fix security issue GHSA-3jrq-66fm-w7xr

fix typo

.gitignore

@@ -153,3 +153,6 @@ media
 flagged
 request_llms/ChatGLM-6b-onnx-u8s8
 .pre-commit-config.yaml
+themes/common.js.min.*.js
+test*
+objdump*

Dockerfile

@@ -12,11 +12,16 @@ RUN echo '[global]' > /etc/pip.conf && \
     echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
 
 
+# 语音输出功能（以下两行，第一行更换阿里源，第二行安装ffmpeg，都可以删除）
+RUN UBUNTU_VERSION=$(awk -F= '/^VERSION_CODENAME=/{print $2}' /etc/os-release); echo "deb https://mirrors.aliyun.com/debian/ $UBUNTU_VERSION main non-free contrib" > /etc/apt/sources.list; apt-get update
+RUN apt-get install ffmpeg -y
+
+
 # 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 （以下三行，可以删除）
+# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下两行，可以删除）
 COPY requirements.txt ./
 RUN pip3 install -r requirements.txt
 

README.md

@@ -1,7 +1,7 @@
 > [!IMPORTANT]
-> 2024.1.18: 更新3.70版本，支持Mermaid绘图库（让大模型绘制脑图）  
+> 2024.6.1: 版本3.80加入插件二级菜单功能（详见wiki）  
-> 2024.1.17: 恭迎GLM4，全力支持Qwen、GLM、DeepseekCoder等国内中文大语言基座模型！  
+> 2024.5.1: 加入Doc2x翻译PDF论文的功能，[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)  
-> 2024.1.17: 某些依赖包尚不兼容python 3.12，推荐python 3.11。  
+> 2024.3.11: 全力支持Qwen、GLM、DeepseekCoder等中文大语言模型！ SoVits语音克隆模块，[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/) 
 > 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
 
 <br>
@@ -67,7 +67,7 @@ Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanes
 读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
 Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
 批量注释生成 | [插件] 一键批量生成函数注释
-Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？就是出自他的手笔
+Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README.English.md)了吗？就是出自他的手笔
 [PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
 [Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
 Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
@@ -87,6 +87,10 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
 <img src="https://user-images.githubusercontent.com/96192199/279702205-d81137c3-affd-4cd1-bb5e-b15610389762.gif" width="700" >
 </div>
 
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/70ff1ec5-e589-4561-a29e-b831079b37fb.gif" width="700" >
+</div>
+
 
