chatgpt_academic/crazy_functions/生成多种Mermaid图表.py
binary-husky c3140ce344
merge frontier branch (#1620)
* Zhipu sdk update 适配最新的智谱SDK,支持GLM4v (#1502)

* 适配 google gemini 优化为从用户input中提取文件

* 适配最新的智谱SDK、支持glm-4v

* requirements.txt fix

* pending history check

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>

* Update "生成多种Mermaid图表" plugin: Separate out the file reading function (#1520)

* Update crazy_functional.py with new functionality deal with PDF

* Update crazy_functional.py and Mermaid.py for plugin_kwargs

* Update crazy_functional.py with new chart type: mind map

* Update SELECT_PROMPT and i_say_show_user messages

* Update ArgsReminder message in get_crazy_functions() function

* Update with read md file and update PROMPTS

* Return the PROMPTS as the test found that the initial version worked best

* Update Mermaid chart generation function

* version 3.71

* 解决issues #1510

* Remove unnecessary text from sys_prompt in 解析历史输入 function

* Remove sys_prompt message in 解析历史输入 function

* Update bridge_all.py: supports gpt-4-turbo-preview (#1517)

* Update bridge_all.py: supports gpt-4-turbo-preview

supports gpt-4-turbo-preview

* Update bridge_all.py

---------

Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>

* Update config.py: supports gpt-4-turbo-preview (#1516)

* Update config.py: supports gpt-4-turbo-preview

supports gpt-4-turbo-preview

* Update config.py

---------

Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>

* Refactor 解析历史输入 function to handle file input

* Update Mermaid chart generation functionality

* rename files and functions

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>
Co-authored-by: hongyi-zhao <hongyi.zhao@gmail.com>
Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>

* 接入mathpix ocr功能 (#1468)

* Update Latex输出PDF结果.py

借助mathpix实现了PDF翻译中文并重新编译PDF

* Update config.py

add mathpix appid & appkey

* Add 'PDF翻译中文并重新编译PDF' feature to plugins.

---------

Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>

* fix zhipuai

* check picture

* remove glm-4 due to bug

* 修改config

* 检查MATHPIX_APPID

* Remove unnecessary code and update
function_plugins dictionary

* capture non-standard token overflow

* bug fix #1524

* change mermaid style

* 支持mermaid 滚动放大缩小重置,鼠标滚动和拖拽 (#1530)

* 支持mermaid 滚动放大缩小重置,鼠标滚动和拖拽

* 微调未果 先stage一下

* update

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>
Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>

* ver 3.72

* change live2d

* save the status of ``clear btn` in cookie

* 前端选择保持

* js ui bug fix

* reset btn bug fix

* update live2d tips

* fix missing get_token_num method

* fix live2d toggle switch

* fix persistent custom btn with cookie

* fix zhipuai feedback with core functionality

* Refactor button update and clean up functions

* tailing space removal

* Fix missing MATHPIX_APPID and MATHPIX_APPKEY
configuration

* Prompt fix、脑图提示词优化 (#1537)

* 适配 google gemini 优化为从用户input中提取文件

* 脑图提示词优化

* Fix missing MATHPIX_APPID and MATHPIX_APPKEY
configuration

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>

* 优化“PDF翻译中文并重新编译PDF”插件 (#1602)

* Add gemini_endpoint to API_URL_REDIRECT (#1560)

* Add gemini_endpoint to API_URL_REDIRECT

* Update gemini-pro and gemini-pro-vision model_info
endpoints

* Update to support new claude models (#1606)

* Add anthropic library and update claude models

* 更新bridge_claude.py文件,添加了对图片输入的支持。修复了一些bug。

* 添加Claude_3_Models变量以限制图片数量

* Refactor code to improve readability and
maintainability

* minor claude bug fix

* more flexible one-api support

* reformat config

* fix one-api new access bug

* dummy

* compat non-standard api

* version 3.73

---------

Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com>
Co-authored-by: Menghuan1918 <menghuan2003@outlook.com>
Co-authored-by: hongyi-zhao <hongyi.zhao@gmail.com>
Co-authored-by: Hao Ma <893017927@qq.com>
Co-authored-by: zeyuan huang <599012428@qq.com>
2024-03-11 17:26:09 +08:00

