Merge branch 'frontier' into master_autogen

[Feature] 更新精准翻译PDF文档(NOUGAT)插件

.gitignore

@@ -146,9 +146,9 @@ debug*
 private*
 crazy_functions/test_project/pdf_and_word
 crazy_functions/test_samples
-request_llm/jittorllms
+request_llms/jittorllms
 multi-language
-request_llm/moss
+request_llms/moss
 media
 flagged
-request_llm/ChatGLM-6b-onnx-u8s8
+request_llms/ChatGLM-6b-onnx-u8s8

Dockerfile

@@ -1,34 +1,35 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，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
+# 此Dockerfile适用于“无本地模型”的迷你运行环境构建
-# 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
+# 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
-# 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
+# - 如何构建: 先修改 `config.py`， 然后 `docker build -t 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 `
+# - 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
 
 
-# 非必要步骤，更换pip源
+# 非必要步骤，更换pip源 （以下三行，可以删除）
 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
 
 
-# 进入工作路径
+# 进入工作路径（必要）
 WORKDIR /gpt
 
 
-# 安装大部分依赖，利用Docker缓存加速以后的构建
+# 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 ./
-COPY ./docs/gradio-3.32.2-py3-none-any.whl ./docs/gradio-3.32.2-py3-none-any.whl
+COPY ./docs/gradio-3.32.6-py3-none-any.whl ./docs/gradio-3.32.6-py3-none-any.whl
 RUN pip3 install -r requirements.txt
 
 
-# 装载项目文件，安装剩余依赖
+# 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
+# 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
+# 启动（必要）
 CMD ["python3", "-u", "main.py"]

README.md

@@ -1,24 +1,27 @@
 > **Note**
 > 
-> 2023.7.8: Gradio, Pydantic依赖调整，已修改 `requirements.txt`。请及时**更新代码**，安装依赖时，请严格选择`requirements.txt`中**指定的版本**
+> 2023.10.28: 紧急修复了若干问题，安装依赖时，请选择`requirements.txt`中**指定的版本**。
 > 
 > `pip install -r requirements.txt`
+>
+> 2023.11.7: 本项目开源免费，近期发现有人蔑视开源协议并利用本项目违规圈钱，请提高警惕，谨防上当受骗。
+
 
 
 # <div align=center><img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)</div>
 
-**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函数插件，欢迎发pull requests！**
+**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. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
+If you like this project, please give it a Star. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
 To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
 
 > **Note**
 >
-> 1.请注意只有 **高亮** 标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR。
+> 1.请注意只有 **高亮** 标识的插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR。
 >
-> 2.本项目中每个文件的功能都在[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)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[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[安装方法](#installation) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。
+> 2.本项目中每个文件的功能都在[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)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[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[常规安装方法](#installation) | [一键安装脚本](https://github.com/binary-husky/gpt_academic/releases) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
 > 
-> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM和Moss等等。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`然后回车键提交后即可生效。
+> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM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`然后回车键提交后即可生效。
 
 
  
@@ -61,7 +64,7 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
 
 - 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）
 <div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
 </div>
 
 
@@ -101,16 +104,16 @@ cd gpt_academic
 
 2. 配置API_KEY
 
-在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。
+在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](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。如您能理解该读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。 」
 
-「 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/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。配置读取优先级: `环境变量` > `config_private.py` > `config.py`。 」
+「 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版本3.9以上，越新越好），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/
+# （选择I: 如熟悉python, python>=3.9）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)：
@@ -126,17 +129,17 @@ python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步
 【可选步骤】如果需要支持清华ChatGLM2/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ChatGLM2。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_llm/requirements_chatglm.txt  
+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_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 注意执行此行代码时，必须处于项目根路径
+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", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+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"]
 ```
 
 </p>
@@ -151,11 +154,11 @@ 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。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个，建议使用方案1）（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）
+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，则不推荐使用这个）
-[![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-audio-assistant.yml)
+[![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。修改docker-compose.yml中方案0的配置，参考其中注释即可
+# 修改docker-compose.yml，保留方案0并删除其他方案。然后运行：
 docker-compose up
 ```
 
@@ -165,7 +168,7 @@ docker-compose up
 [![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。修改docker-compose.yml中方案1的配置，参考其中注释即可
+# 修改docker-compose.yml，保留方案1并删除其他方案。然后运行：
 docker-compose up
 ```
 
@@ -175,48 +178,30 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 [![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。修改docker-compose.yml中方案2的配置，参考其中注释即可
+# 修改docker-compose.yml，保留方案2并删除其他方案。然后运行：
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + 盘古 + RWKV（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）
-[![jittorllms](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml)
-
-``` sh
-# 修改docker-compose.yml，保留方案3并删除其他方案。修改docker-compose.yml中方案3的配置，参考其中注释即可
 docker-compose up
 ```
 
 
 ### 安装方法III：其他部署姿势
-1. 一键运行脚本。
+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。
 脚本的贡献来源是[oobabooga](https://github.com/oobabooga/one-click-installers)。
 
-2. 使用docker-compose运行。
+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)
-请阅读docker-compose.yml后，按照其中的提示操作即可
 
-3. 如何使用反代URL
+3. 云服务器远程部署避坑指南。
-按照`config.py`中的说明配置API_URL_REDIRECT即可。
+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. 微软云AzureAPI
+4. 一些新型的部署平台或方法
-按照`config.py`中的说明配置即可（AZURE_ENDPOINT等四个配置）
+    - 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)
-5. 远程云服务器部署（需要云服务器知识与经验）。
+    - 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)
-请访问[部署wiki-1](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)
-
-6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
-
-7. 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)
-
-8. 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)
 
 
 # Advanced Usage
 ### I：自定义新的便捷按钮（学术快捷键）
-任意文本编辑器打开`core_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。）
+任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序。（如按钮已存在，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": {
@@ -232,14 +217,13 @@ docker-compose up
 </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)。
 
 
-# Latest Update
+# Updates
-### I：新功能动态
+### I：动态
 
 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。
@@ -280,28 +264,23 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 <img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
 </div>
 
-7. 新增MOSS大语言模型支持
+7. OpenAI图像生成
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
-</div>
-
-8. 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>
 
-9. OpenAI音频解析与总结
+8. OpenAI音频解析与总结
 <div align="center">
 <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
 </div>
 
-10. Latex全文校对纠错
+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>
 
-11. 语言、主题切换
+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>
@@ -309,7 +288,11 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 
 
 ### II：版本:
-- version 3.60（todo）: 优化虚空终端，引入code interpreter和更多插件
+- version 3.60（todo）: 优化虚空终端，并引入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
@@ -331,7 +314,7 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 - 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-2：610599535
+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
@@ -342,7 +325,13 @@ gpt_academic开发者QQ群-2：610599535
 1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
 
