version 3.75 (#1702)
* Update version to 3.74 * Add support for Yi Model API (#1635) * 更新以支持零一万物模型 * 删除newbing * 修改config --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * Refactor function signatures in bridge files * fix qwen api change * rename and ref functions * rename and move some cookie functions * 增加haiku模型，新增endpoint配置说明 (#1626) * haiku added * 新增haiku，新增endpoint配置说明 * Haiku added * 将说明同步至最新Endpoint --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * private_upload目录下进行文件鉴权 (#1596) * private_upload目录下进行文件鉴权 * minor fastapi adjustment * Add logging functionality to enable saving conversation records * waiting to fix username retrieve * support 2rd web path * allow accessing default user dir --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * remove yaml deps * fix favicon * fix abs path auth problem * forget to write a return * add `dashscope` to deps * fix GHSA-v9q9-xj86-953p * 用户名重叠越权访问patch (#1681) * add cohere model api access * cohere + can_multi_thread * fix block user access(fail) * fix fastapi bug * change cohere api endpoint * explain version * # fix com_zhipuglm.py illegal temperature problem (#1687) * Update com_zhipuglm.py # fix 用户在使用 zhipuai 界面时遇到了关于温度参数的非法参数错误 * allow store lm model dropdown * add a btn to reverse previous reset * remove extra fns * Add support for glm-4v model (#1700) * 修改chatglm3量化加载方式 (#1688) Co-authored-by: zym9804 <ren990603@gmail.com> * save chat stage 1 * consider null cookie situation * 在点击复制按钮时激活语音 * miss some parts * move all to js * done first stage * add edge tts * bug fix * bug fix * remove console log * bug fix * bug fix * bug fix * audio switch * update tts readme * remove tempfile when done * disable auto audio follow * avoid play queue update after shut up * feat: minimizing common.js * improve tts functionality * deterine whether the cached model is in choices * Add support for Ollama (#1740) * print err when doc2x not successful * add icon * adjust url for doc2x key version * prepare merge --------- Co-authored-by: Menghuan1918 <menghuan2003@outlook.com> Co-authored-by: Skyzayre <120616113+Skyzayre@users.noreply.github.com> Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com> Co-authored-by: Yuki <903728862@qq.com> Co-authored-by: zyren123 <91042213+zyren123@users.noreply.github.com> Co-authored-by: zym9804 <ren990603@gmail.com>
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.gitignore
vendored
@ -153,3 +153,4 @@ media
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flagged
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request_llms/ChatGLM-6b-onnx-u8s8
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.pre-commit-config.yaml
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themes/common.js.min.*.js
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16
config.py
16
config.py
@ -34,7 +34,7 @@ else:
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LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
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AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
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"gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
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"gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo",
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"gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
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"gemini-pro", "chatglm3"
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]
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# --- --- --- ---
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@ -50,9 +50,9 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
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# "yi-34b-chat-0205", "yi-34b-chat-200k"
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# ]
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# --- --- --- ---
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# 此外，为了更灵活地接入one-api多模型管理界面，您还可以在接入one-api时，
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# 使用"one-api-*"前缀直接使用非标准方式接入的模型，例如
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# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)"]
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# 此外，您还可以在接入one-api/vllm/ollama时，
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# 使用"one-api-*","vllm-*","ollama-*"前缀直接使用非标准方式接入的模型，例如
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# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)"]
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# --- --- --- ---
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@ -60,7 +60,7 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
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# 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！）
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# 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"}
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# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
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# 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"}
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API_URL_REDIRECT = {}
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@ -195,6 +195,12 @@ ALIYUN_ACCESSKEY="" # （无需填写）
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ALIYUN_SECRET="" # （无需填写）
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# 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）
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TTS_TYPE = "DISABLE" # LOCAL / LOCAL_SOVITS_API / DISABLE
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GPT_SOVITS_URL = ""
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EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
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# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
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XFYUN_APPID = "00000000"
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XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
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@ -74,8 +74,10 @@ def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, cha
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import requests, json, os
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markdown_dir = get_log_folder(plugin_name="pdf_ocr")
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doc2x_api_key = DOC2X_API_KEY
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# url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
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url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
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if doc2x_api_key.startswith('sk-'):
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url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
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else:
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url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
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chatbot.append((None, "加载PDF文件，发送至DOC2X解析..."))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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58
docs/use_tts.md
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58
docs/use_tts.md
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@ -0,0 +1,58 @@
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# 使用TTS文字转语音
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## 1. 使用EDGE-TTS（简单）
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将本项目配置项修改如下即可
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```
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TTS_TYPE = "EDGE_TTS"
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EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
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```
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## 2. 使用SoVITS（需要有显卡）
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使用以下docker-compose.yml文件，先启动SoVITS服务API
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1. 创建以下文件夹结构
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```shell
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.
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├── docker-compose.yml
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└── reference
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├── clone_target_txt.txt
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└── clone_target_wave.mp3
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```
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2. 其中`docker-compose.yml`为
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```yaml
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version: '3.8'
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services:
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gpt-sovits:
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image: fuqingxu/sovits_gptac_trim:latest
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container_name: sovits_gptac_container
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working_dir: /workspace/gpt_sovits_demo
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environment:
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- is_half=False
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- is_share=False
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volumes:
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- ./reference:/reference
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ports:
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- "19880:9880" # 19880 为 sovits api 的暴露端口，记住它
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shm_size: 16G
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: "all"
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capabilities: [gpu]
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command: bash -c "python3 api.py"
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```
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3. 其中`clone_target_wave.mp3`为需要克隆的角色音频，`clone_target_txt.txt`为该音频对应的文字文本（ https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2%E8%AF%AD%E9%9F%B3 ）