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from toolbox import CatchException, update_ui, report_exception
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
import datetime
#以下是每类图表的PROMPT
SELECT_PROMPT = """
{subject}
=============
以上是从文章中提取的摘要,将会使用这些摘要绘制图表。请你选择一个合适的图表类型:
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
不需要解释原因,仅需要输出单个不带任何标点符号的数字。
"""
#没有思维导图!!!测试发现模型始终会优先选择思维导图
#流程图
PROMPT_1 = """
请你给出围绕“{subject}”的逻辑关系图使用mermaid语法mermaid语法举例
```mermaid
graph TD
P(编程) --> L1(Python)
P(编程) --> L2(C)
P(编程) --> L3(C++)
P(编程) --> L4(Javascipt)
P(编程) --> L5(PHP)
```
"""
#序列图
PROMPT_2 = """
请你给出围绕“{subject}”的序列图使用mermaid语法mermaid语法举例
```mermaid
sequenceDiagram
participant A as 用户
participant B as 系统
A->>B: 登录请求
B->>A: 登录成功
A->>B: 获取数据
B->>A: 返回数据
```
"""
#类图
PROMPT_3 = """
请你给出围绕“{subject}”的类图使用mermaid语法mermaid语法举例
```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```
"""
#饼图
PROMPT_4 = """
请你给出围绕“{subject}”的饼图使用mermaid语法mermaid语法举例
```mermaid
pie title Pets adopted by volunteers
"" : 386
"" : 85
"兔子" : 15
```
"""
#甘特图
PROMPT_5 = """
请你给出围绕“{subject}”的甘特图使用mermaid语法mermaid语法举例
```mermaid
gantt
title 项目开发流程
dateFormat YYYY-MM-DD
section 设计
需求分析 :done, des1, 2024-01-06,2024-01-08
原型设计 :active, des2, 2024-01-09, 3d
UI设计 : des3, after des2, 5d
section 开发
前端开发 :2024-01-20, 10d
后端开发 :2024-01-20, 10d
```
"""
#状态图
PROMPT_6 = """
请你给出围绕“{subject}”的状态图使用mermaid语法mermaid语法举例
```mermaid
stateDiagram-v2
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```
"""
#实体关系图
PROMPT_7 = """
请你给出围绕“{subject}”的实体关系图使用mermaid语法mermaid语法举例
```mermaid
erDiagram
CUSTOMER ||--o{ ORDER : places
ORDER ||--|{ LINE-ITEM : contains
CUSTOMER {
string name
string id
}
ORDER {
string orderNumber
date orderDate
string customerID
}
LINE-ITEM {
number quantity
string productID
}
```
"""
#象限提示图
PROMPT_8 = """
请你给出围绕“{subject}”的象限图使用mermaid语法mermaid语法举例
```mermaid
graph LR
A[Hard skill] --> B(Programming)
A[Hard skill] --> C(Design)
D[Soft skill] --> E(Coordination)
D[Soft skill] --> F(Communication)
```
"""
#思维导图
PROMPT_9 = """
{subject}
==========
请给出上方内容的思维导图充分考虑其之间的逻辑使用mermaid语法mermaid语法举例
```mermaid
mindmap
root((mindmap))
Origins
Long history
::icon(fa fa-book)
Popularisation
British popular psychology author Tony Buzan
Research
On effectiveness<br/>and features
On Automatic creation
Uses
Creative techniques
Strategic planning
Argument mapping
Tools
Pen and paper
Mermaid
```
"""
def 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs):
############################## <第 0 步,切割输入> ##################################
# 借用PDF切割中的函数对文本进行切割
TOKEN_LIMIT_PER_FRAGMENT = 2500
txt = str(history).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
############################## <第 1 步,迭代地历遍整个文章,提取精炼信息> ##################################
results = []
MAX_WORD_TOTAL = 4096
n_txt = len(txt)
last_iteration_result = "从以下文本中提取摘要。"
if n_txt >= 20: print('文章极长,不能达到预期效果')
for i in range(n_txt):
NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[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 content of the previous section is?", last_iteration_result], # 迭代上一次的结果
sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese." # 提示
)
results.append(gpt_say)
last_iteration_result = gpt_say
############################## <第 2 步,根据整理的摘要选择图表类型> ##################################
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
gpt_say = plugin_kwargs.get("advanced_arg", "") #将图表类型参数赋值为插件参数
results_txt = '\n'.join(results) #合并摘要
if gpt_say not in ['1','2','3','4','5','6','7','8','9']: #如插件参数不正确则使用对话模型判断
i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; 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
i_say = SELECT_PROMPT.format(subject=results_txt)
i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为: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。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。'
for i in range(3):
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=""
)
if gpt_say in ['1','2','3','4','5','6','7','8','9']: #判断返回是否正确
break
if gpt_say not in ['1','2','3','4','5','6','7','8','9']:
gpt_say = '1'
############################## <第 3 步,根据选择的图表类型绘制图表> ##################################
if gpt_say == '1':
i_say = PROMPT_1.format(subject=results_txt)
elif gpt_say == '2':
i_say = PROMPT_2.format(subject=results_txt)
elif gpt_say == '3':
i_say = PROMPT_3.format(subject=results_txt)
elif gpt_say == '4':
i_say = PROMPT_4.format(subject=results_txt)
elif gpt_say == '5':
i_say = PROMPT_5.format(subject=results_txt)
elif gpt_say == '6':
i_say = PROMPT_6.format(subject=results_txt)
elif gpt_say == '7':
i_say = PROMPT_7.replace("{subject}", results_txt) #由于实体关系图用到了{}符号
elif gpt_say == '8':
i_say = PROMPT_8.format(subject=results_txt)
elif gpt_say == '9':
i_say = PROMPT_9.format(subject=results_txt)
i_say_show_user = f'请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。'
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=""
)
history.append(gpt_say)
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
@CatchException
def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
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,用于灵活调整复杂功能的各种参数
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
web_port 当前软件运行的端口号
"""
import os
# 基本信息:功能、贡献者
chatbot.append([
"函数插件功能?",
"根据当前聊天历史或指定的路径文件(文件内容优先)绘制多种mermaid图表将会由对话模型首先判断适合的图表类型随后绘制图表。\
\n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
if os.path.exists(txt): #如输入区无内容则直接解析历史记录
from crazy_functions.pdf_fns.parse_word import extract_text_from_files
file_exist, final_result, page_one, file_manifest, excption = extract_text_from_files(txt, chatbot, history)
else:
file_exist = False
excption = ""
file_manifest = []
if excption != "":
if excption == "word":
report_exception(chatbot, history,
a = f"解析项目: {txt}",
b = f"找到了.doc文件但是该文件格式不被支持请先转化为.docx格式。")
elif excption == "pdf":
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```。")
elif excption == "word_pip":
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
else:
if not file_exist:
history.append(txt) #如输入区不是文件则将输入区内容加入历史记录
i_say_show_user = f'首先你从历史记录中提取摘要。'; 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=history) # 更新UI
yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs)
else:
file_num = len(file_manifest)
for i in range(file_num): #依次处理文件
i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"; 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=history) # 更新UI
history = [] #如输入区内容为文件则清空历史记录
history.append(final_result[i])
yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs)