fix count down error

beta
bug fix
2023-03-24 18:53:43 +08:00 · 2023-03-24 18:47:45 +08:00 · 2023-03-24 18:08:48 +08:00 · 2023-03-24 18:04:59 +08:00 · 2023-03-24 16:34:48 +08:00 · 2023-03-24 16:25:40 +08:00
256 changed files with 8209 additions and 47025 deletions
--- a/.gitattributes
+++ b/.gitattributes
@ -1,5 +0,0 @@
-*.h linguist-detectable=false
-*.cpp linguist-detectable=false
-*.tex linguist-detectable=false
-*.cs linguist-detectable=false
-*.tps linguist-detectable=false
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@ -1,71 +0,0 @@
-name: Report Bug | 报告BUG
-description: "Report bug"
-title: "[Bug]: "
-labels: []
-body:
-  - type: dropdown
-    id: download
-    attributes:
-      label: Installation Method | 安装方法与平台
-      options:
-        - Please choose | 请选择
-        - Pip Install (I ignored requirements.txt)
-        - Pip Install (I used latest requirements.txt)
-        - OneKeyInstall (一键安装脚本-windows)
-        - OneKeyInstall (一键安装脚本-mac)
-        - Anaconda (I ignored requirements.txt)
-        - Anaconda (I used latest requirements.txt)
-        - Docker（Windows/Mac）
-        - Docker（Linux）
-        - Docker-Compose（Windows/Mac）
-        - Docker-Compose（Linux）
-        - Huggingface
-        - Others (Please Describe)
-    validations:
-      required: true
-
-  - type: dropdown
-    id: version
-    attributes:
-      label: Version | 版本
-      options:
-        - Please choose | 请选择
-        - Latest | 最新版
-        - Others | 非最新版
-    validations:
-      required: true
-
-  - type: dropdown
-    id: os
-    attributes:
-      label: OS | 操作系统
-      options:
-        - Please choose | 请选择
-        - Windows
-        - Mac
-        - Linux
-        - Docker
-    validations:
-      required: true
-
-  - type: textarea
-    id: describe
-    attributes:
-      label: Describe the bug | 简述
-      description: Describe the bug | 简述
-    validations:
-      required: true
-
-  - type: textarea
-    id: screenshot
-    attributes:
-      label: Screen Shot | 有帮助的截图
-      description: Screen Shot | 有帮助的截图
-    validations:
-      required: true
-
-  - type: textarea
-    id: traceback
-    attributes:
-      label: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
-      description: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@ -1,23 +0,0 @@
-name: Feature Request | 功能请求
-description: "Feature Request"
-title: "[Feature]: "
-labels: []
-body:
-  - type: dropdown
-    id: download
-    attributes:
-      label: Class | 类型
-      options:
-        - Please choose | 请选择
-        - 其他
-        - 函数插件
-        - 大语言模型
-        - 程序主体
-    validations:
-      required: false
-
-  - type: textarea
-    id: traceback
-    attributes:
-      label: Feature Request | 功能请求
-      description: Feature Request | 功能请求
--- a/.github/workflows/build-with-all-capacity-beta.yml
+++ b/.github/workflows/build-with-all-capacity-beta.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-all-capacity-beta
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+AllCapacityBeta
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-all-capacity.yml
+++ b/.github/workflows/build-with-all-capacity.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-all-capacity
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_all_capacity
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+AllCapacity
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-audio-assistant.yml
+++ b/.github/workflows/build-with-audio-assistant.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-audio-assistant
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_audio_assistant
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+NoLocal+AudioAssistant
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-chatglm.yml
+++ b/.github/workflows/build-with-chatglm.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-chatglm
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_chatglm_moss
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+ChatGLM+Moss
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-jittorllms.yml
+++ b/.github/workflows/build-with-jittorllms.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-jittorllms
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_jittorllms
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+JittorLLMs
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-with-latex.yml
+++ b/.github/workflows/build-with-latex.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-latex
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_latex
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+NoLocal+Latex
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/build-without-local-llms.yml
+++ b/.github/workflows/build-without-local-llms.yml
@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-without-local-llms
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_nolocal
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+NoLocal
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@ -1,25 +0,0 @@
-# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
-#
-# You can adjust the behavior by modifying this file.
-# For more information, see:
-# https://github.com/actions/stale
-
-name: 'Close stale issues and PRs'
-on:
-  schedule:
-    - cron: '*/5 * * * *'
-
-jobs:
-  stale:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: read
-
-    steps:
-      - uses: actions/stale@v8
-        with:
-          stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 1 days.'
-          days-before-stale: 100
-          days-before-close: 1
-          debug-only: true
--- a/.gitignore
+++ b/.gitignore
@ -131,26 +131,8 @@ dmypy.json
 # Pyre type checker
 .pyre/

-.vscode
-.idea
-
 history
 ssr_conf
 config_private.py
 gpt_log
 private.md
-private_upload
-other_llms
-cradle*
-debug*
-private*
-crazy_functions/test_project/pdf_and_word
-crazy_functions/test_samples
-request_llms/jittorllms
-multi-language
-request_llms/moss
-media
-flagged
-request_llms/ChatGLM-6b-onnx-u8s8
-.pre-commit-config.yaml
-themes/common.js.min.*.js
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": false
+        }
+    ]
+}
--- a/35
+++ b/35
@ -1,28 +1,17 @@
-# 此Dockerfile适用于“无本地模型”的迷你运行环境构建
-# 如果需要使用chatglm等本地模型或者latex运行依赖，请参考 docker-compose.yml
-# - 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
-# - 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
-# - 如何运行(其他操作系统，选择任意一个固定端口50923): `docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic `
-FROM python:3.11
+FROM ubuntu:latest

+RUN apt-get update && \
+    apt-get install -y python3 python3-pip && \
+    rm -rf /var/lib/apt/lists/*

-# 进入工作路径（必要）
+RUN echo '[global]' > /etc/pip.conf && \
+    echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
+    echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
+
+RUN pip3 install gradio requests[socks] mdtex2html
+
+COPY . /gpt
 WORKDIR /gpt


-# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下三行，可以删除）
-COPY requirements.txt ./
-RUN pip3 install -r requirements.txt
-
-
-# 装载项目文件，安装剩余依赖（必要）
-COPY . .
-RUN pip3 install -r requirements.txt
-
-
-# 非必要步骤，用于预热模块（可以删除）
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-
-# 启动（必要）
-CMD ["python3", "-u", "main.py"]
+CMD ["python3", "main.py"]
--- a/README.md
+++ b/README.md
@ -1,447 +1,155 @@
-> [!IMPORTANT]
-> 2024.5.1: 加入Doc2x翻译PDF论文的功能，[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)  
-> 2024.4.30: 3.75版本引入Edge-TTS和SoVits语音克隆模块，[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/)  
-> 2024.3.11: 恭迎Claude3和Moonshot，全力支持Qwen、GLM、DeepseekCoder等中文大语言模型！  
-> 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
+# ChatGPT 学术优化

-<br>
+**如果喜欢这个项目，请给它一个Star；如果你发明了更好用的学术快捷键，欢迎发issue或者pull requests**

-<div align=center>
-<h1 aligh="center">
-<img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)
-</h1>
+If you like this project, please give it a Star. If you've come up with more useful academic shortcuts, feel free to open an issue or pull request.

-[![Github][Github-image]][Github-url]
-[![License][License-image]][License-url]
-[![Releases][Releases-image]][Releases-url]
-[![Installation][Installation-image]][Installation-url]
-[![Wiki][Wiki-image]][Wiki-url]
-[![PR][PRs-image]][PRs-url]
-
-[Github-image]: https://img.shields.io/badge/github-12100E.svg?style=flat-square
-[License-image]: https://img.shields.io/github/license/binary-husky/gpt_academic?label=License&style=flat-square&color=orange
-[Releases-image]: https://img.shields.io/github/release/binary-husky/gpt_academic?label=Release&style=flat-square&color=blue
-[Installation-image]: https://img.shields.io/badge/dynamic/json?color=blue&url=https://raw.githubusercontent.com/binary-husky/gpt_academic/master/version&query=$.version&label=Installation&style=flat-square
-[Wiki-image]: https://img.shields.io/badge/wiki-项目文档-black?style=flat-square
-[PRs-image]: https://img.shields.io/badge/PRs-welcome-pink?style=flat-square
-
-[Github-url]: https://github.com/binary-husky/gpt_academic
-[License-url]: https://github.com/binary-husky/gpt_academic/blob/master/LICENSE
-[Releases-url]: https://github.com/binary-husky/gpt_academic/releases
-[Installation-url]: https://github.com/binary-husky/gpt_academic#installation
-[Wiki-url]: https://github.com/binary-husky/gpt_academic/wiki
-[PRs-url]: https://github.com/binary-husky/gpt_academic/pulls
-
-
-</div>
-<br>
-
-**如果喜欢这个项目，请给它一个Star；如果您发明了好用的快捷键或插件，欢迎发pull requests！**
-
-If you like this project, please give it a Star.
-Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanese.md) | [한국어](docs/README.Korean.md) | [Русский](docs/README.Russian.md) | [Français](docs/README.French.md). All translations have been provided by the project itself. To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
-<br>
-
-> [!NOTE]
-> 1.本项目中每个文件的功能都在[自译解报告](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)`self_analysis.md`详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题请查阅wiki。
->    [![常规安装方法](https://img.shields.io/static/v1?label=&message=常规安装方法&color=gray)](#installation)  [![一键安装脚本](https://img.shields.io/static/v1?label=&message=一键安装脚本&color=gray)](https://github.com/binary-husky/gpt_academic/releases)  [![配置说明](https://img.shields.io/static/v1?label=&message=配置说明&color=gray)](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明) [![wiki](https://img.shields.io/static/v1?label=&message=wiki&color=gray)]([https://github.com/binary-husky/gpt_academic/wiki/项目配置说明](https://github.com/binary-husky/gpt_academic/wiki))
->
-> 2.本项目兼容并鼓励尝试国内中文大语言基座模型如通义千问，智谱GLM等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交即可生效。
-
-<br><br>
-
-<div align="center">
-
-功能（⭐= 近期新增功能） | 描述
+功能 | 描述
 --- | ---
-⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，[智谱GLM4](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
-⭐支持mermaid图像渲染 | 支持让GPT生成[流程图](https://www.bilibili.com/video/BV18c41147H9/)、状态转移图、甘特图、饼状图、GitGraph等等（3.7版本）
-⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
-⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
-⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
-⭐虚空终端插件 | [插件] 能够使用自然语言直接调度本项目其他插件
-润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
-[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
-模块化设计 | 支持自定义强大的[插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [插件] 一键剖析Python/C/C++/Java/Lua/...项目树 或 [自我剖析](https://www.bilibili.com/video/BV1cj411A7VW)
-读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
-Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
-批量注释生成 | [插件] 一键批量生成函数注释
-Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？就是出自他的手笔
-[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
-[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
-Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
-[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
-互联网信息聚合+GPT | [插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
-公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
-启动暗色[主题](https://github.com/binary-husky/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
-[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)伺候的感觉一定会很不错吧？
-更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
-⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
-更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
-</div>
+一键润色 | 支持一键润色、一键查找论文语法错误
+一键中英互译 | 一键中英互译
+一键代码解释 | 可以正确显示代码、解释代码
+自定义快捷键 | 支持自定义快捷键
+配置代理服务器 | 支持配置代理服务器
+模块化设计 | 支持自定义高阶的实验性功能
+自我程序剖析 | [实验性功能] 可以读懂自己的源代码
+程序剖析 | [实验性功能] 可以剖析其他Python/C++项目
+读论文 | [实验性功能] 解读latex论文写摘要
+批量注释生成 | [实验性功能] 批量生成函数注释
+公式显示 | 可以同时显示公式的tex形式和渲染形式
+图片显示 | 可以在markdown中显示图片
+支持GPT输出的markdown表格 | 可以输出支持GPT的markdown表格


- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
+
+
+
+- 支持GPT输出的markdown表格
 <div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/279702205-d81137c3-affd-4cd1-bb5e-b15610389762.gif" width="700" >
+<img src="demo2.jpg" width="500" >
 </div>

+- 如果输出包含公式，会同时以tex形式和渲染形式显示，方便复制和阅读
 <div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/70ff1ec5-e589-4561-a29e-b831079b37fb.gif" width="700" >
+<img src="demo.jpg" width="500" >
 </div>

-
- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放剪贴板
+- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放粘贴板
 <div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+<img src="公式.gif" width="700" >
 </div>

- 润色/纠错
+- 代码的显示自然也不在话下 https://www.bilibili.com/video/BV1F24y147PD/
 <div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+<img src="润色.gif" width="700" >
 </div>

- 如果输出包含公式，会以tex形式和渲染形式同时显示，方便复制和阅读
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
- 懒得看项目代码？直接把整个工程炫ChatGPT嘴里
+- 懒得看项目代码？整个工程直接给chatgpt炫嘴里
 <div align="center">
 <img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
 </div>

- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + GPT4）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
+## 直接运行 (Windows or Linux or MacOS)

-<br><br>
-
-# Installation
-
-```mermaid
-flowchart TD
-    A{"安装方法"} --> W1("I. 🔑直接运行 (Windows, Linux or MacOS)")
-    W1 --> W11["1. Python pip包管理依赖"]
-    W1 --> W12["2. Anaconda包管理依赖（推荐⭐）"]
-
-    A --> W2["II. 🐳使用Docker (Windows, Linux or MacOS)"]
-
-    W2 --> k1["1. 部署项目全部能力的大镜像（推荐⭐）"]
-    W2 --> k2["2. 仅在线模型（GPT, GLM4等）镜像"]
-    W2 --> k3["3. 在线模型 + Latex的大镜像"]
-
-    A --> W4["IV. 🚀其他部署方法"]
-    W4 --> C1["1. Windows/MacOS 一键安装运行脚本（推荐⭐）"]
-    W4 --> C2["2. Huggingface, Sealos远程部署"]
-    W4 --> C4["3. ... 其他 ..."]
+```
+# 下载项目
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+# 在config.py中，配置 海外Proxy 和 OpenAI API KEY
+- 1.如果你在国内，需要设置海外代理才能够使用 OpenAI API，你可以通过 config.py 文件来进行设置。
+- 2.配置 OpenAI API KEY。你需要在 OpenAI 官网上注册并获取 API KEY。一旦你拿到了 API KEY，在 config.py 文件里配置好即可。
+# 安装依赖
+python -m pip install -r requirements.txt
+# 运行
+python main.py
 ```

-### 安装方法I：直接运行 (Windows, Linux or MacOS)

-1. 下载项目
+## 使用docker (Linux)

-    ```sh
-    git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-    cd gpt_academic
-    ```
+``` sh
+# 下载项目
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+# 配置 海外Proxy 和 OpenAI API KEY
+config.py
+# 安装
+docker build -t gpt-academic .
+# 运行
+docker run --rm -it --net=host gpt-academic

-2. 配置API_KEY等变量
-
-    在`config.py`中，配置API KEY等变量。[特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1)、[Wiki-项目配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
-
-    「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解以上读取逻辑，我们强烈建议您在`config.py`同路径下创建一个名为`config_private.py`的新配置文件，并使用`config_private.py`配置项目，从而确保自动更新时不会丢失配置 」。
-
-    「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。配置读取优先级: `环境变量` > `config_private.py` > `config.py` 」。
-
-
-3. 安装依赖
-    ```sh
-    # （选择I: 如熟悉python, python推荐版本 3.9 ~ 3.11）备注：使用官方pip源或者阿里pip源, 临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-    python -m pip install -r requirements.txt
-
-    # （选择II: 使用Anaconda）步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
-    conda create -n gptac_venv python=3.11    # 创建anaconda环境
-    conda activate gptac_venv                 # 激活anaconda环境
-    python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步骤
-    ```
-
-
-<details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
-<p>
-
-【可选步骤】如果需要支持清华ChatGLM3/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
-
-```sh
-# 【可选步骤I】支持清华ChatGLM3。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llms/requirements_chatglm.txt
-
-# 【可选步骤II】支持复旦MOSS
-python -m pip install -r request_llms/requirements_moss.txt
-git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss  # 注意执行此行代码时，必须处于项目根路径
-
-# 【可选步骤III】支持RWKV Runner
-参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
-
-# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-
-# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
-pip install bitsandbyte
-# windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
-python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
-pip install -U git+https://github.com/huggingface/transformers.git
-pip install -U git+https://github.com/huggingface/accelerate.git
-pip install peft
 ```

-</p>
-</details>

-
-
-4. 运行
-    ```sh
-    python main.py
-    ```
-
-### 安装方法II：使用Docker
-
-0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小，则不推荐该方法部署完整项目）
-[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
-
-    ``` sh
-    # 修改docker-compose.yml，保留方案0并删除其他方案。然后运行：
-    docker-compose up
-    ```
-
-1. 仅ChatGPT + GLM4 + 文心一言+spark等在线模型（推荐大多数人选择）
-[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
-[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
-[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
-
-    ``` sh
-    # 修改docker-compose.yml，保留方案1并删除其他方案。然后运行：
-    docker-compose up
-    ```
-
-P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用方案4或者方案0获取Latex功能。
-
-2. ChatGPT + GLM3 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
-[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
-
-    ``` sh
-    # 修改docker-compose.yml，保留方案2并删除其他方案。然后运行：
-    docker-compose up
-    ```
-
-
-### 安装方法III：其他部署方法
-1. **Windows一键运行脚本**。
-完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。脚本贡献来源：[oobabooga](https://github.com/oobabooga/one-click-installers)。
-
-2. 使用第三方API、Azure等、文心一言、星火等，见[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
-
-3. 云服务器远程部署避坑指南。
-请访问[云服务器远程部署wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-4. 在其他平台部署&二级网址部署
-    - 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
-    - 使用WSL2（Windows Subsystem for Linux 子系统）。请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-    - 如何在二级网址（如`http://localhost/subpath`）下运行。请访问[FastAPI运行说明](docs/WithFastapi.md)
-
-<br><br>
-
-# Advanced Usage
-### I：自定义新的便捷按钮（学术快捷键）
-
-现在已可以通过UI中的`界面外观`菜单中的`自定义菜单`添加新的便捷按钮。如果需要在代码中定义，请使用任意文本编辑器打开`core_functional.py`，添加如下条目即可：
-
-```python
+## 自定义新的便捷按钮（学术快捷键自定义）
+打开functional.py，添加条目如下，然后重启程序即可。（如果按钮已经添加成功并可见，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
+例如
+```
 "超级英译中": {
-    # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-    "Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n",

+    # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
+    "Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n", 
+    
    # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来。
    "Suffix": "",
+    
 },
 ```
-
 <div align="center">
 <img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
 </div>

-### II：自定义函数插件
-编写强大的函数插件来执行任何你想得到的和想不到的任务。
-本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。
-详情请参考[函数插件指南](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。

-<br><br>
+如果你发明了更好用的学术快捷键，欢迎发issue或者pull requests！

-# Updates
-### I：动态
+## 配置代理

-1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件，
-另外在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。
-Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史html存档缓存。
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
-</div>
-
-2. ⭐Latex/Arxiv论文翻译功能⭐
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
-</div>
-
-3. 虚空终端（从自然语言输入中，理解用户意图+自动调用其他插件）
-
- 步骤一：输入 “ 请调用插件翻译PDF论文，地址为https://openreview.net/pdf?id=rJl0r3R9KX ”
- 步骤二：点击“虚空终端”
+在```config.py```中修改端口与代理软件对应

 <div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
+<img src="https://user-images.githubusercontent.com/96192199/226571294-37a47cd9-4d40-4c16-97a2-d360845406f7.png" width="500" >
+<img src="https://user-images.githubusercontent.com/96192199/226838985-e5c95956-69c2-4c23-a4dd-cd7944eeb451.png" width="500" >
 </div>

-4. 模块化功能设计，简单的接口却能支持强大的功能
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-5. 译解其他开源项目
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
-</div>
-
-6. 装饰[live2d](https://github.com/fghrsh/live2d_demo)的小功能（默认关闭，需要修改`config.py`）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
-</div>
-
-7. OpenAI图像生成
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
-</div>
-
-8. 基于mermaid的流图、脑图绘制
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/c518b82f-bd53-46e2-baf5-ad1b081c1da4" width="500" >
-</div>
-
-9. Latex全文校对纠错
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
-</div>
-
-10. 语言、主题切换
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
-</div>
-
-
-
-### II：版本:
- version 3.80(TODO): 优化AutoGen插件主题并设计一系列衍生插件
- version 3.70: 引入Mermaid绘图，实现GPT画脑图等功能   
- version 3.60: 引入AutoGen作为新一代插件的基石
- version 3.57: 支持GLM3，星火v3，文心一言v4，修复本地模型的并发BUG
- version 3.56: 支持动态追加基础功能按钮，新汇报PDF汇总页面
- version 3.55: 重构前端界面，引入悬浮窗口与菜单栏
- version 3.54: 新增动态代码解释器（Code Interpreter）（待完善）
- version 3.53: 支持动态选择不同界面主题，提高稳定性&解决多用户冲突问题
- version 3.50: 使用自然语言调用本项目的所有函数插件（虚空终端），支持插件分类，改进UI，设计新主题
- version 3.49: 支持百度千帆平台和文心一言
- version 3.48: 支持阿里达摩院通义千问，上海AI-Lab书生，讯飞星火
- version 3.46: 支持完全脱手操作的实时语音对话
- version 3.45: 支持自定义ChatGLM2微调模型
- version 3.44: 正式支持Azure，优化界面易用性
- version 3.4: +arxiv论文翻译、latex论文批改功能
- version 3.3: +互联网信息综合功能
- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡
- version 3.0: 对chatglm和其他小型llm的支持
- version 2.6: 重构了插件结构，提高了交互性，加入更多插件
- version 2.5: 自更新，解决总结大工程源代码时文本过长、token溢出的问题
- version 2.4: 新增PDF全文翻译功能; 新增输入区切换位置的功能
- version 2.3: 增强多线程交互性
- version 2.2: 函数插件支持热重载
- version 2.1: 可折叠式布局
- version 2.0: 引入模块化函数插件
- version 1.0: 基础功能
-
-GPT Academic开发者QQ群：`610599535`
-
- 已知问题
-    - 某些浏览器翻译插件干扰此软件前端的运行
-    - 官方Gradio目前有很多兼容性问题，请**务必使用`requirement.txt`安装Gradio**
-
-```mermaid
-timeline LR
-    title GPT-Academic项目发展历程
-    section 2.x
-        1.0~2.2: 基础功能: 引入模块化函数插件: 可折叠式布局: 函数插件支持热重载
-        2.3~2.5: 增强多线程交互性: 新增PDF全文翻译功能: 新增输入区切换位置的功能: 自更新
-        2.6: 重构了插件结构: 提高了交互性: 加入更多插件
-    section 3.x
-        3.0~3.1: 对chatglm支持: 对其他小型llm支持: 支持同时问询多个gpt模型: 支持多个apikey负载均衡
-        3.2~3.3: 函数插件支持更多参数接口: 保存对话功能: 解读任意语言代码: 同时询问任意的LLM组合: 互联网信息综合功能
-        3.4: 加入arxiv论文翻译: 加入latex论文批改功能
-        3.44: 正式支持Azure: 优化界面易用性
-        3.46: 自定义ChatGLM2微调模型: 实时语音对话
-        3.49: 支持阿里达摩院通义千问: 上海AI-Lab书生: 讯飞星火: 支持百度千帆平台 & 文心一言
-        3.50: 虚空终端: 支持插件分类: 改进UI: 设计新主题
-        3.53: 动态选择不同界面主题: 提高稳定性: 解决多用户冲突问题
-        3.55: 动态代码解释器: 重构前端界面: 引入悬浮窗口与菜单栏
-        3.56: 动态追加基础功能按钮: 新汇报PDF汇总页面
-        3.57: GLM3, 星火v3: 支持文心一言v4: 修复本地模型的并发BUG
-        3.60: 引入AutoGen
-        3.70: 引入Mermaid绘图: 实现GPT画脑图等功能
-        3.80(TODO): 优化AutoGen插件主题: 设计衍生插件
-
+配置完成后，你可以用以下命令测试代理是否工作，如果一切正常，下面的代码将输出你的代理服务器所在地：
+```
+python check_proxy.py
 ```

+## 兼容性测试

-### III：主题
-可以通过修改`THEME`选项（config.py）变更主题
-1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
+### 图片显示：
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226906087-b5f1c127-2060-4db9-af05-487643b21ed9.png" width="500" >
+</div>
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226906703-7226495d-6a1f-4a53-9728-ce6778cbdd19.png" width="200" >
+</div>
+
+### 如果一个程序能够读懂并剖析自己：
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
+</div>
+
+### 其他任意Python项目剖析：
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
+</div>


-### IV：本项目的开发分支
-
-1. `master` 分支: 主分支，稳定版
-2. `frontier` 分支: 开发分支，测试版
-3. 如何[接入其他大模型](request_llms/README.md)
-4. 访问GPT-Academic的[在线服务并支持我们](https://github.com/binary-husky/gpt_academic/wiki/online)
-
-### V：参考与学习
-
+## 参考项目
 ```
-代码中参考了很多其他优秀项目中的设计，顺序不分先后：
-
-# 清华ChatGLM2-6B:
-https://github.com/THUDM/ChatGLM2-6B
-
-# 清华JittorLLMs:
-https://github.com/Jittor/JittorLLMs
-
-# ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Oobabooga one-click installer:
-https://github.com/oobabooga/one-click-installers
-
-# More：
+https://github.com/Python-Markdown/markdown
 https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
+https://github.com/polarwinkel/mdtex2html
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
 ```
--- a/check_proxy.py
+++ b/check_proxy.py
@ -5,17 +5,9 @@ def check_proxy(proxies):
    try:
        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
        data = response.json()
-        if 'country_name' in data:
-            country = data['country_name']
-            result = f"代理配置 {proxies_https}, 代理所在地：{country}"
-        elif 'error' in data:
-            alternative = _check_with_backup_source(proxies)
-            if alternative is None:
-                result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
-            else:
-                result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
-        else:
-            result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
+        print(f'查询代理的地理位置，返回的结果是{data}')
+        country = data['country_name']
+        result = f"代理配置 {proxies_https}, 代理所在地：{country}"
        print(result)
        return result
    except:
@ -23,154 +15,8 @@ def check_proxy(proxies):
        print(result)
        return result

-def _check_with_backup_source(proxies):
-    import random, string, requests
-    random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
-    try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
-    except: return None
-
-def backup_and_download(current_version, remote_version):
-    """
-    一键更新协议：备份和下载
-    """
-    from toolbox import get_conf
-    import shutil
-    import os
-    import requests
-    import zipfile
-    os.makedirs(f'./history', exist_ok=True)
-    backup_dir = f'./history/backup-{current_version}/'
-    new_version_dir = f'./history/new-version-{remote_version}/'
-    if os.path.exists(new_version_dir):
-        return new_version_dir
-    os.makedirs(new_version_dir)
-    shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
-    proxies = get_conf('proxies')
-    try:    r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
-    except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
-    zip_file_path = backup_dir+'/master.zip'
-    with open(zip_file_path, 'wb+') as f:
-        f.write(r.content)
-    dst_path = new_version_dir
-    with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
-        for zip_info in zip_ref.infolist():
-            dst_file_path = os.path.join(dst_path, zip_info.filename)
-            if os.path.exists(dst_file_path):
-                os.remove(dst_file_path)
-            zip_ref.extract(zip_info, dst_path)
-    return new_version_dir
-
-
-def patch_and_restart(path):
-    """
-    一键更新协议：覆盖和重启
-    """
-    from distutils import dir_util
-    import shutil
-    import os
-    import sys
-    import time
-    import glob
-    from colorful import print亮黄, print亮绿, print亮红
-    # if not using config_private, move origin config.py as config_private.py
-    if not os.path.exists('config_private.py'):
-        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
-              '另外您可以随时在history子文件夹下找回旧版的程序。')
-        shutil.copyfile('config.py', 'config_private.py')
-    path_new_version = glob.glob(path + '/*-master')[0]
-    dir_util.copy_tree(path_new_version, './')
-    print亮绿('代码已经更新，即将更新pip包依赖……')
-    for i in reversed(range(5)): time.sleep(1); print(i)
-    try:
-        import subprocess
-        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
-    except:
-        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
-    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print(' ------------------------------ -----------------------------------')
-    for i in reversed(range(8)): time.sleep(1); print(i)
-    os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-def get_current_version():
-    import json
-    try:
-        with open('./version', 'r', encoding='utf8') as f:
-            current_version = json.loads(f.read())['version']
-    except:
-        current_version = ""
-    return current_version
-
-
-def auto_update(raise_error=False):
-    """
-    一键更新协议：查询版本和用户意见
-    """
-    try:
-        from toolbox import get_conf
-        import requests
-        import json
-        proxies = get_conf('proxies')
-        try:    response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
-        except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
-        remote_json_data = json.loads(response.text)
-        remote_version = remote_json_data['version']
-        if remote_json_data["show_feature"]:
-            new_feature = "新功能：" + remote_json_data["new_feature"]
-        else:
-            new_feature = ""
-        with open('./version', 'r', encoding='utf8') as f:
-            current_version = f.read()
-            current_version = json.loads(current_version)['version']
-        if (remote_version - current_version) >= 0.01-1e-5:
-            from colorful import print亮黄
-            print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
-            print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
-            user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
-            if user_instruction in ['Y', 'y']:
-                path = backup_and_download(current_version, remote_version)
-                try:
-                    patch_and_restart(path)
-                except:
-                    msg = '更新失败。'
-                    if raise_error:
-                        from toolbox import trimmed_format_exc
-                        msg += trimmed_format_exc()
-                    print(msg)
-            else:
-                print('自动更新程序：已禁用')
-                return
-        else:
-            return
-    except:
-        msg = '自动更新程序：已禁用。建议排查：代理网络配置。'
-        if raise_error:
-            from toolbox import trimmed_format_exc
-            msg += trimmed_format_exc()
-        print(msg)
-
-def warm_up_modules():
-    print('正在执行一些模块的预热 ...')
-    from toolbox import ProxyNetworkActivate
-    from request_llms.bridge_all import model_info
-    with ProxyNetworkActivate("Warmup_Modules"):
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        enc.encode("模块预热", disallowed_special=())
-        enc = model_info["gpt-4"]['tokenizer']
-        enc.encode("模块预热", disallowed_special=())
-
-def warm_up_vectordb():
-    print('正在执行一些模块的预热 ...')
-    from toolbox import ProxyNetworkActivate
-    with ProxyNetworkActivate("Warmup_Modules"):
-        import nltk
-        with ProxyNetworkActivate("Warmup_Modules"): nltk.download("punkt")
-

 if __name__ == '__main__':
-    import os
-    os.environ['no_proxy'] = '*'  # 避免代理网络产生意外污染
-    from toolbox import get_conf
-    proxies = get_conf('proxies')
-    check_proxy(proxies)
+    try: from config_private import proxies # 放自己的秘密如API和代理网址 os.path.exists('config_private.py')
+    except: from config import proxies
+    check_proxy(proxies)
--- a/colorful.py
+++ b/colorful.py
@ -3,7 +3,7 @@ from sys import stdout

 if platform.system()=="Linux":
    pass
-else:
+else: 
    from colorama import init
    init()

@ -34,28 +34,58 @@ def print亮紫(*kw,**kargs):
 def print亮靛(*kw,**kargs):
    print("\033[1;36m",*kw,"\033[0m",**kargs)

-# Do you like the elegance of Chinese characters?
-def sprint红(*kw):
-    return "\033[0;31m"+' '.join(kw)+"\033[0m"
-def sprint绿(*kw):
-    return "\033[0;32m"+' '.join(kw)+"\033[0m"
-def sprint黄(*kw):
-    return "\033[0;33m"+' '.join(kw)+"\033[0m"
-def sprint蓝(*kw):
-    return "\033[0;34m"+' '.join(kw)+"\033[0m"
-def sprint紫(*kw):
-    return "\033[0;35m"+' '.join(kw)+"\033[0m"
-def sprint靛(*kw):
-    return "\033[0;36m"+' '.join(kw)+"\033[0m"
-def sprint亮红(*kw):
-    return "\033[1;31m"+' '.join(kw)+"\033[0m"
-def sprint亮绿(*kw):
-    return "\033[1;32m"+' '.join(kw)+"\033[0m"
-def sprint亮黄(*kw):
-    return "\033[1;33m"+' '.join(kw)+"\033[0m"
-def sprint亮蓝(*kw):
-    return "\033[1;34m"+' '.join(kw)+"\033[0m"
-def sprint亮紫(*kw):
-    return "\033[1;35m"+' '.join(kw)+"\033[0m"
-def sprint亮靛(*kw):
-    return "\033[1;36m"+' '.join(kw)+"\033[0m"
+
+
+def print亮红(*kw,**kargs):
+    print("\033[1;31m",*kw,"\033[0m",**kargs)
+def print亮绿(*kw,**kargs):
+    print("\033[1;32m",*kw,"\033[0m",**kargs)
+def print亮黄(*kw,**kargs):
+    print("\033[1;33m",*kw,"\033[0m",**kargs)
+def print亮蓝(*kw,**kargs):
+    print("\033[1;34m",*kw,"\033[0m",**kargs)
+def print亮紫(*kw,**kargs):
+    print("\033[1;35m",*kw,"\033[0m",**kargs)
+def print亮靛(*kw,**kargs):
+    print("\033[1;36m",*kw,"\033[0m",**kargs)
+    
+print_red = print红
+print_green = print绿
+print_yellow = print黄
+print_blue = print蓝
+print_purple = print紫
+print_indigo = print靛
+
+print_bold_red = print亮红
+print_bold_green = print亮绿
+print_bold_yellow = print亮黄
+print_bold_blue = print亮蓝
+print_bold_purple = print亮紫
+print_bold_indigo = print亮靛
+    
+if not stdout.isatty():
+    # redirection, avoid a fucked up log file
+    print红 = print
+    print绿 = print
+    print黄 = print
+    print蓝 = print
+    print紫 = print
+    print靛 = print
+    print亮红 = print
+    print亮绿 = print
+    print亮黄 = print
+    print亮蓝 = print
+    print亮紫 = print
+    print亮靛 = print
+    print_red = print
+    print_green = print
+    print_yellow = print
+    print_blue = print
+    print_purple = print
+    print_indigo = print
+    print_bold_red = print
+    print_bold_green = print
+    print_bold_yellow = print
+    print_bold_blue = print
+    print_bold_purple = print
+    print_bold_indigo = print
--- a/config.py
+++ b/config.py
@ -1,391 +1,29 @@
-"""
-    以下所有配置也都支持利用环境变量覆写，环境变量配置格式见docker-compose.yml。
-    读取优先级：环境变量 > config_private.py > config.py
-    --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
-    All the following configurations also support using environment variables to override,
-    and the environment variable configuration format can be seen in docker-compose.yml.
-    Configuration reading priority: environment variable > config_private.py > config.py
-"""
+# API_KEY = "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" 此key无效
+API_KEY = "sk-此处填API秘钥"
+API_URL = "https://api.openai.com/v1/chat/completions"

-# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
-API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
-
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
+# 改为True应用代理
 USE_PROXY = False
 if USE_PROXY:
-    """
-    代理网络的地址，打开你的代理软件查看代理协议(socks5h / http)、地址(localhost)和端口(11284)
-    填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
-            <配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
-    [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    [地址] 填localhost或者127.0.0.1（localhost意思是代理软件安装在本机上）
-    [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-    """
-    proxies = {
-        #          [协议]://  [地址]  :[端口]
-        "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
-        "https": "socks5h://localhost:11284",  # 再例如  "https": "http://127.0.0.1:7890",
-    }
+    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
+    proxies = { "http": "socks5h://localhost:11284", "https": "socks5h://localhost:11284", } 
+    print('网络代理状态：运行。')
 else:
    proxies = None
-
-# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
-                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
-                    "gemini-pro", "chatglm3","deepseek-chat","deepseek-coder","command-r","command-r-plus","command-r-plus-internet"
-                    ]
-# --- --- --- ---
-# P.S. 其他可用的模型还包括
-# AVAIL_LLM_MODELS = [
-#   "qianfan", "deepseekcoder",
-#   "spark", "sparkv2", "sparkv3", "sparkv3.5",
-#   "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
-#   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
-#   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125"
-#   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
-#   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
-#   "yi-34b-chat-0205", "yi-34b-chat-200k"
-# ]
-# --- --- --- ---
-# 此外，您还可以在接入one-api/vllm/ollama时，
-# 使用"one-api-*","vllm-*","ollama-*"前缀直接使用非标准方式接入的模型，例如
-# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)"]
-# --- --- --- ---
-
-
-# --------------- 以下配置可以优化体验 ---------------
-
-# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
-API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://api.cdn.18g.me/v1/chat/completions"}
-
-
-# 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 1500
-
-
-# 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
-# 更多主题, 请查阅Gradio主题商店: https://huggingface.co/spaces/gradio/theme-gallery 可选 ["Gstaff/Xkcd", "NoCrypt/Miku", ...]
-THEME = "NoCrypt/Miku"
-AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
-
-
-# 默认的系统提示词（system prompt）
-INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
-
-
-# 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
-CHATBOT_HEIGHT = 1115
-
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT"   # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-
-# 暗色模式 / 亮色模式
-DARK_MODE = True
-
+    print('网络代理状态：未配置。无代理状态下很可能无法访问。')

 # 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
+TIMEOUT_SECONDS = 20

 # 网页的端口, -1代表随机端口
 WEB_PORT = -1

-
 # 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
 MAX_RETRY = 2

+# 选择的OpenAI模型是（gpt4现在只对申请成功的人开放）
+LLM_MODEL = "gpt-3.5-turbo"