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4. 运行`docker-compose up`
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5. 将本项目配置项修改如下即可
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(19880 为 sovits api 的暴露端口，与docker-compose.yml中的端口对应)
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```
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TTS_TYPE = "LOCAL_SOVITS_API"
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GPT_SOVITS_URL = "http://127.0.0.1:19880"
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```
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6. 启动本项目
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34
main.py
34
main.py
@ -1,4 +1,4 @@
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import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
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import os, json; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
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help_menu_description = \
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"""Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic),
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@ -14,7 +14,7 @@ help_menu_description = \
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</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
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def enable_log(PATH_LOGGING):
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import logging, uuid
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import logging
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admin_log_path = os.path.join(PATH_LOGGING, "admin")
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os.makedirs(admin_log_path, exist_ok=True)
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log_dir = os.path.join(admin_log_path, "chat_secrets.log")
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@ -29,13 +29,14 @@ def main():
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if gr.__version__ not in ['3.32.9']:
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raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
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from request_llms.bridge_all import predict
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
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# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
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CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
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ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
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NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
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DARK_MODE, INIT_SYS_PROMPT, ADD_WAIFU = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU')
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DARK_MODE, INIT_SYS_PROMPT, ADD_WAIFU, TTS_TYPE = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU', 'TTS_TYPE')
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if LLM_MODEL not in AVAIL_LLM_MODELS: AVAIL_LLM_MODELS += [LLM_MODEL]
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# 如果WEB_PORT是-1, 则随机选取WEB端口
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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@ -79,15 +80,18 @@ def main():
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cancel_handles = []
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customize_btns = {}
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predefined_btns = {}
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from shared_utils.cookie_manager import make_cookie_cache, make_history_cache
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with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as app_block:
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gr.HTML(title_html)
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secret_css, web_cookie_cache = gr.Textbox(visible=False), gr.Textbox(visible=False)
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cookies = gr.State(load_chat_cookies())
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secret_css = gr.Textbox(visible=False, elem_id="secret_css")
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cookies, web_cookie_cache = make_cookie_cache() # 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
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with gr_L1():
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with gr_L2(scale=2, elem_id="gpt-chat"):
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chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}", elem_id="gpt-chatbot")
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if LAYOUT == "TOP-DOWN": chatbot.style(height=CHATBOT_HEIGHT)
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history = gr.State([])
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history, history_cache, history_cache_update = make_history_cache() # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
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with gr_L2(scale=1, elem_id="gpt-panel"):
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with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
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with gr.Row():
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@ -155,7 +159,7 @@ def main():
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file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload_float")
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with gr.Tab("更换模型", elem_id="interact-panel"):
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md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
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md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, elem_id="elem_model_sel", label="更换LLM模型/请求源").style(container=False)
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top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature", elem_id="elem_temperature")
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max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
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@ -164,6 +168,8 @@ def main():
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_js="""(temperature)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_temperature_cookie", temperature)""")
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system_prompt.change(None, inputs=[system_prompt], outputs=None,
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_js="""(system_prompt)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_system_prompt_cookie", system_prompt)""")
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md_dropdown.change(None, inputs=[md_dropdown], outputs=None,
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_js="""(md_dropdown)=>gpt_academic_gradio_saveload("save", "elem_model_sel", "js_md_dropdown_cookie", md_dropdown)""")
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with gr.Tab("界面外观", elem_id="interact-panel"):
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theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").style(container=False)
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@ -247,8 +253,10 @@ def main():
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cancel_handles.append(submitBtn2.click(**predict_args))
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resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset) # 先在前端快速清除chatbot&status
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resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset) # 先在前端快速清除chatbot&status
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resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) # 再在后端清除history
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resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) # 再在后端清除history
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reset_server_side_args = (lambda history: ([], [], "已重置", json.dumps(history)),
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[history], [chatbot, history, status, history_cache])
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resetBtn.click(*reset_server_side_args) # 再在后端清除history，把history转存history_cache备用
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resetBtn2.click(*reset_server_side_args) # 再在后端清除history，把history转存history_cache备用
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clearBtn.click(None, None, [txt, txt2], _js=js_code_clear)
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clearBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
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if AUTO_CLEAR_TXT:
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@ -271,7 +279,7 @@ def main():
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for k in plugins:
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if not plugins[k].get("AsButton", True): continue
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click_handle = plugins[k]["Button"].click(ArgsGeneralWrapper(plugins[k]["Function"]), [*input_combo], output_combo)
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click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
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click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [plugins[k]["Button"]], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
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cancel_handles.append(click_handle)
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# 函数插件-下拉菜单与随变按钮的互动
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def on_dropdown_changed(k):
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@ -309,7 +317,7 @@ def main():
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if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
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yield from ArgsGeneralWrapper(plugins[k]["Function"])(request, *args, **kwargs)
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click_handle = switchy_bt.click(route,[switchy_bt, *input_combo], output_combo)