 - 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放剪贴板
 <div align="center">
@@ -253,8 +257,7 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 # 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`，添加如下条目，然后重启程序。（如果按钮已存在，那么可以直接修改（前缀、后缀都已支持热修改），无需重启程序即可生效。）
+现在已可以通过UI中的`界面外观`菜单中的`自定义菜单`添加新的便捷按钮。如果需要在代码中定义，请使用任意文本编辑器打开`core_functional.py`，添加如下条目即可：
-例如
 
 ```python
 "超级英译中": {

check_proxy.py

@@ -47,7 +47,7 @@ def backup_and_download(current_version, remote_version):
     shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
     proxies = get_conf('proxies')
     try:    r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
-    except: r = requests.get('https://public.gpt-academic.top/publish/master.zip', proxies=proxies, stream=True)
+    except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
     zip_file_path = backup_dir+'/master.zip'
     with open(zip_file_path, 'wb+') as f:
         f.write(r.content)
@@ -71,7 +71,7 @@ def patch_and_restart(path):
     import sys
     import time
     import glob
-    from colorful import print亮黄, print亮绿, print亮红
+    from shared_utils.colorful import print亮黄, print亮绿, print亮红
     # if not using config_private, move origin config.py as config_private.py
     if not os.path.exists('config_private.py'):
         print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
@@ -113,7 +113,7 @@ def auto_update(raise_error=False):
         import json
         proxies = get_conf('proxies')
         try:    response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
-        except: response = requests.get("https://public.gpt-academic.top/publish/version", proxies=proxies, timeout=5)
+        except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
         remote_json_data = json.loads(response.text)
         remote_version = remote_json_data['version']
         if remote_json_data["show_feature"]:
@@ -124,7 +124,7 @@ def auto_update(raise_error=False):
             current_version = f.read()
             current_version = json.loads(current_version)['version']
         if (remote_version - current_version) >= 0.01-1e-5:
-            from colorful import print亮黄
+            from shared_utils.colorful import print亮黄
             print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
             print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
             user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')

config.py

@@ -30,11 +30,40 @@ if USE_PROXY:
 else:
     proxies = None
 
-# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
+# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
+LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+                    "gpt-4o", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
+                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
+                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
+                    "gemini-pro", "chatglm3"
+                    ]
+# --- --- --- ---
+# P.S. 其他可用的模型还包括
+# AVAIL_LLM_MODELS = [
+#   "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
+#   "qianfan", "deepseekcoder",
+#   "spark", "sparkv2", "sparkv3", "sparkv3.5",
+#   "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
+#   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
+#   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
+#   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
+#   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
+#   "deepseek-chat" ,"deepseek-coder",
+#   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
+# ]
+# --- --- --- ---
+# 此外，您还可以在接入one-api/vllm/ollama时，
+# 使用"one-api-*","vllm-*","ollama-*"前缀直接使用非标准方式接入的模型，例如
+# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)"]
+# --- --- --- ---
+
+
+# --------------- 以下配置可以优化体验 ---------------
 
 # 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
 # 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
+# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
 API_URL_REDIRECT = {}
 
 
@@ -77,6 +106,10 @@ TIMEOUT_SECONDS = 30
 WEB_PORT = -1
 
 
+# 是否自动打开浏览器页面
+AUTO_OPEN_BROWSER = True
+
+
 # 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
 MAX_RETRY = 2
 
@@ -85,20 +118,6 @@ MAX_RETRY = 2
 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-1106","gpt-4-1106-preview","gpt-4-vision-preview",
-                    "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
-                    "gemini-pro", "chatglm3", "claude-2", "zhipuai"]
-# P.S. 其他可用的模型还包括 [
-# "moss", "qwen-turbo", "qwen-plus", "qwen-max"
-# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", 
-# "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
-# "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"
 
@@ -116,7 +135,7 @@ DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
 # 百度千帆（LLM_MODEL="qianfan"）
 BAIDU_CLOUD_API_KEY = ''
 BAIDU_CLOUD_SECRET_KEY = ''
-BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
+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", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
 
 
 # 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
@@ -127,6 +146,7 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
 LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
 LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
 
+
 # 设置gradio的并行线程数（不需要修改）
 CONCURRENT_COUNT = 100
 
@@ -144,7 +164,8 @@ ADD_WAIFU = False
 AUTHENTICATION = []
 
 
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
+# 如果需要在二级路径下运行（常规情况下，不要修改!!）
+# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
 CUSTOM_PATH = "/"
 
 
@@ -172,14 +193,8 @@ AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.
 AZURE_CFG_ARRAY = {}
 
 
-# 使用Newbing (不推荐使用，未来将删除)
+# 阿里云实时语音识别 配置难度较高
-NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
+# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
-NEWBING_COOKIES = """
-put your new bing cookies here
-"""
-
-
-# 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
 ENABLE_AUDIO = False
 ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
 ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
@@ -187,6 +202,12 @@ ALIYUN_ACCESSKEY="" # （无需填写）
 ALIYUN_SECRET=""    # （无需填写）
 
 
+# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
+TTS_TYPE = "EDGE_TTS" # EDGE_TTS / LOCAL_SOVITS_API / DISABLE
+GPT_SOVITS_URL = ""
+EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
+
+
 # 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
 XFYUN_APPID = "00000000"
 XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
@@ -195,19 +216,32 @@ XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 # 接入智谱大模型
 ZHIPUAI_API_KEY = ""
-ZHIPUAI_MODEL = "glm-4" # 可选 "glm-3-turbo" "glm-4"
+ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
-
-
-# # 火山引擎YUNQUE大模型
-# YUNQUE_SECRET_KEY = ""
-# YUNQUE_ACCESS_KEY = ""
-# YUNQUE_MODEL = ""
 
 
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 
 
+# 月之暗面 API KEY
+MOONSHOT_API_KEY = ""
+
+
+# 零一万物(Yi Model) API KEY
+YIMODEL_API_KEY = ""
+
+# 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
+DEEPSEEK_API_KEY = ""
+
+# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
+MATHPIX_APPID = ""
+MATHPIX_APPKEY = ""
+
+
+# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
+DOC2X_API_KEY = ""
+
+
 # 自定义API KEY格式
 CUSTOM_API_KEY_PATTERN = ""
 
@@ -261,7 +295,11 @@ PLUGIN_HOT_RELOAD = False
 # 自定义按钮的最大数量限制
 NUM_CUSTOM_BASIC_BTN = 4
 
+
+
 """
+--------------- 配置关联关系说明 ---------------
+
 在线大模型配置关联关系示意图
 │
 ├── "gpt-3.5-turbo" 等openai模型
@@ -285,7 +323,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── XFYUN_API_SECRET
 │   └── XFYUN_API_KEY
 │
-├── "claude-1-100k" 等claude模型
+├── "claude-3-opus-20240229" 等claude模型
 │   └── ANTHROPIC_API_KEY
 │
 ├── "stack-claude"
@@ -297,9 +335,11 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── BAIDU_CLOUD_API_KEY
 │   └── BAIDU_CLOUD_SECRET_KEY
 │
-├── "zhipuai" 智谱AI大模型chatglm_turbo
+├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
-│   ├── ZHIPUAI_API_KEY
+│   └── ZHIPUAI_API_KEY
-│   └── ZHIPUAI_MODEL
+│
+├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
+│   └── YIMODEL_API_KEY
 │
 ├── "qwen-turbo" 等通义千问大模型
 │   └──  DASHSCOPE_API_KEY
@@ -307,9 +347,10 @@ NUM_CUSTOM_BASIC_BTN = 4
 ├── "Gemini"
 │   └──  GEMINI_API_KEY
 │
-└── "newbing" Newbing接口不再稳定，不推荐使用
+└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
-    ├── NEWBING_STYLE
+    ├── AVAIL_LLM_MODELS
-    └── NEWBING_COOKIES
+    ├── API_KEY
+    └── API_URL_REDIRECT
 
 
 本地大模型示意图
@@ -351,6 +392,9 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   └── ALIYUN_SECRET
 │
 └── PDF文档精准解析
-    └── GROBID_URLS
+    ├── GROBID_URLS
+    ├── MATHPIX_APPID
+    └── MATHPIX_APPKEY
+
 
 """

core_functional.py

@@ -33,17 +33,19 @@ def get_core_functions():
             "AutoClearHistory": False,
             # [6] 文本预处理 （可选参数，默认 None，举例：写个函数移除所有的换行符）
             "PreProcess": None,
+            # [7] 模型选择 （可选参数。如不设置，则使用当前全局模型；如设置，则用指定模型覆盖全局模型。）
+            # "ModelOverride": "gpt-3.5-turbo", # 主要用途：强制点击此基础功能按钮时，使用指定的模型。
         },
 
 
         "总结绘制脑图": {
             # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-            "Prefix":   r"",
+            "Prefix":   '''"""\n\n''',
             # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
             "Suffix":
                 # dedent() 函数用于去除多行字符串的缩进
-                dedent("\n"+r'''
+                dedent("\n\n"+r'''
-                    ==============================
+                    """
 
                     使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
 
@@ -57,7 +59,7 @@ def get_core_functions():
                         C --> |"箭头名2"| F["节点名6"]
                     ```
 
-                    警告：
+                    注意：
                     （1）使用中文
                     （2）节点名字使用引号包裹，如["Laptop"]
                     （3）`|` 和 `"`之间不要存在空格

crazy_functional.py

@@ -15,26 +15,35 @@ def get_crazy_functions():
     from crazy_functions.解析项目源代码 import 解析一个Java项目
     from crazy_functions.解析项目源代码 import 解析一个前端项目
     from crazy_functions.高级功能函数模板 import 高阶功能模板函数
+    from crazy_functions.高级功能函数模板 import Demo_Wrap
     from crazy_functions.Latex全文润色 import Latex英文润色
     from crazy_functions.询问多个大语言模型 import 同时问询
     from crazy_functions.解析项目源代码 import 解析一个Lua项目
     from crazy_functions.解析项目源代码 import 解析一个CSharp项目
     from crazy_functions.总结word文档 import 总结word文档
     from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    from crazy_functions.对话历史存档 import 对话历史存档
+    from crazy_functions.Conversation_To_File import 载入对话历史存档
-    from crazy_functions.对话历史存档 import 载入对话历史存档
+    from crazy_functions.Conversation_To_File import 对话历史存档
-    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
+    from crazy_functions.Conversation_To_File import Conversation_To_File_Wrap
+    from crazy_functions.Conversation_To_File import 删除所有本地对话历史记录
     from crazy_functions.辅助功能 import 清除缓存
-    from crazy_functions.批量Markdown翻译 import Markdown英译中
+    from crazy_functions.Markdown_Translate import Markdown英译中
     from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
+    from crazy_functions.PDF_Translate import 批量翻译PDF文档
     from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
     from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
     from crazy_functions.Latex全文润色 import Latex中文润色
     from crazy_functions.Latex全文润色 import Latex英文纠错
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
+    from crazy_functions.Markdown_Translate import Markdown中译英
     from crazy_functions.虚空终端 import 虚空终端
-    from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表
+    from crazy_functions.生成多种Mermaid图表 import Mermaid_Gen
+    from crazy_functions.PDF_Translate_Wrap import PDF_Tran
+    from crazy_functions.Latex_Function import Latex英文纠错加PDF对比
+    from crazy_functions.Latex_Function import Latex翻译中文并重新编译PDF
+    from crazy_functions.Latex_Function import PDF翻译中文并重新编译PDF
+    from crazy_functions.Latex_Function_Wrap import Arxiv_Localize
+    from crazy_functions.Latex_Function_Wrap import PDF_Localize
+
 
     function_plugins = {
         "虚空终端": {
@@ -70,14 +79,13 @@ def get_crazy_functions():
             "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
             "Function": HotReload(清除缓存),
         },
-        "生成多种Mermaid图表(从当前对话或文件(.pdf/.md)中生产图表）": {
+        "生成多种Mermaid图表(从当前对话或路径(.pdf/.md/.docx)中生产图表）": {
             "Group": "对话",
             "Color": "stop",
             "AsButton": False,
-            "Info" : "基于当前对话或PDF生成多种Mermaid图表,图表类型由模型判断",
+            "Info" : "基于当前对话或文件生成多种Mermaid图表,图表类型由模型判断",
-            "Function": HotReload(生成多种Mermaid图表),
+            "Function": None,
-            "AdvancedArgs": True,
+            "Class": Mermaid_Gen
-            "ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
         },
         "批量总结Word文档": {
             "Group": "学术",
@@ -190,7 +198,8 @@ def get_crazy_functions():
             "Group": "对话",
             "AsButton": True,
             "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档),
+            "Function": HotReload(对话历史存档),    # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
+            "Class": Conversation_To_File_Wrap     # 新一代插件需要注册Class
         },
         "[多线程Demo]解析此项目本身（源码自译解）": {
             "Group": "对话|编程",
@@ -202,14 +211,16 @@ def get_crazy_functions():
             "Group": "对话",
             "AsButton": True,
             "Info": "查看历史上的今天事件 (这是一个面向开发者的插件Demo) | 不需要输入参数",
-            "Function": HotReload(高阶功能模板函数),
+            "Function": None,
+            "Class": Demo_Wrap, # 新一代插件需要注册Class
         },
         "精准翻译PDF论文": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": True,
             "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档),
+            "Function": HotReload(批量翻译PDF文档), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
+            "Class": PDF_Tran,  # 新一代插件需要注册Class
         },
         "询问多个GPT模型": {
             "Group": "对话",
@@ -284,7 +295,51 @@ def get_crazy_functions():
             "Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
             "Function": HotReload(Markdown中译英),
         },
+        "Latex英文纠错+高亮修正位置 [需Latex]": {
+            "Group": "学术",
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True,
+            "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
+            "Function": HotReload(Latex英文纠错加PDF对比),
+        },
+        "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
+            "Group": "学术",
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True,
+            "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                            r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                            r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+            "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+            "Function": HotReload(Latex翻译中文并重新编译PDF),  # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
+            "Class": Arxiv_Localize,    # 新一代插件需要注册Class
+        },
+        "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
+            "Group": "学术",
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True,
+            "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                            r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                            r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+            "Info": "本地Latex论文精细翻译 | 输入参数是路径",
+            "Function": HotReload(Latex翻译中文并重新编译PDF),
+        },
+        "PDF翻译中文并重新编译PDF（上传PDF）[需Latex]": {
+            "Group": "学术",
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True,
+            "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                            r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                            r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+            "Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
+            "Function": HotReload(PDF翻译中文并重新编译PDF),   # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
+            "Class": PDF_Localize   # 新一代插件需要注册Class
         }
+    }
+
 
     # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
     try:
@@ -458,7 +513,7 @@ def get_crazy_functions():
         print("Load function plugin failed")
 
     try:
-        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
+        from crazy_functions.Markdown_Translate import Markdown翻译指定语言
 
         function_plugins.update(
             {
@@ -531,47 +586,6 @@ def get_crazy_functions():
         print(trimmed_format_exc())
         print("Load function plugin failed")
 
-    try:
-        from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
-        from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
-
-        function_plugins.update(
-            {
-                "Latex英文纠错+高亮修正位置 [需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
-                    "Function": HotReload(Latex英文纠错加PDF对比),
-                },
-                "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                    "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-                    "Function": HotReload(Latex翻译中文并重新编译PDF),
-                },
-                "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
-                    "Group": "学术",
-                    "Color": "stop",
-                    "AsButton": False,
-                    "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                    "Info": "本地Latex论文精细翻译 | 输入参数是路径",
-                    "Function": HotReload(Latex翻译中文并重新编译PDF),
-                }
-            }
-        )
-    except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
 
     try:
         from toolbox import get_conf

crazy_functions/CodeInterpreter.py

@@ -1,232 +0,0 @@
-from collections.abc import Callable, Iterable, Mapping
-from typing import Any
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc
-from toolbox import promote_file_to_downloadzone, get_log_folder
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import input_clipping, try_install_deps
-from multiprocessing import Process, Pipe
-import os
-import time
-
-templete = """
-```python
-import ...  # Put dependencies here, e.g. import numpy as np
-
-class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
-
-    def run(self, path):    # The name of the function must be `run`, it takes only a positional argument.
-        # rewrite the function you have just written here 
-        ...
-        return generated_file_path
-```
-"""
-
-def inspect_dependency(chatbot, history):
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return True
-
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) == 1: 
-        return matches[0].strip('python') #  code block
-    for match in matches:
-        if 'class TerminalFunction' in match:
-            return match.strip('python') #  code block
-    raise RuntimeError("GPT is not generating proper code.")
-
-def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
-    # 输入
-    prompt_compose = [
-        f'Your job:\n'
-        f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
-        f"2. You should write this function to perform following task: " + txt + "\n",
-        f"3. Wrap the output python function with markdown codeblock."
-    ]
-    i_say = "".join(prompt_compose)
-    demo = []
-
-    # 第一步
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, 
-        sys_prompt= r"You are a programmer."
-    )
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-    # 第二步
-    prompt_compose = [
-        "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
-        templete
-    ]
-    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=inputs_show_user, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt= r"You are a programmer."
-    )
-    code_to_return = gpt_say
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-    
-    # # 第三步
-    # i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
-    # i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=inputs_show_user, 
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-    #     sys_prompt= r"You are a programmer."
-    # )
-    # # # 第三步  
-    # i_say = "Show me how to use `pip` to install packages to run the code above. "
-    # i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=i_say, 
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-    #     sys_prompt= r"You are a programmer."
-    # )
-    installation_advance = ""
-    
-    return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
-
-def make_module(code):
-    module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
-    with open(f'{get_log_folder()}/{module_file}.py', 'w', encoding='utf8') as f:
-        f.write(code)
-
-    def get_class_name(class_string):
-        import re
-        # Use regex to extract the class name
-        class_name = re.search(r'class (\w+)\(', class_string).group(1)
-        return class_name
-
-    class_name = get_class_name(code)
-    return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}"
-
-def init_module_instance(module):
-    import importlib
-    module_, class_ = module.split('->')
-    init_f = getattr(importlib.import_module(module_), class_)
-    return init_f()
-
-def for_immediate_show_off_when_possible(file_type, fp, chatbot):
-    if file_type in ['png', 'jpg']:
-        image_path = os.path.abspath(fp)
-        chatbot.append(['这是一张图片, 展示如下:',  
-            f'本地文件地址: <br/>`{image_path}`<br/>'+
-            f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-        ])
-    return chatbot
-
-def subprocess_worker(instance, file_path, return_dict):
-    return_dict['result'] = instance.run(file_path)
-
-def have_any_recent_upload_files(chatbot):
-    _5min = 5 * 60
-    if not chatbot: return False    # chatbot is None
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    if not most_recent_uploaded: return False   # most_recent_uploaded is None
-    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
-    else: return False  # most_recent_uploaded is too old
-
-def get_recent_file_prompt_support(chatbot):
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    path = most_recent_uploaded['path']
-    return path
-
-@CatchException
-def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    raise NotImplementedError
-
-    # 清空历史，以免输入溢出
-    history = []; clear_file_downloadzone(chatbot)
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..."
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if have_any_recent_upload_files(chatbot):
-        file_path = get_recent_file_prompt_support(chatbot)
-    else:
-        chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 读取文件
-    if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files")
-    recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None)
-    file_path = recently_uploaded_files[-1]
-    file_type = file_path.split('.')[-1]
-
-    # 粗心检查
-    if is_the_upload_folder(txt):
-        chatbot.append([
-            "...",
-            f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）"
-        ])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # 开始干正事
-    for j in range(5):  # 最多重试5次
-        try:
-            code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
-                yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
-            code = get_code_block(code)
-            res = make_module(code)
-            instance = init_module_instance(res)
-            break
-        except Exception as e:
-            chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 代码生成结束, 开始执行
-    try:
-        import multiprocessing
-        manager = multiprocessing.Manager()
-        return_dict = manager.dict()
-
-        p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict))
-        # only has 10 seconds to run
-        p.start(); p.join(timeout=10)
-        if p.is_alive(): p.terminate(); p.join()
-        p.close()
-        res = return_dict['result']
-        # res = instance.run(file_path)
-    except Exception as e:
-        chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
-        # chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 顺利完成，收尾
-    res = str(res)
-    if os.path.exists(res):
-        chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
-        new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-    else:
-        chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新   
-
-"""
-测试：
-    裁剪图像，保留下半部分
-    交换图像的蓝色通道和红色通道
-    将图像转为灰度图像
-    将csv文件转excel表格
-"""

crazy_functions/Conversation_To_File.py

@@ -1,4 +1,5 @@
 from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
+from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
 import re
 
 f_prefix = 'GPT-Academic对话存档'
@@ -9,27 +10,61 @@ def write_chat_to_file(chatbot, history=None, file_name=None):
     """
     import os
     import time
+    from themes.theme import advanced_css
+
     if file_name is None:
         file_name = f_prefix + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
     fp = os.path.join(get_log_folder(get_user(chatbot), plugin_name='chat_history'), file_name)
+
     with open(fp, 'w', encoding='utf8') as f:
-        from themes.theme import advanced_css
+        from textwrap import dedent
-        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
+        form = dedent("""
+        <!DOCTYPE html><head><meta charset="utf-8"><title>对话存档</title><style>{CSS}</style></head>
+        <body>
+        <div class="test_temp1" style="width:10%; height: 500px; float:left;"></div>
+        <div class="test_temp2" style="width:80%;padding: 40px;float:left;padding-left: 20px;padding-right: 20px;box-shadow: rgba(0, 0, 0, 0.2) 0px 0px 8px 8px;border-radius: 10px;">
+            <div class="chat-body" style="display: flex;justify-content: center;flex-direction: column;align-items: center;flex-wrap: nowrap;">
+                {CHAT_PREVIEW}
+                <div></div>
+                <div></div>
+                <div style="text-align: center;width:80%;padding: 0px;float:left;padding-left:20px;padding-right:20px;box-shadow: rgba(0, 0, 0, 0.05) 0px 0px 1px 2px;border-radius: 1px;">对话（原始数据）</div>
+                {HISTORY_PREVIEW}
+            </div>
+        </div>
+        <div class="test_temp3" style="width:10%; height: 500px; float:left;"></div>
+        </body>
+        """)
+
+        qa_from = dedent("""
+        <div class="QaBox" style="width:80%;padding: 20px;margin-bottom: 20px;box-shadow: rgb(0 255 159 / 50%) 0px 0px 1px 2px;border-radius: 4px;">
+            <div class="Question" style="border-radius: 2px;">{QUESTION}</div>
+            <hr color="blue" style="border-top: dotted 2px #ccc;">
+            <div class="Answer" style="border-radius: 2px;">{ANSWER}</div>
+        </div>
+        """)
+
+        history_from = dedent("""
+        <div class="historyBox" style="width:80%;padding: 0px;float:left;padding-left:20px;padding-right:20px;box-shadow: rgba(0, 0, 0, 0.05) 0px 0px 1px 2px;border-radius: 1px;">
+            <div class="entry" style="border-radius: 2px;">{ENTRY}</div>
+        </div>
+        """)
+        CHAT_PREVIEW_BUF = ""
         for i, contents in enumerate(chatbot):
-            for j, content in enumerate(contents):
+            question, answer = contents[0], contents[1]
-                try:    # 这个bug没找到触发条件，暂时先这样顶一下
+            if question is None: question = ""
-                    if type(content) != str: content = str(content)
+            try: question = str(question)
-                except:
+            except: question = ""
-                    continue
+            if answer is None: answer = ""
-                f.write(content)
+            try: answer = str(answer)
-                if j == 0:
+            except: answer = ""
-                    f.write('<hr style="border-top: dotted 3px #ccc;">')
+            CHAT_PREVIEW_BUF += qa_from.format(QUESTION=question, ANSWER=answer)
-            f.write('<hr color="red"> \n\n')
+
-        f.write('<hr color="blue"> \n\n raw chat context:\n')
+        HISTORY_PREVIEW_BUF = ""
-        f.write('<code>')
         for h in history:
-            f.write("\n>>>" + h)
+            HISTORY_PREVIEW_BUF += history_from.format(ENTRY=h)
-        f.write('</code>')
+        html_content = form.format(CHAT_PREVIEW=CHAT_PREVIEW_BUF, HISTORY_PREVIEW=HISTORY_PREVIEW_BUF, CSS=advanced_css)
+        f.write(html_content)
+
     promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
     return '对话历史写入：' + fp
 
@@ -40,7 +75,7 @@ def gen_file_preview(file_name):
         # pattern to match the text between <head> and </head>
         pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
         file_content = re.sub(pattern, '', file_content)
-        html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
+        html, history = file_content.split('<hr color="blue"> \n\n 对话数据 (无渲染):\n')
         history = history.strip('<code>')
         history = history.strip('</code>')
         history = history.split("\n>>>")
@@ -51,21 +86,25 @@ def gen_file_preview(file_name):
 def read_file_to_chat(chatbot, history, file_name):
     with open(file_name, 'r', encoding='utf8') as f:
         file_content = f.read()
-    # pattern to match the text between <head> and </head>
+    from bs4 import BeautifulSoup
-    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
+    soup = BeautifulSoup(file_content, 'lxml')
-    file_content = re.sub(pattern, '', file_content)
+    # 提取QaBox信息
-    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-    history = history.strip('<code>')
-    history = history.strip('</code>')
-    history = history.split("\n>>>")
-    history = list(filter(lambda x:x!="", history))
-    html = html.split('<hr color="red"> \n\n')
-    html = list(filter(lambda x:x!="", html))
     chatbot.clear()
-    for i, h in enumerate(html):
+    qa_box_list = []
-        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
+    qa_boxes = soup.find_all("div", class_="QaBox")
-        chatbot.append([i_say, gpt_say])
+    for box in qa_boxes:
-    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
+        question = box.find("div", class_="Question").get_text(strip=False)
+        answer = box.find("div", class_="Answer").get_text(strip=False)
+        qa_box_list.append({"Question": question, "Answer": answer})
+        chatbot.append([question, answer])
+    # 提取historyBox信息
+    history_box_list = []
+    history_boxes = soup.find_all("div", class_="historyBox")
+    for box in history_boxes:
+        entry = box.find("div", class_="entry").get_text(strip=False)
+        history_box_list.append(entry)
+    history = history_box_list
+    chatbot.append([None, f"[Local Message] 载入对话{len(qa_box_list)}条，上下文{len(history)}条。"])
     return chatbot, history
 
 @CatchException
@@ -79,11 +118,42 @@ def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
     system_prompt   给gpt的静默提醒
     user_request    当前用户的请求信息（IP地址等）
     """
+    file_name = plugin_kwargs.get("file_name", None)
+    if (file_name is not None) and (file_name != "") and (not file_name.endswith('.html')): file_name += '.html'
+    else: file_name = None
 
-    chatbot.append(("保存当前对话", 
+    chatbot.append((None, f"[Local Message] {write_chat_to_file(chatbot, history, file_name)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
-        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
 
+
+class Conversation_To_File_Wrap(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+
+        第一个参数，名称`file_name`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+        """
+        gui_definition = {
+            "file_name": ArgProperty(title="保存文件名", description="输入对话存档文件名，留空则使用时间作为文件名", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        yield from 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+
+
+
+
+
 def hide_cwd(str):
     import os
     current_path = os.getcwd()

crazy_functions/Internet_GPT.py

@@ -0,0 +1,122 @@
+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_llms.bridge_all import model_info
+import urllib.request
+from functools import lru_cache
+
+
+@lru_cache
+def get_auth_ip():
+    try:
+        external_ip = urllib.request.urlopen('https://v4.ident.me/').read().decode('utf8')
+        return external_ip
+    except:
+        return '114.114.114.114'
+
+def searxng_request(query, proxies):
+    url = 'https://cloud-1.agent-matrix.com/'  # 请替换为实际的API URL
+    params = {
+        'q': query,         # 搜索查询
+        'format': 'json',   # 输出格式为JSON
+        'language': 'zh',   # 搜索语言
+    }
+    headers = {
+        'Accept-Language': 'zh-CN,zh;q=0.9',
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
+        'X-Forwarded-For': get_auth_ip(),
+        'X-Real-IP': get_auth_ip()
+    }
+    results = []
+    response = requests.post(url, params=params, headers=headers, proxies=proxies)
+    if response.status_code == 200:
+        json_result = response.json()
+        for result in json_result['results']:
+            item = {
+                "title": result["title"],
+                "content": result["content"],
+                "link": result["url"],
+            }
+            results.append(item)
+        return results
+    else:
+        raise ValueError("搜索失败，状态码: " + str(response.status_code) + '\t' + response.content.decode('utf-8'))
+
+def scrape_text(url, proxies) -> str:
+    """Scrape text from a webpage
+
+    Args:
+        url (str): The URL to scrape text from
+
+    Returns:
+        str: The scraped text
+    """
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
+        'Content-Type': 'text/plain',
+    }
+    try:
+        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
+        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
+    except:
+        return "无法连接到该网页"
+    soup = BeautifulSoup(response.text, "html.parser")
+    for script in soup(["script", "style"]):
+        script.extract()
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+    return text
+
+@CatchException
+def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    user_request    当前用户的请求信息（IP地址等）
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append((f"请结合互联网信息回答以下问题：{txt}",
+                    "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
+    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
+
+    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
+    from toolbox import get_conf
+    proxies = get_conf('proxies')
+    urls = searxng_request(txt, proxies)
+    history = []
+    if len(urls) == 0:
+        chatbot.append((f"结论：{txt}",
+                        "[Local Message] 受到google限制，无法从google获取信息！"))
+        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
+        return
+    # ------------- < 第2步：依次访问网页 > -------------
+    max_search_result = 5   # 最多收纳多少个网页的结果
+    for index, url in enumerate(urls[:max_search_result]):
+        res = scrape_text(url['link'], proxies)
+        history.extend([f"第{index}份搜索结果：", res])
+        chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
+        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
+
+    # ------------- < 第3步：ChatGPT综合 > -------------
+    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
+    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token
+        inputs=i_say,
+        history=history,
+        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
+    )
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=i_say, inputs_show_user=i_say,
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
+        sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
+    )
+    chatbot[-1] = (i_say, gpt_say)
+    history.append(i_say);history.append(gpt_say)
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+

crazy_functions/Latex_Function.py

@@ -0,0 +1,548 @@
+from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
+from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
+from functools import partial
+import glob, os, requests, time, json, tarfile
+
+pj = os.path.join
+ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
+def switch_prompt(pfg, mode, more_requirement):
+    """
+    Generate prompts and system prompts based on the mode for proofreading or translating.
+    Args:
+    - pfg: Proofreader or Translator instance.
+    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
+
+    Returns:
+    - inputs_array: A list of strings containing prompts for users to respond to.
+    - sys_prompt_array: A list of strings containing prompts for system prompts.
+    """
+    n_split = len(pfg.sp_file_contents)
+    if mode == 'proofread_en':
+        inputs_array = [r"Below is a section from an academic paper, proofread this section." +
+                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
+                        r"Answer me only with the revised text:" +
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
+    elif mode == 'translate_zh':
+        inputs_array = [
+            r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
+            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
+            r"Answer me only with the translated text:" +
+            f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
+    else:
+        assert False, "未知指令"
+    return inputs_array, sys_prompt_array
+
+
+def desend_to_extracted_folder_if_exist(project_folder):
+    """
+    Descend into the extracted folder if it exists, otherwise return the original folder.
+
+    Args:
+    - project_folder: A string specifying the folder path.
+
+    Returns:
+    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
+    """
+    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
+    if len(maybe_dir) == 0: return project_folder
+    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
+    return project_folder
+
+
+def move_project(project_folder, arxiv_id=None):
+    """
+    Create a new work folder and copy the project folder to it.
+
+    Args:
+    - project_folder: A string specifying the folder path of the project.
+
+    Returns:
+    - A string specifying the path to the new work folder.
+    """
+    import shutil, time
+    time.sleep(2)  # avoid time string conflict
+    if arxiv_id is not None:
+        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
+    else:
+        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
+    try:
+        shutil.rmtree(new_workfolder)
+    except:
+        pass
+
+    # align subfolder if there is a folder wrapper
+    items = glob.glob(pj(project_folder, '*'))
+    items = [item for item in items if os.path.basename(item) != '__MACOSX']
+    if len(glob.glob(pj(project_folder, '*.tex'))) == 0 and len(items) == 1:
+        if os.path.isdir(items[0]): project_folder = items[0]
+
+    shutil.copytree(src=project_folder, dst=new_workfolder)
+    return new_workfolder
+
+
+def arxiv_download(chatbot, history, txt, allow_cache=True):
+    def check_cached_translation_pdf(arxiv_id):
+        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
+        if not os.path.exists(translation_dir):
+            os.makedirs(translation_dir)
+        target_file = pj(translation_dir, 'translate_zh.pdf')
+        if os.path.exists(target_file):
+            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
+            target_file_compare = pj(translation_dir, 'comparison.pdf')
+            if os.path.exists(target_file_compare):
+                promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
+            return target_file
+        return False
+
+    def is_float(s):
+        try:
+            float(s)
+            return True
+        except ValueError:
+            return False
+
+    if txt.startswith('https://arxiv.org/pdf/'):
+        arxiv_id = txt.split('/')[-1]   # 2402.14207v2.pdf
+        txt = arxiv_id.split('v')[0]  # 2402.14207
+
+    if ('.' in txt) and ('/' not in txt) and is_float(txt):  # is arxiv ID
+        txt = 'https://arxiv.org/abs/' + txt.strip()
+    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]):  # is arxiv ID
+        txt = 'https://arxiv.org/abs/' + txt[:10]
+
+    if not txt.startswith('https://arxiv.org'):
+        return txt, None    # 是本地文件，跳过下载
+
+    # <-------------- inspect format ------------->
+    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
+    yield from update_ui(chatbot=chatbot, history=history)
+    time.sleep(1)  # 刷新界面
+
+    url_ = txt  # https://arxiv.org/abs/1707.06690
+
+    if not txt.startswith('https://arxiv.org/abs/'):
+        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
+        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
+        return msg, None
+    # <-------------- set format ------------->
+    arxiv_id = url_.split('/abs/')[-1]
+    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
+    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
+    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
+
+    url_tar = url_.replace('/abs/', '/e-print/')
+    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
+    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
+    os.makedirs(translation_dir, exist_ok=True)
+
+    # <-------------- download arxiv source file ------------->
+    dst = pj(translation_dir, arxiv_id + '.tar')
+    if os.path.exists(dst):
+        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
+    else:
+        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
+        proxies = get_conf('proxies')
+        r = requests.get(url_tar, proxies=proxies)
+        with open(dst, 'wb+') as f:
+            f.write(r.content)
+    # <-------------- extract file ------------->
+    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
+    from toolbox import extract_archive
+    extract_archive(file_path=dst, dest_dir=extract_dst)
+    return extract_dst, arxiv_id
+
+
+def pdf2tex_project(pdf_file_path, plugin_kwargs):
+    if plugin_kwargs["method"] == "MATHPIX":
+        # Mathpix API credentials
+        app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
+        headers = {"app_id": app_id, "app_key": app_key}
+
+        # Step 1: Send PDF file for processing
+        options = {
+            "conversion_formats": {"tex.zip": True},
+            "math_inline_delimiters": ["$", "$"],
+            "rm_spaces": True
+        }
+
+        response = requests.post(url="https://api.mathpix.com/v3/pdf",
+                                headers=headers,
+                                data={"options_json": json.dumps(options)},
+                                files={"file": open(pdf_file_path, "rb")})
+
+        if response.ok:
+            pdf_id = response.json()["pdf_id"]
+            print(f"PDF processing initiated. PDF ID: {pdf_id}")
+
+            # Step 2: Check processing status
+            while True:
+                conversion_response = requests.get(f"https://api.mathpix.com/v3/pdf/{pdf_id}", headers=headers)
+                conversion_data = conversion_response.json()
+
+                if conversion_data["status"] == "completed":
+                    print("PDF processing completed.")
+                    break
+                elif conversion_data["status"] == "error":
+                    print("Error occurred during processing.")
+                else:
+                    print(f"Processing status: {conversion_data['status']}")
+                    time.sleep(5)  # wait for a few seconds before checking again
+
+            # Step 3: Save results to local files
+            output_dir = os.path.join(os.path.dirname(pdf_file_path), 'mathpix_output')
+            if not os.path.exists(output_dir):
+                os.makedirs(output_dir)
+
+            url = f"https://api.mathpix.com/v3/pdf/{pdf_id}.tex"
+            response = requests.get(url, headers=headers)
+            file_name_wo_dot = '_'.join(os.path.basename(pdf_file_path).split('.')[:-1])
+            output_name = f"{file_name_wo_dot}.tex.zip"
+            output_path = os.path.join(output_dir, output_name)
+            with open(output_path, "wb") as output_file:
+                output_file.write(response.content)
+            print(f"tex.zip file saved at: {output_path}")
+
+            import zipfile
+            unzip_dir = os.path.join(output_dir, file_name_wo_dot)
+            with zipfile.ZipFile(output_path, 'r') as zip_ref:
+                zip_ref.extractall(unzip_dir)
+
+            return unzip_dir
+
+        else:
+            print(f"Error sending PDF for processing. Status code: {response.status_code}")
+            return None
+    else:
+        from crazy_functions.pdf_fns.parse_pdf_via_doc2x import 解析PDF_DOC2X_转Latex
+        unzip_dir = 解析PDF_DOC2X_转Latex(pdf_file_path)
+        return unzip_dir
+
+
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+
+@CatchException
+def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    # <-------------- information about this plugin ------------->
+    chatbot.append(["函数插件功能？",
+                    "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    history = []
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- if is a zip/tar file ------------->
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+    # <-------------- move latex project away from temp folder ------------->
+    from shared_utils.fastapi_server import validate_path_safety
+    validate_path_safety(project_folder, chatbot.get_user())
+    project_folder = move_project(project_folder, arxiv_id=None)
+
+    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
+        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                       chatbot, history, system_prompt, mode='proofread_en',
+                                       switch_prompt=_switch_prompt_)
+
+    # <-------------- compile PDF ------------->
+    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                   main_file_modified='merge_proofread_en',
+                                   work_folder_original=project_folder, work_folder_modified=project_folder,
+                                   work_folder=project_folder)
+
+    # <-------------- zip PDF ------------->
+    zip_res = zip_result(project_folder)
+    if success:
+        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    else:
+        chatbot.append((f"失败了",
+                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+Conversation_To_File进行反馈 ...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+    # <-------------- we are done ------------->
+    return success
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+@CatchException
+def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    # <-------------- information about this plugin ------------->
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    no_cache = more_req.startswith("--no-cache")
+    if no_cache: more_req.lstrip("--no-cache")
+    allow_cache = not no_cache
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    history = []
+    try:
+        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
+    except tarfile.ReadError as e:
+        yield from update_ui_lastest_msg(
+            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
+            chatbot=chatbot, history=history)
+        return
+
+    if txt.endswith('.pdf'):
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现已经存在翻译好的PDF文档")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- if is a zip/tar file ------------->
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+    # <-------------- move latex project away from temp folder ------------->
+    from shared_utils.fastapi_server import validate_path_safety
+    validate_path_safety(project_folder, chatbot.get_user())
+    project_folder = move_project(project_folder, arxiv_id)
+
+    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
+        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                       chatbot, history, system_prompt, mode='translate_zh',
+                                       switch_prompt=_switch_prompt_)
+
+    # <-------------- compile PDF ------------->
+    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                   main_file_modified='merge_translate_zh', mode='translate_zh',
+                                   work_folder_original=project_folder, work_folder_modified=project_folder,
+                                   work_folder=project_folder)
+
+    # <-------------- zip PDF ------------->
+    zip_res = zip_result(project_folder)
+    if success:
+        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    else:
+        chatbot.append((f"失败了",
+                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+    # <-------------- we are done ------------->
+    return success
+
+
+#  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 插件主程序3  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+@CatchException
+def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # <-------------- information about this plugin ------------->
+    chatbot.append([
+        "函数插件功能？",
+        "将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    no_cache = more_req.startswith("--no-cache")
+    if no_cache: more_req.lstrip("--no-cache")
+    allow_cache = not no_cache
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    if len(file_manifest) != 1:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"不支持同时处理多个pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    if plugin_kwargs.get("method", "") == 'MATHPIX':
+        app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
+        if len(app_id) == 0 or len(app_key) == 0:
+            report_exception(chatbot, history, a="缺失 MATHPIX_APPID 和 MATHPIX_APPKEY。", b=f"请配置 MATHPIX_APPID 和 MATHPIX_APPKEY")
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            return
+    if plugin_kwargs.get("method", "") == 'DOC2X':
+        app_id, app_key = "", ""
+        DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
+        if len(DOC2X_API_KEY) == 0:
+            report_exception(chatbot, history, a="缺失 DOC2X_API_KEY。", b=f"请配置 DOC2X_API_KEY")
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            return
+
+    hash_tag = map_file_to_sha256(file_manifest[0])
+
+    # # <-------------- check repeated pdf ------------->
+    # chatbot.append([f"检查PDF是否被重复上传", "正在检查..."])
+    # yield from update_ui(chatbot=chatbot, history=history)
+    # repeat, project_folder = check_repeat_upload(file_manifest[0], hash_tag)
+
+    # if repeat:
+    #     yield from update_ui_lastest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
+    #     try:
+    #         translate_pdf = [f for f in glob.glob(f'{project_folder}/**/merge_translate_zh.pdf', recursive=True)][0]
+    #         promote_file_to_downloadzone(translate_pdf, rename_file=None, chatbot=chatbot)
+    #         comparison_pdf = [f for f in glob.glob(f'{project_folder}/**/comparison.pdf', recursive=True)][0]
+    #         promote_file_to_downloadzone(comparison_pdf, rename_file=None, chatbot=chatbot)
+    #         zip_res = zip_result(project_folder)
+    #         promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    #         return
+    #     except:
+    #         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现重复上传，但是无法找到相关文件")
+    #         yield from update_ui(chatbot=chatbot, history=history)
+    # else:
+    #     yield from update_ui_lastest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
+
+    # <-------------- convert pdf into tex ------------->
+    chatbot.append([f"解析项目: {txt}", "正在将PDF转换为tex项目，请耐心等待..."])
+    yield from update_ui(chatbot=chatbot, history=history)
+    project_folder = pdf2tex_project(file_manifest[0], plugin_kwargs)
+    if project_folder is None:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"PDF转换为tex项目失败")
+        yield from update_ui(chatbot=chatbot, history=history)
+        return False
+
+    # <-------------- translate latex file into Chinese ------------->
+    yield from update_ui_lastest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- if is a zip/tar file ------------->
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+    # <-------------- move latex project away from temp folder ------------->
+    from shared_utils.fastapi_server import validate_path_safety
+    validate_path_safety(project_folder, chatbot.get_user())
+    project_folder = move_project(project_folder)
+
+    # <-------------- set a hash tag for repeat-checking ------------->
+    with open(pj(project_folder, hash_tag + '.tag'), 'w') as f:
+        f.write(hash_tag)
+        f.close()
+
+
+    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
+        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                    chatbot, history, system_prompt, mode='translate_zh',
+                                    switch_prompt=_switch_prompt_)
+
+    # <-------------- compile PDF ------------->
+    yield from update_ui_lastest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
+    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                main_file_modified='merge_translate_zh', mode='translate_zh',
+                                work_folder_original=project_folder, work_folder_modified=project_folder,
+                                work_folder=project_folder)
+
+    # <-------------- zip PDF ------------->
+    zip_res = zip_result(project_folder)
+    if success:
+        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    else:
+        chatbot.append((f"失败了",
+                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+    # <-------------- we are done ------------->
+    return success

crazy_functions/Latex_Function_Wrap.py

@@ -0,0 +1,78 @@
+
+from crazy_functions.Latex_Function import Latex翻译中文并重新编译PDF, PDF翻译中文并重新编译PDF
+from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
+
+
+class Arxiv_Localize(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+
+        第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+        第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+        第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；
+
+        """
+        gui_definition = {
+            "main_input":
+                ArgProperty(title="ArxivID", description="输入Arxiv的ID或者网址", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
+            "advanced_arg":
+                ArgProperty(title="额外的翻译提示词",
+                            description=r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                        r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                        r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                            default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
+            "allow_cache":
+                ArgProperty(title="是否允许从缓存中调取结果", options=["允许缓存", "从头执行"], default_value="允许缓存", description="无", type="dropdown").model_dump_json(),
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        allow_cache = plugin_kwargs["allow_cache"]
+        advanced_arg = plugin_kwargs["advanced_arg"]
+
+        if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + plugin_kwargs["advanced_arg"]
+        yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+
+
+
+class PDF_Localize(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+        """
+        gui_definition = {
+            "main_input":
+                ArgProperty(title="PDF文件路径", description="未指定路径，请上传文件后，再点击该插件", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
+            "advanced_arg":
+                ArgProperty(title="额外的翻译提示词",
+                            description=r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                        r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                        r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                            default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
+            "method":
+                ArgProperty(title="采用哪种方法执行转换", options=["MATHPIX", "DOC2X"], default_value="DOC2X", description="无", type="dropdown").model_dump_json(),
+
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        yield from PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)

crazy_functions/Latex全文润色.py

@@ -81,8 +81,8 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
     #  <-------- 多线程润色开始 ---------->
     if language == 'en':
         if mode == 'polish':
-            inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " + 
+            inputs_array = [r"Below is a section from an academic paper, polish this section to meet the academic standard, " +
-                            "improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" + 
+                            r"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         else:
             inputs_array = [r"Below is a section from an academic paper, proofread this section." +
@@ -93,10 +93,10 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
     elif language == 'zh':
         if mode == 'polish':
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+            inputs_array = [r"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         else:
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+            inputs_array = [r"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
         sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]

crazy_functions/Latex输出PDF结果.py

@@ -1,313 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
-from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
-from functools import partial
-import glob, os, requests, time, tarfile
-pj = os.path.join
-ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
-def switch_prompt(pfg, mode, more_requirement):
-    """
-    Generate prompts and system prompts based on the mode for proofreading or translating.
-    Args:
-    - pfg: Proofreader or Translator instance.
-    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
-
-    Returns:
-    - inputs_array: A list of strings containing prompts for users to respond to.
-    - sys_prompt_array: A list of strings containing prompts for system prompts.
-    """
-    n_split = len(pfg.sp_file_contents)
-    if mode == 'proofread_en':
-        inputs_array = [r"Below is a section from an academic paper, proofread this section." + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
-                        r"Answer me only with the revised text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif mode == 'translate_zh':
-        inputs_array = [r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + 
-                        r"Answer me only with the translated text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
-    else:
-        assert False, "未知指令"
-    return inputs_array, sys_prompt_array
-
-def desend_to_extracted_folder_if_exist(project_folder):
-    """ 
-    Descend into the extracted folder if it exists, otherwise return the original folder.
-
-    Args:
-    - project_folder: A string specifying the folder path.
-
-    Returns:
-    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
-    """
-    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
-    if len(maybe_dir) == 0: return project_folder
-    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
-    return project_folder
-
-def move_project(project_folder, arxiv_id=None):
-    """ 
-    Create a new work folder and copy the project folder to it.
-
-    Args:
-    - project_folder: A string specifying the folder path of the project.
-
-    Returns:
-    - A string specifying the path to the new work folder.
-    """
-    import shutil, time
-    time.sleep(2)   # avoid time string conflict
-    if arxiv_id is not None:
-        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
-    else:
-        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
-    try:
-        shutil.rmtree(new_workfolder)
-    except:
-        pass
-
-    # align subfolder if there is a folder wrapper
-    items = glob.glob(pj(project_folder,'*'))
-    items = [item for item in items if os.path.basename(item)!='__MACOSX']
-    if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:
-        if os.path.isdir(items[0]): project_folder = items[0]
-
-    shutil.copytree(src=project_folder, dst=new_workfolder)
-    return new_workfolder
-
-def arxiv_download(chatbot, history, txt, allow_cache=True):
-    def check_cached_translation_pdf(arxiv_id):
-        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
-        if not os.path.exists(translation_dir):
-            os.makedirs(translation_dir)
-        target_file = pj(translation_dir, 'translate_zh.pdf')
-        if os.path.exists(target_file):
-            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
-            target_file_compare = pj(translation_dir, 'comparison.pdf')
-            if os.path.exists(target_file_compare):
-                promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
-            return target_file
-        return False
-    def is_float(s):
-        try:
-            float(s)
-            return True
-        except ValueError:
-            return False
-    if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt.strip()
-    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt[:10]
-    if not txt.startswith('https://arxiv.org'): 
-        return txt, None    # 是本地文件，跳过下载
-    
-    # <-------------- inspect format ------------->
-    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...']) 
-    yield from update_ui(chatbot=chatbot, history=history)
-    time.sleep(1) # 刷新界面
-
-    url_ = txt   # https://arxiv.org/abs/1707.06690
-    if not txt.startswith('https://arxiv.org/abs/'): 
-        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
-        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
-        return msg, None
-    # <-------------- set format ------------->
-    arxiv_id = url_.split('/abs/')[-1]
-    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
-    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
-    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
-
-    url_tar = url_.replace('/abs/', '/e-print/')
-    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
-    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
-    os.makedirs(translation_dir, exist_ok=True)
-    
-    # <-------------- download arxiv source file ------------->
-    dst = pj(translation_dir, arxiv_id+'.tar')
-    if os.path.exists(dst):
-        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
-    else:
-        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
-        proxies = get_conf('proxies')
-        r = requests.get(url_tar, proxies=proxies)
-        with open(dst, 'wb+') as f:
-            f.write(r.content)
-    # <-------------- extract file ------------->
-    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
-    from toolbox import extract_archive
-    extract_archive(file_path=dst, dest_dir=extract_dst)
-    return extract_dst, arxiv_id
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
-
-
-@CatchException
-def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([ "函数插件功能？",
-        "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id=None)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-    
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-    # <-------------- we are done ------------->
-    return success
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
-
-@CatchException
-def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    no_cache = more_req.startswith("--no-cache")
-    if no_cache: more_req.lstrip("--no-cache")
-    allow_cache = not no_cache
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    try:
-        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
-    except tarfile.ReadError as e:
-        yield from update_ui_lastest_msg(
-            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。", 
-            chatbot=chatbot, history=history)
-        return
-
-    if txt.endswith('.pdf'):
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-
-    # <-------------- we are done ------------->
-    return success

crazy_functions/Markdown_Translate.py

@@ -1,5 +1,5 @@
-import glob, time, os, re, logging
+import glob, shutil, os, re, logging
-from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
+from toolbox import update_ui, trimmed_format_exc, gen_time_str
 from toolbox import CatchException, report_exception, get_log_folder
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 fast_debug = False
@@ -18,7 +18,7 @@ class PaperFileGroup():
         def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
         self.get_token_num = get_token_num
 
-    def run_file_split(self, max_token_limit=1900):
+    def run_file_split(self, max_token_limit=2048):
         """
         将长文本分离开来
         """
@@ -64,25 +64,25 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
             pfg.file_contents.append(file_content)
 
     #  <-------- 拆分过长的Markdown文件 ---------->
-    pfg.run_file_split(max_token_limit=1500)
+    pfg.run_file_split(max_token_limit=2048)
     n_split = len(pfg.sp_file_contents)
 
     #  <-------- 多线程翻译开始 ---------->
     if language == 'en->zh':
-        inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" + 
+        inputs_array = ["This is a Markdown file, translate it into Chinese, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
                         f"\n\n{frag}" for frag in pfg.sp_file_contents]
         inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
     elif language == 'zh->en':
-        inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" + 
+        inputs_array = [f"This is a Markdown file, translate it into English, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
                         f"\n\n{frag}" for frag in pfg.sp_file_contents]
         inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
     else:
-        inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" + 
+        inputs_array = [f"This is a Markdown file, translate it into {language}, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
                         f"\n\n{frag}" for frag in pfg.sp_file_contents]
         inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
 
     gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array=inputs_array,
@@ -99,7 +99,12 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
             pfg.sp_file_result.append(gpt_say)
         pfg.merge_result()
-        pfg.write_result(language)
+        output_file_arr = pfg.write_result(language)
+        for output_file in output_file_arr:
+            promote_file_to_downloadzone(output_file, chatbot=chatbot)
+            if 'markdown_expected_output_path' in plugin_kwargs:
+                expected_f_name = plugin_kwargs['markdown_expected_output_path']
+                shutil.copyfile(output_file, expected_f_name)
     except:
         logging.error(trimmed_format_exc())
 
@@ -159,7 +164,6 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
@@ -199,7 +203,6 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
@@ -232,7 +235,6 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:

crazy_functions/PDF_Translate.py

@@ -0,0 +1,83 @@
+from toolbox import CatchException, check_packages, get_conf
+from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import trimmed_format_exc_markdown
+from crazy_functions.crazy_utils import get_files_from_everything
+from crazy_functions.pdf_fns.parse_pdf import get_avail_grobid_url
+from crazy_functions.pdf_fns.parse_pdf_via_doc2x import 解析PDF_基于DOC2X
+from crazy_functions.pdf_fns.parse_pdf_legacy import 解析PDF_简单拆解
+from crazy_functions.pdf_fns.parse_pdf_grobid import 解析PDF_基于GROBID
+from shared_utils.colorful import *
+
+@CatchException
+def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+
+    disable_auto_promotion(chatbot)
+    # 基本信息：功能、贡献者
+    chatbot.append([None, "插件功能：批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        check_packages(["fitz", "tiktoken", "scipdf"])
+    except:
+        chatbot.append([None, f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if (not success) and txt == "": txt = '空空如也的输入栏。提示：请先上传文件（把PDF文件拖入对话）。'
+
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        chatbot.append([None, f"找不到任何.pdf拓展名的文件: {txt}"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 开始正式执行任务
+    method = plugin_kwargs.get("pdf_parse_method", None)
+    if method == "DOC2X":
+        # ------- 第一种方法，效果最好，但是需要DOC2X服务 -------
+        DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
+        if len(DOC2X_API_KEY) != 0:
+            try:
+                yield from 解析PDF_基于DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
+                return
+            except:
+                chatbot.append([None, f"DOC2X服务不可用，现在将执行效果稍差的旧版代码。{trimmed_format_exc_markdown()}"])
+                yield from update_ui(chatbot=chatbot, history=history)
+
+    if method == "GROBID":
+        # ------- 第二种方法，效果次优 -------
+        grobid_url = get_avail_grobid_url()
+        if grobid_url is not None:
+            yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
+            return
+
+    if method == "ClASSIC":
+        # ------- 第三种方法，早期代码，效果不理想 -------
+        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
+        yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+        return
+
+    if method is None:
+        # ------- 以上三种方法都试一遍 -------
+        DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
+        if len(DOC2X_API_KEY) != 0:
+            try:
+                yield from 解析PDF_基于DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
+                return
+            except:
+                chatbot.append([None, f"DOC2X服务不可用，正在尝试GROBID。{trimmed_format_exc_markdown()}"])
+                yield from update_ui(chatbot=chatbot, history=history)
+        grobid_url = get_avail_grobid_url()
+        if grobid_url is not None:
+            yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
+            return
+        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
+        yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+        return
+

crazy_functions/PDF_Translate_Wrap.py

@@ -0,0 +1,33 @@
+from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
+from .PDF_Translate import 批量翻译PDF文档
+
+
+class PDF_Tran(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+        """
+        gui_definition = {
+            "main_input":
+                ArgProperty(title="PDF文件路径", description="未指定路径，请上传文件后，再点击该插件", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
+            "additional_prompt":
+                ArgProperty(title="额外提示词", description="例如：对专有名词、翻译语气等方面的要求", default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
+            "pdf_parse_method":
+                ArgProperty(title="PDF解析方法", options=["DOC2X", "GROBID", "ClASSIC"], description="无", default_value="GROBID", type="dropdown").model_dump_json(),
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        main_input = plugin_kwargs["main_input"]
+        additional_prompt = plugin_kwargs["additional_prompt"]
+        pdf_parse_method = plugin_kwargs["pdf_parse_method"]
+        yield from 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)

crazy_functions/crazy_utils.py

@@ -135,14 +135,26 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
     return final_result
 
-def can_multi_process(llm):
+def can_multi_process(llm) -> bool:
+    from request_llms.bridge_all import model_info
+
+    def default_condition(llm) -> bool:
+        # legacy condition
         if llm.startswith('gpt-'): return True
         if llm.startswith('api2d-'): return True
         if llm.startswith('azure-'): return True
         if llm.startswith('spark'): return True
-    if llm.startswith('zhipuai'): return True
+        if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
         return False
 
+    if llm in model_info:
+        if 'can_multi_thread' in model_info[llm]:
+            return model_info[llm]['can_multi_thread']
+        else:
+            return default_condition(llm)
+    else:
+        return default_condition(llm)
+
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array, inputs_show_user_array, llm_kwargs,
         chatbot, history_array, sys_prompt_array,
@@ -337,7 +349,7 @@ def read_and_clean_pdf_text(fp):
     import fitz, copy
     import re
     import numpy as np
-    from colorful import print亮黄, print亮绿
+    from shared_utils.colorful import print亮黄, print亮绿
     fc = 0  # Index 0 文本
     fs = 1  # Index 1 字体
     fb = 2  # Index 2 框框
@@ -556,7 +568,7 @@ class nougat_interface():
         from toolbox import ProxyNetworkActivate
         logging.info(f'正在执行命令 {command}')
         with ProxyNetworkActivate("Nougat_Download"):
-            process = subprocess.Popen(command, shell=True, cwd=cwd, env=os.environ)
+            process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
         try:
             stdout, stderr = process.communicate(timeout=timeout)
         except subprocess.TimeoutExpired:
@@ -580,7 +592,8 @@ 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)
+        command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
+        self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
         res = glob.glob(os.path.join(dst,'*.mmd'))
         if len(res) == 0:
             self.threadLock.release()

crazy_functions/json_fns/pydantic_io.py

@@ -62,8 +62,8 @@ class GptJsonIO():
         if "type" in reduced_schema:
             del reduced_schema["type"]
         # Ensure json in context is well-formed with double quotes.
-        if self.example_instruction:
         schema_str = json.dumps(reduced_schema)
+        if self.example_instruction:
             return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
         else:
             return PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE.format(schema=schema_str)

crazy_functions/latex_fns/latex_actions.py

@@ -1,10 +1,11 @@
 from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
-from toolbox import get_conf, objdump, objload, promote_file_to_downloadzone
+from toolbox import get_conf, promote_file_to_downloadzone
 from .latex_toolbox import PRESERVE, TRANSFORM
 from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
 from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
 from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
 from .latex_toolbox import find_title_and_abs
+from .latex_pickle_io import objdump, objload
 
 import os, shutil
 import re

crazy_functions/latex_fns/latex_pickle_io.py

@@ -0,0 +1,38 @@
+import pickle
+
+
+class SafeUnpickler(pickle.Unpickler):
+
+    def get_safe_classes(self):
+        from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
+        # 定义允许的安全类
+        safe_classes = {
+            # 在这里添加其他安全的类
+            'LatexPaperFileGroup': LatexPaperFileGroup,
+            'LatexPaperSplit' : LatexPaperSplit,
+        }
+        return safe_classes
+
+    def find_class(self, module, name):
+        # 只允许特定的类进行反序列化
+        self.safe_classes = self.get_safe_classes()
+        if f'{module}.{name}' in self.safe_classes:
+            return self.safe_classes[f'{module}.{name}']
+        # 如果尝试加载未授权的类，则抛出异常
+        raise pickle.UnpicklingError(f"Attempted to deserialize unauthorized class '{name}' from module '{module}'")
+
+def objdump(obj, file="objdump.tmp"):
+
+    with open(file, "wb+") as f:
+        pickle.dump(obj, f)
+    return
+
+
+def objload(file="objdump.tmp"):
+    import os
+
+    if not os.path.exists(file):
+        return
+    with open(file, "rb") as f:
+        unpickler = SafeUnpickler(f)
+        return unpickler.load()

crazy_functions/pdf_fns/parse_pdf.py

@@ -4,7 +4,7 @@ from toolbox import promote_file_to_downloadzone
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 from toolbox import get_conf
 from toolbox import ProxyNetworkActivate
-from colorful import *
+from shared_utils.colorful import *
 import requests
 import random
 import copy
@@ -72,7 +72,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
     generated_conclusion_files.append(res_path)
     return res_path
 
-def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
+def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs={}):
     from crazy_functions.pdf_fns.report_gen_html import construct_html
     from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
     from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@@ -138,7 +138,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
         chatbot=chatbot,
         history_array=[meta for _ in inputs_array],
         sys_prompt_array=[
-            "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
+            "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "") for _ in inputs_array],
     )
     # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
     produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)

crazy_functions/pdf_fns/parse_pdf_grobid.py

@@ -0,0 +1,26 @@
+import os
+from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
+from crazy_functions.pdf_fns.parse_pdf import parse_pdf, translate_pdf
+
+def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
+    import copy, json
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
+    generated_conclusion_files = []
+    generated_html_files = []
+    DST_LANG = "中文"
+    from crazy_functions.pdf_fns.report_gen_html import construct_html
+    for index, fp in enumerate(file_manifest):
+        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        article_dict = parse_pdf(fp, grobid_url)
+        grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
+        with open(grobid_json_res, 'w+', encoding='utf8') as f:
+            f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
+        promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
+        if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
+        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs=plugin_kwargs)
+    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+

crazy_functions/pdf_fns/parse_pdf_legacy.py

@@ -1,83 +1,15 @@
-from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
+from toolbox import get_log_folder
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import update_ui, promote_file_to_downloadzone
 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_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
+from crazy_functions.crazy_utils import read_and_clean_pdf_text
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
+from shared_utils.colorful import *
-from colorful import *
 import os
 
-
+def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-
-    disable_auto_promotion(chatbot)
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        check_packages(["fitz", "tiktoken", "scipdf"])
-    except:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    grobid_url = get_avail_grobid_url()
-    if grobid_url is not None:
-        yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
-    else:
-        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
-        yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
-    import copy, json
-    TOKEN_LIMIT_PER_FRAGMENT = 1024
-    generated_conclusion_files = []
-    generated_html_files = []
-    DST_LANG = "中文"
-    from crazy_functions.pdf_fns.report_gen_html import construct_html
-    for index, fp in enumerate(file_manifest):
-        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        article_dict = parse_pdf(fp, grobid_url)
-        grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
-        with open(grobid_json_res, 'w+', encoding='utf8') as f:
-            f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
-        promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
-        
-        if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
-        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     """
-    此函数已经弃用
+    注意：此函数已经弃用！！新函数位于：crazy_functions/pdf_fns/parse_pdf.py
     """
     import copy
     TOKEN_LIMIT_PER_FRAGMENT = 1024
@@ -116,7 +48,8 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
             chatbot=chatbot,
             history_array=[[paper_meta] for _ in paper_fragments],
             sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
+                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "")
+                for _ in paper_fragments],
             # max_workers=5  # OpenAI所允许的最大并行过载
         )
         gpt_response_collection_md = copy.deepcopy(gpt_response_collection)

crazy_functions/pdf_fns/parse_pdf_via_doc2x.py

@@ -0,0 +1,211 @@
+from toolbox import get_log_folder, gen_time_str, get_conf
+from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import promote_file_to_downloadzone, extract_archive
+from toolbox import generate_file_link, zip_folder
+from crazy_functions.crazy_utils import get_files_from_everything
+from shared_utils.colorful import *
+import os
+
+def refresh_key(doc2x_api_key):
+    import requests, json
+    url = "https://api.doc2x.noedgeai.com/api/token/refresh"
+    res = requests.post(
+        url,
+        headers={"Authorization": "Bearer " + doc2x_api_key}
+    )
+    res_json = []
+    if res.status_code == 200:
+        decoded = res.content.decode("utf-8")
+        res_json = json.loads(decoded)
+        doc2x_api_key = res_json['data']['token']
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+    return doc2x_api_key
+
+def 解析PDF_DOC2X_转Latex(pdf_file_path):
+    import requests, json, os
+    DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
+    latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
+    doc2x_api_key = DOC2X_API_KEY
+    if doc2x_api_key.startswith('sk-'):
+        url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
+    else:
+        doc2x_api_key = refresh_key(doc2x_api_key)
+        url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
+
+    res = requests.post(
+        url,
+        files={"file": open(pdf_file_path, "rb")},
+        data={"ocr": "1"},
+        headers={"Authorization": "Bearer " + doc2x_api_key}
+    )
+    res_json = []
+    if res.status_code == 200:
+        decoded = res.content.decode("utf-8")
+        for z_decoded in decoded.split('\n'):
+            if len(z_decoded) == 0: continue
+            assert z_decoded.startswith("data: ")
+            z_decoded = z_decoded[len("data: "):]
+            decoded_json = json.loads(z_decoded)
+            res_json.append(decoded_json)
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+
+    uuid = res_json[0]['uuid']
+    to = "latex" # latex, md, docx
+    url = "https://api.doc2x.noedgeai.com/api/export"+"?request_id="+uuid+"&to="+to
+
+    res = requests.get(url, headers={"Authorization": "Bearer " + doc2x_api_key})
+    latex_zip_path = os.path.join(latex_dir, gen_time_str() + '.zip')
+    latex_unzip_path = os.path.join(latex_dir, gen_time_str())
+    if res.status_code == 200:
+        with open(latex_zip_path, "wb") as f: f.write(res.content)
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+
+    import zipfile
+    with zipfile.ZipFile(latex_zip_path, 'r') as zip_ref:
+        zip_ref.extractall(latex_unzip_path)
+
+
+    return latex_unzip_path
+
+
+
+
+def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request):
+
+
+    def pdf2markdown(filepath):
+        import requests, json, os
+        markdown_dir = get_log_folder(plugin_name="pdf_ocr")
+        doc2x_api_key = DOC2X_API_KEY
+        if doc2x_api_key.startswith('sk-'):
+            url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
+        else:
+            doc2x_api_key = refresh_key(doc2x_api_key)
+            url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
+
+        chatbot.append((None, "加载PDF文件，发送至DOC2X解析..."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        res = requests.post(
+            url,
+            files={"file": open(filepath, "rb")},
+            data={"ocr": "1"},
+            headers={"Authorization": "Bearer " + doc2x_api_key}
+        )
+        res_json = []
+        if res.status_code == 200:
+            decoded = res.content.decode("utf-8")
+            for z_decoded in decoded.split('\n'):
+                if len(z_decoded) == 0: continue
+                assert z_decoded.startswith("data: ")
+                z_decoded = z_decoded[len("data: "):]
+                decoded_json = json.loads(z_decoded)
+                res_json.append(decoded_json)
+        else:
+            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+        uuid = res_json[0]['uuid']
+        to = "md" # latex, md, docx
+        url = "https://api.doc2x.noedgeai.com/api/export"+"?request_id="+uuid+"&to="+to
+
+        chatbot.append((None, f"读取解析: {url} ..."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        res = requests.get(url, headers={"Authorization": "Bearer " + doc2x_api_key})
+        md_zip_path = os.path.join(markdown_dir, gen_time_str() + '.zip')
+        if res.status_code == 200:
+            with open(md_zip_path, "wb") as f: f.write(res.content)
+        else:
+            raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+        promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
+        chatbot.append((None, f"完成解析 {md_zip_path} ..."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return md_zip_path
+
+    def deliver_to_markdown_plugin(md_zip_path, user_request):
+        from crazy_functions.Markdown_Translate import Markdown英译中
+        import shutil, re
+
+        time_tag = gen_time_str()
+        target_path_base = get_log_folder(chatbot.get_user())
+        file_origin_name = os.path.basename(md_zip_path)
+        this_file_path = os.path.join(target_path_base, file_origin_name)
+        os.makedirs(target_path_base, exist_ok=True)
+        shutil.copyfile(md_zip_path, this_file_path)
+        ex_folder = this_file_path + ".extract"
+        extract_archive(
+            file_path=this_file_path, dest_dir=ex_folder
+        )
+
+        # edit markdown files
+        success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md')
+        for generated_fp in file_manifest:
+            # 修正一些公式问题
+            with open(generated_fp, 'r', encoding='utf8') as f:
+                content = f.read()
+            # 将公式中的\[ \]替换成$$
+            content = content.replace(r'\[', r'$$').replace(r'\]', r'$$')
+            # 将公式中的\( \)替换成$
+            content = content.replace(r'\(', r'$').replace(r'\)', r'$')
+            content = content.replace('```markdown', '\n').replace('```', '\n')
+            with open(generated_fp, 'w', encoding='utf8') as f:
+                f.write(content)
+            promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
+            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+            # 生成在线预览html
+            file_name = '在线预览翻译（原文）' + gen_time_str() + '.html'
+            preview_fp = os.path.join(ex_folder, file_name)
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
+            with open(generated_fp, "r", encoding="utf-8") as f:
+                md = f.read()
+                # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
+                md = re.sub(r'^<table>', r'😃<table>', md, flags=re.MULTILINE)
+            html = markdown_convertion_for_file(md)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
+            chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
+            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
+
+
+
+        chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
+        plugin_kwargs['markdown_expected_output_dir'] = ex_folder
+
+        translated_f_name = 'translated_markdown.md'
+        generated_fp = plugin_kwargs['markdown_expected_output_path'] = os.path.join(ex_folder, translated_f_name)
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        yield from Markdown英译中(ex_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+        if os.path.exists(generated_fp):
+            # 修正一些公式问题
+            with open(generated_fp, 'r', encoding='utf8') as f: content = f.read()
+            content = content.replace('```markdown', '\n').replace('```', '\n')
+            # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
+            content = re.sub(r'^<table>', r'😃<table>', content, flags=re.MULTILINE)
+            with open(generated_fp, 'w', encoding='utf8') as f: f.write(content)
+            # 生成在线预览html
+            file_name = '在线预览翻译' + gen_time_str() + '.html'
+            preview_fp = os.path.join(ex_folder, file_name)
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
+            with open(generated_fp, "r", encoding="utf-8") as f:
+                md = f.read()
+            html = markdown_convertion_for_file(md)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
+            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
+            # 生成包含图片的压缩包
+            dest_folder = get_log_folder(chatbot.get_user())
+            zip_name = '翻译后的带图文档.zip'
+            zip_folder(source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name)
+            zip_fp = os.path.join(dest_folder, zip_name)
+            promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
+            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    md_zip_path = yield from pdf2markdown(fp)
+    yield from deliver_to_markdown_plugin(md_zip_path, user_request)
+
+def 解析PDF_基于DOC2X(file_manifest, *args):
+    for index, fp in enumerate(file_manifest):
+        yield from 解析PDF_DOC2X_单文件(fp, *args)
+    return
+
+

crazy_functions/pdf_fns/parse_word.py

@@ -0,0 +1,85 @@
+from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
+import os
+import re
+def extract_text_from_files(txt, chatbot, history):
+    """
+    查找pdf/md/word并获取文本内容并返回状态以及文本
+
+    输入参数 Args:
+        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
+        history (list): List of chat history （历史，对话历史列表）
+
+    输出 Returns:
+        文件是否存在(bool)
+        final_result(list):文本内容
+        page_one(list):第一页内容/摘要
+        file_manifest(list):文件路径
+        excption(string):需要用户手动处理的信息,如没出错则保持为空
+    """
+
+    final_result = []
+    page_one = []
+    file_manifest = []
+    excption = ""
+
+    if txt == "":
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    #查找输入区内容中的文件
+    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
+    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
+    file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx')
+    file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')
+
+    if file_doc:
+        excption = "word"
+        return False, final_result, page_one, file_manifest, excption
+
+    file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
+    if file_num == 0:
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    if file_pdf:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            import fitz
+        except:
+            excption = "pdf"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(pdf_manifest):
+            file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
+            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+            pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
+            final_result.append(file_content)
+            page_one.append(pdf_one)
+            file_manifest.append(os.path.relpath(fp, folder_pdf))
+
+    if file_md:
+        for index, fp in enumerate(md_manifest):
+            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+                file_content = f.read()
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE)  #接下来提取md中的一级/二级标题作为摘要
+            if len(headers) > 0:
+                page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
+            else:
+                page_one.append("")
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_md))
+
+    if file_word:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            from docx import Document
+        except:
+            excption = "word_pip"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(word_manifest):
+            doc = Document(fp)
+            file_content = '\n'.join([p.text for p in doc.paragraphs])
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            page_one.append(file_content[:200])
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_word))
+
+    return True, final_result, page_one, file_manifest, excption

crazy_functions/pdf_fns/report_template_v2.html

@@ -0,0 +1,73 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
+    <title>GPT-Academic 翻译报告书</title>
+    <style>
+        .centered-a {
+            color: red;
+            text-align: center;
+            margin-bottom: 2%;
+            font-size: 1.5em;
+        }
+        .centered-b {
+            color: red;
+            text-align: center;
+            margin-top: 10%;
+            margin-bottom: 20%;
+            font-size: 1.5em;
+        }
+        .centered-c {
+            color: rgba(255, 0, 0, 0);
+            text-align: center;
+            margin-top: 2%;
+            margin-bottom: 20%;
+            font-size: 7em;
+        }
+    </style>
+<script>
+        // Configure MathJax settings
+        MathJax = {
+            tex: {
+                inlineMath: [
+                    ['$', '$'],
+                    ['\(', '\)']
+                ]
+            }
+        }
+        addEventListener('zero-md-rendered', () => {MathJax.typeset(); console.log('MathJax typeset!');})
+    </script>
+    <!-- Load MathJax library -->
+    <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
+    <script
+        type="module"
+        src="https://cdn.jsdelivr.net/gh/zerodevx/zero-md@2/dist/zero-md.min.js"
+    ></script>
+
+</head>
+
+<body>
+    <div class="test_temp1" style="width:10%; height: 500px; float:left;">
+
+    </div>
+    <div class="test_temp2" style="width:80%; height: 500px; float:left;">
+        <!-- Simply set the `src` attribute to your MD file and win -->
+        <div class="centered-a">
+            请按Ctrl+S保存此页面，否则该页面可能在几分钟后失效。
+        </div>
+        <zero-md src="translated_markdown.md" no-shadow>
+        </zero-md>
+        <div class="centered-b">
+            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
+        </div>
+        <div class="centered-c">
+            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
+        </div>
+    </div>
+    <div class="test_temp3" style="width:10%; height: 500px; float:left;">
+    </div>
+
+    </body>
+
+</html>

crazy_functions/plugin_template/plugin_class_template.py

@@ -0,0 +1,52 @@
+import os, json, base64
+from pydantic import BaseModel, Field
+from textwrap import dedent
+from typing import List
+
+class ArgProperty(BaseModel): # PLUGIN_ARG_MENU
+    title: str = Field(description="The title", default="")
+    description: str = Field(description="The description", default="")
+    default_value: str = Field(description="The default value", default="")
+    type: str = Field(description="The type", default="")   # currently we support ['string', 'dropdown']
+    options: List[str] = Field(default=[], description="List of options available for the argument") # only used when type is 'dropdown'
+
+class GptAcademicPluginTemplate():
+    def __init__(self):
+        # please note that `execute` method may run in different threads,
+        # thus you should not store any state in the plugin instance,
+        # which may be accessed by multiple threads
+        pass
+
+
+    def define_arg_selection_menu(self):
+        """
+        An example as below:
+            ```
+            def define_arg_selection_menu(self):
+                gui_definition = {
+                    "main_input":
+                        ArgProperty(title="main input", description="description", default_value="default_value", type="string").model_dump_json(),
+                    "advanced_arg":
+                        ArgProperty(title="advanced arguments", description="description", default_value="default_value", type="string").model_dump_json(),
+                    "additional_arg_01":
+                        ArgProperty(title="additional", description="description", default_value="default_value", type="string").model_dump_json(),
+                }
+                return gui_definition
+            ```
+        """
+        raise NotImplementedError("You need to implement this method in your plugin class")
+
+
+    def get_js_code_for_generating_menu(self, btnName):
+        define_arg_selection = self.define_arg_selection_menu()
+
+        if len(define_arg_selection.keys()) > 8:
+            raise ValueError("You can only have up to 8 arguments in the define_arg_selection")
+        # if "main_input" not in define_arg_selection:
+        #     raise ValueError("You must have a 'main_input' in the define_arg_selection")
+
+        DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
+        return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        raise NotImplementedError("You need to implement this method in your plugin class")

crazy_functions/vt_fns/vt_call_plugin.py

@@ -10,7 +10,7 @@ def read_avail_plugin_enum():
     from crazy_functional import get_crazy_functions
     plugin_arr = get_crazy_functions()
     # remove plugins with out explaination
-    plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
+    plugin_arr = {k:v for k, v in plugin_arr.items() if ('Info' in v) and ('Function' in v)}
     plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}

crazy_functions/批量翻译PDF文档_NOUGAT.py

@@ -5,7 +5,7 @@ from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from .crazy_utils import read_and_clean_pdf_text
 from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
-from colorful import *
+from shared_utils.colorful import *
 import copy
 import os
 import math

crazy_functions/生成多种Mermaid图表.py

@@ -1,7 +1,9 @@
 from toolbox import CatchException, update_ui, report_exception
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import read_and_clean_pdf_text
+from crazy_functions.plugin_template.plugin_class_template import (
-import datetime
+    GptAcademicPluginTemplate,
+)
+from crazy_functions.plugin_template.plugin_class_template import ArgProperty
 
 # 以下是每类图表的PROMPT
 SELECT_PROMPT = """
@@ -21,19 +23,21 @@ SELECT_PROMPT = """
 # 没有思维导图!!!测试发现模型始终会优先选择思维导图
 # 流程图
 PROMPT_1 = """
-请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 graph TD
-    P(编程) --> L1(Python)
+    P("编程") --> L1("Python")
-    P(编程) --> L2(C)
+    P("编程") --> L2("C")
-    P(编程) --> L3(C++)
+    P("编程") --> L3("C++")
-    P(编程) --> L4(Javascipt)
+    P("编程") --> L4("Javascipt")
-    P(编程) --> L5(PHP)
+    P("编程") --> L5("PHP")
 ```
 """
 # 序列图
 PROMPT_2 = """
-请你给出围绕“{subject}”的序列图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的序列图，使用mermaid语法。
+mermaid语法举例：
 ```mermaid
 sequenceDiagram
     participant A as 用户
@@ -46,7 +50,8 @@ sequenceDiagram
 """
 # 类图
 PROMPT_3 = """
-请你给出围绕“{subject}”的类图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的类图，使用mermaid语法。
+mermaid语法举例：
 ```mermaid
 classDiagram
     Class01 <|-- AveryLongClass : Cool
@@ -66,7 +71,8 @@ classDiagram
 """
 # 饼图
 PROMPT_4 = """
-请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的饼图，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 pie title Pets adopted by volunteers
     "狗" : 386
@@ -76,36 +82,39 @@ pie title Pets adopted by volunteers
 """
 # 甘特图
 PROMPT_5 = """
-请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的甘特图，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 gantt
-    title 项目开发流程
+    title "项目开发流程"
     dateFormat  YYYY-MM-DD
-    section 设计
+    section "设计"
-    需求分析 :done, des1, 2024-01-06,2024-01-08
+    "需求分析" :done, des1, 2024-01-06,2024-01-08
-    原型设计 :active, des2, 2024-01-09, 3d
+    "原型设计" :active, des2, 2024-01-09, 3d
-    UI设计 : des3, after des2, 5d
+    "UI设计" : des3, after des2, 5d
-    section 开发
+    section "开发"
-    前端开发 :2024-01-20, 10d
+    "前端开发" :2024-01-20, 10d
-    后端开发 :2024-01-20, 10d
+    "后端开发" :2024-01-20, 10d
 ```
 """
 # 状态图
 PROMPT_6 = """
-请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的状态图，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 stateDiagram-v2
-   [*] --> Still
+   [*] --> "Still"
-    Still --> [*]
+    "Still" --> [*]
-    Still --> Moving
+    "Still" --> "Moving"
-    Moving --> Still
+    "Moving" --> "Still"
-    Moving --> Crash
+    "Moving" --> "Crash"
-    Crash --> [*]
+    "Crash" --> [*]
 ```
 """
 # 实体关系图
 PROMPT_7 = """
-请你给出围绕“{subject}”的实体关系图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的实体关系图，使用mermaid语法。
+mermaid语法举例：
 ```mermaid
 erDiagram
     CUSTOMER ||--o{ ORDER : places
@@ -127,142 +136,171 @@ erDiagram
 """
 # 象限提示图
 PROMPT_8 = """
-请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例：
+请你给出围绕“{subject}”的象限图，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 graph LR
-    A[Hard skill] --> B(Programming)
+    A["Hard skill"] --> B("Programming")
-    A[Hard skill] --> C(Design)
+    A["Hard skill"] --> C("Design")
-    D[Soft skill] --> E(Coordination)
+    D["Soft skill"] --> E("Coordination")
-    D[Soft skill] --> F(Communication)
+    D["Soft skill"] --> F("Communication")
 ```
 """
 # 思维导图
 PROMPT_9 = """
 {subject}
 ==========
-请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，mermaid语法举例：
+请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，注意需要使用双引号将内容括起来。
+mermaid语法举例：
 ```mermaid
 mindmap
   root((mindmap))
-    Origins
+    ("Origins")
-      Long history
+      ("Long history")
       ::icon(fa fa-book)
-      Popularisation
+      ("Popularisation")
-        British popular psychology author Tony Buzan
+        ("British popular psychology author Tony Buzan")
-    Research
+        ::icon(fa fa-user)
-      On effectiveness<br/>and features
+    ("Research")
-      On Automatic creation
+      ("On effectiveness<br/>and features")
-        Uses
+      ::icon(fa fa-search)
-            Creative techniques
+      ("On Automatic creation")
-            Strategic planning
+      ::icon(fa fa-robot)
-            Argument mapping
+        ("Uses")
-    Tools
+            ("Creative techniques")
-      Pen and paper
+            ::icon(fa fa-lightbulb-o)
-      Mermaid
+            ("Strategic planning")
+            ::icon(fa fa-flag)
+            ("Argument mapping")
+            ::icon(fa fa-comments)
+    ("Tools")
+      ("Pen and paper")
+      ::icon(fa fa-pencil)
+      ("Mermaid")
+      ::icon(fa fa-code)
 ```
 """
 