-# 插件分类默认选项
-DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
-
-
-# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
-MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
-
-
-# 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
-# 如果你选择Qwen系列的模型，那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型
-# 也可以是具体的模型路径
-QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
-
-
-# 接入通义千问在线大模型 https://dashscope.console.aliyun.com/
-DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
-
-
-# 百度千帆（LLM_MODEL="qianfan"）
-BAIDU_CLOUD_API_KEY = ''
-BAIDU_CLOUD_SECRET_KEY = ''
-BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
-
-
-# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
-CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"
-
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
-
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-
-# 是否在提交时自动清空输入框
-AUTO_CLEAR_TXT = False
-
-
-# 加一个live2d装饰
-ADD_WAIFU = False
-
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
-
-
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）
-# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
-CUSTOM_PATH = "/"
-
-
-# HTTPS 秘钥和证书（不需要修改）
-SSL_KEYFILE = ""
-SSL_CERTFILE = ""
-
-
-# 极少数情况下，openai的官方KEY需要伴随组织编码（格式如org-xxxxxxxxxxxxxxxxxxxxxxxx）使用
-API_ORG = ""
-
-
-# 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
-SLACK_CLAUDE_BOT_ID = ''
-SLACK_CLAUDE_USER_TOKEN = ''
-
-
-# 如果需要使用AZURE（方法一：单个azure模型部署）详情请见额外文档 docs\use_azure.md
-AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
-AZURE_API_KEY = "填入azure openai api的密钥"    # 建议直接在API_KEY处填写，该选项即将被弃用
-AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.md
-
-
-# 如果需要使用AZURE（方法二：多个azure模型部署+动态切换）详情请见额外文档 docs\use_azure.md
-AZURE_CFG_ARRAY = {}
-
-
-# 阿里云实时语音识别 配置难度较高
-# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
-ENABLE_AUDIO = False
-ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
-ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
-ALIYUN_ACCESSKEY="" # （无需填写）
-ALIYUN_SECRET=""    # （无需填写）
-
-
-# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
-TTS_TYPE = "DISABLE" # LOCAL / LOCAL_SOVITS_API / DISABLE
-GPT_SOVITS_URL = ""
-EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
-
-
-# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
-XFYUN_APPID = "00000000"
-XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
-XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
-
-
-# 接入智谱大模型
-ZHIPUAI_API_KEY = ""
-ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
-
-
-# Claude API KEY
-ANTHROPIC_API_KEY = ""
-
-
-# 月之暗面 API KEY
-MOONSHOT_API_KEY = ""
-
-
-# 零一万物(Yi Model) API KEY
-YIMODEL_API_KEY = ""
-
-
-# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
-MATHPIX_APPID = ""
-MATHPIX_APPKEY = ""
-
-
-# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
-DOC2X_API_KEY = "11329.h5.327469d394a3b5afc674bf2ccf20b66ab20fbe7e8146b625a60981d6d2f2cc7c"
-
-
-# 自定义API KEY格式
-CUSTOM_API_KEY_PATTERN = ""
-
-
-# Google Gemini API-Key
-GEMINI_API_KEY = ''
-
-
-# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
-HUGGINGFACE_ACCESS_TOKEN = "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"
-
-
-# GROBID服务器地址（填写多个可以均衡负载），用于高质量地读取PDF文档
-# 获取方法：复制以下空间https://huggingface.co/spaces/qingxu98/grobid，设为public，然后GROBID_URL = "https://(你的hf用户名如qingxu98)-(你的填写的空间名如grobid).hf.space"
-GROBID_URLS = [
-    "https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space",
-    "https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space",
-    "https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space",
-]
-
-
-# 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
-ALLOW_RESET_CONFIG = False
-
-
-# 在使用AutoGen插件时，是否使用Docker容器运行代码
-AUTOGEN_USE_DOCKER = False
-
-
-# 临时的上传文件夹位置，请勿修改
-PATH_PRIVATE_UPLOAD = "private_upload"
-
-
-# 日志文件夹的位置，请勿修改
-PATH_LOGGING = "gpt_log"
-
-
-# 除了连接OpenAI之外，还有哪些场合允许使用代理，请勿修改
-WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
-                     "Warmup_Modules", "Nougat_Download", "AutoGen"]
-
-
-# *实验性功能*: 自动检测并屏蔽失效的KEY，请勿使用
-BLOCK_INVALID_APIKEY = False
-
-
-# 启用插件热加载
-PLUGIN_HOT_RELOAD = False
-
-
-# 自定义按钮的最大数量限制
-NUM_CUSTOM_BASIC_BTN = 4
-
-
-
-"""
--------------- 配置关联关系说明 ---------------
-
-在线大模型配置关联关系示意图
-│
-├── "gpt-3.5-turbo" 等openai模型
-│   ├── API_KEY
-│   ├── CUSTOM_API_KEY_PATTERN（不常用）
-│   ├── API_ORG（不常用）
-│   └── API_URL_REDIRECT（不常用）
-│
-├── "azure-gpt-3.5" 等azure模型（单个azure模型，不需要动态切换）
-│   ├── API_KEY
-│   ├── AZURE_ENDPOINT
-│   ├── AZURE_API_KEY
-│   ├── AZURE_ENGINE
-│   └── API_URL_REDIRECT
-│
-├── "azure-gpt-3.5" 等azure模型（多个azure模型，需要动态切换，高优先级）
-│   └── AZURE_CFG_ARRAY
-│
-├── "spark" 星火认知大模型 spark & sparkv2
-│   ├── XFYUN_APPID
-│   ├── XFYUN_API_SECRET
-│   └── XFYUN_API_KEY
-│
-├── "claude-3-opus-20240229" 等claude模型
-│   └── ANTHROPIC_API_KEY
-│
-├── "stack-claude"
-│   ├── SLACK_CLAUDE_BOT_ID
-│   └── SLACK_CLAUDE_USER_TOKEN
-│
-├── "qianfan" 百度千帆大模型库
-│   ├── BAIDU_CLOUD_QIANFAN_MODEL
-│   ├── BAIDU_CLOUD_API_KEY
-│   └── BAIDU_CLOUD_SECRET_KEY
-│
-├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
-│   └── ZHIPUAI_API_KEY
-│
-├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
-│   └── YIMODEL_API_KEY
-│
-├── "qwen-turbo" 等通义千问大模型
-│   └──  DASHSCOPE_API_KEY
-│
-├── "Gemini"
-│   └──  GEMINI_API_KEY
-│
-└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
-    ├── AVAIL_LLM_MODELS
-    ├── API_KEY
-    └── API_URL_REDIRECT
-
-
-本地大模型示意图
-│
-├── "chatglm3"
-├── "chatglm"
-├── "chatglm_onnx"
-├── "chatglmft"
-├── "internlm"
-├── "moss"
-├── "jittorllms_pangualpha"
-├── "jittorllms_llama"
-├── "deepseekcoder"
-├── "qwen-local"
-├──  RWKV的支持见Wiki
-└── "llama2"
-
-
-用户图形界面布局依赖关系示意图
-│
-├── CHATBOT_HEIGHT 对话窗的高度
-├── CODE_HIGHLIGHT 代码高亮
-├── LAYOUT 窗口布局
-├── DARK_MODE 暗色模式 / 亮色模式
-├── DEFAULT_FN_GROUPS 插件分类默认选项
-├── THEME 色彩主题
-├── AUTO_CLEAR_TXT 是否在提交时自动清空输入框
-├── ADD_WAIFU 加一个live2d装饰
-└── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性
-
-
-插件在线服务配置依赖关系示意图
-│
-├── 语音功能
-│   ├── ENABLE_AUDIO
-│   ├── ALIYUN_TOKEN
-│   ├── ALIYUN_APPKEY
-│   ├── ALIYUN_ACCESSKEY
-│   └── ALIYUN_SECRET
-│
-└── PDF文档精准解析
-    ├── GROBID_URLS
-    ├── MATHPIX_APPID
-    └── MATHPIX_APPKEY
-
-
-"""
+# 检查一下是不是忘了改config
+if API_KEY == "sk-此处填API秘钥":
+    assert False, "请在config文件中修改API密钥, 添加海外代理之后再运行"
--- a/core_functional.py
+++ b/core_functional.py
@ -1,173 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-import importlib
-from toolbox import clear_line_break
-from toolbox import apply_gpt_academic_string_mask_langbased
-from toolbox import build_gpt_academic_masked_string_langbased
-from textwrap import dedent
-
-def get_core_functions():
-    return {
-
-        "学术语料润色": {
-            # [1*] 前缀字符串，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等。
-            #      这里填一个提示词字符串就行了，这里为了区分中英文情景搞复杂了一点
-            "Prefix":   build_gpt_academic_masked_string_langbased(
-                            text_show_english=
-                                r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
-                                r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
-                                r"Firstly, you should provide the polished paragraph. "
-                                r"Secondly, you should list all your modification and explain the reasons to do so in markdown table.",
-                            text_show_chinese=
-                                r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，"
-                                r"同时分解长句，减少重复，并提供改进建议。请先提供文本的更正版本，然后在markdown表格中列出修改的内容，并给出修改的理由:"
-                        ) + "\n\n",
-            # [2*] 后缀字符串，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
-            "Suffix":   r"",
-            # [3] 按钮颜色 (可选参数，默认 secondary)
-            "Color":    r"secondary",
-            # [4] 按钮是否可见 (可选参数，默认 True，即可见)
-            "Visible": True,
-            # [5] 是否在触发时清除历史 (可选参数，默认 False，即不处理之前的对话历史)
-            "AutoClearHistory": False,
-            # [6] 文本预处理 （可选参数，默认 None，举例：写个函数移除所有的换行符）
-            "PreProcess": None,
-        },
-
-
-        "总结绘制脑图": {
-            # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-            "Prefix":   '''"""\n\n''',
-            # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
-            "Suffix":
-                # dedent() 函数用于去除多行字符串的缩进
-                dedent("\n\n"+r'''
-                    """
-
-                    使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
-
-                    以下是对以上文本的总结，以mermaid flowchart的形式展示：
-                    ```mermaid
-                    flowchart LR
-                        A["节点名1"] --> B("节点名2")
-                        B --> C{"节点名3"}
-                        C --> D["节点名4"]
-                        C --> |"箭头名1"| E["节点名5"]
-                        C --> |"箭头名2"| F["节点名6"]
-                    ```
-
-                    注意：
-                    （1）使用中文
-                    （2）节点名字使用引号包裹，如["Laptop"]
-                    （3）`|` 和 `"`之间不要存在空格
-                    （4）根据情况选择flowchart LR（从左到右）或者flowchart TD（从上到下）
-                '''),
-        },
-
-
-        "查找语法错误": {
-            "Prefix":   r"Help me ensure that the grammar and the spelling is correct. "
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good. "
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, "
-                        r"put the original text the first column, "
-                        r"put the corrected text in the second column and highlight the key words you fixed. "
-                        r"Finally, please provide the proofreaded text.""\n\n"
-                        r"Example:""\n"
-                        r"Paragraph: How is you? Do you knows what is it?""\n"
-                        r"| Original sentence | Corrected sentence |""\n"
-                        r"| :--- | :--- |""\n"
-                        r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n\n"
-                        r"Below is a paragraph from an academic paper. "
-                        r"You need to report all grammar and spelling mistakes as the example before."
-                        + "\n\n",
-            "Suffix":   r"",
-            "PreProcess": clear_line_break,    # 预处理：清除换行符
-        },
-
-
-        "中译英": {
-            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
-            "Suffix":   r"",
-        },
-
-
-        "学术英中互译": {
-            "Prefix":   build_gpt_academic_masked_string_langbased(
-                            text_show_chinese=
-                                r"I want you to act as a scientific English-Chinese translator, "
-                                r"I will provide you with some paragraphs in one language "
-                                r"and your task is to accurately and academically translate the paragraphs only into the other language. "
-                                r"Do not repeat the original provided paragraphs after translation. "
-                                r"You should use artificial intelligence tools, "
-                                r"such as natural language processing, and rhetorical knowledge "
-                                r"and experience about effective writing techniques to reply. "
-                                r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:",
-                            text_show_english=
-                                r"你是经验丰富的翻译，请把以下学术文章段落翻译成中文，"
-                                r"并同时充分考虑中文的语法、清晰、简洁和整体可读性，"
-                                r"必要时，你可以修改整个句子的顺序以确保翻译后的段落符合中文的语言习惯。"
-                                r"你需要翻译的文本如下："
-                        ) + "\n\n",
-            "Suffix":   r"",
-        },
-
-
-        "英译中": {
-            "Prefix":   r"翻译成地道的中文：" + "\n\n",
-            "Suffix":   r"",
-            "Visible":  False,
-        },
-
-
-        "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，"
-                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
-            "Suffix":   r"",
-            "Visible":  False,
-        },
-
-
-        "解释代码": {
-            "Prefix":   r"请解释以下代码：" + "\n```\n",
-            "Suffix":   "\n```\n",
-        },
-
-
-        "参考文献转Bib": {
-            "Prefix":   r"Here are some bibliography items, please transform them into bibtex style."
-                        r"Note that, reference styles maybe more than one kind, you should transform each item correctly."
-                        r"Items need to be transformed:" + "\n\n",
-            "Visible":  False,
-            "Suffix":   r"",
-        }
-    }
-
-
-def handle_core_functionality(additional_fn, inputs, history, chatbot):
-    import core_functional
-    importlib.reload(core_functional)    # 热更新prompt
-    core_functional = core_functional.get_core_functions()
-    addition = chatbot._cookies['customize_fn_overwrite']
-    if additional_fn in addition:
-        # 自定义功能
-        inputs = addition[additional_fn]["Prefix"] + inputs + addition[additional_fn]["Suffix"]
-        return inputs, history
-    else:
-        # 预制功能
-        if "PreProcess" in core_functional[additional_fn]:
-            if core_functional[additional_fn]["PreProcess"] is not None:
-                inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        # 为字符串加上上面定义的前缀和后缀。
-        inputs = apply_gpt_academic_string_mask_langbased(
-            string = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"],
-            lang_reference = inputs,
-        )
-        if core_functional[additional_fn].get("AutoClearHistory", False):
-            history = []
-        return inputs, history
-
-if __name__ == "__main__":
-    t = get_core_functions()["总结绘制脑图"]
-    print(t["Prefix"] + t["Suffix"])
--- a/crazy_functional.py
+++ b/crazy_functional.py
@ -1,723 +0,0 @@
-from toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
-from toolbox import trimmed_format_exc
-
-
-def get_crazy_functions():
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个Matlab项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.解析项目源代码 import 解析一个Golang项目
-    from crazy_functions.解析项目源代码 import 解析一个Rust项目
-    from crazy_functions.解析项目源代码 import 解析一个Java项目
-    from crazy_functions.解析项目源代码 import 解析一个前端项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    from crazy_functions.询问多个大语言模型 import 同时问询
-    from crazy_functions.解析项目源代码 import 解析一个Lua项目
-    from crazy_functions.解析项目源代码 import 解析一个CSharp项目
-    from crazy_functions.总结word文档 import 总结word文档
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    from crazy_functions.对话历史存档 import 对话历史存档
-    from crazy_functions.对话历史存档 import 载入对话历史存档
-    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
-    from crazy_functions.辅助功能 import 清除缓存
-    from crazy_functions.批量Markdown翻译 import Markdown英译中
-    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.PDF批量翻译 import 批量翻译PDF文档
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
-    from crazy_functions.Latex全文润色 import Latex中文润色
-    from crazy_functions.Latex全文润色 import Latex英文纠错
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    from crazy_functions.虚空终端 import 虚空终端
-    from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表
-
-    function_plugins = {
-        "虚空终端": {
-            "Group": "对话|编程|学术|智能体",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(虚空终端),
-        },
-        "解析整个Python项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "解析一个Python项目的所有源文件(.py) | 输入参数为路径",
-            "Function": HotReload(解析一个Python项目),
-        },
-        "载入对话历史存档（先上传存档或输入路径）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "载入对话历史存档 | 输入参数为路径",
-            "Function": HotReload(载入对话历史存档),
-        },
-        "删除所有本地对话历史记录（谨慎操作）": {
-            "Group": "对话",
-            "AsButton": False,
-            "Info": "删除所有本地对话历史记录，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(删除所有本地对话历史记录),
-        },
-        "清除所有缓存文件（谨慎操作）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(清除缓存),
-        },
-        "生成多种Mermaid图表(从当前对话或路径(.pdf/.md/.docx)中生产图表）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,
-            "Info" : "基于当前对话或文件生成多种Mermaid图表,图表类型由模型判断",
-            "Function": HotReload(生成多种Mermaid图表),
-            "AdvancedArgs": True,
-            "ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
-        },
-        "批量总结Word文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "批量总结word文档 | 输入参数为路径",
-            "Function": HotReload(总结word文档),
-        },
-        "解析整个Matlab项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "解析一个Matlab项目的所有源文件(.m) | 输入参数为路径",
-            "Function": HotReload(解析一个Matlab项目),
-        },
-        "解析整个C++项目头文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有头文件(.h/.hpp) | 输入参数为路径",
-            "Function": HotReload(解析一个C项目的头文件),
-        },
-        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有源文件（.cpp/.hpp/.c/.h）| 输入参数为路径",
-            "Function": HotReload(解析一个C项目),
-        },
-        "解析整个Go项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Go项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Golang项目),
-        },
-        "解析整个Rust项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Rust项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Rust项目),
-        },
-        "解析整个Java项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Java项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Java项目),
-        },
-        "解析整个前端项目（js,ts,css等）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个前端项目的所有源文件（js,ts,css等） | 输入参数为路径",
-            "Function": HotReload(解析一个前端项目),
-        },
-        "解析整个Lua项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Lua项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Lua项目),
-        },
-        "解析整个CSharp项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个CSharp项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个CSharp项目),
-        },
-        "解析Jupyter Notebook文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "解析Jupyter Notebook文件 | 输入参数为路径",
-            "Function": HotReload(解析ipynb文件),
-            "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-            "ArgsReminder": "若输入0，则不解析notebook中的Markdown块",  # 高级参数输入区的显示提示
-        },
-        "读Tex论文写摘要": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "读取Tex论文并写摘要 | 输入参数为路径",
-            "Function": HotReload(读文章写摘要),
-        },
-        "翻译README或MD": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "将Markdown翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中),
-        },
-        "翻译Markdown或README（支持Github链接）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "将Markdown或README翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中),
-        },
-        "批量生成函数注释": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量生成函数的注释 | 输入参数为路径",
-            "Function": HotReload(批量生成函数注释),
-        },
-        "保存当前的对话": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档),
-        },
-        "[多线程Demo]解析此项目本身（源码自译解）": {
-            "Group": "对话|编程",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "多线程解析并翻译此项目的源码 | 不需要输入参数",
-            "Function": HotReload(解析项目本身),
-        },
-        "历史上的今天": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "查看历史上的今天事件 (这是一个面向开发者的插件Demo) | 不需要输入参数",
-            "Function": HotReload(高阶功能模板函数),
-        },
-        "精准翻译PDF论文": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档),
-        },
-        "询问多个GPT模型": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(同时问询),
-        },
-        "批量总结PDF文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量总结PDF文档的内容 | 输入参数为路径",
-            "Function": HotReload(批量总结PDF文档),
-        },
-        "谷歌学术检索助手（输入谷歌学术搜索页url）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
-            "Function": HotReload(谷歌检索小助手),
-        },
-        "理解PDF文档内容 （模仿ChatPDF）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
-            "Function": HotReload(理解PDF文档内容标准文件输入),
-        },
-        "英文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对英文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文润色),
-        },
-
-        "中文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对中文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中文润色),
-        },
-        # 已经被新插件取代
-        # "英文Latex项目全文纠错（输入路径或上传压缩包）": {
-        #     "Group": "学术",
-        #     "Color": "stop",
-        #     "AsButton": False,  # 加入下拉菜单中
-        #     "Info": "对英文Latex项目全文进行纠错处理 | 输入参数为路径或上传压缩包",
-        #     "Function": HotReload(Latex英文纠错),
-        # },
-        # 已经被新插件取代
-        # "Latex项目全文中译英（输入路径或上传压缩包）": {
-        #     "Group": "学术",
-        #     "Color": "stop",
-        #     "AsButton": False,  # 加入下拉菜单中
-        #     "Info": "对Latex项目全文进行中译英处理 | 输入参数为路径或上传压缩包",
-        #     "Function": HotReload(Latex中译英)
-        # },
-        # 已经被新插件取代
-        # "Latex项目全文英译中（输入路径或上传压缩包）": {
-        #     "Group": "学术",
-        #     "Color": "stop",
-        #     "AsButton": False,  # 加入下拉菜单中
-        #     "Info": "对Latex项目全文进行英译中处理 | 输入参数为路径或上传压缩包",
-        #     "Function": HotReload(Latex英译中)
-        # },
-        "批量Markdown中译英（输入路径或上传压缩包）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Markdown中译英),
-        },
-    }
-
-    # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
-    try:
-        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-
-        function_plugins.update(
-            {
-                "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,  # 加入下拉菜单中
-                    # "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
-                    "Function": HotReload(下载arxiv论文并翻译摘要),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.联网的ChatGPT import 连接网络回答问题
-
-        function_plugins.update(
-            {
-                "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,  # 加入下拉菜单中
-                    # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-                    "Function": HotReload(连接网络回答问题),
-                }
-            }
-        )
-        from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
-
-        function_plugins.update(
-            {
-                "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,  # 加入下拉菜单中
-                    "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
-                    "Function": HotReload(连接bing搜索回答问题),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.解析项目源代码 import 解析任意code项目
-
-        function_plugins.update(
-            {
-                "解析项目源代码（手动指定和筛选源代码文件类型）": {
-                    "Group": "编程",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                    "ArgsReminder": '输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: "*.c, ^*.cpp, config.toml, ^*.toml"',  # 高级参数输入区的显示提示
-                    "Function": HotReload(解析任意code项目),
-                },
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
-
-        function_plugins.update(
-            {
-                "询问多个GPT模型（手动指定询问哪些模型）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                    "ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&gpt-4",  # 高级参数输入区的显示提示
-                    "Function": HotReload(同时问询_指定模型),
-                },
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
-
-        function_plugins.update(
-            {
-                "图片生成_DALLE2 （先切换模型到gpt-*）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                    "ArgsReminder": "在这里输入分辨率, 如1024x1024（默认），支持 256x256, 512x512, 1024x1024",  # 高级参数输入区的显示提示
-                    "Info": "使用DALLE2生成图片 | 输入参数字符串，提供图像的内容",
-                    "Function": HotReload(图片生成_DALLE2),
-                },
-            }
-        )
-        function_plugins.update(
-            {
-                "图片生成_DALLE3 （先切换模型到gpt-*）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                    "ArgsReminder": "在这里输入自定义参数「分辨率-质量(可选)-风格(可选)」, 参数示例「1024x1024-hd-vivid」 || 分辨率支持 「1024x1024」(默认) /「1792x1024」/「1024x1792」 || 质量支持 「-standard」(默认) /「-hd」 || 风格支持 「-vivid」(默认) /「-natural」",  # 高级参数输入区的显示提示
-                    "Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
-                    "Function": HotReload(图片生成_DALLE3),
-                },
-            }
-        )
-        function_plugins.update(
-            {
-                "图片修改_DALLE2 （先切换模型到gpt-*）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": False,  # 调用时，唤起高级参数输入区（默认False）
-                    # "Info": "使用DALLE2修改图片 | 输入参数字符串，提供图像的内容",
-                    "Function": HotReload(图片修改_DALLE2),
-                },
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.总结音视频 import 总结音视频
-
-        function_plugins.update(
-            {
-                "批量总结音视频（输入路径或上传压缩包）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
-                    "Info": "批量总结音频或视频 | 输入参数为路径",
-                    "Function": HotReload(总结音视频),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.数学动画生成manim import 动画生成
-
-        function_plugins.update(
-            {
-                "数学动画生成（Manim）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
-                    "Function": HotReload(动画生成),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
-
-        function_plugins.update(
-            {
-                "Markdown翻译（指定翻译成何种语言）": {
-                    "Group": "编程",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "请输入要翻译成哪种语言，默认为Chinese。",
-                    "Function": HotReload(Markdown翻译指定语言),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.知识库问答 import 知识库文件注入
-
-        function_plugins.update(
-            {
-                "构建知识库（先上传文件素材,再运行此插件）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
-                    "Function": HotReload(知识库文件注入),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.知识库问答 import 读取知识库作答
-
-        function_plugins.update(
-            {
-                "知识库文件注入（构建知识库后,再运行此插件）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要构建知识库后再运行此插件。",
-                    "Function": HotReload(读取知识库作答),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.交互功能函数模板 import 交互功能模板函数
-
-        function_plugins.update(
-            {
-                "交互功能模板Demo函数（查找wallhaven.cc的壁纸）": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Function": HotReload(交互功能模板函数),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.Latex输出PDF import Latex英文纠错加PDF对比
-        from crazy_functions.Latex输出PDF import Latex翻译中文并重新编译PDF
-        from crazy_functions.Latex输出PDF import PDF翻译中文并重新编译PDF
-
-        function_plugins.update(
-            {
-                "Latex英文纠错+高亮修正位置 [需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
-                    "Function": HotReload(Latex英文纠错加PDF对比),
-                },
-                "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                    "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-                    "Function": HotReload(Latex翻译中文并重新编译PDF),
-                },
-                "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                    "Info": "本地Latex论文精细翻译 | 输入参数是路径",
-                    "Function": HotReload(Latex翻译中文并重新编译PDF),
-                },
-                "PDF翻译中文并重新编译PDF（上传PDF）[需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                    "Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
-                    "Function": HotReload(PDF翻译中文并重新编译PDF)
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from toolbox import get_conf
-
-        ENABLE_AUDIO = get_conf("ENABLE_AUDIO")
-        if ENABLE_AUDIO:
-            from crazy_functions.语音助手 import 语音助手
-
-            function_plugins.update(
-                {
-                    "实时语音对话": {
-                        "Group": "对话",
-                        "Color": "stop",
-                        "AsButton": True,
-                        "Info": "这是一个时刻聆听着的语音对话助手 | 没有输入参数",
-                        "Function": HotReload(语音助手),
-                    }
-                }
-            )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档
-
-        function_plugins.update(
-            {
-                "精准翻译PDF文档（NOUGAT）": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Function": HotReload(批量翻译PDF文档),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.函数动态生成 import 函数动态生成
-
-        function_plugins.update(
-            {
-                "动态代码解释器（CodeInterpreter）": {
-                    "Group": "智能体",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Function": HotReload(函数动态生成),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.多智能体 import 多智能体终端
-
-        function_plugins.update(
-            {
-                "AutoGen多智能体终端（仅供测试）": {
-                    "Group": "智能体",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Function": HotReload(多智能体终端),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    try:
-        from crazy_functions.互动小游戏 import 随机小游戏
-
-        function_plugins.update(
-            {
-                "随机互动小游戏（仅供测试）": {
-                    "Group": "智能体",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "Function": HotReload(随机小游戏),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
-
-    # try:
-    #     from crazy_functions.高级功能函数模板 import 测试图表渲染
-    #     function_plugins.update({
-    #         "绘制逻辑关系（测试图表渲染）": {
-    #             "Group": "智能体",
-    #             "Color": "stop",
-    #             "AsButton": True,
-    #             "Function": HotReload(测试图表渲染)
-    #         }
-    #     })
-    # except:
-    #     print(trimmed_format_exc())
-    #     print('Load function plugin failed')
-
-    # try:
-    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
-    #     function_plugins.update({
-    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
-    #             "Color": "stop",
-    #             "AsButton": False,
-    #             "AdvancedArgs": True,
-    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
-    #             "Function": HotReload(微调数据集生成)
-    #         }
-    #     })
-    # except:
-    #     print('Load function plugin failed')
-
-    """
-    设置默认值:
-    - 默认 Group = 对话
-    - 默认 AsButton = True
-    - 默认 AdvancedArgs = False
-    - 默认 Color = secondary
-    """
-    for name, function_meta in function_plugins.items():
-        if "Group" not in function_meta:
-            function_plugins[name]["Group"] = "对话"
-        if "AsButton" not in function_meta:
-            function_plugins[name]["AsButton"] = True
-        if "AdvancedArgs" not in function_meta:
-            function_plugins[name]["AdvancedArgs"] = False
-        if "Color" not in function_meta:
-            function_plugins[name]["Color"] = "secondary"
-
-    return function_plugins
--- a/crazy_functions/Latex全文润色.py
+++ b/crazy_functions/Latex全文润色.py
@ -1,245 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
-from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
-
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llms.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-        print('Segmentation: done')
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            with open(path + '.polish.tex', 'w', encoding='utf8') as f:
-                manifest.append(path + '.polish.tex')
-                f.write(res)
-        return manifest
-
-    def zip_result(self):
-        import os, time
-        folder = os.path.dirname(self.file_paths[0])
-        t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-        zip_folder(folder, get_log_folder(), f'{t}-polished.zip')
-
-
-def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):
-    import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-
-    #  <-------- 读取Latex文件，删除其中的所有注释 ---------->
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 定义注释的正则表达式
-            comment_pattern = r'(?<!\\)%.*'
-            # 使用正则表达式查找注释，并替换为空字符串
-            clean_tex_content = re.sub(comment_pattern, '', file_content)
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(clean_tex_content)
-
-    #  <-------- 拆分过长的latex文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-
-    #  <-------- 多线程润色开始 ---------->
-    if language == 'en':
-        if mode == 'polish':
-            inputs_array = [r"Below is a section from an academic paper, polish this section to meet the academic standard, " +
-                            r"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        else:
-            inputs_array = [r"Below is a section from an academic paper, proofread this section." +
-                            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
-                            r"Answer me only with the revised text:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"Polish {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif language == 'zh':
-        if mode == 'polish':
-            inputs_array = [r"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        else:
-            inputs_array = [r"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]
-
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # 并行任务数量限制，最多同时执行5个，其他的排队等待
-        scroller_max_len = 80
-    )
-
-    #  <-------- 文本碎片重组为完整的tex文件，整理结果为压缩包 ---------->
-    try:
-        pfg.sp_file_result = []
-        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-        pfg.write_result()
-        pfg.zip_result()
-    except:
-        print(trimmed_format_exc())
-
-    #  <-------- 整理结果，退出 ---------->
-    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky。（注意，此插件不调用Latex，如果有Latex环境，请使用「Latex英文纠错+高亮修正位置(需Latex)插件」"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')
-
-
-
-
-
-
-@CatchException
-def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')
-
-
-
-
-@CatchException
-def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行纠错。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')
-
-
-
--- a/crazy_functions/Latex全文翻译.py
+++ b/crazy_functions/Latex全文翻译.py
@ -1,176 +0,0 @@
-from toolbox import update_ui, promote_file_to_downloadzone
-from toolbox import CatchException, report_exception, write_history_to_file
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llms.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-        print('Segmentation: done')
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Latex文件，删除其中的所有注释 ---------->
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 定义注释的正则表达式
-            comment_pattern = r'(?<!\\)%.*'
-            # 使用正则表达式查找注释，并替换为空字符串
-            clean_tex_content = re.sub(comment_pattern, '', file_content)
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(clean_tex_content)
-
-    #  <-------- 拆分过长的latex文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 抽取摘要 ---------->
-    # if language == 'en':
-    #     abs_extract_inputs = f"Please write an abstract for this paper"
-
-    # # 单线，获取文章meta信息
-    # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=abs_extract_inputs,
-    #     inputs_show_user=f"正在抽取摘要信息。",
-    #     llm_kwargs=llm_kwargs,
-    #     chatbot=chatbot, history=[],
-    #     sys_prompt="Your job is to collect information from materials。",
-    # )
-
-    #  <-------- 多线程润色开始 ---------->
-    if language == 'en->zh':
-        inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-
-    #  <-------- 整理结果，退出 ---------->
-    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_history_to_file(gpt_response_collection, create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-
-
-
-@CatchException
-def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
--- a/crazy_functions/Latex输出PDF.py
+++ b/crazy_functions/Latex输出PDF.py
@ -1,543 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
-from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
-from functools import partial
-import glob, os, requests, time, json, tarfile
-
-pj = os.path.join
-ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
-
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
-def switch_prompt(pfg, mode, more_requirement):
-    """
-    Generate prompts and system prompts based on the mode for proofreading or translating.
-    Args:
-    - pfg: Proofreader or Translator instance.
-    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
-
-    Returns:
-    - inputs_array: A list of strings containing prompts for users to respond to.
-    - sys_prompt_array: A list of strings containing prompts for system prompts.
-    """
-    n_split = len(pfg.sp_file_contents)
-    if mode == 'proofread_en':
-        inputs_array = [r"Below is a section from an academic paper, proofread this section." +
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
-                        r"Answer me only with the revised text:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif mode == 'translate_zh':
-        inputs_array = [
-            r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
-            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
-            r"Answer me only with the translated text:" +
-            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
-    else:
-        assert False, "未知指令"
-    return inputs_array, sys_prompt_array
-
-
-def desend_to_extracted_folder_if_exist(project_folder):
-    """
-    Descend into the extracted folder if it exists, otherwise return the original folder.
-
-    Args:
-    - project_folder: A string specifying the folder path.
-
-    Returns:
-    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
-    """
-    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
-    if len(maybe_dir) == 0: return project_folder
-    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
-    return project_folder
-
-
-def move_project(project_folder, arxiv_id=None):
-    """
-    Create a new work folder and copy the project folder to it.
-
-    Args:
-    - project_folder: A string specifying the folder path of the project.
-
-    Returns:
-    - A string specifying the path to the new work folder.
-    """
-    import shutil, time
-    time.sleep(2)  # avoid time string conflict
-    if arxiv_id is not None:
-        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
-    else:
-        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
-    try:
-        shutil.rmtree(new_workfolder)
-    except:
-        pass
-
-    # align subfolder if there is a folder wrapper
-    items = glob.glob(pj(project_folder, '*'))
-    items = [item for item in items if os.path.basename(item) != '__MACOSX']
-    if len(glob.glob(pj(project_folder, '*.tex'))) == 0 and len(items) == 1:
-        if os.path.isdir(items[0]): project_folder = items[0]
-
-    shutil.copytree(src=project_folder, dst=new_workfolder)
-    return new_workfolder
-
-
-def arxiv_download(chatbot, history, txt, allow_cache=True):
-    def check_cached_translation_pdf(arxiv_id):
-        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
-        if not os.path.exists(translation_dir):
-            os.makedirs(translation_dir)
-        target_file = pj(translation_dir, 'translate_zh.pdf')
-        if os.path.exists(target_file):
-            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
-            target_file_compare = pj(translation_dir, 'comparison.pdf')
-            if os.path.exists(target_file_compare):
-                promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
-            return target_file
-        return False
-
-    def is_float(s):
-        try:
-            float(s)
-            return True
-        except ValueError:
-            return False
-
-    if txt.startswith('https://arxiv.org/pdf/'):
-        arxiv_id = txt.split('/')[-1]   # 2402.14207v2.pdf
-        txt = arxiv_id.split('v')[0]  # 2402.14207
-
-    if ('.' in txt) and ('/' not in txt) and is_float(txt):  # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt.strip()
-    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]):  # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt[:10]
-
-    if not txt.startswith('https://arxiv.org'):
-        return txt, None    # 是本地文件，跳过下载
-
-    # <-------------- inspect format ------------->
-    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
-    yield from update_ui(chatbot=chatbot, history=history)
-    time.sleep(1)  # 刷新界面
-
-    url_ = txt  # https://arxiv.org/abs/1707.06690
-
-    if not txt.startswith('https://arxiv.org/abs/'):
-        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
-        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
-        return msg, None
-    # <-------------- set format ------------->
-    arxiv_id = url_.split('/abs/')[-1]
-    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
-    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
-    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
-
-    url_tar = url_.replace('/abs/', '/e-print/')
-    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
-    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
-    os.makedirs(translation_dir, exist_ok=True)
-
-    # <-------------- download arxiv source file ------------->
-    dst = pj(translation_dir, arxiv_id + '.tar')
-    if os.path.exists(dst):
-        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
-    else:
-        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
-        proxies = get_conf('proxies')
-        r = requests.get(url_tar, proxies=proxies)
-        with open(dst, 'wb+') as f:
-            f.write(r.content)
-    # <-------------- extract file ------------->
-    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
-    from toolbox import extract_archive
-    extract_archive(file_path=dst, dest_dir=extract_dst)
-    return extract_dst, arxiv_id
-
-
-def pdf2tex_project(pdf_file_path):
-    # Mathpix API credentials
-    app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
-    headers = {"app_id": app_id, "app_key": app_key}
-
-    # Step 1: Send PDF file for processing
-    options = {
-        "conversion_formats": {"tex.zip": True},
-        "math_inline_delimiters": ["$", "$"],
-        "rm_spaces": True
-    }
-
-    response = requests.post(url="https://api.mathpix.com/v3/pdf",
-                             headers=headers,
-                             data={"options_json": json.dumps(options)},
-                             files={"file": open(pdf_file_path, "rb")})
-
-    if response.ok:
-        pdf_id = response.json()["pdf_id"]
-        print(f"PDF processing initiated. PDF ID: {pdf_id}")
-
-        # Step 2: Check processing status
-        while True:
-            conversion_response = requests.get(f"https://api.mathpix.com/v3/pdf/{pdf_id}", headers=headers)
-            conversion_data = conversion_response.json()
-
-            if conversion_data["status"] == "completed":
-                print("PDF processing completed.")
-                break
-            elif conversion_data["status"] == "error":
-                print("Error occurred during processing.")
-            else:
-                print(f"Processing status: {conversion_data['status']}")
-                time.sleep(5)  # wait for a few seconds before checking again
-
-        # Step 3: Save results to local files
-        output_dir = os.path.join(os.path.dirname(pdf_file_path), 'mathpix_output')
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-
-        url = f"https://api.mathpix.com/v3/pdf/{pdf_id}.tex"
-        response = requests.get(url, headers=headers)
-        file_name_wo_dot = '_'.join(os.path.basename(pdf_file_path).split('.')[:-1])
-        output_name = f"{file_name_wo_dot}.tex.zip"
-        output_path = os.path.join(output_dir, output_name)
-        with open(output_path, "wb") as output_file:
-            output_file.write(response.content)
-        print(f"tex.zip file saved at: {output_path}")
-
-        import zipfile
-        unzip_dir = os.path.join(output_dir, file_name_wo_dot)
-        with zipfile.ZipFile(output_path, 'r') as zip_ref:
-            zip_ref.extractall(unzip_dir)
-
-        return unzip_dir
-
-    else:
-        print(f"Error sending PDF for processing. Status code: {response.status_code}")
-        return None
-
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-
-@CatchException
-def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append(["函数插件功能？",
-                    "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([f"解析项目: {txt}",
-                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id=None)
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
-                                       chatbot, history, system_prompt, mode='proofread_en',
-                                       switch_prompt=_switch_prompt_)
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
-                                   main_file_modified='merge_proofread_en',
-                                   work_folder_original=project_folder, work_folder_modified=project_folder,
-                                   work_folder=project_folder)
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history);
-        time.sleep(1)  # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了",
-                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
-        yield from update_ui(chatbot=chatbot, history=history);
-        time.sleep(1)  # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-    # <-------------- we are done ------------->
-    return success
-
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-@CatchException
-def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    no_cache = more_req.startswith("--no-cache")
-    if no_cache: more_req.lstrip("--no-cache")
-    allow_cache = not no_cache
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([f"解析项目: {txt}",
-                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    try:
-        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
-    except tarfile.ReadError as e:
-        yield from update_ui_lastest_msg(
-            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
-            chatbot=chatbot, history=history)
-        return
-
-    if txt.endswith('.pdf'):
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现已经存在翻译好的PDF文档")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id)
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
-                                       chatbot, history, system_prompt, mode='translate_zh',
-                                       switch_prompt=_switch_prompt_)
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
-                                   main_file_modified='merge_translate_zh', mode='translate_zh',
-                                   work_folder_original=project_folder, work_folder_modified=project_folder,
-                                   work_folder=project_folder)
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history);
-        time.sleep(1)  # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了",
-                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-        yield from update_ui(chatbot=chatbot, history=history);
-        time.sleep(1)  # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-    # <-------------- we are done ------------->
-    return success
-
-
-#  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 插件主程序3  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-@CatchException
-def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([
-        "函数插件功能？",
-        "将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    no_cache = more_req.startswith("--no-cache")
-    if no_cache: more_req.lstrip("--no-cache")
-    allow_cache = not no_cache
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([f"解析项目: {txt}",
-                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # <-------------- clear history and read input ------------->
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    if len(file_manifest) != 1:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"不支持同时处理多个pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
-    if len(app_id) == 0 or len(app_key) == 0:
-        report_exception(chatbot, history, a="缺失 MATHPIX_APPID 和 MATHPIX_APPKEY。", b=f"请配置 MATHPIX_APPID 和 MATHPIX_APPKEY")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    hash_tag = map_file_to_sha256(file_manifest[0])
-
-    # <-------------- check repeated pdf ------------->
-    chatbot.append([f"检查PDF是否被重复上传", "正在检查..."])
-    yield from update_ui(chatbot=chatbot, history=history)
-    repeat, project_folder = check_repeat_upload(file_manifest[0], hash_tag)
-
-    except_flag = False
-
-    if repeat:
-        yield from update_ui_lastest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
-
-        try:
-            trans_html_file = [f for f in glob.glob(f'{project_folder}/**/*.trans.html', recursive=True)][0]
-            promote_file_to_downloadzone(trans_html_file, rename_file=None, chatbot=chatbot)
-
-            translate_pdf = [f for f in glob.glob(f'{project_folder}/**/merge_translate_zh.pdf', recursive=True)][0]
-            promote_file_to_downloadzone(translate_pdf, rename_file=None, chatbot=chatbot)
-
-            comparison_pdf = [f for f in glob.glob(f'{project_folder}/**/comparison.pdf', recursive=True)][0]
-            promote_file_to_downloadzone(comparison_pdf, rename_file=None, chatbot=chatbot)
-
-            zip_res = zip_result(project_folder)
-            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-            return True
-
-        except:
-            report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现重复上传，但是无法找到相关文件")
-            yield from update_ui(chatbot=chatbot, history=history)
-
-            chatbot.append([f"没有相关文件", '尝试重新翻译PDF...'])
-            yield from update_ui(chatbot=chatbot, history=history)
-
-            except_flag = True
-
-
-    elif not repeat or except_flag:
-        yield from update_ui_lastest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
-
-        # <-------------- convert pdf into tex ------------->
-        chatbot.append([f"解析项目: {txt}", "正在将PDF转换为tex项目，请耐心等待..."])
-        yield from update_ui(chatbot=chatbot, history=history)
-        project_folder = pdf2tex_project(file_manifest[0])
-        if project_folder is None:
-            report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"PDF转换为tex项目失败")
-            yield from update_ui(chatbot=chatbot, history=history)
-            return False
-
-        # <-------------- translate latex file into Chinese ------------->
-        yield from update_ui_lastest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-        if len(file_manifest) == 0:
-            report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-            return
-
-        # <-------------- if is a zip/tar file ------------->
-        project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-        # <-------------- move latex project away from temp folder ------------->
-        project_folder = move_project(project_folder)
-
-        # <-------------- set a hash tag for repeat-checking ------------->
-        with open(pj(project_folder, hash_tag + '.tag'), 'w') as f:
-            f.write(hash_tag)
-            f.close()
-
-
-        # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-        if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
-            yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
-                                        chatbot, history, system_prompt, mode='translate_zh',
-                                        switch_prompt=_switch_prompt_)
-
-        # <-------------- compile PDF ------------->
-        yield from update_ui_lastest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
-        success = yield from 编译Latex(chatbot, history, main_file_original='merge',
-                                    main_file_modified='merge_translate_zh', mode='translate_zh',
-                                    work_folder_original=project_folder, work_folder_modified=project_folder,
-                                    work_folder=project_folder)
-
-        # <-------------- zip PDF ------------->
-        zip_res = zip_result(project_folder)
-        if success:
-            chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-            yield from update_ui(chatbot=chatbot, history=history);
-            time.sleep(1)  # 刷新界面
-            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-        else:
-            chatbot.append((f"失败了",
-                            '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-            yield from update_ui(chatbot=chatbot, history=history);
-            time.sleep(1)  # 刷新界面
-            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-        # <-------------- we are done ------------->
-        return success
--- a/crazy_functions/PDF批量翻译.py
+++ b/crazy_functions/PDF批量翻译.py
@ -1,324 +0,0 @@
-from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
-from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
-from toolbox import generate_file_link, zip_folder, trimmed_format_exc, trimmed_format_exc_markdown
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import get_files_from_everything
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
-from colorful import *
-import os
-
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-
-    disable_auto_promotion(chatbot)
-    # 基本信息：功能、贡献者
-    chatbot.append([None, "插件功能：批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        check_packages(["fitz", "tiktoken", "scipdf"])
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
-    # ------- 第一种方法，效果最好，但是需要DOC2X服务 -------
-    if len(DOC2X_API_KEY) != 0:
-        try:
-            yield from 解析PDF_DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
-            return
-        except:
-            chatbot.append([None, f"DOC2X服务不可用，现在将执行效果稍差的旧版代码。{trimmed_format_exc_markdown()}"])
-            yield from update_ui(chatbot=chatbot, history=history)
-
-    # ------- 第二种方法，效果次优 -------
-    grobid_url = get_avail_grobid_url()
-    if grobid_url is not None:
-        yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
-        return
-
-    # ------- 第三种方法，早期代码，效果不理想 -------
-    yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    return
-
-
-
-def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request):
-
-    def pdf2markdown(filepath):
-        import requests, json, os
-        markdown_dir = get_log_folder(plugin_name="pdf_ocr")
-        doc2x_api_key = DOC2X_API_KEY
-        if doc2x_api_key.startswith('sk-'):
-            url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
-        else:
-            url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
-
-        chatbot.append((None, "加载PDF文件，发送至DOC2X解析..."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        res = requests.post(
-            url,
-            files={"file": open(filepath, "rb")},
-            data={"ocr": "1"},
-            headers={"Authorization": "Bearer " + doc2x_api_key}
-        )
-        res_json = []
-        if res.status_code == 200:
-            decoded = res.content.decode("utf-8")
-            for z_decoded in decoded.split('\n'):
-                if len(z_decoded) == 0: continue
-                assert z_decoded.startswith("data: ")
-                z_decoded = z_decoded[len("data: "):]
-                decoded_json = json.loads(z_decoded)
-                res_json.append(decoded_json)
-        else:
-            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
-        uuid = res_json[0]['uuid']
-        to = "md" # latex, md, docx
-        url = "https://api.doc2x.noedgeai.com/api/export"+"?request_id="+uuid+"&to="+to
-
-        chatbot.append((None, f"读取解析: {url} ..."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        res = requests.get(url, headers={"Authorization": "Bearer " + doc2x_api_key})
-        md_zip_path = os.path.join(markdown_dir, gen_time_str() + '.zip')
-        if res.status_code == 200:
-            with open(md_zip_path, "wb") as f: f.write(res.content)
-        else:
-            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
-        promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
-        chatbot.append((None, f"完成解析 {md_zip_path} ..."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return md_zip_path
-
-    def deliver_to_markdown_plugin(md_zip_path, user_request):
-        from crazy_functions.批量Markdown翻译 import Markdown英译中
-        import shutil, re
-
-        time_tag = gen_time_str()
-        target_path_base = get_log_folder(chatbot.get_user())
-        file_origin_name = os.path.basename(md_zip_path)
-        this_file_path = os.path.join(target_path_base, file_origin_name)
-        os.makedirs(target_path_base, exist_ok=True)
-        shutil.copyfile(md_zip_path, this_file_path)