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click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
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click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [switchy_bt], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
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cancel_handles.append(click_handle)
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# 终止按钮的回调函数注册
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stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
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@ -342,7 +350,7 @@ def main():
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app_block.load(load_web_cookie_cache, inputs = [web_cookie_cache, cookies],
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outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
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app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}")""") # 配置暗色主题或亮色主题
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app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}","{TTS_TYPE}")""") # 配置暗色主题或亮色主题
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# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
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def run_delayed_tasks():
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@ -67,7 +67,8 @@ newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
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gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
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claude_endpoint = "https://api.anthropic.com/v1/messages"
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yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
|
||||
cohere_endpoint = 'https://api.cohere.ai/v1/chat'
|
||||
cohere_endpoint = "https://api.cohere.ai/v1/chat"
|
||||
ollama_endpoint = "http://localhost:11434/api/chat"
|
||||
|
||||
if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
|
||||
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
|
||||
@ -87,6 +88,7 @@ if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemin
|
||||
if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
|
||||
if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
|
||||
if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
|
||||
if ollama_endpoint in API_URL_REDIRECT: ollama_endpoint = API_URL_REDIRECT[ollama_endpoint]
|
||||
|
||||
# 获取tokenizer
|
||||
tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
|
||||
@ -266,6 +268,14 @@ model_info = {
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
"glm-4v": {
|
||||
"fn_with_ui": zhipu_ui,
|
||||
"fn_without_ui": zhipu_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 1000,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
"glm-3-turbo": {
|
||||
"fn_with_ui": zhipu_ui,
|
||||
"fn_without_ui": zhipu_noui,
|
||||
@ -827,7 +837,32 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("vllm-")]:
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
})
|
||||
|
||||
# -=-=-=-=-=-=- ollama 对齐支持 -=-=-=-=-=-=-
|
||||
for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
|
||||
from .bridge_ollama import predict_no_ui_long_connection as ollama_noui
|
||||
from .bridge_ollama import predict as ollama_ui
|
||||
break
|
||||
for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
|
||||
# 为了更灵活地接入ollama多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["ollama-phi3(max_token=6666)"]
|
||||
# 其中
|
||||
# "ollama-" 是前缀（必要）
|
||||
# "phi3" 是模型名（必要）
|
||||
# "(max_token=6666)" 是配置（非必要）
|
||||
try:
|
||||
_, max_token_tmp = read_one_api_model_name(model)
|
||||
except:
|
||||
print(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
|
||||
continue
|
||||
model_info.update({
|
||||
model: {
|
||||
"fn_with_ui": ollama_ui,
|
||||
"fn_without_ui": ollama_noui,
|
||||
"endpoint": ollama_endpoint,
|
||||
"max_token": max_token_tmp,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
})
|
||||
|
||||
# -=-=-=-=-=-=- azure模型对齐支持 -=-=-=-=-=-=-
|
||||
AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY") # <-- 用于定义和切换多个azure模型 -->
|
||||
|
||||
@ -6,7 +6,6 @@ from toolbox import get_conf, ProxyNetworkActivate
|
||||
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
|
||||
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 Local Model
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
@ -23,20 +22,45 @@ class GetGLM3Handle(LocalLLMHandle):
|
||||
import os, glob
|
||||
import os
|
||||
import platform
|
||||
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
|
||||
|
||||
if LOCAL_MODEL_QUANT == "INT4": # INT4
|
||||
_model_name_ = "THUDM/chatglm3-6b-int4"
|
||||
elif LOCAL_MODEL_QUANT == "INT8": # INT8
|
||||
_model_name_ = "THUDM/chatglm3-6b-int8"
|
||||
else:
|
||||
_model_name_ = "THUDM/chatglm3-6b" # FP16
|
||||
with ProxyNetworkActivate('Download_LLM'):
|
||||
chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
|
||||
if device=='cpu':
|
||||
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cpu').float()
|
||||
LOCAL_MODEL_QUANT, device = get_conf("LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
|
||||
_model_name_ = "THUDM/chatglm3-6b"
|
||||
# if LOCAL_MODEL_QUANT == "INT4": # INT4
|
||||
# _model_name_ = "THUDM/chatglm3-6b-int4"
|
||||
# elif LOCAL_MODEL_QUANT == "INT8": # INT8
|
||||
# _model_name_ = "THUDM/chatglm3-6b-int8"
|
||||
# else:
|
||||
# _model_name_ = "THUDM/chatglm3-6b" # FP16
|
||||
with ProxyNetworkActivate("Download_LLM"):
|
||||
chatglm_tokenizer = AutoTokenizer.from_pretrained(
|
||||
_model_name_, trust_remote_code=True
|
||||
)
|
||||
if device == "cpu":
|
||||
chatglm_model = AutoModel.from_pretrained(
|
||||
_model_name_,
|
||||
trust_remote_code=True,
|
||||
device="cpu",
|
||||
).float()
|
||||
elif LOCAL_MODEL_QUANT == "INT4": # INT4
|
||||
chatglm_model = AutoModel.from_pretrained(
|
||||
pretrained_model_name_or_path=_model_name_,
|
||||
trust_remote_code=True,
|
||||
device="cuda",
|
||||
load_in_4bit=True,
|
||||
)
|
||||
elif LOCAL_MODEL_QUANT == "INT8": # INT8
|
||||
chatglm_model = AutoModel.from_pretrained(
|
||||
pretrained_model_name_or_path=_model_name_,
|
||||
trust_remote_code=True,
|
||||
device="cuda",
|
||||
load_in_8bit=True,
|
||||
)
|
||||
else:
|
||||
chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cuda')
|
||||
chatglm_model = AutoModel.from_pretrained(
|
||||
pretrained_model_name_or_path=_model_name_,
|
||||
trust_remote_code=True,
|
||||
device="cuda",
|
||||
)
|
||||
chatglm_model = chatglm_model.eval()
|
||||
|
||||
self._model = chatglm_model
|
||||
@ -46,32 +70,36 @@ class GetGLM3Handle(LocalLLMHandle):
|
||||
def llm_stream_generator(self, **kwargs):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
def adaptor(kwargs):
|
||||
query = kwargs['query']
|
||||
max_length = kwargs['max_length']
|
||||
top_p = kwargs['top_p']
|
||||
temperature = kwargs['temperature']
|
||||
history = kwargs['history']
|
||||
query = kwargs["query"]
|
||||
max_length = kwargs["max_length"]
|
||||
top_p = kwargs["top_p"]
|
||||
temperature = kwargs["temperature"]
|
||||
history = kwargs["history"]
|
||||
return query, max_length, top_p, temperature, history
|
||||
|
||||
query, max_length, top_p, temperature, history = adaptor(kwargs)
|
||||
|
||||
for response, history in self._model.stream_chat(self._tokenizer,
|
||||
query,
|
||||
history,
|
||||
max_length=max_length,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
):
|
||||
for response, history in self._model.stream_chat(
|
||||
self._tokenizer,
|
||||
query,
|
||||
history,
|
||||
max_length=max_length,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
):
|
||||
yield response
|
||||
|
||||
def try_to_import_special_deps(self, **kwargs):
|
||||
# import something that will raise error if the user does not install requirement_*.txt
|
||||
# 🏃♂️🏃♂️🏃♂️ 主进程执行
|
||||
import importlib
|
||||
|
||||
# importlib.import_module('modelscope')
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 GPT-Academic Interface
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetGLM3Handle, model_name, history_format='chatglm3')
|
||||
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(
|
||||
GetGLM3Handle, model_name, history_format="chatglm3"
|
||||
)
|
||||
|
||||
272
request_llms/bridge_ollama.py
Normal file
272
request_llms/bridge_ollama.py
Normal file
@ -0,0 +1,272 @@
|
||||
# 借鉴自同目录下的bridge_chatgpt.py
|
||||
|
||||
"""
|
||||
该文件中主要包含三个函数
|
||||
|
||||
不具备多线程能力的函数：
|
||||
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
|
||||
|
||||
具备多线程调用能力的函数
|
||||
2. predict_no_ui_long_connection：支持多线程
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
import gradio as gr
|
||||
import logging
|
||||
import traceback
|
||||
import requests
|
||||
import importlib
|
||||
import random
|
||||
|
||||
# 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
|
||||
from toolbox import get_conf, update_ui, trimmed_format_exc, is_the_upload_folder, read_one_api_model_name
|
||||
proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
|
||||
"proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
|
||||
)
|
||||
|
||||
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
|
||||
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
|
||||
|
||||
def get_full_error(chunk, stream_response):
|
||||
"""
|
||||
获取完整的从Openai返回的报错
|
||||
"""
|
||||
while True:
|
||||
try:
|
||||
chunk += next(stream_response)
|
||||
except:
|
||||
break
|
||||
return chunk
|
||||
|
||||
def decode_chunk(chunk):
|
||||
# 提前读取一些信息（用于判断异常）
|
||||
chunk_decoded = chunk.decode()
|
||||
chunkjson = None
|
||||
is_last_chunk = False
|
||||
try:
|
||||
chunkjson = json.loads(chunk_decoded)
|
||||
is_last_chunk = chunkjson.get("done", False)
|
||||
except:
|
||||
pass
|
||||
return chunk_decoded, chunkjson, is_last_chunk
|
||||
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||||
"""
|
||||
发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
|
||||
inputs：
|
||||
是本次问询的输入
|
||||
sys_prompt:
|
||||
系统静默prompt
|
||||
llm_kwargs：
|
||||
chatGPT的内部调优参数
|
||||
history：
|
||||
是之前的对话列表
|
||||
observe_window = None：
|
||||
用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
|
||||
"""
|
||||
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