 
-### IV：参考与学习
+### 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
+
+
+### V：参考与学习
 
 ```
 代码中参考了很多其他优秀项目中的设计，顺序不分先后：

check_proxy.py

@@ -46,7 +46,7 @@ def backup_and_download(current_version, remote_version):
         return new_version_dir
     os.makedirs(new_version_dir)
     shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     r = requests.get(
         'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
     zip_file_path = backup_dir+'/master.zip'
@@ -113,7 +113,7 @@ def auto_update(raise_error=False):
         import requests
         import time
         import json
-        proxies, = get_conf('proxies')
+        proxies = get_conf('proxies')
         response = requests.get(
             "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
         remote_json_data = json.loads(response.text)
@@ -156,7 +156,7 @@ def auto_update(raise_error=False):
 def warm_up_modules():
     print('正在执行一些模块的预热...')
     from toolbox import ProxyNetworkActivate
-    from request_llm.bridge_all import model_info
+    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=())
@@ -167,5 +167,5 @@ 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')
+    proxies = get_conf('proxies')
     check_proxy(proxies)

config.py

@@ -48,6 +48,11 @@ DEFAULT_WORKER_NUM = 3
 THEME = "Default"
 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
 
@@ -58,7 +63,10 @@ CODE_HIGHLIGHT = True
 
 # 窗口布局
 LAYOUT = "LEFT-RIGHT"   # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-DARK_MODE = True        # 暗色模式 / 亮色模式
+
+
+# 暗色模式 / 亮色模式
+DARK_MODE = True        
 
 
 # 发送请求到OpenAI后，等待多久判定为超时
@@ -79,16 +87,23 @@ DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
 
 # 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" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", 
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview",
-                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
+                    "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-# P.S. 其他可用的模型还包括 ["qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", 
+                    "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
-# "spark", "sparkv2", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
+                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
+                    "chatglm3", "moss", "newbing", "claude-2"]
+# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random"
+# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
+
+
+# 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"
 
 
 # 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"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
+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
@@ -121,22 +136,31 @@ AUTHENTICATION = []
 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_llm/README.md
+# 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 详情请见额外文档 docs\use_azure.md
+# 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
 
 
-# 使用Newbing
+# 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 = {}
+
+
+# 使用Newbing (不推荐使用，未来将删除)
 NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
 NEWBING_COOKIES = """
 put your new bing cookies here
@@ -157,6 +181,11 @@ XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
 XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 
+# 接入智谱大模型
+ZHIPUAI_API_KEY = ""
+ZHIPUAI_MODEL = "chatglm_turbo"
+
+
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 
@@ -173,20 +202,39 @@ HUGGINGFACE_ACCESS_TOKEN = "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"
 # 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://shaocongma-grobid.hf.space","https://FBR123-grobid.hf.space", "https://yeku-grobid.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"]
 
 
+# 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
+
+
+# 自定义按钮的最大数量限制
+NUM_CUSTOM_BASIC_BTN = 4
+
 """
 在线大模型配置关联关系示意图
 │
@@ -196,13 +244,16 @@ WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
 │   ├── API_ORG（不常用）
 │   └── API_URL_REDIRECT（不常用）
 │
-├── "azure-gpt-3.5" 等azure模型
+├── "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

core_functional.py

@@ -91,6 +91,13 @@ 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]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
         inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
         if core_functional[additional_fn].get("AutoClearHistory", False):

crazy_functional.py

@@ -190,10 +190,10 @@ def get_crazy_functions():
             "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)
         },
-        "[插件demo]历史上的今天": {
+        "历史上的今天": {
             "Group": "对话",
             "AsButton": True,
-            "Info": "查看历史上的今天事件 | 不需要输入参数",
+            "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": {
@@ -252,7 +252,7 @@ def get_crazy_functions():
             "Function": HotReload(Latex中文润色)
         },
 
-        # 被新插件取代
+        # 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",
@@ -260,6 +260,8 @@ def get_crazy_functions():
         #     "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",
@@ -347,18 +349,40 @@ def get_crazy_functions():
         print('Load function plugin failed')
 
     try:
-        from crazy_functions.图片生成 import 图片生成
+        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（先切换模型到openai或api2d）": {
+            "图片生成_DALLE2（先切换模型到openai或api2d）": {
                 "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256x256（默认）",  # 高级参数输入区的显示提示
+                "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": "图片生成 | 输入参数字符串，提供图像的内容",
+                "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)
+                "Function": HotReload(图片生成_DALLE2)
             },
         })
+        function_plugins.update({
+            "图片生成_DALLE3（先切换模型到openai或api2d）": {
+                "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 1024x1024, 1792x1024, 1024x1792",  # 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（启动DALLE2图像修改向导程序）": {
+        #         "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 1024x1024, 1792x1024, 1024x1792",  # 高级参数输入区的显示提示
+        #         # "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('Load function plugin failed')
 
@@ -395,7 +419,7 @@ def get_crazy_functions():
     try:
         from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
         function_plugins.update({
-            "Markdown翻译（手动指定语言）": {
+            "Markdown翻译（指定翻译成何种语言）": {
                 "Group": "编程",
                 "Color": "stop",
                 "AsButton": False,
@@ -440,7 +464,7 @@ def get_crazy_functions():
     try:
         from crazy_functions.交互功能函数模板 import 交互功能模板函数
         function_plugins.update({
-            "交互功能模板函数": {
+            "交互功能模板Demo函数（查找wallhaven.cc的壁纸）": {
                 "Group": "对话",
                 "Color": "stop",
                 "AsButton": False,
@@ -496,15 +520,15 @@ def get_crazy_functions():
 
     try:
         from toolbox import get_conf
-        ENABLE_AUDIO, = get_conf('ENABLE_AUDIO')
+        ENABLE_AUDIO = get_conf('ENABLE_AUDIO')
         if ENABLE_AUDIO:
             from crazy_functions.语音助手 import 语音助手
             function_plugins.update({
-                "实时音频采集": {
+                "实时语音对话": {
                     "Group": "对话",
                     "Color": "stop",
                     "AsButton": True,
-                    "Info": "开始语言对话 | 没有输入参数",
+                    "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)
                 }
             })
@@ -537,18 +561,15 @@ def get_crazy_functions():
     except:
         print('Load function plugin failed')
 
-    # try:
+    from crazy_functions.多智能体 import 多智能体终端
-    #     from crazy_functions.CodeInterpreter import 虚空终端CodeInterpreter
+    function_plugins.update({
-    #     function_plugins.update({
+        "多智能体终端（微软AutoGen）": {
-    #         "CodeInterpreter（开发中，仅供测试）": {
+            "Group": "智能体",
-    #             "Group": "编程|对话",
+            "Color": "stop",
-    #             "Color": "stop",
+            "AsButton": True,
-    #             "AsButton": False,
+            "Function": HotReload(多智能体终端)
-    #             "Function": HotReload(虚空终端CodeInterpreter)
+        }
-    #         }
+    })
-    #     })
-    # except:
-    #     print('Load function plugin failed')
 
     # try:
     #     from crazy_functions.chatglm微调工具 import 微调数据集生成

crazy_functions/Latex全文润色.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
-from toolbox import CatchException, report_execption, write_history_to_file, zip_folder
+from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
 
 
 class PaperFileGroup():
@@ -11,7 +11,7 @@ class PaperFileGroup():
         self.sp_file_tag = []
 
         # count_token
-        from request_llm.bridge_all import model_info
+        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
@@ -146,7 +146,7 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -157,12 +157,12 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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')
@@ -184,7 +184,7 @@ def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -195,12 +195,12 @@ def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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')
@@ -220,7 +220,7 @@ def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -231,12 +231,12 @@ def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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')

crazy_functions/Latex全文翻译.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui, promote_file_to_downloadzone
-from toolbox import CatchException, report_execption, write_history_to_file
+from toolbox import CatchException, report_exception, write_history_to_file
 fast_debug = False
 
 class PaperFileGroup():
@@ -11,7 +11,7 @@ class PaperFileGroup():
         self.sp_file_tag = []
 
         # count_token
-        from request_llm.bridge_all import model_info
+        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
@@ -117,7 +117,7 @@ def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -128,12 +128,12 @@ def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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')
@@ -154,7 +154,7 @@ def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -165,12 +165,12 @@ def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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')

crazy_functions/Latex输出PDF结果.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
-from toolbox import CatchException, report_execption, update_ui_lastest_msg, zip_result, gen_time_str
+from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
 from functools import partial
 import glob, os, requests, time
 pj = os.path.join
@@ -129,7 +129,7 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
         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')
+        proxies = get_conf('proxies')
         r = requests.get(url_tar, proxies=proxies)
         with open(dst, 'wb+') as f:
             f.write(r.content)
@@ -171,12 +171,12 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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
     
@@ -249,7 +249,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
     history = []
     txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
     if txt.endswith('.pdf'):
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
+        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
     
@@ -258,13 +258,13 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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
     

crazy_functions/agent_fns/auto_agent.py

@@ -0,0 +1,23 @@
+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.
+            },
+        ]

crazy_functions/agent_fns/echo_agent.py

@@ -0,0 +1,19 @@
+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')

crazy_functions/agent_fns/general.py

@@ -0,0 +1,134 @@
+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
+        self.child_conn.send(PipeCom("show", sender.name + "\n\n---\n\n" + message["content"]))
+
+    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
+        with ProxyNetworkActivate("AutoGen"):
+            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")
+                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

crazy_functions/agent_fns/persistent.py

@@ -0,0 +1,16 @@
+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]
+

crazy_functions/agent_fns/pipe.py

@@ -0,0 +1,194 @@
+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, web_port):
+        # ⭐ 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.web_port = web_port
+        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

crazy_functions/agent_fns/watchdog.py

@@ -0,0 +1,28 @@
+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()

crazy_functions/crazy_utils.py

@@ -5,7 +5,7 @@ import logging
 
 def input_clipping(inputs, history, max_token_limit):
     import numpy as np
-    from request_llm.bridge_all import model_info
+    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=()))
 
@@ -63,18 +63,21 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     """
     import time
     from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
+    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]) > 5: 
+            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
@@ -174,11 +177,11 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     """
     import time, random
     from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
+    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')
+        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
@@ -193,6 +196,9 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     # 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 = ""
@@ -201,7 +207,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         mutable[index][2] = "执行中"
         while True:
             # watchdog error
-            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
+            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > watch_dog_patience: 
                 raise RuntimeError("检测到程序终止。")
             try:
                 # 【第一种情况】：顺利完成
@@ -275,7 +281,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         # 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('\n', '').replace('`', '.').replace(
                     ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
             observe_win.append(print_something_really_funny)
         # 在前端打印些好玩的东西
@@ -301,7 +307,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             gpt_res = f.result()
             chatbot.append([inputs_show_user, gpt_res])
             yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-            time.sleep(0.3)
+            time.sleep(0.5)
     return gpt_response_collection
 
 
@@ -596,7 +602,7 @@ def get_files_from_everything(txt, type): # type='.md'
         import requests
         from toolbox import get_conf
         from toolbox import get_log_folder, gen_time_str
-        proxies, = get_conf('proxies')
+        proxies = get_conf('proxies')
         try:
             r = requests.get(txt, proxies=proxies)
         except:
@@ -715,8 +721,10 @@ class nougat_interface():
 
     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}')
-        process = subprocess.Popen(command, shell=True, cwd=cwd)
+        with ProxyNetworkActivate("Nougat_Download"):
+            process = subprocess.Popen(command, shell=True, cwd=cwd, env=os.environ)
         try:
             stdout, stderr = process.communicate(timeout=timeout)
         except subprocess.TimeoutExpired:
@@ -740,7 +748,7 @@ class nougat_interface():
 
         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)
-        self.nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}"', os.getcwd(), timeout=3600)
+        self.nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}" --recompute --no-skipping --markdown --batchsize 8', os.getcwd(), timeout=3600)
         res = glob.glob(os.path.join(dst,'*.mmd'))
         if len(res) == 0:
             self.threadLock.release()
@@ -761,54 +769,6 @@ def try_install_deps(deps, reload_m=[]):
         importlib.reload(__import__(m))
 
 
-HTML_CSS = """
-.row {
-  display: flex;
-  flex-wrap: wrap;
-}
-.column {
-  flex: 1;
-  padding: 10px;
-}
-.table-header {
-  font-weight: bold;
-  border-bottom: 1px solid black;
-}
-.table-row {
-  border-bottom: 1px solid lightgray;
-}
-.table-cell {
-  padding: 5px;
-}
-"""
-
-TABLE_CSS = """
-<div class="row table-row">
-    <div class="column table-cell">REPLACE_A</div>
-    <div class="column table-cell">REPLACE_B</div>
-</div>
-"""
-
-class construct_html():
-    def __init__(self) -> None:
-        self.css = HTML_CSS
-        self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
-
-
-    def add_row(self, a, b):
-        tmp = TABLE_CSS
-        from toolbox import markdown_convertion
-        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
-        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
-        self.html_string += tmp
-
-
-    def save_file(self, file_name):
-        with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f:
-            f.write(self.html_string.encode('utf-8', 'ignore').decode())
-        return os.path.join(get_log_folder(), file_name)
-
-
 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)

crazy_functions/latex_fns/latex_actions.py

@@ -165,7 +165,7 @@ class LatexPaperFileGroup():
         self.sp_file_tag = []
 
         # count_token
-        from request_llm.bridge_all import model_info
+        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
@@ -423,7 +423,7 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
     # write html
     try:
         import shutil
-        from ..crazy_utils import construct_html
+        from crazy_functions.pdf_fns.report_gen_html import construct_html
         from toolbox import gen_time_str
         ch = construct_html() 
         orig = ""

crazy_functions/latex_fns/latex_toolbox.py

@@ -308,7 +308,10 @@ def merge_tex_files_(project_foler, main_file, mode):
         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)
@@ -342,10 +345,41 @@ def merge_tex_files(project_foler, main_file, mode):
         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

crazy_functions/live_audio/aliyunASR.py

@@ -1,4 +1,106 @@
-import time, logging, json
+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():
@@ -66,12 +168,22 @@ class AliyunASR():
                     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) 
@@ -79,12 +191,32 @@ class AliyunASR():
                 # convert to pcm file
                 temp_file = f'{temp_folder}/{uuid.hex}.pcm' # 
                 dsdata = change_sample_rate(audio, rad.rate, NEW_SAMPLERATE) # 48000 --> 16000
-                io.wavfile.write(temp_file, NEW_SAMPLERATE, dsdata)
+                write_numpy_to_wave(temp_file, NEW_SAMPLERATE, dsdata)
                 # read pcm binary
                 with open(temp_file, "rb") as f: data = f.read()
-                # print('audio len:', len(audio), '\t ds len:', len(dsdata), '\t need n send:', len(data)//640)
+                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)
 

crazy_functions/live_audio/audio_io.py

@@ -35,7 +35,7 @@ class RealtimeAudioDistribution():
     def read(self, uuid):
         if uuid in self.data:
             res = self.data.pop(uuid)
-            print('\r read-', len(res), '-', max(res), end='', flush=True)
+            # print('\r read-', len(res), '-', max(res), end='', flush=True)
         else:
             res = None
         return res

crazy_functions/multi_stage/multi_stage_utils.py

@@ -0,0 +1,45 @@
+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 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 lock_plugin(self, chatbot):
+        chatbot._cookies['plugin_state'] = pickle.dumps(self)
+
+    def unlock_plugin(self, chatbot):
+        self.reset()
+        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 GatherMaterials():
+    def __init__(self, materials) -> None:
+        materials = ['image', 'prompt']

crazy_functions/pdf_fns/parse_pdf.py

@@ -14,7 +14,7 @@ import math
 class GROBID_OFFLINE_EXCEPTION(Exception): pass
 
 def get_avail_grobid_url():
-    GROBID_URLS, = get_conf('GROBID_URLS')
+    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
@@ -73,7 +73,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
     return res_path
 
 def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
-    from crazy_functions.crazy_utils import construct_html
+    from crazy_functions.pdf_fns.report_gen_html import construct_html
     from crazy_functions.crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
     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
@@ -103,7 +103,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
     inputs_show_user_array = []
 
     # get_token_num
-    from request_llm.bridge_all import model_info
+    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=()))
 

crazy_functions/pdf_fns/report_gen_html.py

@@ -0,0 +1,58 @@
+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'`')
+            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)

crazy_functions/vt_fns/vt_call_plugin.py

@@ -1,7 +1,7 @@
 from pydantic import BaseModel, Field
 from typing import List
 from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from request_llm.bridge_all import predict_no_ui_long_connection
+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
 

crazy_functions/vt_fns/vt_modify_config.py

@@ -1,13 +1,13 @@
 from pydantic import BaseModel, Field
 from typing import List
 from toolbox import update_ui_lastest_msg, get_conf
-from request_llm.bridge_all import predict_no_ui_long_connection
+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')
+    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。", 
@@ -66,7 +66,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         )
 
 def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    ALLOW_RESET_CONFIG, = get_conf('ALLOW_RESET_CONFIG')
+    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。", 

crazy_functions/下载arxiv论文翻译摘要.py

@@ -1,6 +1,6 @@
 from toolbox import update_ui, get_log_folder
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-from toolbox import CatchException, report_execption, get_conf
+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):
@@ -43,7 +43,7 @@ def download_arxiv_(url_pdf):
     file_path = download_dir+title_str
 
     print('下载中')
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     r = requests.get(requests_pdf_url, proxies=proxies)
     with open(file_path, 'wb+') as f:
         f.write(r.content)
@@ -77,7 +77,7 @@ def get_name(_url_):
     #     print('在缓存中')
     #     return arxiv_recall[_url_]
 
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     res = requests.get(_url_, proxies=proxies)
 
     bs = BeautifulSoup(res.text, 'html.parser')
@@ -144,7 +144,7 @@ def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, hi
     try:
         import bs4
     except:
-        report_execption(chatbot, history, 
+        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
@@ -157,7 +157,7 @@ def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, hi
     try:
         pdf_path, info = download_arxiv_(txt)
     except:
-        report_execption(chatbot, history, 
+        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

crazy_functions/图片生成.py

@@ -1,13 +1,12 @@
 from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
-import datetime
 
 
-def gen_image(llm_kwargs, prompt, resolution="256x256"):
+def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2"):
     import requests, json, time, os
-    from request_llm.bridge_all import model_info
+    from request_llms.bridge_all import model_info
 
-    proxies, = get_conf('proxies')
+    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']
@@ -23,6 +22,7 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
         'prompt': prompt,
         'n': 1,
         'size': resolution,
+        'model': model,
         'response_format': 'url'
     }
     response = requests.post(url, headers=headers, json=data, proxies=proxies)
@@ -42,9 +42,48 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
     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
+    headers = {
+        'Authorization': f"Bearer {api_key}",
+        'Content-Type': 'application/json'
+    }
+    data = {
+        'image': open(image_path, 'rb'),
+        'prompt': prompt,
+        'n': 1,
+        'size': resolution,
+        'model': model,
+        'response_format': 'url'
+    }
+    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
+
 
 @CatchException
-def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+def 图片生成_DALLE2(prompt, 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
@@ -58,7 +97,7 @@ def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 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", '256x256')
+    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/>'+
@@ -67,3 +106,92 @@ def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
         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, web_port):
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 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
+
+
+class ImageEditState(GptAcademicState):
+    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 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',   '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',                 '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.set_state(chatbot, 'dummy_key', 'dummy_value')
+                    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, web_port):
+    history = []    # 清空历史
+    state = ImageEditState.get_state(chatbot, ImageEditState)
+    state = state.feed(prompt, chatbot)
+    if not state.already_obtained_all_materials():
+        chatbot.append(["图片修改（先上传图片，再输入修改需求，最后输入分辨率）", state.next_req()])
+        yield from update_ui(chatbot=chatbot, history=history)
+        return
+
+    image_path = state.req[0]
+    resolution = state.req[1]
+    prompt = state.req[2]
+    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([state.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
+

crazy_functions/多智能体.py

@@ -0,0 +1,108 @@
+# 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, 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
+    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
+    """
+    # 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'])
+    
+    # 检查当前的模型是否符合要求
+    API_URL_REDIRECT = get_conf('API_URL_REDIRECT')
+    if len(API_URL_REDIRECT) > 0:
+        chatbot.append([f"处理任务: {txt}", f"暂不支持中转."])
+        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
+        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, web_port)
+        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

crazy_functions/总结word文档.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+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
@@ -32,7 +32,7 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
         print(file_content)
         # 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_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
+        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_txt_to_satisfy_token_limit_for_pdf(
@@ -97,7 +97,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
     try:
         from docx import Document
     except:
-        report_execption(chatbot, history,
+        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
@@ -111,7 +111,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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
 
@@ -124,7 +124,7 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
 
     # 如果没找到任何文件
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
+        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
 

crazy_functions/总结音视频.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, report_execption, select_api_key, update_ui, get_conf
+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
 
@@ -41,7 +41,7 @@ def split_audio_file(filename, split_duration=1000):
 def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
     import os, requests
     from moviepy.editor import AudioFileClip
-    from request_llm.bridge_all import model_info
+    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'])
@@ -79,7 +79,7 @@ def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
 
             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')
+            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])
@@ -144,7 +144,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     try:
         from moviepy.editor import AudioFileClip
     except:
-        report_execption(chatbot, history,
+        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
@@ -158,7 +158,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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
 
@@ -174,7 +174,7 @@ def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
 
     # 如果没找到任何文件
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
+        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
 

crazy_functions/批量Markdown翻译.py

@@ -1,6 +1,6 @@
 import glob, time, os, re, logging
 from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
-from toolbox import CatchException, report_execption, get_log_folder
+from toolbox import CatchException, report_exception, get_log_folder
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 fast_debug = False
 
@@ -13,7 +13,7 @@ class PaperFileGroup():
         self.sp_file_tag = []
 
         # count_token
-        from request_llm.bridge_all import model_info
+        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
@@ -118,7 +118,7 @@ def get_files_from_everything(txt, preference=''):
     if txt.startswith('http'):
         import requests
         from toolbox import get_conf
-        proxies, = get_conf('proxies')
+        proxies = get_conf('proxies')
         # 网络的远程文件
         if preference == 'Github':
             logging.info('正在从github下载资源 ...')
@@ -165,7 +165,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -177,12 +177,12 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     if not success:
         # 什么都没有
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        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
 
@@ -205,7 +205,7 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -215,11 +215,11 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     if not success:
         # 什么都没有
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        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')
@@ -238,7 +238,7 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
     try:
         import tiktoken
     except:
-        report_execption(chatbot, history,
+        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
@@ -248,11 +248,11 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
     if not success:
         # 什么都没有
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        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
     

crazy_functions/批量总结PDF文档.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
-from toolbox import CatchException, report_execption
+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
@@ -21,7 +21,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         TOKEN_LIMIT_PER_FRAGMENT = 2500
 
         from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
+        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=()))
         paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
@@ -119,7 +119,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     try:
         import fitz
     except:
-        report_execption(chatbot, history, 
+        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
@@ -133,7 +133,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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
 
@@ -142,7 +142,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     
     # 如果没找到任何文件
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
+        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
 

crazy_functions/批量总结PDF文档pdfminer.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+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
 
@@ -138,7 +138,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
     try:
         import pdfminer, bs4
     except:
-        report_execption(chatbot, history, 
+        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
@@ -147,7 +147,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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)] + \
@@ -155,7 +155,7 @@ def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, histo
                     # [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_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
+        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)

crazy_functions/批量翻译PDF文档_NOUGAT.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, report_execption, get_log_folder, gen_time_str
+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
@@ -57,30 +57,35 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         "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:
-        import nougat
-        import tiktoken
-    except:
-        report_execption(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
-
     # 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_execption(chatbot, history,
+        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
+                         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
 
@@ -97,12 +102,17 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa
     generated_conclusion_files = []
     generated_html_files = []
     DST_LANG = "中文"
-    from crazy_functions.crazy_utils import nougat_interface, construct_html
+    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)

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, report_execption, get_log_folder, gen_time_str
+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
@@ -26,7 +26,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         import tiktoken
         import scipdf
     except:
-        report_execption(chatbot, history,
+        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
@@ -43,8 +43,8 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
 
     # 如果没找到任何文件
     if len(file_manifest) == 0:
-        report_execption(chatbot, history,
+        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
+                         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
 
@@ -63,7 +63,7 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa
     generated_conclusion_files = []
     generated_html_files = []
     DST_LANG = "中文"
-    from crazy_functions.crazy_utils import construct_html
+    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)
@@ -86,7 +86,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
     TOKEN_LIMIT_PER_FRAGMENT = 1024
     generated_conclusion_files = []
     generated_html_files = []
-    from crazy_functions.crazy_utils import construct_html
+    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)
@@ -95,7 +95,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
 
         # 递归地切割PDF文件
         from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
+        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=()))
         paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(

crazy_functions/理解PDF文档内容.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+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
@@ -19,7 +19,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     TOKEN_LIMIT_PER_FRAGMENT = 2500
 
     from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
+    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=()))
     paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
@@ -49,7 +49,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
         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."  # 提示
+                                                                           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
@@ -81,7 +81,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
     try:
         import fitz
     except:
-        report_execption(chatbot, history, 
+        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
@@ -96,7 +96,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
     else:
         if txt == "":
             txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
+        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
@@ -105,7 +105,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
     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_execption(chatbot, history,
+        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

crazy_functions/生成函数注释.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+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
@@ -43,14 +43,14 @@ def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.py', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
 
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/联网的ChatGPT.py

@@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
 import requests
 from bs4 import BeautifulSoup
-from request_llm.bridge_all import model_info
+from request_llms.bridge_all import model_info
 
 def google(query, proxies):
     query = query # 在此处替换您要搜索的关键词
@@ -72,7 +72,7 @@ def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
 
     # ------------- < 第1步：爬取搜索引擎的结果 > -------------
     from toolbox import get_conf
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     urls = google(txt, proxies)
     history = []
     if len(urls) == 0:

crazy_functions/联网的ChatGPT_bing版.py

@@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
 import requests
 from bs4 import BeautifulSoup
-from request_llm.bridge_all import model_info
+from request_llms.bridge_all import model_info
 
 
 def bing_search(query, proxies=None):
@@ -72,7 +72,7 @@ def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, histor
 
     # ------------- < 第1步：爬取搜索引擎的结果 > -------------
     from toolbox import get_conf
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     urls = bing_search(txt, proxies)
     history = []
     if len(urls) == 0:

crazy_functions/虚空终端.py

@@ -48,7 +48,7 @@ from pydantic import BaseModel, Field
 from typing import List
 from toolbox import CatchException, update_ui, is_the_upload_folder
 from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from request_llm.bridge_all import predict_no_ui_long_connection
+from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError

crazy_functions/解析JupyterNotebook.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+from toolbox import CatchException, report_exception
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 fast_debug = True
 
@@ -13,7 +13,7 @@ class PaperFileGroup():
         self.sp_file_tag = []
 
         # count_token
-        from request_llm.bridge_all import model_info
+        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=()))
@@ -131,7 +131,7 @@ def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
     else:
         if txt == "":
             txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
+        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
@@ -141,7 +141,7 @@ def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         file_manifest = [f for f in glob.glob(
             f'{project_folder}/**/*.ipynb', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history,
+        report_exception(chatbot, history,
                          a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
         return

crazy_functions/解析项目源代码.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
-from toolbox import CatchException, report_execption, write_history_to_file
+from toolbox import CatchException, report_exception, write_history_to_file
 from .crazy_utils import input_clipping
 
 def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
@@ -113,7 +113,7 @@ def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
                     [f for f in glob.glob('./*/*.py')]
     project_folder = './'
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -126,12 +126,12 @@ def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.py', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -144,12 +144,12 @@ def 解析一个Matlab项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        report_exception(chatbot, history, a = f"解析Matlab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}/**/*.m', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到任何`.m`源文件: {txt}")
+        report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到任何`.m`源文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -162,14 +162,14 @@ def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, his
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.h', recursive=True)]  + \
                     [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
                     # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -182,7 +182,7 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.h', recursive=True)]  + \
@@ -190,7 +190,7 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
                     [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -204,7 +204,7 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.java', recursive=True)] + \
@@ -212,7 +212,7 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
                     [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -226,7 +226,7 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.ts', recursive=True)] + \
@@ -241,7 +241,7 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
                     [f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -255,7 +255,7 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.go', recursive=True)] + \
@@ -263,7 +263,7 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
                     [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -276,14 +276,14 @@ def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.rs', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -296,7 +296,7 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.lua', recursive=True)] + \
@@ -304,7 +304,7 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
                     [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -318,13 +318,13 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.cs', recursive=True)] + \
                     [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
@@ -352,7 +352,7 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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
     # 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
@@ -365,7 +365,7 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
                       os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
+        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
     yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/询问多个大语言模型.py

@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui
+from toolbox import CatchException, update_ui, get_conf
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 import datetime
 @CatchException
@@ -13,11 +13,12 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
     web_port        当前软件运行的端口号
     """
     history = []    # 清空历史，以免输入溢出
-    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
+    MULTI_QUERY_LLM_MODELS = get_conf('MULTI_QUERY_LLM_MODELS')
+    chatbot.append((txt, "正在同时咨询" + MULTI_QUERY_LLM_MODELS))
     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
 
     # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
+    llm_kwargs['llm_model'] = MULTI_QUERY_LLM_MODELS # 支持任意数量的llm接口，用&符号分隔
     gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
         inputs=txt, inputs_show_user=txt, 
         llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 

crazy_functions/语音助手.py

@@ -1,47 +1,35 @@
 from toolbox import update_ui
 from toolbox import CatchException, get_conf, markdown_convertion
 from crazy_functions.crazy_utils import input_clipping
-from request_llm.bridge_all import predict_no_ui_long_connection
+from crazy_functions.agent_fns.watchdog import WatchDog
+from request_llms.bridge_all import predict_no_ui_long_connection
 import threading, time
 import numpy as np
 from .live_audio.aliyunASR import AliyunASR
 import json
+import re
 
-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()
 
 def chatbot2history(chatbot):
     history = []
     for c in chatbot:
         for q in c:
-            if q not in ["[请讲话]", "[等待GPT响应]", "[正在等您说完问题]"]:
+            if q in ["[ 请讲话 ]", "[ 等待GPT响应 ]", "[ 正在等您说完问题 ]"]:
+                continue
+            elif q.startswith("[ 正在等您说完问题 ]"):
+                continue
+            else:
                 history.append(q.strip('<div class="markdown-body">').strip('</div>').strip('<p>').strip('</p>'))
     return history
 
+def visualize_audio(chatbot, audio_shape):
+    if len(chatbot) == 0: chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
+    chatbot[-1] = list(chatbot[-1])
+    p1 = '「'
+    p2 = '」'
+    chatbot[-1][-1] = re.sub(p1+r'(.*)'+p2, '', chatbot[-1][-1])
+    chatbot[-1][-1] += (p1+f"`{audio_shape}`"+p2)
+
 class AsyncGptTask():
     def __init__(self) -> None:
         self.observe_future = []
@@ -81,8 +69,9 @@ class InterviewAssistant(AliyunASR):
         self.capture_interval = 0.5 # second
         self.stop = False
         self.parsed_text = ""   # 下个句子中已经说完的部分, 由 test_on_result_chg() 写入
-        self.parsed_sentence = ""   # 某段话的整个句子,由 test_on_sentence_end() 写入
+        self.parsed_sentence = ""   # 某段话的整个句子, 由 test_on_sentence_end() 写入
         self.buffered_sentence = ""    #
+        self.audio_shape = ""   # 音频的可视化表现, 由 audio_convertion_thread() 写入
         self.event_on_result_chg = threading.Event()
         self.event_on_entence_end = threading.Event()
         self.event_on_commit_question = threading.Event()
@@ -117,7 +106,7 @@ class InterviewAssistant(AliyunASR):
     def begin(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
         # main plugin function
         self.init(chatbot)
-        chatbot.append(["[请讲话]", "[正在等您说完问题]"])
+        chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         self.plugin_wd.begin_watch()
         self.agt = AsyncGptTask()
@@ -157,14 +146,18 @@ class InterviewAssistant(AliyunASR):
 
                 self.commit_wd.begin_watch()
                 chatbot[-1] = list(chatbot[-1])
-                chatbot[-1] = [self.buffered_sentence, "[等待GPT响应]"]
+                chatbot[-1] = [self.buffered_sentence, "[ 等待GPT响应 ]"]
                 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
                 # add gpt task 创建子线程请求gpt，避免线程阻塞
                 history = chatbot2history(chatbot)
                 self.agt.add_async_gpt_task(self.buffered_sentence, len(chatbot)-1, llm_kwargs, history, system_prompt)
                 
                 self.buffered_sentence = ""
-                chatbot.append(["[请讲话]", "[正在等您说完问题]"])
+                chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
+                yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+            if not self.event_on_result_chg.is_set() and not self.event_on_entence_end.is_set() and not self.event_on_commit_question.is_set():
+                visualize_audio(chatbot, self.audio_shape)
                 yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
         if len(self.stop_msg) != 0:
@@ -183,7 +176,7 @@ def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
         import nls
         from scipy import io
     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 --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
+        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 --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
 

crazy_functions/读文章写摘要.py

@@ -1,5 +1,5 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption
+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
 
@@ -51,14 +51,14 @@ def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
         project_folder = txt
     else:
         if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        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}/**/*.cpp', recursive=True)] + \
                     # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        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 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/谷歌检索小助手.py

@@ -1,5 +1,5 @@
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, promote_file_to_downloadzone
+from toolbox import CatchException, report_exception, promote_file_to_downloadzone
 from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
 import logging
 import requests
@@ -17,7 +17,7 @@ def get_meta_information(url, chatbot, history):
     from urllib.parse import urlparse
     session = requests.session()
 
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     headers = {
         'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
         'Accept-Encoding': 'gzip, deflate, br', 
@@ -26,7 +26,13 @@ def get_meta_information(url, chatbot, history):
         'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 
         'Connection': 'keep-alive'
     }
+    try:
         session.proxies.update(proxies)
+    except:
+        report_exception(chatbot, history,
+                    a=f"获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议：检查USE_PROXY选项是否修改。",
+                    b=f"尝试直接连接")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
     session.headers.update(headers)
 
     response = session.get(url)
@@ -140,7 +146,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
         import math
         from bs4 import BeautifulSoup
     except:
-        report_execption(chatbot, history, 
+        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 arxiv```。")
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

docker-compose.yml

@@ -1,4 +1,28 @@
-#【请修改完参数后，删除此行】请在以下方案中选择一种，然后删除其他的方案，最后docker-compose up运行 | Please choose from one of these options below, delete other options as well as This Line
+## ===================================================
+# docker-compose.yml
+## ===================================================
+# 1. 请在以下方案中选择任意一种，然后删除其他的方案
+# 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py
+# 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改：
+    # 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置
+    # network_mode: "host"
+    # 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
+    # ports:
+    #   - "12345:12345"  # 注意！12345必须与WEB_PORT环境变量相互对应
+# 4. 最后`docker-compose up`运行
+# 5. 如果希望使用显卡，请关注 LOCAL_MODEL_DEVICE 和 英伟达显卡运行时 选项
+## ===================================================
+# 1. Please choose one of the following options and delete the others.
+# 2. Modify the environment variables in the selected option, see GitHub wiki or config.py for more details.
+# 3. Choose a method to expose the server port and make the corresponding configuration changes:
+    # [Method 1: Suitable for Linux, convenient, but not supported for Windows] Fusion with the host network, this is the default configuration
+    # network_mode: "host"
+    # [Method 2: Suitable for all systems including Windows and MacOS] Port mapping, mapping the container port to the host port (note that you need to delete network_mode: "host" first, and then add the following content)
+    # ports:
+    # - "12345: 12345" # Note! 12345 must correspond to the WEB_PORT environment variable.
+# 4. Finally, run `docker-compose up`.
+# 5. If you want to use a graphics card, pay attention to the LOCAL_MODEL_DEVICE and Nvidia GPU runtime options.
+## ===================================================
 
 ## ===================================================
 ## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
@@ -39,10 +63,14 @@ services:
     #             count: 1
     #             capabilities: [gpu]
 
-    # 与宿主的网络融合
+    # 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合
     network_mode: "host"
 
-    # 不使用代理网络拉取最新代码
+    # 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
+    # ports:
+    #   - "12345:12345"  # 12345必须与WEB_PORT相互对应
+
+    # 启动容器后，运行main.py主程序
     command: >
       bash -c "python3 -u main.py"
 
@@ -109,7 +137,7 @@ services:
 
     # P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
     # command: >
-    #   bash -c "pip install -r request_llm/requirements_qwen.txt && python3 -u main.py"
+    #   bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
 
 ### ===================================================
 ### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型

docs/GithubAction+AllCapacity

@@ -14,18 +14,18 @@ RUN python3 -m pip install colorama Markdown pygments pymupdf
 RUN python3 -m pip install python-docx moviepy pdfminer
 RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
 RUN python3 -m pip install rarfile py7zr
-RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
 # 下载分支
 WORKDIR /gpt
 RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
 WORKDIR /gpt/gpt_academic
-RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss
+RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
 
 RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llm/requirements_moss.txt
+RUN python3 -m pip install -r request_llms/requirements_moss.txt
-RUN python3 -m pip install -r request_llm/requirements_qwen.txt
+RUN python3 -m pip install -r request_llms/requirements_qwen.txt
-RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llm/requirements_newbing.txt
+RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 RUN python3 -m pip install nougat-ocr
 
 

docs/GithubAction+ChatGLM+Moss

@@ -14,12 +14,12 @@ RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/
 WORKDIR /gpt
 RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
 WORKDIR /gpt/gpt_academic
-RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss
+RUN git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss
 RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llm/requirements_moss.txt
+RUN python3 -m pip install -r request_llms/requirements_moss.txt
-RUN python3 -m pip install -r request_llm/requirements_qwen.txt
+RUN python3 -m pip install -r request_llms/requirements_qwen.txt
-RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llm/requirements_newbing.txt
+RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 
 
 

docs/GithubAction+JittorLLMs

@@ -16,12 +16,12 @@ WORKDIR /gpt
 RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
 WORKDIR /gpt/gpt_academic
 RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llm/requirements_newbing.txt
+RUN python3 -m pip install -r request_llms/requirements_newbing.txt
-RUN python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
+RUN python3 -m pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
 
 # 下载JittorLLMs
-RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
+RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llms/jittorllms
 
 # 禁用缓存，确保更新代码
 ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache

docs/GithubAction+NoLocal+Latex

@@ -4,16 +4,19 @@
 # - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
 
 FROM fuqingxu/python311_texlive_ctex:latest
+ENV PATH "$PATH:/usr/local/texlive/2022/bin/x86_64-linux"
+ENV PATH "$PATH:/usr/local/texlive/2023/bin/x86_64-linux"
+ENV PATH "$PATH:/usr/local/texlive/2024/bin/x86_64-linux"
+ENV PATH "$PATH:/usr/local/texlive/2025/bin/x86_64-linux"
+ENV PATH "$PATH:/usr/local/texlive/2026/bin/x86_64-linux"
 
 # 指定路径
 WORKDIR /gpt
 
-RUN pip3 install gradio openai numpy arxiv rich
+RUN pip3 install openai numpy arxiv rich
 RUN pip3 install colorama Markdown pygments pymupdf
-RUN pip3 install python-docx moviepy pdfminer 
+RUN pip3 install python-docx pdfminer 
-RUN pip3 install zh_langchain==0.2.1
 RUN pip3 install nougat-ocr
-RUN pip3 install aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
 
 # 装载项目文件
 COPY . .

docs/README.md.German.md

@@ -103,12 +103,12 @@ python -m pip install -r requirements.txt # Same step as pip installation
 
 [Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
 ```sh
-# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llms/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # [Optional Step II] Support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss # When executing this line of code, you must be in the project root path
 
 # [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README.md.Italian.md

@@ -109,12 +109,12 @@ python -m pip install -r requirements.txt # questo passaggio funziona allo stess
 
 【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
 ```sh
-# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llms/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
 
 # 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README.md.Korean.md

@@ -104,11 +104,11 @@ python -m pip install -r requirements.txt # 이 단계도 pip install의 단계
 # 1 : 기본 설치된 것들은 torch + cpu 버전입니다. cuda를 사용하려면 torch를 제거한 다음 torch + cuda를 다시 설치해야합니다.
 # 2 : 모델을 로드할 수 없는 기계 구성 때문에, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)를
 # AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)로 변경합니다.
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # [선택 사항 II] Fudan MOSS 지원
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
 
 # [선택 사항III] AVAIL_LLM_MODELS config.py 구성 파일에 기대하는 모델이 포함되어 있는지 확인하십시오.
 # 현재 지원되는 전체 모델 :

docs/README.md.Portuguese.md

@@ -119,12 +119,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 
 [Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong):
 ```sh
-# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # 【Optional Step II】support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note: When executing this line of code, you must be in the project root path
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # Note: When executing this line of code, you must be in the project root path
 
 # 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README_EN.md

@@ -106,12 +106,12 @@ python -m pip install -r requirements.txt # this step is the same as pip install
 
 [Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (prerequisites: familiar with Python + used Pytorch + computer configuration is strong enough):
 ```sh
-# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
+# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
-python -m pip install -r request_llm/requirements_chatglm.txt 
+python -m pip install -r request_llms/requirements_chatglm.txt 
 
 # [Optional Step II] Support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # When executing this line of code, you must be in the root directory of the project
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # When executing this line of code, you must be in the root directory of the project
 
 # [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. Currently supported models are as follows (the jittorllms series only supports the docker solution for the time being):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README_FR.md

@@ -111,12 +111,12 @@ python -m pip install -r requirements.txt # Same step as pip instalation
 
 【Optional】 Si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend, des dépendances supplémentaires doivent être installées (prérequis: compétent en Python + utilisez Pytorch + configuration suffisante de l'ordinateur):
 ```sh
-# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llm/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llms/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # 【Optional Step II】 Support FDU MOSS
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note: When running this line of code, you must be in the project root path.
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # Note: When running this line of code, you must be in the project root path.
 
 # 【Optional Step III】Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the desired model. Currently, all models supported are as follows (the jittorllms series currently only supports the docker scheme):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README_JP.md

@@ -120,12 +120,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 [Optional Steps] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (precondition: familiar with Python + used Pytorch + computer configuration). Strong enough):
 
 ```sh
-# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
+# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # Optional Step II: Support Fudan MOSS.
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note that when executing this line of code, it must be in the project root.
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # Note that when executing this line of code, it must be in the project root.
 
 # 【Optional Step III】Ensure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports the docker solution):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/README_RS.md

@@ -108,12 +108,12 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 
 [Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, you need to install more dependencies (prerequisites: familiar with Python + have used Pytorch + computer configuration is strong):
 ```sh
-# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llms/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt  
 
 # [Optional step II] Support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
+python -m pip install -r request_llms/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note that when executing this line of code, you must be in the project root path
+git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss  # Note that when executing this line of code, you must be in the project root path
 
 # [Optional step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently, all supported models are as follows (the jittorllms series currently only supports the docker solution):
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]

docs/WithFastapi.md

@@ -16,7 +16,7 @@ nano config.py
     + demo.queue(concurrency_count=CONCURRENT_COUNT)
 
     - # 如果需要在二级路径下运行
-    - # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
+    - # CUSTOM_PATH = get_conf('CUSTOM_PATH')
     - # if CUSTOM_PATH != "/": 
     - #     from toolbox import run_gradio_in_subpath
     - #     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
@@ -24,7 +24,7 @@ nano config.py
     - #     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
 
     + 如果需要在二级路径下运行
-    + CUSTOM_PATH, = get_conf('CUSTOM_PATH')
+    + CUSTOM_PATH = get_conf('CUSTOM_PATH')
     + if CUSTOM_PATH != "/": 
     +     from toolbox import run_gradio_in_subpath
     +     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)

docs/self_analysis.md

@@ -38,20 +38,20 @@
 | crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
 | crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
 | crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
+| request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
+| request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
+| request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
+| request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
+| request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
+| request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
+| request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
+| request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
+| request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
+| request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
+| request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
+| request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
+| request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
+| request_llms\test_llms.py | 对llm模型进行单元测试。 |
 
 ## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
 
@@ -129,7 +129,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
 2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
 
-这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
+这两个函数都依赖于从 `toolbox` 和 `request_llms` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
 
 ## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
 
@@ -137,7 +137,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 ## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
 
-这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
+这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llms` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
 
 ## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
 
@@ -217,7 +217,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 ## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py
 
-这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_execption、write_results_to_file等。
+这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_exception、write_results_to_file等。
 
 ## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py
 
@@ -227,19 +227,19 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
 
-## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py
+## [34/48] 请对下面的程序文件做一个概述: request_llms\bridge_all.py
 
 该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
 
-## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py
+## [35/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatglm.py
 
 这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
 
-## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py
+## [36/48] 请对下面的程序文件做一个概述: request_llms\bridge_chatgpt.py
 
-该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
+该文件为 Python 代码文件，文件名为 request_llms\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
 
-## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py
+## [37/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_llama.py
 
 该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
 1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
@@ -248,17 +248,17 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
 
-## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py
+## [38/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_pangualpha.py
 
 这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
 
-## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py
+## [39/48] 请对下面的程序文件做一个概述: request_llms\bridge_jittorllms_rwkv.py
 
 这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
 
-## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py
+## [40/48] 请对下面的程序文件做一个概述: request_llms\bridge_moss.py
 
-该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
+该文件为一个Python源代码文件，文件名为 request_llms\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
 
 GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
 
@@ -266,7 +266,7 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
 
 函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
 
-## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py
+## [41/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbing.py
 
 这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
 
@@ -276,11 +276,11 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
 
 第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
 
-## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py
+## [42/48] 请对下面的程序文件做一个概述: request_llms\bridge_newbingfree.py
 
 这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
 
-## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py
+## [43/48] 请对下面的程序文件做一个概述: request_llms\bridge_stackclaude.py
 
 这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
 
@@ -290,21 +290,21 @@ GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个
 
 第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
 
-## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py
+## [44/48] 请对下面的程序文件做一个概述: request_llms\bridge_tgui.py
 
 该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
 
-## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py
+## [45/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt.py
 
 该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
 
-## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py
+## [46/48] 请对下面的程序文件做一个概述: request_llms\edge_gpt_free.py
 
 该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
 
-## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py
+## [47/48] 请对下面的程序文件做一个概述: request_llms\test_llms.py
 
-这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
+这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llms.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
 
 ## 用一张Markdown表格简要描述以下文件的功能：
 check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
@@ -355,24 +355,24 @@ crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生
 概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
 
 ## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
+crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llms\bridge_all.py, request_llms\bridge_chatglm.py, request_llms\bridge_chatgpt.py, request_llms\bridge_jittorllms_llama.py, request_llms\bridge_jittorllms_pangualpha.py, request_llms\bridge_jittorllms_rwkv.py, request_llms\bridge_moss.py, request_llms\bridge_newbing.py, request_llms\bridge_newbingfree.py, request_llms\bridge_stackclaude.py, request_llms\bridge_tgui.py, request_llms\edge_gpt.py, request_llms\edge_gpt_free.py, request_llms\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
 
 | 文件名 | 功能描述 |
 | --- | --- |
 | crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
 | crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
+| request_llms\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
+| request_llms\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
+| request_llms\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
+| request_llms\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
+| request_llms\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
+| request_llms\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
+| request_llms\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
+| request_llms\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
+| request_llms\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
+| request_llms\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
+| request_llms\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
+| request_llms\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
+| request_llms\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
+| request_llms\test_llms.py | 对llm模型进行单元测试。 |
 | 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |

docs/translate_english.json

@@ -265,7 +265,7 @@
     "例如chatglm&gpt-3.5-turbo&api2d-gpt-4": "e.g. chatglm&gpt-3.5-turbo&api2d-gpt-4",
     "先切换模型到openai或api2d": "Switch the model to openai or api2d first",
     "在这里输入分辨率": "Enter the resolution here",
-    "如256x256": "e.g. 256x256",
+    "如1024x1024": "e.g. 1024x1024",
     "默认": "Default",
     "建议您复制一个config_private.py放自己的秘密": "We suggest you to copy a config_private.py file to keep your secrets, such as API and proxy URLs, from being accidentally uploaded to Github and seen by others.",
     "如API和代理网址": "Such as API and proxy URLs",
@@ -322,7 +322,7 @@
     "任何文件": "Any file",
     "但推荐上传压缩文件": "But it is recommended to upload compressed files",
     "更换模型 & SysPrompt & 交互界面布局": "Change model & SysPrompt & interactive interface layout",
-    "底部输入区": "Bottom input area",
+    "浮动输入区": "Floating input area",
     "输入清除键": "Input clear key",
     "插件参数区": "Plugin parameter area",
     "显示/隐藏功能区": "Show/hide function area",
@@ -1184,7 +1184,7 @@
     "Call ChatGLM fail 不能正常加载ChatGLM的参数": "Call ChatGLM fail, unable to load parameters for ChatGLM",
     "不能正常加载ChatGLM的参数！": "Unable to load parameters for ChatGLM!",
     "多线程方法": "Multithreading method",
-    "函数的说明请见 request_llm/bridge_all.py": "For function details, please see request_llm/bridge_all.py",
+    "函数的说明请见 request_llms/bridge_all.py": "For function details, please see request_llms/bridge_all.py",
     "程序终止": "Program terminated",
     "单线程方法": "Single-threaded method",
     "等待ChatGLM响应中": "Waiting for response from ChatGLM",
@@ -1543,7 +1543,7 @@
     "str类型": "str type",
     "所有音频都总结完成了吗": "Are all audio summaries completed?",
     "SummaryAudioVideo内容": "SummaryAudioVideo content",
-    "使用教程详情见 request_llm/README.md": "See request_llm/README.md for detailed usage instructions",
+    "使用教程详情见 request_llms/README.md": "See request_llms/README.md for detailed usage instructions",
     "删除中间文件夹": "Delete intermediate folder",
     "Claude组件初始化成功": "Claude component initialized successfully",
     "$c$ 是光速": "$c$ is the speed of light",
@@ -2513,5 +2513,280 @@
     "此处待注入的知识库名称id": "The knowledge base name ID to be injected here",
     "您需要构建知识库后再运行此插件": "You need to build the knowledge base before running this plugin",
     "判定是否为公式 | 测试1 写出洛伦兹定律": "Determine whether it is a formula | Test 1 write out the Lorentz law",
-    "构建知识库后": "After building the knowledge base"
+    "构建知识库后": "After building the knowledge base",
+    "找不到本地项目或无法处理": "Unable to find local project or unable to process",
+    "再做一个小修改": "Make another small modification",
+    "解析整个Matlab项目": "Parse the entire Matlab project",
+    "需要用GPT提取参数": "Need to extract parameters using GPT",
+    "文件路径": "File path",
+    "正在排队": "In queue",
+    "-=-=-=-=-=-=-=-= 写出第1个文件": "-=-=-=-=-=-=-=-= Write the first file",
+    "仅翻译后的文本 -=-=-=-=-=-=-=-=": "Translated text only -=-=-=-=-=-=-=-=",
+    "对话通道": "Conversation channel",
+    "找不到任何": "Unable to find any",
+    "正在启动": "Starting",
+    "开始创建新进程并执行代码! 时间限制": "Start creating a new process and executing the code! Time limit",
+    "解析Matlab项目": "Parse Matlab project",
+    "更换UI主题": "Change UI theme",
+    "⭐ 开始啦 ！": "⭐ Let's start!",
+    "先提取当前英文标题": "First extract the current English title",
+    "睡一会防止触发google反爬虫": "Sleep for a while to prevent triggering Google anti-crawler",
+    "测试": "Test",
+    "-=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=": "-=-=-=-=-=-=-=-= Write out Markdown file",
+    "如果index是1的话": "If the index is 1",
+    "VoidTerminal已经实现了类似的代码": "VoidTerminal has already implemented similar code",
+    "等待线程锁": "Waiting for thread lock",
+    "那么我们默认代理生效": "Then we default to proxy",
+    "结果是一个有效文件": "The result is a valid file",
+    "⭐ 检查模块": "⭐ Check module",
+    "备份一份History作为记录": "Backup a copy of History as a record",
+    "作者Binary-Husky": "Author Binary-Husky",
+    "将csv文件转excel表格": "Convert CSV file to Excel table",
+    "获取文章摘要": "Get article summary",
+    "次代码生成尝试": "Attempt to generate code",
+    "如果参数是空的": "If the parameter is empty",
+    "请配置讯飞星火大模型的XFYUN_APPID": "Please configure XFYUN_APPID for the Xunfei Starfire model",
+    "-=-=-=-=-=-=-=-= 写出第2个文件": "Write the second file",
+    "代码生成阶段结束": "Code generation phase completed",
+    "则进行提醒": "Then remind",
+    "处理异常": "Handle exception",
+    "可能触发了google反爬虫机制": "May have triggered Google anti-crawler mechanism",
+    "AnalyzeAMatlabProject的所有源文件": "All source files of AnalyzeAMatlabProject",
+    "写入": "Write",
+    "我们5秒后再试一次...": "Let's try again in 5 seconds...",
+    "判断一下用户是否错误地通过对话通道进入": "Check if the user entered through the dialogue channel by mistake",
+    "结果": "Result",
+    "2. 如果没有文件": "2. If there is no file",
+    "由 test_on_sentence_end": "By test_on_sentence_end",
+    "则直接使用first section name": "Then directly use the first section name",
+    "太懒了": "Too lazy",
+    "记录当前的大章节标题": "Record the current chapter title",
+    "然后再次点击该插件! 至于您的文件": "Then click the plugin again! As for your file",
+    "此次我们的错误追踪是": "This time our error tracking is",
+    "首先在arxiv上搜索": "First search on arxiv",
+    "被新插件取代": "Replaced by a new plugin",
+    "正在处理文件": "Processing file",
+    "除了连接OpenAI之外": "In addition to connecting OpenAI",
+    "我们检查一下": "Let's check",
+    "进度": "Progress",
+    "处理少数情况下的特殊插件的锁定状态": "Handle the locked state of special plugins in a few cases",
+    "⭐ 开始执行": "⭐ Start execution",
+    "正常情况": "Normal situation",
+    "下个句子中已经说完的部分": "The part that has already been said in the next sentence",
+    "首次运行需要花费较长时间下载NOUGAT参数": "The first run takes a long time to download NOUGAT parameters",
+    "使用tex格式公式 测试2 给出柯西不等式": "Use the tex format formula to test 2 and give the Cauchy inequality",
+    "无法从bing获取信息！": "Unable to retrieve information from Bing!",
+    "秒. 请等待任务完成": "Wait for the task to complete",
+    "开始干正事": "Start doing real work",
+    "需要花费较长时间下载NOUGAT参数": "It takes a long time to download NOUGAT parameters",
+    "然后再次点击该插件": "Then click the plugin again",
+    "受到bing限制": "Restricted by Bing",
+    "检索文章的历史版本的题目": "Retrieve the titles of historical versions of the article",
+    "收尾": "Wrap up",
+    "给定了task": "Given a task",
+    "某段话的整个句子": "The whole sentence of a paragraph",
+    "-=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-=": "-=-=-=-=-=-=-=-= Write out HTML file -=-=-=-=-=-=-=-=",
+    "当前文件": "Current file",
+    "请在输入框内填写需求": "Please fill in the requirements in the input box",
+    "结果是一个字符串": "The result is a string",
+    "用插件实现」": "Implemented with a plugin",
+    "⭐ 到最后一步了": "⭐ Reached the final step",
+    "重新修改当前part的标题": "Modify the title of the current part again",
+    "请勿点击“提交”按钮或者“基础功能区”按钮": "Do not click the 'Submit' button or the 'Basic Function Area' button",
+    "正在执行命令": "Executing command",
+    "检测到**滞留的缓存文档**": "Detected **stuck cache document**",
+    "第三步": "Step three",
+    "失败了~ 别担心": "Failed~ Don't worry",
+    "动态代码解释器": "Dynamic code interpreter",
+    "开始执行": "Start executing",
+    "不给定task": "No task given",
+    "正在加载NOUGAT...": "Loading NOUGAT...",
+    "精准翻译PDF文档": "Accurate translation of PDF documents",
+    "时间限制TIME_LIMIT": "Time limit TIME_LIMIT",
+    "翻译前后混合 -=-=-=-=-=-=-=-=": "Mixed translation before and after -=-=-=-=-=-=-=-=",
+    "搞定代码生成": "Code generation is done",
+    "插件通道": "Plugin channel",
+    "智能体": "Intelligent agent",
+    "切换界面明暗 ☀": "Switch interface brightness ☀",
+    "交换图像的蓝色通道和红色通道": "Swap blue channel and red channel of the image",
+    "作为函数参数": "As a function parameter",
+    "先挑选偶数序列号": "First select even serial numbers",
+    "仅供测试": "For testing only",
+    "执行成功了": "Execution succeeded",
+    "开始逐个文件进行处理": "Start processing files one by one",
+    "当前文件处理列表": "Current file processing list",
+    "执行失败了": "Execution failed",
+    "请及时处理": "Please handle it in time",
+    "源文件": "Source file",
+    "裁剪图像": "Crop image",
+    "插件动态生成插件": "Dynamic generation of plugins",
+    "正在验证上述代码的有效性": "Validating the above code",
+    "⭐ = 关键步骤": "⭐ = Key step",
+    "!= 0 代表“提交”键对话通道": "!= 0 represents the 'Submit' key dialogue channel",
+    "解析python源代码项目": "Parsing Python source code project",
+    "请检查PDF是否损坏": "Please check if the PDF is damaged",
+    "插件动态生成": "Dynamic generation of plugins",
+    "⭐ 分离代码块": "⭐ Separating code blocks",
+    "已经被记忆": "Already memorized",
+    "默认用英文的": "Default to English",
+    "错误追踪": "Error tracking",
+    "对话|编程|学术|智能体": "Dialogue|Programming|Academic|Intelligent agent",
+    "请检查": "Please check",
+    "检测到被滞留的缓存文档": "Detected cached documents being left behind",
+    "还有哪些场合允许使用代理": "What other occasions allow the use of proxies",
+    "1. 如果有文件": "1. If there is a file",
+    "执行开始": "Execution starts",
+    "代码生成结束": "Code generation ends",
+    "请及时点击“**保存当前对话**”获取所有滞留文档": "Please click '**Save Current Dialogue**' in time to obtain all cached documents",
+    "需点击“**函数插件区**”按钮进行处理": "Click the '**Function Plugin Area**' button for processing",
+    "此函数已经弃用": "This function has been deprecated",
+    "以后再写": "Write it later",
+    "返回给定的url解析出的arxiv_id": "Return the arxiv_id parsed from the given URL",
+    "⭐ 文件上传区是否有东西": "⭐ Is there anything in the file upload area",
+    "Nougat解析论文失败": "Nougat failed to parse the paper",
+    "本源代码中": "In this source code",
+    "或者基础功能通道": "Or the basic function channel",
+    "使用zip压缩格式": "Using zip compression format",
+    "受到google限制": "Restricted by Google",
+    "如果是": "If it is",
+    "不用担心": "don't worry",
+    "显示/隐藏自定义菜单": "Show/Hide Custom Menu",
+    "1. 输入文本": "1. Enter Text",
+    "微软AutoGen": "Microsoft AutoGen",
+    "在没有声音之后": "After No Sound",
+    "⭐ 主进程 Docker 外挂文件夹监控": "⭐ Main Process Docker External Folder Monitoring",
+    "请求任务": "Request Task",
+    "推荐上传压缩文件": "Recommend Uploading Compressed File",
+    "我准备好处理下一个问题了": "I'm ready to handle the next question",
+    "输入要反馈的内容": "Enter the content to be feedbacked",
+    "当已经存在一个正在运行的MultiAgentTerminal时": "When there is already a running MultiAgentTerminal",
+    "也根据时间间隔": "Also according to the time interval",
+    "自定义功能": "Custom Function",
+    "上传文件后会自动把输入区修改为相应路径": "After uploading the file, the input area will be automatically modified to the corresponding path",
+    "缺少docker运行环境！": "Missing docker runtime environment!",
+    "暂不支持中转": "Transit is not supported temporarily",
+    "一些第三方接口的出现这样的错误": "Some third-party interfaces encounter such errors",
+    "项目Wiki": "Project Wiki",
+    "但是我们把上一帧同样加上": "But we also add the previous frame",
+    "AutoGen 执行失败": "AutoGen execution failed",
+    "程序抵达用户反馈节点": "The program reaches the user feedback node",
+    "预制功能": "Prefabricated Function",
+    "输入新按钮名称": "Enter the new button name",
+    "| 不需要输入参数": "| No input parameters required",
+    "如果有新文件出现": "If there is a new file",
+    "Bug反馈": "Bug Feedback",
+    "指定翻译成何种语言": "Specify the language to translate into",
+    "点击保存当前的对话按钮": "Click the save current conversation button",
+    "如果您需要补充些什么": "If you need to add something",
+    "HTTPS 秘钥和证书": "HTTPS Key and Certificate",
+    "输入exit": "Enter exit",
+    "输入新提示后缀": "Enter a new prompt suffix",
+    "如果是文本文件": "If it is a text file",
+    "支持动态切换主题": "Support dynamic theme switching",
+    "并与self.previous_work_dir_files中所记录的文件进行对比": "And compare with the files recorded in self.previous_work_dir_files",
+    "作者 Microsoft & Binary-Husky": "Author Microsoft & Binary-Husky",
+    "请在自定义菜单中定义提示词前缀": "Please define the prefix of the prompt word in the custom menu",
+    "一般情况下您不需要说什么": "In general, you don't need to say anything",
+    "「暗色主题已启用": "Dark theme enabled",
+    "继续向服务器发送n次音频数据": "Continue to send audio data to the server n times",
+    "获取fp的拓展名": "Get the extension name of fp",
+    "指令安装内置Gradio及其他依赖": "Command to install built-in Gradio and other dependencies",
+    "查看自动更新": "Check for automatic updates",
+    "则更新self.previous_work_dir_files中": "Then update in self.previous_work_dir_files",
+    "看门狗耐心": "Watchdog patience",
+    "检测到新生图像": "Detected new image",
+    "等待AutoGen执行结果": "Waiting for AutoGen execution result",
+    "自定义菜单": "Custom menu",
+    "保持链接激活": "Keep the link active",
+    "已经被新插件取代": "Has been replaced by a new plugin",
+    "检查当前的模型是否符合要求": "Check if the current model meets the requirements",
+    "交互功能模板Demo函数": "Interactive function template Demo function",
+    "上一帧没有人声": "No human voice in the previous frame",
+    "用于判断异常": "Used to judge exceptions",
+    "请阅读Wiki": "Please read the Wiki",
+    "查找wallhaven.cc的壁纸": "Search for wallpapers on wallhaven.cc",
+    "2. 点击任意基础功能区按钮": "2. Click any button in the basic function area",
+    "一些垃圾第三方接口的出现这样的错误": "Some errors caused by garbage third-party interfaces",
+    "再次点击VoidTerminal": "Click VoidTerminal again",
+    "结束信号已明确": "The end signal is clear",
+    "获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议": "Failed to get proxy. It is very likely that you will not be able to access OpenAI family models and Google Scholar without a proxy. It is recommended",
+    "界面外观": "Interface appearance",
+    "如果您想终止程序": "If you want to terminate the program",
+    "2. 点击任意函数插件区按钮": "Click any function plugin area button",
+    "绕过openai访问频率限制": "Bypass openai access frequency limit",
+    "配置暗色主题或亮色主题": "Configure dark theme or light theme",
+    "自定义按钮的最大数量限制": "Maximum number limit for custom buttons",
+    "函数插件区使用说明": "Instructions for function plugin area",
+    "如何语音对话": "How to have a voice conversation",
+    "清空输入区": "Clear input area",
+    "文档清单如下": "The document list is as follows",
+    "由 audio_convertion_thread": "By audio_convertion_thread",
+    "音频的可视化表现": "Visual representation of audio",
+    "然后直接点击“提交”以继续": "Then click 'Submit' to continue",
+    "运行MultiAgentTerminal": "Run MultiAgentTerminal",
+    "自定义按钮1": "Custom button 1",
+    "查看历史上的今天事件": "View events from history",
+    "如遇到Bug请前往": "If you encounter a bug, please go to",
+    "当前插件只支持": "The current plugin only supports",
+    "而不是再次启动一个新的MultiAgentTerminal": "Instead of starting a new MultiAgentTerminal again",
+    "用户代理或助理代理未定义": "User agent or assistant agent is not defined",
+    "运行阶段-": "Running phase-",
+    "随机选择": "Random selection",
+    "直接点击“提交”以继续": "Click 'Submit' to continue",
+    "使用项目内置Gradio获取最优体验! 请运行": "Use the built-in Gradio for the best experience! Please run",
+    "直接点击“提交”以终止AutoGen并解锁": "Click 'Submit' to terminate AutoGen and unlock",
+    "Github源代码开源和更新": "Github source code is open source and updated",
+    "直接将用户输入传递给它": "Pass user input directly to it",
+    "这是一个面向开发者的插件Demo": "This is a plugin demo for developers",
+    "帮助": "Help",
+    "普通对话使用说明": "Instructions for normal conversation",
+    "自定义按钮": "Custom button",
+    "即使没有声音": "Even without sound",
+    "⭐ 主进程": "⭐ Main process",
+    "基础功能区使用说明": "Basic Function Area Usage Instructions",
+    "提前读取一些信息": "Read some information in advance",
+    "当用户点击了“等待反馈”按钮时": "When the user clicks the 'Wait for Feedback' button",
+    "选择一个需要自定义基础功能区按钮": "Select a button in the Basic Function Area that needs to be customized",
+    "VoidTerminal使用说明": "VoidTerminal Usage Instructions",
+    "兼容一下吧": "Let's make it compatible",
+    "⭐⭐ 子进程执行": "⭐⭐ Subprocess execution",
+    "首次": "For the first time",
+    "则直接显示文本内容": "Then display the text content directly",
+    "更新状态": "Update status",
+    "2. 点击提交": "2. Click Submit",
+    "⭐⭐ 子进程": "⭐⭐ Subprocess",
+    "输入新提示前缀": "Enter a new prompt prefix",
+    "等待用户输入超时": "Wait for user input timeout",
+    "把新文件和发生变化的文件的路径记录到 change_list 中": "Record the paths of new files and files that have changed in change_list",
+    "或者上传文件": "Or upload a file",
+    "或者文件的修改时间发生变化": "Or the modification time of the file has changed",
+    "1. 输入路径/问题": "1. Enter path/question",
+    "尝试直接连接": "Try to connect directly",
+    "未来将删除": "Will be deleted in the future",
+    "请在自定义菜单中定义提示词后缀": "Please define the suffix of the prompt word in the custom menu",
+    "将executor存储到cookie中": "Store the executor in the cookie",
+    "1. 输入问题": "1. Enter question",
+    "发送一些音频片段给服务器": "Send some audio clips to the server",
+    "点击VoidTerminal": "Click VoidTerminal",
+    "扫描路径下的所有文件": "Scan all files under the path",
+    "检测到新生文档": "Detect new documents",
+    "预热tiktoken模块": "Preheat the tiktoken module",
+    "等待您的进一步指令": "Waiting for your further instructions",
+    "实时语音对话": "Real-time voice conversation",
+    "确认并保存": "Confirm and save",
+    "「亮色主题已启用": "Light theme enabled",
+    "终止AutoGen程序": "Terminate AutoGen program",
+    "然后根据提示输入指令": "Then enter the command as prompted",
+    "请上传本地文件/压缩包供“函数插件区”功能调用": "Please upload local files/zip packages for 'Function Plugin Area' function call",
+    "上传文件": "Upload file",
+    "上一帧是否有人说话": "Was there anyone speaking in the previous frame",
+    "这是一个时刻聆听着的语音对话助手 | 没有输入参数": "This is a voice conversation assistant that is always listening | No input parameters",
+    "常见问题请查阅": "Please refer to the FAQ for common questions",
+    "更换模型 & Prompt": "Change model & Prompt",
+    "如何保存对话": "How to save the conversation",
+    "处理任务": "Process task",
+    "加载已保存": "Load saved",
+    "打开浏览器页面": "Open browser page",
+    "解锁插件": "Unlock plugin",
+    "如果话筒激活 / 如果处于回声收尾阶段": "If the microphone is active / If it is in the echo tail stage"
 }

docs/translate_japanese.json

@@ -782,7 +782,7 @@
     "主进程统一调用函数接口": "メインプロセスが関数インターフェースを統一的に呼び出します",
     "再例如一个包含了待处理文件的路径": "処理待ちのファイルを含むパスの例",
     "负责把学术论文准确翻译成中文": "学術論文を正確に中国語に翻訳する責任があります",
-    "函数的说明请见 request_llm/bridge_all.py": "関数の説明については、request_llm/bridge_all.pyを参照してください",
+    "函数的说明请见 request_llms/bridge_all.py": "関数の説明については、request_llms/bridge_all.pyを参照してください",
     "然后回车提交": "そしてEnterを押して提出してください",
     "防止爆token": "トークンの爆発を防止する",
     "Latex项目全文中译英": "LaTeXプロジェクト全文の中国語から英語への翻訳",
@@ -854,7 +854,7 @@
     "查询版本和用户意见": "バージョンとユーザーの意見を検索する",
     "提取摘要": "要約を抽出する",
     "在gpt输出代码的中途": "GPTがコードを出力する途中で",
-    "如256x256": "256x256のように",
+    "如1024x1024": "1024x1024のように",
     "概括其内容": "内容を要約する",
     "剩下的情况都开头除去": "残りの場合はすべて先頭を除去する",
     "至少一个线程任务意外失败": "少なくとも1つのスレッドタスクが予期しない失敗をした",
@@ -1007,7 +1007,6 @@
     "第一部分": "第1部分",
     "的分析如下": "の分析は以下の通りです",
     "解决一个mdx_math的bug": "mdx_mathのバグを解決する",
-    "底部输入区": "下部の入力エリア",
     "函数插件输入输出接驳区": "関数プラグインの入出力接続エリア",
     "打开浏览器": "ブラウザを開く",
     "免费用户填3": "無料ユーザーは3を入力してください",
@@ -1617,7 +1616,7 @@
     "正在重试": "再試行中",
     "从而更全面地理解项目的整体功能": "プロジェクトの全体的な機能をより理解するために",
     "正在等您说完问题": "質問が完了するのをお待ちしています",
-    "使用教程详情见 request_llm/README.md": "使用方法の詳細については、request_llm/README.mdを参照してください",
+    "使用教程详情见 request_llms/README.md": "使用方法の詳細については、request_llms/README.mdを参照してください",
     "6.25 加入判定latex模板的代码": "6.25 テンプレートの判定コードを追加",
     "找不到任何音频或视频文件": "音声またはビデオファイルが見つかりません",
     "请求GPT模型的": "GPTモデルのリクエスト",

docs/translate_std.json

@@ -90,5 +90,9 @@
     "解析PDF_基于GROBID": "ParsePDF_BasedOnGROBID",
     "虚空终端主路由": "VoidTerminalMainRoute",
     "批量翻译PDF文档_NOUGAT": "BatchTranslatePDFDocuments_NOUGAT",
-    "解析PDF_基于NOUGAT": "ParsePDF_NOUGAT"
+    "解析PDF_基于NOUGAT": "ParsePDF_NOUGAT",
+    "解析一个Matlab项目": "AnalyzeAMatlabProject",
+    "函数动态生成": "DynamicFunctionGeneration",
+    "多智能体终端": "MultiAgentTerminal",
+    "多智能体": "MultiAgent"
 }

docs/translate_traditionalchinese.json

@@ -123,7 +123,7 @@
     "的第": "的第",
     "减少重复": "減少重複",
     "如果超过期限没有喂狗": "如果超過期限沒有餵狗",
-    "函数的说明请见 request_llm/bridge_all.py": "函數的說明請見 request_llm/bridge_all.py",
+    "函数的说明请见 request_llms/bridge_all.py": "函數的說明請見 request_llms/bridge_all.py",
     "第7步": "第7步",
     "说": "說",
     "中途接收可能的终止指令": "中途接收可能的終止指令",
@@ -346,7 +346,6 @@
     "情况会好转": "情況會好轉",
     "超过512个": "超過512個",
     "多线": "多線",
-    "底部输入区": "底部輸入區",
     "合并小写字母开头的段落块并替换为空格": "合併小寫字母開頭的段落塊並替換為空格",
     "暗色主题": "暗色主題",
     "提高限制请查询": "提高限制請查詢",
@@ -1148,7 +1147,7 @@
     "Y+回车=确认": "Y+回車=確認",
     "正在同时咨询ChatGPT和ChatGLM……": "正在同時諮詢ChatGPT和ChatGLM……",
     "根据 heuristic 规则": "根據heuristic規則",
-    "如256x256": "如256x256",
+    "如1024x1024": "如1024x1024",
     "函数插件区": "函數插件區",
     "*** API_KEY 导入成功": "*** API_KEY 導入成功",
     "请对下面的程序文件做一个概述文件名是": "請對下面的程序文件做一個概述文件名是",
@@ -1888,7 +1887,7 @@
     "请继续分析其他源代码": "請繼續分析其他源代碼",
     "质能方程式": "質能方程式",
     "功能尚不稳定": "功能尚不穩定",
-    "使用教程详情见 request_llm/README.md": "使用教程詳情見 request_llm/README.md",
+    "使用教程详情见 request_llms/README.md": "使用教程詳情見 request_llms/README.md",
     "从以上搜索结果中抽取信息": "從以上搜索結果中抽取信息",
     "虽然PDF生成失败了": "雖然PDF生成失敗了",
     "找图片": "尋找圖片",

docs/use_azure.md

@@ -1,3 +1,42 @@
+# 微软Azure云接入指南
+
+## 方法一（旧方法，只能接入一个Azure模型）
+
+- 通过以下教程，获取AZURE_ENDPOINT，AZURE_API_KEY，AZURE_ENGINE，直接修改 config 配置即可。配置的修改方法见本项目wiki。
+
+## 方法二（新方法，接入多个Azure模型，并支持动态切换）
+
+- 在方法一的基础上，注册并获取多组 AZURE_ENDPOINT，AZURE_API_KEY，AZURE_ENGINE
+- 修改config中的AZURE_CFG_ARRAY和AVAIL_LLM_MODELS配置项，按照格式填入多个Azure模型的配置，如下所示：
+
+```
+AZURE_CFG_ARRAY = {
+    "azure-gpt-3.5": # 第一个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-3.5"加入AVAIL_LLM_MODELS（模型下拉菜单）
+    {
+        "AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
+        "AZURE_API_KEY": "cccccccccccccccccccccccccccccccc",
+        "AZURE_ENGINE": "填入你亲手写的部署名1",
+        "AZURE_MODEL_MAX_TOKEN": 4096,
+    },
+    "azure-gpt-4":  # 第二个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-4"加入AVAIL_LLM_MODELS（模型下拉菜单）
+    {
+        "AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
+        "AZURE_API_KEY": "dddddddddddddddddddddddddddddddd",
+        "AZURE_ENGINE": "填入你亲手写的部署名2",
+        "AZURE_MODEL_MAX_TOKEN": 8192,
+    },
+    "azure-gpt-3.5-16k":  # 第三个模型，azure模型必须以"azure-"开头，注意您还需要将"azure-gpt-3.5-16k"加入AVAIL_LLM_MODELS（模型下拉菜单）
+    {
+        "AZURE_ENDPOINT": "https://你亲手写的api名称.openai.azure.com/",
+        "AZURE_API_KEY": "eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee",
+        "AZURE_ENGINE": "填入你亲手写的部署名3",
+        "AZURE_MODEL_MAX_TOKEN": 16384,
+    },
+}
+```
+
+
+
 # 通过微软Azure云服务申请 Openai API
 
 由于Openai和微软的关系，现在是可以通过微软的Azure云计算服务直接访问openai的api，免去了注册和网络的问题。

main.py

@@ -1,24 +1,36 @@
 import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
+import pickle
+import base64
 
 def main():
     import gradio as gr
-    if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖，请务必用 pip install -r requirements.txt 指令安装依赖，详情信息见requirements.txt"
+    if gr.__version__ not in ['3.32.6']: 
-    from request_llm.bridge_all import predict
+        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.")
+    from request_llms.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
     # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
     ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
+    DARK_MODE, NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('DARK_MODE', 'NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
+    INIT_SYS_PROMPT = get_conf('INIT_SYS_PROMPT')
 
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
     from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme
 
-    initial_prompt = "Serve me as a writing and programming assistant."
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
-    description =  "代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic)，"
+    description =  "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), "
-    description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)"
+    description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)."
+    description += "</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), "
+    description += "如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues)."
+    description += "</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交"
+    description += "</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮"
+    description += "</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮"
+    description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端"
+    description += "</br></br>如何保存对话: 点击保存当前的对话按钮"
+    description += "</br></br>如何语音对话: 请阅读Wiki"
 
     # 问询记录, python 版本建议3.9+（越新越好）
     import logging, uuid
@@ -35,7 +47,7 @@ def main():
 
     # 高级函数插件
     from crazy_functional import get_crazy_functions
-    DEFAULT_FN_GROUPS, = get_conf('DEFAULT_FN_GROUPS')
+    DEFAULT_FN_GROUPS = get_conf('DEFAULT_FN_GROUPS')
     plugins = get_crazy_functions()
     all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
     match_group = lambda tags, groups: any([g in groups for g in tags.split('|')])
@@ -58,9 +70,11 @@ def main():
         CHATBOT_HEIGHT /= 2
 
     cancel_handles = []
+    customize_btns = {}
+    predefined_btns = {}
     with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
         gr.HTML(title_html)
-        secret_css, secret_font = gr.Textbox(visible=False), gr.Textbox(visible=False)
+        secret_css, dark_mode, persistent_cookie = gr.Textbox(visible=False), gr.Textbox(DARK_MODE, visible=False), gr.Textbox(visible=False)
         cookies = gr.State(load_chat_cookies())
         with gr_L1():
             with gr_L2(scale=2, elem_id="gpt-chat"):
@@ -72,23 +86,28 @@ def main():
                     with gr.Row():
                         txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
                     with gr.Row():
-                        submitBtn = gr.Button("提交", variant="primary")
+                        submitBtn = gr.Button("提交", elem_id="elem_submit", variant="primary")
                     with gr.Row():
-                        resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
+                        resetBtn = gr.Button("重置", elem_id="elem_reset", variant="secondary"); resetBtn.style(size="sm")
-                        stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
+                        stopBtn = gr.Button("停止", elem_id="elem_stop", variant="secondary"); stopBtn.style(size="sm")
-                        clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm")
+                        clearBtn = gr.Button("清除", elem_id="elem_clear", variant="secondary", visible=False); clearBtn.style(size="sm")
                     if ENABLE_AUDIO: 
                         with gr.Row():
-                            audio_mic = gr.Audio(source="microphone", type="numpy", streaming=True, show_label=False).style(container=False)
+                            audio_mic = gr.Audio(source="microphone", type="numpy", elem_id="elem_audio", streaming=True, show_label=False).style(container=False)
                     with gr.Row():
                         status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}", elem_id="state-panel")
                 with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
                     with gr.Row():
+                        for k in range(NUM_CUSTOM_BASIC_BTN):
+                            customize_btn = gr.Button("自定义按钮" + str(k+1), visible=False, variant="secondary", info_str=f'基础功能区: 自定义按钮')
+                            customize_btn.style(size="sm")
+                            customize_btns.update({"自定义按钮" + str(k+1): customize_btn})
                         for k in functional:
                             if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
                             variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
-                            functional[k]["Button"] = gr.Button(k, variant=variant)
+                            functional[k]["Button"] = gr.Button(k, variant=variant, info_str=f'基础功能区: {k}')
                             functional[k]["Button"].style(size="sm")
+                            predefined_btns.update({k: functional[k]["Button"]})
                 with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
                     with gr.Row():
                         gr.Markdown("插件可读取“输入区”文本/路径作为参数（上传文件自动修正路径）")
@@ -100,7 +119,9 @@ def main():
                             if not plugin.get("AsButton", True): continue
                             visible = True if match_group(plugin['Group'], DEFAULT_FN_GROUPS) else False
                             variant = plugins[k]["Color"] if "Color" in plugin else "secondary"
-                            plugin['Button'] = plugins[k]['Button'] = gr.Button(k, variant=variant, visible=visible).style(size="sm")
+                            info = plugins[k].get("Info", k)
+                            plugin['Button'] = plugins[k]['Button'] = gr.Button(k, variant=variant, 
+                                visible=visible, info_str=f'函数插件区: {info}').style(size="sm")
                     with gr.Row():
                         with gr.Accordion("更多函数插件", open=True):
                             dropdown_fn_list = []
@@ -117,15 +138,28 @@ def main():
                                 switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary").style(size="sm")
                     with gr.Row():
                         with gr.Accordion("点击展开“文件上传区”。上传本地文件/压缩包供函数插件调用。", open=False) as area_file_up:
-                            file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
+                            file_upload = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload")
-                with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN"), elem_id="interact-panel"):
+
-                    system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
+
+        with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden"):
+            with gr.Row():
+                with gr.Tab("上传文件", elem_id="interact-panel"):
+                    gr.Markdown("请上传本地文件/压缩包供“函数插件区”功能调用。请注意: 上传文件后会自动把输入区修改为相应路径。")
+                    file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple")
+    
+                with gr.Tab("更换模型 & Prompt", elem_id="interact-panel"):
+                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
                     top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
                     temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
-                    max_length_sl = gr.Slider(minimum=256, maximum=8192, value=4096, step=1, interactive=True, label="Local LLM MaxLength",)
+                    max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
-                    checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
+                    system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=INIT_SYS_PROMPT)
-                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
+
+                with gr.Tab("界面外观", elem_id="interact-panel"):
                     theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").style(container=False)
+                    checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "浮动输入区", "输入清除键", "插件参数区"], 
+                                                  value=["基础功能区", "函数插件区"], label="显示/隐藏功能区", elem_id='cbs').style(container=False)
+                    checkboxes_2 = gr.CheckboxGroup(["自定义菜单"], 
+                                                  value=[], label="显示/隐藏自定义菜单", elem_id='cbs').style(container=False)
                     dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")
                     dark_mode_btn.click(None, None, None, _js="""() => {
                             if (document.querySelectorAll('.dark').length) {
@@ -135,30 +169,113 @@ def main():
                             }
                         }""",
                     )
+                with gr.Tab("帮助", elem_id="interact-panel"):
                     gr.Markdown(description)
-                with gr.Accordion("备选输入区", open=True, visible=False, elem_id="input-panel2") as area_input_secondary:
+
-                    with gr.Row():
+        with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_input_secondary:
-                        txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
+            with gr.Accordion("浮动输入区", open=True, elem_id="input-panel2"):
-                    with gr.Row():
+                with gr.Row() as row:
-                        submitBtn2 = gr.Button("提交", variant="primary")
+                    row.style(equal_height=True)
-                    with gr.Row():
+                    with gr.Column(scale=10):
+                        txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", lines=8, label="输入区2").style(container=False)
+                    with gr.Column(scale=1, min_width=40):
+                        submitBtn2 = gr.Button("提交", variant="primary"); submitBtn2.style(size="sm")
                         resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm")
                         stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
                         clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
 
+        def to_cookie_str(d):
+            # Pickle the dictionary and encode it as a string
+            pickled_dict = pickle.dumps(d)
+            cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
+            return cookie_value
+        
+        def from_cookie_str(c):
+            # Decode the base64-encoded string and unpickle it into a dictionary
+            pickled_dict = base64.b64decode(c.encode('utf-8'))
+            return pickle.loads(pickled_dict)
+
+        with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_customize:
+            with gr.Accordion("自定义菜单", open=True, elem_id="edit-panel"):
+                with gr.Row() as row:
+                    with gr.Column(scale=10):
+                        AVAIL_BTN = [btn for btn in customize_btns.keys()] + [k for k in functional]
+                        basic_btn_dropdown = gr.Dropdown(AVAIL_BTN, value="自定义按钮1", label="选择一个需要自定义基础功能区按钮").style(container=False)
+                        basic_fn_title = gr.Textbox(show_label=False, placeholder="输入新按钮名称", lines=1).style(container=False)
+                        basic_fn_prefix = gr.Textbox(show_label=False, placeholder="输入新提示前缀", lines=4).style(container=False)
+                        basic_fn_suffix = gr.Textbox(show_label=False, placeholder="输入新提示后缀", lines=4).style(container=False)
+                    with gr.Column(scale=1, min_width=70):
+                        basic_fn_confirm = gr.Button("确认并保存", variant="primary"); basic_fn_confirm.style(size="sm")
+                        basic_fn_load    = gr.Button("加载已保存", variant="primary"); basic_fn_load.style(size="sm")
+                        def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix):
+                            ret = {}
+                            customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
+                            customize_fn_overwrite_.update({
+                                basic_btn_dropdown_:
+                                    {
+                                        "Title":basic_fn_title,
+                                        "Prefix":basic_fn_prefix,
+                                        "Suffix":basic_fn_suffix,
+                                    }
+                                }
+                            )
+                            cookies_.update(customize_fn_overwrite_)
+                            if basic_btn_dropdown_ in customize_btns:
+                                ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
+                            else:
+                                ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
+                            ret.update({cookies: cookies_})
+                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
+                            except: persistent_cookie_ = {}
+                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_   # dict update new value
+                            persistent_cookie_ = to_cookie_str(persistent_cookie_)         # persistent cookie to dict
+                            ret.update({persistent_cookie: persistent_cookie_})                             # write persistent cookie
+                            return ret
+                        
+                        def reflesh_btn(persistent_cookie_, cookies_):
+                            ret = {}
+                            for k in customize_btns:
+                                ret.update({customize_btns[k]: gr.update(visible=False, value="")})
+
+                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
+                            except: return ret
+                            
+                            customize_fn_overwrite_ = persistent_cookie_.get("custom_bnt", {})
+                            cookies_['customize_fn_overwrite'] = customize_fn_overwrite_
+                            ret.update({cookies: cookies_})
+
+                            for k,v in persistent_cookie_["custom_bnt"].items():
+                                if v['Title'] == "": continue
+                                if k in customize_btns: ret.update({customize_btns[k]: gr.update(visible=True, value=v['Title'])})
+                                else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
+                            return ret
+                        
+                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies],[cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h = basic_fn_confirm.click(assign_btn, [persistent_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix], 
+                                                   [persistent_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""") # save persistent cookie
+
         # 功能区显示开关与功能区的互动
         def fn_area_visibility(a):
             ret = {}
             ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
             ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
-            ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))})
+            ret.update({area_input_primary: gr.update(visible=("浮动输入区" not in a))})
-            ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
+            ret.update({area_input_secondary: gr.update(visible=("浮动输入区" in a))})
             ret.update({clearBtn: gr.update(visible=("输入清除键" in a))})
             ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))})
             ret.update({plugin_advanced_arg: gr.update(visible=("插件参数区" in a))})
-            if "底部输入区" in a: ret.update({txt: gr.update(value="")})
+            if "浮动输入区" in a: ret.update({txt: gr.update(value="")})
             return ret
         checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2, plugin_advanced_arg] )
+
+        # 功能区显示开关与功能区的互动
+        def fn_area_visibility_2(a):
+            ret = {}
+            ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
+            return ret
+        checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
+
         # 整理反复出现的控件句柄组合
         input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
         output_combo = [cookies, chatbot, history, status]
@@ -182,8 +299,12 @@ def main():
             if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
             click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
             cancel_handles.append(click_handle)
+        for btn in customize_btns.values():
+            click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
+            cancel_handles.append(click_handle)
         # 文件上传区，接收文件后与chatbot的互动
         file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
+        file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
         # 函数插件-固定按钮区
         for k in plugins:
             if not plugins[k].get("AsButton", True): continue
@@ -193,7 +314,8 @@ def main():
         # 函数插件-下拉菜单与随变按钮的互动
         def on_dropdown_changed(k):
             variant = plugins[k]["Color"] if "Color" in plugins[k] else "secondary"
-            ret = {switchy_bt: gr.update(value=k, variant=variant)}
+            info = plugins[k].get("Info", k)
+            ret = {switchy_bt: gr.update(value=k, variant=variant, info_str=f'函数插件区: {info}')}
             if plugins[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
                 ret.update({plugin_advanced_arg: gr.update(visible=True,  label=f"插件[{k}]的高级参数说明：" + plugins[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
             else:
@@ -266,40 +388,60 @@ def main():
             cookies.update({'uuid': uuid.uuid4()})
             return cookies
         demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
-        demo.load(lambda: 0, inputs=None, outputs=None, _js='()=>{GptAcademicJavaScriptInit();}')
+        darkmode_js = """(dark) => {
+            dark = dark == "True";
+            if (document.querySelectorAll('.dark').length) {
+                if (!dark){
+                    document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
+                }
+            } else {
+                if (dark){
+                    document.querySelector('body').classList.add('dark');
+                }
+            }
+        }"""
+        load_cookie_js = """(persistent_cookie) => {
+            return getCookie("persistent_cookie");
+        }"""
+        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=load_cookie_js)
+        demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js)    # 配置暗色主题或亮色主题
+        demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
         
     # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
-    def auto_opentab_delay():
+    def run_delayed_tasks():
         import threading, webbrowser, time
         print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
-        print(f"\t（亮色主题）: http://localhost:{PORT}")
+        if DARK_MODE:   print(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
-        print(f"\t（暗色主题）: http://localhost:{PORT}/?__theme=dark")
+        else:           print(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
-        def open():
-            time.sleep(2)       # 打开浏览器
-            DARK_MODE, = get_conf('DARK_MODE')
-            if DARK_MODE: webbrowser.open_new_tab(f"http://localhost:{PORT}/?__theme=dark")
-            else: webbrowser.open_new_tab(f"http://localhost:{PORT}")
-        threading.Thread(target=open, name="open-browser", daemon=True).start()
-        threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
-        threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start()
 
-    auto_opentab_delay()
+        def auto_updates(): time.sleep(0); auto_update()
+        def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
+        def warm_up_mods(): time.sleep(4); warm_up_modules()
+        
+        threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
+        threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面
+        threading.Thread(target=warm_up_mods, name="warm-up", daemon=True).start()      # 预热tiktoken模块
+
+    run_delayed_tasks()
     demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
         quiet=True,
         server_name="0.0.0.0", 
+        ssl_keyfile=None if SSL_KEYFILE == "" else SSL_KEYFILE,
+        ssl_certfile=None if SSL_CERTFILE == "" else SSL_CERTFILE,
+        ssl_verify=False,
         server_port=PORT,
-        favicon_path="docs/logo.png", 
+        favicon_path=os.path.join(os.path.dirname(__file__), "docs/logo.png"), 
         auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None,
-        blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
+        blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
 
     # 如果需要在二级路径下运行
-    # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
+    # CUSTOM_PATH = get_conf('CUSTOM_PATH')
     # if CUSTOM_PATH != "/": 
     #     from toolbox import run_gradio_in_subpath
     #     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
     # else: 
     #     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png",
-    #                 blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
+    #                 blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
 
 if __name__ == "__main__":
     main()

multi_language.py

@@ -13,6 +13,7 @@
 
         4. Run `python multi_language.py`. 
             Note: You need to run it multiple times to increase translation coverage because GPT makes mistakes sometimes.
+           (You can also run `CACHE_ONLY=True python multi_language.py` to use cached translation mapping)
 
         5. Find the translated program in `multi-language\English\*`
    
@@ -35,7 +36,9 @@ import pickle
 import time
 from toolbox import get_conf
 
-CACHE_FOLDER, = get_conf('PATH_LOGGING')
+CACHE_ONLY = os.environ.get('CACHE_ONLY', False)
+
+CACHE_FOLDER = get_conf('PATH_LOGGING')
 
 blacklist = ['multi-language', CACHE_FOLDER, '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv']
 
@@ -336,6 +339,9 @@ def step_1_core_key_translate():
         if d not in cached_translation_keys: 
             need_translate.append(d)
 
+    if CACHE_ONLY:
+        need_translate_mapping = {}
+    else:
         need_translate_mapping = trans(need_translate, language=LANG_STD, special=True)
     map_to_json(need_translate_mapping, language=LANG_STD)
     cached_translation = read_map_from_json(language=LANG_STD)
@@ -476,7 +482,9 @@ def step_2_core_key_translate():
         if d not in cached_translation_keys: 
             need_translate.append(d)
 
-
+    if CACHE_ONLY:
+        up = {}
+    else:
         up = trans_json(need_translate, language=LANG, special=False)
     map_to_json(up, language=LANG)
     cached_translation = read_map_from_json(language=LANG)

request_llm/bridge_chatglm.py

@@ -1,167 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf, ProxyNetworkActivate
-from multiprocessing import Process, Pipe
-
-load_message = "ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-#################################################################################
-class GetGLMHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.chatglm_model = None
-        self.chatglm_tokenizer = None
-        self.info = ""
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            import sentencepiece
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            self.info = "缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
-            self.success = False
-
-    def ready(self):
-        return self.chatglm_model is not None
-
-    def run(self):
-        # 子进程执行
-        # 第一次运行，加载参数
-        retry = 0
-        LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
-
-        if LOCAL_MODEL_QUANT == "INT4":         # INT4
-            _model_name_ = "THUDM/chatglm2-6b-int4"
-        elif LOCAL_MODEL_QUANT == "INT8":       # INT8
-            _model_name_ = "THUDM/chatglm2-6b-int8"
-        else:
-            _model_name_ = "THUDM/chatglm2-6b"  # FP16
-
-        while True:
-            try:
-                with ProxyNetworkActivate('Download_LLM'):
-                    if self.chatglm_model is None:
-                        self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
-                        if device=='cpu':
-                            self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
-                        else:
-                            self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
-                        self.chatglm_model = self.chatglm_model.eval()
-                        break
-                    else:
-                        break
-            except:
-                retry += 1
-                if retry > 3: 
-                    self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。')
-                    raise RuntimeError("不能正常加载ChatGLM的参数！")
-
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            # 收到消息，开始请求
-            try:
-                for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
-                    self.child.send(response)
-                    # # 中途接收可能的终止指令（如果有的话）
-                    # if self.child.poll(): 
-                    #     command = self.child.recv()
-                    #     if command == '[Terminate]': break
-            except:
-                from toolbox import trimmed_format_exc
-                self.child.send('[Local Message] Call ChatGLM fail.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # 主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-global glm_handle
-glm_handle = None
-#################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global glm_handle
-    if glm_handle is None:
-        glm_handle = GetGLMHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + glm_handle.info
-        if not glm_handle.success: 
-            error = glm_handle.info
-            glm_handle = None
-            raise RuntimeError(error)
-
-    # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    history_feedin.append(["What can I do?", sys_prompt])
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    global glm_handle
-    if glm_handle is None:
-        glm_handle = GetGLMHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + glm_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not glm_handle.success: 
-            glm_handle = None
-            return
-
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    # 处理历史信息
-    history_feedin = []
-    history_feedin.append(["What can I do?", system_prompt] )
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收chatglm的回复
-    response = "[Local Message]: 等待ChatGLM响应中 ..."
-    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == "[Local Message]: 等待ChatGLM响应中 ...":
-        response = "[Local Message]: ChatGLM响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)

request_llm/local_llm_class.py

@@ -1,180 +0,0 @@
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf, Singleton
-from multiprocessing import Process, Pipe
-
-def SingletonLocalLLM(cls):
-    """
-    一个单实例装饰器
-    """
-    _instance = {}
-    def _singleton(*args, **kargs):
-        if cls not in _instance:
-            _instance[cls] = cls(*args, **kargs)
-            return _instance[cls]
-        elif _instance[cls].corrupted:
-            _instance[cls] = cls(*args, **kargs)
-            return _instance[cls]
-        else:
-            return _instance[cls]
-    return _singleton
-
-class LocalLLMHandle(Process):
-    def __init__(self):
-        # ⭐主进程执行
-        super().__init__(daemon=True)
-        self.corrupted = False
-        self.load_model_info()
-        self.parent, self.child = Pipe()
-        self.running = True
-        self._model = None
-        self._tokenizer = None
-        self.info = ""
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-
-    def load_model_info(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        raise NotImplementedError("Method not implemented yet")
-        self.model_name = ""
-        self.cmd_to_install = ""
-
-    def load_model_and_tokenizer(self):
-        """
-        This function should return the model and the tokenizer
-        """
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        raise NotImplementedError("Method not implemented yet")
-
-    def llm_stream_generator(self, **kwargs):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        raise NotImplementedError("Method not implemented yet")
-        
-    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
-        raise NotImplementedError("Method not implemented yet")
-
-    def check_dependency(self):
-        # ⭐主进程执行
-        try:
-            self.try_to_import_special_deps()
-            self.info = "依赖检测通过"
-            self.running = True
-        except:
-            self.info = f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。"
-            self.running = False
-
-    def run(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # 第一次运行，加载参数
-        try:
-            self._model, self._tokenizer = self.load_model_and_tokenizer()
-        except:
-            self.running = False
-            from toolbox import trimmed_format_exc
-            self.child.send(f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            self.child.send('[FinishBad]')
-            raise RuntimeError(f"不能正常加载{self.model_name}的参数！")
-
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            # 收到消息，开始请求
-            try:
-                for response_full in self.llm_stream_generator(**kwargs):
-                    self.child.send(response_full)
-                self.child.send('[Finish]')
-                # 请求处理结束，开始下一个循环
-            except:
-                from toolbox import trimmed_format_exc
-                self.child.send(f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-                self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # ⭐主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res == '[Finish]': 
-                break
-            if res == '[FinishBad]': 
-                self.running = False
-                self.corrupted = True
-                break
-            else: 
-                yield res
-        self.threadLock.release()
-    
-
-
-def get_local_llm_predict_fns(LLMSingletonClass, model_name):
-    load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-    def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-        """
-            ⭐多线程方法
-            函数的说明请见 request_llm/bridge_all.py
-        """
-        _llm_handle = LLMSingletonClass()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
-        if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
-
-        # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
-        history_feedin = []
-        history_feedin.append([sys_prompt, "Certainly!"])
-        for i in range(len(history)//2):
-            history_feedin.append([history[2*i], history[2*i+1]] )
-
-        watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-        response = ""
-        for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-            if len(observe_window) >= 1:
-                observe_window[0] = response
-            if len(observe_window) >= 2:  
-                if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
-        return response
-
-
-
-    def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-        """
-            ⭐单线程方法
-            函数的说明请见 request_llm/bridge_all.py
-        """
-        chatbot.append((inputs, ""))
-
-        _llm_handle = LLMSingletonClass()
-        chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
-
-        if additional_fn is not None:
-            from core_functional import handle_core_functionality
-            inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-        # 处理历史信息
-        history_feedin = []
-        history_feedin.append([system_prompt, "Certainly!"])
-        for i in range(len(history)//2):
-            history_feedin.append([history[2*i], history[2*i+1]] )
-
-        # 开始接收回复
-        response = f"[Local Message]: 等待{model_name}响应中 ..."
-        for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-            chatbot[-1] = (inputs, response)
-            yield from update_ui(chatbot=chatbot, history=history)
-
-        # 总结输出
-        if response == f"[Local Message]: 等待{model_name}响应中 ...":
-            response = f"[Local Message]: {model_name}响应异常 ..."
-        history.extend([inputs, response])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    return predict_no_ui_long_connection, predict

request_llms/README.md

@@ -2,7 +2,7 @@
 
 ## ChatGLM
 
-- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt`
+- 安装依赖 `pip install -r request_llms/requirements_chatglm.txt`
 - 修改配置，在config.py中将LLM_MODEL的值改为"chatglm"
 
 ``` sh

request_llms/bridge_all.py

@@ -19,8 +19,8 @@ from .bridge_chatgpt import predict as chatgpt_ui
 from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
 from .bridge_chatglm import predict as chatglm_ui
 
-from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
+from .bridge_chatglm3 import predict_no_ui_long_connection as chatglm3_noui
-from .bridge_chatglm import predict as chatglm_ui
+from .bridge_chatglm3 import predict as chatglm3_ui
 
 from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
 from .bridge_qianfan import predict as qianfan_ui
@@ -56,7 +56,7 @@ if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
 azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
 # 兼容旧版的配置
 try:
-    API_URL, = get_conf("API_URL")
+    API_URL = get_conf("API_URL")
     if API_URL != "https://api.openai.com/v1/chat/completions": 
         openai_endpoint = API_URL
         print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
@@ -94,7 +94,7 @@ model_info = {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
-        "max_token": 1024*16,
+        "max_token": 16385,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
@@ -112,7 +112,16 @@ model_info = {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
-        "max_token": 1024 * 16,
+        "max_token": 16385,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+    "gpt-3.5-turbo-1106": {#16k
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 16385,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
@@ -135,6 +144,24 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    "gpt-4-1106-preview": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 128000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+    },
+
+    "gpt-3.5-random": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 4096,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+    },
+    
     # azure openai
     "azure-gpt-3.5":{
         "fn_with_ui": chatgpt_ui,
@@ -150,11 +177,11 @@ model_info = {
         "fn_without_ui": chatgpt_noui,
         "endpoint": azure_endpoint,
         "max_token": 8192,
-        "tokenizer": tokenizer_gpt35,
+        "tokenizer": tokenizer_gpt4,
-        "token_cnt": get_token_num_gpt35,
+        "token_cnt": get_token_num_gpt4,
     },
 
-    # api_2d
+    # api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
     "api2d-gpt-3.5-turbo": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -190,6 +217,14 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "chatglm3": {
+        "fn_with_ui": chatglm3_ui,
+        "fn_without_ui": chatglm3_noui,
+        "endpoint": None,
+        "max_token": 8192,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
     "qianfan": {
         "fn_with_ui": qianfan_ui,
         "fn_without_ui": qianfan_noui,
@@ -200,6 +235,20 @@ model_info = {
     },
 }
 
+# -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
+for model in AVAIL_LLM_MODELS:
+    if model.startswith('api2d-') and (model.replace('api2d-','') in model_info.keys()):
+        mi = model_info[model.replace('api2d-','')]
+        mi.update({"endpoint": api2d_endpoint})
+        model_info.update({model: mi})
+
+# -=-=-=-=-=-=- azure 对齐支持 -=-=-=-=-=-=-
+for model in AVAIL_LLM_MODELS:
+    if model.startswith('azure-') and (model.replace('azure-','') in model_info.keys()):
+        mi = model_info[model.replace('azure-','')]
+        mi.update({"endpoint": azure_endpoint})
+        model_info.update({model: mi})
+
 # -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
 if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
     from .bridge_claude import predict_no_ui_long_connection as claude_noui
@@ -433,6 +482,22 @@ if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
         })
     except:
         print(trimmed_format_exc())
+if "sparkv3" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+    try:
+        from .bridge_spark import predict_no_ui_long_connection as spark_noui
+        from .bridge_spark import predict as spark_ui
+        model_info.update({
+            "sparkv3": {
+                "fn_with_ui": spark_ui,
+                "fn_without_ui": spark_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
 if "llama2" in AVAIL_LLM_MODELS:   # llama2
     try:
         from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
@@ -449,6 +514,46 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
         })
     except:
         print(trimmed_format_exc())
+if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai
+    try:
+        from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
+        from .bridge_zhipu import predict as zhipu_ui
+        model_info.update({
+            "zhipuai": {
+                "fn_with_ui": zhipu_ui,
+                "fn_without_ui": zhipu_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
+
+# <-- 用于定义和切换多个azure模型 -->
+AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
+if len(AZURE_CFG_ARRAY) > 0:
+    for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
+        # 可能会覆盖之前的配置，但这是意料之中的
+        if not azure_model_name.startswith('azure'): 
+            raise ValueError("AZURE_CFG_ARRAY中配置的模型必须以azure开头")
+        endpoint_ = azure_cfg_dict["AZURE_ENDPOINT"] + \
+            f'openai/deployments/{azure_cfg_dict["AZURE_ENGINE"]}/chat/completions?api-version=2023-05-15'
+        model_info.update({
+            azure_model_name: {
+                "fn_with_ui": chatgpt_ui,
+                "fn_without_ui": chatgpt_noui,
+                "endpoint": endpoint_,
+                "azure_api_key": azure_cfg_dict["AZURE_API_KEY"],
+                "max_token": azure_cfg_dict["AZURE_MODEL_MAX_TOKEN"],
+                "tokenizer": tokenizer_gpt35,   # tokenizer只用于粗估token数量
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+        if azure_model_name not in AVAIL_LLM_MODELS:
+            AVAIL_LLM_MODELS += [azure_model_name]
+
 
 
 
@@ -466,7 +571,7 @@ def LLM_CATCH_EXCEPTION(f):
     return decorated
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
     """
     发送至LLM，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：

request_llms/bridge_chatglm.py

@@ -0,0 +1,78 @@
+model_name = "ChatGLM"
+cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
+
+
+from transformers import AutoModel, AutoTokenizer
+from toolbox import get_conf, ProxyNetworkActivate
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+class GetGLM2Handle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        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/chatglm2-6b-int4"
+        elif LOCAL_MODEL_QUANT == "INT8":       # INT8
+            _model_name_ = "THUDM/chatglm2-6b-int8"
+        else:
+            _model_name_ = "THUDM/chatglm2-6b"  # 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).float()
+            else:
+                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
+            chatglm_model = chatglm_model.eval()
+
+        self._model = chatglm_model
+        self._tokenizer = chatglm_tokenizer
+        return self._model, self._tokenizer
+
+    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']
+            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,
+                                                         ):
+            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(GetGLM2Handle, model_name)

request_llms/bridge_chatglm3.py

@@ -0,0 +1,77 @@
+model_name = "ChatGLM3"
+cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
+
+
+from transformers import AutoModel, AutoTokenizer
+from toolbox import get_conf, ProxyNetworkActivate
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+class GetGLM3Handle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        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()
+            else:
+                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cuda')
+            chatglm_model = chatglm_model.eval()
+
+        self._model = chatglm_model
+        self._tokenizer = chatglm_tokenizer
+        return self._model, self._tokenizer
+
+    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']
+            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,
+                                                         ):
+            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')

request_llms/bridge_chatglmft.py

@@ -44,7 +44,7 @@ class GetGLMFTHandle(Process):
             self.info = "依赖检测通过"
             self.success = True
         except:
-            self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
+            self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_chatglm.txt`安装ChatGLM的依赖。"
             self.success = False
 
     def ready(self):
@@ -59,11 +59,11 @@ class GetGLMFTHandle(Process):
                 if self.chatglmft_model is None:
                     from transformers import AutoConfig
                     import torch
-                    # conf = 'request_llm/current_ptune_model.json'
+                    # conf = 'request_llms/current_ptune_model.json'
                     # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
                     # with open(conf, 'r', encoding='utf8') as f:
                     #     model_args = json.loads(f.read())
-                    CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT')
+                    CHATGLM_PTUNING_CHECKPOINT = get_conf('CHATGLM_PTUNING_CHECKPOINT')
                     assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
                     conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
                     with open(conf, 'r', encoding='utf8') as f:
@@ -87,7 +87,7 @@ class GetGLMFTHandle(Process):
                             new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
                     model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
 
-                    if model_args['quantization_bit'] is not None:
+                    if model_args['quantization_bit'] is not None and model_args['quantization_bit'] != 0:
                         print(f"Quantized to {model_args['quantization_bit']} bit")
                         model = model.quantize(model_args['quantization_bit'])
                     model = model.cuda()
@@ -140,7 +140,7 @@ glmft_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global glmft_handle
     if glmft_handle is None:
@@ -171,7 +171,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -195,13 +195,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收chatglmft的回复
-    response = "[Local Message]: 等待ChatGLMFT响应中 ..."
+    response = "[Local Message] 等待ChatGLMFT响应中 ..."
     for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待ChatGLMFT响应中 ...":
+    if response == "[Local Message] 等待ChatGLMFT响应中 ...":
-        response = "[Local Message]: ChatGLMFT响应异常 ..."
+        response = "[Local Message] ChatGLMFT响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_chatglmonnx.py

@@ -1,5 +1,5 @@
 model_name = "ChatGLM-ONNX"
-cmd_to_install = "`pip install -r request_llm/requirements_chatglm_onnx.txt`"
+cmd_to_install = "`pip install -r request_llms/requirements_chatglm_onnx.txt`"
 
 
 from transformers import AutoModel, AutoTokenizer
@@ -8,7 +8,7 @@ import threading
 import importlib
 from toolbox import update_ui, get_conf
 from multiprocessing import Process, Pipe
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 from .chatglmoonx import ChatGLMModel, chat_template
 
@@ -17,7 +17,6 @@ from .chatglmoonx import ChatGLMModel, chat_template
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
-@SingletonLocalLLM
 class GetONNXGLMHandle(LocalLLMHandle):
 
     def load_model_info(self):
@@ -28,13 +27,13 @@ class GetONNXGLMHandle(LocalLLMHandle):
     def load_model_and_tokenizer(self):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         import os, glob
-        if not len(glob.glob("./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件
+        if not len(glob.glob("./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: # 该模型有七个 bin 文件
             from huggingface_hub import snapshot_download
-            snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llm/ChatGLM-6b-onnx-u8s8")
+            snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llms/ChatGLM-6b-onnx-u8s8")
         def create_model():
             return ChatGLMModel(
-                tokenizer_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model",
+                tokenizer_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model",
-                onnx_model_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"
+                onnx_model_path = "./request_llms/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"
             )
         self._model = create_model()
         return self._model, None

request_llms/bridge_chatgpt.py

@@ -7,8 +7,7 @@
     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：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
+    2. predict_no_ui_long_connection：支持多线程
-    3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
 """
 
 import json
@@ -18,12 +17,13 @@ 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, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder
-proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
+proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
-    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
+    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
 
 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。'
@@ -39,6 +39,33 @@ def get_full_error(chunk, stream_response):
             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
+    has_choices = False
+    choice_valid = False
+    has_content = False
+    has_role = False
+    try: 
+        chunkjson = json.loads(chunk_decoded[6:])
+        has_choices = 'choices' in chunkjson
+        if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
+        if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
+        if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
+    except: 
+        pass
+    return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
+
+from functools import lru_cache
+@lru_cache(maxsize=32)
+def verify_endpoint(endpoint):
+    """
+        检查endpoint是否可用
+    """
+    if "你亲手写的api名称" in endpoint:
+        raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
+    return endpoint
 
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
     """
@@ -61,7 +88,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
         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']
+            endpoint = verify_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:
@@ -153,14 +180,22 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
         return
         
+    # 检查endpoint是否合法
+    try:
+        from .bridge_all import model_info
+        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+    except:
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (inputs, tb_str)
+        yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
+        return
+    
     history.append(inputs); history.append("")
 
     retry = 0
     while True:
         try:
             # make a POST request to the API endpoint, stream=True
-            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:
@@ -191,23 +226,36 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
                 return
             
-            chunk_decoded = chunk.decode()
+            # 提前读取一些信息 （用于判断异常）
+            chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+
             if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
                 # 数据流的第一帧不携带content
                 is_head_of_the_stream = False; continue
             
             if chunk:
                 try:
+                    if has_choices and not choice_valid:
+                        # 一些垃圾第三方接口的出现这样的错误
+                        continue
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
-                    if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
+                    if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
                         # 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
-                    chunkjson = json.loads(chunk_decoded[6:])
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
                     # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    if has_content:
+                        # 正常情况
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
+                    elif has_role:
+                        # 一些第三方接口的出现这样的错误，兼容一下吧
+                        continue
+                    else:
+                        # 一些垃圾第三方接口的出现这样的错误
+                        gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
+
                     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
@@ -239,6 +287,8 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
     elif "associated with a deactivated account" in error_msg:
         chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+    elif "API key has been deactivated" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website)
     elif "bad forward key" in error_msg:
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
     elif "Not enough point" in error_msg:
@@ -263,7 +313,11 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         "Authorization": f"Bearer {api_key}"
     }
     if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
-    if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
+    if llm_kwargs['llm_model'].startswith('azure-'): 
+        headers.update({"api-key": api_key})
+        if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
+            azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
+            headers.update({"api-key": azure_api_key_unshared})
 
     conversation_cnt = len(history) // 2
 
@@ -288,9 +342,23 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     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('api2d-'):
+        model = llm_kwargs['llm_model'][len('api2d-'):]
+
+    if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
+        model = random.choice([
+            "gpt-3.5-turbo", 
+            "gpt-3.5-turbo-16k",
+            "gpt-3.5-turbo-1106",
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k-0613",
+            "gpt-3.5-turbo-0301",
+        ])
+        logging.info("Random select model:" + model)
 
     payload = {
-        "model": llm_kwargs['llm_model'].strip('api2d-'),
+        "model": model,
         "messages": messages, 
         "temperature": llm_kwargs['temperature'],  # 1.0,
         "top_p": llm_kwargs['top_p'],  # 1.0,

request_llms/bridge_chatgpt_website.py

@@ -7,8 +7,7 @@
     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：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
+    2. predict_no_ui_long_connection：支持多线程
-    3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
 """
 
 import json

request_llms/bridge_claude.py

@@ -7,7 +7,7 @@
     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：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
+    2. predict_no_ui_long_connection：支持多线程
 """
 
 import os

request_llms/bridge_internlm.py

@@ -1,13 +1,13 @@
 model_name = "InternLM"
-cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
+cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
 
 from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
-from toolbox import update_ui, get_conf
+from toolbox import update_ui, get_conf, ProxyNetworkActivate
 from multiprocessing import Process, Pipe
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
 # ------------------------------------------------------------------------------------------------------------------------
@@ -34,7 +34,6 @@ def combine_history(prompt, hist):
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
-@SingletonLocalLLM
 class GetInternlmHandle(LocalLLMHandle):
 
     def load_model_info(self):
@@ -52,7 +51,8 @@ class GetInternlmHandle(LocalLLMHandle):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         import torch
         from transformers import AutoModelForCausalLM, AutoTokenizer
-        device, = get_conf('LOCAL_MODEL_DEVICE')
+        device = get_conf('LOCAL_MODEL_DEVICE')
+        with ProxyNetworkActivate('Download_LLM'):
             if self._model is None:
                 tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
                 if device=='cpu':
@@ -94,8 +94,9 @@ class GetInternlmHandle(LocalLLMHandle):
 
         inputs = tokenizer([prompt], padding=True, return_tensors="pt")
         input_length = len(inputs["input_ids"][0])
+        device = get_conf('LOCAL_MODEL_DEVICE')
         for k, v in inputs.items():
-            inputs[k] = v.cuda()
+            inputs[k] = v.to(device)
         input_ids = inputs["input_ids"]
         batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
         if generation_config is None:

request_llms/bridge_jittorllms_llama.py

@@ -28,8 +28,8 @@ class GetGLMHandle(Process):
             self.success = True
         except:
             from toolbox import trimmed_format_exc
-            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
+            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
-                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
+                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
                         r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
             self.success = False
 
@@ -45,15 +45,15 @@ class GetGLMHandle(Process):
             env = os.environ.get("PATH", "")
             os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/jittorllms')
+            os.chdir(root_dir_assume + '/request_llms/jittorllms')
-            sys.path.append(root_dir_assume + '/request_llm/jittorllms')
+            sys.path.append(root_dir_assume + '/request_llms/jittorllms')
         validate_path() # validate path so you can run from base directory
 
         def load_model():
             import types
             try:
                 if self.jittorllms_model is None:
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
+                    device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
                     # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'llama'}
@@ -109,7 +109,7 @@ llama_glm_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global llama_glm_handle
     if llama_glm_handle is None:
@@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_jittorllms_pangualpha.py

@@ -28,8 +28,8 @@ class GetGLMHandle(Process):
             self.success = True
         except:
             from toolbox import trimmed_format_exc
-            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
+            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
-                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
+                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
                         r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
             self.success = False
 
@@ -45,15 +45,15 @@ class GetGLMHandle(Process):
             env = os.environ.get("PATH", "")
             os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/jittorllms')
+            os.chdir(root_dir_assume + '/request_llms/jittorllms')
-            sys.path.append(root_dir_assume + '/request_llm/jittorllms')
+            sys.path.append(root_dir_assume + '/request_llms/jittorllms')
         validate_path() # validate path so you can run from base directory
 
         def load_model():
             import types
             try:
                 if self.jittorllms_model is None:
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
+                    device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
                     # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'pangualpha'}
@@ -109,7 +109,7 @@ pangu_glm_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global pangu_glm_handle
     if pangu_glm_handle is None:
@@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_jittorllms_rwkv.py

@@ -28,8 +28,8 @@ class GetGLMHandle(Process):
             self.success = True
         except:
             from toolbox import trimmed_format_exc
-            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
+            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
-                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
+                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
                         r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
             self.success = False
 
@@ -45,15 +45,15 @@ class GetGLMHandle(Process):
             env = os.environ.get("PATH", "")
             os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/jittorllms')
+            os.chdir(root_dir_assume + '/request_llms/jittorllms')
-            sys.path.append(root_dir_assume + '/request_llm/jittorllms')
+            sys.path.append(root_dir_assume + '/request_llms/jittorllms')
         validate_path() # validate path so you can run from base directory
 
         def load_model():
             import types
             try:
                 if self.jittorllms_model is None:
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
+                    device = get_conf('LOCAL_MODEL_DEVICE')
                     from .jittorllms.models import get_model
                     # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
                     args_dict = {'model': 'chatrwkv'}
@@ -109,7 +109,7 @@ rwkv_glm_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global rwkv_glm_handle
     if rwkv_glm_handle is None:
@@ -140,7 +140,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_llama2.py

@@ -1,18 +1,17 @@
 model_name = "LLaMA"
-cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
+cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
 
 
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 from toolbox import update_ui, get_conf, ProxyNetworkActivate
 from multiprocessing import Process, Pipe
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 from threading import Thread
 
 
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
-@SingletonLocalLLM
 class GetONNXGLMHandle(LocalLLMHandle):
 
     def load_model_info(self):

request_llms/bridge_moss.py

@@ -24,12 +24,12 @@ class GetGLMHandle(Process):
     def check_dependency(self): # 主进程执行
         try:
             import datasets, os
-            assert os.path.exists('request_llm/moss/models')
+            assert os.path.exists('request_llms/moss/models')
             self.info = "依赖检测通过"
             self.success = True
         except:
             self.info = """
-            缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss`安装MOSS的依赖。
+            缺少MOSS的依赖，如果要使用MOSS，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_moss.txt`和`git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss`安装MOSS的依赖。
             """
             self.success = False
         return self.success
@@ -110,8 +110,8 @@ class GetGLMHandle(Process):
         def validate_path():
             import os, sys
             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/moss')
+            os.chdir(root_dir_assume + '/request_llms/moss')
-            sys.path.append(root_dir_assume + '/request_llm/moss')
+            sys.path.append(root_dir_assume + '/request_llms/moss')
         validate_path() # validate path so you can run from base directory
 
         try:
@@ -176,7 +176,7 @@ moss_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global moss_handle
     if moss_handle is None:
@@ -206,7 +206,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -219,7 +219,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             moss_handle = None
             return
     else:
-        response = "[Local Message]: 等待MOSS响应中 ..."
+        response = "[Local Message] 等待MOSS响应中 ..."
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
@@ -238,7 +238,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待MOSS响应中 ...":
+    if response == "[Local Message] 等待MOSS响应中 ...":
-        response = "[Local Message]: MOSS响应异常 ..."
+        response = "[Local Message] MOSS响应异常 ..."
     history.extend([inputs, response.strip('<|MOSS|>: ')])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_newbingfree.py

@@ -54,7 +54,7 @@ class NewBingHandle(Process):
             self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
             self.success = True
         except:
-            self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
+            self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_newbing.txt`安装Newbing的依赖。"
             self.success = False
 
     def ready(self):
@@ -62,8 +62,8 @@ class NewBingHandle(Process):
 
     async def async_run(self):
         # 读取配置
-        NEWBING_STYLE, = get_conf('NEWBING_STYLE')
+        NEWBING_STYLE = get_conf('NEWBING_STYLE')
-        from request_llm.bridge_all import model_info
+        from request_llms.bridge_all import model_info
         endpoint = model_info['newbing']['endpoint']
         while True:
             # 等待
@@ -141,10 +141,10 @@ class NewBingHandle(Process):
             except:
                 self.success = False
                 tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
+                self.child.send(f'[Local Message] 不能加载Newbing组件，请注意Newbing组件已不再维护。{tb_str}')
                 self.child.send('[Fail]')
                 self.child.send('[Finish]')
-                raise RuntimeError(f"不能加载Newbing组件。")
+                raise RuntimeError(f"不能加载Newbing组件，请注意Newbing组件已不再维护。")
 
         self.success = True
         try:
@@ -181,7 +181,7 @@ newbingfree_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global newbingfree_handle
     if (newbingfree_handle is None) or (not newbingfree_handle.success):
@@ -199,7 +199,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
-    if len(observe_window) >= 1: observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
+    if len(observe_window) >= 1: observe_window[0] = "[Local Message] 等待NewBing响应中 ..."
     for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         if len(observe_window) >= 1:  observe_window[0] = preprocess_newbing_out_simple(response)
         if len(observe_window) >= 2:  
@@ -210,9 +210,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
+    chatbot.append((inputs, "[Local Message] 等待NewBing响应中 ..."))
 
     global newbingfree_handle
     if (newbingfree_handle is None) or (not newbingfree_handle.success):
@@ -231,13 +231,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     for i in range(len(history)//2):
         history_feedin.append([history[2*i], history[2*i+1]] )
 
-    chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
+    chatbot[-1] = (inputs, "[Local Message] 等待NewBing响应中 ...")
-    response = "[Local Message]: 等待NewBing响应中 ..."
+    response = "[Local Message] 等待NewBing响应中 ..."
     yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
     for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
         yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
+    if response == "[Local Message] 等待NewBing响应中 ...": response = "[Local Message] NewBing响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
     logging.info(f'[raw_input] {inputs}')
     logging.info(f'[response] {response}')

request_llms/bridge_qianfan.py

@@ -75,11 +75,12 @@ def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
 
 
 def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
-    BAIDU_CLOUD_QIANFAN_MODEL,  = get_conf('BAIDU_CLOUD_QIANFAN_MODEL')
+    BAIDU_CLOUD_QIANFAN_MODEL = get_conf('BAIDU_CLOUD_QIANFAN_MODEL')
 
     url_lib = {
-        "ERNIE-Bot":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions" ,
+        "ERNIE-Bot-4":          "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro",
-        "ERNIE-Bot-turbo":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant"  ,
+        "ERNIE-Bot":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions",
+        "ERNIE-Bot-turbo":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant",
         "BLOOMZ-7B":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1",
 
         "Llama-2-70B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b",
@@ -119,7 +120,7 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     watch_dog_patience = 5
     response = ""
@@ -134,7 +135,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         ⭐单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
 
@@ -158,8 +159,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         return
     
     # 总结输出
-    response = f"[Local Message]: {model_name}响应异常 ..."
+    response = f"[Local Message] {model_name}响应异常 ..."
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
+        response = f"[Local Message] {model_name}响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_qwen.py

@@ -1,21 +1,20 @@
 model_name = "Qwen"
-cmd_to_install = "`pip install -r request_llm/requirements_qwen.txt`"
+cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
 
 
 from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
-from toolbox import update_ui, get_conf
+from toolbox import update_ui, get_conf, ProxyNetworkActivate
 from multiprocessing import Process, Pipe
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
 
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
-@SingletonLocalLLM
 class GetONNXGLMHandle(LocalLLMHandle):
 
     def load_model_info(self):
@@ -30,11 +29,11 @@ class GetONNXGLMHandle(LocalLLMHandle):
         import platform
         from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
+        with ProxyNetworkActivate('Download_LLM'):
             model_id = 'qwen/Qwen-7B-Chat'
-        revision = 'v1.0.1'
+            self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
-        self._tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, trust_remote_code=True)
             # use fp16
-        model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", revision=revision, trust_remote_code=True, fp16=True).eval()
+            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
             model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True)  # 可指定不同的生成长度、top_p等相关超参
             self._model = model
 

request_llms/bridge_spark.py

@@ -8,7 +8,7 @@ from multiprocessing import Process, Pipe
 model_name = '星火认知大模型'
 
 def validate_key():
-    XFYUN_APPID,  = get_conf('XFYUN_APPID', )
+    XFYUN_APPID = get_conf('XFYUN_APPID')
     if XFYUN_APPID == '00000000' or XFYUN_APPID == '': 
         return False
     return True
@@ -16,7 +16,7 @@ def validate_key():
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     watch_dog_patience = 5
     response = ""
@@ -36,13 +36,13 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
         ⭐单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history)
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message]: 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:
@@ -57,7 +57,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
+        response = f"[Local Message] {model_name}响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_stackclaude.py

@@ -36,7 +36,7 @@ try:
         CHANNEL_ID = None
 
         async def open_channel(self):
-            response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID')[0])
+            response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID'))
             self.CHANNEL_ID = response["channel"]["id"]
 
         async def chat(self, text):
@@ -51,7 +51,7 @@ try:
                 # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题
                 resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)
                 msg = [msg for msg in resp["messages"]
-                    if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')[0]]
+                    if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')]
                 return msg
             except (SlackApiError, KeyError) as e:
                 raise RuntimeError(f"获取Slack消息失败。")
@@ -99,7 +99,7 @@ class ClaudeHandle(Process):
             self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
             self.success = True
         except:
-            self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
+            self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
             self.success = False
 
     def ready(self):
@@ -146,14 +146,14 @@ class ClaudeHandle(Process):
         self.local_history = []
         if (self.claude_model is None) or (not self.success):
             # 代理设置
-            proxies, = get_conf('proxies')
+            proxies = get_conf('proxies')
             if proxies is None:
                 self.proxies_https = None
             else:
                 self.proxies_https = proxies['https']
 
             try:
-                SLACK_CLAUDE_USER_TOKEN, = get_conf('SLACK_CLAUDE_USER_TOKEN')
+                SLACK_CLAUDE_USER_TOKEN = get_conf('SLACK_CLAUDE_USER_TOKEN')
                 self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)
                 print('Claude组件初始化成功。')
             except:
@@ -204,7 +204,7 @@ claude_handle = None
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
     """
         多线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
     global claude_handle
     if (claude_handle is None) or (not claude_handle.success):
@@ -222,7 +222,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
-    observe_window[0] = "[Local Message]: 等待Claude响应中 ..."
+    observe_window[0] = "[Local Message] 等待Claude响应中 ..."
     for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         observe_window[0] = preprocess_newbing_out_simple(response)
         if len(observe_window) >= 2:
@@ -234,9 +234,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
     """
         单线程方法
-        函数的说明请见 request_llm/bridge_all.py
+        函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ..."))
+    chatbot.append((inputs, "[Local Message] 等待Claude响应中 ..."))
 
     global claude_handle
     if (claude_handle is None) or (not claude_handle.success):
@@ -255,14 +255,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     for i in range(len(history)//2):
         history_feedin.append([history[2*i], history[2*i+1]])
 
-    chatbot[-1] = (inputs, "[Local Message]: 等待Claude响应中 ...")
+    chatbot[-1] = (inputs, "[Local Message] 等待Claude响应中 ...")
-    response = "[Local Message]: 等待Claude响应中 ..."
+    response = "[Local Message] 等待Claude响应中 ..."
     yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
     for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
         yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待Claude响应中 ...":
+    if response == "[Local Message] 等待Claude响应中 ...":
-        response = "[Local Message]: Claude响应异常，请刷新界面重试 ..."
+        response = "[Local Message] Claude响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
     logging.info(f'[raw_input] {inputs}')
     logging.info(f'[response] {response}')

request_llms/bridge_zhipu.py

@@ -0,0 +1,59 @@
+
+import time
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
+
+model_name = '智谱AI大模型'
+
+def validate_key():
+    ZHIPUAI_API_KEY = get_conf("ZHIPUAI_API_KEY")
+    if ZHIPUAI_API_KEY == '': return False
+    return True
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    if validate_key() is False:
+        raise RuntimeError('请配置ZHIPUAI_API_KEY')
+
+    from .com_zhipuapi import ZhipuRequestInstance
+    sri = ZhipuRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+
+    if validate_key() is False:
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
+        return
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    # 开始接收回复    
+    from .com_zhipuapi import ZhipuRequestInstance
+    sri = ZhipuRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+        chatbot[-1] = (inputs, response)
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    # 总结输出
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
+        response = f"[Local Message] {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)

request_llms/com_sparkapi.py

@@ -64,6 +64,7 @@ class SparkRequestInstance():
         self.api_key = XFYUN_API_KEY
         self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
         self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
+        self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
 
         self.time_to_yield_event = threading.Event()
         self.time_to_exit_event = threading.Event()
@@ -87,6 +88,8 @@ class SparkRequestInstance():
     def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
         if llm_kwargs['llm_model'] == 'sparkv2':
             gpt_url = self.gpt_url_v2
+        elif llm_kwargs['llm_model'] == 'sparkv3':
+            gpt_url = self.gpt_url_v3
         else:
             gpt_url = self.gpt_url
 
@@ -168,6 +171,11 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
     """
     通过appid和用户的提问来生成请参数
     """
+    domains = {
+        "spark": "general",
+        "sparkv2": "generalv2",
+        "sparkv3": "generalv3",
+    }
     data = {
         "header": {
             "app_id": appid,
@@ -175,7 +183,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
         },
         "parameter": {
             "chat": {
-                "domain": "generalv2" if llm_kwargs['llm_model'] == 'sparkv2' else "general",
+                "domain": domains[llm_kwargs['llm_model']],
                 "temperature": llm_kwargs["temperature"],
                 "random_threshold": 0.5,
                 "max_tokens": 4096,

request_llms/com_zhipuapi.py

@@ -0,0 +1,67 @@
+from toolbox import get_conf
+import threading
+import logging
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+
+class ZhipuRequestInstance():
+    def __init__(self):
+
+        self.time_to_yield_event = threading.Event()
+        self.time_to_exit_event = threading.Event()
+
+        self.result_buf = ""
+
+    def generate(self, inputs, llm_kwargs, history, system_prompt):
+        # import _thread as thread
+        import zhipuai
+        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
+        zhipuai.api_key = ZHIPUAI_API_KEY
+        self.result_buf = ""
+        response = zhipuai.model_api.sse_invoke(
+            model=ZHIPUAI_MODEL,
+            prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
+            top_p=llm_kwargs['top_p'],
+            temperature=llm_kwargs['temperature'],
+        )
+        for event in response.events():
+            if event.event == "add":
+                self.result_buf += event.data
+                yield self.result_buf
+            elif event.event == "error" or event.event == "interrupted":
+                raise RuntimeError("Unknown error:" + event.data)
+            elif event.event == "finish":
+                yield self.result_buf
+                break
+            else:
+                raise RuntimeError("Unknown error:" + str(event))
+            
+        logging.info(f'[raw_input] {inputs}')
+        logging.info(f'[response] {self.result_buf}')
+        return self.result_buf
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+    conversation_cnt = len(history) // 2
+    messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
+    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)
+    return messages

request_llms/key_manager.py

@@ -0,0 +1,29 @@
+import random
+
+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 OpenAI_ApiKeyManager():
+    def __init__(self, mode='blacklist') -> None:
+        # self.key_avail_list = []
+        self.key_black_list = []
+    
+    def add_key_to_blacklist(self, key):
+        self.key_black_list.append(key)
+
+    def select_avail_key(self, key_list):
+        # select key from key_list, but avoid keys also in self.key_black_list, raise error if no key can be found
+        available_keys = [key for key in key_list if key not in self.key_black_list]
+        if not available_keys:
+            raise KeyError("No available key found.")
+        selected_key = random.choice(available_keys)
+        return selected_key