-        ex_folder = this_file_path + ".extract"
-        extract_archive(
-            file_path=this_file_path, dest_dir=ex_folder
-        )
-
-        # edit markdown files
-        success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md')
-        for generated_fp in file_manifest:
-            # 修正一些公式问题
-            with open(generated_fp, 'r', encoding='utf8') as f:
-                content = f.read()
-            # 将公式中的\[ \]替换成$$
-            content = content.replace(r'\[', r'$$').replace(r'\]', r'$$')
-            # 将公式中的\( \)替换成$
-            content = content.replace(r'\(', r'$').replace(r'\)', r'$')
-            content = content.replace('```markdown', '\n').replace('```', '\n')
-            with open(generated_fp, 'w', encoding='utf8') as f:
-                f.write(content)
-            promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-            # 生成在线预览html
-            file_name = '在线预览翻译（原文）' + gen_time_str() + '.html'
-            preview_fp = os.path.join(ex_folder, file_name)
-            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
-            with open(generated_fp, "r", encoding="utf-8") as f:
-                md = f.read()
-                # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
-                md = re.sub(r'^<table>', r'😃<table>', md, flags=re.MULTILINE)
-            html = markdown_convertion_for_file(md)
-            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
-            chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
-            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
-
-
-
-        chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
-        plugin_kwargs['markdown_expected_output_dir'] = ex_folder
-
-        translated_f_name = 'translated_markdown.md'
-        generated_fp = plugin_kwargs['markdown_expected_output_path'] = os.path.join(ex_folder, translated_f_name)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        yield from Markdown英译中(ex_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-        if os.path.exists(generated_fp):
-            # 修正一些公式问题
-            with open(generated_fp, 'r', encoding='utf8') as f: content = f.read()
-            content = content.replace('```markdown', '\n').replace('```', '\n')
-            # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
-            content = re.sub(r'^<table>', r'😃<table>', content, flags=re.MULTILINE)
-            with open(generated_fp, 'w', encoding='utf8') as f: f.write(content)
-            # 生成在线预览html
-            file_name = '在线预览翻译' + gen_time_str() + '.html'
-            preview_fp = os.path.join(ex_folder, file_name)
-            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
-            with open(generated_fp, "r", encoding="utf-8") as f:
-                md = f.read()
-            html = markdown_convertion_for_file(md)
-            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
-            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
-            # 生成包含图片的压缩包
-            dest_folder = get_log_folder(chatbot.get_user())
-            zip_name = '翻译后的带图文档.zip'
-            zip_folder(source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name)
-            zip_fp = os.path.join(dest_folder, zip_name)
-            promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    md_zip_path = yield from pdf2markdown(fp)
-    yield from deliver_to_markdown_plugin(md_zip_path, user_request)
-
-def 解析PDF_DOC2X(file_manifest, *args):
-    for index, fp in enumerate(file_manifest):
-        yield from 解析PDF_DOC2X_单文件(fp, *args)
-    return
-
-def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
-    import copy, json
-    TOKEN_LIMIT_PER_FRAGMENT = 1024
-    generated_conclusion_files = []
-    generated_html_files = []
-    DST_LANG = "中文"
-    from crazy_functions.pdf_fns.report_gen_html import construct_html
-    for index, fp in enumerate(file_manifest):
-        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        article_dict = parse_pdf(fp, grobid_url)
-        grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
-        with open(grobid_json_res, 'w+', encoding='utf8') as f:
-            f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
-        promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
-
-        if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
-        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    """
-    此函数已经弃用
-    """
-    import copy
-    TOKEN_LIMIT_PER_FRAGMENT = 1024
-    generated_conclusion_files = []
-    generated_html_files = []
-    from crazy_functions.pdf_fns.report_gen_html import construct_html
-    for index, fp in enumerate(file_manifest):
-        # 读取PDF文件
-        file_content, page_one = read_and_clean_pdf_text(fp)
-        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
-
-        # 递归地切割PDF文件
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
-        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
-
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
-            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="Your job is to collect information from materials。",
-        )
-
-        # 多线，翻译
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[
-                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
-            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[paper_meta] for _ in paper_fragments],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
-            # max_workers=5  # OpenAI所允许的最大并行过载
-        )
-        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
-        # 整理报告的格式
-        for i,k in enumerate(gpt_response_collection_md):
-            if i%2==0:
-                gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
-            else:
-                gpt_response_collection_md[i] = gpt_response_collection_md[i]
-        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
-        final.extend(gpt_response_collection_md)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
-        res = write_history_to_file(final, create_report_file_name)
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-
-        # 更新UI
-        generated_conclusion_files.append(f'{get_log_folder()}/{create_report_file_name}')
-        chatbot.append((f"{fp}完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        # write html
-        try:
-            ch = construct_html()
-            orig = ""
-            trans = ""
-            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-            for i,k in enumerate(gpt_response_collection_html):
-                if i%2==0:
-                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
-                else:
-                    gpt_response_collection_html[i] = gpt_response_collection_html[i]
-            final = ["论文概况", paper_meta_info.replace('# ', '### '),  "二、论文翻译",  ""]
-            final.extend(gpt_response_collection_html)
-            for i, k in enumerate(final):
-                if i%2==0:
-                    orig = k
-                if i%2==1:
-                    trans = k
-                    ch.add_row(a=orig, b=trans)
-            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-            generated_html_files.append(ch.save_file(create_report_file_name))
-        except:
-            from toolbox import trimmed_format_exc
-            print('writing html result failed:', trimmed_format_exc())
-
-    # 准备文件的下载
-    for pdf_path in generated_conclusion_files:
-        # 重命名文件
-        rename_file = f'翻译-{os.path.basename(pdf_path)}'
-        promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
-    for html_path in generated_html_files:
-        # 重命名文件
-        rename_file = f'翻译-{os.path.basename(html_path)}'
-        promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
--- a/crazy_functions/init.py
+++ b/crazy_functions/init.py
--- a/crazy_functions/agent_fns/auto_agent.py
+++ b/crazy_functions/agent_fns/auto_agent.py
@ -1,23 +0,0 @@
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
-from toolbox import report_exception, get_log_folder, update_ui_lastest_msg, Singleton
-from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
-from crazy_functions.agent_fns.general import AutoGenGeneral
-
-
-
-class AutoGenMath(AutoGenGeneral):
-
-    def define_agents(self):
-        from autogen import AssistantAgent, UserProxyAgent
-        return [
-            {
-                "name": "assistant",            # name of the agent.
-                "cls":  AssistantAgent,         # class of the agent.
-            },
-            {
-                "name": "user_proxy",           # name of the agent.
-                "cls":  UserProxyAgent,         # class of the agent.
-                "human_input_mode": "ALWAYS",   # always ask for human input.
-                "llm_config": False,            # disables llm-based auto reply.
-            },
-        ]
--- a/crazy_functions/agent_fns/echo_agent.py
+++ b/crazy_functions/agent_fns/echo_agent.py
@ -1,19 +0,0 @@
-from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
-
-class EchoDemo(PluginMultiprocessManager):
-    def subprocess_worker(self, child_conn):
-        # ⭐⭐ 子进程
-        self.child_conn = child_conn
-        while True:
-            msg = self.child_conn.recv() # PipeCom
-            if msg.cmd == "user_input":
-                # wait futher user input
-                self.child_conn.send(PipeCom("show", msg.content))
-                wait_success = self.subprocess_worker_wait_user_feedback(wait_msg="我准备好处理下一个问题了.")
-                if not wait_success:
-                    # wait timeout, terminate this subprocess_worker
-                    break
-            elif msg.cmd == "terminate":
-                self.child_conn.send(PipeCom("done", ""))
-                break
-        print('[debug] subprocess_worker terminated')
--- a/crazy_functions/agent_fns/general.py
+++ b/crazy_functions/agent_fns/general.py
@ -1,138 +0,0 @@
-from toolbox import trimmed_format_exc, get_conf, ProxyNetworkActivate
-from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
-from request_llms.bridge_all import predict_no_ui_long_connection
-import time
-
-def gpt_academic_generate_oai_reply(
-    self,
-    messages,
-    sender,
-    config,
-):
-    llm_config = self.llm_config if config is None else config
-    if llm_config is False:
-        return False, None
-    if messages is None:
-        messages = self._oai_messages[sender]
-
-    inputs = messages[-1]['content']
-    history = []
-    for message in messages[:-1]:
-        history.append(message['content'])
-    context=messages[-1].pop("context", None)
-    assert context is None, "预留参数 context 未实现"
-
-    reply = predict_no_ui_long_connection(
-        inputs=inputs,
-        llm_kwargs=llm_config,
-        history=history,
-        sys_prompt=self._oai_system_message[0]['content'],
-        console_slience=True
-    )
-    assumed_done = reply.endswith('\nTERMINATE')
-    return True, reply
-
-class AutoGenGeneral(PluginMultiprocessManager):
-    def gpt_academic_print_override(self, user_proxy, message, sender):
-        # ⭐⭐ run in subprocess
-        try:
-            print_msg = sender.name + "\n\n---\n\n" + message["content"]
-        except:
-            print_msg = sender.name + "\n\n---\n\n" + message
-        self.child_conn.send(PipeCom("show", print_msg))
-
-    def gpt_academic_get_human_input(self, user_proxy, message):
-        # ⭐⭐ run in subprocess
-        patience = 300
-        begin_waiting_time = time.time()
-        self.child_conn.send(PipeCom("interact", message))
-        while True:
-            time.sleep(0.5)
-            if self.child_conn.poll():
-                wait_success = True
-                break
-            if time.time() - begin_waiting_time > patience:
-                self.child_conn.send(PipeCom("done", ""))
-                wait_success = False
-                break
-        if wait_success:
-            return self.child_conn.recv().content
-        else:
-            raise TimeoutError("等待用户输入超时")
-
-    def define_agents(self):
-        raise NotImplementedError
-
-    def exe_autogen(self, input):
-        # ⭐⭐ run in subprocess
-        input = input.content
-        code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
-        agents = self.define_agents()
-        user_proxy = None
-        assistant = None
-        for agent_kwargs in agents:
-            agent_cls = agent_kwargs.pop('cls')
-            kwargs = {
-                'llm_config':self.llm_kwargs,
-                'code_execution_config':code_execution_config
-            }
-            kwargs.update(agent_kwargs)
-            agent_handle = agent_cls(**kwargs)
-            agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b)
-            for d in agent_handle._reply_func_list:
-                if hasattr(d['reply_func'],'__name__') and d['reply_func'].__name__ == 'generate_oai_reply':
-                    d['reply_func'] = gpt_academic_generate_oai_reply
-            if agent_kwargs['name'] == 'user_proxy':
-                agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
-                user_proxy = agent_handle
-            if agent_kwargs['name'] == 'assistant': assistant = agent_handle
-        try:
-            if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义")
-            with ProxyNetworkActivate("AutoGen"):
-                user_proxy.initiate_chat(assistant, message=input)
-        except Exception as e:
-            tb_str = '```\n' + trimmed_format_exc() + '```'
-            self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
-
-    def subprocess_worker(self, child_conn):
-        # ⭐⭐ run in subprocess
-        self.child_conn = child_conn
-        while True:
-            msg = self.child_conn.recv()  # PipeCom
-            self.exe_autogen(msg)
-
-
-class AutoGenGroupChat(AutoGenGeneral):
-    def exe_autogen(self, input):
-        # ⭐⭐ run in subprocess
-        import autogen
-
-        input = input.content
-        with ProxyNetworkActivate("AutoGen"):
-            code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
-            agents = self.define_agents()
-            agents_instances = []
-            for agent_kwargs in agents:
-                agent_cls = agent_kwargs.pop("cls")
-                kwargs = {"code_execution_config": code_execution_config}
-                kwargs.update(agent_kwargs)
-                agent_handle = agent_cls(**kwargs)
-                agent_handle._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
-                agents_instances.append(agent_handle)
-                if agent_kwargs["name"] == "user_proxy":
-                    user_proxy = agent_handle
-                    user_proxy.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
-            try:
-                groupchat = autogen.GroupChat(agents=agents_instances, messages=[], max_round=50)
-                manager = autogen.GroupChatManager(groupchat=groupchat, **self.define_group_chat_manager_config())
-                manager._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
-                manager.get_human_input = lambda a: self.gpt_academic_get_human_input(manager, a)
-                if user_proxy is None:
-                    raise Exception("user_proxy is not defined")
-                user_proxy.initiate_chat(manager, message=input)
-            except Exception:
-                tb_str = "```\n" + trimmed_format_exc() + "```"
-                self.child_conn.send(PipeCom("done", "AutoGen exe failed: \n\n" + tb_str))
-
-    def define_group_chat_manager_config(self):
-        raise NotImplementedError
--- a/crazy_functions/agent_fns/persistent.py
+++ b/crazy_functions/agent_fns/persistent.py
@ -1,16 +0,0 @@
-from toolbox import Singleton
-@Singleton
-class GradioMultiuserManagerForPersistentClasses():
-    def __init__(self):
-        self.mapping = {}
-
-    def already_alive(self, key):
-        return (key in self.mapping) and (self.mapping[key].is_alive())
-
-    def set(self, key, x):
-        self.mapping[key] = x
-        return self.mapping[key]
-
-    def get(self, key):
-        return self.mapping[key]
-
--- a/crazy_functions/agent_fns/pipe.py
+++ b/crazy_functions/agent_fns/pipe.py
@ -1,194 +0,0 @@
-from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_file_to_downloadzone
-from crazy_functions.agent_fns.watchdog import WatchDog
-import time, os
-
-class PipeCom:
-    def __init__(self, cmd, content) -> None:
-        self.cmd = cmd
-        self.content = content
-
-
-class PluginMultiprocessManager:
-    def __init__(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-        # ⭐ run in main process
-        self.autogen_work_dir = os.path.join(get_log_folder("autogen"), gen_time_str())
-        self.previous_work_dir_files = {}
-        self.llm_kwargs = llm_kwargs
-        self.plugin_kwargs = plugin_kwargs
-        self.chatbot = chatbot
-        self.history = history
-        self.system_prompt = system_prompt
-        # self.user_request = user_request
-        self.alive = True
-        self.use_docker = get_conf("AUTOGEN_USE_DOCKER")
-        self.last_user_input = ""
-        # create a thread to monitor self.heartbeat, terminate the instance if no heartbeat for a long time
-        timeout_seconds = 5 * 60
-        self.heartbeat_watchdog = WatchDog(timeout=timeout_seconds, bark_fn=self.terminate, interval=5)
-        self.heartbeat_watchdog.begin_watch()
-
-    def feed_heartbeat_watchdog(self):
-        # feed this `dog`, so the dog will not `bark` (bark_fn will terminate the instance)
-        self.heartbeat_watchdog.feed()
-
-    def is_alive(self):
-        return self.alive
-
-    def launch_subprocess_with_pipe(self):
-        # ⭐ run in main process
-        from multiprocessing import Process, Pipe
-
-        parent_conn, child_conn = Pipe()
-        self.p = Process(target=self.subprocess_worker, args=(child_conn,))
-        self.p.daemon = True
-        self.p.start()
-        return parent_conn
-
-    def terminate(self):
-        self.p.terminate()
-        self.alive = False
-        print("[debug] instance terminated")
-
-    def subprocess_worker(self, child_conn):
-        # ⭐⭐ run in subprocess
-        raise NotImplementedError
-
-    def send_command(self, cmd):
-        # ⭐ run in main process
-        repeated = False
-        if cmd == self.last_user_input:
-            repeated = True
-            cmd = ""
-        else:
-            self.last_user_input = cmd
-        self.parent_conn.send(PipeCom("user_input", cmd))
-        return repeated, cmd
-
-    def immediate_showoff_when_possible(self, fp):
-        # ⭐ 主进程
-        # 获取fp的拓展名
-        file_type = fp.split('.')[-1]
-        # 如果是文本文件, 则直接显示文本内容
-        if file_type.lower() in ['png', 'jpg']:
-            image_path = os.path.abspath(fp)
-            self.chatbot.append([
-                '检测到新生图像:',
-                f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-            ])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-
-    def overwatch_workdir_file_change(self):
-        # ⭐ 主进程 Docker 外挂文件夹监控
-        path_to_overwatch = self.autogen_work_dir
-        change_list = []
-        # 扫描路径下的所有文件, 并与self.previous_work_dir_files中所记录的文件进行对比，
-        # 如果有新文件出现，或者文件的修改时间发生变化，则更新self.previous_work_dir_files中
-        # 把新文件和发生变化的文件的路径记录到 change_list 中
-        for root, dirs, files in os.walk(path_to_overwatch):
-            for file in files:
-                file_path = os.path.join(root, file)
-                if file_path not in self.previous_work_dir_files.keys():
-                    last_modified_time = os.stat(file_path).st_mtime
-                    self.previous_work_dir_files.update({file_path: last_modified_time})
-                    change_list.append(file_path)
-                else:
-                    last_modified_time = os.stat(file_path).st_mtime
-                    if last_modified_time != self.previous_work_dir_files[file_path]:
-                        self.previous_work_dir_files[file_path] = last_modified_time
-                        change_list.append(file_path)
-        if len(change_list) > 0:
-            file_links = ""
-            for f in change_list:
-                res = promote_file_to_downloadzone(f)
-                file_links += f'<br/><a href="file={res}" target="_blank">{res}</a>'
-                yield from self.immediate_showoff_when_possible(f)
-
-            self.chatbot.append(['检测到新生文档.', f'文档清单如下: {file_links}'])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-        return change_list
-
-
-    def main_process_ui_control(self, txt, create_or_resume) -> str:
-        # ⭐ 主进程
-        if create_or_resume == 'create':
-            self.cnt = 1
-            self.parent_conn = self.launch_subprocess_with_pipe() # ⭐⭐⭐
-        repeated, cmd_to_autogen = self.send_command(txt)
-        if txt == 'exit':
-            self.chatbot.append([f"结束", "结束信号已明确，终止AutoGen程序。"])
-            yield from update_ui(chatbot=self.chatbot, history=self.history)
-            self.terminate()
-            return "terminate"
-
-        # patience = 10
-
-        while True:
-            time.sleep(0.5)
-            if not self.alive:
-                # the heartbeat watchdog might have it killed
-                self.terminate()
-                return "terminate"
-            if self.parent_conn.poll():
-                self.feed_heartbeat_watchdog()
-                if "[GPT-Academic] 等待中" in self.chatbot[-1][-1]:
-                    self.chatbot.pop(-1)  # remove the last line
-                if "等待您的进一步指令" in self.chatbot[-1][-1]:
-                    self.chatbot.pop(-1)  # remove the last line
-                if '[GPT-Academic] 等待中' in self.chatbot[-1][-1]:
-                    self.chatbot.pop(-1)    # remove the last line
-                msg = self.parent_conn.recv() # PipeCom
-                if msg.cmd == "done":
-                    self.chatbot.append([f"结束", msg.content])
-                    self.cnt += 1
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    self.terminate()
-                    break
-                if msg.cmd == "show":
-                    yield from self.overwatch_workdir_file_change()
-                    notice = ""
-                    if repeated: notice = "（自动忽略重复的输入）"
-                    self.chatbot.append([f"运行阶段-{self.cnt}（上次用户反馈输入为: 「{cmd_to_autogen}」{notice}", msg.content])
-                    self.cnt += 1
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                if msg.cmd == "interact":
-                    yield from self.overwatch_workdir_file_change()
-                    self.chatbot.append([f"程序抵达用户反馈节点.", msg.content +
-                                         "\n\n等待您的进一步指令." +
-                                         "\n\n(1) 一般情况下您不需要说什么, 清空输入区, 然后直接点击“提交”以继续. " +
-                                         "\n\n(2) 如果您需要补充些什么, 输入要反馈的内容, 直接点击“提交”以继续. " +
-                                         "\n\n(3) 如果您想终止程序, 输入exit, 直接点击“提交”以终止AutoGen并解锁. "
-                    ])
-                    yield from update_ui(chatbot=self.chatbot, history=self.history)
-                    # do not terminate here, leave the subprocess_worker instance alive
-                    return "wait_feedback"
-            else:
-                self.feed_heartbeat_watchdog()
-                if '[GPT-Academic] 等待中' not in self.chatbot[-1][-1]:
-                    # begin_waiting_time = time.time()
-                    self.chatbot.append(["[GPT-Academic] 等待AutoGen执行结果 ...", "[GPT-Academic] 等待中"])
-                self.chatbot[-1] = [self.chatbot[-1][0], self.chatbot[-1][1].replace("[GPT-Academic] 等待中", "[GPT-Academic] 等待中.")]
-                yield from update_ui(chatbot=self.chatbot, history=self.history)
-                # if time.time() - begin_waiting_time > patience:
-                #     self.chatbot.append([f"结束", "等待超时, 终止AutoGen程序。"])
-                #     yield from update_ui(chatbot=self.chatbot, history=self.history)
-                #     self.terminate()
-                #     return "terminate"
-
-        self.terminate()
-        return "terminate"
-
-    def subprocess_worker_wait_user_feedback(self, wait_msg="wait user feedback"):
-        # ⭐⭐ run in subprocess
-        patience = 5 * 60
-        begin_waiting_time = time.time()
-        self.child_conn.send(PipeCom("interact", wait_msg))
-        while True:
-            time.sleep(0.5)
-            if self.child_conn.poll():
-                wait_success = True
-                break
-            if time.time() - begin_waiting_time > patience:
-                self.child_conn.send(PipeCom("done", ""))
-                wait_success = False
-                break
-        return wait_success
--- a/crazy_functions/agent_fns/watchdog.py
+++ b/crazy_functions/agent_fns/watchdog.py
@ -1,28 +0,0 @@
-import threading, time
-
-class WatchDog():
-    def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
-        self.last_feed = None
-        self.timeout = timeout
-        self.bark_fn = bark_fn
-        self.interval = interval
-        self.msg = msg
-        self.kill_dog = False
-
-    def watch(self):
-        while True:
-            if self.kill_dog: break
-            if time.time() - self.last_feed > self.timeout:
-                if len(self.msg) > 0: print(self.msg)
-                self.bark_fn()
-                break
-            time.sleep(self.interval)
-
-    def begin_watch(self):
-        self.last_feed = time.time()
-        th = threading.Thread(target=self.watch)
-        th.daemon = True
-        th.start()
-
-    def feed(self):
-        self.last_feed = time.time()
--- a/crazy_functions/chatglm微调工具.py
+++ b/crazy_functions/chatglm微调工具.py
@ -1,141 +0,0 @@
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-import datetime, json
-
-def fetch_items(list_of_items, batch_size):
-    for i in range(0, len(list_of_items), batch_size):
-        yield list_of_items[i:i + batch_size]
-
-def string_to_options(arguments):
-    import argparse
-    import shlex
-
-    # Create an argparse.ArgumentParser instance
-    parser = argparse.ArgumentParser()
-
-    # Add command-line arguments
-    parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
-    parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
-    parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
-    parser.add_argument("--batch", type=int, help="System prompt", default=50)
-    parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
-    parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
-    parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
-    parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
-    parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
-
-
-
-    # Parse the arguments
-    args = parser.parse_args(shlex.split(arguments))
-
-    return args
-
-@CatchException
-def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    args = plugin_kwargs.get("advanced_arg", None)
-    if args is None:
-        chatbot.append(("没给定指令", "退出"))
-        yield from update_ui(chatbot=chatbot, history=history); return
-    else:
-        arguments = string_to_options(arguments=args)
-
-    dat = []
-    with open(txt, 'r', encoding='utf8') as f:
-        for line in f.readlines():
-            json_dat = json.loads(line)
-            dat.append(json_dat["content"])
-
-    llm_kwargs['llm_model'] = arguments.llm_to_learn
-    for batch in fetch_items(dat, arguments.batch):
-        res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
-            inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[] for _ in (batch)],
-            sys_prompt_array=[arguments.system_prompt for _ in (batch)],
-            max_workers=10  # OpenAI所允许的最大并行过载
-        )
-
-        with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
-            for b, r in zip(batch, res[1::2]):
-                f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
-
-    promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
-    return
-
-
-
-@CatchException
-def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    import subprocess
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    args = plugin_kwargs.get("advanced_arg", None)
-    if args is None:
-        chatbot.append(("没给定指令", "退出"))
-        yield from update_ui(chatbot=chatbot, history=history); return
-    else:
-        arguments = string_to_options(arguments=args)
-
-
-
-    pre_seq_len = arguments.pre_seq_len             # 128
-    learning_rate = arguments.learning_rate                               # 2e-2
-    num_gpus = arguments.num_gpus                   # 1
-    json_dataset = arguments.json_dataset                 # 't_code.json'
-    ptuning_directory = arguments.ptuning_directory       # '/home/hmp/ChatGLM2-6B/ptuning'
-
-    command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
-        --do_train \
-        --train_file AdvertiseGen/{json_dataset} \
-        --validation_file AdvertiseGen/{json_dataset} \
-        --preprocessing_num_workers 20 \
-        --prompt_column content \
-        --response_column summary \
-        --overwrite_cache \
-        --model_name_or_path THUDM/chatglm2-6b \
-        --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
-        --overwrite_output_dir \
-        --max_source_length 256 \
-        --max_target_length 256 \
-        --per_device_train_batch_size 1 \
-        --per_device_eval_batch_size 1 \
-        --gradient_accumulation_steps 16 \
-        --predict_with_generate \
-        --max_steps 100 \
-        --logging_steps 10 \
-        --save_steps 20 \
-        --learning_rate {learning_rate} \
-        --pre_seq_len {pre_seq_len} \
-        --quantization_bit 4"
-
-    process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
-    try:
-        process.communicate(timeout=3600*24)
-    except subprocess.TimeoutExpired:
-        process.kill()
-    return
--- a/crazy_functions/crazy_utils.py
+++ b/crazy_functions/crazy_utils.py
@ -1,621 +0,0 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
-import threading
-import os
-import logging
-
-def input_clipping(inputs, history, max_token_limit):
-    import numpy as np
-    from request_llms.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-
-    mode = 'input-and-history'
-    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
-    input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit//2:
-        mode = 'only-history'
-        max_token_limit = max_token_limit - input_token_num
-
-    everything = [inputs] if mode == 'input-and-history' else ['']
-    everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
-    everything_token = [get_token_num(e) for e in everything]
-    delta = max(everything_token) // 16 # 截断时的颗粒度
-
-    while n_token > max_token_limit:
-        where = np.argmax(everything_token)
-        encoded = enc.encode(everything[where], disallowed_special=())
-        clipped_encoded = encoded[:len(encoded)-delta]
-        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
-        everything_token[where] = get_token_num(everything[where])
-        n_token = get_token_num('\n'.join(everything))
-
-    if mode == 'input-and-history':
-        inputs = everything[0]
-    else:
-        pass
-    history = everything[1:]
-    return inputs, history
-
-def request_gpt_model_in_new_thread_with_ui_alive(
-        inputs, inputs_show_user, llm_kwargs,
-        chatbot, history, sys_prompt, refresh_interval=0.2,
-        handle_token_exceed=True,
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model，请求GPT模型同时维持用户界面活跃。
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs (string): List of inputs （输入）
-        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
-        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
-        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
-        history (list): List of chat history （历史，对话历史列表）
-        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        retry_times_at_unknown_error：失败时的重试次数
-
-    输出 Returns:
-        future: 输出，GPT返回的结果
-    """
-    import time
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llms.bridge_all import predict_no_ui_long_connection
-    # 用户反馈
-    chatbot.append([inputs_show_user, ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    executor = ThreadPoolExecutor(max_workers=16)
-    mutable = ["", time.time(), ""]
-    # 看门狗耐心
-    watch_dog_patience = 5
-    # 请求任务
-    def _req_gpt(inputs, history, sys_prompt):
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        while True:
-            # watchdog error
-            if len(mutable) >= 2 and (time.time()-mutable[1]) > watch_dog_patience:
-                raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                result = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs,
-                    history=history, sys_prompt=sys_prompt, observe_window=mutable)
-                return result
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = get_max_token(llm_kwargs)
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    return mutable[0] # 放弃
-            except:
-                # 【第三种情况】：其他错误：重试几次
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if retry_op > 0:
-                    retry_op -= 1
-                    mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        time.sleep(30)
-                    time.sleep(5)
-                    continue # 返回重试
-                else:
-                    time.sleep(5)
-                    return mutable[0] # 放弃
-
-    # 提交任务
-    future = executor.submit(_req_gpt, inputs, history, sys_prompt)
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        # “喂狗”（看门狗）
-        mutable[1] = time.time()
-        if future.done():
-            break
-        chatbot[-1] = [chatbot[-1][0], mutable[0]]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-
-    final_result = future.result()
-    chatbot[-1] = [chatbot[-1][0], final_result]
-    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
-    return final_result
-
-def can_multi_process(llm) -> bool:
-    from request_llms.bridge_all import model_info
-
-    def default_condition(llm) -> bool:
-        # legacy condition
-        if llm.startswith('gpt-'): return True
-        if llm.startswith('api2d-'): return True
-        if llm.startswith('azure-'): return True
-        if llm.startswith('spark'): return True
-        if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
-        return False
-
-    if llm in model_info:
-        if 'can_multi_thread' in model_info[llm]:
-            return model_info[llm]['can_multi_thread']
-        else:
-            return default_condition(llm)
-    else:
-        return default_condition(llm)
-
-def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array, inputs_show_user_array, llm_kwargs,
-        chatbot, history_array, sys_prompt_array,
-        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
-        handle_token_exceed=True, show_user_at_complete=False,
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model using multiple threads with UI and high efficiency
-    请求GPT模型的[多线程]版。
-    具备以下功能：
-        实时在UI上反馈远程数据流
-        使用线程池，可调节线程池的大小避免openai的流量限制错误
-        处理中途中止的情况
-        网络等出问题时，会把traceback和已经接收的数据转入输出
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs_array (list): List of inputs （每个子任务的输入）
-        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        llm_kwargs: llm_kwargs参数
-        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
-        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
-        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
-        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
-        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
-        retry_times_at_unknown_error：子任务失败时的重试次数
-
-    输出 Returns:
-        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
-    """
-    import time, random
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llms.bridge_all import predict_no_ui_long_connection
-    assert len(inputs_array) == len(history_array)
-    assert len(inputs_array) == len(sys_prompt_array)
-    if max_workers == -1: # 读取配置文件
-        try: max_workers = get_conf('DEFAULT_WORKER_NUM')
-        except: max_workers = 8
-        if max_workers <= 0: max_workers = 3
-    # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
-    if not can_multi_process(llm_kwargs['llm_model']):
-        max_workers = 1
-
-    executor = ThreadPoolExecutor(max_workers=max_workers)
-    n_frag = len(inputs_array)
-    # 用户反馈
-    chatbot.append(["请开始多线程操作。", ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    # 跨线程传递
-    mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
-
-    # 看门狗耐心
-    watch_dog_patience = 5
-
-    # 子线程任务
-    def _req_gpt(index, inputs, history, sys_prompt):
-        gpt_say = ""
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        mutable[index][2] = "执行中"
-        detect_timeout = lambda: len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > watch_dog_patience
-        while True:
-            # watchdog error
-            if detect_timeout(): raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                gpt_say = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs, history=history,
-                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
-                )
-                mutable[index][2] = "已成功"
-                return gpt_say
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = get_max_token(llm_kwargs)
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    mutable[index][2] = f"截断重试"
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                    mutable[index][2] = "输入过长已放弃"
-                    return gpt_say # 放弃
-            except:
-                # 【第三种情况】：其他错误
-                if detect_timeout(): raise RuntimeError("检测到程序终止。")
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                if retry_op > 0:
-                    retry_op -= 1
-                    wait = random.randint(5, 20)
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        wait = wait * 3
-                        fail_info = "OpenAI绑定信用卡可解除频率限制 "
-                    else:
-                        fail_info = ""
-                    # 也许等待十几秒后，情况会好转
-                    for i in range(wait):
-                        mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
-                    # 开始重试
-                    if detect_timeout(): raise RuntimeError("检测到程序终止。")
-                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
-                    continue # 返回重试
-                else:
-                    mutable[index][2] = "已失败"
-                    wait = 5
-                    time.sleep(5)
-                    return gpt_say # 放弃
-
-    # 异步任务开始
-    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
-        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
-    cnt = 0
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        cnt += 1
-        worker_done = [h.done() for h in futures]
-        # 更好的UI视觉效果
-        observe_win = []
-        # 每个线程都要“喂狗”（看门狗）
-        for thread_index, _ in enumerate(worker_done):
-            mutable[thread_index][1] = time.time()
-        # 在前端打印些好玩的东西
-        for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
-                replace('\n', '').replace('`', '.').replace(' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
-            observe_win.append(print_something_really_funny)
-        # 在前端打印些好玩的东西
-        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
-                            if not done else f'`{mutable[thread_index][2]}`\n\n'
-                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
-        # 在前端打印些好玩的东西
-        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-        if all(worker_done):
-            executor.shutdown()
-            break
-
-    # 异步任务结束
-    gpt_response_collection = []
-    for inputs_show_user, f in zip(inputs_show_user_array, futures):
-        gpt_res = f.result()
-        gpt_response_collection.extend([inputs_show_user, gpt_res])
-
-    # 是否在结束时，在界面上显示结果
-    if show_user_at_complete:
-        for inputs_show_user, f in zip(inputs_show_user_array, futures):
-            gpt_res = f.result()
-            chatbot.append([inputs_show_user, gpt_res])
-            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-            time.sleep(0.5)
-    return gpt_response_collection
-
-
-
-def read_and_clean_pdf_text(fp):
-    """
-    这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
-
-    **输入参数说明**
-    - `fp`：需要读取和清理文本的pdf文件路径
-
-    **输出参数说明**
-    - `meta_txt`：清理后的文本内容字符串
-    - `page_one_meta`：第一页清理后的文本内容列表
-
-    **函数功能**
-    读取pdf文件并清理其中的文本内容，清理规则包括：
-    - 提取所有块元的文本信息，并合并为一个字符串
-    - 去除短块（字符数小于100）并替换为回车符
-    - 清理多余的空行
-    - 合并小写字母开头的段落块并替换为空格
-    - 清除重复的换行
-    - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
-    """
-    import fitz, copy
-    import re
-    import numpy as np
-    from colorful import print亮黄, print亮绿
-    fc = 0  # Index 0 文本
-    fs = 1  # Index 1 字体
-    fb = 2  # Index 2 框框
-    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
-    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
-    def primary_ffsize(l):
-        """
-        提取文本块主字体
-        """
-        fsize_statiscs = {}
-        for wtf in l['spans']:
-            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
-            fsize_statiscs[wtf['size']] += len(wtf['text'])
-        return max(fsize_statiscs, key=fsize_statiscs.get)
-
-    def ffsize_same(a,b):
-        """
-        提取字体大小是否近似相等
-        """
-        return abs((a-b)/max(a,b)) < 0.02
-
-    with fitz.open(fp) as doc:
-        meta_txt = []
-        meta_font = []
-
-        meta_line = []
-        meta_span = []
-        ############################## <第 1 步，搜集初始信息> ##################################
-        for index, page in enumerate(doc):
-            # file_content += page.get_text()
-            text_areas = page.get_text("dict")  # 获取页面上的文本信息
-            for t in text_areas['blocks']:
-                if 'lines' in t:
-                    pf = 998
-                    for l in t['lines']:
-                        txt_line = "".join([wtf['text'] for wtf in l['spans']])
-                        if len(txt_line) == 0: continue
-                        pf = primary_ffsize(l)
-                        meta_line.append([txt_line, pf, l['bbox'], l])
-                        for wtf in l['spans']: # for l in t['lines']:
-                            meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
-                    # meta_line.append(["NEW_BLOCK", pf])
-            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
-            meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                '- ', '') for t in text_areas['blocks'] if 'lines' in t])
-            meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
-                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
-            if index == 0:
-                page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                    '- ', '') for t in text_areas['blocks'] if 'lines' in t]
-
-        ############################## <第 2 步，获取正文主字体> ##################################
-        try:
-            fsize_statiscs = {}
-            for span in meta_span:
-                if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
-                fsize_statiscs[span[1]] += span[2]
-            main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
-            if REMOVE_FOOT_NOTE:
-                give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
-        except:
-            raise RuntimeError(f'抱歉, 我们暂时无法解析此PDF文档: {fp}。')
-        ############################## <第 3 步，切分和重新整合> ##################################
-        mega_sec = []
-        sec = []
-        for index, line in enumerate(meta_line):
-            if index == 0:
-                sec.append(line[fc])
-                continue
-            if REMOVE_FOOT_NOTE:
-                if meta_line[index][fs] <= give_up_fize_threshold:
-                    continue
-            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
-                # 尝试识别段落
-                if meta_line[index][fc].endswith('.') and\
-                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \
-                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
-                    sec[-1] += line[fc]
-                    sec[-1] += "\n\n"
-                else:
-                    sec[-1] += " "
-                    sec[-1] += line[fc]
-            else:
-                if (index+1 < len(meta_line)) and \
-                    meta_line[index][fs] > main_fsize:
-                    # 单行 + 字体大
-                    mega_sec.append(copy.deepcopy(sec))
-                    sec = []
-                    sec.append("# " + line[fc])
-                else:
-                    # 尝试识别section
-                    if meta_line[index-1][fs] > meta_line[index][fs]:
-                        sec.append("\n" + line[fc])
-                    else:
-                        sec.append(line[fc])
-        mega_sec.append(copy.deepcopy(sec))
-
-        finals = []
-        for ms in mega_sec:
-            final = " ".join(ms)
-            final = final.replace('- ', ' ')
-            finals.append(final)
-        meta_txt = finals
-
-        ############################## <第 4 步，乱七八糟的后处理> ##################################
-        def 把字符太少的块清除为回车(meta_txt):
-            for index, block_txt in enumerate(meta_txt):
-                if len(block_txt) < 100:
-                    meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 把字符太少的块清除为回车(meta_txt)
-
-        def 清理多余的空行(meta_txt):
-            for index in reversed(range(1, len(meta_txt))):
-                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
-                    meta_txt.pop(index)
-            return meta_txt
-        meta_txt = 清理多余的空行(meta_txt)
-
-        def 合并小写开头的段落块(meta_txt):
-            def starts_with_lowercase_word(s):
-                pattern = r"^[a-z]+"
-                match = re.match(pattern, s)
-                if match:
-                    return True
-                else:
-                    return False
-            # 对于某些PDF会有第一个段落就以小写字母开头,为了避免索引错误将其更改为大写
-            if starts_with_lowercase_word(meta_txt[0]):
-                meta_txt[0] = meta_txt[0].capitalize()
-            for _ in range(100):
-                for index, block_txt in enumerate(meta_txt):
-                    if starts_with_lowercase_word(block_txt):
-                        if meta_txt[index-1] != '\n':
-                            meta_txt[index-1] += ' '
-                        else:
-                            meta_txt[index-1] = ''
-                        meta_txt[index-1] += meta_txt[index]
-                        meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 合并小写开头的段落块(meta_txt)
-        meta_txt = 清理多余的空行(meta_txt)
-
-        meta_txt = '\n'.join(meta_txt)
-        # 清除重复的换行
-        for _ in range(5):
-            meta_txt = meta_txt.replace('\n\n', '\n')
-
-        # 换行 -> 双换行
-        meta_txt = meta_txt.replace('\n', '\n\n')
-
-        ############################## <第 5 步，展示分割效果> ##################################
-        # for f in finals:
-        #    print亮黄(f)
-        #    print亮绿('***************************')
-
-    return meta_txt, page_one_meta
-
-
-def get_files_from_everything(txt, type): # type='.md'
-    """
-    这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
-    下面是对每个参数和返回值的说明：
-    参数
-    - txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。
-    - type: 字符串，表示要搜索的文件类型。默认是.md。
-    返回值
-    - success: 布尔值，表示函数是否成功执行。
-    - file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。
-    - project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
-    该函数详细注释已添加，请确认是否满足您的需要。
-    """
-    import glob, os
-
-    success = True
-    if txt.startswith('http'):
-        # 网络的远程文件
-        import requests
-        from toolbox import get_conf
-        from toolbox import get_log_folder, gen_time_str
-        proxies = get_conf('proxies')
-        try:
-            r = requests.get(txt, proxies=proxies)
-        except:
-            raise ConnectionRefusedError(f"无法下载资源{txt}，请检查。")
-        path = os.path.join(get_log_folder(plugin_name='web_download'), gen_time_str()+type)
-        with open(path, 'wb+') as f: f.write(r.content)
-        project_folder = get_log_folder(plugin_name='web_download')
-        file_manifest = [path]
-    elif txt.endswith(type):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
-        if len(file_manifest) == 0:
-            success = False
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder
-
-
-
-@Singleton
-class nougat_interface():
-    def __init__(self):
-        self.threadLock = threading.Lock()
-
-    def nougat_with_timeout(self, command, cwd, timeout=3600):
-        import subprocess
-        from toolbox import ProxyNetworkActivate
-        logging.info(f'正在执行命令 {command}')
-        with ProxyNetworkActivate("Nougat_Download"):
-            process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
-        try:
-            stdout, stderr = process.communicate(timeout=timeout)
-        except subprocess.TimeoutExpired:
-            process.kill()
-            stdout, stderr = process.communicate()
-            print("Process timed out!")
-            return False
-        return True
-
-
-    def NOUGAT_parse_pdf(self, fp, chatbot, history):
-        from toolbox import update_ui_lastest_msg
-
-        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
-                                         chatbot=chatbot, history=history, delay=0)
-        self.threadLock.acquire()
-        import glob, threading, os
-        from toolbox import get_log_folder, gen_time_str
-        dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str())
-        os.makedirs(dst)
-
-        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
-                                         chatbot=chatbot, history=history, delay=0)
-        command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
-        self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
-        res = glob.glob(os.path.join(dst,'*.mmd'))
-        if len(res) == 0:
-            self.threadLock.release()
-            raise RuntimeError("Nougat解析论文失败。")
-        self.threadLock.release()
-        return res[0]
-
-
-
-
-def try_install_deps(deps, reload_m=[]):
-    import subprocess, sys, importlib
-    for dep in deps:
-        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--user', dep])
-    import site
-    importlib.reload(site)
-    for m in reload_m:
-        importlib.reload(__import__(m))
-
-
-def get_plugin_arg(plugin_kwargs, key, default):
-    # 如果参数是空的
-    if (key in plugin_kwargs) and (plugin_kwargs[key] == ""): plugin_kwargs.pop(key)
-    # 正常情况
-    return plugin_kwargs.get(key, default)
--- a/crazy_functions/diagram_fns/file_tree.py
+++ b/crazy_functions/diagram_fns/file_tree.py
@ -1,122 +0,0 @@
-import os
-from textwrap import indent
-
-class FileNode:
-    def __init__(self, name):
-        self.name = name
-        self.children = []
-        self.is_leaf = False
-        self.level = 0
-        self.parenting_ship = []
-        self.comment = ""
-        self.comment_maxlen_show = 50
-
-    @staticmethod
-    def add_linebreaks_at_spaces(string, interval=10):
-        return '\n'.join(string[i:i+interval] for i in range(0, len(string), interval))
-
-    def sanitize_comment(self, comment):
-        if len(comment) > self.comment_maxlen_show: suf = '...'
-        else: suf = ''
-        comment = comment[:self.comment_maxlen_show]
-        comment = comment.replace('\"', '').replace('`', '').replace('\n', '').replace('`', '').replace('$', '')
-        comment = self.add_linebreaks_at_spaces(comment, 10)
-        return '`' + comment + suf + '`'
-
-    def add_file(self, file_path, file_comment):
-        directory_names, file_name = os.path.split(file_path)
-        current_node = self
-        level = 1
-        if directory_names == "":
-            new_node = FileNode(file_name)
-            current_node.children.append(new_node)
-            new_node.is_leaf = True
-            new_node.comment = self.sanitize_comment(file_comment)
-            new_node.level = level
-            current_node = new_node
-        else:
-            dnamesplit = directory_names.split(os.sep)
-            for i, directory_name in enumerate(dnamesplit):
-                found_child = False
-                level += 1
-                for child in current_node.children:
-                    if child.name == directory_name:
-                        current_node = child
-                        found_child = True
-                        break
-                if not found_child:
-                    new_node = FileNode(directory_name)
-                    current_node.children.append(new_node)
-                    new_node.level = level - 1
-                    current_node = new_node
-            term = FileNode(file_name)
-            term.level = level
-            term.comment = self.sanitize_comment(file_comment)
-            term.is_leaf = True
-            current_node.children.append(term)
-
-    def print_files_recursively(self, level=0, code="R0"):
-        print('    '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
-        for j, child in enumerate(self.children):
-            child.print_files_recursively(level=level+1, code=code+str(j))
-            self.parenting_ship.extend(child.parenting_ship)
-            p1 = f"""{code}[\"🗎{self.name}\"]""" if self.is_leaf else f"""{code}[[\"📁{self.name}\"]]"""
-            p2 = """ --> """
-            p3 = f"""{code+str(j)}[\"🗎{child.name}\"]""" if child.is_leaf else f"""{code+str(j)}[[\"📁{child.name}\"]]"""
-            edge_code = p1 + p2 + p3
-            if edge_code in self.parenting_ship:
-                continue
-            self.parenting_ship.append(edge_code)
-        if self.comment != "":
-            pc1 = f"""{code}[\"🗎{self.name}\"]""" if self.is_leaf else f"""{code}[[\"📁{self.name}\"]]"""
-            pc2 = f""" -.-x """
-            pc3 = f"""C{code}[\"{self.comment}\"]:::Comment"""
-            edge_code = pc1 + pc2 + pc3
-            self.parenting_ship.append(edge_code)
-
-
-MERMAID_TEMPLATE = r"""
-```mermaid
-flowchart LR
-    %% <gpt_academic_hide_mermaid_code> 一个特殊标记，用于在生成mermaid图表时隐藏代码块
-    classDef Comment stroke-dasharray: 5 5
-    subgraph {graph_name}
-{relationship}
-    end
-```
-"""
-
-def build_file_tree_mermaid_diagram(file_manifest, file_comments, graph_name):
-    # Create the root node
-    file_tree_struct = FileNode("root")
-    # Build the tree structure
-    for file_path, file_comment in zip(file_manifest, file_comments):
-        file_tree_struct.add_file(file_path, file_comment)
-    file_tree_struct.print_files_recursively()
-    cc = "\n".join(file_tree_struct.parenting_ship)
-    ccc = indent(cc, prefix=" "*8)
-    return MERMAID_TEMPLATE.format(graph_name=graph_name, relationship=ccc)
-
-if __name__ == "__main__":
-    # File manifest
-    file_manifest = [
-        "cradle_void_terminal.ipynb",
-        "tests/test_utils.py",
-        "tests/test_plugins.py",
-        "tests/test_llms.py",
-        "config.py",
-        "build/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/model_weights_0.bin",
-        "crazy_functions/latex_fns/latex_actions.py",
-        "crazy_functions/latex_fns/latex_toolbox.py"
-    ]
-    file_comments = [
-        "根据位置和名称，可能是一个模块的初始化文件根据位置和名称，可能是一个模块的初始化文件根据位置和名称，可能是一个模块的初始化文件",
-        "包含一些用于文本处理和模型微调的函数和装饰器包含一些用于文本处理和模型微调的函数和装饰器包含一些用于文本处理和模型微调的函数和装饰器",
-        "用于构建HTML报告的类和方法用于构建HTML报告的类和方法用于构建HTML报告的类和方法",
-        "包含了用于文本切分的函数，以及处理PDF文件的示例代码包含了用于文本切分的函数，以及处理PDF文件的示例代码包含了用于文本切分的函数，以及处理PDF文件的示例代码",
-        "用于解析和翻译PDF文件的功能和相关辅助函数用于解析和翻译PDF文件的功能和相关辅助函数用于解析和翻译PDF文件的功能和相关辅助函数",
-        "是一个包的初始化文件，用于初始化包的属性和导入模块是一个包的初始化文件，用于初始化包的属性和导入模块是一个包的初始化文件，用于初始化包的属性和导入模块",
-        "用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器",
-        "包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类",
-    ]
-    print(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
--- a/crazy_functions/game_fns/game_ascii_art.py
+++ b/crazy_functions/game_fns/game_ascii_art.py
@ -1,42 +0,0 @@
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-import random
-
-
-class MiniGame_ASCII_Art(GptAcademicGameBaseState):
-    def step(self, prompt, chatbot, history):
-        if self.step_cnt == 0:
-            chatbot.append(["我画你猜（动物）", "请稍等..."])
-        else:
-            if prompt.strip() == 'exit':
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
-                return
-            chatbot.append([prompt, ""])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-        if self.step_cnt == 0:
-            self.lock_plugin(chatbot)
-            self.cur_task = 'draw'
-
-        if self.cur_task == 'draw':
-            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
-            self.obj = random.choice(avail_obj)
-            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + \
-                f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
-            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
-            self.cur_task = 'identify user guess'
-            res = get_code_block(raw_res)
-            history += ['', f'the answer is {self.obj}', inputs, res]
-            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
-
-        elif self.cur_task == 'identify user guess':
-            if is_same_thing(self.obj, prompt, self.llm_kwargs):
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
-            else:
-                self.cur_task = 'identify user guess'
-                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
--- a/crazy_functions/game_fns/game_interactive_story.py
+++ b/crazy_functions/game_fns/game_interactive_story.py
@ -1,212 +0,0 @@
-prompts_hs = """ 请以“{headstart}”为开头，编写一个小说的第一幕。
-
- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
- 出现人物时，给出人物的名字。
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
- 字数要求：第一幕的字数少于300字，且少于2个段落。
-"""
-
-prompts_interact = """ 小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，根据以上的情节，给出4种不同的后续剧情发展方向，每个发展方向都精明扼要地用一句话说明。稍后，我将在这4个选择中，挑选一种剧情发展。
-
-输出格式例如：
-1. 后续剧情发展1
-2. 后续剧情发展2
-3. 后续剧情发展3
-4. 后续剧情发展4
-"""
-
-
-prompts_resume = """小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，我们正在互相讨论，确定后续剧情的发展。
-在以下的剧情发展中，
-「
-{choice}
-」
-我认为更合理的是：{user_choice}。
-请在前文的基础上（不要重复前文），围绕我选定的剧情情节，编写小说的下一幕。
-
- 禁止杜撰不符合我选择的剧情。
- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
- 不要重复前文。
- 出现人物时，给出人物的名字。
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
- 小说的下一幕字数少于300字，且少于2个段落。
-"""
-
-
-prompts_terminate = """小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，我们正在互相讨论，确定后续剧情的发展。
-现在，故事该结束了，我认为最合理的故事结局是：{user_choice}。
-
-请在前文的基础上（不要重复前文），编写小说的最后一幕。
-
- 不要重复前文。
- 出现人物时，给出人物的名字。
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
- 字数要求：最后一幕的字数少于1000字。
-"""
-
-
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-import random
-
-
-class MiniGame_ResumeStory(GptAcademicGameBaseState):
-    story_headstart = [
-        '先行者知道，他现在是全宇宙中唯一的一个人了。',
-        '深夜，一个年轻人穿过天安门广场向纪念堂走去。在二十二世纪编年史中，计算机把他的代号定为M102。',
-        '他知道，这最后一课要提前讲了。又一阵剧痛从肝部袭来，几乎使他晕厥过去。',
-        '在距地球五万光年的远方，在银河系的中心，一场延续了两万年的星际战争已接近尾声。那里的太空中渐渐隐现出一个方形区域，仿佛灿烂的群星的背景被剪出一个方口。',
-        '伊依一行三人乘坐一艘游艇在南太平洋上做吟诗航行，他们的目的地是南极，如果几天后能顺利到达那里，他们将钻出地壳去看诗云。',
-        '很多人生来就会莫名其妙地迷上一样东西，仿佛他的出生就是要和这东西约会似的，正是这样，圆圆迷上了肥皂泡。'
-    ]
-
-
-    def begin_game_step_0(self, prompt, chatbot, history):
-        # init game at step 0
-        self.headstart = random.choice(self.story_headstart)
-        self.story = []
-        chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
-        self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
-
-
-    def generate_story_image(self, story_paragraph):
-        try:
-            from crazy_functions.图片生成 import gen_image
-            prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
-            image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
-            return f'<br/><div align="center"><img src="file={image_path}"></div>'
-        except:
-            return ''
-
-    def step(self, prompt, chatbot, history):
-
-        """
-        首先，处理游戏初始化等特殊情况
-        """
-        if self.step_cnt == 0:
-            self.begin_game_step_0(prompt, chatbot, history)
-            self.lock_plugin(chatbot)
-            self.cur_task = 'head_start'
-        else:
-            if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
-                # should we terminate game here?
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
-                return
-            if '剧情收尾' in prompt:
-                self.cur_task = 'story_terminate'
-            # # well, game resumes
-            # chatbot.append([prompt, ""])
-        # update ui, don't keep the user waiting
-        yield from update_ui(chatbot=chatbot, history=history)
-
-
-        """
-        处理游戏的主体逻辑
-        """
-        if self.cur_task == 'head_start':
-            """
-            这是游戏的第一步
-            """
-            inputs_ = prompts_hs.format(headstart=self.headstart)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, '故事开头', self.llm_kwargs,
-                chatbot, history_, self.sys_prompt_
-            )
-            self.story.append(story_paragraph)
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # # 构建后续剧情引导
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
-            history_ = []
-            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs,
-                chatbot,
-                history_,
-                self.sys_prompt_
-            )
-            self.cur_task = 'user_choice'
-
-
-        elif self.cur_task == 'user_choice':
-            """
-            根据用户的提示，确定故事的下一步
-            """
-            if '请在以下几种故事走向中，选择一种' in chatbot[-1][0]: chatbot.pop(-1)
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs,
-                chatbot, history_, self.sys_prompt_
-            )
-            self.story.append(story_paragraph)
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # # 构建后续剧情引导
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
-            history_ = []
-            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_,
-                '请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs,
-                chatbot,
-                history_,
-                self.sys_prompt_
-            )
-            self.cur_task = 'user_choice'
-
-
-        elif self.cur_task == 'story_terminate':
-            """
-            根据用户的提示，确定故事的结局
-            """
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs,
-                chatbot, history_, self.sys_prompt_
-            )
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # terminate game
-            self.delete_game = True
-            return
--- a/crazy_functions/game_fns/game_utils.py
+++ b/crazy_functions/game_fns/game_utils.py
@ -1,35 +0,0 @@
-
-from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
-from request_llms.bridge_all import predict_no_ui_long_connection
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) == 1:
-        return "```" + matches[0] + "```" #  code block
-    raise RuntimeError("GPT is not generating proper code.")
-
-def is_same_thing(a, b, llm_kwargs):
-    from pydantic import BaseModel, Field
-    class IsSameThing(BaseModel):
-        is_same_thing: bool = Field(description="determine whether two objects are same thing.", default=False)
-
-    def run_gpt_fn(inputs, sys_prompt, history=[]):
-        return predict_no_ui_long_connection(
-            inputs=inputs, llm_kwargs=llm_kwargs,
-            history=history, sys_prompt=sys_prompt, observe_window=[]
-        )
-
-    gpt_json_io = GptJsonIO(IsSameThing)
-    inputs_01 = "Identity whether the user input and the target is the same thing: \n target object: {a} \n user input object: {b} \n\n\n".format(a=a, b=b)
-    inputs_01 += "\n\n\n Note that the user may describe the target object with a different language, e.g. cat and 猫 are the same thing."
-    analyze_res_cot_01 = run_gpt_fn(inputs_01, "", [])
-
-    inputs_02 = inputs_01 + gpt_json_io.format_instructions
-    analyze_res = run_gpt_fn(inputs_02, "", [inputs_01, analyze_res_cot_01])
-
-    try:
-        res = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
-        return res.is_same_thing
-    except JsonStringError as e:
-        return False
--- a/crazy_functions/gen_fns/gen_fns_shared.py
+++ b/crazy_functions/gen_fns/gen_fns_shared.py
@ -1,70 +0,0 @@
-import time
-import importlib
-from toolbox import trimmed_format_exc, gen_time_str, get_log_folder
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
-from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
-import multiprocessing
-
-def get_class_name(class_string):
-    import re
-    # Use regex to extract the class name
-    class_name = re.search(r'class (\w+)\(', class_string).group(1)
-    return class_name
-
-def try_make_module(code, chatbot):
-    module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
-    fn_path = f'{get_log_folder(plugin_name="gen_plugin_verify")}/{module_file}.py'
-    with open(fn_path, 'w', encoding='utf8') as f: f.write(code)
-    promote_file_to_downloadzone(fn_path, chatbot=chatbot)
-    class_name = get_class_name(code)
-    manager = multiprocessing.Manager()
-    return_dict = manager.dict()
-    p = multiprocessing.Process(target=is_function_successfully_generated, args=(fn_path, class_name, return_dict))
-    # only has 10 seconds to run
-    p.start(); p.join(timeout=10)
-    if p.is_alive(): p.terminate(); p.join()
-    p.close()
-    return return_dict["success"], return_dict['traceback']
-
-# check is_function_successfully_generated
-def is_function_successfully_generated(fn_path, class_name, return_dict):
-    return_dict['success'] = False
-    return_dict['traceback'] = ""
-    try:
-        # Create a spec for the module
-        module_spec = importlib.util.spec_from_file_location('example_module', fn_path)
-        # Load the module
-        example_module = importlib.util.module_from_spec(module_spec)
-        module_spec.loader.exec_module(example_module)
-        # Now you can use the module
-        some_class = getattr(example_module, class_name)
-        # Now you can create an instance of the class
-        instance = some_class()
-        return_dict['success'] = True
-        return
-    except:
-        return_dict['traceback'] = trimmed_format_exc()
-        return
-
-def subprocess_worker(code, file_path, return_dict):
-    return_dict['result'] = None
-    return_dict['success'] = False
-    return_dict['traceback'] = ""
-    try:
-        module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
-        fn_path = f'{get_log_folder(plugin_name="gen_plugin_run")}/{module_file}.py'
-        with open(fn_path, 'w', encoding='utf8') as f: f.write(code)
-        class_name = get_class_name(code)
-        # Create a spec for the module
-        module_spec = importlib.util.spec_from_file_location('example_module', fn_path)
-        # Load the module
-        example_module = importlib.util.module_from_spec(module_spec)
-        module_spec.loader.exec_module(example_module)
-        # Now you can use the module
-        some_class = getattr(example_module, class_name)
-        # Now you can create an instance of the class
-        instance = some_class()
-        return_dict['result'] = instance.run(file_path)
-        return_dict['success'] = True
-    except:
-        return_dict['traceback'] = trimmed_format_exc()
--- a/crazy_functions/ipc_fns/mp.py
+++ b/crazy_functions/ipc_fns/mp.py
@ -1,37 +0,0 @@
-import platform
-import pickle
-import multiprocessing
-
-def run_in_subprocess_wrapper_func(v_args):
-    func, args, kwargs, return_dict, exception_dict = pickle.loads(v_args)
-    import sys
-    try:
-        result = func(*args, **kwargs)
-        return_dict['result'] = result
-    except Exception as e:
-        exc_info = sys.exc_info()
-        exception_dict['exception'] = exc_info
-
-def run_in_subprocess_with_timeout(func, timeout=60):
-    if platform.system() == 'Linux':
-        def wrapper(*args, **kwargs):
-            return_dict = multiprocessing.Manager().dict()
-            exception_dict = multiprocessing.Manager().dict()
-            v_args = pickle.dumps((func, args, kwargs, return_dict, exception_dict))
-            process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, args=(v_args,))
-            process.start()
-            process.join(timeout)
-            if process.is_alive():
-                process.terminate()
-                raise TimeoutError(f'功能单元{str(func)}未能在规定时间内完成任务')
-            process.close()
-            if 'exception' in exception_dict:
-                # ooops, the subprocess ran into an exception
-                exc_info = exception_dict['exception']
-                raise exc_info[1].with_traceback(exc_info[2])
-            if 'result' in return_dict.keys():
-                # If the subprocess ran successfully, return the result
-                return return_dict['result']
-        return wrapper
-    else:
-        return func
--- a/crazy_functions/json_fns/pydantic_io.py
+++ b/crazy_functions/json_fns/pydantic_io.py
@ -1,111 +0,0 @@
-"""
-https://github.com/langchain-ai/langchain/blob/master/docs/extras/modules/model_io/output_parsers/pydantic.ipynb
-
-Example 1.
-
-# Define your desired data structure.
-class Joke(BaseModel):
-    setup: str = Field(description="question to set up a joke")
-    punchline: str = Field(description="answer to resolve the joke")
-
-    # You can add custom validation logic easily with Pydantic.
-    @validator("setup")
-    def question_ends_with_question_mark(cls, field):
-        if field[-1] != "?":
-            raise ValueError("Badly formed question!")
-        return field
-
-
-Example 2.
-
-# Here's another example, but with a compound typed field.
-class Actor(BaseModel):
-    name: str = Field(description="name of an actor")
-    film_names: List[str] = Field(description="list of names of films they starred in")
-"""
-
-import json, re, logging
-
-
-PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
-
-As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
-the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
-
-Here is the output schema:
-```
-{schema}
-```"""
-
-
-PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
-```
-{schema}
-```"""
-
-class JsonStringError(Exception): ...
-
-class GptJsonIO():
-
-    def __init__(self, schema, example_instruction=True):
-        self.pydantic_object = schema
-        self.example_instruction = example_instruction
-        self.format_instructions = self.generate_format_instructions()
-
-    def generate_format_instructions(self):
-        schema = self.pydantic_object.schema()
-
-        # Remove extraneous fields.
-        reduced_schema = schema
-        if "title" in reduced_schema:
-            del reduced_schema["title"]
-        if "type" in reduced_schema:
-            del reduced_schema["type"]
-        # Ensure json in context is well-formed with double quotes.
-        schema_str = json.dumps(reduced_schema)
-        if self.example_instruction:
-            return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
-        else:
-            return PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE.format(schema=schema_str)
-
-    def generate_output(self, text):
-        # Greedy search for 1st json candidate.
-        match = re.search(
-            r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL
-        )
-        json_str = ""
-        if match: json_str = match.group()
-        json_object = json.loads(json_str, strict=False)
-        final_object = self.pydantic_object.parse_obj(json_object)
-        return final_object
-
-    def generate_repair_prompt(self, broken_json, error):
-        prompt = "Fix a broken json string.\n\n" + \
-                 "(1) The broken json string need to fix is: \n\n" + \
-                 "```" + "\n" + \
-                 broken_json + "\n" + \
-                 "```" + "\n\n" + \
-                 "(2) The error message is: \n\n" + \
-                 error + "\n\n" + \
-                "Now, fix this json string. \n\n"
-        return prompt
-
-    def generate_output_auto_repair(self, response, gpt_gen_fn):
-        """
-        response: string containing canidate json
-        gpt_gen_fn: gpt_gen_fn(inputs, sys_prompt)
-        """
-        try:
-            result = self.generate_output(response)
-        except Exception as e:
-            try:
-                logging.info(f'Repairing json：{response}')
-                repair_prompt = self.generate_repair_prompt(broken_json = response, error=repr(e))
-                result = self.generate_output(gpt_gen_fn(repair_prompt, self.format_instructions))
-                logging.info('Repaire json success.')
-            except Exception as e:
-                # 没辙了，放弃治疗
-                logging.info('Repaire json fail.')
-                raise JsonStringError('Cannot repair json.', str(e))
-        return result
-
--- a/crazy_functions/latex_fns/latex_actions.py
+++ b/crazy_functions/latex_fns/latex_actions.py
@ -1,468 +0,0 @@
-from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
-from toolbox import get_conf, promote_file_to_downloadzone
-from .latex_toolbox import PRESERVE, TRANSFORM
-from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
-from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
-from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
-from .latex_toolbox import find_title_and_abs
-from .latex_pickle_io import objdump, objload
-
-import os, shutil
-import re
-import numpy as np
-
-pj = os.path.join
-
-
-def split_subprocess(txt, project_folder, return_dict, opts):
-    """
-    break down latex file to a linked list,
-    each node use a preserve flag to indicate whether it should
-    be proccessed by GPT.
-    """
-    text = txt
-    mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
-
-    # 吸收title与作者以上的部分
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\maketitle", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\begin{document}", re.DOTALL)
-    # 吸收iffalse注释
-    text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
-    # 吸收在42行以内的begin-end组合
-    text, mask = set_forbidden_text_begin_end(text, mask, r"\\begin\{([a-z\*]*)\}(.*?)\\end\{\1\}", re.DOTALL, limit_n_lines=42)
-    # 吸收匿名公式
-    text, mask = set_forbidden_text(text, mask, [ r"\$\$([^$]+)\$\$",  r"\\\[.*?\\\]" ], re.DOTALL)
-    # 吸收其他杂项
-    text, mask = set_forbidden_text(text, mask, [ r"\\section\{(.*?)\}", r"\\section\*\{(.*?)\}", r"\\subsection\{(.*?)\}", r"\\subsubsection\{(.*?)\}" ])
-    text, mask = set_forbidden_text(text, mask, [ r"\\bibliography\{(.*?)\}", r"\\bibliographystyle\{(.*?)\}" ])
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{thebibliography\}.*?\\end\{thebibliography\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{lstlisting\}(.*?)\\end\{lstlisting\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{wraptable\}(.*?)\\end\{wraptable\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{wrapfigure\}(.*?)\\end\{wrapfigure\}", r"\\begin\{wrapfigure\*\}(.*?)\\end\{wrapfigure\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{figure\}(.*?)\\end\{figure\}", r"\\begin\{figure\*\}(.*?)\\end\{figure\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{multline\}(.*?)\\end\{multline\}", r"\\begin\{multline\*\}(.*?)\\end\{multline\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{table\}(.*?)\\end\{table\}", r"\\begin\{table\*\}(.*?)\\end\{table\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{minipage\}(.*?)\\end\{minipage\}", r"\\begin\{minipage\*\}(.*?)\\end\{minipage\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{align\*\}(.*?)\\end\{align\*\}", r"\\begin\{align\}(.*?)\\end\{align\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{equation\}(.*?)\\end\{equation\}", r"\\begin\{equation\*\}(.*?)\\end\{equation\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\includepdf\[(.*?)\]\{(.*?)\}", r"\\clearpage", r"\\newpage", r"\\appendix", r"\\tableofcontents", r"\\include\{(.*?)\}"])
-    text, mask = set_forbidden_text(text, mask, [r"\\vspace\{(.*?)\}", r"\\hspace\{(.*?)\}", r"\\label\{(.*?)\}", r"\\begin\{(.*?)\}", r"\\end\{(.*?)\}", r"\\item "])
-    text, mask = set_forbidden_text_careful_brace(text, mask, r"\\hl\{(.*?)\}", re.DOTALL)
-    # reverse 操作必须放在最后
-    text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\caption\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
-    text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\abstract\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
-    text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
-    root = convert_to_linklist(text, mask)
-
-    # 最后一步处理，增强稳健性
-    root = post_process(root)
-
-    # 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）
-    with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:
-        segment_parts_for_gpt = []
-        nodes = []
-        node = root
-        while True:
-            nodes.append(node)
-            show_html = node.string.replace('\n','<br/>')
-            if not node.preserve:
-                segment_parts_for_gpt.append(node.string)
-                f.write(f'<p style="color:black;">#{node.range}{show_html}#</p>')
-            else:
-                f.write(f'<p style="color:red;">{show_html}</p>')
-            node = node.next
-            if node is None: break
-
-    for n in nodes: n.next = None   # break
-    return_dict['nodes'] = nodes
-    return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt
-    return return_dict
-
-class LatexPaperSplit():
-    """
-    break down latex file to a linked list,
-    each node use a preserve flag to indicate whether it should
-    be proccessed by GPT.
-    """
-    def __init__(self) -> None:
-        self.nodes = None
-        self.msg = "*{\\scriptsize\\textbf{警告：该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成，" + \
-            "版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。" + \
-            "项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
-        # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
-        self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
-        self.title = "unknown"
-        self.abstract = "unknown"
-
-    def read_title_and_abstract(self, txt):
-        try:
-            title, abstract = find_title_and_abs(txt)
-            if title is not None:
-                self.title = title.replace('\n', ' ').replace('\\\\', ' ').replace('  ', '').replace('  ', '')
-            if abstract is not None:
-                self.abstract = abstract.replace('\n', ' ').replace('\\\\', ' ').replace('  ', '').replace('  ', '')
-        except:
-            pass
-
-    def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):
-        """
-        Merge the result after the GPT process completed
-        """
-        result_string = ""
-        node_cnt = 0
-        line_cnt = 0
-
-        for node in self.nodes:
-            if node.preserve:
-                line_cnt += node.string.count('\n')
-                result_string += node.string
-            else:
-                translated_txt = fix_content(arr[node_cnt], node.string)
-                begin_line = line_cnt
-                end_line = line_cnt + translated_txt.count('\n')
-
-                # reverse translation if any error
-                if any([begin_line-buggy_line_surgery_n_lines <= b_line <= end_line+buggy_line_surgery_n_lines for b_line in buggy_lines]):
-                    translated_txt = node.string
-
-                result_string += translated_txt
-                node_cnt += 1
-                line_cnt += translated_txt.count('\n')
-
-        if mode == 'translate_zh':
-            pattern = re.compile(r'\\begin\{abstract\}.*\n')
-            match = pattern.search(result_string)
-            if not match:
-                # match \abstract{xxxx}
-                pattern_compile = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
-                match = pattern_compile.search(result_string)
-                position = match.regs[1][0]
-            else:
-                # match \begin{abstract}xxxx\end{abstract}
-                position = match.end()
-            result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]
-        return result_string
-
-
-    def split(self, txt, project_folder, opts):
-        """
-        break down latex file to a linked list,
-        each node use a preserve flag to indicate whether it should
-        be proccessed by GPT.
-        P.S. use multiprocessing to avoid timeout error
-        """
-        import multiprocessing
-        manager = multiprocessing.Manager()
-        return_dict = manager.dict()
-        p = multiprocessing.Process(
-            target=split_subprocess,
-            args=(txt, project_folder, return_dict, opts))
-        p.start()
-        p.join()
-        p.close()
-        self.nodes = return_dict['nodes']
-        self.sp = return_dict['segment_parts_for_gpt']
-        return self.sp
-
-
-class LatexPaperFileGroup():
-    """
-    use tokenizer to break down text according to max_token_limit
-    """
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-        # count_token
-        from request_llms.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        use tokenizer to break down text according to max_token_limit
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            with open(path + '.polish.tex', 'w', encoding='utf8') as f:
-                manifest.append(path + '.polish.tex')
-                f.write(res)
-        return manifest
-
-
-def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):
-    import time, os, re
-    from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
-
-    #  <-------- 寻找主tex文件 ---------->
-    maintex = find_main_tex_file(file_manifest, mode)
-    chatbot.append((f"定位主Latex文件", f'[Local Message] 分析结果：该项目的Latex主文件是{maintex}, 如果分析错误, 请立即终止程序, 删除或修改歧义文件, 然后重试。主程序即将开始, 请稍候。'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    time.sleep(3)
-
-    #  <-------- 读取Latex文件, 将多文件tex工程融合为一个巨型tex ---------->
-    main_tex_basename = os.path.basename(maintex)
-    assert main_tex_basename.endswith('.tex')
-    main_tex_basename_bare = main_tex_basename[:-4]
-    may_exist_bbl = pj(project_folder, f'{main_tex_basename_bare}.bbl')
-    if os.path.exists(may_exist_bbl):
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge.bbl'))
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_{mode}.bbl'))
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_diff.bbl'))
-
-    with open(maintex, 'r', encoding='utf-8', errors='replace') as f:
-        content = f.read()
-        merged_content = merge_tex_files(project_folder, content, mode)
-
-    with open(project_folder + '/merge.tex', 'w', encoding='utf-8', errors='replace') as f:
-        f.write(merged_content)
-
-    #  <-------- 精细切分latex文件 ---------->
-    chatbot.append((f"Latex文件融合完成", f'[Local Message] 正在精细切分latex文件，这需要一段时间计算，文档越长耗时越长，请耐心等待。'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    lps = LatexPaperSplit()
-    lps.read_title_and_abstract(merged_content)
-    res = lps.split(merged_content, project_folder, opts) # 消耗时间的函数
-    #  <-------- 拆分过长的latex片段 ---------->
-    pfg = LatexPaperFileGroup()
-    for index, r in enumerate(res):
-        pfg.file_paths.append('segment-' + str(index))
-        pfg.file_contents.append(r)
-
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 根据需要切换prompt ---------->
-    inputs_array, sys_prompt_array = switch_prompt(pfg, mode)
-    inputs_show_user_array = [f"{mode} {f}" for f in pfg.sp_file_tag]
-
-    if os.path.exists(pj(project_folder,'temp.pkl')):
-
-        #  <-------- 【仅调试】如果存在调试缓存文件，则跳过GPT请求环节 ---------->
-        pfg = objload(file=pj(project_folder,'temp.pkl'))
-
-    else:
-        #  <-------- gpt 多线程请求 ---------->
-        history_array = [[""] for _ in range(n_split)]
-        # LATEX_EXPERIMENTAL, = get_conf('LATEX_EXPERIMENTAL')
-        # if LATEX_EXPERIMENTAL:
-        #     paper_meta = f"The paper you processing is `{lps.title}`, a part of the abstraction is `{lps.abstract}`"
-        #     paper_meta_max_len = 888
-        #     history_array = [[ paper_meta[:paper_meta_max_len] + '...',  "Understand, what should I do?"] for _ in range(n_split)]
-
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=history_array,
-            sys_prompt_array=sys_prompt_array,
-            # max_workers=5,  # 并行任务数量限制, 最多同时执行5个, 其他的排队等待
-            scroller_max_len = 40
-        )
-
-        #  <-------- 文本碎片重组为完整的tex片段 ---------->
-        pfg.sp_file_result = []
-        for i_say, gpt_say, orig_content in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], pfg.sp_file_contents):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-
-        # <-------- 临时存储用于调试 ---------->
-        pfg.get_token_num = None
-        objdump(pfg, file=pj(project_folder,'temp.pkl'))
-
-    write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)
-
-    #  <-------- 写出文件 ---------->
-    msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
-    final_tex = lps.merge_result(pfg.file_result, mode, msg)
-    objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
-
-    with open(project_folder + f'/merge_{mode}.tex', 'w', encoding='utf-8', errors='replace') as f:
-        if mode != 'translate_zh' or "binary" in final_tex: f.write(final_tex)
-
-
-    #  <-------- 整理结果, 退出 ---------->
-    chatbot.append((f"完成了吗？", 'GPT结果已输出, 即将编译PDF'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    #  <-------- 返回 ---------->
-    return project_folder + f'/merge_{mode}.tex'
-
-
-def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified, fixed_line=[]):
-    try:
-        with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
-            log = f.read()
-        import re
-        buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)
-        buggy_lines = [int(l) for l in buggy_lines]
-        buggy_lines = sorted(buggy_lines)
-        buggy_line = buggy_lines[0]-1
-        print("reversing tex line that has errors", buggy_line)
-
-        # 重组，逆转出错的段落
-        if buggy_line not in fixed_line:
-            fixed_line.append(buggy_line)
-
-        lps, file_result, mode, msg = objload(file=pj(work_folder_modified,'merge_result.pkl'))
-        final_tex = lps.merge_result(file_result, mode, msg, buggy_lines=fixed_line, buggy_line_surgery_n_lines=5*n_fix)
-
-        with open(pj(work_folder_modified, f"{tex_name_pure}_fix_{n_fix}.tex"), 'w', encoding='utf-8', errors='replace') as f:
-            f.write(final_tex)
-
-        return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
-    except:
-        print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
-        return False, -1, [-1]
-
-
-def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):
-    import os, time
-    n_fix = 1
-    fixed_line = []
-    max_try = 32
-    chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
-    chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
-    yield from update_ui_lastest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面
-
-    while True:
-        import os
-        may_exist_bbl = pj(work_folder_modified, f'merge.bbl')
-        target_bbl = pj(work_folder_modified, f'{main_file_modified}.bbl')
-        if os.path.exists(may_exist_bbl) and not os.path.exists(target_bbl):
-            shutil.copyfile(may_exist_bbl, target_bbl)
-
-        # https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
-        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-
-        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-
-        if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
-            # 只有第二步成功，才能继续下面的步骤
-            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history)    # 刷新Gradio前端界面
-            if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):
-                ok = compile_latex_with_timeout(f'bibtex  {main_file_original}.aux', work_folder_original)
-            if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):
-                ok = compile_latex_with_timeout(f'bibtex  {main_file_modified}.aux', work_folder_modified)
-
-            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history)  # 刷新Gradio前端界面
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-
-            if mode!='translate_zh':
-                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
-                print(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
-                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())
-
-                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-                ok = compile_latex_with_timeout(f'bibtex    merge_diff.aux', work_folder)
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-
-        # <---------- 检查结果 ----------->
-        results_ = ""
-        original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
-        modified_pdf_success = os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf'))
-        diff_pdf_success     = os.path.exists(pj(work_folder, f'merge_diff.pdf'))
-        results_ += f"原始PDF编译是否成功: {original_pdf_success};"
-        results_ += f"转化PDF编译是否成功: {modified_pdf_success};"
-        results_ += f"对比PDF编译是否成功: {diff_pdf_success};"
-        yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
-
-        if diff_pdf_success:
-            result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
-            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-        if modified_pdf_success:
-            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
-            result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
-            origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
-            if os.path.exists(pj(work_folder, '..', 'translation')):
-                shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))
-            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-            # 将两个PDF拼接
-            if original_pdf_success:
-                try:
-                    from .latex_toolbox import merge_pdfs
-                    concat_pdf = pj(work_folder_modified, f'comparison.pdf')
-                    merge_pdfs(origin_pdf, result_pdf, concat_pdf)
-                    if os.path.exists(pj(work_folder, '..', 'translation')):
-                        shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
-                    promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-                except Exception as e:
-                    print(e)
-                    pass
-            return True # 成功啦
-        else:
-            if n_fix>=max_try: break
-            n_fix += 1
-            can_retry, main_file_modified, buggy_lines = remove_buggy_lines(
-                file_path=pj(work_folder_modified, f'{main_file_modified}.tex'),
-                log_path=pj(work_folder_modified, f'{main_file_modified}.log'),
-                tex_name=f'{main_file_modified}.tex',
-                tex_name_pure=f'{main_file_modified}',
-                n_fix=n_fix,
-                work_folder_modified=work_folder_modified,
-                fixed_line=fixed_line
-            )
-            yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面
-            if not can_retry: break
-
-    return False # 失败啦
-
-
-def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
-    # write html
-    try:
-        import shutil
-        from crazy_functions.pdf_fns.report_gen_html import construct_html
-        from toolbox import gen_time_str
-        ch = construct_html()
-        orig = ""
-        trans = ""
-        final = []
-        for c,r in zip(sp_file_contents, sp_file_result):
-            final.append(c)
-            final.append(r)
-        for i, k in enumerate(final):
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{gen_time_str()}.trans.html"
-        res = ch.save_file(create_report_file_name)
-        shutil.copyfile(res, pj(project_folder, create_report_file_name))
-        promote_file_to_downloadzone(file=res, chatbot=chatbot)
-    except:
-        from toolbox import trimmed_format_exc
-        print('writing html result failed:', trimmed_format_exc())
--- a/crazy_functions/latex_fns/latex_pickle_io.py
+++ b/crazy_functions/latex_fns/latex_pickle_io.py
@ -1,38 +0,0 @@
-import pickle
-
-
-class SafeUnpickler(pickle.Unpickler):
-
-    def get_safe_classes(self):
-        from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
-        # 定义允许的安全类
-        safe_classes = {
-            # 在这里添加其他安全的类
-            'LatexPaperFileGroup': LatexPaperFileGroup,
-            'LatexPaperSplit' : LatexPaperSplit,
-        }
-        return safe_classes
-
-    def find_class(self, module, name):
-        # 只允许特定的类进行反序列化
-        self.safe_classes = self.get_safe_classes()
-        if f'{module}.{name}' in self.safe_classes:
-            return self.safe_classes[f'{module}.{name}']
-        # 如果尝试加载未授权的类，则抛出异常
-        raise pickle.UnpicklingError(f"Attempted to deserialize unauthorized class '{name}' from module '{module}'")
-
-def objdump(obj, file="objdump.tmp"):
-
-    with open(file, "wb+") as f:
-        pickle.dump(obj, f)
-    return
-
-
-def objload(file="objdump.tmp"):
-    import os
-
-    if not os.path.exists(file):
-        return
-    with open(file, "rb") as f:
-        unpickler = SafeUnpickler(f)
-        return unpickler.load()
--- a/crazy_functions/latex_fns/latex_toolbox.py
+++ b/crazy_functions/latex_fns/latex_toolbox.py
@ -1,694 +0,0 @@
-import os, shutil
-import re
-import numpy as np
-
-PRESERVE = 0
-TRANSFORM = 1
-
-pj = os.path.join
-
-
-class LinkedListNode:
-    """
-    Linked List Node
-    """
-
-    def __init__(self, string, preserve=True) -> None:
-        self.string = string
-        self.preserve = preserve
-        self.next = None
-        self.range = None
-        # self.begin_line = 0
-        # self.begin_char = 0
-
-
-def convert_to_linklist(text, mask):
-    root = LinkedListNode("", preserve=True)
-    current_node = root
-    for c, m, i in zip(text, mask, range(len(text))):
-        if (m == PRESERVE and current_node.preserve) or (
-            m == TRANSFORM and not current_node.preserve
-        ):
-            # add
-            current_node.string += c
-        else:
-            current_node.next = LinkedListNode(c, preserve=(m == PRESERVE))
-            current_node = current_node.next
-    return root
-
-
-def post_process(root):
-    # 修复括号
-    node = root
-    while True:
-        string = node.string
-        if node.preserve:
-            node = node.next
-            if node is None:
-                break
-            continue
-
-        def break_check(string):
-            str_stack = [""]  # (lv, index)
-            for i, c in enumerate(string):
-                if c == "{":
-                    str_stack.append("{")
-                elif c == "}":
-                    if len(str_stack) == 1:
-                        print("stack fix")
-                        return i
-                    str_stack.pop(-1)
-                else:
-                    str_stack[-1] += c
-            return -1
-
-        bp = break_check(string)
-
-        if bp == -1:
-            pass
-        elif bp == 0:
-            node.string = string[:1]
-            q = LinkedListNode(string[1:], False)
-            q.next = node.next
-            node.next = q
-        else:
-            node.string = string[:bp]
-            q = LinkedListNode(string[bp:], False)
-            q.next = node.next
-            node.next = q
-
-        node = node.next
-        if node is None:
-            break
-
-    # 屏蔽空行和太短的句子
-    node = root
-    while True:
-        if len(node.string.strip("\n").strip("")) == 0:
-            node.preserve = True
-        if len(node.string.strip("\n").strip("")) < 42:
-            node.preserve = True
-        node = node.next
-        if node is None:
-            break
-    node = root
-    while True:
-        if node.next and node.preserve and node.next.preserve:
-            node.string += node.next.string
-            node.next = node.next.next
-        node = node.next
-        if node is None:
-            break
-
-    # 将前后断行符脱离
-    node = root
-    prev_node = None
-    while True:
-        if not node.preserve:
-            lstriped_ = node.string.lstrip().lstrip("\n")
-            if (
-                (prev_node is not None)
-                and (prev_node.preserve)
-                and (len(lstriped_) != len(node.string))
-            ):
-                prev_node.string += node.string[: -len(lstriped_)]
-                node.string = lstriped_
-            rstriped_ = node.string.rstrip().rstrip("\n")
-            if (
-                (node.next is not None)
-                and (node.next.preserve)
-                and (len(rstriped_) != len(node.string))
-            ):
-                node.next.string = node.string[len(rstriped_) :] + node.next.string
-                node.string = rstriped_
-        # =-=-=
-        prev_node = node
-        node = node.next
-        if node is None:
-            break
-
-    # 标注节点的行数范围
-    node = root
-    n_line = 0
-    expansion = 2
-    while True:
-        n_l = node.string.count("\n")
-        node.range = [n_line - expansion, n_line + n_l + expansion]  # 失败时，扭转的范围
-        n_line = n_line + n_l
-        node = node.next
-        if node is None:
-            break
-    return root
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-
-
-def set_forbidden_text(text, mask, pattern, flags=0):
-    """
-    Add a preserve text area in this paper
-    e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
-    you can mask out (mask = PRESERVE so that text become untouchable for GPT)
-    everything between "\begin{equation}" and "\end{equation}"
-    """
-    if isinstance(pattern, list):
-        pattern = "|".join(pattern)
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        mask[res.span()[0] : res.span()[1]] = PRESERVE
-    return text, mask
-
-
-def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
-    """
-    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area.
-    e.g.
-    \begin{abstract} blablablablablabla. \end{abstract}
-    """
-    if isinstance(pattern, list):
-        pattern = "|".join(pattern)
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        if not forbid_wrapper:
-            mask[res.span()[0] : res.span()[1]] = TRANSFORM
-        else:
-            mask[res.regs[0][0] : res.regs[1][0]] = PRESERVE  # '\\begin{abstract}'
-            mask[res.regs[1][0] : res.regs[1][1]] = TRANSFORM  # abstract
-            mask[res.regs[1][1] : res.regs[0][1]] = PRESERVE  # abstract
-    return text, mask
-
-
-def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
-    """
-    Add a preserve text area in this paper (text become untouchable for GPT).
-    count the number of the braces so as to catch compelete text area.
-    e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.}
-    """
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        brace_level = -1
-        p = begin = end = res.regs[0][0]
-        for _ in range(1024 * 16):
-            if text[p] == "}" and brace_level == 0:
-                break
-            elif text[p] == "}":
-                brace_level -= 1
-            elif text[p] == "{":
-                brace_level += 1
-            p += 1
-        end = p + 1
-        mask[begin:end] = PRESERVE
-    return text, mask
-
-
-def reverse_forbidden_text_careful_brace(
-    text, mask, pattern, flags=0, forbid_wrapper=True
-):
-    """
-    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area.
-    e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.}
-    """
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        brace_level = 0
-        p = begin = end = res.regs[1][0]
-        for _ in range(1024 * 16):
-            if text[p] == "}" and brace_level == 0:
-                break
-            elif text[p] == "}":
-                brace_level -= 1
-            elif text[p] == "{":
-                brace_level += 1
-            p += 1
-        end = p
-        mask[begin:end] = TRANSFORM
-        if forbid_wrapper:
-            mask[res.regs[0][0] : begin] = PRESERVE
-            mask[end : res.regs[0][1]] = PRESERVE
-    return text, mask
-
-
-def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
-    """
-    Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
-    Add it to preserve area
-    """
-    pattern_compile = re.compile(pattern, flags)
-
-    def search_with_line_limit(text, mask):
-        for res in pattern_compile.finditer(text):
-            cmd = res.group(1)  # begin{what}
-            this = res.group(2)  # content between begin and end
-            this_mask = mask[res.regs[2][0] : res.regs[2][1]]
-            white_list = [
-                "document",
-                "abstract",
-                "lemma",
-                "definition",
-                "sproof",
-                "em",
-                "emph",
-                "textit",
-                "textbf",
-                "itemize",
-                "enumerate",
-            ]
-            if (cmd in white_list) or this.count(
-                "\n"
-            ) >= limit_n_lines:  # use a magical number 42
-                this, this_mask = search_with_line_limit(this, this_mask)
-                mask[res.regs[2][0] : res.regs[2][1]] = this_mask
-            else:
-                mask[res.regs[0][0] : res.regs[0][1]] = PRESERVE
-        return text, mask
-
-    return search_with_line_limit(text, mask)
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Latex Merge File
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-
-
-def find_main_tex_file(file_manifest, mode):
-    """
-    在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
-    P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
-    """
-    canidates = []
-    for texf in file_manifest:
-        if os.path.basename(texf).startswith("merge"):
-            continue
-        with open(texf, "r", encoding="utf8", errors="ignore") as f:
-            file_content = f.read()
-        if r"\documentclass" in file_content:
-            canidates.append(texf)
-        else:
-            continue
-
-    if len(canidates) == 0:
-        raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
-    elif len(canidates) == 1:
-        return canidates[0]
-    else:  # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
-        canidates_score = []
-        # 给出一些判定模板文档的词作为扣分项
-        unexpected_words = [
-            "\\LaTeX",
-            "manuscript",
-            "Guidelines",
-            "font",
-            "citations",
-            "rejected",
-            "blind review",
-            "reviewers",
-        ]
-        expected_words = ["\\input", "\\ref", "\\cite"]
-        for texf in canidates:
-            canidates_score.append(0)
-            with open(texf, "r", encoding="utf8", errors="ignore") as f:
-                file_content = f.read()
-                file_content = rm_comments(file_content)
-            for uw in unexpected_words:
-                if uw in file_content:
-                    canidates_score[-1] -= 1
-            for uw in expected_words:
-                if uw in file_content:
-                    canidates_score[-1] += 1
-        select = np.argmax(canidates_score)  # 取评分最高者返回
-        return canidates[select]
-
-
-def rm_comments(main_file):
-    new_file_remove_comment_lines = []
-    for l in main_file.splitlines():
-        # 删除整行的空注释
-        if l.lstrip().startswith("%"):
-            pass
-        else:
-            new_file_remove_comment_lines.append(l)
-    main_file = "\n".join(new_file_remove_comment_lines)
-    # main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file)  # 将 \include 命令转换为 \input 命令
-    main_file = re.sub(r"(?<!\\)%.*", "", main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
-    return main_file
-
-
-def find_tex_file_ignore_case(fp):
-    dir_name = os.path.dirname(fp)
-    base_name = os.path.basename(fp)
-    # 如果输入的文件路径是正确的
-    if os.path.isfile(pj(dir_name, base_name)):
-        return pj(dir_name, base_name)
-    # 如果不正确，试着加上.tex后缀试试
-    if not base_name.endswith(".tex"):
-        base_name += ".tex"
-    if os.path.isfile(pj(dir_name, base_name)):
-        return pj(dir_name, base_name)
-    # 如果还找不到，解除大小写限制，再试一次
-    import glob
-
-    for f in glob.glob(dir_name + "/*.tex"):
-        base_name_s = os.path.basename(fp)
-        base_name_f = os.path.basename(f)
-        if base_name_s.lower() == base_name_f.lower():
-            return f
-        # 试着加上.tex后缀试试
-        if not base_name_s.endswith(".tex"):
-            base_name_s += ".tex"
-        if base_name_s.lower() == base_name_f.lower():
-            return f
-    return None
-
-
-def merge_tex_files_(project_foler, main_file, mode):
-    """
-    Merge Tex project recrusively
-    """
-    main_file = rm_comments(main_file)
-    for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
-        f = s.group(1)
-        fp = os.path.join(project_foler, f)
-        fp_ = find_tex_file_ignore_case(fp)
-        if fp_:
-            try:
-                with open(fp_, "r", encoding="utf-8", errors="replace") as fx:
-                    c = fx.read()
-            except:
-                c = f"\n\nWarning from GPT-Academic: LaTex source file is missing!\n\n"
-        else:
-            raise RuntimeError(f"找不到{fp}，Tex源文件缺失！")
-        c = merge_tex_files_(project_foler, c, mode)
-        main_file = main_file[: s.span()[0]] + c + main_file[s.span()[1] :]
-    return main_file
-
-
-def find_title_and_abs(main_file):
-    def extract_abstract_1(text):
-        pattern = r"\\abstract\{(.*?)\}"
-        match = re.search(pattern, text, re.DOTALL)
-        if match:
-            return match.group(1)
-        else:
-            return None
-
-    def extract_abstract_2(text):
-        pattern = r"\\begin\{abstract\}(.*?)\\end\{abstract\}"
-        match = re.search(pattern, text, re.DOTALL)
-        if match:
-            return match.group(1)
-        else:
-            return None
-
-    def extract_title(string):
-        pattern = r"\\title\{(.*?)\}"
-        match = re.search(pattern, string, re.DOTALL)
-
-        if match:
-            return match.group(1)
-        else:
-            return None
-
-    abstract = extract_abstract_1(main_file)
-    if abstract is None:
-        abstract = extract_abstract_2(main_file)
-    title = extract_title(main_file)
-    return title, abstract
-
-
-def merge_tex_files(project_foler, main_file, mode):
-    """
-    Merge Tex project recrusively
-    P.S. 顺便把CTEX塞进去以支持中文
-    P.S. 顺便把Latex的注释去除
-    """
-    main_file = merge_tex_files_(project_foler, main_file, mode)
-    main_file = rm_comments(main_file)
-
-    if mode == "translate_zh":
-        # find paper documentclass
-        pattern = re.compile(r"\\documentclass.*\n")
-        match = pattern.search(main_file)
-        assert match is not None, "Cannot find documentclass statement!"
-        position = match.end()
-        add_ctex = "\\usepackage{ctex}\n"
-        add_url = "\\usepackage{url}\n" if "{url}" not in main_file else ""
-        main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
-        # fontset=windows
-        import platform
-
-        main_file = re.sub(
-            r"\\documentclass\[(.*?)\]{(.*?)}",
-            r"\\documentclass[\1,fontset=windows,UTF8]{\2}",
-            main_file,
-        )
-        main_file = re.sub(
-            r"\\documentclass{(.*?)}",
-            r"\\documentclass[fontset=windows,UTF8]{\1}",
-            main_file,
-        )
-        # find paper abstract
-        pattern_opt1 = re.compile(r"\\begin\{abstract\}.*\n")
-        pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
-        match_opt1 = pattern_opt1.search(main_file)
-        match_opt2 = pattern_opt2.search(main_file)
-        if (match_opt1 is None) and (match_opt2 is None):
-            # "Cannot find paper abstract section!"
-            main_file = insert_abstract(main_file)
-        match_opt1 = pattern_opt1.search(main_file)
-        match_opt2 = pattern_opt2.search(main_file)
-        assert (match_opt1 is not None) or (
-            match_opt2 is not None
-        ), "Cannot find paper abstract section!"
-    return main_file
-
-
-insert_missing_abs_str = r"""
-\begin{abstract}
-The GPT-Academic program cannot find abstract section in this paper.
-\end{abstract}
-"""
-
-
-def insert_abstract(tex_content):
-    if "\\maketitle" in tex_content:
-        # find the position of "\maketitle"
-        find_index = tex_content.index("\\maketitle")
-        # find the nearest ending line
-        end_line_index = tex_content.find("\n", find_index)
-        # insert "abs_str" on the next line
-        modified_tex = (
-            tex_content[: end_line_index + 1]
-            + "\n\n"
-            + insert_missing_abs_str
-            + "\n\n"
-            + tex_content[end_line_index + 1 :]
-        )
-        return modified_tex
-    elif r"\begin{document}" in tex_content:
-        # find the position of "\maketitle"
-        find_index = tex_content.index(r"\begin{document}")
-        # find the nearest ending line
-        end_line_index = tex_content.find("\n", find_index)
-        # insert "abs_str" on the next line
-        modified_tex = (
-            tex_content[: end_line_index + 1]
-            + "\n\n"
-            + insert_missing_abs_str
-            + "\n\n"
-            + tex_content[end_line_index + 1 :]
-        )
-        return modified_tex
-    else:
-        return tex_content
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Post process
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-
-
-def mod_inbraket(match):
-    """
-    为啥chatgpt会把cite里面的逗号换成中文逗号呀
-    """
-    # get the matched string
-    cmd = match.group(1)
-    str_to_modify = match.group(2)
-    # modify the matched string
-    str_to_modify = str_to_modify.replace("：", ":")  # 前面是中文冒号，后面是英文冒号
-    str_to_modify = str_to_modify.replace("，", ",")  # 前面是中文逗号，后面是英文逗号
-    # str_to_modify = 'BOOM'
-    return "\\" + cmd + "{" + str_to_modify + "}"
-
-
-def fix_content(final_tex, node_string):
-    """
-    Fix common GPT errors to increase success rate
-    """
-    final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
-    final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
-    final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
-    final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
-
-    if "Traceback" in final_tex and "[Local Message]" in final_tex:
-        final_tex = node_string  # 出问题了，还原原文
-    if node_string.count("\\begin") != final_tex.count("\\begin"):
-        final_tex = node_string  # 出问题了，还原原文
-    if node_string.count("\_") > 0 and node_string.count("\_") > final_tex.count("\_"):
-        # walk and replace any _ without \
-        final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
-
-    def compute_brace_level(string):
-        # this function count the number of { and }
-        brace_level = 0
-        for c in string:
-            if c == "{":
-                brace_level += 1
-            elif c == "}":
-                brace_level -= 1
-        return brace_level
-
-    def join_most(tex_t, tex_o):
-        # this function join translated string and original string when something goes wrong
-        p_t = 0
-        p_o = 0
-
-        def find_next(string, chars, begin):
-            p = begin
-            while p < len(string):
-                if string[p] in chars:
-                    return p, string[p]
-                p += 1
-            return None, None
-
-        while True:
-            res1, char = find_next(tex_o, ["{", "}"], p_o)
-            if res1 is None:
-                break
-            res2, char = find_next(tex_t, [char], p_t)
-            if res2 is None:
-                break
-            p_o = res1 + 1
-            p_t = res2 + 1
-        return tex_t[:p_t] + tex_o[p_o:]
-
-    if compute_brace_level(final_tex) != compute_brace_level(node_string):
-        # 出问题了，还原部分原文，保证括号正确
-        final_tex = join_most(final_tex, node_string)
-    return final_tex
-
-
-def compile_latex_with_timeout(command, cwd, timeout=60):
-    import subprocess
-
-    process = subprocess.Popen(
-        command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd
-    )
-    try:
-        stdout, stderr = process.communicate(timeout=timeout)
-    except subprocess.TimeoutExpired:
-        process.kill()
-        stdout, stderr = process.communicate()
-        print("Process timed out!")
-        return False
-    return True
-
-
-def run_in_subprocess_wrapper_func(func, args, kwargs, return_dict, exception_dict):