|
||||
if inputs == "": inputs = "空空如也的输入栏"
|
||||
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
|
||||
retry = 0
|
||||
while True:
|
||||
try:
|
||||
# make a POST request to the API endpoint, stream=False
|
||||
from .bridge_all import model_info
|
||||
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||||
response = requests.post(endpoint, headers=headers, proxies=proxies,
|
||||
json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
|
||||
except requests.exceptions.ReadTimeout as e:
|
||||
retry += 1
|
||||
traceback.print_exc()
|
||||
if retry > MAX_RETRY: raise TimeoutError
|
||||
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
|
||||
|
||||
stream_response = response.iter_lines()
|
||||
result = ''
|
||||
while True:
|
||||
try: chunk = next(stream_response)
|
||||
except StopIteration:
|
||||
break
|
||||
except requests.exceptions.ConnectionError:
|
||||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||||
chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
|
||||
if chunk:
|
||||
try:
|
||||
if is_last_chunk:
|
||||
# 判定为数据流的结束，gpt_replying_buffer也写完了
|
||||
logging.info(f'[response] {result}')
|
||||
break
|
||||
result += chunkjson['message']["content"]
|
||||
if not console_slience: print(chunkjson['message']["content"], end='')
|
||||
if observe_window is not None:
|
||||
# 观测窗，把已经获取的数据显示出去
|
||||
if len(observe_window) >= 1:
|
||||
observe_window[0] += chunkjson['message']["content"]
|
||||
# 看门狗，如果超过期限没有喂狗，则终止
|
||||
if len(observe_window) >= 2:
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("用户取消了程序。")
|
||||
except Exception as e:
|
||||
chunk = get_full_error(chunk, stream_response)
|
||||
chunk_decoded = chunk.decode()
|
||||
error_msg = chunk_decoded
|
||||
print(error_msg)
|
||||
raise RuntimeError("Json解析不合常规")
|
||||
return result
|
||||
|
||||
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
发送至chatGPT，流式获取输出。
|
||||
用于基础的对话功能。
|
||||
inputs 是本次问询的输入
|
||||
top_p, temperature是chatGPT的内部调优参数
|
||||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||||
"""
|
||||
if inputs == "": inputs = "空空如也的输入栏"
|
||||
user_input = inputs
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
|
||||
raw_input = inputs
|
||||
logging.info(f'[raw_input] {raw_input}')
|
||||
chatbot.append((inputs, ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
|
||||
|
||||
# check mis-behavior
|
||||
if is_the_upload_folder(user_input):
|
||||
chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
|
||||
time.sleep(2)
|
||||
|
||||
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
|
||||
|
||||
from .bridge_all import model_info
|
||||
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||||
|
||||
history.append(inputs); history.append("")
|
||||
|
||||
retry = 0
|
||||
while True:
|
||||
try:
|
||||
# make a POST request to the API endpoint, stream=True
|
||||
response = requests.post(endpoint, headers=headers, proxies=proxies,
|
||||
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
|
||||
except:
|
||||
retry += 1
|
||||
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
|
||||
retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
|
||||
if retry > MAX_RETRY: raise TimeoutError
|
||||
|
||||
gpt_replying_buffer = ""
|
||||
|
||||
if stream:
|
||||
stream_response = response.iter_lines()
|
||||
while True:
|
||||
try:
|
||||
chunk = next(stream_response)
|
||||
except StopIteration:
|
||||
break
|
||||
except requests.exceptions.ConnectionError:
|
||||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||||
|
||||
# 提前读取一些信息 （用于判断异常）
|
||||
chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
|
||||
|
||||
if chunk:
|
||||
try:
|
||||
if is_last_chunk:
|
||||
# 判定为数据流的结束，gpt_replying_buffer也写完了
|
||||
logging.info(f'[response] {gpt_replying_buffer}')
|
||||
break
|
||||
# 处理数据流的主体
|
||||
try:
|
||||
status_text = f"finish_reason: {chunkjson['error'].get('message', 'null')}"
|
||||
except:
|
||||
status_text = "finish_reason: null"
|
||||
gpt_replying_buffer = gpt_replying_buffer + chunkjson['message']["content"]
|
||||
# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
|
||||
history[-1] = gpt_replying_buffer
|
||||
chatbot[-1] = (history[-2], history[-1])
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
|
||||
except Exception as e:
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
|
||||
chunk = get_full_error(chunk, stream_response)
|
||||
chunk_decoded = chunk.decode()
|
||||
error_msg = chunk_decoded
|
||||
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
||||
print(error_msg)
|
||||
return
|
||||
|
||||
def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
|
||||
from .bridge_all import model_info
|
||||
if "bad_request" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] 已经超过了模型的最大上下文或是模型格式错误,请尝试削减单次输入的文本量。")
|
||||
elif "authentication_error" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. 请确保API key有效。")
|
||||
elif "not_found" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] {llm_kwargs['llm_model']} 无效，请确保使用小写的模型名称。")
|
||||
elif "rate_limit" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] 遇到了控制请求速率限制，请一分钟后重试。")
|
||||
elif "system_busy" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] 系统繁忙，请一分钟后重试。")
|
||||
else:
|
||||
from toolbox import regular_txt_to_markdown
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
|
||||
return chatbot, history
|
||||
|
||||
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||||
"""
|
||||
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
|
||||
"""
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
conversation_cnt = len(history) // 2
|
||||
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
if conversation_cnt:
|
||||
for index in range(0, 2*conversation_cnt, 2):
|
||||
what_i_have_asked = {}
|
||||
what_i_have_asked["role"] = "user"
|
||||
what_i_have_asked["content"] = history[index]
|
||||
what_gpt_answer = {}
|
||||
what_gpt_answer["role"] = "assistant"
|
||||
what_gpt_answer["content"] = history[index+1]
|
||||
if what_i_have_asked["content"] != "":
|
||||
if what_gpt_answer["content"] == "": continue
|
||||
if what_gpt_answer["content"] == timeout_bot_msg: continue
|
||||
messages.append(what_i_have_asked)
|
||||
messages.append(what_gpt_answer)
|
||||
else:
|
||||
messages[-1]['content'] = what_gpt_answer['content']
|
||||
|
||||
what_i_ask_now = {}
|
||||
what_i_ask_now["role"] = "user"
|
||||
what_i_ask_now["content"] = inputs
|
||||
messages.append(what_i_ask_now)
|
||||
model = llm_kwargs['llm_model']
|
||||
if llm_kwargs['llm_model'].startswith('ollama-'):
|
||||
model = llm_kwargs['llm_model'][len('ollama-'):]
|
||||
model, _ = read_one_api_model_name(model)
|
||||
options = {"temperature": llm_kwargs['temperature']}
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"options": options,
|
||||
}
|
||||
try:
|
||||
print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
|
||||
except:
|
||||
print('输入中可能存在乱码。')
|
||||
return headers,payload
|
||||
@ -75,6 +75,10 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
|
||||
llm_kwargs["llm_model"] = zhipuai_default_model
|
||||
|
||||
if llm_kwargs["llm_model"] in ["glm-4v"]:
|
||||
if (len(inputs) + sum(len(temp) for temp in history) + 1047) > 2000:
|
||||
chatbot.append((inputs, "上下文长度超过glm-4v上限2000tokens，注意图片大约占用1,047个tokens"))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
|
||||
if not have_recent_file:
|
||||
chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
|
||||
|
||||
@ -36,8 +36,14 @@ class ZhipuChatInit:
|
||||
what_i_have_asked = {"role": "user", "content": []}
|
||||
what_i_have_asked['content'].append({"type": 'text', "text": user_input})
|
||||
if encode_img:
|
||||
if len(encode_img) > 1:
|
||||
logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
|
||||
print("glm-4v只支持一张图片,将只取第一张图片进行处理")
|
||||
img_d = {"type": "image_url",
|
||||
"image_url": {'url': encode_img}}
|
||||
"image_url": {
|
||||
"url": encode_img[0]['data']
|
||||
}
|
||||
}
|
||||
what_i_have_asked['content'].append(img_d)
|
||||
return what_i_have_asked
|
||||
|
||||
|
||||
@ -24,6 +24,7 @@ Markdown
|
||||
pygments
|
||||
pymupdf
|
||||
openai
|
||||
rjsmin
|
||||
arxiv
|
||||
numpy
|
||||
rich
|
||||
@ -1,4 +1,6 @@
|
||||
import json
|
||||
from typing import Callable
|
||||
|
||||
def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)->Callable:
|
||||
def load_web_cookie_cache(persistent_cookie_, cookies_):
|
||||
import gradio as gr
|
||||
@ -22,7 +24,6 @@ def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)-
|
||||
return ret
|
||||
return load_web_cookie_cache
|
||||
|
||||
|
||||
def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)->Callable:
|
||||
def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
|
||||
import gradio as gr
|
||||
@ -59,3 +60,29 @@ def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_
|
||||
return ret
|
||||
return assign_btn
|
||||
|
||||
# cookies, web_cookie_cache = make_cookie_cache()
|
||||
def make_cookie_cache():
|
||||
# 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
|
||||
import gradio as gr
|
||||
from toolbox import load_chat_cookies
|
||||
# 定义cookies的后端state
|
||||
cookies = gr.State(load_chat_cookies())
|
||||
# 定义cookies的一个孪生的前端存储区（隐藏）
|
||||
web_cookie_cache = gr.Textbox(visible=False, elem_id="web_cookie_cache")
|
||||
return cookies, web_cookie_cache
|
||||
|
||||
# history, history_cache, history_cache_update = make_history_cache()
|
||||
def make_history_cache():
|
||||
# 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
|
||||
import gradio as gr
|
||||
# 定义history的后端state
|
||||
history = gr.State([])
|
||||
# 定义history的一个孪生的前端存储区（隐藏）
|
||||
history_cache = gr.Textbox(visible=False, elem_id="history_cache")
|
||||
# 定义history_cache->history的更新方法（隐藏）。在触发这个按钮时，会先执行js代码更新history_cache，然后再执行python代码更新history
|
||||
def process_history_cache(history_cache):
|
||||
return json.loads(history_cache)
|
||||
# 另一种更简单的setter方法
|
||||
history_cache_update = gr.Button("", elem_id="elem_update_history", visible=False).click(
|
||||
process_history_cache, inputs=[history_cache], outputs=[history])
|
||||
return history, history_cache, history_cache_update
|
||||
|
||||
@ -137,6 +137,47 @@ def start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SS
|
||||
return "越权访问!"