-def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
+
+def 解析历史输入(history, llm_kwargs, file_manifest, chatbot, plugin_kwargs):
     ############################## <第 0 步，切割输入> ##################################
     # 借用PDF切割中的函数对文本进行切割
     TOKEN_LIMIT_PER_FRAGMENT = 2500
-    txt = str(history).encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+    txt = (
-    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        str(history).encode("utf-8", "ignore").decode()
-    txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+    )  # avoid reading non-utf8 chars
+    from crazy_functions.pdf_fns.breakdown_txt import (
+        breakdown_text_to_satisfy_token_limit,
+    )
+
+    txt = breakdown_text_to_satisfy_token_limit(
+        txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs["llm_model"]
+    )
     ############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你从历史记录或文件中提取摘要。'; gpt_say = "[Local Message] 收到。"   # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history)    # 更新UI
     results = []
     MAX_WORD_TOTAL = 4096
     n_txt = len(txt)
     last_iteration_result = "从以下文本中提取摘要。"
-    if n_txt >= 20: print('文章极长，不能达到预期效果')
+    if n_txt >= 20:
+        print("文章极长，不能达到预期效果")
     for i in range(n_txt):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i]}"
+        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
         i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i][:200]} ...."
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                                                                           llm_kwargs, chatbot, 
+            i_say,
-                                                                           history=["The main content of the previous section is?", last_iteration_result], # 迭代上一次的结果
+            i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese."  # 提示
+            llm_kwargs,
+            chatbot,
+            history=[
+                "The main content of the previous section is?",
+                last_iteration_result,
+            ],  # 迭代上一次的结果
+            sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese.",  # 提示
         )
         results.append(gpt_say)
         last_iteration_result = gpt_say
     ############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    gpt_say = str(plugin_kwargs)  # 将图表类型参数赋值为插件参数
-    gpt_say = plugin_kwargs.get("advanced_arg", "")     #将图表类型参数赋值为插件参数  
+    results_txt = "\n".join(results)  # 合并摘要
-    results_txt = '\n'.join(results)    #合并摘要
+    if gpt_say not in [
-    if gpt_say not in ['1','2','3','4','5','6','7','8','9']:    #如插件参数不正确则使用对话模型判断
+        "1",
-        i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; gpt_say = "[Local Message] 收到。"   # 用户提示
+        "2",
-        chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+    ]:  # 如插件参数不正确则使用对话模型判断
+        i_say_show_user = (
+            f"接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制"
+        )
+        gpt_say = "[Local Message] 收到。"  # 用户提示
+        chatbot.append([i_say_show_user, gpt_say])
+        yield from update_ui(chatbot=chatbot, history=[])  # 更新UI
         i_say = SELECT_PROMPT.format(subject=results_txt)
         i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。'
         for i in range(3):
             gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
                 inputs=i_say,
                 inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+                llm_kwargs=llm_kwargs,
-                sys_prompt=""
+                chatbot=chatbot,
+                history=[],
+                sys_prompt="",
             )
-            if gpt_say in ['1','2','3','4','5','6','7','8','9']:     #判断返回是否正确
+            if gpt_say in [
+                "1",
+                "2",
+                "3",
+                "4",
+                "5",
+                "6",
+                "7",
+                "8",
+                "9",
+            ]:  # 判断返回是否正确
                 break
-        if gpt_say not in ['1','2','3','4','5','6','7','8','9']:
+        if gpt_say not in ["1", "2", "3", "4", "5", "6", "7", "8", "9"]:
-            gpt_say = '1'
+            gpt_say = "1"
     ############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
-    if gpt_say == '1':
+    if gpt_say == "1":
         i_say = PROMPT_1.format(subject=results_txt)
-    elif gpt_say == '2':
+    elif gpt_say == "2":
         i_say = PROMPT_2.format(subject=results_txt)
-    elif gpt_say == '3':
+    elif gpt_say == "3":
         i_say = PROMPT_3.format(subject=results_txt)
-    elif gpt_say == '4':
+    elif gpt_say == "4":
         i_say = PROMPT_4.format(subject=results_txt)
-    elif gpt_say == '5':
+    elif gpt_say == "5":
         i_say = PROMPT_5.format(subject=results_txt)
-    elif gpt_say == '6':
+    elif gpt_say == "6":
         i_say = PROMPT_6.format(subject=results_txt)
-    elif gpt_say == '7':
+    elif gpt_say == "7":
         i_say = PROMPT_7.replace("{subject}", results_txt)  # 由于实体关系图用到了{}符号
-    elif gpt_say == '8':
+    elif gpt_say == "8":
         i_say = PROMPT_8.format(subject=results_txt)
-    elif gpt_say == '9':
+    elif gpt_say == "9":
         i_say = PROMPT_9.format(subject=results_txt)
-    i_say_show_user = f'请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。'
+    i_say_show_user = f"请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。"
     gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
         inputs=i_say,
         inputs_show_user=i_say_show_user,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+        llm_kwargs=llm_kwargs,
-        sys_prompt="你精通使用mermaid语法来绘制图表,首先确保语法正确,其次避免在mermaid语法中使用不允许的字符,此外也应当分考虑图表的可读性。"
+        chatbot=chatbot,
+        history=[],
+        sys_prompt="",
     )
     history.append(gpt_say)
     yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面 # 界面更新
 
-def 输入区文件处理(txt):
-    if txt == "": return False, txt
-    success = True
-    import glob
-    from .crazy_utils import get_files_from_everything
-    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
-    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
-    if len(pdf_manifest) == 0 and len(md_manifest) == 0:
-        return False, txt   #如输入区内容不是文件则直接返回输入区内容
-    
-    final_result = ""
-    if file_pdf:
-        for index, fp in enumerate(pdf_manifest):
-            file_content, page_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
-            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-            final_result += "\n" + file_content
-    if file_md:
-        for index, fp in enumerate(md_manifest):
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-            file_content = file_content.encode('utf-8', 'ignore').decode()
-            final_result += "\n" + file_content
-    return True, final_result
 
 @CatchException
-def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+def 生成多种Mermaid图表(
+    txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
+):
     """
     txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
     llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
@@ -275,28 +313,126 @@ def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history,
     import os
 
     # 基本信息：功能、贡献者
-    chatbot.append([
+    chatbot.append(
+        [
             "函数插件功能？",
-        "根据当前聊天历史或文件中(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
+            "根据当前聊天历史或指定的路径文件(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
-        \n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"])
+        \n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918",
+        ]
+    )
     yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
 
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_exception(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
     if os.path.exists(txt):  # 如输入区无内容则直接解析历史记录
-        file_exist, txt = 输入区文件处理(txt)
+        from crazy_functions.pdf_fns.parse_word import extract_text_from_files
+
+        file_exist, final_result, page_one, file_manifest, excption = (
+            extract_text_from_files(txt, chatbot, history)
+        )
     else:
         file_exist = False
+        excption = ""
+        file_manifest = []
 
-    if file_exist : history = []    #如输入区内容为文件则清空历史记录
+    if excption != "":
-    history.append(txt)     #将解析后的txt传递加入到历史中
+        if excption == "word":
+            report_exception(
+                chatbot,
+                history,
+                a=f"解析项目: {txt}",
+                b=f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。",
+            )
 
-    yield from 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs)  
+        elif excption == "pdf":
+            report_exception(
+                chatbot,
+                history,
+                a=f"解析项目: {txt}",
+                b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。",
+            )
+
+        elif excption == "word_pip":
+            report_exception(
+                chatbot,
+                history,
+                a=f"解析项目: {txt}",
+                b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。",
+            )
+
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    else:
+        if not file_exist:
+            history.append(txt)  # 如输入区不是文件则将输入区内容加入历史记录
+            i_say_show_user = f"首先你从历史记录中提取摘要。"
+            gpt_say = "[Local Message] 收到。"  # 用户提示
+            chatbot.append([i_say_show_user, gpt_say])
+            yield from update_ui(chatbot=chatbot, history=history)  # 更新UI
+            yield from 解析历史输入(
+                history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
+            )
+        else:
+            file_num = len(file_manifest)
+            for i in range(file_num):  # 依次处理文件
+                i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"
+                gpt_say = "[Local Message] 收到。"  # 用户提示
+                chatbot.append([i_say_show_user, gpt_say])
+                yield from update_ui(chatbot=chatbot, history=history)  # 更新UI
+                history = []  # 如输入区内容为文件则清空历史记录
+                history.append(final_result[i])
+                yield from 解析历史输入(
+                    history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
+                )
+
+
+class Mermaid_Gen(GptAcademicPluginTemplate):
+    def __init__(self):
+        pass
+
+    def define_arg_selection_menu(self):
+        gui_definition = {
+            "Type_of_Mermaid": ArgProperty(
+                title="绘制的Mermaid图表类型",
+                options=[
+                    "由LLM决定",
+                    "流程图",
+                    "序列图",
+                    "类图",
+                    "饼图",
+                    "甘特图",
+                    "状态图",
+                    "实体关系图",
+                    "象限提示图",
+                    "思维导图",
+                ],
+                default_value="由LLM决定",
+                description="选择'由LLM决定'时将由对话模型判断适合的图表类型(不包括思维导图)，选择其他类型时将直接绘制指定的图表类型。",
+                type="dropdown",
+            ).model_dump_json(),
+        }
+        return gui_definition
+
+    def execute(
+        txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request
+    ):
+        options = [
+            "由LLM决定",
+            "流程图",
+            "序列图",
+            "类图",
+            "饼图",
+            "甘特图",
+            "状态图",
+            "实体关系图",
+            "象限提示图",
+            "思维导图",
+        ]
+        plugin_kwargs = options.index(plugin_kwargs['Type_of_Mermaid'])
+        yield from 生成多种Mermaid图表(
+            txt,
+            llm_kwargs,
+            plugin_kwargs,
+            chatbot,
+            history,
+            system_prompt,
+            user_request,
+        )

crazy_functions/解析JupyterNotebook.py

@@ -12,6 +12,12 @@ class PaperFileGroup():
         self.sp_file_index = []
         self.sp_file_tag = []
 
+        # count_token
+        from request_llms.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
     def run_file_split(self, max_token_limit=1900):
         """
         将长文本分离开来

crazy_functions/解析项目源代码.py

@@ -1,6 +1,7 @@
 from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
 from toolbox import CatchException, report_exception, write_history_to_file
-from .crazy_utils import input_clipping
+from shared_utils.fastapi_server import validate_path_safety
+from crazy_functions.crazy_utils import input_clipping
 
 def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import os, copy
@@ -128,6 +129,7 @@ def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
@@ -146,6 +148,7 @@ def 解析一个Matlab项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         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}")
@@ -164,6 +167,7 @@ def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, his
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
@@ -184,6 +188,7 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
@@ -206,6 +211,7 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
@@ -228,6 +234,7 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
@@ -257,6 +264,7 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
@@ -278,6 +286,7 @@ def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
@@ -298,6 +307,7 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
@@ -320,6 +330,7 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     import glob, os
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
@@ -345,15 +356,19 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
     pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
     # 将要忽略匹配的文件名(例如: ^README.md)
-    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
+    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", r"\.") # 移除左边通配符，移除右侧逗号，转义点号
+                           for _ in txt_pattern.split(" ") # 以空格分割
+                           if (_ != "" and _.strip().startswith("^") and not _.strip().startswith("^*."))   # ^开始，但不是^*.开始
+                           ]
     # 生成正则表达式
-    pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
+    pattern_except = r'/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
     pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
 
     history.clear()
     import glob, os, re
     if os.path.exists(txt):
         project_folder = txt
+        validate_path_safety(project_folder, chatbot.get_user())
     else:
         if txt == "": txt = '空空如也的输入栏'
         report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")

crazy_functions/高级功能函数模板.py

@@ -2,6 +2,10 @@ from toolbox import CatchException, update_ui
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 import datetime
 
+####################################################################################################################
+# Demo 1: 一个非常简单的插件 #########################################################################################
+####################################################################################################################
+
 高阶功能模板函数示意图 = f"""
 ```mermaid
 flowchart TD
@@ -26,7 +30,7 @@ flowchart TD
 """
 
 @CatchException
-def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, num_day=5):
     """
     # 高阶功能模板函数示意图：https://mermaid.live/edit#pako:eNptk1tvEkEYhv8KmattQpvlvOyFCcdeeaVXuoYssBwie8gyhCIlqVoLhrbbtAWNUpEGUkyMEDW2Fmn_DDOL_8LZHdOwxrnamX3f7_3mmZk6yKhZCfAgV1KrmYKoQ9fDuKC4yChX0nld1Aou1JzjznQ5fWmejh8LYHW6vG2a47YAnlCLNSIRolnenKBXI_zRIBrcuqRT890u7jZx7zMDt-AaMbnW1--5olGiz2sQjwfoQxsZL0hxplSSU0-rop4vrzmKR6O2JxYjHmwcL2Y_HDatVMkXlf86YzHbGY9bO5j8XE7O8Nsbc3iNB3ukL2SMcH-XIQBgWoVOZzxuOxOJOyc63EPGV6ZQLENVrznViYStTiaJ2vw2M2d9bByRnOXkgCnXylCSU5quyto_IcmkbdvctELmJ-j1ASW3uB3g5xOmKqVTmqr_Na3AtuS_dtBFm8H90XJyHkDDT7S9xXWb4HGmRChx64AOL5HRpUm411rM5uh4H78Z4V7fCZzytjZz2seto9XaNPFue07clLaVZF8UNLygJ-VES8lah_n-O-5Ozc7-77NzJ0-K0yr0ZYrmHdqAk50t2RbA4qq9uNohBASw7YpSgaRkLWCCAtxAlnRZLGbJba9bPwUAC5IsCYAnn1kpJ1ZKUACC0iBSsQLVBzUlA3ioVyQ3qGhZEUrxokiehAz4nFgqk1VNVABfB1uAD_g2_AGPl-W8nMcbCvsDblADfNCz4feyobDPy3rYEMtxwYYbPFNVUoHdCPmDHBv2cP4AMfrCbiBli-Q-3afv0X6WdsIjW2-10fgDy1SAig
 
@@ -43,7 +47,7 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         "您正在调用插件：历史上的今天",
         "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！" + 高阶功能模板函数示意图))
     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
-    for i in range(5):
+    for i in range(int(num_day)):
         currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
         currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
         i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
@@ -59,6 +63,56 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
 
 
 
+
+
+
+####################################################################################################################
+# Demo 2: 一个带二级菜单的插件 #######################################################################################
+####################################################################################################################
+
+from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
+class Demo_Wrap(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+        """
+        gui_definition = {
+            "num_day":
+                ArgProperty(title="日期选择", options=["仅今天", "未来3天", "未来5天"], default_value="未来3天", description="无", type="dropdown").model_dump_json(),
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        num_day = plugin_kwargs["num_day"]
+        if num_day == "仅今天": num_day = 1
+        if num_day == "未来3天": num_day = 3
+        if num_day == "未来5天": num_day = 5
+        yield from 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, num_day=num_day)
+
+
+
+
+
+
+
+
+
+
+
+
+####################################################################################################################
+# Demo 3: 绘制脑图的Demo ############################################################################################
+####################################################################################################################
+
 PROMPT = """
 请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
 ```mermaid

docker-compose.yml

@@ -4,9 +4,9 @@
 # 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
+    # 「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）
+    # 「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
@@ -25,7 +25,7 @@
 ## ===================================================
 
 ## ===================================================
-## 【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）
+## 「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）
 ## ===================================================
 version: '3'
 services:
@@ -63,10 +63,10 @@ services:
     #             count: 1
     #             capabilities: [gpu]
 
-    # 【WEB_PORT暴露方法1: 适用于Linux】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"
 
-    # 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
+    # 「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
 
@@ -75,10 +75,8 @@ services:
       bash -c "python3 -u main.py"
 
 
-
-
 ## ===================================================
-## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
+## 「方案一」 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
 ## ===================================================
 version: '3'
 services:
@@ -97,16 +95,16 @@ services:
       # DEFAULT_WORKER_NUM:       '    10                                                                                             '
       # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
 
-    # 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
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
 
 ### ===================================================
-### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
+### 「方案二」 如果需要运行ChatGLM + Qwen + MOSS等本地模型
 ### ===================================================
 version: '3'
 services:
@@ -130,8 +128,10 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 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"
+
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
@@ -139,8 +139,9 @@ services:
     # command: >
     #   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
+### 「方案三」 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
 ### ===================================================
 version: '3'
 services:
@@ -164,16 +165,16 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 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
+    # 启动命令
     command: >
       python3 -u main.py
 
 
 ## ===================================================
-## 【方案四】 ChatGPT + Latex
+## 「方案四」 ChatGPT + Latex
 ## ===================================================
 version: '3'
 services:
@@ -190,16 +191,16 @@ services:
       DEFAULT_WORKER_NUM:       '    10                                                                               '
       WEB_PORT:                 '    12303                                                                            '
 
-    # 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
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
 
 ## ===================================================
-## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
+## 「方案五」 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
 ## ===================================================
 version: '3'
 services:
@@ -223,9 +224,9 @@ services:
       # (无需填写) ALIYUN_ACCESSKEY:         '    LTAI5q6BrFUzoRXVGUWnekh1                                         '
       # (无需填写) ALIYUN_SECRET:            '    eHmI20AVWIaQZ0CiTD2bGQVsaP9i68                                   '
 
-    # 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
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"

docs/GithubAction+AllCapacity

@@ -28,6 +28,8 @@ RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 RUN python3 -m pip install nougat-ocr
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
 
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'

docs/GithubAction+AllCapacityBeta

@@ -36,6 +36,9 @@ RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 RUN python3 -m pip install nougat-ocr
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
+
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 

docs/GithubAction+ChatGLM+Moss

@@ -21,7 +21,8 @@ RUN python3 -m pip install -r request_llms/requirements_qwen.txt
 RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 
-
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
 
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'

docs/GithubAction+JittorLLMs

@@ -23,6 +23,9 @@ RUN python3 -m pip install -r request_llms/requirements_jittorllms.txt -i https:
 # 下载JittorLLMs
 RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llms/jittorllms
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
+
 # 禁用缓存，确保更新代码
 ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
 RUN git pull

docs/GithubAction+NoLocal

@@ -12,6 +12,8 @@ COPY . .
 # 安装依赖
 RUN pip3 install -r requirements.txt
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
 
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'

docs/GithubAction+NoLocal+AudioAssistant

@@ -13,7 +13,10 @@ COPY . .
 RUN pip3 install -r requirements.txt
 
 # 安装语音插件的额外依赖
-RUN pip3 install pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+RUN pip3 install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
 
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'

docs/GithubAction+NoLocal+Latex

@@ -25,6 +25,9 @@ COPY . .
 # 安装依赖
 RUN pip3 install -r requirements.txt
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
+
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 

docs/GithubAction+NoLocal+Vectordb

@@ -19,6 +19,9 @@ RUN pip3 install transformers protobuf langchain sentence-transformers  faiss-cp
 RUN pip3 install unstructured[all-docs] --upgrade
 RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
 
+# edge-tts需要的依赖
+RUN apt update && apt install ffmpeg -y
+
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 

docs/plugin_with_secondary_menu.md

@@ -0,0 +1,189 @@
+# 实现带二级菜单的插件
+
+## 一、如何写带有二级菜单的插件
+
+1. 声明一个 `Class`，继承父类 `GptAcademicPluginTemplate`
+
+    ```python
+    from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate
+    from crazy_functions.plugin_template.plugin_class_template import ArgProperty
+
+    class Demo_Wrap(GptAcademicPluginTemplate):
+        def __init__(self): ...
+    ```
+
+2. 声明二级菜单中需要的变量，覆盖父类的`define_arg_selection_menu`函数。
+
+    ```python
+    class Demo_Wrap(GptAcademicPluginTemplate):
+        ...
+
+        def define_arg_selection_menu(self):
+            """
+            定义插件的二级选项菜单
+
+            第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+            第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+            第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；
+            """
+            gui_definition = {
+                "main_input":
+                    ArgProperty(title="ArxivID", description="输入Arxiv的ID或者网址", default_value="", type="string").model_dump_json(),
+                "advanced_arg":
+                    ArgProperty(title="额外的翻译提示词",
+                                description=r"如果有必要, 请在此处给出自定义翻译命令",
+                                default_value="", type="string").model_dump_json(),
+                "allow_cache":
+                    ArgProperty(title="是否允许从缓存中调取结果", options=["允许缓存", "从头执行"], default_value="允许缓存", description="无", type="dropdown").model_dump_json(),
+            }
+            return gui_definition
+        ...
+    ```
+
+
+    > [!IMPORTANT]
+    >
+    > ArgProperty 中每个条目对应一个参数，`type == "string"`时，使用文本块，`type == dropdown`时，使用下拉菜单。
+    >
+    > 注意：`main_input` 和 `advanced_arg`是两个特殊的参数。`main_input`会自动与界面右上角的`输入区`进行同步，而`advanced_arg`会自动与界面右下角的`高级参数输入区`同步。除此之外，参数名称可以任意选取。其他细节详见`crazy_functions/plugin_template/plugin_class_template.py`。
+
+
+
+
+3. 编写插件程序，覆盖父类的`execute`函数。
+
+    例如：
+
+    ```python
+    class Demo_Wrap(GptAcademicPluginTemplate):
+        ...
+        ...
+
+        def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+            """
+            执行插件
+
+            plugin_kwargs字典中会包含用户的选择，与上述 `define_arg_selection_menu` 一一对应
+            """
+            allow_cache = plugin_kwargs["allow_cache"]
+            advanced_arg = plugin_kwargs["advanced_arg"]
+
+            if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + plugin_kwargs["advanced_arg"]
+            yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+
+    ```
+
+
+
+4. 注册插件
+
+    将以下条目插入`crazy_functional.py`即可。注意，与旧插件不同的是，`Function`键值应该为None，而`Class`键值为上述插件的类名称（`Demo_Wrap`）。
+    ```
+        "新插件": {
+            "Group": "学术",
+            "Color": "stop",
+            "AsButton": True,
+            "Info": "插件说明",
+            "Function": None,
+            "Class": Demo_Wrap,
+        },
+    ```
+
+5. 已经结束了，启动程序测试吧~！
+
+
+
+## 二、背后的原理（需要JavaScript的前置知识）
+
+
+### (I) 首先介绍三个Gradio官方没有的重要前端函数
+
+主javascript程序`common.js`中有三个Gradio官方没有的重要API
+
+1. `get_data_from_gradio_component`
+    这个函数可以获取任意gradio组件的当前值，例如textbox中的字符，dropdown中的当前选项，chatbot当前的对话等等。调用方法举例：
+    ```javascript
+    // 获取当前的对话
+    let chatbot = await get_data_from_gradio_component('gpt-chatbot');
+    ```
+
+2. `get_gradio_component`
+    有时候我们不仅需要gradio组件的当前值，还需要它的label值、是否隐藏、下拉菜单其他可选选项等等，而通过这个函数可以直接获取这个组件的句柄。举例：
+    ```javascript
+    // 获取下拉菜单组件的句柄
+    var model_sel = await get_gradio_component("elem_model_sel");
+    // 获取它的所有属性，包括其所有可选选项
+    console.log(model_sel.props)
+    ```
+
+
+3. `push_data_to_gradio_component`
+    这个函数可以将数据推回gradio组件，例如textbox中的字符，dropdown中的当前选项等等。调用方法举例：
+
+    ```javascript
+    // 修改一个按钮上面的文本
+    push_data_to_gradio_component("btnName", "gradio_element_id", "string");
+
+    // 隐藏一个组件
+    push_data_to_gradio_component({ visible: false, __type__: 'update' }, "plugin_arg_menu", "obj");
+
+    // 修改组件label
+    push_data_to_gradio_component({ label: '新label的值', __type__: 'update' }, "gpt-chatbot", "obj")
+
+    // 第一个参数是value，
+    //     - 可以是字符串（调整textbox的文本，按钮的文本）；
+    //     - 还可以是 { visible: false, __type__: 'update' }  这样的字典（调整visible, label, choices）
+    // 第二个参数是elem_id
+    // 第三个参数是"string" 或者 "obj"
+    ```
+
+
+### (II) 从点击插件到执行插件的逻辑过程
+
+简述：程序启动时把每个插件的二级菜单编码为BASE64，存储在用户的浏览器前端，用户调用对应功能时，会按照插件的BASE64编码，将平时隐藏的菜单（有选择性地）显示出来。
+
+1. 启动阶段（主函数 `main.py` 中），遍历每个插件，生成二级菜单的BASE64编码，存入变量`register_advanced_plugin_init_code_arr`。
+    ```python
+    def get_js_code_for_generating_menu(self, btnName):
+        define_arg_selection = self.define_arg_selection_menu()
+        DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
+        return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
+    ```
+
+
+2. 用户加载阶段（主javascript程序`common.js`中），浏览器加载`register_advanced_plugin_init_code_arr`，存入本地的字典`advanced_plugin_init_code_lib`：
+
+    ```javascript
+    advanced_plugin_init_code_lib = {}
+    function register_advanced_plugin_init_code(key, code){
+        advanced_plugin_init_code_lib[key] = code;
+    }
+    ```
+
+3. 用户点击插件按钮（主函数 `main.py` 中）时，仅执行以下javascript代码，唤醒隐藏的二级菜单（生成菜单的代码在`common.js`中的`generate_menu`函数上）：
+
+
+    ```javascript
+    // 生成高级插件的选择菜单
+    function run_advanced_plugin_launch_code(key){
+        generate_menu(advanced_plugin_init_code_lib[key], key);
+    }
+    function on_flex_button_click(key){
+        run_advanced_plugin_launch_code(key);
+    }
+    ```
+
+    ```python
+    click_handle = plugins[k]["Button"].click(None, inputs=[], outputs=None, _js=f"""()=>run_advanced_plugin_launch_code("{k}")""")
+    ```
+
+4. 当用户点击二级菜单的执行键时，通过javascript脚本模拟点击一个隐藏按钮，触发后续程序（`common.js`中的`execute_current_pop_up_plugin`，会把二级菜单中的参数缓存到`invisible_current_pop_up_plugin_arg_final`，然后模拟点击`invisible_callback_btn_for_plugin_exe`按钮）。隐藏按钮的定义在（主函数 `main.py` ），该隐藏按钮会最终触发`route_switchy_bt_with_arg`函数（定义于`themes/gui_advanced_plugin_class.py`）：
+
+    ```python
+    click_handle_ng = new_plugin_callback.click(route_switchy_bt_with_arg, [
+            gr.State(["new_plugin_callback", "usr_confirmed_arg"] + input_combo_order),
+            new_plugin_callback, usr_confirmed_arg, *input_combo
+        ], output_combo)
+    ```
+
+5. 最后，`route_switchy_bt_with_arg`中，会搜集所有用户参数，统一集中到`plugin_kwargs`参数中，并执行对应插件的`execute`函数。