-    import sys
-
-    try:
-        result = func(*args, **kwargs)
-        return_dict["result"] = result
-    except Exception as e:
-        exc_info = sys.exc_info()
-        exception_dict["exception"] = exc_info
-
-
-def run_in_subprocess(func):
-    import multiprocessing
-
-    def wrapper(*args, **kwargs):
-        return_dict = multiprocessing.Manager().dict()
-        exception_dict = multiprocessing.Manager().dict()
-        process = multiprocessing.Process(
-            target=run_in_subprocess_wrapper_func,
-            args=(func, args, kwargs, return_dict, exception_dict),
-        )
-        process.start()
-        process.join()
-        process.close()
-        if "exception" in exception_dict:
-            # ooops, the subprocess ran into an exception
-            exc_info = exception_dict["exception"]
-            raise exc_info[1].with_traceback(exc_info[2])
-        if "result" in return_dict.keys():
-            # If the subprocess ran successfully, return the result
-            return return_dict["result"]
-
-    return wrapper
-
-
-def _merge_pdfs(pdf1_path, pdf2_path, output_path):
-    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
-
-    Percent = 0.95
-    # raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
-    # Open the first PDF file
-    with open(pdf1_path, "rb") as pdf1_file:
-        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
-        # Open the second PDF file
-        with open(pdf2_path, "rb") as pdf2_file:
-            pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
-            # Create a new PDF file to store the merged pages
-            output_writer = PyPDF2.PdfFileWriter()
-            # Determine the number of pages in each PDF file
-            num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
-            # Merge the pages from the two PDF files
-            for page_num in range(num_pages):
-                # Add the page from the first PDF file
-                if page_num < pdf1_reader.numPages:
-                    page1 = pdf1_reader.getPage(page_num)
-                else:
-                    page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Add the page from the second PDF file
-                if page_num < pdf2_reader.numPages:
-                    page2 = pdf2_reader.getPage(page_num)
-                else:
-                    page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Create a new empty page with double width
-                new_page = PyPDF2.PageObject.createBlankPage(
-                    width=int(
-                        int(page1.mediaBox.getWidth())
-                        + int(page2.mediaBox.getWidth()) * Percent
-                    ),
-                    height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
-                )
-                new_page.mergeTranslatedPage(page1, 0, 0)
-                new_page.mergeTranslatedPage(
-                    page2,
-                    int(
-                        int(page1.mediaBox.getWidth())
-                        - int(page2.mediaBox.getWidth()) * (1 - Percent)
-                    ),
-                    0,
-                )
-                output_writer.addPage(new_page)
-            # Save the merged PDF file
-            with open(output_path, "wb") as output_file:
-                output_writer.write(output_file)
-
-
-merge_pdfs = run_in_subprocess(_merge_pdfs)  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
--- a/crazy_functions/live_audio/aliyunASR.py
+++ b/crazy_functions/live_audio/aliyunASR.py
@ -1,261 +0,0 @@
-import time, logging, json, sys, struct
-import numpy as np
-from scipy.io.wavfile import WAVE_FORMAT
-
-def write_numpy_to_wave(filename, rate, data, add_header=False):
-    """
-    Write a NumPy array as a WAV file.
-    """
-    def _array_tofile(fid, data):
-        # ravel gives a c-contiguous buffer
-        fid.write(data.ravel().view('b').data)
-
-    if hasattr(filename, 'write'):
-        fid = filename
-    else:
-        fid = open(filename, 'wb')
-
-    fs = rate
-
-    try:
-        dkind = data.dtype.kind
-        if not (dkind == 'i' or dkind == 'f' or (dkind == 'u' and
-                                                 data.dtype.itemsize == 1)):
-            raise ValueError("Unsupported data type '%s'" % data.dtype)
-
-        header_data = b''
-
-        header_data += b'RIFF'
-        header_data += b'\x00\x00\x00\x00'
-        header_data += b'WAVE'
-
-        # fmt chunk
-        header_data += b'fmt '
-        if dkind == 'f':
-            format_tag = WAVE_FORMAT.IEEE_FLOAT
-        else:
-            format_tag = WAVE_FORMAT.PCM
-        if data.ndim == 1:
-            channels = 1
-        else:
-            channels = data.shape[1]
-        bit_depth = data.dtype.itemsize * 8
-        bytes_per_second = fs*(bit_depth // 8)*channels
-        block_align = channels * (bit_depth // 8)
-
-        fmt_chunk_data = struct.pack('<HHIIHH', format_tag, channels, fs,
-                                     bytes_per_second, block_align, bit_depth)
-        if not (dkind == 'i' or dkind == 'u'):
-            # add cbSize field for non-PCM files
-            fmt_chunk_data += b'\x00\x00'
-
-        header_data += struct.pack('<I', len(fmt_chunk_data))
-        header_data += fmt_chunk_data
-
-        # fact chunk (non-PCM files)
-        if not (dkind == 'i' or dkind == 'u'):
-            header_data += b'fact'
-            header_data += struct.pack('<II', 4, data.shape[0])
-
-        # check data size (needs to be immediately before the data chunk)
-        if ((len(header_data)-4-4) + (4+4+data.nbytes)) > 0xFFFFFFFF:
-            raise ValueError("Data exceeds wave file size limit")
-        if add_header:
-            fid.write(header_data)
-            # data chunk
-            fid.write(b'data')
-            fid.write(struct.pack('<I', data.nbytes))
-            if data.dtype.byteorder == '>' or (data.dtype.byteorder == '=' and
-                                            sys.byteorder == 'big'):
-                data = data.byteswap()
-        _array_tofile(fid, data)
-
-        if add_header:
-            # Determine file size and place it in correct
-            #  position at start of the file.
-            size = fid.tell()
-            fid.seek(4)
-            fid.write(struct.pack('<I', size-8))
-
-    finally:
-        if not hasattr(filename, 'write'):
-            fid.close()
-        else:
-            fid.seek(0)
-
-def is_speaker_speaking(vad, data, sample_rate):
-    # Function to detect if the speaker is speaking
-    # The WebRTC VAD only accepts 16-bit mono PCM audio,
-    # sampled at 8000, 16000, 32000 or 48000 Hz.
-    # A frame must be either 10, 20, or 30 ms in duration:
-    frame_duration = 30
-    n_bit_each = int(sample_rate * frame_duration / 1000)*2 # x2 because audio is 16 bit (2 bytes)
-    res_list = []
-    for t in range(len(data)):
-        if t!=0 and t % n_bit_each == 0:
-            res_list.append(vad.is_speech(data[t-n_bit_each:t], sample_rate))
-
-    info = ''.join(['^' if r else '.' for r in res_list])
-    info = info[:10]
-    if any(res_list):
-        return True, info
-    else:
-        return False, info
-
-
-class AliyunASR():
-
-    def test_on_sentence_begin(self, message, *args):
-        # print("test_on_sentence_begin:{}".format(message))
-        pass
-
-    def test_on_sentence_end(self, message, *args):
-        # print("test_on_sentence_end:{}".format(message))
-        message = json.loads(message)
-        self.parsed_sentence = message['payload']['result']
-        self.event_on_entence_end.set()
-        # print(self.parsed_sentence)
-
-    def test_on_start(self, message, *args):
-        # print("test_on_start:{}".format(message))
-        pass
-
-    def test_on_error(self, message, *args):
-        logging.error("on_error args=>{}".format(args))
-        pass
-
-    def test_on_close(self, *args):
-        self.aliyun_service_ok = False
-        pass
-
-    def test_on_result_chg(self, message, *args):
-        # print("test_on_chg:{}".format(message))
-        message = json.loads(message)
-        self.parsed_text = message['payload']['result']
-        self.event_on_result_chg.set()
-
-    def test_on_completed(self, message, *args):
-        # print("on_completed:args=>{} message=>{}".format(args, message))
-        pass
-
-    def audio_convertion_thread(self, uuid):
-        # 在一个异步线程中采集音频
-        import nls  # pip install git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
-        import tempfile
-        from scipy import io
-        from toolbox import get_conf
-        from .audio_io import change_sample_rate
-        from .audio_io import RealtimeAudioDistribution
-        NEW_SAMPLERATE = 16000
-        rad = RealtimeAudioDistribution()
-        rad.clean_up()
-        temp_folder = tempfile.gettempdir()
-        TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
-        if len(TOKEN) == 0:
-            TOKEN = self.get_token()
-        self.aliyun_service_ok = True
-        URL="wss://nls-gateway.aliyuncs.com/ws/v1"
-        sr = nls.NlsSpeechTranscriber(
-                    url=URL,
-                    token=TOKEN,
-                    appkey=APPKEY,
-                    on_sentence_begin=self.test_on_sentence_begin,
-                    on_sentence_end=self.test_on_sentence_end,
-                    on_start=self.test_on_start,
-                    on_result_changed=self.test_on_result_chg,
-                    on_completed=self.test_on_completed,
-                    on_error=self.test_on_error,
-                    on_close=self.test_on_close,
-                    callback_args=[uuid.hex]
-                )
-        timeout_limit_second = 20
-        r = sr.start(aformat="pcm",
-                timeout=timeout_limit_second,
-                enable_intermediate_result=True,
-                enable_punctuation_prediction=True,
-                enable_inverse_text_normalization=True)
-
-        import webrtcvad
-        vad = webrtcvad.Vad()
-        vad.set_mode(1)
-
-        is_previous_frame_transmitted = False   # 上一帧是否有人说话
-        previous_frame_data = None
-        echo_cnt = 0        # 在没有声音之后，继续向服务器发送n次音频数据
-        echo_cnt_max = 4   # 在没有声音之后，继续向服务器发送n次音频数据
-        keep_alive_last_send_time = time.time()
-        while not self.stop:
-            # time.sleep(self.capture_interval)
-            audio = rad.read(uuid.hex)
-            if audio is not None:
-                # convert to pcm file
-                temp_file = f'{temp_folder}/{uuid.hex}.pcm' #
-                dsdata = change_sample_rate(audio, rad.rate, NEW_SAMPLERATE) # 48000 --> 16000
-                write_numpy_to_wave(temp_file, NEW_SAMPLERATE, dsdata)
-                # read pcm binary
-                with open(temp_file, "rb") as f: data = f.read()
-                is_speaking, info = is_speaker_speaking(vad, data, NEW_SAMPLERATE)
-
-                if is_speaking or echo_cnt > 0:
-                    # 如果话筒激活 / 如果处于回声收尾阶段
-                    echo_cnt -= 1
-                    if not is_previous_frame_transmitted:   # 上一帧没有人声，但是我们把上一帧同样加上
-                        if previous_frame_data is not None: data = previous_frame_data + data
-                    if is_speaking:
-                        echo_cnt = echo_cnt_max
-                    slices = zip(*(iter(data),) * 640)      # 640个字节为一组
-                    for i in slices: sr.send_audio(bytes(i))
-                    keep_alive_last_send_time = time.time()
-                    is_previous_frame_transmitted = True
-                else:
-                    is_previous_frame_transmitted = False
-                    echo_cnt = 0
-                    # 保持链接激活，即使没有声音，也根据时间间隔，发送一些音频片段给服务器
-                    if time.time() - keep_alive_last_send_time > timeout_limit_second/2:
-                        slices = zip(*(iter(data),) * 640)    # 640个字节为一组
-                        for i in slices: sr.send_audio(bytes(i))
-                        keep_alive_last_send_time = time.time()
-                        is_previous_frame_transmitted = True
-                self.audio_shape = info
-            else:
-                time.sleep(0.1)
-
-            if not self.aliyun_service_ok:
-                self.stop = True
-                self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'
-        r = sr.stop()
-
-    def get_token(self):
-        from toolbox import get_conf
-        import json
-        from aliyunsdkcore.request import CommonRequest
-        from aliyunsdkcore.client import AcsClient
-        AccessKey_ID, AccessKey_secret = get_conf('ALIYUN_ACCESSKEY', 'ALIYUN_SECRET')
-
-        # 创建AcsClient实例
-        client = AcsClient(
-            AccessKey_ID,
-            AccessKey_secret,
-            "cn-shanghai"
-        )
-
-        # 创建request，并设置参数。
-        request = CommonRequest()
-        request.set_method('POST')
-        request.set_domain('nls-meta.cn-shanghai.aliyuncs.com')
-        request.set_version('2019-02-28')
-        request.set_action_name('CreateToken')
-
-        try:
-            response = client.do_action_with_exception(request)
-            print(response)
-            jss = json.loads(response)
-            if 'Token' in jss and 'Id' in jss['Token']:
-                token = jss['Token']['Id']
-                expireTime = jss['Token']['ExpireTime']
-                print("token = " + token)
-                print("expireTime = " + str(expireTime))
-        except Exception as e:
-            print(e)
-
-        return token
--- a/crazy_functions/live_audio/audio_io.py
+++ b/crazy_functions/live_audio/audio_io.py
@ -1,51 +0,0 @@
-import numpy as np
-from scipy import interpolate
-
-def Singleton(cls):
-    _instance = {}
-
-    def _singleton(*args, **kargs):
-        if cls not in _instance:
-            _instance[cls] = cls(*args, **kargs)
-        return _instance[cls]
-
-    return _singleton
-
-
-@Singleton
-class RealtimeAudioDistribution():
-    def __init__(self) -> None:
-        self.data = {}
-        self.max_len = 1024*1024
-        self.rate = 48000   # 只读，每秒采样数量
-
-    def clean_up(self):
-        self.data = {}
-
-    def feed(self, uuid, audio):
-        self.rate, audio_ = audio
-        # print('feed', len(audio_), audio_[-25:])
-        if uuid not in self.data:
-            self.data[uuid] = audio_
-        else:
-            new_arr = np.concatenate((self.data[uuid], audio_))
-            if len(new_arr) > self.max_len: new_arr = new_arr[-self.max_len:]
-            self.data[uuid] = new_arr
-
-    def read(self, uuid):
-        if uuid in self.data:
-            res = self.data.pop(uuid)
-            # print('\r read-', len(res), '-', max(res), end='', flush=True)
-        else:
-            res = None
-        return res
-
-def change_sample_rate(audio, old_sr, new_sr):
-    duration = audio.shape[0] / old_sr
-
-    time_old  = np.linspace(0, duration, audio.shape[0])
-    time_new  = np.linspace(0, duration, int(audio.shape[0] * new_sr / old_sr))
-
-    interpolator = interpolate.interp1d(time_old, audio.T)
-    new_audio = interpolator(time_new).T
-    return new_audio.astype(np.int16)
--- a/crazy_functions/multi_stage/multi_stage_utils.py
+++ b/crazy_functions/multi_stage/multi_stage_utils.py
@ -1,93 +0,0 @@
-from pydantic import BaseModel, Field
-from typing import List
-from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
-import time
-import pickle
-
-def have_any_recent_upload_files(chatbot):
-    _5min = 5 * 60
-    if not chatbot: return False    # chatbot is None
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    if not most_recent_uploaded: return False   # most_recent_uploaded is None
-    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
-    else: return False  # most_recent_uploaded is too old
-
-class GptAcademicState():
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        pass
-
-    def dump_state(self, chatbot):
-        chatbot._cookies['plugin_state'] = pickle.dumps(self)
-
-    def set_state(self, chatbot, key, value):
-        setattr(self, key, value)
-        chatbot._cookies['plugin_state'] = pickle.dumps(self)
-
-    def get_state(chatbot, cls=None):
-        state = chatbot._cookies.get('plugin_state', None)
-        if state is not None:   state = pickle.loads(state)
-        elif cls is not None:   state = cls()
-        else:                   state = GptAcademicState()
-        state.chatbot = chatbot
-        return state
-
-
-class GptAcademicGameBaseState():
-    """
-    1. first init: __init__ ->
-    """
-    def init_game(self, chatbot, lock_plugin):
-        self.plugin_name = None
-        self.callback_fn = None
-        self.delete_game = False
-        self.step_cnt = 0
-
-    def lock_plugin(self, chatbot):
-        if self.callback_fn is None:
-            raise ValueError("callback_fn is None")
-        chatbot._cookies['lock_plugin'] = self.callback_fn
-        self.dump_state(chatbot)
-
-    def get_plugin_name(self):
-        if self.plugin_name is None:
-            raise ValueError("plugin_name is None")
-        return self.plugin_name
-
-    def dump_state(self, chatbot):
-        chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
-
-    def set_state(self, chatbot, key, value):
-        setattr(self, key, value)
-        chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
-
-    @staticmethod
-    def sync_state(chatbot, llm_kwargs, cls, plugin_name, callback_fn, lock_plugin=True):
-        state = chatbot._cookies.get(f'plugin_state/{plugin_name}', None)
-        if state is not None:
-            state = pickle.loads(state)
-        else:
-            state = cls()
-            state.init_game(chatbot, lock_plugin)
-        state.plugin_name = plugin_name
-        state.llm_kwargs = llm_kwargs
-        state.chatbot = chatbot
-        state.callback_fn = callback_fn
-        return state
-
-    def continue_game(self, prompt, chatbot, history):
-        # 游戏主体
-        yield from self.step(prompt, chatbot, history)
-        self.step_cnt += 1
-        # 保存状态，收尾
-        self.dump_state(chatbot)
-        # 如果游戏结束，清理
-        if self.delete_game:
-            chatbot._cookies['lock_plugin'] = None
-            chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = None
-        yield from update_ui(chatbot=chatbot, history=history)
--- a/crazy_functions/pdf_fns/breakdown_txt.py
+++ b/crazy_functions/pdf_fns/breakdown_txt.py
@ -1,125 +0,0 @@
-from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
-
-def force_breakdown(txt, limit, get_token_fn):
-    """ 当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-
-def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
-    """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
-    """
-    _min = int(5e4)
-    _max = int(1e5)
-    # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
-    if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
-        remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
-        remain_txt_to_cut_storage = ""
-    if len(remain_txt_to_cut) > _max:
-        remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
-        remain_txt_to_cut = remain_txt_to_cut[:_max]
-    return remain_txt_to_cut, remain_txt_to_cut_storage
-
-
-def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
-    """ 文本切分
-    """
-    res = []
-    total_len = len(txt_tocut)
-    fin_len = 0
-    remain_txt_to_cut = txt_tocut
-    remain_txt_to_cut_storage = ""
-    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-
-    while True:
-        if get_token_fn(remain_txt_to_cut) <= limit:
-            # 如果剩余文本的token数小于限制，那么就不用切了
-            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
-            break
-        else:
-            # 如果剩余文本的token数大于限制，那么就切
-            lines = remain_txt_to_cut.split('\n')
-
-            # 估计一个切分点
-            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-
-            # 开始查找合适切分点的偏移（cnt）
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    # 首先尝试用双空行（\n\n）作为切分点
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-
-            if cnt == 0:
-                # 如果没有找到合适的切分点
-                if break_anyway:
-                    # 是否允许暴力切分
-                    prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
-                else:
-                    # 不允许直接报错
-                    raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
-
-            # 追加列表
-            res.append(prev); fin_len+=len(prev)
-            # 准备下一次迭代
-            remain_txt_to_cut = post
-            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-            process = fin_len/total_len
-            print(f'正在文本切分 {int(process*100)}%')
-            if len(remain_txt_to_cut.strip()) == 0:
-                break
-    return res
-
-
-def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
-    """ 使用多种方式尝试切分文本，以满足 token 限制
-    """
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_model]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(limit, get_token_fn, txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(limit, get_token_fn, txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下吧
-                    return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
-
-breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
-
-if __name__ == '__main__':
-    from crazy_functions.crazy_utils import read_and_clean_pdf_text
-    file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
-
-    from request_llms.bridge_all import model_info
-    for i in range(5):
-        file_content += file_content
-
-    print(len(file_content))
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-    res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
-
--- a/crazy_functions/pdf_fns/parse_pdf.py
+++ b/crazy_functions/pdf_fns/parse_pdf.py
@ -1,171 +0,0 @@
-from functools import lru_cache
-from toolbox import gen_time_str
-from toolbox import promote_file_to_downloadzone
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-from toolbox import get_conf
-from toolbox import ProxyNetworkActivate
-from colorful import *
-import requests
-import random
-import copy
-import os
-import math
-
-class GROBID_OFFLINE_EXCEPTION(Exception): pass
-
-def get_avail_grobid_url():
-    GROBID_URLS = get_conf('GROBID_URLS')
-    if len(GROBID_URLS) == 0: return None
-    try:
-        _grobid_url = random.choice(GROBID_URLS) # 随机负载均衡
-        if _grobid_url.endswith('/'): _grobid_url = _grobid_url.rstrip('/')
-        with ProxyNetworkActivate('Connect_Grobid'):
-            res = requests.get(_grobid_url+'/api/isalive')
-        if res.text=='true': return _grobid_url
-        else: return None
-    except:
-        return None
-
-@lru_cache(maxsize=32)
-def parse_pdf(pdf_path, grobid_url):
-    import scipdf   # pip install scipdf_parser
-    if grobid_url.endswith('/'): grobid_url = grobid_url.rstrip('/')
-    try:
-        with ProxyNetworkActivate('Connect_Grobid'):
-            article_dict = scipdf.parse_pdf_to_dict(pdf_path, grobid_url=grobid_url)
-    except GROBID_OFFLINE_EXCEPTION:
-        raise GROBID_OFFLINE_EXCEPTION("GROBID服务不可用，请修改config中的GROBID_URL，可修改成本地GROBID服务。")
-    except:
-        raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
-    return article_dict
-
-
-def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files):
-    # -=-=-=-=-=-=-=-= 写出第1个文件：翻译前后混合 -=-=-=-=-=-=-=-=
-    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=f"{gen_time_str()}translated_and_original.md", file_fullname=None)
-    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
-    generated_conclusion_files.append(res_path)
-
-    # -=-=-=-=-=-=-=-= 写出第2个文件：仅翻译后的文本 -=-=-=-=-=-=-=-=
-    translated_res_array = []
-    # 记录当前的大章节标题：
-    last_section_name = ""
-    for index, value in enumerate(gpt_response_collection):
-        # 先挑选偶数序列号：
-        if index % 2 != 0:
-            # 先提取当前英文标题：
-            cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0]
-            # 如果index是1的话，则直接使用first section name：
-            if cur_section_name != last_section_name:
-                cur_value = cur_section_name + '\n'
-                last_section_name = copy.deepcopy(cur_section_name)
-            else:
-                cur_value = ""
-            # 再做一个小修改：重新修改当前part的标题，默认用英文的
-            cur_value += value
-            translated_res_array.append(cur_value)
-    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + translated_res_array,
-                                     file_basename = f"{gen_time_str()}-translated_only.md",
-                                     file_fullname = None,
-                                     auto_caption = False)
-    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
-    generated_conclusion_files.append(res_path)
-    return res_path
-
-def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
-    from crazy_functions.pdf_fns.report_gen_html import construct_html
-    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-    from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    prompt = "以下是一篇学术论文的基本信息:\n"
-    # title
-    title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
-    # authors
-    authors = article_dict.get('authors', '无法获取 authors')[:100]; prompt += f'authors:{authors}\n\n'
-    # abstract
-    abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
-    # command
-    prompt += f"请将题目和摘要翻译为{DST_LANG}。"
-    meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
-
-    # 单线，获取文章meta信息
-    paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=prompt,
-        inputs_show_user=prompt,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot, history=[],
-        sys_prompt="You are an academic paper reader。",
-    )
-
-    # 多线，翻译
-    inputs_array = []
-    inputs_show_user_array = []
-
-    # get_token_num
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_kwargs['llm_model']]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-
-    def break_down(txt):
-        raw_token_num = get_token_num(txt)
-        if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
-            return [txt]
-        else:
-            # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
-            # find a smooth token limit to achieve even seperation
-            count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
-            token_limit_smooth = raw_token_num // count + count
-            return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
-
-    for section in article_dict.get('sections'):
-        if len(section['text']) == 0: continue
-        section_frags = break_down(section['text'])
-        for i, fragment in enumerate(section_frags):
-            heading = section['heading']
-            if len(section_frags) > 1: heading += f' Part-{i+1}'
-            inputs_array.append(
-                f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
-            )
-            inputs_show_user_array.append(
-                f"# {heading}\n\n{fragment}"
-            )
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[meta for _ in inputs_array],
-        sys_prompt_array=[
-            "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
-    )
-    # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
-    produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)
-
-    # -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-=
-    ch = construct_html()
-    orig = ""
-    trans = ""
-    gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-    for i,k in enumerate(gpt_response_collection_html):
-        if i%2==0:
-            gpt_response_collection_html[i] = inputs_show_user_array[i//2]
-        else:
-            # 先提取当前英文标题：
-            cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0]
-            cur_value = cur_section_name + "\n" + gpt_response_collection_html[i]
-            gpt_response_collection_html[i] = cur_value
-
-    final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
-    final.extend(gpt_response_collection_html)
-    for i, k in enumerate(final):
-        if i%2==0:
-            orig = k
-        if i%2==1:
-            trans = k
-            ch.add_row(a=orig, b=trans)
-    create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-    html_file = ch.save_file(create_report_file_name)
-    generated_conclusion_files.append(html_file)
-    promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
--- a/crazy_functions/pdf_fns/parse_word.py
+++ b/crazy_functions/pdf_fns/parse_word.py
@ -1,85 +0,0 @@
-from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
-import os
-import re
-def extract_text_from_files(txt, chatbot, history):
-    """
-    查找pdf/md/word并获取文本内容并返回状态以及文本
-
-    输入参数 Args:
-        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
-        history (list): List of chat history （历史，对话历史列表）
-
-    输出 Returns:
-        文件是否存在(bool)
-        final_result(list):文本内容
-        page_one(list):第一页内容/摘要
-        file_manifest(list):文件路径
-        excption(string):需要用户手动处理的信息,如没出错则保持为空
-    """
-
-    final_result = []
-    page_one = []
-    file_manifest = []
-    excption = ""
-
-    if txt == "":
-        final_result.append(txt)
-        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
-
-    #查找输入区内容中的文件
-    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
-    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
-    file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx')
-    file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')
-
-    if file_doc:
-        excption = "word"
-        return False, final_result, page_one, file_manifest, excption
-
-    file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
-    if file_num == 0:
-        final_result.append(txt)
-        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
-
-    if file_pdf:
-        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-            import fitz
-        except:
-            excption = "pdf"
-            return False, final_result, page_one, file_manifest, excption
-        for index, fp in enumerate(pdf_manifest):
-            file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
-            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-            pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-            final_result.append(file_content)
-            page_one.append(pdf_one)
-            file_manifest.append(os.path.relpath(fp, folder_pdf))
-
-    if file_md:
-        for index, fp in enumerate(md_manifest):
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-            file_content = file_content.encode('utf-8', 'ignore').decode()
-            headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE)  #接下来提取md中的一级/二级标题作为摘要
-            if len(headers) > 0:
-                page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
-            else:
-                page_one.append("")
-            final_result.append(file_content)
-            file_manifest.append(os.path.relpath(fp, folder_md))
-
-    if file_word:
-        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-            from docx import Document
-        except:
-            excption = "word_pip"
-            return False, final_result, page_one, file_manifest, excption
-        for index, fp in enumerate(word_manifest):
-            doc = Document(fp)
-            file_content = '\n'.join([p.text for p in doc.paragraphs])
-            file_content = file_content.encode('utf-8', 'ignore').decode()
-            page_one.append(file_content[:200])
-            final_result.append(file_content)
-            file_manifest.append(os.path.relpath(fp, folder_word))
-
-    return True, final_result, page_one, file_manifest, excption
--- a/crazy_functions/pdf_fns/report_gen_html.py
+++ b/crazy_functions/pdf_fns/report_gen_html.py
@ -1,58 +0,0 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc, get_log_folder
-import os
-
-
-
-
-class construct_html():
-    def __init__(self) -> None:
-        self.html_string = ""
-
-    def add_row(self, a, b):
-        from toolbox import markdown_convertion
-        template = """
-            {
-                primary_col: {
-                    header: String.raw`__PRIMARY_HEADER__`,
-                    msg: String.raw`__PRIMARY_MSG__`,
-                },
-                secondary_rol: {
-                    header: String.raw`__SECONDARY_HEADER__`,
-                    msg: String.raw`__SECONDARY_MSG__`,
-                }
-            },
-        """
-        def std(str):
-            str = str.replace(r'`',r'&#96;')
-            if str.endswith("\\"): str += ' '
-            if str.endswith("}"): str += ' '
-            if str.endswith("$"): str += ' '
-            return str
-
-        template_ = template
-        a_lines = a.split('\n')
-        b_lines = b.split('\n')
-
-        if len(a_lines) == 1 or len(a_lines[0]) > 50:
-            template_ = template_.replace("__PRIMARY_HEADER__", std(a[:20]))
-            template_ = template_.replace("__PRIMARY_MSG__", std(markdown_convertion(a)))
-        else:
-            template_ = template_.replace("__PRIMARY_HEADER__", std(a_lines[0]))
-            template_ = template_.replace("__PRIMARY_MSG__", std(markdown_convertion('\n'.join(a_lines[1:]))))
-
-        if len(b_lines) == 1 or len(b_lines[0]) > 50:
-            template_ = template_.replace("__SECONDARY_HEADER__", std(b[:20]))
-            template_ = template_.replace("__SECONDARY_MSG__", std(markdown_convertion(b)))
-        else:
-            template_ = template_.replace("__SECONDARY_HEADER__", std(b_lines[0]))
-            template_ = template_.replace("__SECONDARY_MSG__", std(markdown_convertion('\n'.join(b_lines[1:]))))
-        self.html_string += template_
-
-    def save_file(self, file_name):
-        from toolbox import get_log_folder
-        with open('crazy_functions/pdf_fns/report_template.html', 'r', encoding='utf8') as f:
-            html_template = f.read()
-        html_template = html_template.replace("__TF_ARR__", self.html_string)
-        with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f:
-            f.write(html_template.encode('utf-8', 'ignore').decode())
-        return os.path.join(get_log_folder(), file_name)
--- a/crazy_functions/pdf_fns/report_template.html
+++ b/crazy_functions/pdf_fns/report_template.html
--- a/crazy_functions/pdf_fns/report_template_v2.html
+++ b/crazy_functions/pdf_fns/report_template_v2.html
@ -1,73 +0,0 @@
-<!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml">
-
-<head>
-    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
-    <title>GPT-Academic 翻译报告书</title>
-    <style>
-        .centered-a {
-            color: red;
-            text-align: center;
-            margin-bottom: 2%;
-            font-size: 1.5em;
-        }
-        .centered-b {
-            color: red;
-            text-align: center;
-            margin-top: 10%;
-            margin-bottom: 20%;
-            font-size: 1.5em;
-        }
-        .centered-c {
-            color: rgba(255, 0, 0, 0);
-            text-align: center;
-            margin-top: 2%;
-            margin-bottom: 20%;
-            font-size: 7em;
-        }
-    </style>
-<script>
-        // Configure MathJax settings
-        MathJax = {
-            tex: {
-                inlineMath: [
-                    ['$', '$'],
-                    ['\(', '\)']
-                ]
-            }
-        }
-        addEventListener('zero-md-rendered', () => {MathJax.typeset(); console.log('MathJax typeset!');})
-    </script>
-    <!-- Load MathJax library -->
-    <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
-    <script
-        type="module"
-        src="https://cdn.jsdelivr.net/gh/zerodevx/zero-md@2/dist/zero-md.min.js"
-    ></script>
-
-</head>
-
-<body>
-    <div class="test_temp1" style="width:10%; height: 500px; float:left;">
-
-    </div>
-    <div class="test_temp2" style="width:80%; height: 500px; float:left;">
-        <!-- Simply set the `src` attribute to your MD file and win -->
-        <div class="centered-a">
-            请按Ctrl+S保存此页面，否则该页面可能在几分钟后失效。
-        </div>
-        <zero-md src="translated_markdown.md" no-shadow>
-        </zero-md>
-        <div class="centered-b">
-            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
-        </div>
-        <div class="centered-c">
-            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
-        </div>
-    </div>
-    <div class="test_temp3" style="width:10%; height: 500px; float:left;">
-    </div>
-
-    </body>
-
-</html>
--- a/crazy_functions/test_project/cpp/cppipc/buffer.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/buffer.cpp
@ -0,0 +1,87 @@
+#include "libipc/buffer.h"
+#include "libipc/utility/pimpl.h"
+
+#include <cstring>
+
+namespace ipc {
+
+bool operator==(buffer const & b1, buffer const & b2) {
+    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
+}
+
+bool operator!=(buffer const & b1, buffer const & b2) {
+    return !(b1 == b2);
+}
+
+class buffer::buffer_ : public pimpl<buffer_> {
+public:
+    void*       p_;
+    std::size_t s_;
+    void*       a_;
+    buffer::destructor_t d_;
+
+    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
+        : p_(p), s_(s), a_(a), d_(d) {
+    }
+
+    ~buffer_() {
+        if (d_ == nullptr) return;
+        d_((a_ == nullptr) ? p_ : a_, s_);
+    }
+};
+
+buffer::buffer()
+    : buffer(nullptr, 0, nullptr, nullptr) {
+}
+
+buffer::buffer(void* p, std::size_t s, destructor_t d)
+    : p_(p_->make(p, s, d, nullptr)) {
+}
+
+buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
+    : p_(p_->make(p, s, d, additional)) {
+}
+
+buffer::buffer(void* p, std::size_t s)
+    : buffer(p, s, nullptr) {
+}
+
+buffer::buffer(char const & c)
+    : buffer(const_cast<char*>(&c), 1) {
+}
+
+buffer::buffer(buffer&& rhs)
+    : buffer() {
+    swap(rhs);
+}
+
+buffer::~buffer() {
+    p_->clear();
+}
+
+void buffer::swap(buffer& rhs) {
+    std::swap(p_, rhs.p_);
+}
+
+buffer& buffer::operator=(buffer rhs) {
+    swap(rhs);
+    return *this;
+}
+
+bool buffer::empty() const noexcept {
+    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
+}
+
+void* buffer::data() noexcept {
+    return impl(p_)->p_;
+}
+
+void const * buffer::data() const noexcept {
+    return impl(p_)->p_;
+}
+
+std::size_t buffer::size() const noexcept {
+    return impl(p_)->s_;
+}
+
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/ipc.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/ipc.cpp
@ -0,0 +1,701 @@
+
+#include <type_traits>
+#include <cstring>
+#include <algorithm>
+#include <utility>          // std::pair, std::move, std::forward
+#include <atomic>
+#include <type_traits>      // aligned_storage_t
+#include <string>
+#include <vector>
+#include <array>
+#include <cassert>
+
+#include "libipc/ipc.h"
+#include "libipc/def.h"
+#include "libipc/shm.h"
+#include "libipc/pool_alloc.h"
+#include "libipc/queue.h"
+#include "libipc/policy.h"
+#include "libipc/rw_lock.h"
+#include "libipc/waiter.h"
+
+#include "libipc/utility/log.h"
+#include "libipc/utility/id_pool.h"
+#include "libipc/utility/scope_guard.h"
+#include "libipc/utility/utility.h"
+
+#include "libipc/memory/resource.h"
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_array.h"
+
+namespace {
+
+using msg_id_t = std::uint32_t;
+using acc_t    = std::atomic<msg_id_t>;
+
+template <std::size_t DataSize, std::size_t AlignSize>
+struct msg_t;
+
+template <std::size_t AlignSize>
+struct msg_t<0, AlignSize> {
+    msg_id_t     cc_id_;
+    msg_id_t     id_;
+    std::int32_t remain_;
+    bool         storage_;
+};
+
+template <std::size_t DataSize, std::size_t AlignSize>
+struct msg_t : msg_t<0, AlignSize> {
+    std::aligned_storage_t<DataSize, AlignSize> data_ {};
+
+    msg_t() = default;
+    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
+        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
+        if (this->storage_) {
+            if (data != nullptr) {
+                // copy storage-id
+                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
+                     *static_cast<ipc::storage_id_t const *>(data);
+            }
+        }
+        else std::memcpy(&data_, data, size);
+    }
+};
+
+template <typename T>
+ipc::buff_t make_cache(T& data, std::size_t size) {
+    auto ptr = ipc::mem::alloc(size);
+    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
+    return { ptr, size, ipc::mem::free };
+}
+
+struct cache_t {
+    std::size_t fill_;
+    ipc::buff_t buff_;
+
+    cache_t(std::size_t f, ipc::buff_t && b)
+        : fill_(f), buff_(std::move(b))
+    {}
+
+    void append(void const * data, std::size_t size) {
+        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
+        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
+        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
+        fill_ = new_fill;
+    }
+};
+
+auto cc_acc() {
+    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
+    return static_cast<acc_t*>(acc_h.get());
+}
+
+IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
+    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
+}
+
+IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
+    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
+           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
+}
+
+struct chunk_t {
+    std::atomic<ipc::circ::cc_t> &conns() noexcept {
+        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
+    }
+
+    void *data() noexcept {
+        return reinterpret_cast<ipc::byte_t *>(this)
+             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
+    }
+};
+
+struct chunk_info_t {
+    ipc::id_pool<> pool_;
+    ipc::spin_lock lock_;
+
+    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
+        return ipc::id_pool<>::max_count * chunk_size;
+    }
+
+    ipc::byte_t *chunks_mem() noexcept {
+        return reinterpret_cast<ipc::byte_t *>(this + 1);
+    }
+
+    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
+        if (id < 0) return nullptr;
+        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
+    }
+};
+
+auto& chunk_storages() {
+    class chunk_handle_t {
+        ipc::shm::handle handle_;
+
+    public:
+        chunk_info_t *get_info(std::size_t chunk_size) {
+            if (!handle_.valid() &&
+                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
+                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
+                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
+                return nullptr;
+            }
+            auto info = static_cast<chunk_info_t*>(handle_.get());
+            if (info == nullptr) {
+                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
+                return nullptr;
+            }
+            return info;
+        }
+    };
+    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
+    return chunk_hs;
+}
+
+chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
+    auto &storages = chunk_storages();
+    std::decay_t<decltype(storages)>::iterator it;
+    {
+        static ipc::rw_lock lock;
+        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
+        if ((it = storages.find(chunk_size)) == storages.end()) {
+            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
+            guard.unlock();
+            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
+            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
+        }
+    }
+    return it->second.get_info(chunk_size);
+}
+
+std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return {};
+
+    info->lock_.lock();
+    info->pool_.prepare();
+    // got an unique id
+    auto id = info->pool_.acquire();
+    info->lock_.unlock();
+
+    auto chunk = info->at(chunk_size, id);
+    if (chunk == nullptr) return {};
+    chunk->conns().store(conns, std::memory_order_relaxed);
+    return { id, chunk->data() };
+}
+
+void *find_storage(ipc::storage_id_t id, std::size_t size) {
+    if (id < 0) {
+        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return nullptr;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return nullptr;
+    return info->at(chunk_size, id)->data();
+}
+
+void release_storage(ipc::storage_id_t id, std::size_t size) {
+    if (id < 0) {
+        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return;
+    info->lock_.lock();
+    info->pool_.release(id);
+    info->lock_.unlock();
+}
+
+template <ipc::relat Rp, ipc::relat Rc>
+bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
+            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
+    return true;
+}
+
+template <ipc::relat Rp, ipc::relat Rc>
+bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
+            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
+    auto last_conns = curr_conns & ~conn_id;
+    for (unsigned k = 0;;) {
+        auto chunk_conns  = conns.load(std::memory_order_acquire);
+        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
+            return (chunk_conns & last_conns) == 0;
+        }
+        ipc::yield(k);
+    }
+}
+
+template <typename Flag>
+void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
+    if (id < 0) {
+        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return;
+
+    auto chunk = info->at(chunk_size, id);
+    if (chunk == nullptr) return;
+
+    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
+        return;
+    }
+    info->lock_.lock();
+    info->pool_.release(id);
+    info->lock_.unlock();
+}
+
+template <typename MsgT>
+bool clear_message(void* p) {
+    auto msg = static_cast<MsgT*>(p);
+    if (msg->storage_) {
+        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
+        if (r_size <= 0) {
+            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
+            return true;
+        }
+        release_storage(
+            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
+            static_cast<std::size_t>(r_size));
+    }
+    return true;
+}
+
+struct conn_info_head {
+
+    ipc::string name_;
+    msg_id_t    cc_id_; // connection-info id
+    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
+    ipc::shm::handle acc_h_;
+
+    conn_info_head(char const * name)
+        : name_     {name}
+        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
+        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
+        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
+        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
+        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
+    }
+
+    void quit_waiting() {
+        cc_waiter_.quit_waiting();
+        wt_waiter_.quit_waiting();
+        rd_waiter_.quit_waiting();
+    }
+
+    auto acc() {
+        return static_cast<acc_t*>(acc_h_.get());
+    }
+
+    auto& recv_cache() {
+        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
+        return tls;
+    }
+};
+
+template <typename W, typename F>
+bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
+    if (tm == 0) return !pred();
+    for (unsigned k = 0; pred();) {
+        bool ret = true;
+        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
+            ret = waiter.wait_if(std::forward<F>(pred), tm);
+            k   = 0;
+        });
+        if (!ret) return false; // timeout or fail
+        if (k == 0) break; // k has been reset
+    }
+    return true;
+}
+
+template <typename Policy,
+          std::size_t DataSize  = ipc::data_length,
+          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
+struct queue_generator {
+
+    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
+
+    struct conn_info_t : conn_info_head {
+        queue_t que_;
+
+        conn_info_t(char const * name)
+            : conn_info_head{name}
+            , que_{("__QU_CONN__" +
+                    ipc::to_string(DataSize) + "__" +
+                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
+        }
+
+        void disconnect_receiver() {
+            bool dis = que_.disconnect();
+            this->quit_waiting();
+            if (dis) {
+                this->recv_cache().clear();
+            }
+        }
+    };
+};
+
+template <typename Policy>
+struct detail_impl {
+
+using policy_t    = Policy;
+using flag_t      = typename policy_t::flag_t;
+using queue_t     = typename queue_generator<policy_t>::queue_t;
+using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
+
+constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
+    return static_cast<conn_info_t*>(h);
+}
+
+constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
+    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
+}
+
+/* API implementations */
+
+static void disconnect(ipc::handle_t h) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return;
+    }
+    que->shut_sending();
+    assert(info_of(h) != nullptr);
+    info_of(h)->disconnect_receiver();
+}
+
+static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
+    assert(ph != nullptr);
+    assert(*ph != nullptr);
+    auto que = queue_of(*ph);
+    if (que == nullptr) {
+        return false;
+    }
+    if (start_to_recv) {
+        que->shut_sending();
+        if (que->connect()) { // wouldn't connect twice
+            info_of(*ph)->cc_waiter_.broadcast();
+            return true;
+        }
+        return false;
+    }
+    // start_to_recv == false
+    if (que->connected()) {
+        info_of(*ph)->disconnect_receiver();
+    }
+    return que->ready_sending();
+}
+
+static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
+    assert(ph != nullptr);
+    if (*ph == nullptr) {
+        *ph = ipc::mem::alloc<conn_info_t>(name);
+    }
+    return reconnect(ph, start_to_recv);
+}
+
+static void destroy(ipc::handle_t h) {
+    disconnect(h);
+    ipc::mem::free(info_of(h));
+}
+
+static std::size_t recv_count(ipc::handle_t h) noexcept {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return ipc::invalid_value;
+    }
+    return que->conn_count();
+}
+
+static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return false;
+    }
+    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
+        return que->conn_count() < r_count;
+    }, tm);
+}
+
+template <typename F>
+static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
+    if (data == nullptr || size == 0) {
+        ipc::error("fail: send(%p, %zd)\n", data, size);
+        return false;
+    }
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        ipc::error("fail: send, queue_of(h) == nullptr\n");
+        return false;
+    }
+    if (que->elems() == nullptr) {
+        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
+        return false;
+    }
+    if (!que->ready_sending()) {
+        ipc::error("fail: send, que->ready_sending() == false\n");
+        return false;
+    }
+    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
+    if (conns == 0) {
+        ipc::error("fail: send, there is no receiver on this connection.\n");
+        return false;
+    }
+    // calc a new message id
+    auto acc = info_of(h)->acc();
+    if (acc == nullptr) {
+        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
+        return false;
+    }
+    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
+    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
+    if (size > ipc::large_msg_limit) {
+        auto   dat = acquire_storage(size, conns);
+        void * buf = dat.second;
+        if (buf != nullptr) {
+            std::memcpy(buf, data, size);
+            return try_push(static_cast<std::int32_t>(size) - 
+                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
+        }
+        // try using message fragment
+        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
+    }
+    // push message fragment
+    std::int32_t offset = 0;
+    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
+        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
+                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
+            return false;
+        }
+    }
+    // if remain > 0, this is the last message fragment
+    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
+    if (remain > 0) {
+        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
+                      static_cast<ipc::byte_t const *>(data) + offset, 
+                      static_cast<std::size_t>(remain))) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return send([tm](auto info, auto que, auto msg_id) {
+        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
+            if (!wait_for(info->wt_waiter_, [&] {
+                    return !que->push(
+                        [](void*) { return true; },
+                        info->cc_id_, msg_id, remain, data, size);
+                }, tm)) {
+                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
+                if (!que->force_push(
+                        clear_message<typename queue_t::value_t>,
+                        info->cc_id_, msg_id, remain, data, size)) {
+                    return false;
+                }
+            }
+            info->rd_waiter_.broadcast();
+            return true;
+        };
+    }, h, data, size);
+}
+
+static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return send([tm](auto info, auto que, auto msg_id) {
+        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
+            if (!wait_for(info->wt_waiter_, [&] {
+                    return !que->push(
+                        [](void*) { return true; },
+                        info->cc_id_, msg_id, remain, data, size);
+                }, tm)) {
+                return false;
+            }
+            info->rd_waiter_.broadcast();
+            return true;
+        };
+    }, h, data, size);
+}
+
+static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        ipc::error("fail: recv, queue_of(h) == nullptr\n");
+        return {};
+    }
+    if (!que->connected()) {
+        // hasn't connected yet, just return.
+        return {};
+    }
+    auto& rc = info_of(h)->recv_cache();
+    for (;;) {
+        // pop a new message
+        typename queue_t::value_t msg;
+        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
+                return !que->pop(msg);
+            }, tm)) {
+            // pop failed, just return.
+            return {};
+        }
+        info_of(h)->wt_waiter_.broadcast();
+        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
+            continue; // ignore message to self
+        }
+        // msg.remain_ may minus & abs(msg.remain_) < data_length
+        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
+        if (r_size <= 0) {
+            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
+            return {};
+        }
+        std::size_t msg_size = static_cast<std::size_t>(r_size);
+        // large message
+        if (msg.storage_) {
+            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
+            void* buf = find_storage(buf_id, msg_size);
+            if (buf != nullptr) {
+                struct recycle_t {
+                    ipc::storage_id_t storage_id;
+                    ipc::circ::cc_t   curr_conns;
+                    ipc::circ::cc_t   conn_id;
+                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
+                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
+                });
+                if (r_info == nullptr) {
+                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
+                    return ipc::buff_t{buf, msg_size}; // no recycle
+                } else {
+                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
+                        auto r_info = static_cast<recycle_t *>(p_info);
+                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
+                            ipc::mem::free(r_info);
+                        });
+                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
+                    }, r_info};
+                }
+            } else {
+                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
+                continue;
+            }
+        }
+        // find cache with msg.id_
+        auto cac_it = rc.find(msg.id_);
+        if (cac_it == rc.end()) {
+            if (msg_size <= ipc::data_length) {
+                return make_cache(msg.data_, msg_size);
+            }
+            // gc
+            if (rc.size() > 1024) {
+                std::vector<msg_id_t> need_del;
+                for (auto const & pair : rc) {