|
||||
return await endpoint(path_or_url, request)
|
||||
|
||||
TTS_TYPE = get_conf("TTS_TYPE")
|
||||
if TTS_TYPE != "DISABLE":
|
||||
# audio generation functionality
|
||||
import httpx
|
||||
from fastapi import FastAPI, Request, HTTPException
|
||||
from starlette.responses import Response
|
||||
async def forward_request(request: Request, method: str) -> Response:
|
||||
async with httpx.AsyncClient() as client:
|
||||
try:
|
||||
# Forward the request to the target service
|
||||
if TTS_TYPE == "EDGE_TTS":
|
||||
import tempfile
|
||||
import edge_tts
|
||||
import wave
|
||||
import uuid
|
||||
from pydub import AudioSegment
|
||||
json = await request.json()
|
||||
voice = get_conf("EDGE_TTS_VOICE")
|
||||
tts = edge_tts.Communicate(text=json['text'], voice=voice)
|
||||
temp_folder = tempfile.gettempdir()
|
||||
temp_file_name = str(uuid.uuid4().hex)
|
||||
temp_file = os.path.join(temp_folder, f'{temp_file_name}.mp3')
|
||||
await tts.save(temp_file)
|
||||
mp3_audio = AudioSegment.from_file(temp_file, format="mp3")
|
||||
mp3_audio.export(temp_file, format="wav")
|
||||
with open(temp_file, 'rb') as wav_file: t = wav_file.read()
|
||||
os.remove(temp_file)
|
||||
return Response(content=t)
|
||||
if TTS_TYPE == "LOCAL_SOVITS_API":
|
||||
# Forward the request to the target service
|
||||
TARGET_URL = get_conf("GPT_SOVITS_URL")
|
||||
body = await request.body()
|
||||
resp = await client.post(TARGET_URL, content=body, timeout=60)
|
||||
# Return the response from the target service
|
||||
return Response(content=resp.content, status_code=resp.status_code, headers=dict(resp.headers))
|
||||
except httpx.RequestError as e:
|
||||
raise HTTPException(status_code=400, detail=f"Request to the target service failed: {str(e)}")
|
||||
@gradio_app.post("/vits")
|
||||
async def forward_post_request(request: Request):
|
||||
return await forward_request(request, "POST")
|
||||
|
||||
# --- --- app_lifespan --- ---
|
||||
from contextlib import asynccontextmanager
|
||||
@asynccontextmanager
|
||||
|
||||
@ -38,6 +38,7 @@
|
||||
left: calc(100% + 3px);
|
||||
top: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
justify-content: space-between;
|
||||
}
|
||||
/* .message-btn-row-leading, .message-btn-row-trailing {
|
||||
|
||||
604
themes/common.js
604
themes/common.js
@ -7,6 +7,9 @@ function push_data_to_gradio_component(DAT, ELEM_ID, TYPE) {
|
||||
if (TYPE == "str") {
|
||||
// convert dat to string: do nothing
|
||||
}
|
||||
else if (TYPE == "obj") {
|
||||
// convert dat to string: do nothing
|
||||
}
|
||||
else if (TYPE == "no_conversion") {
|
||||
// no nothing
|
||||
}
|
||||
@ -254,11 +257,22 @@ function cancel_loading_status() {
|
||||
// 第 2 部分: 复制按钮
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function addCopyButton(botElement) {
|
||||
|
||||
var allow_auto_read_continously = true;
|
||||
var allow_auto_read_tts_flag = false;
|
||||
function addCopyButton(botElement, index, is_last_in_arr) {
|
||||
// https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
|
||||
// Copy bot button
|
||||
const copiedIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
|
||||
const copyIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
|
||||
// const audioIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
|
||||
const audioIcon = '<span><svg t="1713628577799" fill="currentColor" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4587" width=".9em" height=".9em"><path d="M113.7664 540.4672c0-219.9552 178.2784-398.2336 398.2336-398.2336S910.2336 320.512 910.2336 540.4672v284.4672c0 31.4368-25.4976 56.9344-56.9344 56.9344h-56.9344c-31.4368 0-56.9344-25.4976-56.9344-56.9344V597.2992c0-31.4368 25.4976-56.9344 56.9344-56.9344h56.9344c0-188.5184-152.7808-341.2992-341.2992-341.2992S170.7008 351.9488 170.7008 540.4672h56.9344c31.4368 0 56.9344 25.4976 56.9344 56.9344v227.5328c0 31.4368-25.4976 56.9344-56.9344 56.9344h-56.9344c-31.4368 0-56.9344-25.4976-56.9344-56.9344V540.4672z" p-id="4588"></path></svg></span>';
|
||||
// const cancelAudioIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
|
||||
|
||||
// 此功能没准备好
|
||||
if (allow_auto_read_continously && is_last_in_arr && allow_auto_read_tts_flag) {
|
||||
process_latest_text_output(botElement.innerText, index);
|
||||
}
|
||||
|
||||
const messageBtnColumnElement = botElement.querySelector('.message-btn-row');
|
||||
if (messageBtnColumnElement) {
|
||||
@ -273,6 +287,7 @@ function addCopyButton(botElement) {
|
||||
copyButton.addEventListener('click', async () => {
|
||||
const textToCopy = botElement.innerText;
|
||||
try {
|
||||
// push_text_to_audio(textToCopy).catch(console.error);
|
||||
if ("clipboard" in navigator) {
|
||||
await navigator.clipboard.writeText(textToCopy);
|
||||
copyButton.innerHTML = copiedIcon;
|
||||
@ -299,9 +314,35 @@ function addCopyButton(botElement) {
|
||||
console.error("Copy failed: ", error);
|
||||
}
|
||||
});
|
||||
|
||||
if (enable_tts){
|
||||
var audioButton = document.createElement('button');
|
||||
audioButton.classList.add('audio-toggle-btn');
|
||||
audioButton.innerHTML = audioIcon;
|
||||
audioButton.addEventListener('click', async () => {
|
||||
if (audioPlayer.isPlaying) {
|
||||
allow_auto_read_tts_flag = false;
|
||||
toast_push('自动朗读已禁用。', 3000);
|
||||
audioPlayer.stop();
|
||||
setCookie("js_auto_read_cookie", "False", 365);
|
||||
|
||||
} else {
|
||||
allow_auto_read_tts_flag = true;
|
||||
toast_push('正在合成语音 & 自动朗读已开启 (再次点击此按钮可禁用自动朗读)。', 3000);
|
||||
// toast_push('正在合成语音', 3000);
|
||||
const readText = botElement.innerText;
|
||||
push_text_to_audio(readText);
|
||||
setCookie("js_auto_read_cookie", "True", 365);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
var messageBtnColumn = document.createElement('div');
|
||||
messageBtnColumn.classList.add('message-btn-row');
|
||||
messageBtnColumn.appendChild(copyButton);
|
||||
if (enable_tts){
|
||||
messageBtnColumn.appendChild(audioButton);
|
||||
}
|
||||
botElement.appendChild(messageBtnColumn);
|
||||
}
|
||||
|
||||
@ -337,7 +378,15 @@ function chatbotContentChanged(attempt = 1, force = false) {
|
||||
// https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
|
||||
for (var i = 0; i < attempt; i++) {
|
||||
setTimeout(() => {
|
||||
gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
|
||||
const messages = gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot');
|
||||
messages.forEach((message, index, arr) => {
|
||||
// Check if the current message is the last in the array
|
||||
const is_last_in_arr = index === arr.length - 1;
|
||||
|
||||
// Now pass both the message element and the is_last_in_arr boolean to addCopyButton
|
||||
addCopyButton(message, index, is_last_in_arr);
|
||||
});
|
||||
// gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
|
||||
}, i === 0 ? 0 : 200);
|
||||
}
|
||||
// we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0
|
||||
@ -621,16 +670,16 @@ function monitoring_input_box() {
|
||||
|
||||
if (elem_input_main) {
|
||||