+                    auto cmp = std::minmax(msg.id_, pair.first);
+                    if (cmp.second - cmp.first > 8192) {
+                        need_del.push_back(pair.first);
+                    }
+                }
+                for (auto id : need_del) rc.erase(id);
+            }
+            // cache the first message fragment
+            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
+        }
+        // has cached before this message
+        else {
+            auto& cac = cac_it->second;
+            // this is the last message fragment
+            if (msg.remain_ <= 0) {
+                cac.append(&(msg.data_), msg_size);
+                // finish this message, erase it from cache
+                auto buff = std::move(cac.buff_);
+                rc.erase(cac_it);
+                return buff;
+            }
+            // there are remain datas after this message
+            cac.append(&(msg.data_), ipc::data_length);
+        }
+    }
+}
+
+static ipc::buff_t try_recv(ipc::handle_t h) {
+    return recv(h, 0);
+}
+
+}; // detail_impl<Policy>
+
+template <typename Flag>
+using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
+
+} // internal-linkage
+
+namespace ipc {
+
+template <typename Flag>
+ipc::handle_t chan_impl<Flag>::inited() {
+    ipc::detail::waiter::init();
+    return nullptr;
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
+    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
+    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
+}
+
+template <typename Flag>
+void chan_impl<Flag>::disconnect(ipc::handle_t h) {
+    detail_impl<policy_t<Flag>>::disconnect(h);
+}
+
+template <typename Flag>
+void chan_impl<Flag>::destroy(ipc::handle_t h) {
+    detail_impl<policy_t<Flag>>::destroy(h);
+}
+
+template <typename Flag>
+char const * chan_impl<Flag>::name(ipc::handle_t h) {
+    auto info = detail_impl<policy_t<Flag>>::info_of(h);
+    return (info == nullptr) ? nullptr : info->name_.c_str();
+}
+
+template <typename Flag>
+std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
+    return detail_impl<policy_t<Flag>>::recv_count(h);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
+}
+
+template <typename Flag>
+buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::recv(h, tm);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
+}
+
+template <typename Flag>
+buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
+    return detail_impl<policy_t<Flag>>::try_recv(h);
+}
+
+template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
+// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
+// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
+template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
+template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
+
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/policy.h
+++ b/crazy_functions/test_project/cpp/cppipc/policy.h
@ -0,0 +1,25 @@
+#pragma once
+
+#include <type_traits>
+
+#include "libipc/def.h"
+#include "libipc/prod_cons.h"
+
+#include "libipc/circ/elem_array.h"
+
+namespace ipc {
+namespace policy {
+
+template <template <typename, std::size_t...> class Elems, typename Flag>
+struct choose;
+
+template <typename Flag>
+struct choose<circ::elem_array, Flag> {
+    using flag_t = Flag;
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    using elems_t = circ::elem_array<ipc::prod_cons_impl<flag_t>, DataSize, AlignSize>;
+};
+
+} // namespace policy
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
@ -0,0 +1,17 @@
+#include "libipc/pool_alloc.h"
+
+#include "libipc/memory/resource.h"
+
+namespace ipc {
+namespace mem {
+
+void* pool_alloc::alloc(std::size_t size) {
+    return async_pool_alloc::alloc(size);
+}
+
+void pool_alloc::free(void* p, std::size_t size) {
+    async_pool_alloc::free(p, size);
+}
+
+} // namespace mem
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ b/crazy_functions/test_project/cpp/cppipc/prod_cons.h
@ -0,0 +1,433 @@
+#pragma once
+
+#include <atomic>
+#include <utility>
+#include <cstring>
+#include <type_traits>
+#include <cstdint>
+
+#include "libipc/def.h"
+
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_def.h"
+#include "libipc/utility/log.h"
+#include "libipc/utility/utility.h"
+
+namespace ipc {
+
+////////////////////////////////////////////////////////////////
+/// producer-consumer implementation
+////////////////////////////////////////////////////////////////
+
+template <typename Flag>
+struct prod_cons_impl;
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
+
+    constexpr circ::u2_t cursor() const noexcept {
+        return 0;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
+        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
+            return false; // full
+        }
+        std::forward<F>(f)(&(elems[cur_wt].data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    /**
+     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
+     * So we could just disconnect all connections of receiver, and return false.
+    */
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
+        return false;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
+        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
+        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
+            return false; // empty
+        }
+        std::forward<F>(f)(&(elems[cur_rd].data_));
+        std::forward<R>(out)(true);
+        rd_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            if (circ::index_of(cur_rd) ==
+                circ::index_of(wt_.load(std::memory_order_acquire))) {
+                return false; // empty
+            }
+            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                std::forward<F>(f)(buff);
+                std::forward<R>(out)(true);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
+
+    using flag_t = std::uint64_t;
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        circ::u2_t cur_ct, nxt_ct;
+        for (unsigned k = 0;;) {
+            cur_ct = ct_.load(std::memory_order_relaxed);
+            if (circ::index_of(nxt_ct = cur_ct + 1) ==
+                circ::index_of(rd_.load(std::memory_order_acquire))) {
+                return false; // full
+            }
+            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        auto* el = elems + circ::index_of(cur_ct);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        while (1) {
+            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
+                return true;
+            }
+            if ((~cac_ct) != cur_ct) {
+                return true;
+            }
+            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
+                return true;
+            }
+            wt_.store(nxt_ct, std::memory_order_release);
+            cur_ct = nxt_ct;
+            nxt_ct = cur_ct + 1;
+            el = elems + circ::index_of(cur_ct);
+        }
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            auto cur_wt = wt_.load(std::memory_order_acquire);
+            auto id_rd  = circ::index_of(cur_rd);
+            auto id_wt  = circ::index_of(cur_wt);
+            if (id_rd == id_wt) {
+                auto* el = elems + id_wt;
+                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+                if ((~cac_ct) != cur_wt) {
+                    return false; // empty
+                }
+                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
+                    wt_.store(cur_wt + 1, std::memory_order_release);
+                }
+                k = 0;
+            }
+            else {
+                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                    std::forward<F>(f)(buff);
+                    std::forward<R>(out)(true);
+                    return true;
+                }
+                ipc::yield(k);
+            }
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
+
+    using rc_t = std::uint64_t;
+
+    enum : rc_t {
+        ep_mask = 0x00000000ffffffffull,
+        ep_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t> rc_ { 0 }; // read-counter
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
+    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
+
+    circ::u2_t cursor() const noexcept {
+        return wt_.load(std::memory_order_acquire);
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
+                return false; // has not finished yet
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        epoch_ += ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
+        if (cur == cursor()) return false; // acquire
+        auto* el = elems + circ::index_of(cur++);
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & ep_mask) == 0) {
+                std::forward<R>(out)(true);
+                return true;
+            }
+            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
+
+    using rc_t   = std::uint64_t;
+    using flag_t = std::uint64_t;
+
+    enum : rc_t {
+        rc_mask = 0x00000000ffffffffull,
+        ep_mask = 0x00ffffffffffffffull,
+        ep_incr = 0x0100000000000000ull,
+        ic_mask = 0xff000000ffffffffull,
+        ic_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t  > rc_   { 0 }; // read-counter
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
+    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
+
+    circ::u2_t cursor() const noexcept {
+        return ct_.load(std::memory_order_acquire);
+    }
+
+    constexpr static rc_t inc_rc(rc_t rc) noexcept {
+        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
+    }
+
+    constexpr static rc_t inc_mask(rc_t rc) noexcept {
+        return inc_rc(rc) & ~rc_mask;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.load(std::memory_order_acquire);
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
+                return false; // has not finished yet
+            }
+            else if (!rem_cc) {
+                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+                if ((cur_fl != cur_ct) && cur_fl) {
+                    return false; // full
+                }
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
+                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
+                if (epoch == epoch_.load(std::memory_order_acquire)) {
+                    break;
+                }
+                else if (push(wrapper, std::forward<F>(f), elems)) {
+                    return true;
+                }
+                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E, std::size_t N>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
+        auto* el = elems + circ::index_of(cur);
+        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+        if (cur_fl != ~static_cast<flag_t>(cur)) {
+            return false; // empty
+        }
+        ++cur;
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & rc_mask) == 0) {
+                std::forward<R>(out)(true);
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+                return true;
+            }
+            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
+            bool last_one = false;
+            if ((last_one = (nxt_rc & rc_mask) == 0)) {
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+            }
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)(last_one);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/queue.h
+++ b/crazy_functions/test_project/cpp/cppipc/queue.h
@ -0,0 +1,216 @@
+#pragma once
+
+#include <type_traits>
+#include <new>
+#include <utility>  // [[since C++14]]: std::exchange
+#include <algorithm>
+#include <atomic>
+#include <tuple>
+#include <thread>
+#include <chrono>
+#include <string>
+#include <cassert>  // assert
+
+#include "libipc/def.h"
+#include "libipc/shm.h"
+#include "libipc/rw_lock.h"
+
+#include "libipc/utility/log.h"
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_def.h"
+
+namespace ipc {
+namespace detail {
+
+class queue_conn {
+protected:
+    circ::cc_t connected_ = 0;
+    shm::handle elems_h_;
+
+    template <typename Elems>
+    Elems* open(char const * name) {
+        if (name == nullptr || name[0] == '\0') {
+            ipc::error("fail open waiter: name is empty!\n");
+            return nullptr;
+        }
+        if (!elems_h_.acquire(name, sizeof(Elems))) {
+            return nullptr;
+        }
+        auto elems = static_cast<Elems*>(elems_h_.get());
+        if (elems == nullptr) {
+            ipc::error("fail acquire elems: %s\n", name);
+            return nullptr;
+        }
+        elems->init();
+        return elems;
+    }
+
+    void close() {
+        elems_h_.release();
+    }
+
+public:
+    queue_conn() = default;
+    queue_conn(const queue_conn&) = delete;
+    queue_conn& operator=(const queue_conn&) = delete;
+
+    bool connected() const noexcept {
+        return connected_ != 0;
+    }
+
+    circ::cc_t connected_id() const noexcept {
+        return connected_;
+    }
+
+    template <typename Elems>
+    auto connect(Elems* elems) noexcept
+                         /*needs 'optional' here*/
+     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
+        if (elems == nullptr) return {};
+        // if it's already connected, just return
+        if (connected()) return {connected(), false, 0};
+        connected_ = elems->connect_receiver();
+        return {connected(), true, elems->cursor()};
+    }
+
+    template <typename Elems>
+    bool disconnect(Elems* elems) noexcept {
+        if (elems == nullptr) return false;
+        // if it's already disconnected, just return false
+        if (!connected()) return false;
+        elems->disconnect_receiver(std::exchange(connected_, 0));
+        return true;
+    }
+};
+
+template <typename Elems>
+class queue_base : public queue_conn {
+    using base_t = queue_conn;
+
+public:
+    using elems_t  = Elems;
+    using policy_t = typename elems_t::policy_t;
+
+protected:
+    elems_t * elems_ = nullptr;
+    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
+    bool sender_flag_ = false;
+
+public:
+    using base_t::base_t;
+
+    queue_base() = default;
+
+    explicit queue_base(char const * name)
+        : queue_base{} {
+        elems_ = open<elems_t>(name);
+    }
+
+    explicit queue_base(elems_t * elems) noexcept
+        : queue_base{} {
+        assert(elems != nullptr);
+        elems_ = elems;
+    }
+
+    /* not virtual */ ~queue_base() {
+        base_t::close();
+    }
+
+    elems_t       * elems()       noexcept { return elems_; }
+    elems_t const * elems() const noexcept { return elems_; }
+
+    bool ready_sending() noexcept {
+        if (elems_ == nullptr) return false;
+        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
+    }
+
+    void shut_sending() noexcept {
+        if (elems_ == nullptr) return;
+        if (!sender_flag_) return;
+        elems_->disconnect_sender();
+    }
+
+    bool connect() noexcept {
+        auto tp = base_t::connect(elems_);
+        if (std::get<0>(tp) && std::get<1>(tp)) {
+            cursor_ = std::get<2>(tp);
+            return true;
+        }
+        return std::get<0>(tp);
+    }
+
+    bool disconnect() noexcept {
+        return base_t::disconnect(elems_);
+    }
+
+    std::size_t conn_count() const noexcept {
+        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
+    }
+
+    bool valid() const noexcept {
+        return elems_ != nullptr;
+    }
+
+    bool empty() const noexcept {
+        return !valid() || (cursor_ == elems_->cursor());
+    }
+
+    template <typename T, typename F, typename... P>
+    bool push(F&& prep, P&&... params) {
+        if (elems_ == nullptr) return false;
+        return elems_->push(this, [&](void* p) {
+            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
+        });
+    }
+
+    template <typename T, typename F, typename... P>
+    bool force_push(F&& prep, P&&... params) {
+        if (elems_ == nullptr) return false;
+        return elems_->force_push(this, [&](void* p) {
+            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
+        });
+    }
+
+    template <typename T, typename F>
+    bool pop(T& item, F&& out) {
+        if (elems_ == nullptr) {
+            return false;
+        }
+        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
+            ::new (&item) T(std::move(*static_cast<T*>(p)));
+        }, std::forward<F>(out));
+    }
+};
+
+} // namespace detail
+
+template <typename T, typename Policy>
+class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
+    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
+
+public:
+    using value_t = T;
+
+    using base_t::base_t;
+
+    template <typename... P>
+    bool push(P&&... params) {
+        return base_t::template push<T>(std::forward<P>(params)...);
+    }
+
+    template <typename... P>
+    bool force_push(P&&... params) {
+        return base_t::template force_push<T>(std::forward<P>(params)...);
+    }
+
+    bool pop(T& item) {
+        return base_t::pop(item, [](bool) {});
+    }
+
+    template <typename F>
+    bool pop(T& item, F&& out) {
+        return base_t::pop(item, std::forward<F>(out));
+    }
+};
+
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/shm.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/shm.cpp
@ -0,0 +1,103 @@
+
+#include <string>
+#include <utility>
+
+#include "libipc/shm.h"
+
+#include "libipc/utility/pimpl.h"
+#include "libipc/memory/resource.h"
+
+namespace ipc {
+namespace shm {
+
+class handle::handle_ : public pimpl<handle_> {
+public:
+    shm::id_t id_ = nullptr;
+    void*     m_  = nullptr;
+
+    ipc::string n_;
+    std::size_t s_ = 0;
+};
+
+handle::handle()
+    : p_(p_->make()) {
+}
+
+handle::handle(char const * name, std::size_t size, unsigned mode)
+    : handle() {
+    acquire(name, size, mode);
+}
+
+handle::handle(handle&& rhs)
+    : handle() {
+    swap(rhs);
+}
+
+handle::~handle() {
+    release();
+    p_->clear();
+}
+
+void handle::swap(handle& rhs) {
+    std::swap(p_, rhs.p_);
+}
+
+handle& handle::operator=(handle rhs) {
+    swap(rhs);
+    return *this;
+}
+
+bool handle::valid() const noexcept {
+    return impl(p_)->m_ != nullptr;
+}
+
+std::size_t handle::size() const noexcept {
+    return impl(p_)->s_;
+}
+
+char const * handle::name() const noexcept {
+    return impl(p_)->n_.c_str();
+}
+
+std::int32_t handle::ref() const noexcept {
+    return shm::get_ref(impl(p_)->id_);
+}
+
+void handle::sub_ref() noexcept {
+    shm::sub_ref(impl(p_)->id_);
+}
+
+bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
+    release();
+    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
+    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
+    return valid();
+}
+
+std::int32_t handle::release() {
+    if (impl(p_)->id_ == nullptr) return -1;
+    return shm::release(detach());
+}
+
+void* handle::get() const {
+    return impl(p_)->m_;
+}
+
+void handle::attach(id_t id) {
+    if (id == nullptr) return;
+    release();
+    impl(p_)->id_ = id;
+    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
+}
+
+id_t handle::detach() {
+    auto old = impl(p_)->id_;
+    impl(p_)->id_ = nullptr;
+    impl(p_)->m_  = nullptr;
+    impl(p_)->s_  = 0;
+    impl(p_)->n_.clear();
+    return old;
+}
+
+} // namespace shm
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/waiter.h
+++ b/crazy_functions/test_project/cpp/cppipc/waiter.h
@ -0,0 +1,83 @@
+#pragma once
+
+#include <utility>
+#include <string>
+#include <mutex>
+#include <atomic>
+
+#include "libipc/def.h"
+#include "libipc/mutex.h"
+#include "libipc/condition.h"
+#include "libipc/platform/detail.h"
+
+namespace ipc {
+namespace detail {
+
+class waiter {
+    ipc::sync::condition cond_;
+    ipc::sync::mutex     lock_;
+    std::atomic<bool>    quit_ {false};
+
+public:
+    static void init();
+
+    waiter() = default;
+    waiter(char const *name) {
+        open(name);
+    }
+
+    ~waiter() {
+        close();
+    }
+
+    bool valid() const noexcept {
+        return cond_.valid() && lock_.valid();
+    }
+
+    bool open(char const *name) noexcept {
+        quit_.store(false, std::memory_order_relaxed);
+        if (!cond_.open((std::string{"_waiter_cond_"} + name).c_str())) {
+            return false;
+        }
+        if (!lock_.open((std::string{"_waiter_lock_"} + name).c_str())) {
+            cond_.close();
+            return false;
+        }
+        return valid();
+    }
+
+    void close() noexcept {
+        cond_.close();
+        lock_.close();
+    }
+
+    template <typename F>
+    bool wait_if(F &&pred, std::uint64_t tm = ipc::invalid_value) noexcept {
+        IPC_UNUSED_ std::lock_guard<ipc::sync::mutex> guard {lock_};
+        while ([this, &pred] {
+                    return !quit_.load(std::memory_order_relaxed)
+                        && std::forward<F>(pred)();
+                }()) {
+            if (!cond_.wait(lock_, tm)) return false;
+        }
+        return true;
+    }
+
+    bool notify() noexcept {
+        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
+        return cond_.notify(lock_);
+    }
+
+    bool broadcast() noexcept {
+        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
+        return cond_.broadcast(lock_);
+    }
+
+    bool quit_waiting() {
+        quit_.store(true, std::memory_order_release);
+        return broadcast();
+    }
+};
+
+} // namespace detail
+} // namespace ipc
--- a/crazy_functions/test_project/cpp/libJPG/JpegLibrary.tps
+++ b/crazy_functions/test_project/cpp/libJPG/JpegLibrary.tps
@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<TpsData xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+  <Name>Jpeg Library</Name>
+  <Location>/Engine/Source/ThirdParty/libJPG/</Location>
+  <Date>2016-06-10T14:04:17.9005402-04:00</Date>
+  <Function>We need it because it is a 3rd party lib in GFx</Function>
+  <Justification />
+  <Eula> See license in download: http://www.ijg.org/</Eula>
+  <RedistributeTo>
+    <EndUserGroup>Licensees</EndUserGroup>
+    <EndUserGroup>Git</EndUserGroup>
+    <EndUserGroup>P4</EndUserGroup>
+  </RedistributeTo>
+  <LicenseFolder>/Engine/Source/ThirdParty/Licenses/JPEG_License.txt</LicenseFolder>
+</TpsData>
--- a/crazy_functions/test_project/cpp/libJPG/UElibJPG.Build.cs
+++ b/crazy_functions/test_project/cpp/libJPG/UElibJPG.Build.cs
@ -0,0 +1,17 @@
+// Copyright Epic Games, Inc. All Rights Reserved.
+
+using UnrealBuildTool;
+
+public class UElibJPG : ModuleRules
+{
+	public UElibJPG(ReadOnlyTargetRules Target) : base(Target)
+    {
+        Type = ModuleType.External;
+
+		string libJPGPath = Target.UEThirdPartySourceDirectory + "libJPG";
+		PublicIncludePaths.Add(libJPGPath);
+
+		ShadowVariableWarningLevel = WarningLevel.Off;
+	}
+}
+
--- a/crazy_functions/test_project/cpp/libJPG/jpeg-compressor.tps
+++ b/crazy_functions/test_project/cpp/libJPG/jpeg-compressor.tps
@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<TpsData xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+  <Name>jpeg-compressor</Name>
+  <Location>/Engine/Source/ThirdParty/libJPG/</Location>
+  <Date>2016-06-10T14:07:13.8351319-04:00</Date>
+  <Function>Allows JPEG compression and decompression.</Function>
+  <Justification>Compressing video frames at runtime for reduced memory usage. Decompression to access the data afterwards.</Justification>
+  <Eula>https://code.google.com/archive/p/jpeg-compressor/</Eula>
+  <RedistributeTo>
+    <EndUserGroup>Licensees</EndUserGroup>
+    <EndUserGroup>Git</EndUserGroup>
+    <EndUserGroup>P4</EndUserGroup>
+  </RedistributeTo>
+  <LicenseFolder>None</LicenseFolder>
+</TpsData>
--- a/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ b/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
--- a/crazy_functions/test_project/cpp/libJPG/jpgd.h
+++ b/crazy_functions/test_project/cpp/libJPG/jpgd.h
@ -0,0 +1,316 @@
+// jpgd.h - C++ class for JPEG decompression.
+// Public domain, Rich Geldreich <richgel99@gmail.com>
+#ifndef JPEG_DECODER_H
+#define JPEG_DECODER_H
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <setjmp.h>
+
+namespace jpgd
+{
+  typedef unsigned char  uint8;
+  typedef   signed short int16;
+  typedef unsigned short uint16;
+  typedef unsigned int   uint;
+  typedef   signed int   int32;
+
+  // Loads a JPEG image from a memory buffer or a file.
+  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
+  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
+  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
+  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
+// BEGIN EPIC MOD
+//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
+  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
+// END EPIC MOD
+  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
+
+  // Success/failure error codes.
+  enum jpgd_status
+  {
+    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
+    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, 
+    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, 
+    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
+    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
+    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
+    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
+    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
+    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
+  };
+    
+  // Input stream interface.
+  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
+  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
+  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
+  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
+  class jpeg_decoder_stream
+  {
+  public:
+    jpeg_decoder_stream() { }
+    virtual ~jpeg_decoder_stream() { }
+
+    // The read() method is called when the internal input buffer is empty.
+    // Parameters:
+    // pBuf - input buffer
+    // max_bytes_to_read - maximum bytes that can be written to pBuf
+    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
+    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
+    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
+  };
+
+  // stdio FILE stream class.
+  class jpeg_decoder_file_stream : public jpeg_decoder_stream
+  {
+    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
+    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
+
+    FILE *m_pFile;
+    bool m_eof_flag, m_error_flag;
+
+  public:
+    jpeg_decoder_file_stream();
+    virtual ~jpeg_decoder_file_stream();
+    
+    bool open(const char *Pfilename);
+    void close();
+
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
+  };
+
+  // Memory stream class.
+  class jpeg_decoder_mem_stream : public jpeg_decoder_stream
+  {
+    const uint8 *m_pSrc_data;
+    uint m_ofs, m_size;
+
+  public:
+    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
+    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
+
+    virtual ~jpeg_decoder_mem_stream() { }
+
+    bool open(const uint8 *pSrc_data, uint size);
+    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
+    
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
+  };
+
+  // Loads JPEG file from a jpeg_decoder_stream.
+  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
+
+  enum 
+  { 
+    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, 
+    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 
+  };
+          
+  typedef int16 jpgd_quant_t;
+  typedef int16 jpgd_block_t;
+
+  class jpeg_decoder
+  {
+  public:
+    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
+    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
+    jpeg_decoder(jpeg_decoder_stream *pStream);
+
+    ~jpeg_decoder();
+
+    // Call this method after constructing the object to begin decompression.
+    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
+    int begin_decoding();
+
+    // Returns the next scan line.
+    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). 
+    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
+    // Returns JPGD_SUCCESS if a scan line has been returned.
+    // Returns JPGD_DONE if all scan lines have been returned.
+    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
+    int decode(const void** pScan_line, uint* pScan_line_len);
+    
+    inline jpgd_status get_error_code() const { return m_error_code; }
+
+    inline int get_width() const { return m_image_x_size; }
+    inline int get_height() const { return m_image_y_size; }
+
+    inline int get_num_components() const { return m_comps_in_frame; }
+
+    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
+    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
+
+    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
+    inline int get_total_bytes_read() const { return m_total_bytes_read; }
+    
+  private:
+    jpeg_decoder(const jpeg_decoder &);
+    jpeg_decoder &operator =(const jpeg_decoder &);
+
+    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
+
+    struct huff_tables
+    {
+      bool ac_table;
+      uint  look_up[256];
+      uint  look_up2[256];
+      uint8 code_size[256];
+      uint  tree[512];
+    };
+
+    struct coeff_buf
+    {
+      uint8 *pData;
+      int block_num_x, block_num_y;
+      int block_len_x, block_len_y;
+      int block_size;
+    };
+
+    struct mem_block
+    {
+      mem_block *m_pNext;
+      size_t m_used_count;
+      size_t m_size;
+      char m_data[1];
+    };
+
+    jmp_buf m_jmp_state;
+    mem_block *m_pMem_blocks;
+    int m_image_x_size;
+    int m_image_y_size;
+    jpeg_decoder_stream *m_pStream;
+    int m_progressive_flag;
+    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
+    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size
+    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size
+    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
+    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
+    int m_comps_in_frame;                         // # of components in frame
+    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor
+    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor
+    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector
+    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID
+    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
+    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
+    int m_comps_in_scan;                          // # of components in scan
+    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan
+    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector
+    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector
+    int m_spectral_start;                         // spectral selection start
+    int m_spectral_end;                           // spectral selection end
+    int m_successive_low;                         // successive approximation low
+    int m_successive_high;                        // successive approximation high
+    int m_max_mcu_x_size;                         // MCU's max. X size in pixels
+    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels
+    int m_blocks_per_mcu;
+    int m_max_blocks_per_row;
+    int m_mcus_per_row, m_mcus_per_col;
+    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
+    int m_total_lines_left;                       // total # lines left in image
+    int m_mcu_lines_left;                         // total # lines left in this MCU
+    int m_real_dest_bytes_per_scan_line;
+    int m_dest_bytes_per_scan_line;               // rounded up
+    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)
+    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
+    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
+    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
+    int m_eob_run;
+    int m_block_y_mcu[JPGD_MAX_COMPONENTS];
+    uint8* m_pIn_buf_ofs;
+    int m_in_buf_left;
+    int m_tem_flag;
+    bool m_eof_flag;
+    uint8 m_in_buf_pad_start[128];
+    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
+    uint8 m_in_buf_pad_end[128];
+    int m_bits_left;
+    uint m_bit_buf;
+    int m_restart_interval;
+    int m_restarts_left;
+    int m_next_restart_num;
+    int m_max_mcus_per_row;
+    int m_max_blocks_per_mcu;
+    int m_expanded_blocks_per_mcu;
+    int m_expanded_blocks_per_row;
+    int m_expanded_blocks_per_component;
+    bool  m_freq_domain_chroma_upsample;
+    int m_max_mcus_per_col;
+    uint m_last_dc_val[JPGD_MAX_COMPONENTS];
+    jpgd_block_t* m_pMCU_coefficients;
+    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
+    uint8* m_pSample_buf;
+    int m_crr[256];
+    int m_cbb[256];
+    int m_crg[256];
+    int m_cbg[256];
+    uint8* m_pScan_line_0;
+    uint8* m_pScan_line_1;
+    jpgd_status m_error_code;
+    bool m_ready_flag;
+    int m_total_bytes_read;
+
+    void free_all_blocks();
+    // BEGIN EPIC MOD
+    UE_NORETURN void stop_decoding(jpgd_status status);
+    // END EPIC MOD
+    void *alloc(size_t n, bool zero = false);
+    void word_clear(void *p, uint16 c, uint n);
+    void prep_in_buffer();
+    void read_dht_marker();
+    void read_dqt_marker();
+    void read_sof_marker();
+    void skip_variable_marker();
+    void read_dri_marker();
+    void read_sos_marker();
+    int next_marker();
+    int process_markers();
+    void locate_soi_marker();
+    void locate_sof_marker();
+    int locate_sos_marker();
+    void init(jpeg_decoder_stream * pStream);
+    void create_look_ups();
+    void fix_in_buffer();
+    void transform_mcu(int mcu_row);
+    void transform_mcu_expand(int mcu_row);
+    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
+    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
+    void load_next_row();
+    void decode_next_row();
+    void make_huff_table(int index, huff_tables *pH);
+    void check_quant_tables();
+    void check_huff_tables();
+    void calc_mcu_block_order();
+    int init_scan();
+    void init_frame();
+    void process_restart();
+    void decode_scan(pDecode_block_func decode_block_func);
+    void init_progressive();
+    void init_sequential();
+    void decode_start();
+    void decode_init(jpeg_decoder_stream * pStream);
+    void H2V2Convert();
+    void H2V1Convert();
+    void H1V2Convert();
+    void H1V1Convert();
+    void gray_convert();
+    void expanded_convert();
+    void find_eoi();
+    inline uint get_char();
+    inline uint get_char(bool *pPadding_flag);
+    inline void stuff_char(uint8 q);
+    inline uint8 get_octet();
+    inline uint get_bits(int num_bits);
+    inline uint get_bits_no_markers(int numbits);
+    inline int huff_decode(huff_tables *pH);
+    inline int huff_decode(huff_tables *pH, int& extrabits);
+    static inline uint8 clamp(int i);
+    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+  };
+  
+} // namespace jpgd
+
+#endif // JPEG_DECODER_H
--- a/crazy_functions/test_project/cpp/libJPG/jpge.cpp
+++ b/crazy_functions/test_project/cpp/libJPG/jpge.cpp
--- a/crazy_functions/test_project/cpp/libJPG/jpge.h
+++ b/crazy_functions/test_project/cpp/libJPG/jpge.h
@ -0,0 +1,172 @@
+
+// jpge.h - C++ class for JPEG compression.
+// Public domain, Rich Geldreich <richgel99@gmail.com>
+// Alex Evans: Added RGBA support, linear memory allocator.
+#ifndef JPEG_ENCODER_H
+#define JPEG_ENCODER_H
+
+#include <stdint.h>
+
+namespace jpge
+{
+  typedef unsigned char  uint8;
+  typedef signed short   int16;
+  typedef signed int     int32;
+  typedef unsigned short uint16;
+  typedef unsigned int   uint32;
+  typedef unsigned int   uint;
+
+  // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common.
+  enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 };
+
+  // JPEG compression parameters structure.
+  struct params
+  {
+    inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { }
+
+    inline bool check_valid() const
+    {
+      if ((m_quality < 1) || (m_quality > 100)) return false;
+      if ((uint)m_subsampling > (uint)H2V2) return false;
+      return true;
+    }
+
+    // Quality: 1-100, higher is better. Typical values are around 50-95.
+    int m_quality;
+
+    // m_subsampling:
+    // 0 = Y (grayscale) only
+    // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU)
+    // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU)
+    // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common)
+    subsampling_t m_subsampling;
+
+    // Disables CbCr discrimination - only intended for testing.
+    // If true, the Y quantization table is also used for the CbCr channels.
+    bool m_no_chroma_discrim_flag;
+
+    bool m_two_pass_flag;
+  };
+  
+  // Writes JPEG image to a file. 
+  // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels.
+  bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
+
+  // Writes JPEG image to memory buffer. 
+  // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. 
+  // If return value is true, buf_size will be set to the size of the compressed data.
+  bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
+    
+  // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. 
+  // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts.
+  class output_stream
+  {
+  public:
+    virtual ~output_stream() { };
+    virtual bool put_buf(const void* Pbuf, int64_t len) = 0;
+    template<class T> inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); }
+  };
+    
+  // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions.
+  class jpeg_encoder
+  {
+  public:
+    jpeg_encoder();
+    ~jpeg_encoder();
+
+    // Initializes the compressor.
+    // pStream: The stream object to use for writing compressed data.
+    // params - Compression parameters structure, defined above.
+    // width, height  - Image dimensions.
+    // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data.
+    // Returns false on out of memory or if a stream write fails.
+    bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params());
+    
+    const params &get_params() const { return m_params; }
+    
+    // Deinitializes the compressor, freeing any allocated memory. May be called at any time.
+    void deinit();
+
+    uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; }
+    inline uint get_cur_pass() { return m_pass_num; }
+
+    // Call this method with each source scanline.
+    // width * src_channels bytes per scanline is expected (RGB or Y format).
+    // You must call with NULL after all scanlines are processed to finish compression.
+    // Returns false on out of memory or if a stream write fails.
+    bool process_scanline(const void* pScanline);
+        
+  private:
+    jpeg_encoder(const jpeg_encoder &);
+    jpeg_encoder &operator =(const jpeg_encoder &);
+
+    typedef int32 sample_array_t;
+        
+    output_stream *m_pStream;
+    params m_params;
+    uint8 m_num_components;
+    uint8 m_comp_h_samp[3], m_comp_v_samp[3];
+    int m_image_x, m_image_y, m_image_bpp, m_image_bpl;
+    int m_image_x_mcu, m_image_y_mcu;
+    int m_image_bpl_xlt, m_image_bpl_mcu;
+    int m_mcus_per_row;
+    int m_mcu_x, m_mcu_y;
+    uint8 *m_mcu_lines[16];
+    uint8 m_mcu_y_ofs;
+    sample_array_t m_sample_array[64];
+    int16 m_coefficient_array[64];
+    int32 m_quantization_tables[2][64];
+    uint m_huff_codes[4][256];
+    uint8 m_huff_code_sizes[4][256];
+    uint8 m_huff_bits[4][17];
+    uint8 m_huff_val[4][256];
+    uint32 m_huff_count[4][256];
+    int m_last_dc_val[3];
+    enum { JPGE_OUT_BUF_SIZE = 2048 };
+    uint8 m_out_buf[JPGE_OUT_BUF_SIZE];
+    uint8 *m_pOut_buf;
+    uint m_out_buf_left;
+    uint32 m_bit_buffer;
+    uint m_bits_in;
+    uint8 m_pass_num;
+    bool m_all_stream_writes_succeeded;
+        
+    void optimize_huffman_table(int table_num, int table_len);
+    void emit_byte(uint8 i);
+    void emit_word(uint i);
+    void emit_marker(int marker);
+    void emit_jfif_app0();
+    void emit_dqt();
+    void emit_sof();
+    void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag);
+    void emit_dhts();
+    void emit_sos();
+    void emit_markers();
+    void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val);
+    void compute_quant_table(int32 *dst, int16 *src);
+    void adjust_quant_table(int32 *dst, int32 *src);
+    void first_pass_init();
+    bool second_pass_init();
+    bool jpg_open(int p_x_res, int p_y_res, int src_channels);
+    void load_block_8_8_grey(int x);
+    void load_block_8_8(int x, int y, int c);
+    void load_block_16_8(int x, int c);
+    void load_block_16_8_8(int x, int c);
+    void load_quantized_coefficients(int component_num);
+    void flush_output_buffer();
+    void put_bits(uint bits, uint len);
+    void code_coefficients_pass_one(int component_num);
+    void code_coefficients_pass_two(int component_num);
+    void code_block(int component_num);
+    void process_mcu_row();
+    bool terminate_pass_one();
+    bool terminate_pass_two();
+    bool process_end_of_image();
+    void load_mcu(const void* src);
+    void clear();
+    void init();
+  };
+
+} // namespace jpge
+
+#endif // JPEG_ENCODER
--- a/crazy_functions/test_project/cpp/libJPG/来源
+++ b/crazy_functions/test_project/cpp/libJPG/来源
@ -0,0 +1,3 @@
+jpge.h - C++ class for JPEG compression.
+Public domain, Rich Geldreich <richgel99@gmail.com>
+Alex Evans: Added RGBA support, linear memory allocator.
--- a/crazy_functions/test_project/latex/attention/background.tex
+++ b/crazy_functions/test_project/latex/attention/background.tex
@ -0,0 +1,58 @@
+The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
+
+Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
+
+End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
+
+To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
+In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
+
+
+%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
+
+%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
+
+%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
+
+%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
+
+%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
+
+%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
+
+%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
+
+
+
+%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
+
+%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
+
+%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
+
+%\begin{table}[h!]
+%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
+%\label{tab:op_complexities}
+%\begin{center}
+%\vspace{-5pt}
+%\scalebox{0.75}{
+
+%\begin{tabular}{l|c|c|c}
+%\hline \hline
+%Layer Type & Receptive & Complexity & Sequential  \\
+%           & Field     &            & Operations  \\
+%\hline
+%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
+%\hline
+%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
+%\hline
+%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
+%\hline
+%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
+%\hline
+%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
+%\hline \hline
+%\end{tabular}
+%}
+%\end{center}
+%\end{table}
--- a/crazy_functions/test_project/latex/attention/introduction.tex
+++ b/crazy_functions/test_project/latex/attention/introduction.tex
@ -0,0 +1,18 @@
+Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
+
+Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
+%\marginpar{not sure if the memory constraints are understandable here}
+Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
+
+%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} 
+
+Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
+
+%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
+%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
+
+In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 
+%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
+
+% Just a standard paragraph with citations, rewrite.
+%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
--- a/crazy_functions/test_project/latex/attention/model_architecture.tex
+++ b/crazy_functions/test_project/latex/attention/model_architecture.tex
@ -0,0 +1,155 @@
+
+\begin{figure}
+  \centering
+  \includegraphics[scale=0.6]{Figures/ModalNet-21}
+  \caption{The Transformer - model architecture.}
+  \label{fig:model-arch}
+\end{figure}
+
+% Although the primary workhorse of our model is attention, 
+%Our model maintains the encoder-decoder structure that is common to many so-called sequence-to-sequence models \citep{bahdanau2014neural,sutskever14}.  As in all such architectures, the encoder computes a representation of the input sequence, and the decoder consumes these representations along with the output tokens to autoregressively produce the output sequence.  Where, traditionally, the encoder and decoder contain stacks of recurrent or convolutional layers, our encoder and decoder stacks are composed of attention layers and position-wise feed-forward layers (Figure~\ref{fig:model-arch}).  The following sections describe the gross architecture and these particular components in detail.
+
+Most competitive neural sequence transduction models have an encoder-decoder structure \citep{cho2014learning,bahdanau2014neural,sutskever14}. Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\mathbf{z} = (z_1, ..., z_n)$. Given $\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive \citep{graves2013generating}, consuming the previously generated symbols as additional input when generating the next.
+
+The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure~\ref{fig:model-arch}, respectively.
+
+\subsection{Encoder and Decoder Stacks}
+
+\paragraph{Encoder:}The encoder is composed of a stack of $N=6$ identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.   We employ a residual connection \citep{he2016deep} around each of the two sub-layers, followed by layer normalization \cite{layernorm2016}.  That is, the output of each sub-layer is $\mathrm{LayerNorm}(x + \mathrm{Sublayer}(x))$, where $\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $\dmodel=512$.
+
+\paragraph{Decoder:}The decoder is also composed of a stack of $N=6$ identical layers.  In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.
+
+% In our model (Figure~\ref{fig:model-arch}), the encoder and decoder are composed of stacks of alternating self-attention layers (for cross-positional communication) and position-wise feed-forward layers (for in-place computation).  In addition, the decoder stack contains encoder-decoder attention layers.  Since attention is agnostic to the distances between words, our model requires a "positional encoding" to be added to the encoder and decoder input. The following sections describe all of these components in detail.
+
+\subsection{Attention} \label{sec:attention}
+An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
+
+\subsubsection{Scaled Dot-Product Attention} \label{sec:scaled-dot-prod}
+
+% \begin{figure}
+%   \centering
+%   \includegraphics[scale=0.6]{Figures/ModalNet-19}
+%   \caption{Scaled Dot-Product Attention.}
+%   \label{fig:multi-head-att}
+% \end{figure}
+
+We call our particular attention "Scaled Dot-Product Attention" (Figure~\ref{fig:multi-head-att}).   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.
+
+In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:
+
+\begin{equation}
+   \mathrm{Attention}(Q, K, V) = \mathrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V
+\end{equation}
+
+The two most commonly used attention functions are additive attention \citep{bahdanau2014neural}, and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code. 
+
+%We scale the dot products by $1/\sqrt{d_k}$ to limit the magnitude of the dot products, which works well in practice. Otherwise, we found applying the softmax to often result in weights very close to 0 or 1, and hence minuscule gradients.
+
+% Already described in the subsequent section
+%When used as part of decoder self-attention, an optional mask function is applied just before the softmax to prevent positions from attending to subsequent positions.   This mask simply sets the logits corresponding to all illegal connections (those outside of the lower triangle) to $-\infty$.
+
+%\paragraph{Comparison to Additive Attention: } We choose dot product attention over additive attention \citep{bahdanau2014neural} since it can be computed using highly optimized matrix multiplication code.  This optimization is particularly important to us, as we employ many attention layers in our model.
+
+While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ \citep{DBLP:journals/corr/BritzGLL17}. We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  \footnote{To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \cdot k = \sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.}. To counteract this effect, we scale the dot products by $\frac{1}{\sqrt{d_k}}$.
+
+
+%We suspect this to be caused by the dot products growing too large in magnitude to result in useful gradients after applying the softmax function.  To counteract this, we scale the dot product by $1/\sqrt{d_k}$.
+
+
+\subsubsection{Multi-Head Attention} \label{sec:multihead}
+
+\begin{figure}
+\begin{minipage}[t]{0.5\textwidth}
+  \centering
+  Scaled Dot-Product Attention \\
+  \vspace{0.5cm}
+  \includegraphics[scale=0.6]{Figures/ModalNet-19}
+\end{minipage}
+\begin{minipage}[t]{0.5\textwidth}
+  \centering 
+  Multi-Head Attention \\
+  \vspace{0.1cm}
+  \includegraphics[scale=0.6]{Figures/ModalNet-20}  
+\end{minipage}
+
+
+  % \centering
+
+  \caption{(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.}
+  \label{fig:multi-head-att}
+\end{figure}
+
+Instead of performing a single attention function with $\dmodel$-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values $h$ times with different, learned linear projections to $d_k$, $d_k$ and $d_v$ dimensions, respectively.
+On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding $d_v$-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure~\ref{fig:multi-head-att}.
+
+Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
+
+\begin{align*}
+    \mathrm{MultiHead}(Q, K, V) &= \mathrm{Concat}(\mathrm{head_1}, ..., \mathrm{head_h})W^O\\
+%    \mathrm{where} \mathrm{head_i} &= \mathrm{Attention}(QW_Q_i^{\dmodel \times d_q}, KW_K_i^{\dmodel \times d_k}, VW^V_i^{\dmodel \times d_v})\\
+    \text{where}~\mathrm{head_i} &= \mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)\\
+\end{align*}
+
+Where the projections are parameter matrices $W^Q_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^K_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^V_i \in \mathbb{R}^{\dmodel \times d_v}$ and $W^O \in \mathbb{R}^{hd_v \times \dmodel}$.
+
+
+%find it better (and no more expensive) to have multiple parallel attention layers (each over the full set of positions) with proportionally lower-dimensional keys, values and queries.  We call this "Multi-Head Attention" (Figure~\ref{fig:multi-head-att}).  The keys, values, and queries for each of these parallel attention layers are computed by learned linear transformations of the inputs to the multi-head attention.  We use different linear transformations across different parallel attention layers.  The output of the parallel attention layers are concatenated, and then passed through a final learned linear transformation. 
+
+In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=\dmodel/h=64$.
+Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
+
+\subsubsection{Applications of Attention in our Model}
+
+The Transformer uses multi-head attention in three different ways: 
+\begin{itemize}
+ \item In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as \citep{wu2016google, bahdanau2014neural,JonasFaceNet2017}.
+
+ \item The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.
+
+ \item Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\infty$) all values in the input of the softmax which correspond to illegal connections.  See Figure~\ref{fig:multi-head-att}.
+
+\end{itemize}
+
+\subsection{Position-wise Feed-Forward Networks}\label{sec:ffn}
+
+In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.
+
+\begin{equation}
+   \mathrm{FFN}(x)=\max(0, xW_1 + b_1) W_2 + b_2
+\end{equation}
+
+While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $\dmodel=512$, and the inner-layer has dimensionality $d_{ff}=2048$.
+
+
+
+%In the appendix, we describe how the position-wise feed-forward network can also be seen as a form of attention.
+
+%from Jakob: The number of operations required for the model to relate signals from two arbitrary input or output positions grows in the distance between positions in input or output, linearly for ConvS2S and logarithmically for ByteNet, making it harder to learn dependencies between these positions \citep{hochreiter2001gradient}. In the transformer this is reduced to a constant number of operations, albeit at the cost of effective resolution caused by averaging attention-weighted positions, an effect we aim to counteract with multi-headed attention.
+
+
+%Figure~\ref{fig:simple-att} presents a simple attention function, $A$, with a single head, that forms the basis of our multi-head attention. $A$ takes a query key vector $\kq$, matrices of memory keys $\km$ and memory values $\vm$ ,and produces a query value vector $\vq$ as 
+%\begin{equation*} \label{eq:attention}
+%    A(\kq, \km, \vm) = {\vm}^T (Softmax(\km \kq).
+%\end{equation*}
+%We linearly transform $\kq,\,\km$, and $\vm$ with learned matrices ${\Wkq \text{,} \, \Wkm}$, and ${\Wvm}$ before calling the attention function, and transform the output query with $\Wvq$ before handing it to the feed forward layer. Each attention layer has it's own set of transformation matrices, which are shared across all query positions. $A$ is applied in parallel for each query position, and is implemented very efficiently as a batch of matrix multiplies. The self-attention and encoder-decoder attention layers use $A$, but with different arguments. For example, in encdoder self-attention, queries in encoder layer $i$ attention to memories in encoder layer $i-1$. To ensure that decoder self-attention layers do not look at future words, we add $- \inf$ to the softmax logits in positions $j+1$ to query length for query position $l$.  
+
+%In simple attention, the query value is a weighted combination of the memory values where the attention weights sum to one. Although this function performs well in practice, the constraint on attention weights can restrict the amount of information that flows from memories to queries because the query cannot focus on multiple memory positions at once, which might be desirable when translating long sequences. \marginpar{@usz, could you think of an example of this ?} We remedy this by maintaining multiple attention heads at each query position that attend to all memory positions in parallel, with a different set of parameters  per attention head $h$. 
+%\marginpar{}
+
+\subsection{Embeddings and Softmax}
+Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $\dmodel$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to \citep{press2016using}.   In the embedding layers, we multiply those weights by $\sqrt{\dmodel}$.
+
+
+\subsection{Positional Encoding}
+Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $\dmodel$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed \citep{JonasFaceNet2017}.
+
+In this work, we use sine and cosine functions of different frequencies:
+
+\begin{align*}
+    PE_{(pos,2i)} = sin(pos / 10000^{2i/\dmodel}) \\
+    PE_{(pos,2i+1)} = cos(pos / 10000^{2i/\dmodel})
+\end{align*}
+
+where $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\pi$ to $10000 \cdot 2\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.
+
+We also experimented with using learned positional embeddings \citep{JonasFaceNet2017} instead, and found that the two versions produced nearly identical results (see Table~\ref{tab:variations} row (E)).  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
--- a/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ b/crazy_functions/test_project/latex/attention/parameter_attention.tex
@ -0,0 +1,45 @@
+\pagebreak
+\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
+
+In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
+
+\begin{align*}
+    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
+    A(q, K, V) = Softmax(qK^T)V
+\end{align*}
+
+Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
+
+%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
+
+Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
+
+In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
+
+In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