if (elem_input_main.querySelector("textarea")) {
|
||||
register_func_paste(elem_input_main.querySelector("textarea"))
|
||||
register_func_paste(elem_input_main.querySelector("textarea"));
|
||||
}
|
||||
}
|
||||
if (elem_input_float) {
|
||||
if (elem_input_float.querySelector("textarea")) {
|
||||
register_func_paste(elem_input_float.querySelector("textarea"))
|
||||
register_func_paste(elem_input_float.querySelector("textarea"));
|
||||
}
|
||||
}
|
||||
if (elem_chatbot) {
|
||||
register_func_drag(elem_chatbot)
|
||||
register_func_drag(elem_chatbot);
|
||||
}
|
||||
|
||||
}
|
||||
@ -737,7 +786,7 @@ function minor_ui_adjustment() {
|
||||
}
|
||||
|
||||
setInterval(function () {
|
||||
auto_hide_toolbar()
|
||||
auto_hide_toolbar();
|
||||
}, 200); // 每50毫秒执行一次
|
||||
}
|
||||
|
||||
@ -857,8 +906,8 @@ function gpt_academic_gradio_saveload(
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
|
||||
enable_tts = false;
|
||||
async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
|
||||
// 第一部分，布局初始化
|
||||
audio_fn_init();
|
||||
minor_ui_adjustment();
|
||||
@ -873,7 +922,6 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
|
||||
// 第二部分，读取Cookie，初始话界面
|
||||
let searchString = "";
|
||||
let bool_value = "";
|
||||
|
||||
// darkmode 深色模式
|
||||
if (getCookie("js_darkmode_cookie")) {
|
||||
dark = getCookie("js_darkmode_cookie")
|
||||
@ -889,11 +937,39 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
|
||||
}
|
||||
}
|
||||
|
||||
// 自动朗读
|
||||
if (tts != "DISABLE"){
|
||||
enable_tts = true;
|
||||
if (getCookie("js_auto_read_cookie")) {
|
||||
auto_read_tts = getCookie("js_auto_read_cookie")
|
||||
auto_read_tts = auto_read_tts == "True";
|
||||
if (auto_read_tts) {
|
||||
allow_auto_read_tts_flag = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// SysPrompt 系统静默提示词
|
||||
gpt_academic_gradio_saveload("load", "elem_prompt", "js_system_prompt_cookie", null, "str");
|
||||
|
||||
// Temperature 大模型温度参数
|
||||
gpt_academic_gradio_saveload("load", "elem_temperature", "js_temperature_cookie", null, "float");
|
||||
// md_dropdown 大模型类型选择
|
||||
if (getCookie("js_md_dropdown_cookie")) {
|
||||
const cached_model = getCookie("js_md_dropdown_cookie");
|
||||
var model_sel = await get_gradio_component("elem_model_sel");
|
||||
// deterine whether the cached model is in the choices
|
||||
if (model_sel.props.choices.includes(cached_model)){
|
||||
// change dropdown
|
||||
gpt_academic_gradio_saveload("load", "elem_model_sel", "js_md_dropdown_cookie", null, "str");
|
||||
// 连锁修改chatbot的label
|
||||
push_data_to_gradio_component({
|
||||
label: '当前模型：' + getCookie("js_md_dropdown_cookie"),
|
||||
__type__: 'update'
|
||||
}, "gpt-chatbot", "obj")
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
// clearButton 自动清除按钮
|
||||
if (getCookie("js_clearbtn_show_cookie")) {
|
||||
@ -953,4 +1029,510 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
function reset_conversation(a, b) {
|
||||
console.log("js_code_reset");
|
||||
a = btoa(unescape(encodeURIComponent(JSON.stringify(a))));
|
||||
setCookie("js_previous_chat_cookie", a, 1);
|
||||
gen_restore_btn();
|
||||
return [[], [], "已重置"];
|
||||
}
|
||||
|
||||
// clear -> 将 history 缓存至 history_cache -> 点击复原 -> restore_previous_chat() -> 触发elem_update_history -> 读取 history_cache
|
||||
function restore_previous_chat() {
|
||||
console.log("restore_previous_chat");
|
||||
let chat = getCookie("js_previous_chat_cookie");
|
||||
chat = JSON.parse(decodeURIComponent(escape(atob(chat))));
|
||||
push_data_to_gradio_component(chat, "gpt-chatbot", "obj");
|
||||
document.querySelector("#elem_update_history").click(); // in order to call set_history_gr_state, and send history state to server
|
||||
}
|
||||
|
||||
function gen_restore_btn() {
|
||||
|
||||
|
||||
// 创建按钮元素
|
||||
const button = document.createElement('div');
|
||||
// const recvIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
|
||||
const rec_svg = '<svg t="1714361184567" style="transform:translate(1px, 2.5px)" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4389" width="35" height="35"><path d="M320 512h384v64H320zM320 384h384v64H320zM320 640h192v64H320z" p-id="4390" fill="#ffffff"></path><path d="M863.7 544c-1.9 44-11.4 86.8-28.5 127.2-18.5 43.8-45.1 83.2-78.9 117-33.8 33.8-73.2 60.4-117 78.9C593.9 886.3 545.7 896 496 896s-97.9-9.7-143.2-28.9c-43.8-18.5-83.2-45.1-117-78.9-33.8-33.8-60.4-73.2-78.9-117C137.7 625.9 128 577.7 128 528s9.7-97.9 28.9-143.2c18.5-43.8 45.1-83.2 78.9-117s73.2-60.4 117-78.9C398.1 169.7 446.3 160 496 160s97.9 9.7 143.2 28.9c23.5 9.9 45.8 22.2 66.5 36.7l-119.7 20 9.9 59.4 161.6-27 59.4-9.9-9.9-59.4-27-161.5-59.4 9.9 19 114.2C670.3 123.8 586.4 96 496 96 257.4 96 64 289.4 64 528s193.4 432 432 432c233.2 0 423.3-184.8 431.7-416h-64z" p-id="4391" fill="#ffffff"></path></svg>'
|
||||
const recvIcon = '<span>' + rec_svg + '</span>';
|
||||
|
||||
// 设置按钮的样式和属性
|
||||
button.id = 'floatingButton';
|
||||
button.className = 'glow';
|
||||
button.style.textAlign = 'center';
|
||||
button.style.position = 'fixed';
|
||||
button.style.bottom = '10px';
|
||||
button.style.left = '10px';
|
||||
button.style.width = '50px';
|
||||
button.style.height = '50px';
|
||||
button.style.borderRadius = '50%';
|
||||
button.style.backgroundColor = '#007bff';
|
||||
button.style.color = 'white';
|
||||
button.style.display = 'flex';
|
||||
button.style.alignItems = 'center';
|
||||
button.style.justifyContent = 'center';
|
||||
button.style.cursor = 'pointer';
|
||||
button.style.transition = 'all 0.3s ease';
|
||||
button.style.boxShadow = '0 0 10px rgba(0,0,0,0.2)';
|
||||
|
||||
button.innerHTML = recvIcon;
|
||||
|
||||
// 添加发光动画的关键帧
|
||||
const styleSheet = document.createElement('style');
|
||||
styleSheet.id = 'floatingButtonStyle';
|
||||
styleSheet.innerText = `
|
||||
@keyframes glow {
|
||||
from {
|
||||
box-shadow: 0 0 10px rgba(0,0,0,0.2);
|
||||
}
|
||||
to {
|
||||
box-shadow: 0 0 13px rgba(0,0,0,0.5);
|
||||
}
|
||||
}
|
||||
#floatingButton.glow {
|
||||
animation: glow 1s infinite alternate;
|
||||
}
|
||||
#floatingButton:hover {
|
||||
transform: scale(1.2);
|
||||
box-shadow: 0 0 20px rgba(0,0,0,0.4);
|
||||
}
|
||||
#floatingButton.disappearing {
|
||||
animation: shrinkAndDisappear 0.5s forwards;
|
||||
}
|
||||
`;
|
||||
|
||||
// only add when not exist
|
||||
if (!document.getElementById('recvButtonStyle'))
|
||||
{
|
||||
document.head.appendChild(styleSheet);
|
||||
}
|
||||
|
||||
// 鼠标悬停和移开的事件监听器
|
||||
button.addEventListener('mouseover', function () {
|
||||
this.textContent = "还原\n对话";
|
||||
});
|
||||
|
||||
button.addEventListener('mouseout', function () {
|
||||
this.innerHTML = recvIcon;
|
||||
});
|
||||
|
||||
// 点击事件监听器
|
||||
button.addEventListener('click', function () {
|
||||
// 添加一个类来触发缩小和消失的动画
|
||||
restore_previous_chat();
|
||||
this.classList.add('disappearing');
|
||||
// 在动画结束后移除按钮
|
||||
document.body.removeChild(this);
|
||||
});
|
||||
// only add when not exist
|
||||
if (!document.getElementById('recvButton'))
|
||||
{
|
||||
document.body.appendChild(button);
|
||||
}
|
||||
|
||||
// 将按钮添加到页面中
|
||||
|
||||
}
|
||||
|
||||
async function on_plugin_exe_complete(fn_name) {
|
||||
console.log(fn_name);
|
||||
if (fn_name === "保存当前的对话") {
|
||||
// get chat profile path
|
||||
let chatbot = await get_data_from_gradio_component('gpt-chatbot');
|
||||
let may_have_chat_profile_info = chatbot[chatbot.length - 1][1];
|
||||
|
||||
function get_href(htmlString) {
|
||||
const parser = new DOMParser();
|
||||
const doc = parser.parseFromString(htmlString, 'text/html');
|
||||
const anchor = doc.querySelector('a');
|
||||
|
||||
if (anchor) {
|
||||
return anchor.getAttribute('href');
|
||||
} else {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
let href = get_href(may_have_chat_profile_info);
|
||||
if (href) {
|
||||
const cleanedHref = href.replace('file=', ''); // /home/fuqingxu/chatgpt_academic/gpt_log/default_user/chat_history/GPT-Academic对话存档2024-04-12-00-35-06.html
|
||||
console.log(cleanedHref);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 8 部分: TTS语音生成函数
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
class AudioPlayer {
|
||||
constructor() {
|
||||
this.audioCtx = new (window.AudioContext || window.webkitAudioContext)();
|
||||
this.queue = [];
|
||||
this.isPlaying = false;
|
||||
this.currentSource = null; // 添加属性来保存当前播放的源
|
||||
}
|
||||
|
||||
// Base64 编码的字符串转换为 ArrayBuffer
|
||||
base64ToArrayBuffer(base64) {
|
||||
const binaryString = window.atob(base64);
|
||||
const len = binaryString.length;
|
||||
const bytes = new Uint8Array(len);
|
||||
for (let i = 0; i < len; i++) {
|
||||
bytes[i] = binaryString.charCodeAt(i);
|
||||
}
|
||||
return bytes.buffer;
|
||||
}
|
||||
|
||||
// 检查音频播放队列并播放音频
|
||||
checkQueue() {
|
||||
if (!this.isPlaying && this.queue.length > 0) {
|
||||
this.isPlaying = true;
|
||||
const nextAudio = this.queue.shift();
|
||||
this.play_wave(nextAudio);
|
||||
}
|
||||
}
|
||||
|
||||
// 将音频添加到播放队列
|
||||
enqueueAudio(audio_buf_wave) {
|
||||
if (allow_auto_read_tts_flag) {
|
||||
this.queue.push(audio_buf_wave);
|
||||
this.checkQueue();
|
||||
}
|
||||
}
|
||||
|
||||
// 播放音频
|
||||
async play_wave(encodedAudio) {
|
||||
//const audioData = this.base64ToArrayBuffer(encodedAudio);
|
||||
const audioData = encodedAudio;
|
||||
try {
|
||||
const buffer = await this.audioCtx.decodeAudioData(audioData);
|
||||
const source = this.audioCtx.createBufferSource();
|
||||
source.buffer = buffer;
|
||||
source.connect(this.audioCtx.destination);
|
||||
source.onended = () => {
|
||||
if (allow_auto_read_tts_flag) {
|
||||
this.isPlaying = false;
|
||||
this.currentSource = null; // 播放结束后清空当前源
|
||||
this.checkQueue();
|
||||
}
|
||||
};
|
||||
this.currentSource = source; // 保存当前播放的源
|
||||
source.start();
|
||||
} catch (e) {
|
||||
console.log("Audio error!", e);
|
||||
this.isPlaying = false;
|
||||
this.currentSource = null; // 出错时也应清空当前源
|
||||
this.checkQueue();
|
||||
}
|
||||
}
|
||||
|
||||
// 新增：立即停止播放音频的方法
|
||||
stop() {
|
||||
if (this.currentSource) {
|
||||
this.queue = []; // 清空队列
|
||||
this.currentSource.stop(); // 停止当前源
|
||||
this.currentSource = null; // 清空当前源
|
||||
this.isPlaying = false; // 更新播放状态
|
||||
// 关闭音频上下文可能会导致无法再次播放音频，因此仅停止当前源
|
||||
// this.audioCtx.close(); // 可选：如果需要可以关闭音频上下文
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const audioPlayer = new AudioPlayer();
|
||||
|
||||
class FIFOLock {
|
||||
constructor() {
|
||||
this.queue = [];
|
||||
this.currentTaskExecuting = false;
|
||||
}
|
||||
|
||||
lock() {
|
||||
let resolveLock;
|
||||
const lock = new Promise(resolve => {
|
||||
resolveLock = resolve;
|
||||
});
|
||||
|
||||
this.queue.push(resolveLock);
|
||||
|
||||
if (!this.currentTaskExecuting) {
|
||||
this._dequeueNext();
|
||||
}
|
||||
|
||||
return lock;
|
||||
}
|
||||
|
||||
_dequeueNext() {
|
||||
if (this.queue.length === 0) {
|
||||
this.currentTaskExecuting = false;
|
||||
return;
|
||||
}
|
||||
this.currentTaskExecuting = true;
|
||||
const resolveLock = this.queue.shift();
|
||||
resolveLock();
|
||||
}
|
||||
|
||||
unlock() {
|
||||
this.currentTaskExecuting = false;
|
||||
this._dequeueNext();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
function delay(ms) {
|
||||
return new Promise(resolve => setTimeout(resolve, ms));
|
||||
}
|
||||
|
||||
// Define the trigger function with delay parameter T in milliseconds
|
||||
function trigger(T, fire) {
|
||||
// Variable to keep track of the timer ID
|
||||
let timeoutID = null;
|
||||
// Variable to store the latest arguments
|
||||
let lastArgs = null;
|
||||
|
||||
return function (...args) {
|
||||
// Update lastArgs with the latest arguments
|
||||
lastArgs = args;
|
||||
// Clear the existing timer if the function is called again
|
||||
if (timeoutID !== null) {
|
||||
clearTimeout(timeoutID);
|
||||
}
|
||||
// Set a new timer that calls the `fire` function with the latest arguments after T milliseconds
|
||||
timeoutID = setTimeout(() => {
|
||||
fire(...lastArgs);
|
||||
}, T);
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
prev_text = "";
|
||||
prev_text_already_pushed = "";
|
||||
prev_chatbot_index = -1;
|
||||
const delay_live_text_update = trigger(3000, on_live_stream_terminate);
|
||||
|
||||
function on_live_stream_terminate(latest_text) {
|
||||
// remove `prev_text_already_pushed` from `latest_text`
|
||||
console.log("on_live_stream_terminate", latest_text)
|
||||
remaining_text = latest_text.slice(prev_text_already_pushed.length);
|
||||
if ((!isEmptyOrWhitespaceOnly(remaining_text)) && remaining_text.length != 0) {
|
||||
prev_text_already_pushed = latest_text;
|
||||
push_text_to_audio(remaining_text);
|
||||
}
|
||||
}
|
||||
function is_continue_from_prev(text, prev_text) {
|
||||
abl = 5
|
||||
if (text.length < prev_text.length - abl) {
|
||||
return false;
|
||||
}
|
||||
if (prev_text.length > 10) {
|
||||
return text.startsWith(prev_text.slice(0, Math.min(prev_text.length - abl, 100)));
|
||||
} else {
|
||||
return text.startsWith(prev_text);
|
||||
}
|
||||
}
|
||||
function isEmptyOrWhitespaceOnly(remaining_text) {
|
||||
// Replace \n and 。 with empty strings
|
||||
let textWithoutSpecifiedCharacters = remaining_text.replace(/[\n。]/g, '');
|
||||
// Check if the remaining string is empty
|
||||
return textWithoutSpecifiedCharacters.trim().length === 0;
|
||||
}
|
||||
function process_increased_text(remaining_text) {
|
||||
// console.log('[is continue], remaining_text: ', remaining_text)
|
||||
// remaining_text starts with \n or 。, then move these chars into prev_text_already_pushed
|
||||
while (remaining_text.startsWith('\n') || remaining_text.startsWith('。')) {
|
||||
prev_text_already_pushed = prev_text_already_pushed + remaining_text[0];
|
||||
remaining_text = remaining_text.slice(1);
|
||||
}
|
||||
if (remaining_text.includes('\n') || remaining_text.includes('。')) { // determine remaining_text contain \n or 。
|
||||
// new message begin!