+
+Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
+
+\begin{table}[h]
+\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
+\label{tab:parameter_attention}
+\begin{center}
+\vspace{-2mm}
+%\scalebox{1.0}{
+\begin{tabular}{c|cccccc|cccc}
+\hline\rule{0pt}{2.0ex}
+ & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
+\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
+ \multirow{2}{*}{$n_p$} &
+ PPL & BLEU & params & training\\
+ & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
+\hline\rule{0pt}{2.0ex}
+base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
+\hline\rule{0pt}{2.0ex}
+AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
+AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
+\hline
+\end{tabular}
+%}
+\end{center}
+\end{table}
--- a/crazy_functions/test_project/latex/attention/来源
+++ b/crazy_functions/test_project/latex/attention/来源
@ -0,0 +1,8 @@
+chatgpt的老祖宗《Attention is all you need》
+
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
+
+真实的摘要如下
+The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
+
+https://arxiv.org/abs/1706.03762
--- a/crazy_functions/test_project/python/dqn/init.py
+++ b/crazy_functions/test_project/python/dqn/init.py
@ -0,0 +1,2 @@
+from stable_baselines3.dqn.dqn import DQN
+from stable_baselines3.dqn.policies import CnnPolicy, MlpPolicy
--- a/crazy_functions/test_project/python/dqn/dqn.py
+++ b/crazy_functions/test_project/python/dqn/dqn.py
@ -0,0 +1,245 @@
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+import gym
+import numpy as np
+import torch as th
+from torch.nn import functional as F
+
+from stable_baselines3.common import logger
+from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
+from stable_baselines3.common.preprocessing import maybe_transpose
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
+from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
+from stable_baselines3.dqn.policies import DQNPolicy
+
+
+class DQN(OffPolicyAlgorithm):
+    """
+    Deep Q-Network (DQN)
+
+    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
+    Default hyperparameters are taken from the nature paper,
+    except for the optimizer and learning rate that were taken from Stable Baselines defaults.
+
+    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
+    :param env: The environment to learn from (if registered in Gym, can be str)
+    :param learning_rate: The learning rate, it can be a function
+        of the current progress remaining (from 1 to 0)
+    :param buffer_size: size of the replay buffer
+    :param learning_starts: how many steps of the model to collect transitions for before learning starts
+    :param batch_size: Minibatch size for each gradient update
+    :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
+    :param gamma: the discount factor
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
+    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
+        Set to ``-1`` means to do as many gradient steps as steps done in the environment
+        during the rollout.
+    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
+        at a cost of more complexity.
+        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
+    :param target_update_interval: update the target network every ``target_update_interval``
+        environment steps.
+    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
+    :param exploration_initial_eps: initial value of random action probability
+    :param exploration_final_eps: final value of random action probability
+    :param max_grad_norm: The maximum value for the gradient clipping
+    :param tensorboard_log: the log location for tensorboard (if None, no logging)
+    :param create_eval_env: Whether to create a second environment that will be
+        used for evaluating the agent periodically. (Only available when passing string for the environment)
+    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
+    :param seed: Seed for the pseudo random generators
+    :param device: Device (cpu, cuda, ...) on which the code should be run.
+        Setting it to auto, the code will be run on the GPU if possible.
+    :param _init_setup_model: Whether or not to build the network at the creation of the instance
+    """
+
+    def __init__(
+        self,
+        policy: Union[str, Type[DQNPolicy]],
+        env: Union[GymEnv, str],
+        learning_rate: Union[float, Schedule] = 1e-4,
+        buffer_size: int = 1000000,
+        learning_starts: int = 50000,
+        batch_size: Optional[int] = 32,
+        tau: float = 1.0,
+        gamma: float = 0.99,
+        train_freq: Union[int, Tuple[int, str]] = 4,
+        gradient_steps: int = 1,
+        optimize_memory_usage: bool = False,
+        target_update_interval: int = 10000,
+        exploration_fraction: float = 0.1,
+        exploration_initial_eps: float = 1.0,
+        exploration_final_eps: float = 0.05,
+        max_grad_norm: float = 10,
+        tensorboard_log: Optional[str] = None,
+        create_eval_env: bool = False,
+        policy_kwargs: Optional[Dict[str, Any]] = None,
+        verbose: int = 0,
+        seed: Optional[int] = None,
+        device: Union[th.device, str] = "auto",
+        _init_setup_model: bool = True,
+    ):
+
+        super(DQN, self).__init__(
+            policy,
+            env,
+            DQNPolicy,
+            learning_rate,
+            buffer_size,
+            learning_starts,
+            batch_size,
+            tau,
+            gamma,
+            train_freq,
+            gradient_steps,
+            action_noise=None,  # No action noise
+            policy_kwargs=policy_kwargs,
+            tensorboard_log=tensorboard_log,
+            verbose=verbose,
+            device=device,
+            create_eval_env=create_eval_env,
+            seed=seed,
+            sde_support=False,
+            optimize_memory_usage=optimize_memory_usage,
+            supported_action_spaces=(gym.spaces.Discrete,),
+        )
+
+        self.exploration_initial_eps = exploration_initial_eps
+        self.exploration_final_eps = exploration_final_eps
+        self.exploration_fraction = exploration_fraction
+        self.target_update_interval = target_update_interval
+        self.max_grad_norm = max_grad_norm
+        # "epsilon" for the epsilon-greedy exploration
+        self.exploration_rate = 0.0
+        # Linear schedule will be defined in `_setup_model()`
+        self.exploration_schedule = None
+        self.q_net, self.q_net_target = None, None
+
+        if _init_setup_model:
+            self._setup_model()
+
+    def _setup_model(self) -> None:
+        super(DQN, self)._setup_model()
+        self._create_aliases()
+        self.exploration_schedule = get_linear_fn(
+            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
+        )
+
+    def _create_aliases(self) -> None:
+        self.q_net = self.policy.q_net
+        self.q_net_target = self.policy.q_net_target
+
+    def _on_step(self) -> None:
+        """
+        Update the exploration rate and target network if needed.
+        This method is called in ``collect_rollouts()`` after each step in the environment.
+        """
+        if self.num_timesteps % self.target_update_interval == 0:
+            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
+
+        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
+        logger.record("rollout/exploration rate", self.exploration_rate)
+
+    def train(self, gradient_steps: int, batch_size: int = 100) -> None:
+        # Update learning rate according to schedule
+        self._update_learning_rate(self.policy.optimizer)
+
+        losses = []
+        for _ in range(gradient_steps):
+            # Sample replay buffer
+            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
+
+            with th.no_grad():
+                # Compute the next Q-values using the target network
+                next_q_values = self.q_net_target(replay_data.next_observations)
+                # Follow greedy policy: use the one with the highest value
+                next_q_values, _ = next_q_values.max(dim=1)
+                # Avoid potential broadcast issue
+                next_q_values = next_q_values.reshape(-1, 1)
+                # 1-step TD target
+                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
+
+            # Get current Q-values estimates
+            current_q_values = self.q_net(replay_data.observations)
+
+            # Retrieve the q-values for the actions from the replay buffer
+            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
+
+            # Compute Huber loss (less sensitive to outliers)
+            loss = F.smooth_l1_loss(current_q_values, target_q_values)
+            losses.append(loss.item())
+
+            # Optimize the policy
+            self.policy.optimizer.zero_grad()
+            loss.backward()
+            # Clip gradient norm
+            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
+            self.policy.optimizer.step()
+
+        # Increase update counter
+        self._n_updates += gradient_steps
+
+        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
+        logger.record("train/loss", np.mean(losses))
+
+    def predict(
+        self,
+        observation: np.ndarray,
+        state: Optional[np.ndarray] = None,
+        mask: Optional[np.ndarray] = None,
+        deterministic: bool = False,
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Overrides the base_class predict function to include epsilon-greedy exploration.
+
+        :param observation: the input observation
+        :param state: The last states (can be None, used in recurrent policies)
+        :param mask: The last masks (can be None, used in recurrent policies)
+        :param deterministic: Whether or not to return deterministic actions.
+        :return: the model's action and the next state
+            (used in recurrent policies)
+        """
+        if not deterministic and np.random.rand() < self.exploration_rate:
+            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
+                n_batch = observation.shape[0]
+                action = np.array([self.action_space.sample() for _ in range(n_batch)])
+            else:
+                action = np.array(self.action_space.sample())
+        else:
+            action, state = self.policy.predict(observation, state, mask, deterministic)
+        return action, state
+
+    def learn(
+        self,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        log_interval: int = 4,
+        eval_env: Optional[GymEnv] = None,
+        eval_freq: int = -1,
+        n_eval_episodes: int = 5,
+        tb_log_name: str = "DQN",
+        eval_log_path: Optional[str] = None,
+        reset_num_timesteps: bool = True,
+    ) -> OffPolicyAlgorithm:
+
+        return super(DQN, self).learn(
+            total_timesteps=total_timesteps,
+            callback=callback,
+            log_interval=log_interval,
+            eval_env=eval_env,
+            eval_freq=eval_freq,
+            n_eval_episodes=n_eval_episodes,
+            tb_log_name=tb_log_name,
+            eval_log_path=eval_log_path,
+            reset_num_timesteps=reset_num_timesteps,
+        )
+
+    def _excluded_save_params(self) -> List[str]:
+        return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
+
+    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
+        state_dicts = ["policy", "policy.optimizer"]
+
+        return state_dicts, []
--- a/crazy_functions/test_project/python/dqn/policies.py
+++ b/crazy_functions/test_project/python/dqn/policies.py
@ -0,0 +1,237 @@
+from typing import Any, Dict, List, Optional, Type
+
+import gym
+import torch as th
+from torch import nn
+
+from stable_baselines3.common.policies import BasePolicy, register_policy
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
+from stable_baselines3.common.type_aliases import Schedule
+
+
+class QNetwork(BasePolicy):
+    """
+    Action-Value (Q-Value) network for DQN
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        features_extractor: nn.Module,
+        features_dim: int,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        normalize_images: bool = True,
+    ):
+        super(QNetwork, self).__init__(
+            observation_space,
+            action_space,
+            features_extractor=features_extractor,
+            normalize_images=normalize_images,
+        )
+
+        if net_arch is None:
+            net_arch = [64, 64]
+
+        self.net_arch = net_arch
+        self.activation_fn = activation_fn
+        self.features_extractor = features_extractor
+        self.features_dim = features_dim
+        self.normalize_images = normalize_images
+        action_dim = self.action_space.n  # number of actions
+        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
+        self.q_net = nn.Sequential(*q_net)
+
+    def forward(self, obs: th.Tensor) -> th.Tensor:
+        """
+        Predict the q-values.
+
+        :param obs: Observation
+        :return: The estimated Q-Value for each action.
+        """
+        return self.q_net(self.extract_features(obs))
+
+    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        q_values = self.forward(observation)
+        # Greedy action
+        action = q_values.argmax(dim=1).reshape(-1)
+        return action
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_arch,
+                features_dim=self.features_dim,
+                activation_fn=self.activation_fn,
+                features_extractor=self.features_extractor,
+            )
+        )
+        return data
+
+
+class DQNPolicy(BasePolicy):
+    """
+    Policy class with Q-Value Net and target net for DQN
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param features_extractor_class: Features extractor to use.
+    :param features_extractor_kwargs: Keyword arguments
+        to pass to the features extractor.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super(DQNPolicy, self).__init__(
+            observation_space,
+            action_space,
+            features_extractor_class,
+            features_extractor_kwargs,
+            optimizer_class=optimizer_class,
+            optimizer_kwargs=optimizer_kwargs,
+        )
+
+        if net_arch is None:
+            if features_extractor_class == FlattenExtractor:
+                net_arch = [64, 64]
+            else:
+                net_arch = []
+
+        self.net_arch = net_arch
+        self.activation_fn = activation_fn
+        self.normalize_images = normalize_images
+
+        self.net_args = {
+            "observation_space": self.observation_space,
+            "action_space": self.action_space,
+            "net_arch": self.net_arch,
+            "activation_fn": self.activation_fn,
+            "normalize_images": normalize_images,
+        }
+
+        self.q_net, self.q_net_target = None, None
+        self._build(lr_schedule)
+
+    def _build(self, lr_schedule: Schedule) -> None:
+        """
+        Create the network and the optimizer.
+
+        :param lr_schedule: Learning rate schedule
+            lr_schedule(1) is the initial learning rate
+        """
+
+        self.q_net = self.make_q_net()
+        self.q_net_target = self.make_q_net()
+        self.q_net_target.load_state_dict(self.q_net.state_dict())
+
+        # Setup optimizer with initial learning rate
+        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
+
+    def make_q_net(self) -> QNetwork:
+        # Make sure we always have separate networks for features extractors etc
+        net_args = self._update_features_extractor(self.net_args, features_extractor=None)
+        return QNetwork(**net_args).to(self.device)
+
+    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        return self._predict(obs, deterministic=deterministic)
+
+    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        return self.q_net._predict(obs, deterministic=deterministic)
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_args["net_arch"],
+                activation_fn=self.net_args["activation_fn"],
+                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
+                optimizer_class=self.optimizer_class,
+                optimizer_kwargs=self.optimizer_kwargs,
+                features_extractor_class=self.features_extractor_class,
+                features_extractor_kwargs=self.features_extractor_kwargs,
+            )
+        )
+        return data
+
+
+MlpPolicy = DQNPolicy
+
+
+class CnnPolicy(DQNPolicy):
+    """
+    Policy class for DQN when using images as input.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param features_extractor_class: Features extractor to use.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super(CnnPolicy, self).__init__(
+            observation_space,
+            action_space,
+            lr_schedule,
+            net_arch,
+            activation_fn,
+            features_extractor_class,
+            features_extractor_kwargs,
+            normalize_images,
+            optimizer_class,
+            optimizer_kwargs,
+        )
+
+
+register_policy("MlpPolicy", MlpPolicy)
+register_policy("CnnPolicy", CnnPolicy)
--- a/crazy_functions/test_project/python/dqn/来源
+++ b/crazy_functions/test_project/python/dqn/来源
@ -0,0 +1,2 @@
+github stablebaseline3
+https://github.com/DLR-RM/stable-baselines3
--- a/crazy_functions/test_project/其他测试
+++ b/crazy_functions/test_project/其他测试
@ -0,0 +1,27 @@
+"In practice, we found that a high-entropy initial state is more likely to increase the speed of training.
+The entropy is calculated by:
+$$H=-\sum_{k= 1}^{n_k} p(k) \cdot \log p(k), p(k)=\frac{|A_k|}{|\mathcal{A}|}$$
+where $H$ is the entropy, $|A_k|$ is the number of agent nodes in $k$-th cluster, $|\mathcal{A}|$ is the total number of agents.
+To ensure the Cooperation Graph initialization has higher entropy,
+we will randomly generate multiple initial states, 
+rank by their entropy and then pick the one with maximum $H$."
+
+```
+FROM ubuntu:latest
+
+RUN apt-get update && \
+    apt-get install -y python3 python3-pip && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN echo '[global]' > /etc/pip.conf && \
+    echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
+    echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
+
+RUN pip3 install gradio requests[socks] mdtex2html
+
+COPY . /gpt
+WORKDIR /gpt
+
+
+CMD ["python3", "main.py"]
+```
--- a/crazy_functions/vector_fns/init.py
+++ b/crazy_functions/vector_fns/init.py
--- a/crazy_functions/vector_fns/general_file_loader.py
+++ b/crazy_functions/vector_fns/general_file_loader.py
@ -1,70 +0,0 @@
-# From project chatglm-langchain
-
-
-from langchain.document_loaders import UnstructuredFileLoader
-from langchain.text_splitter import CharacterTextSplitter
-import re
-from typing import List
-
-class ChineseTextSplitter(CharacterTextSplitter):
-    def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
-        super().__init__(**kwargs)
-        self.pdf = pdf
-        self.sentence_size = sentence_size
-
-    def split_text1(self, text: str) -> List[str]:
-        if self.pdf:
-            text = re.sub(r"\n{3,}", "\n", text)
-            text = re.sub('\s', ' ', text)
-            text = text.replace("\n\n", "")
-        sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')  # del ：；
-        sent_list = []
-        for ele in sent_sep_pattern.split(text):
-            if sent_sep_pattern.match(ele) and sent_list:
-                sent_list[-1] += ele
-            elif ele:
-                sent_list.append(ele)
-        return sent_list
-
-    def split_text(self, text: str) -> List[str]:   ##此处需要进一步优化逻辑
-        if self.pdf:
-            text = re.sub(r"\n{3,}", r"\n", text)
-            text = re.sub('\s', " ", text)
-            text = re.sub("\n\n", "", text)
-
-        text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text)  # 单字符断句符
-        text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text)  # 英文省略号
-        text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text)  # 中文省略号
-        text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
-        # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
-        text = text.rstrip()  # 段尾如果有多余的\n就去掉它
-        # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
-        ls = [i for i in text.split("\n") if i]
-        for ele in ls:
-            if len(ele) > self.sentence_size:
-                ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
-                ele1_ls = ele1.split("\n")
-                for ele_ele1 in ele1_ls:
-                    if len(ele_ele1) > self.sentence_size:
-                        ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
-                        ele2_ls = ele_ele2.split("\n")
-                        for ele_ele2 in ele2_ls:
-                            if len(ele_ele2) > self.sentence_size:
-                                ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
-                                ele2_id = ele2_ls.index(ele_ele2)
-                                ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
-                                                                                                       ele2_id + 1:]
-                        ele_id = ele1_ls.index(ele_ele1)
-                        ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
-
-                id = ls.index(ele)
-                ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
-        return ls
-
-def load_file(filepath, sentence_size):
-    loader = UnstructuredFileLoader(filepath, mode="elements")
-    textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
-    docs = loader.load_and_split(text_splitter=textsplitter)
-    # write_check_file(filepath, docs)
-    return docs
-
--- a/crazy_functions/vector_fns/vector_database.py
+++ b/crazy_functions/vector_fns/vector_database.py
@ -1,338 +0,0 @@
-# From project chatglm-langchain
-
-import threading
-from toolbox import Singleton
-import os
-import shutil
-import os
-import uuid
-import tqdm
-from langchain.vectorstores import FAISS
-from langchain.docstore.document import Document
-from typing import List, Tuple
-import numpy as np
-from crazy_functions.vector_fns.general_file_loader import load_file
-
-embedding_model_dict = {
-    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
-    "ernie-base": "nghuyong/ernie-3.0-base-zh",
-    "text2vec-base": "shibing624/text2vec-base-chinese",
-    "text2vec": "GanymedeNil/text2vec-large-chinese",
-}
-
-# Embedding model name
-EMBEDDING_MODEL = "text2vec"
-
-# Embedding running device
-EMBEDDING_DEVICE = "cpu"
-
-# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
-PROMPT_TEMPLATE = """已知信息：
-{context}
-
-根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
-
-# 文本分句长度
-SENTENCE_SIZE = 100
-
-# 匹配后单段上下文长度
-CHUNK_SIZE = 250
-
-# LLM input history length
-LLM_HISTORY_LEN = 3
-
-# return top-k text chunk from vector store
-VECTOR_SEARCH_TOP_K = 5
-
-# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
-VECTOR_SEARCH_SCORE_THRESHOLD = 0
-
-NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
-
-FLAG_USER_NAME = uuid.uuid4().hex
-
-# 是否开启跨域，默认为False，如果需要开启，请设置为True
-# is open cross domain
-OPEN_CROSS_DOMAIN = False
-
-def similarity_search_with_score_by_vector(
-        self, embedding: List[float], k: int = 4
-) -> List[Tuple[Document, float]]:
-
-    def seperate_list(ls: List[int]) -> List[List[int]]:
-        lists = []
-        ls1 = [ls[0]]
-        for i in range(1, len(ls)):
-            if ls[i - 1] + 1 == ls[i]:
-                ls1.append(ls[i])
-            else:
-                lists.append(ls1)
-                ls1 = [ls[i]]
-        lists.append(ls1)
-        return lists
-
-    scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
-    docs = []
-    id_set = set()
-    store_len = len(self.index_to_docstore_id)
-    for j, i in enumerate(indices[0]):
-        if i == -1 or 0 < self.score_threshold < scores[0][j]:
-            # This happens when not enough docs are returned.
-            continue
-        _id = self.index_to_docstore_id[i]
-        doc = self.docstore.search(_id)
-        if not self.chunk_conent:
-            if not isinstance(doc, Document):
-                raise ValueError(f"Could not find document for id {_id}, got {doc}")
-            doc.metadata["score"] = int(scores[0][j])
-            docs.append(doc)
-            continue
-        id_set.add(i)
-        docs_len = len(doc.page_content)
-        for k in range(1, max(i, store_len - i)):
-            break_flag = False
-            for l in [i + k, i - k]:
-                if 0 <= l < len(self.index_to_docstore_id):
-                    _id0 = self.index_to_docstore_id[l]
-                    doc0 = self.docstore.search(_id0)
-                    if docs_len + len(doc0.page_content) > self.chunk_size:
-                        break_flag = True
-                        break
-                    elif doc0.metadata["source"] == doc.metadata["source"]:
-                        docs_len += len(doc0.page_content)
-                        id_set.add(l)
-            if break_flag:
-                break
-    if not self.chunk_conent:
-        return docs
-    if len(id_set) == 0 and self.score_threshold > 0:
-        return []
-    id_list = sorted(list(id_set))
-    id_lists = seperate_list(id_list)
-    for id_seq in id_lists:
-        for id in id_seq:
-            if id == id_seq[0]:
-                _id = self.index_to_docstore_id[id]
-                doc = self.docstore.search(_id)
-            else:
-                _id0 = self.index_to_docstore_id[id]
-                doc0 = self.docstore.search(_id0)
-                doc.page_content += " " + doc0.page_content
-        if not isinstance(doc, Document):
-            raise ValueError(f"Could not find document for id {_id}, got {doc}")
-        doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
-        doc.metadata["score"] = int(doc_score)
-        docs.append(doc)
-    return docs
-
-
-class LocalDocQA:
-    llm: object = None
-    embeddings: object = None
-    top_k: int = VECTOR_SEARCH_TOP_K
-    chunk_size: int = CHUNK_SIZE
-    chunk_conent: bool = True
-    score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
-
-    def init_cfg(self,
-                 top_k=VECTOR_SEARCH_TOP_K,
-                 ):
-
-        self.llm = None
-        self.top_k = top_k
-
-    def init_knowledge_vector_store(self,
-                                    filepath,
-                                    vs_path: str or os.PathLike = None,
-                                    sentence_size=SENTENCE_SIZE,
-                                    text2vec=None):
-        loaded_files = []
-        failed_files = []
-        if isinstance(filepath, str):
-            if not os.path.exists(filepath):
-                print("路径不存在")
-                return None
-            elif os.path.isfile(filepath):
-                file = os.path.split(filepath)[-1]
-                try:
-                    docs = load_file(filepath, SENTENCE_SIZE)
-                    print(f"{file} 已成功加载")
-                    loaded_files.append(filepath)
-                except Exception as e:
-                    print(e)
-                    print(f"{file} 未能成功加载")
-                    return None
-            elif os.path.isdir(filepath):
-                docs = []
-                for file in tqdm(os.listdir(filepath), desc="加载文件"):
-                    fullfilepath = os.path.join(filepath, file)
-                    try:
-                        docs += load_file(fullfilepath, SENTENCE_SIZE)
-                        loaded_files.append(fullfilepath)
-                    except Exception as e:
-                        print(e)
-                        failed_files.append(file)
-
-                if len(failed_files) > 0:
-                    print("以下文件未能成功加载：")
-                    for file in failed_files:
-                        print(f"{file}\n")
-
-        else:
-            docs = []
-            for file in filepath:
-                docs += load_file(file, SENTENCE_SIZE)
-                print(f"{file} 已成功加载")
-                loaded_files.append(file)
-
-        if len(docs) > 0:
-            print("文件加载完毕，正在生成向量库")
-            if vs_path and os.path.isdir(vs_path):
-                try:
-                    self.vector_store = FAISS.load_local(vs_path, text2vec)
-                    self.vector_store.add_documents(docs)
-                except:
-                    self.vector_store = FAISS.from_documents(docs, text2vec)
-            else:
-                self.vector_store = FAISS.from_documents(docs, text2vec)  # docs 为Document列表
-
-            self.vector_store.save_local(vs_path)
-            return vs_path, loaded_files
-        else:
-            raise RuntimeError("文件加载失败，请检查文件格式是否正确")
-
-    def get_loaded_file(self, vs_path):
-        ds = self.vector_store.docstore
-        return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
-
-
-    # query      查询内容
-    # vs_path    知识库路径
-    # chunk_conent   是否启用上下文关联
-    # score_threshold    搜索匹配score阈值
-    # vector_search_top_k   搜索知识库内容条数，默认搜索5条结果
-    # chunk_sizes    匹配单段内容的连接上下文长度
-    def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
-                                        score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
-                                        vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
-                                        text2vec=None):
-        self.vector_store = FAISS.load_local(vs_path, text2vec)
-        self.vector_store.chunk_conent = chunk_conent
-        self.vector_store.score_threshold = score_threshold
-        self.vector_store.chunk_size = chunk_size
-
-        embedding = self.vector_store.embedding_function.embed_query(query)
-        related_docs_with_score = similarity_search_with_score_by_vector(self.vector_store, embedding, k=vector_search_top_k)
-
-        if not related_docs_with_score:
-            response = {"query": query,
-                        "source_documents": []}
-            return response, ""
-        # prompt = f"{query}. You should answer this question using information from following documents: \n\n"
-        prompt = f"{query}. 你必须利用以下文档中包含的信息回答这个问题: \n\n---\n\n"
-        prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
-        prompt += "\n\n---\n\n"
-        prompt = prompt.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        # print(prompt)
-        response = {"query": query, "source_documents": related_docs_with_score}
-        return response, prompt
-
-
-
-
-def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
-    for file in files:
-        assert os.path.exists(file), "输入文件不存在：" + file
-    import nltk
-    if NLTK_DATA_PATH not in nltk.data.path: nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
-    local_doc_qa = LocalDocQA()
-    local_doc_qa.init_cfg()
-    filelist = []
-    if not os.path.exists(os.path.join(vs_path, vs_id)):
-        os.makedirs(os.path.join(vs_path, vs_id))
-    for file in files:
-        file_name = file.name if not isinstance(file, str) else file
-        filename = os.path.split(file_name)[-1]
-        shutil.copyfile(file_name, os.path.join(vs_path, vs_id, filename))
-        filelist.append(os.path.join(vs_path, vs_id, filename))
-    vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, os.path.join(vs_path, vs_id), sentence_size, text2vec)
-
-    if len(loaded_files):
-        file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
-    else:
-        pass
-        # file_status = "文件未成功加载，请重新上传文件"
-    # print(file_status)
-    return local_doc_qa, vs_path
-
-@Singleton
-class knowledge_archive_interface():
-    def __init__(self) -> None:
-        self.threadLock = threading.Lock()
-        self.current_id = ""
-        self.kai_path = None
-        self.qa_handle = None
-        self.text2vec_large_chinese = None
-
-    def get_chinese_text2vec(self):
-        if self.text2vec_large_chinese is None:
-            # < -------------------预热文本向量化模组--------------- >
-            from toolbox import ProxyNetworkActivate
-            print('Checking Text2vec ...')
-            from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-            with ProxyNetworkActivate('Download_LLM'):    # 临时地激活代理网络
-                self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
-
-        return self.text2vec_large_chinese
-
-
-    def feed_archive(self, file_manifest, vs_path, id="default"):
-        self.threadLock.acquire()
-        # import uuid
-        self.current_id = id
-        self.qa_handle, self.kai_path = construct_vector_store(
-            vs_id=self.current_id,
-            vs_path=vs_path,
-            files=file_manifest,
-            sentence_size=100,
-            history=[],
-            one_conent="",
-            one_content_segmentation="",
-            text2vec = self.get_chinese_text2vec(),
-        )
-        self.threadLock.release()
-
-    def get_current_archive_id(self):
-        return self.current_id
-
-    def get_loaded_file(self, vs_path):
-        return self.qa_handle.get_loaded_file(vs_path)
-
-    def answer_with_archive_by_id(self, txt, id, vs_path):
-        self.threadLock.acquire()
-        if not self.current_id == id:
-            self.current_id = id
-            self.qa_handle, self.kai_path = construct_vector_store(
-                vs_id=self.current_id,
-                vs_path=vs_path,
-                files=[],
-                sentence_size=100,
-                history=[],
-                one_conent="",
-                one_content_segmentation="",
-                text2vec = self.get_chinese_text2vec(),
-            )
-        VECTOR_SEARCH_SCORE_THRESHOLD = 0
-        VECTOR_SEARCH_TOP_K = 4
-        CHUNK_SIZE = 512
-        resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
-            query = txt,
-            vs_path = self.kai_path,
-            score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
-            vector_search_top_k=VECTOR_SEARCH_TOP_K,
-            chunk_conent=True,
-            chunk_size=CHUNK_SIZE,
-            text2vec = self.get_chinese_text2vec(),
-        )
-        self.threadLock.release()
-        return resp, prompt
--- a/crazy_functions/vt_fns/vt_call_plugin.py
+++ b/crazy_functions/vt_fns/vt_call_plugin.py
@ -1,114 +0,0 @@
-from pydantic import BaseModel, Field
-from typing import List
-from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
-import copy, json, pickle, os, sys, time
-
-
-def read_avail_plugin_enum():
-    from crazy_functional import get_crazy_functions
-    plugin_arr = get_crazy_functions()
-    # remove plugins with out explaination
-    plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
-    plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
-    plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
-    plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
-    plugin_arr_dict_parse.update({f"F_{i}":v for i, v in enumerate(plugin_arr.values(), start=1)})
-    prompt = json.dumps(plugin_arr_info, ensure_ascii=False, indent=2)
-    prompt = "\n\nThe defination of PluginEnum:\nPluginEnum=" + prompt
-    return prompt, plugin_arr_dict, plugin_arr_dict_parse
-
-def wrap_code(txt):
-    txt = txt.replace('```','')
-    return f"\n```\n{txt}\n```\n"
-
-def have_any_recent_upload_files(chatbot):
-    _5min = 5 * 60
-    if not chatbot: return False    # chatbot is None
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    if not most_recent_uploaded: return False   # most_recent_uploaded is None
-    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
-    else: return False  # most_recent_uploaded is too old
-
-def get_recent_file_prompt_support(chatbot):
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    path = most_recent_uploaded['path']
-    prompt =   "\nAdditional Information:\n"
-    prompt =   "In case that this plugin requires a path or a file as argument,"
-    prompt += f"it is important for you to know that the user has recently uploaded a file, located at: `{path}`"
-    prompt += f"Only use it when necessary, otherwise, you can ignore this file."
-    return prompt
-
-def get_inputs_show_user(inputs, plugin_arr_enum_prompt):
-    # remove plugin_arr_enum_prompt from inputs string
-    inputs_show_user = inputs.replace(plugin_arr_enum_prompt, "")
-    inputs_show_user += plugin_arr_enum_prompt[:200] + '...'
-    inputs_show_user += '\n...\n'
-    inputs_show_user += '...\n'
-    inputs_show_user += '...}'
-    return inputs_show_user
-
-def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    plugin_arr_enum_prompt, plugin_arr_dict, plugin_arr_dict_parse = read_avail_plugin_enum()
-    class Plugin(BaseModel):
-        plugin_selection: str = Field(description="The most related plugin from one of the PluginEnum.", default="F_0000")
-        reason_of_selection: str = Field(description="The reason why you should select this plugin.", default="This plugin satisfy user requirement most")
-    # ⭐ ⭐ ⭐ 选择插件
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
-    gpt_json_io = GptJsonIO(Plugin)
-    gpt_json_io.format_instructions = "The format of your output should be a json that can be parsed by json.loads.\n"
-    gpt_json_io.format_instructions += """Output example: {"plugin_selection":"F_1234", "reason_of_selection":"F_1234 plugin satisfy user requirement most"}\n"""
-    gpt_json_io.format_instructions += "The plugins you are authorized to use are listed below:\n"
-    gpt_json_io.format_instructions += plugin_arr_enum_prompt
-    inputs = "Choose the correct plugin according to user requirements, the user requirement is: \n\n" + \
-             ">> " + txt.rstrip('\n').replace('\n','\n>> ') + '\n\n' + gpt_json_io.format_instructions
-
-    run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(
-        inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-    try:
-        gpt_reply = run_gpt_fn(inputs, "")
-        plugin_sel = gpt_json_io.generate_output_auto_repair(gpt_reply, run_gpt_fn)
-    except JsonStringError:
-        msg = f"抱歉, {llm_kwargs['llm_model']}无法理解您的需求。"
-        msg += "请求的Prompt为：\n" + wrap_code(get_inputs_show_user(inputs, plugin_arr_enum_prompt))
-        msg += "语言模型回复为：\n" + wrap_code(gpt_reply)
-        msg += "\n但您可以尝试再试一次\n"
-        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
-        return
-    if plugin_sel.plugin_selection not in plugin_arr_dict_parse:
-        msg = f"抱歉, 找不到合适插件执行该任务, 或者{llm_kwargs['llm_model']}无法理解您的需求。"
-        msg += f"语言模型{llm_kwargs['llm_model']}选择了不存在的插件：\n" + wrap_code(gpt_reply)
-        msg += "\n但您可以尝试再试一次\n"
-        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
-        return
-
-    # ⭐ ⭐ ⭐ 确认插件参数
-    if not have_any_recent_upload_files(chatbot):
-        appendix_info = ""
-    else:
-        appendix_info = get_recent_file_prompt_support(chatbot)
-
-    plugin = plugin_arr_dict_parse[plugin_sel.plugin_selection]
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
-    class PluginExplicit(BaseModel):
-        plugin_selection: str = plugin_sel.plugin_selection
-        plugin_arg: str = Field(description="The argument of the plugin.", default="")
-    gpt_json_io = GptJsonIO(PluginExplicit)
-    gpt_json_io.format_instructions += "The information about this plugin is:" + plugin["Info"]
-    inputs = f"A plugin named {plugin_sel.plugin_selection} is selected, " + \
-             "you should extract plugin_arg from the user requirement, the user requirement is: \n\n" + \
-             ">> " + (txt + appendix_info).rstrip('\n').replace('\n','\n>> ') + '\n\n' + \
-             gpt_json_io.format_instructions
-    run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(
-        inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-    plugin_sel = gpt_json_io.generate_output_auto_repair(run_gpt_fn(inputs, ""), run_gpt_fn)
-
-
-    # ⭐ ⭐ ⭐ 执行插件
-    fn = plugin['Function']
-    fn_name = fn.__name__
-    msg = f'{llm_kwargs["llm_model"]}为您选择了插件: `{fn_name}`\n\n插件说明：{plugin["Info"]}\n\n插件参数：{plugin_sel.plugin_arg}\n\n假如偏离了您的要求，按停止键终止。'
-    yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
-    yield from fn(plugin_sel.plugin_arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, -1)
-    return
--- a/crazy_functions/vt_fns/vt_modify_config.py
+++ b/crazy_functions/vt_fns/vt_modify_config.py
@ -1,81 +0,0 @@
-from pydantic import BaseModel, Field
-from typing import List
-from toolbox import update_ui_lastest_msg, get_conf
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.json_fns.pydantic_io import GptJsonIO
-import copy, json, pickle, os, sys
-
-
-def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
-    if not ALLOW_RESET_CONFIG:
-        yield from update_ui_lastest_msg(
-            lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
-            chatbot=chatbot, history=history, delay=2
-        )
-        return
-
-    # ⭐ ⭐ ⭐ 读取可配置项目条目
-    names = {}
-    from enum import Enum
-    import config
-    for k, v in config.__dict__.items():
-        if k.startswith('__'): continue
-        names.update({k:k})
-        # if len(names) > 20: break   # 限制最多前10个配置项，如果太多了会导致gpt无法理解
-
-    ConfigOptions = Enum('ConfigOptions', names)
-    class ModifyConfigurationIntention(BaseModel):
-        which_config_to_modify: ConfigOptions = Field(description="the name of the configuration to modify, you must choose from one of the ConfigOptions enum.", default=None)
-        new_option_value: str = Field(description="the new value of the option", default=None)
-
-    # ⭐ ⭐ ⭐ 分析用户意图
-    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
-    gpt_json_io = GptJsonIO(ModifyConfigurationIntention)
-    inputs = "Analyze how to change configuration according to following user input, answer me with json: \n\n" + \
-             ">> " + txt.rstrip('\n').replace('\n','\n>> ') + '\n\n' + \
-             gpt_json_io.format_instructions
-
-    run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(
-        inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-    user_intention = gpt_json_io.generate_output_auto_repair(run_gpt_fn(inputs, ""), run_gpt_fn)
-
-    explicit_conf = user_intention.which_config_to_modify.value
-
-    ok = (explicit_conf in txt)
-    if ok:
-        yield from update_ui_lastest_msg(
-            lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}",
-            chatbot=chatbot, history=history, delay=1
-        )
-        yield from update_ui_lastest_msg(
-            lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}\n\n正在修改配置中",
-            chatbot=chatbot, history=history, delay=2
-        )
-
-        # ⭐ ⭐ ⭐ 立即应用配置
-        from toolbox import set_conf
-        set_conf(explicit_conf, user_intention.new_option_value)
-
-        yield from update_ui_lastest_msg(
-            lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，重新页面即可生效。", chatbot=chatbot, history=history, delay=1
-        )
-    else:
-        yield from update_ui_lastest_msg(
-            lastmsg=f"失败，如果需要配置{explicit_conf}，您需要明确说明并在指令中提到它。", chatbot=chatbot, history=history, delay=5
-        )
-
-def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
-    if not ALLOW_RESET_CONFIG:
-        yield from update_ui_lastest_msg(
-            lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
-            chatbot=chatbot, history=history, delay=2
-        )
-        return
-
-    yield from modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
-    yield from update_ui_lastest_msg(
-        lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，五秒后即将重启！若出现报错请无视即可。", chatbot=chatbot, history=history, delay=5
-    )
-    os.execl(sys.executable, sys.executable, *sys.argv)
--- a/crazy_functions/vt_fns/vt_state.py
+++ b/crazy_functions/vt_fns/vt_state.py
@ -1,28 +0,0 @@
-import pickle
-
-class VoidTerminalState():
-    def __init__(self):
-        self.reset_state()
-
-    def reset_state(self):
-        self.has_provided_explaination = False
-
-    def lock_plugin(self, chatbot):
-        chatbot._cookies['lock_plugin'] = 'crazy_functions.虚空终端->虚空终端'
-        chatbot._cookies['plugin_state'] = pickle.dumps(self)
-
-    def unlock_plugin(self, chatbot):
-        self.reset_state()
-        chatbot._cookies['lock_plugin'] = None
-        chatbot._cookies['plugin_state'] = pickle.dumps(self)
-
-    def set_state(self, chatbot, key, value):
-        setattr(self, key, value)
-        chatbot._cookies['plugin_state'] = pickle.dumps(self)
-
-    def get_state(chatbot):
-        state = chatbot._cookies.get('plugin_state', None)
-        if state is not None:   state = pickle.loads(state)
-        else:                   state = VoidTerminalState()
-        state.chatbot = chatbot
-        return state
--- a/crazy_functions/下载arxiv论文翻译摘要.py
+++ b/crazy_functions/下载arxiv论文翻译摘要.py
@ -1,191 +0,0 @@
-from toolbox import update_ui, get_log_folder
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-from toolbox import CatchException, report_exception, get_conf
-import re, requests, unicodedata, os
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-def download_arxiv_(url_pdf):
-    if 'arxiv.org' not in url_pdf:
-        if ('.' in url_pdf) and ('/' not in url_pdf):
-            new_url = 'https://arxiv.org/abs/'+url_pdf
-            print('下载编号：', url_pdf, '自动定位：', new_url)
-            # download_arxiv_(new_url)
-            return download_arxiv_(new_url)
-        else:
-            print('不能识别的URL！')
-            return None
-    if 'abs' in url_pdf:
-        url_pdf = url_pdf.replace('abs', 'pdf')
-        url_pdf = url_pdf + '.pdf'
-
-    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
-    title, other_info = get_name(_url_=url_abs)
-
-    paper_id = title.split()[0]  # '[1712.00559]'
-    if '2' in other_info['year']:
-        title = other_info['year'] + ' ' + title
-
-    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
-    for k in known_conf:
-        if k in other_info['comment']:
-            title = k + ' ' + title
-
-    download_dir = get_log_folder(plugin_name='arxiv')
-    os.makedirs(download_dir, exist_ok=True)
-
-    title_str = title.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-
-    requests_pdf_url = url_pdf
-    file_path = download_dir+title_str
-
-    print('下载中')
-    proxies = get_conf('proxies')
-    r = requests.get(requests_pdf_url, proxies=proxies)
-    with open(file_path, 'wb+') as f:
-        f.write(r.content)
-    print('下载完成')
-
-    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
-    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
-
-    x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
-    x = x.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-    return file_path, other_info
-
-
-def get_name(_url_):
-    import os
-    from bs4 import BeautifulSoup
-    print('正在获取文献名！')
-    print(_url_)
-
-    # arxiv_recall = {}
-    # if os.path.exists('./arxiv_recall.pkl'):
-    #     with open('./arxiv_recall.pkl', 'rb') as f:
-    #         arxiv_recall = pickle.load(f)
-
-    # if _url_ in arxiv_recall:
-    #     print('在缓存中')
-    #     return arxiv_recall[_url_]
-
-    proxies = get_conf('proxies')
-    res = requests.get(_url_, proxies=proxies)
-
-    bs = BeautifulSoup(res.text, 'html.parser')
-    other_details = {}
-
-    # get year
-    try:
-        year = bs.find_all(class_='dateline')[0].text
-        year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
-        other_details['year'] = year
-        abstract = bs.find_all(class_='abstract mathjax')[0].text
-        other_details['abstract'] = abstract
-    except:
-        other_details['year'] = ''
-        print('年份获取失败')
-
-    # get author
-    try:
-        authors = bs.find_all(class_='authors')[0].text
-        authors = authors.split('Authors:')[1]
-        other_details['authors'] = authors
-    except:
-        other_details['authors'] = ''
-        print('authors获取失败')
-
-    # get comment
-    try:
-        comment = bs.find_all(class_='metatable')[0].text
-        real_comment = None
-        for item in comment.replace('\n', ' ').split('   '):
-            if 'Comments' in item:
-                real_comment = item
-        if real_comment is not None:
-            other_details['comment'] = real_comment
-        else:
-            other_details['comment'] = ''
-    except:
-        other_details['comment'] = ''
-        print('年份获取失败')
-
-    title_str = BeautifulSoup(
-        res.text, 'html.parser').find('title').contents[0]
-    print('获取成功：', title_str)
-    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
-    # with open('./arxiv_recall.pkl', 'wb') as f:
-    #     pickle.dump(arxiv_recall, f)
-
-    return title_str+'.pdf', other_details
-
-
-
-@CatchException
-def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-
-    CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import bs4
-    except:
-        report_exception(chatbot, history,
-            a = f"解析项目: {txt}",
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 提取摘要，下载PDF文档
-    try:
-        pdf_path, info = download_arxiv_(txt)
-    except:
-        report_exception(chatbot, history,
-            a = f"解析项目: {txt}",
-            b = f"下载pdf文件未成功")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 翻译摘要等
-    i_say =            f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
-    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
-    chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    msg = '正常'
-    # ** gpt request **
-    # 单线，获取文章meta信息
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say,
-        inputs_show_user=i_say_show_user,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot, history=[],
-        sys_prompt="Your job is to collect information from materials and translate to Chinese。",
-    )
-
-    chatbot[-1] = (i_say_show_user, gpt_say)
-    history.append(i_say_show_user); history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    res = write_history_to_file(history)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    promote_file_to_downloadzone(pdf_path, chatbot=chatbot)
-
-    chatbot.append(("完成了吗？", res + "\n\nPDF文件也已经下载"))
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
--- a/crazy_functions/互动小游戏.py
+++ b/crazy_functions/互动小游戏.py
@ -1,40 +0,0 @@
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-
-@CatchException
-def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    from crazy_functions.game_fns.game_interactive_story import MiniGame_ResumeStory
-    # 清空历史
-    history = []
-    # 选择游戏
-    cls = MiniGame_ResumeStory
-    # 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
-    state = cls.sync_state(chatbot,
-                           llm_kwargs,
-                           cls,
-                           plugin_name='MiniGame_ResumeStory',
-                           callback_fn='crazy_functions.互动小游戏->随机小游戏',
-                           lock_plugin=True
-                           )
-    yield from state.continue_game(prompt, chatbot, history)
-
-
-@CatchException
-def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    from crazy_functions.game_fns.game_ascii_art import MiniGame_ASCII_Art
-    # 清空历史
-    history = []
-    # 选择游戏
-    cls = MiniGame_ASCII_Art
-    # 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
-    state = cls.sync_state(chatbot,
-                           llm_kwargs,
-                           cls,
-                           plugin_name='MiniGame_ASCII_Art',
-                           callback_fn='crazy_functions.互动小游戏->随机小游戏1',
-                           lock_plugin=True
-                           )
-    yield from state.continue_game(prompt, chatbot, history)
--- a/crazy_functions/交互功能函数模板.py
+++ b/crazy_functions/交互功能函数模板.py
@ -1,63 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-
-@CatchException
-def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数, 如温度和top_p等, 一般原样传递下去就行
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
-
-    if state is None:
-        chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数'      # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数
-        chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword'                              # 赋予插件状态
-
-        chatbot.append(("第一次调用：", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if state == 'wait_user_keyword':
-        chatbot._cookies['lock_plugin'] = None          # 解除插件锁定，避免遗忘导致死锁
-        chatbot._cookies['plugin_state_0001'] = None    # 解除插件状态，避免遗忘导致死锁
-
-        # 解除插件锁定
-        chatbot.append((f"获取关键词：{txt}", ""))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        page_return = get_image_page_by_keyword(txt)
-        inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=inputs, inputs_show_user=inputs_show_user,
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
-            sys_prompt="When you want to show an image, use markdown format. e.g. ![image_description](image_url). If there are no image url provided, answer 'no image url provided'"
-        )
-        chatbot[-1] = [chatbot[-1][0], gpt_say]
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-
-
-# ---------------------------------------------------------------------------------
-
-def get_image_page_by_keyword(keyword):
-    import requests
-    from bs4 import BeautifulSoup
-    response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
-    res = "image urls: \n"
-    for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
-        try:
-            res += image_element["data-src"]
-            res += "\n"
-        except:
-            pass
-    return res
--- a/crazy_functions/函数动态生成.py
+++ b/crazy_functions/函数动态生成.py
@ -1,252 +0,0 @@
-# 本源代码中, ⭐ = 关键步骤
-"""
-测试：
-    - 裁剪图像，保留下半部分
-    - 交换图像的蓝色通道和红色通道
-    - 将图像转为灰度图像
-    - 将csv文件转excel表格
-
-Testing:
-    - Crop the image, keeping the bottom half.
-    - Swap the blue channel and red channel of the image.
-    - Convert the image to grayscale.
-    - Convert the CSV file to an Excel spreadsheet.
-"""
-
-
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
-from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
-from .crazy_utils import input_clipping, try_install_deps
-from crazy_functions.gen_fns.gen_fns_shared import is_function_successfully_generated
-from crazy_functions.gen_fns.gen_fns_shared import get_class_name
-from crazy_functions.gen_fns.gen_fns_shared import subprocess_worker
-from crazy_functions.gen_fns.gen_fns_shared import try_make_module
-import os
-import time
-import glob
-import multiprocessing
-
-templete = """
-```python
-import ...  # Put dependencies here, e.g. import numpy as np.
-
-class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
-
-    def run(self, path):    # The name of the function must be `run`, it takes only a positional argument.
-        # rewrite the function you have just written here
-        ...
-        return generated_file_path
-```
-"""
-
-def inspect_dependency(chatbot, history):
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return True
-
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) == 1:
-        return matches[0].strip('python') #  code block
-    for match in matches:
-        if 'class TerminalFunction' in match:
-            return match.strip('python') #  code block
-    raise RuntimeError("GPT is not generating proper code.")
-
-def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
-    # 输入
-    prompt_compose = [
-        f'Your job:\n'
-        f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
-        f"2. You should write this function to perform following task: " + txt + "\n",
-        f"3. Wrap the output python function with markdown codeblock."
-    ]
-    i_say = "".join(prompt_compose)
-    demo = []
-
-    # 第一步
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo,
-        sys_prompt= r"You are a world-class programmer."
-    )
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-    # 第二步
-    prompt_compose = [
-        "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
-        templete
-    ]
-    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=inputs_show_user,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
-        sys_prompt= r"You are a programmer. You need to replace `...` with valid packages, do not give `...` in your answer!"
-    )
-    code_to_return = gpt_say
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-    # # 第三步
-    # i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
-    # i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=inputs_show_user,
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
-    #     sys_prompt= r"You are a programmer."
-    # )
-
-    # # # 第三步
-    # i_say = "Show me how to use `pip` to install packages to run the code above. "
-    # i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=i_say,
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
-    #     sys_prompt= r"You are a programmer."
-    # )
-    installation_advance = ""
-
-    return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
-
-
-
-
-def for_immediate_show_off_when_possible(file_type, fp, chatbot):
-    if file_type in ['png', 'jpg']:
-        image_path = os.path.abspath(fp)
-        chatbot.append(['这是一张图片, 展示如下:',
-            f'本地文件地址: <br/>`{image_path}`<br/>'+
-            f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-        ])
-    return chatbot
-
-
-
-def have_any_recent_upload_files(chatbot):
-    _5min = 5 * 60
-    if not chatbot: return False    # chatbot is None
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    if not most_recent_uploaded: return False   # most_recent_uploaded is None
-    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
-    else: return False  # most_recent_uploaded is too old
-
-def get_recent_file_prompt_support(chatbot):
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    path = most_recent_uploaded['path']
-    return path
-
-@CatchException
-def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-
-    # 清空历史
-    history = []
-
-    # 基本信息：功能、贡献者
-    chatbot.append(["正在启动: 插件动态生成插件", "插件动态生成, 执行开始, 作者Binary-Husky."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # ⭐ 文件上传区是否有东西
-    # 1. 如果有文件: 作为函数参数
-    # 2. 如果没有文件：需要用GPT提取参数 （太懒了，以后再写，虚空终端已经实现了类似的代码）
-    file_list = []
-    if get_plugin_arg(plugin_kwargs, key="file_path_arg", default=False):
-        file_path = get_plugin_arg(plugin_kwargs, key="file_path_arg", default=None)
-        file_list.append(file_path)
-        yield from update_ui_lastest_msg(f"当前文件: {file_path}", chatbot, history, 1)
-    elif have_any_recent_upload_files(chatbot):
-        file_dir = get_recent_file_prompt_support(chatbot)
-        file_list = glob.glob(os.path.join(file_dir, '**/*'), recursive=True)
-        yield from update_ui_lastest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
-    else:
-        chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
-        return  # 2. 如果没有文件
-    if len(file_list) == 0:
-        chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
-        return  # 2. 如果没有文件
-
-    # 读取文件
-    file_type = file_list[0].split('.')[-1]
-
-    # 粗心检查
-    if is_the_upload_folder(txt):
-        yield from update_ui_lastest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
-        return
-
-    # 开始干正事
-    MAX_TRY = 3
-    for j in range(MAX_TRY):  # 最多重试5次
-        traceback = ""
-        try:
-            # ⭐ 开始啦 ！
-            code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
-                yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
-            chatbot.append(["代码生成阶段结束", ""])
-            yield from update_ui_lastest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
-            # ⭐ 分离代码块
-            code = get_code_block(code)
-            # ⭐ 检查模块
-            ok, traceback = try_make_module(code, chatbot)
-            # 搞定代码生成
-            if ok: break
-        except Exception as e:
-            if not traceback: traceback = trimmed_format_exc()
-        # 处理异常
-        if not traceback: traceback = trimmed_format_exc()
-        yield from update_ui_lastest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
-
-    # 代码生成结束, 开始执行
-    TIME_LIMIT = 15
-    yield from update_ui_lastest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
-    manager = multiprocessing.Manager()
-    return_dict = manager.dict()
-
-    # ⭐ 到最后一步了，开始逐个文件进行处理
-    for file_path in file_list:
-        if os.path.exists(file_path):
-            chatbot.append([f"正在处理文件: {file_path}", f"请稍等..."])
-            chatbot = for_immediate_show_off_when_possible(file_type, file_path, chatbot)
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-        else:
-            continue
-
-        # ⭐⭐⭐ subprocess_worker ⭐⭐⭐
-        p = multiprocessing.Process(target=subprocess_worker, args=(code, file_path, return_dict))
-        # ⭐ 开始执行，时间限制TIME_LIMIT
-        p.start(); p.join(timeout=TIME_LIMIT)
-        if p.is_alive(): p.terminate(); p.join()
-        p.close()
-        res = return_dict['result']
-        success = return_dict['success']
-        traceback = return_dict['traceback']
-        if not success:
-            if not traceback: traceback = trimmed_format_exc()
-            chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
-            # chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-            return
-
-        # 顺利完成，收尾
-        res = str(res)
-        if os.path.exists(res):
-            chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
-            new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
-            chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-        else:
-            chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
--- a/crazy_functions/命令行助手.py
+++ b/crazy_functions/命令行助手.py
@ -1,31 +0,0 @@
-from toolbox import CatchException, update_ui, gen_time_str
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import input_clipping
-import copy, json
-
-@CatchException
-def 命令行助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本, 例如需要翻译的一段话, 再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数, 暂时没有用武之地
-    chatbot         聊天显示框的句柄, 用于显示给用户
-    history         聊天历史, 前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    # 清空历史, 以免输入溢出
-    history = []
-
-    # 输入
-    i_say = "请写bash命令实现以下功能：" + txt
-    # 开始
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=txt,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
-        sys_prompt="你是一个Linux大师级用户。注意，当我要求你写bash命令时，尽可能地仅用一行命令解决我的要求。"
-    )
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-
--- a/crazy_functions/图片生成.py
+++ b/crazy_functions/图片生成.py
@ -1,276 +0,0 @@
-from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
-
-
-def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", quality=None, style=None):
-    import requests, json, time, os
-    from request_llms.bridge_all import model_info
-
-    proxies = get_conf('proxies')
-    # Set up OpenAI API key and model
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-    # 'https://api.openai.com/v1/chat/completions'
-    img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
-    # # Generate the image
-    url = img_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}",
-        'Content-Type': 'application/json'
-    }
-    data = {
-        'prompt': prompt,
-        'n': 1,
-        'size': resolution,
-        'model': model,
-        'response_format': 'url'
-    }
-    if quality is not None:
-        data['quality'] = quality
-    if style is not None:
-        data['style'] = style
-    response = requests.post(url, headers=headers, json=data, proxies=proxies)
-    print(response.content)
-    try:
-        image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-    except:
-        raise RuntimeError(response.content.decode())
-    # 文件保存到本地
-    r = requests.get(image_url, proxies=proxies)
-    file_path = f'{get_log_folder()}/image_gen/'
-    os.makedirs(file_path, exist_ok=True)
-    file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
-    with open(file_path+file_name, 'wb+') as f: f.write(r.content)
-
-
-    return image_url, file_path+file_name
-
-
-def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="dall-e-2"):
-    import requests, json, time, os
-    from request_llms.bridge_all import model_info
-
-    proxies = get_conf('proxies')
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-    # 'https://api.openai.com/v1/chat/completions'
-    img_endpoint = chat_endpoint.replace('chat/completions','images/edits')
-    # # Generate the image
-    url = img_endpoint
-    n = 1
-    headers = {
-        'Authorization': f"Bearer {api_key}",
-    }
-    make_transparent(image_path, image_path+'.tsp.png')
-    make_square_image(image_path+'.tsp.png', image_path+'.tspsq.png')
-    resize_image(image_path+'.tspsq.png', image_path+'.ready.png', max_size=1024)
-    image_path = image_path+'.ready.png'
-    with open(image_path, 'rb') as f:
-        file_content = f.read()
-        files = {
-            'image': (os.path.basename(image_path), file_content),
-            # 'mask': ('mask.png', open('mask.png', 'rb'))
-            'prompt':   (None, prompt),
-            "n":        (None, str(n)),
-            'size':     (None, resolution),
-        }
-
-    response = requests.post(url, headers=headers, files=files, proxies=proxies)
-    print(response.content)
-    try:
-        image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-    except:
-        raise RuntimeError(response.content.decode())
-    # 文件保存到本地
-    r = requests.get(image_url, proxies=proxies)
-    file_path = f'{get_log_folder()}/image_gen/'
-    os.makedirs(file_path, exist_ok=True)
-    file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
-    with open(file_path+file_name, 'wb+') as f: f.write(r.content)
-
-
-    return image_url, file_path+file_name
-
-
-@CatchException
-def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本,例如需要翻译的一段话,再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数,如温度和top_p等,一般原样传递下去就行
-    plugin_kwargs   插件模型的参数,暂时没有用武之地
-    chatbot         聊天显示框的句柄,用于显示给用户
-    history         聊天历史,前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    history = []    # 清空历史,以免输入溢出
-    if prompt.strip() == "":
-        chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
-        return
-    chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    resolution = plugin_kwargs.get("advanced_arg", '1024x1024')
-    image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
-    chatbot.append([prompt,
-        f'图像中转网址: <br/>`{image_url}`<br/>'+
-        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
-        f'本地文件地址: <br/>`{image_path}`<br/>'+
-        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
-
-
-@CatchException
-def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    history = []    # 清空历史,以免输入溢出
-    if prompt.strip() == "":
-        chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
-        return
-    chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    resolution_arg = plugin_kwargs.get("advanced_arg", '1024x1024-standard-vivid').lower()
-    parts = resolution_arg.split('-')
-    resolution = parts[0] # 解析分辨率
-    quality = 'standard' # 质量与风格默认值
-    style = 'vivid'
-    # 遍历检查是否有额外参数
-    for part in parts[1:]:
-        if part in ['hd', 'standard']:
-            quality = part
-        elif part in ['vivid', 'natural']:
-            style = part
-    image_url, image_path = gen_image(llm_kwargs, prompt, resolution, model="dall-e-3", quality=quality, style=style)
-    chatbot.append([prompt,
-        f'图像中转网址: <br/>`{image_url}`<br/>'+
-        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
-        f'本地文件地址: <br/>`{image_path}`<br/>'+
-        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
-
-
-class ImageEditState(GptAcademicState):
-    # 尚未完成
-    def get_image_file(self, x):
-        import os, glob
-        if len(x) == 0:             return False, None
-        if not os.path.exists(x):   return False, None
-        if x.endswith('.png'):      return True, x
-        file_manifest = [f for f in glob.glob(f'{x}/**/*.png', recursive=True)]
-        confirm = (len(file_manifest) >= 1 and file_manifest[0].endswith('.png') and os.path.exists(file_manifest[0]))
-        file = None if not confirm else file_manifest[0]
-        return confirm, file
-
-    def lock_plugin(self, chatbot):
-        chatbot._cookies['lock_plugin'] = 'crazy_functions.图片生成->图片修改_DALLE2'
-        self.dump_state(chatbot)
-
-    def unlock_plugin(self, chatbot):
-        self.reset()
-        chatbot._cookies['lock_plugin'] = None
-        self.dump_state(chatbot)
-
-    def get_resolution(self, x):
-        return (x in ['256x256', '512x512', '1024x1024']), x
-
-    def get_prompt(self, x):
-        confirm = (len(x)>=5) and (not self.get_resolution(x)[0]) and (not self.get_image_file(x)[0])
-        return confirm, x
-
-    def reset(self):
-        self.req = [
-            {'value':None, 'description': '请先上传图像（必须是.png格式）, 然后再次点击本插件',                      'verify_fn': self.get_image_file},
-            {'value':None, 'description': '请输入分辨率,可选：256x256, 512x512 或 1024x1024, 然后再次点击本插件',   'verify_fn': self.get_resolution},
-            {'value':None, 'description': '请输入修改需求,建议您使用英文提示词, 然后再次点击本插件',                 'verify_fn': self.get_prompt},
-        ]
-        self.info = ""
-
-    def feed(self, prompt, chatbot):
-        for r in self.req:
-            if r['value'] is None:
-                confirm, res = r['verify_fn'](prompt)
-                if confirm:
-                    r['value'] = res
-                    self.dump_state(chatbot)
-                    break
-        return self
-
-    def next_req(self):
-        for r in self.req:
-            if r['value'] is None:
-                return r['description']
-        return "已经收集到所有信息"
-
-    def already_obtained_all_materials(self):
-        return all([x['value'] is not None for x in self.req])
-
-@CatchException
-def 图片修改_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 尚未完成
-    history = []    # 清空历史
-    state = ImageEditState.get_state(chatbot, ImageEditState)
-    state = state.feed(prompt, chatbot)
-    state.lock_plugin(chatbot)
-    if not state.already_obtained_all_materials():
-        chatbot.append(["图片修改\n\n1. 上传图片（图片中需要修改的位置用橡皮擦擦除为纯白色，即RGB=255,255,255）\n2. 输入分辨率 \n3. 输入修改需求", state.next_req()])
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-    image_path = state.req[0]['value']
-    resolution = state.req[1]['value']
-    prompt = state.req[2]['value']
-    chatbot.append(["图片修改, 执行中", f"图片:`{image_path}`<br/>分辨率:`{resolution}`<br/>修改需求:`{prompt}`"])
-    yield from update_ui(chatbot=chatbot, history=history)
-    image_url, image_path = edit_image(llm_kwargs, prompt, image_path, resolution)
-    chatbot.append([prompt,
-        f'图像中转网址: <br/>`{image_url}`<br/>'+
-        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
-        f'本地文件地址: <br/>`{image_path}`<br/>'+
-        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
-    state.unlock_plugin(chatbot)
-
-def make_transparent(input_image_path, output_image_path):
-    from PIL import Image
-    image = Image.open(input_image_path)
-    image = image.convert("RGBA")
-    data = image.getdata()
-    new_data = []
-    for item in data:
-        if item[0] == 255 and item[1] == 255 and item[2] == 255:
-            new_data.append((255, 255, 255, 0))
-        else:
-            new_data.append(item)
-    image.putdata(new_data)
-    image.save(output_image_path, "PNG")
-
-def resize_image(input_path, output_path, max_size=1024):
-    from PIL import Image
-    with Image.open(input_path) as img:
-        width, height = img.size
-        if width > max_size or height > max_size:
-            if width >= height:
-                new_width = max_size
-                new_height = int((max_size / width) * height)
-            else:
-                new_height = max_size
-                new_width = int((max_size / height) * width)
-
-            resized_img = img.resize(size=(new_width, new_height))
-            resized_img.save(output_path)
-        else:
-            img.save(output_path)
-
-def make_square_image(input_path, output_path):
-    from PIL import Image
-    with Image.open(input_path) as img:
-        width, height = img.size
-        size = max(width, height)
-        new_img = Image.new("RGBA", (size, size), color="black")
-        new_img.paste(img, ((size - width) // 2, (size - height) // 2))
-        new_img.save(output_path)
--- a/crazy_functions/多智能体.py
+++ b/crazy_functions/多智能体.py
@ -1,101 +0,0 @@
-# 本源代码中, ⭐ = 关键步骤
-"""
-测试：
-    - show me the solution of $x^2=cos(x)$, solve this problem with figure, and plot and save image to t.jpg
-
-"""
-
-
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
-from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
-from crazy_functions.crazy_utils import input_clipping, try_install_deps
-from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
-from crazy_functions.agent_fns.auto_agent import AutoGenMath
-import time
-
-def remove_model_prefix(llm):
-    if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
-    if llm.startswith('azure-'): llm = llm.replace('azure-', '')
-    return llm
-
-
-@CatchException
-def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    # 检查当前的模型是否符合要求
-    supported_llms = [
-        "gpt-3.5-turbo-16k",
-        'gpt-3.5-turbo-1106',
-        "gpt-4",
-        "gpt-4-32k",
-        'gpt-4-1106-preview',
-        "azure-gpt-3.5-turbo-16k",
-        "azure-gpt-3.5-16k",
-        "azure-gpt-4",
-        "azure-gpt-4-32k",
-    ]
-    from request_llms.bridge_all import model_info
-    if model_info[llm_kwargs['llm_model']]["max_token"] < 8000: # 至少是8k上下文的模型
-        chatbot.append([f"处理任务: {txt}", f"当前插件只支持{str(supported_llms)}, 当前模型{llm_kwargs['llm_model']}的最大上下文长度太短, 不能支撑AutoGen运行。"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if model_info[llm_kwargs['llm_model']]["endpoint"] is not None: # 如果不是本地模型，加载API_KEY
-        llm_kwargs['api_key'] = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import autogen
-        if get_conf("AUTOGEN_USE_DOCKER"):
-            import docker
-    except:
-        chatbot.append([ f"处理任务: {txt}",
-            f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pyautogen docker```。"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import autogen
-        import glob, os, time, subprocess
-        if get_conf("AUTOGEN_USE_DOCKER"):
-            subprocess.Popen(["docker", "--version"])
-    except:
-        chatbot.append([f"处理任务: {txt}", f"缺少docker运行环境！"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 解锁插件
-    chatbot.get_cookies()['lock_plugin'] = None
-    persistent_class_multi_user_manager = GradioMultiuserManagerForPersistentClasses()
-    user_uuid = chatbot.get_cookies().get('uuid')
-    persistent_key = f"{user_uuid}->多智能体终端"
-    if persistent_class_multi_user_manager.already_alive(persistent_key):
-        # 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
-        print('[debug] feed new user input')
-        executor = persistent_class_multi_user_manager.get(persistent_key)
-        exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
-    else:
-        # 运行多智能体终端 (首次)
-        print('[debug] create new executor instance')
-        history = []
-        chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        executor = AutoGenMath(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-        persistent_class_multi_user_manager.set(persistent_key, executor)
-        exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="create")
-
-    if exit_reason == "wait_feedback":
-        # 当用户点击了“等待反馈”按钮时，将executor存储到cookie中，等待用户的再次调用
-        executor.chatbot.get_cookies()['lock_plugin'] = 'crazy_functions.多智能体->多智能体终端'
-    else:
-        executor.chatbot.get_cookies()['lock_plugin'] = None
-    yield from update_ui(chatbot=executor.chatbot, history=executor.history) # 更新状态
--- a/crazy_functions/对话历史存档.py
+++ b/crazy_functions/对话历史存档.py
@ -1,152 +0,0 @@
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
-import re
-
-f_prefix = 'GPT-Academic对话存档'
-
-def write_chat_to_file(chatbot, history=None, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        file_name = f_prefix + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
-    fp = os.path.join(get_log_folder(get_user(chatbot), plugin_name='chat_history'), file_name)
-    with open(fp, 'w', encoding='utf8') as f:
-        from themes.theme import advanced_css
-        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
-        for i, contents in enumerate(chatbot):
-            for j, content in enumerate(contents):
-                try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                    if type(content) != str: content = str(content)
-                except:
-                    continue
-                f.write(content)
-                if j == 0:
-                    f.write('<hr style="border-top: dotted 3px #ccc;">')
-            f.write('<hr color="red"> \n\n')
-        f.write('<hr color="blue"> \n\n raw chat context:\n')
-        f.write('<code>')
-        for h in history:
-            f.write("\n>>>" + h)
-        f.write('</code>')
-    promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
-    return '对话历史写入：' + fp
-
-def gen_file_preview(file_name):
-    try:
-        with open(file_name, 'r', encoding='utf8') as f:
-            file_content = f.read()
-        # pattern to match the text between <head> and </head>
-        pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-        file_content = re.sub(pattern, '', file_content)
-        html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-        history = history.strip('<code>')
-        history = history.strip('</code>')
-        history = history.split("\n>>>")
-        return list(filter(lambda x:x!="", history))[0][:100]
-    except:
-        return ""
-
-def read_file_to_chat(chatbot, history, file_name):
-    with open(file_name, 'r', encoding='utf8') as f:
-        file_content = f.read()
-    # pattern to match the text between <head> and </head>
-    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-    file_content = re.sub(pattern, '', file_content)
-    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-    history = history.strip('<code>')
-    history = history.strip('</code>')
-    history = history.split("\n>>>")
-    history = list(filter(lambda x:x!="", history))
-    html = html.split('<hr color="red"> \n\n')
-    html = list(filter(lambda x:x!="", html))
-    chatbot.clear()
-    for i, h in enumerate(html):
-        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
-        chatbot.append([i_say, gpt_say])
-    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
-    return chatbot, history
-
-@CatchException
-def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-
-    chatbot.append(("保存当前对话",
-        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-def hide_cwd(str):
-    import os
-    current_path = os.getcwd()
-    replace_path = "."
-    return str.replace(current_path, replace_path)
-
-@CatchException
-def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, _ = get_files_from_everything(txt, type='.html')
-
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-        import glob
-        local_history = "<br/>".join([
-            "`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`"
-            for f in glob.glob(
-                f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html',
-                recursive=True
-            )])
-        chatbot.append([f"正在查找对话历史文件（html格式）: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    try:
-        chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    except:
-        chatbot.append([f"载入对话历史文件", f"对话历史文件损坏！"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-@CatchException
-def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-
-    import glob, os
-    local_history = "<br/>".join([
-        "`"+hide_cwd(f)+"`"
-        for f in glob.glob(
-            f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html', recursive=True
-        )])
-    for f in glob.glob(f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html', recursive=True):
-        os.remove(f)
-    chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return
-
-
--- a/crazy_functions/总结word文档.py
+++ b/crazy_functions/总结word文档.py
@ -1,127 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_exception
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    # pip install python-docx 用于docx格式，跨平台
-    # pip install pywin32 用于doc格式，仅支持Win平台
-    for index, fp in enumerate(file_manifest):
-        if fp.split(".")[-1] == "docx":
-            from docx import Document
-            doc = Document(fp)
-            file_content = "\n".join([para.text for para in doc.paragraphs])
-        else:
-            try:
-                import win32com.client
-                word = win32com.client.Dispatch("Word.Application")
-                word.visible = False
-                # 打开文件
-                doc = word.Documents.Open(os.getcwd() + '/' + fp)
-                # file_content = doc.Content.Text
-                doc = word.ActiveDocument
-                file_content = doc.Range().Text
-                doc.Close()
-                word.Quit()
-            except:
-                raise RuntimeError('请先将.doc文档转换为.docx文档。')
-
-        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-        from request_llms.bridge_all import model_info
-        max_token = model_info[llm_kwargs['llm_model']]['max_token']
-        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
-        this_paper_history = []
-        for i, paper_frag in enumerate(paper_fragments):
-            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
-            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt="总结文章。"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user,gpt_say])
-            this_paper_history.extend([i_say_show_user,gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
-        if len(paper_fragments) > 1:
-            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=this_paper_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say,gpt_say])
-            this_paper_history.extend([i_say,gpt_say])
-
-        res = write_history_to_file(history)
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    res = write_history_to_file(history)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    chatbot.append(("所有文件都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        from docx import Document
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    if txt.endswith('.docx') or txt.endswith('.doc'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
-                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
--- a/crazy_functions/总结音视频.py
+++ b/crazy_functions/总结音视频.py
@ -1,186 +0,0 @@
-from toolbox import CatchException, report_exception, select_api_key, update_ui, get_conf
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder
-
-def split_audio_file(filename, split_duration=1000):
-    """
-    根据给定的切割时长将音频文件切割成多个片段。
-
-    Args:
-        filename (str): 需要被切割的音频文件名。
-        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
-
-    Returns:
-        filelist (list): 一个包含所有切割音频片段文件路径的列表。
-
-    """
-    from moviepy.editor import AudioFileClip
-    import os
-    os.makedirs(f"{get_log_folder(plugin_name='audio')}/mp3/cut/", exist_ok=True)  # 创建存储切割音频的文件夹
-
-    # 读取音频文件
-    audio = AudioFileClip(filename)
-
-    # 计算文件总时长和切割点
-    total_duration = audio.duration
-    split_points = list(range(0, int(total_duration), split_duration))
-    split_points.append(int(total_duration))
-    filelist = []
-
-    # 切割音频文件
-    for i in range(len(split_points) - 1):
-        start_time = split_points[i]
-        end_time = split_points[i + 1]
-        split_audio = audio.subclip(start_time, end_time)
-        split_audio.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")
-        filelist.append(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")
-
-    audio.close()
-    return filelist
-
-def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
-    import os, requests
-    from moviepy.editor import AudioFileClip
-    from request_llms.bridge_all import model_info
-
-    # 设置OpenAI密钥和模型
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-
-    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
-    url = whisper_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}"
-    }
-
-    os.makedirs(f"{get_log_folder(plugin_name='audio')}/mp3/", exist_ok=True)
-    for index, fp in enumerate(file_manifest):
-        audio_history = []
-        # 提取文件扩展名
-        ext = os.path.splitext(fp)[1]
-        # 提取视频中的音频
-        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
-            audio_clip = AudioFileClip(fp)
-            audio_clip.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/output{index}.mp3")
-            fp = f"{get_log_folder(plugin_name='audio')}/mp3/output{index}.mp3"
-        # 调用whisper模型音频转文字
-        voice = split_audio_file(fp)
-        for j, i in enumerate(voice):
-            with open(i, 'rb') as f:
-                file_content = f.read()  # 读取文件内容到内存
-                files = {
-                    'file': (os.path.basename(i), file_content),
-                }
-                data = {
-                    "model": "whisper-1",
-                    "prompt": parse_prompt,
-                    'response_format': "text"
-                }
-
-            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-            proxies = get_conf('proxies')
-            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
-
-            chatbot.append(["音频解析结果", response])
-            history.extend(["音频解析结果", response])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
-            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt=f"总结音频。音频文件名{fp}"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user, gpt_say])
-            audio_history.extend([i_say_show_user, gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了
-        result = "".join(audio_history)
-        if len(audio_history) > 1:
-            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
-            i_say_show_user = f'第{index + 1}段音频的主要内容：'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=audio_history,
-                sys_prompt="总结文章。"
-            )
-            history.extend([i_say, gpt_say])
-            audio_history.extend([i_say, gpt_say])
-
-        res = write_history_to_file(history)
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # 删除中间文件夹
-    import shutil
-    shutil.rmtree(f"{get_log_folder(plugin_name='audio')}/mp3")
-    res = write_history_to_file(history)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    chatbot.append(("所有音频都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history)
-
-
-@CatchException
-def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    try:
-        from moviepy.editor import AudioFileClip
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
-
-    if txt.endswith(tuple(extensions)):
-        file_manifest = [txt]
-    else:
-        file_manifest = []
-        for extension in extensions:
-            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 开始正式执行任务
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
-    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
-
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
--- a/crazy_functions/批量Markdown翻译.py
+++ b/crazy_functions/批量Markdown翻译.py
@ -1,263 +0,0 @@
-import glob, shutil, os, re, logging
-from toolbox import update_ui, trimmed_format_exc, gen_time_str
-from toolbox import CatchException, report_exception, get_log_folder
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llms.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=2048):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
-        logging.info('Segmentation: done')
-
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self, language):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')
-            with open(dst_file, 'w', encoding='utf8') as f:
-                manifest.append(dst_file)
-                f.write(res)
-        return manifest
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Markdown文件，删除其中的所有注释 ---------->
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(file_content)
-
-    #  <-------- 拆分过长的Markdown文件 ---------->
-    pfg.run_file_split(max_token_limit=2048)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 多线程翻译开始 ---------->
-    if language == 'en->zh':
-        inputs_array = ["This is a Markdown file, translate it into Chinese, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"This is a Markdown file, translate it into English, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    else:
-        inputs_array = [f"This is a Markdown file, translate it into {language}, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-    try:
-        pfg.sp_file_result = []
-        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-        output_file_arr = pfg.write_result(language)
-        for output_file in output_file_arr:
-            promote_file_to_downloadzone(output_file, chatbot=chatbot)
-            if 'markdown_expected_output_path' in plugin_kwargs:
-                expected_f_name = plugin_kwargs['markdown_expected_output_path']
-                shutil.copyfile(output_file, expected_f_name)
-    except:
-        logging.error(trimmed_format_exc())
-
-    #  <-------- 整理结果，退出 ---------->
-    create_report_file_name = gen_time_str() + f"-chatgpt.md"
-    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def get_files_from_everything(txt, preference=''):
-    if txt == "": return False, None, None
-    success = True
-    if txt.startswith('http'):
-        import requests
-        from toolbox import get_conf
-        proxies = get_conf('proxies')
-        # 网络的远程文件
-        if preference == 'Github':
-            logging.info('正在从github下载资源 ...')
-            if not txt.endswith('.md'):
-                # Make a request to the GitHub API to retrieve the repository information
-                url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'
-                response = requests.get(url, proxies=proxies)
-                txt = response.json()['download_url']
-            else:
-                txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
-                txt = txt.replace("/blob/", "/")
-
-        r = requests.get(txt, proxies=proxies)
-        download_local = f'{get_log_folder(plugin_name="批量Markdown翻译")}/raw-readme-{gen_time_str()}.md'
-        project_folder = f'{get_log_folder(plugin_name="批量Markdown翻译")}'
-        with open(download_local, 'wb+') as f: f.write(r.content)
-        file_manifest = [download_local]
-    elif txt.endswith('.md'):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder
-
-
-@CatchException
-def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-
-    success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
-
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
-
-
-@CatchException
-def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    language = plugin_kwargs.get("advanced_arg", 'Chinese')
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)
--- a/crazy_functions/批量总结PDF文档.py
+++ b/crazy_functions/批量总结PDF文档.py
@ -1,145 +0,0 @@
-from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
-from toolbox import CatchException, report_exception
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import input_clipping
-
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    file_write_buffer = []
-    for file_name in file_manifest:
-        print('begin analysis on:', file_name)
-        ############################## <第 0 步，切割PDF> ##################################
-        # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-        # 的长度必须小于 2500 个 Token
-        file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-
-        TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content,  limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
-        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-
-        ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-        final_results = []
-        final_results.append(paper_meta)
-
-        ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-        i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-        chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-        iteration_results = []
-        last_iteration_result = paper_meta  # 初始值是摘要
-        MAX_WORD_TOTAL = 4096 * 0.7
-        n_fragment = len(paper_fragments)
-        if n_fragment >= 20: print('文章极长，不能达到预期效果')
-        for i in range(n_fragment):
-            NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-            i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
-            i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                                llm_kwargs, chatbot,
-                                                                                history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                                sys_prompt="Extract the main idea of this section with Chinese."  # 提示
-                                                                                )
-            iteration_results.append(gpt_say)
-            last_iteration_result = gpt_say
-
-        ############################## <第 3 步，整理history，提取总结> ##################################
-        final_results.extend(iteration_results)
-        final_results.append(f'Please conclude this paper discussed above。')
-        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
-        NUM_OF_WORD = 1000
-        i_say = """
-1. Mark the title of the paper (with Chinese translation)
-2. list all the authors' names (use English)
-3. mark the first author's affiliation (output Chinese translation only)
-4. mark the keywords of this article (use English)
-5. link to the paper, Github code link (if available, fill in Github:None if not)
-6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)
-    - (1):What is the research background of this article?
-    - (2):What are the past methods? What are the problems with them? Is the approach well motivated?
-    - (3):What is the research methodology proposed in this paper?
-    - (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?
-Follow the format of the output that follows:
-1. Title: xxx\n\n
-2. Authors: xxx\n\n
-3. Affiliation: xxx\n\n
-4. Keywords: xxx\n\n
-5. Urls: xxx or xxx , xxx \n\n
-6. Summary: \n\n
-    - (1):xxx;\n
-    - (2):xxx;\n
-    - (3):xxx;\n
-    - (4):xxx.\n\n
-Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible,
-do not have too much repetitive information, numerical values using the original numbers.
-        """
-        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
-        file_write_buffer.extend(final_results)
-        i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000)
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user='开始最终总结',
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results,
-            sys_prompt= f"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters"
-        )
-        final_results.append(gpt_say)
-        file_write_buffer.extend([i_say, gpt_say])
-        ############################## <第 4 步，设置一个token上限> ##################################
-        _, final_results = input_clipping("", final_results, max_token_limit=3200)
-        yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-    res = write_history_to_file(file_write_buffer)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面
-
-
-@CatchException
-def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_exception(chatbot, history,
-            a = f"解析项目: {txt}",
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
--- a/crazy_functions/批量总结PDF文档pdfminer.py
+++ b/crazy_functions/批量总结PDF文档pdfminer.py
@ -1,162 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_exception
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-
-fast_debug = False
-
-def readPdf(pdfPath):
-    """
-    读取pdf文件，返回文本内容
-    """
-    import pdfminer
-    from pdfminer.pdfparser import PDFParser
-    from pdfminer.pdfdocument import PDFDocument
-    from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
-    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
-    from pdfminer.pdfdevice import PDFDevice
-    from pdfminer.layout import LAParams
-    from pdfminer.converter import PDFPageAggregator
-
-    fp = open(pdfPath, 'rb')
-
-    # Create a PDF parser object associated with the file object
-    parser = PDFParser(fp)
-
-    # Create a PDF document object that stores the document structure.
-    # Password for initialization as 2nd parameter
-    document = PDFDocument(parser)
-    # Check if the document allows text extraction. If not, abort.
-    if not document.is_extractable:
-        raise PDFTextExtractionNotAllowed
-
-    # Create a PDF resource manager object that stores shared resources.
-    rsrcmgr = PDFResourceManager()
-
-    # Create a PDF device object.
-    # device = PDFDevice(rsrcmgr)
-
-    # BEGIN LAYOUT ANALYSIS.
-    # Set parameters for analysis.
-    laparams = LAParams(
-        char_margin=10.0,
-        line_margin=0.2,
-        boxes_flow=0.2,
-        all_texts=False,
-    )
-    # Create a PDF page aggregator object.
-    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
-    # Create a PDF interpreter object.
-    interpreter = PDFPageInterpreter(rsrcmgr, device)
-
-    # loop over all pages in the document
-    outTextList = []
-    for page in PDFPage.create_pages(document):
-        # read the page into a layout object
-        interpreter.process_page(page)
-        layout = device.get_result()
-        for obj in layout._objs:
-            if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
-                # print(obj.get_text())
-                outTextList.append(obj.get_text())
-
-    return outTextList
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    from bs4 import BeautifulSoup
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        if ".tex" in fp:
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-        if ".pdf" in fp.lower():
-            file_content = readPdf(fp)
-            file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug:
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug:
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say,
-            inputs_show_user=i_say,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_history_to_file(history)
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import pdfminer, bs4
-    except:
-        report_exception(chatbot, history,
-            a = f"解析项目: {txt}",
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
--- a/crazy_functions/批量翻译PDF文档_NOUGAT.py
+++ b/crazy_functions/批量翻译PDF文档_NOUGAT.py
@ -1,125 +0,0 @@
-from toolbox import CatchException, report_exception, get_log_folder, gen_time_str
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
-from colorful import *
-import copy
-import os
-import math
-import logging
-
-def markdown_to_dict(article_content):
-    import markdown
-    from bs4 import BeautifulSoup
-    cur_t = ""
-    cur_c = ""
-    results = {}
-    for line in article_content:
-        if line.startswith('#'):
-            if cur_t!="":
-                if cur_t not in results:
-                    results.update({cur_t:cur_c.lstrip('\n')})
-                else:
-                    # 处理重名的章节
-                    results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')})
-            cur_t = line.rstrip('\n')
-            cur_c = ""
-        else:
-            cur_c += line
-    results_final = {}
-    for k in list(results.keys()):
-        if k.startswith('# '):
-            results_final['title'] = k.split('# ')[-1]
-            results_final['authors'] = results.pop(k).lstrip('\n')
-        if k.startswith('###### Abstract'):
-            results_final['abstract'] = results.pop(k).lstrip('\n')
-
-    results_final_sections = []
-    for k,v in results.items():
-        results_final_sections.append({
-            'heading':k.lstrip("# "),
-            'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section."
-        })
-    results_final['sections'] = results_final_sections
-    return results_final
-
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-
-    disable_auto_promotion(chatbot)
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
-    if len(file_manifest) > 0:
-        # 尝试导入依赖，如果缺少依赖，则给出安装建议
-        try:
-            import nougat
-            import tiktoken
-        except:
-            report_exception(chatbot, history,
-                             a=f"解析项目: {txt}",
-                             b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade nougat-ocr tiktoken```。")
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-            return
-    success_mmd, file_manifest_mmd, _ = get_files_from_everything(txt, type='.mmd')
-    success = success or success_mmd
-    file_manifest += file_manifest_mmd
-    chatbot.append(["文件列表：", ", ".join([e.split('/')[-1] for e in file_manifest])]);
-    yield from update_ui(      chatbot=chatbot, history=history)
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-
-
-def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import copy
-    import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1024
-    generated_conclusion_files = []
-    generated_html_files = []
-    DST_LANG = "中文"
-    from crazy_functions.crazy_utils import nougat_interface
-    from crazy_functions.pdf_fns.report_gen_html import construct_html
-    nougat_handle = nougat_interface()
-    for index, fp in enumerate(file_manifest):
-        if fp.endswith('pdf'):
-            chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-            fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history)
-            promote_file_to_downloadzone(fpp, rename_file=os.path.basename(fpp)+'.nougat.mmd', chatbot=chatbot)
-        else:
-            chatbot.append(["当前论文无需解析：", fp]); yield from update_ui(      chatbot=chatbot, history=history)
-            fpp = fp
-        with open(fpp, 'r', encoding='utf8') as f:
-            article_content = f.readlines()
-        article_dict = markdown_to_dict(article_content)
-        logging.info(article_dict)
-        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
-
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
--- a/crazy_functions/数学动画生成manim.py
+++ b/crazy_functions/数学动画生成manim.py
@ -1,191 +0,0 @@
-import os
-from toolbox import CatchException, update_ui, gen_time_str, promote_file_to_downloadzone
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.crazy_utils import input_clipping
-
-def inspect_dependency(chatbot, history):
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import manim
-        return True
-    except:
-        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install manim manimgl```"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return False
-
-def eval_manim(code):
-    import subprocess, sys, os, shutil
-
-    with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f:
-        f.write(code)
-
-    def get_class_name(class_string):
-        import re
-        # Use regex to extract the class name
-        class_name = re.search(r'class (\w+)\(', class_string).group(1)
-        return class_name
-
-    class_name = get_class_name(code)
-
-    try:
-        time_str = gen_time_str()
-        subprocess.check_output([sys.executable, '-c', f"from gpt_log.MyAnimation import {class_name}; {class_name}().render()"])
-        shutil.move(f'media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{time_str}.mp4')
-        return f'gpt_log/{time_str}.mp4'
-    except subprocess.CalledProcessError as e:
-        output = e.output.decode()
-        print(f"Command returned non-zero exit status {e.returncode}: {output}.")
-        return f"Evaluating python script failed: {e.output}."
-    except:
-        print('generating mp4 failed')
-        return "Generating mp4 failed."
-
-
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) != 1:
-        raise RuntimeError("GPT is not generating proper code.")
-    return matches[0].strip('python') #  code block
-
-@CatchException
-def 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..."
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖, 如果缺少依赖, 则给出安装建议
-    dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
-    if not dep_ok: return
-
-    # 输入
-    i_say = f'Generate a animation to show: ' + txt
-    demo = ["Here is some examples of manim", examples_of_manim()]
-    _, demo = input_clipping(inputs="", history=demo, max_token_limit=2560)
-    # 开始
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo,
-        sys_prompt=
-        r"Write a animation script with 3blue1brown's manim. "+
-        r"Please begin with `from manim import *`. " +
-        r"Answer me with a code block wrapped by ```."
-    )
-    chatbot.append(["开始生成动画", "..."])
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-    # 将代码转为动画
-    code = get_code_block(gpt_say)
-    res = eval_manim(code)
-
-    chatbot.append(("生成的视频文件路径", res))
-    if os.path.exists(res):
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-# 在这里放一些网上搜集的demo，辅助gpt生成代码
-def examples_of_manim():
-    return r"""
-
-
-```
-
-class MovingGroupToDestination(Scene):
-    def construct(self):
-        group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4)
-        dest = Dot([4, 3, 0], color=YELLOW)
-        self.add(group, dest)
-        self.play(group.animate.shift(dest.get_center() - group[2].get_center()))
-        self.wait(0.5)
-
-```
-
-
-```
-
-class LatexWithMovingFramebox(Scene):
-    def construct(self):
-        text=MathTex(
-            "\\frac{d}{dx}f(x)g(x)=","f(x)\\frac{d}{dx}g(x)","+",
-            "g(x)\\frac{d}{dx}f(x)"
-        )
-        self.play(Write(text))
-        framebox1 = SurroundingRectangle(text[1], buff = .1)
-        framebox2 = SurroundingRectangle(text[3], buff = .1)
-        self.play(
-            Create(framebox1),
-        )
-        self.wait()
-        self.play(
-            ReplacementTransform(framebox1,framebox2),
-        )
-        self.wait()
-
-```
-
-
-
-```
-
-class PointWithTrace(Scene):
-    def construct(self):
-        path = VMobject()
-        dot = Dot()
-        path.set_points_as_corners([dot.get_center(), dot.get_center()])
-        def update_path(path):
-            previous_path = path.copy()
-            previous_path.add_points_as_corners([dot.get_center()])
-            path.become(previous_path)
-        path.add_updater(update_path)
-        self.add(path, dot)
-        self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))
-        self.wait()
-        self.play(dot.animate.shift(UP))
-        self.play(dot.animate.shift(LEFT))
-        self.wait()
-
-```
-
-```
-
-# do not use get_graph, this funciton is deprecated
-
-class ExampleFunctionGraph(Scene):
-    def construct(self):
-        cos_func = FunctionGraph(
-            lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t),
-            color=RED,
-        )
-
-        sin_func_1 = FunctionGraph(
-            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
-            color=BLUE,
-        )
-
-        sin_func_2 = FunctionGraph(
-            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
-            x_range=[-4, 4],
-            color=GREEN,
-        ).move_to([0, 1, 0])
-
-        self.add(cos_func, sin_func_1, sin_func_2)
-
-```
-"""
--- a/crazy_functions/理解PDF文档内容.py
+++ b/crazy_functions/理解PDF文档内容.py
@ -1,109 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_exception
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-    file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-    page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
-    paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
-    page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]} ...."
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot,
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section, answer me with Chinese."  # 提示
-                                                                        )
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_exception(chatbot, history,
-            a = f"解析项目: {txt}",
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Your Name	4c72e6cc80	fix count down error	2023-03-24 18:53:43 +08:00
Your Name	38705af90b	beta	2023-03-24 18:47:45 +08:00
Your Name	35e15aab8c	bug fix	2023-03-24 18:08:48 +08:00
Your Name	1cad53f641	模块化封装	2023-03-24 18:04:59 +08:00
Your Name	818690037a	up	2023-03-24 16:34:48 +08:00
Your Name	8053f696d5	用gpt给自己生成注释	2023-03-24 16:25:40 +08:00
Your Name	0b1afab5dd	muban	2023-03-24 16:22:26 +08:00
Your Name	aa87d031f5	易读性+	2023-03-24 16:17:01 +08:00
Your Name	ba2dd3d0ff	批量生成函数注释	2023-03-24 16:14:25 +08:00
Your Name	c19419e1fb	生成文本报告	2023-03-24 15:42:09 +08:00
Your Name	031232c986	better traceback	2023-03-24 15:25:14 +08:00
Your Name	ea4cd5a75a	增加读latex文章的功能，添加测试样例	2023-03-24 14:56:57 +08:00
Your Name	3fb519bf83	Merge remote-tracking branch 'origin/master' into test-3-24	2023-03-24 13:29:37 +08:00
Your Name	1e4bcc0757	设置及时响应	2023-03-24 13:28:12 +08:00
Your Name	44b40ff726	update	2023-03-24 13:12:25 +08:00
binary-husky	791dbf8592	Update functional_crazy.py	2023-03-24 13:11:41 +08:00
binary-husky	82fe0f758d	Update functional_crazy.py	2023-03-24 13:06:42 +08:00
binary-husky	8b1def3425	Update functional_crazy.py	2023-03-24 13:06:34 +08:00
binary-husky	8e85e83cec	Update functional_crazy.py	2023-03-24 13:02:47 +08:00
qingxu fu	ecdeda8e92	Merge branch 'master' of github.com:binary-husky/chatgpt_academic into master	2023-03-24 11:43:24 +08:00
qingxu fu	831009f027	auto retry	2023-03-24 11:42:39 +08:00
binary-husky	c6da02422b	Update README.md	2023-03-24 00:43:33 +08:00
binary-husky	2ad73e9ec3	Update README.md	2023-03-23 22:15:31 +08:00
binary-husky	a0f73a61ea	Update predict.py	2023-03-23 22:13:09 +08:00
binary-husky	1b6d611888	Update README.md	2023-03-23 17:03:08 +08:00
Your Name	47d9ea4921	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-03-23 00:35:01 +08:00
Your Name	b1e33b0f7a	正确地显示requests错误	2023-03-23 00:34:55 +08:00
binary-husky	1e125ca05f	Update README.md	2023-03-23 00:20:39 +08:00
binary-husky	16253ed53c	Update README.md	2023-03-23 00:15:44 +08:00
binary-husky	8beb75762c	Update README.md	2023-03-22 22:52:15 +08:00
Your Name	8da4a0af45	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-03-22 22:42:56 +08:00
Your Name	371bef6e1a	bug fix	2023-03-22 22:42:50 +08:00
Your Name	2aaa836d81	程序自解析功能	2023-03-22 22:37:14 +08:00
binary-husky	14d53a288f	Update README.md	2023-03-22 22:34:15 +08:00
binary-husky	b1d6e711c0	Update README.md	2023-03-22 20:47:28 +08:00
binary-husky	32b005199d	Update README.md	2023-03-22 20:06:09 +08:00
binary-husky	a8886562a1	Update README.md	2023-03-22 20:02:03 +08:00
binary-husky	a60d881268	Update README.md	2023-03-22 19:58:10 +08:00
binary-husky	b9967686a1	私密配置 # config_private.py放自己的秘密如API和代理网址 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件	2023-03-22 19:49:45 +08:00
binary-husky	dd3b3524bd	借鉴github.com/GaiZhenbiao/ChuanhuChatGPT项目	2023-03-22 19:47:49 +08:00
binary-husky	a880819ffd	from github.com/polarwinkel/mdtex2html	2023-03-22 19:46:08 +08:00
binary-husky	b8d802a922	Update README.md	2023-03-22 19:39:02 +08:00
binary-husky	01aafd4df2	Update README.md	2023-03-22 19:36:39 +08:00
binary-husky	7b3b6f5241	Update README.md	2023-03-22 19:35:34 +08:00
binary-husky	7a50af1897	Update README.md	2023-03-22 19:33:26 +08:00
binary-husky	5dc406c641	修复gradio不吃代理的问题	2023-03-22 19:22:42 +08:00
qingxu fu	3103817990	add private conf	2023-03-22 17:54:15 +08:00
qingxu fu	88aaf310c4	upload	2023-03-22 17:48:25 +08:00
qingxu fu	e8d86a3242	代理位置	2023-03-22 17:45:10 +08:00
qingxu fu	37fd1b1c97	upload	2023-03-22 17:35:23 +08:00
qingxu fu	e3e313beab	logging	2023-03-22 17:32:48 +08:00
qingxu fu	380a8a9d4d	fix logging encoding	2023-03-22 17:30:30 +08:00
qingxu fu	d00f6bb1a6	add proxy debug funtion	2023-03-22 17:25:37 +08:00
binary-husky	f8a44a82a9	Update README.md	2023-03-22 16:09:37 +08:00
binary-husky	2be45c386d	Update predict.py	2023-03-21 21:24:38 +08:00
binary-husky	2b7e8b9278	Update README.md	2023-03-21 17:53:40 +08:00
binary-husky	8590014462	Update README.md	2023-03-21 17:53:04 +08:00
binary-husky	8215caddb2	Update README.md	2023-03-21 15:49:52 +08:00
505030475	93a9d27b1e	ok	2023-03-21 13:53:24 +08:00
binary-husky	e128523103	Update README.md	2023-03-21 13:45:08 +08:00
505030475	da0caab4cf	add deploy method for windows	2023-03-21 13:35:53 +08:00
binary-husky	6e9813565a	Update README.md	2023-03-20 18:56:09 +08:00
binary-husky	01cc409f99	Update README.md	2023-03-20 18:55:06 +08:00
Your Name	3438f8f291	readme	2023-03-20 18:39:48 +08:00