|
||||
index_of_last_sep = Math.max(remaining_text.lastIndexOf('\n'), remaining_text.lastIndexOf('。'));
|
||||
// break the text into two parts
|
||||
tobe_pushed = remaining_text.slice(0, index_of_last_sep + 1);
|
||||
prev_text_already_pushed = prev_text_already_pushed + tobe_pushed;
|
||||
// console.log('[is continue], push: ', tobe_pushed)
|
||||
// console.log('[is continue], update prev_text_already_pushed: ', prev_text_already_pushed)
|
||||
if (!isEmptyOrWhitespaceOnly(tobe_pushed)) {
|
||||
// console.log('[is continue], remaining_text is empty')
|
||||
push_text_to_audio(tobe_pushed);
|
||||
}
|
||||
}
|
||||
}
|
||||
function process_latest_text_output(text, chatbot_index) {
|
||||
if (text.length == 0) {
|
||||
prev_text = text;
|
||||
prev_text_mask = text;
|
||||
// console.log('empty text')
|
||||
return;
|
||||
}
|
||||
if (text == prev_text) {
|
||||
// console.log('[nothing changed]')
|
||||
return;
|
||||
}
|
||||
|
||||
var is_continue = is_continue_from_prev(text, prev_text_already_pushed);
|
||||
if (chatbot_index == prev_chatbot_index && is_continue) {
|
||||
// on_text_continue_grow
|
||||
remaining_text = text.slice(prev_text_already_pushed.length);
|
||||
process_increased_text(remaining_text);
|
||||
delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
|
||||
}
|
||||
else if (chatbot_index == prev_chatbot_index && !is_continue) {
|
||||
console.log('---------------------')
|
||||
console.log('text twisting!')
|
||||
console.log('[new message begin]', 'text', text, 'prev_text_already_pushed', prev_text_already_pushed)
|
||||
console.log('---------------------')
|
||||
prev_text_already_pushed = "";
|
||||
delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
|
||||
}
|
||||
else {
|
||||
// on_new_message_begin, we have to clear `prev_text_already_pushed`
|
||||
console.log('---------------------')
|
||||
console.log('new message begin!')
|
||||
console.log('[new message begin]', 'text', text, 'prev_text_already_pushed', prev_text_already_pushed)
|
||||
console.log('---------------------')
|
||||
prev_text_already_pushed = "";
|
||||
process_increased_text(text);
|
||||
delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
|
||||
}
|
||||
prev_text = text;
|
||||
prev_chatbot_index = chatbot_index;
|
||||
}
|
||||
|
||||
const audio_push_lock = new FIFOLock();
|
||||
async function push_text_to_audio(text) {
|
||||
if (!allow_auto_read_tts_flag) {
|
||||
return;
|
||||
}
|
||||
await audio_push_lock.lock();
|
||||
var lines = text.split(/[\n。]/);
|
||||
for (const audio_buf_text of lines) {
|
||||
if (audio_buf_text) {
|
||||
// Append '/vits' to the current URL to form the target endpoint
|
||||
const url = `${window.location.href}vits`;
|
||||
// Define the payload to be sent in the POST request
|
||||
const payload = {
|
||||
text: audio_buf_text, // Ensure 'audio_buf_text' is defined with valid data
|
||||
text_language: "zh"
|
||||
};
|
||||
// Call the async postData function and log the response
|
||||
post_text(url, payload, send_index);
|
||||
send_index = send_index + 1;
|
||||
console.log(send_index, audio_buf_text)
|
||||
// sleep 2 seconds
|
||||
if (allow_auto_read_tts_flag) {
|
||||
await delay(3000);
|
||||
}
|
||||
}
|
||||
}
|
||||
audio_push_lock.unlock();
|
||||
}
|
||||
|
||||
|
||||
send_index = 0;
|
||||
recv_index = 0;
|
||||
to_be_processed = [];
|
||||
async function UpdatePlayQueue(cnt, audio_buf_wave) {
|
||||
if (cnt != recv_index) {
|
||||
to_be_processed.push([cnt, audio_buf_wave]);
|
||||
console.log('cache', cnt);
|
||||
}
|
||||
else {
|
||||
console.log('processing', cnt);
|
||||
recv_index = recv_index + 1;
|
||||
if (audio_buf_wave) {
|
||||
audioPlayer.enqueueAudio(audio_buf_wave);
|
||||
}
|
||||
// deal with other cached audio
|
||||
while (true) {
|
||||
find_any = false;
|
||||
for (i = to_be_processed.length - 1; i >= 0; i--) {
|
||||
if (to_be_processed[i][0] == recv_index) {
|
||||
console.log('processing cached', recv_index);
|
||||
if (to_be_processed[i][1]) {
|
||||
audioPlayer.enqueueAudio(to_be_processed[i][1]);
|
||||
}
|
||||
to_be_processed.pop(i);
|
||||
find_any = true;
|
||||
recv_index = recv_index + 1;
|
||||
}
|
||||
}
|
||||
if (!find_any) { break; }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function post_text(url, payload, cnt) {
|
||||
if (allow_auto_read_tts_flag) {
|
||||
postData(url, payload, cnt)
|
||||
.then(data => {
|
||||
UpdatePlayQueue(cnt, data);
|
||||
return;
|
||||
});
|
||||
} else {
|
||||
UpdatePlayQueue(cnt, null);
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
notify_user_error = false
|
||||
// Create an async function to perform the POST request
|
||||
async function postData(url = '', data = {}) {
|
||||
try {
|
||||
// Use the Fetch API with await
|
||||
const response = await fetch(url, {
|
||||
method: 'POST', // Specify the request method
|
||||
body: JSON.stringify(data), // Convert the JavaScript object to a JSON string
|
||||
});
|
||||
// Check if the response is ok (status in the range 200-299)
|
||||
if (!response.ok) {
|
||||
// If not OK, throw an error
|
||||
console.info('There was a problem during audio generation requests:', response.status);
|
||||
// if (!notify_user_error){
|
||||
// notify_user_error = true;
|
||||
// alert('There was a problem during audio generation requests:', response.status);
|
||||
// }
|
||||
return null;
|
||||
}
|
||||
// If OK, parse and return the JSON response
|
||||
return await response.arrayBuffer();
|
||||
} catch (error) {
|
||||
// Log any errors that occur during the fetch operation
|
||||
console.info('There was a problem during audio generation requests:', error);
|
||||
// if (!notify_user_error){
|
||||
// notify_user_error = true;
|
||||
// alert('There was a problem during audio generation requests:', error);
|
||||
// }
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,10 +1,34 @@
|
||||
from toolbox import get_conf
|
||||
CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf("CODE_HIGHLIGHT", "ADD_WAIFU", "LAYOUT")
|
||||
|
||||
def minimize_js(common_js_path):
|
||||
try:
|
||||
import rjsmin, hashlib, glob, os
|
||||
# clean up old minimized js files, matching `common_js_path + '.min.*'`
|
||||
for old_min_js in glob.glob(common_js_path + '.min.*.js'):
|
||||
os.remove(old_min_js)
|
||||
# use rjsmin to minimize `common_js_path`
|
||||
c_jsmin = rjsmin.jsmin
|
||||
with open(common_js_path, "r") as f:
|
||||
js_content = f.read()
|
||||
minimized_js_content = c_jsmin(js_content)
|
||||
# compute sha256 hash of minimized js content
|
||||
sha_hash = hashlib.sha256(minimized_js_content.encode()).hexdigest()[:8]
|
||||
minimized_js_path = common_js_path + '.min.' + sha_hash + '.js'
|
||||
# save to minimized js file
|
||||
with open(minimized_js_path, "w") as f:
|
||||
f.write(minimized_js_content)
|
||||
# return minimized js file path
|
||||
return minimized_js_path
|
||||
except:
|
||||
return common_js_path
|
||||
|
||||
def get_common_html_javascript_code():
|
||||
js = "\n"
|
||||
common_js_path = "themes/common.js"
|
||||
minimized_js_path = minimize_js(common_js_path)
|
||||
for jsf in [
|
||||
"file=themes/common.js",
|
||||
f"file={minimized_js_path}",
|
||||
]:
|
||||
js += f"""<script src="{jsf}"></script>\n"""
|
||||
|
||||
|
||||
0
themes/sovits_audio.js
Normal file
0
themes/sovits_audio.js
Normal file
@ -111,10 +111,10 @@ js_code_for_persistent_cookie_init = """(web_cookie_cache, cookie) => {
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
# 详见 themes/common.js
|
||||
js_code_reset = """
|
||||
(a,b,c)=>{
|
||||
return [[], [], "已重置"];
|
||||
return reset_conversation(a,b);
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user