docs/self_analysis.md

@@ -22,13 +22,13 @@
 | crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
 | crazy_functions\代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
 | crazy_functions\图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| crazy_functions\对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
+| crazy_functions\Conversation_To_File.py | 将每次对话记录写入Markdown格式的文件中 |
 | crazy_functions\总结word文档.py | 对输入的word文档进行摘要生成 |
 | crazy_functions\总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| crazy_functions\批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| crazy_functions\Markdown_Translate.py | 将指定目录下的Markdown文件进行中英文翻译 |
 | crazy_functions\批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
 | crazy_functions\批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
+| crazy_functions\PDF_Translate.py | 将指定目录下的PDF文件进行中英文翻译 |
 | crazy_functions\理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
 | crazy_functions\生成函数注释.py | 自动生成Python函数的注释 |
 | crazy_functions\联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
@@ -155,9 +155,9 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 该程序文件提供了一个用于生成图像的函数`图片生成`。函数实现的过程中，会调用`gen_image`函数来生成图像，并返回图像生成的网址和本地文件地址。函数有多个参数，包括`prompt`(激励文本)、`llm_kwargs`(GPT模型的参数)、`plugin_kwargs`(插件模型的参数)等。函数核心代码使用了`requests`库向OpenAI API请求图像，并做了简单的处理和保存。函数还更新了交互界面，清空聊天历史并显示正在生成图像的消息和最终的图像网址和预览。
 
-## [18/48] 请对下面的程序文件做一个概述: crazy_functions\对话历史存档.py
+## [18/48] 请对下面的程序文件做一个概述: crazy_functions\Conversation_To_File.py
 
-这个文件是名为crazy_functions\对话历史存档.py的Python程序文件，包含了4个函数：
+这个文件是名为crazy_functions\Conversation_To_File.py的Python程序文件，包含了4个函数：
 
 1. write_chat_to_file(chatbot, history=None, file_name=None)：用来将对话记录以Markdown格式写入文件中，并且生成文件名，如果没指定文件名则用当前时间。写入完成后将文件路径打印出来。
 
@@ -165,7 +165,7 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 3. read_file_to_chat(chatbot, history, file_name)：从传入的文件中读取内容，解析出对话历史记录并更新聊天显示框。
 
-4. 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)：一个主要函数，用于保存当前对话记录并提醒用户。如果用户希望加载历史记录，则调用read_file_to_chat()来更新聊天显示框。如果用户希望删除历史记录，调用删除所有本地对话历史记录()函数完成删除操作。
+4. Conversation_To_File(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)：一个主要函数，用于保存当前对话记录并提醒用户。如果用户希望加载历史记录，则调用read_file_to_chat()来更新聊天显示框。如果用户希望删除历史记录，调用删除所有本地对话历史记录()函数完成删除操作。
 
 ## [19/48] 请对下面的程序文件做一个概述: crazy_functions\总结word文档.py
 
@@ -175,9 +175,9 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 该程序文件包括两个函数：split_audio_file()和AnalyAudio()，并且导入了一些必要的库并定义了一些工具函数。split_audio_file用于将音频文件分割成多个时长相等的片段，返回一个包含所有切割音频片段文件路径的列表，而AnalyAudio用来分析音频文件，通过调用whisper模型进行音频转文字并使用GPT模型对音频内容进行概述，最终将所有总结结果写入结果文件中。
 
-## [21/48] 请对下面的程序文件做一个概述: crazy_functions\批量Markdown翻译.py
+## [21/48] 请对下面的程序文件做一个概述: crazy_functions\Markdown_Translate.py
 
-该程序文件名为`批量Markdown翻译.py`，包含了以下功能：读取Markdown文件，将长文本分离开来，将Markdown文件进行翻译（英译中和中译英），整理结果并退出。程序使用了多线程以提高效率。程序使用了`tiktoken`依赖库，可能需要额外安装。文件中还有一些其他的函数和类，但与文件名所描述的功能无关。
+该程序文件名为`Markdown_Translate.py`，包含了以下功能：读取Markdown文件，将长文本分离开来，将Markdown文件进行翻译（英译中和中译英），整理结果并退出。程序使用了多线程以提高效率。程序使用了`tiktoken`依赖库，可能需要额外安装。文件中还有一些其他的函数和类，但与文件名所描述的功能无关。
 
 ## [22/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档.py
 
@@ -187,9 +187,9 @@ toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和
 
 该程序文件是一个用于批量总结PDF文档的函数插件，使用了pdfminer插件和BeautifulSoup库来提取PDF文档的文本内容，对每个PDF文件分别进行处理并生成中英文摘要。同时，该程序文件还包括一些辅助工具函数和处理异常的装饰器。
 
-## [24/48] 请对下面的程序文件做一个概述: crazy_functions\批量翻译PDF文档_多线程.py
+## [24/48] 请对下面的程序文件做一个概述: crazy_functions\PDF_Translate.py
 
-这个程序文件是一个Python脚本，文件名为“批量翻译PDF文档_多线程.py”。它主要使用了“toolbox”、“request_gpt_model_in_new_thread_with_ui_alive”、“request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency”、“colorful”等Python库和自定义的模块“crazy_utils”的一些函数。程序实现了一个批量翻译PDF文档的功能，可以自动解析PDF文件中的基础信息，递归地切割PDF文件，翻译和处理PDF论文中的所有内容，并生成相应的翻译结果文件（包括md文件和html文件）。功能比较复杂，其中需要调用多个函数和依赖库，涉及到多线程操作和UI更新。文件中有详细的注释和变量命名，代码比较清晰易读。
+这个程序文件是一个Python脚本，文件名为“PDF_Translate.py”。它主要使用了“toolbox”、“request_gpt_model_in_new_thread_with_ui_alive”、“request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency”、“colorful”等Python库和自定义的模块“crazy_utils”的一些函数。程序实现了一个批量翻译PDF文档的功能，可以自动解析PDF文件中的基础信息，递归地切割PDF文件，翻译和处理PDF论文中的所有内容，并生成相应的翻译结果文件（包括md文件和html文件）。功能比较复杂，其中需要调用多个函数和依赖库，涉及到多线程操作和UI更新。文件中有详细的注释和变量命名，代码比较清晰易读。
 
 ## [25/48] 请对下面的程序文件做一个概述: crazy_functions\理解PDF文档内容.py
 
@@ -331,19 +331,19 @@ check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, c
 这些程序源文件提供了基础的文本和语言处理功能、工具函数和高级插件，使 Chatbot 能够处理各种复杂的学术文本问题，包括润色、翻译、搜索、下载、解析等。
 
 ## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生成.py, crazy_functions\对话历史存档.py, crazy_functions\总结word文档.py, crazy_functions\总结音视频.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\联网的ChatGPT.py, crazy_functions\解析JupyterNotebook.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py。根据以上分析，用一句话概括程序的整体功能。
+crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生成.py, crazy_functions\Conversation_To_File.py, crazy_functions\总结word文档.py, crazy_functions\总结音视频.py, crazy_functions\Markdown_Translate.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\PDF_Translate.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\联网的ChatGPT.py, crazy_functions\解析JupyterNotebook.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py。根据以上分析，用一句话概括程序的整体功能。
 
 | 文件名 | 功能简述 |
 | --- | --- |
 | 代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
 | 图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| 对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
+| Conversation_To_File.py | 将每次对话记录写入Markdown格式的文件中 |
 | 总结word文档.py | 对输入的word文档进行摘要生成 |
 | 总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| 批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| Markdown_Translate.py | 将指定目录下的Markdown文件进行中英文翻译 |
 | 批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
 | 批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| 批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
+| PDF_Translate.py | 将指定目录下的PDF文件进行中英文翻译 |
 | 理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
 | 生成函数注释.py | 自动生成Python函数的注释 |
 | 联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |

docs/translate_japanese.json

@@ -36,15 +36,15 @@
     "总结word文档": "SummarizeWordDocument",
     "解析ipynb文件": "ParseIpynbFile",
     "解析JupyterNotebook": "ParseJupyterNotebook",
-    "对话历史存档": "ConversationHistoryArchive",
+    "Conversation_To_File": "ConversationHistoryArchive",
-    "载入对话历史存档": "LoadConversationHistoryArchive",
+    "载入Conversation_To_File": "LoadConversationHistoryArchive",
     "删除所有本地对话历史记录": "DeleteAllLocalChatHistory",
     "Markdown英译中": "MarkdownTranslateFromEngToChi",
-    "批量Markdown翻译": "BatchTranslateMarkdown",
+    "Markdown_Translate": "BatchTranslateMarkdown",
     "批量总结PDF文档": "BatchSummarizePDFDocuments",
     "批量总结PDF文档pdfminer": "BatchSummarizePDFDocumentsUsingPDFMiner",
     "批量翻译PDF文档": "BatchTranslatePDFDocuments",
-    "批量翻译PDF文档_多线程": "BatchTranslatePDFDocumentsUsingMultiThreading",
+    "PDF_Translate": "BatchTranslatePDFDocumentsUsingMultiThreading",
     "谷歌检索小助手": "GoogleSearchAssistant",
     "理解PDF文档内容标准文件输入": "StandardFileInputForUnderstandingPDFDocumentContent",
     "理解PDF文档内容": "UnderstandingPDFDocumentContent",
@@ -1492,7 +1492,7 @@
     "交互功能模板函数": "InteractiveFunctionTemplateFunction",
     "交互功能函数模板": "InteractiveFunctionFunctionTemplate",
     "Latex英文纠错加PDF对比": "LatexEnglishErrorCorrectionWithPDFComparison",
-    "Latex输出PDF结果": "LatexOutputPDFResult",
+    "Latex_Function": "LatexOutputPDFResult",
     "Latex翻译中文并重新编译PDF": "TranslateChineseAndRecompilePDF",
     "语音助手": "VoiceAssistant",
     "微调数据集生成": "FineTuneDatasetGeneration",

docs/translate_std.json

@@ -6,17 +6,14 @@
     "Latex英文纠错加PDF对比": "CorrectEnglishInLatexWithPDFComparison",
     "下载arxiv论文并翻译摘要": "DownloadArxivPaperAndTranslateAbstract",
     "Markdown翻译指定语言": "TranslateMarkdownToSpecifiedLanguage",
-    "批量翻译PDF文档_多线程": "BatchTranslatePDFDocuments_MultiThreaded",
     "下载arxiv论文翻译摘要": "DownloadArxivPaperTranslateAbstract",
     "解析一个Python项目": "ParsePythonProject",
     "解析一个Golang项目": "ParseGolangProject",
     "代码重写为全英文_多线程": "RewriteCodeToEnglish_MultiThreaded",
     "解析一个CSharp项目": "ParsingCSharpProject",
     "删除所有本地对话历史记录": "DeleteAllLocalConversationHistoryRecords",
-    "批量Markdown翻译": "BatchTranslateMarkdown",
     "连接bing搜索回答问题": "ConnectBingSearchAnswerQuestion",
     "Langchain知识库": "LangchainKnowledgeBase",
-    "Latex输出PDF结果": "OutputPDFFromLatex",
     "把字符太少的块清除为回车": "ClearBlocksWithTooFewCharactersToNewline",
     "Latex精细分解与转化": "DecomposeAndConvertLatex",
     "解析一个C项目的头文件": "ParseCProjectHeaderFiles",
@@ -46,7 +43,7 @@
     "高阶功能模板函数": "HighOrderFunctionTemplateFunctions",
     "高级功能函数模板": "AdvancedFunctionTemplate",
     "总结word文档": "SummarizingWordDocuments",
-    "载入对话历史存档": "LoadConversationHistoryArchive",
+    "载入Conversation_To_File": "LoadConversationHistoryArchive",
     "Latex中译英": "LatexChineseToEnglish",
     "Latex英译中": "LatexEnglishToChinese",
     "连接网络回答问题": "ConnectToNetworkToAnswerQuestions",
@@ -70,7 +67,6 @@
     "读文章写摘要": "ReadArticleWriteSummary",
     "生成函数注释": "GenerateFunctionComments",
     "解析项目本身": "ParseProjectItself",
-    "对话历史存档": "ConversationHistoryArchive",
     "专业词汇声明": "ProfessionalTerminologyDeclaration",
     "解析docx": "ParseDocx",
     "解析源代码新": "ParsingSourceCodeNew",
@@ -97,5 +93,18 @@
     "多智能体": "MultiAgent",
     "图片生成_DALLE2": "ImageGeneration_DALLE2",
     "图片生成_DALLE3": "ImageGeneration_DALLE3",
-    "图片修改_DALLE2": "ImageModification_DALLE2"
+    "图片修改_DALLE2": "ImageModification_DALLE2",
+    "生成多种Mermaid图表": "GenerateMultipleMermaidCharts",
+    "知识库文件注入": "InjectKnowledgeBaseFiles",
+    "PDF翻译中文并重新编译PDF": "TranslatePDFToChineseAndRecompilePDF",
+    "随机小游戏": "RandomMiniGame",
+    "互动小游戏": "InteractiveMiniGame",
+    "解析历史输入": "ParseHistoricalInput",
+    "高阶功能模板函数示意图": "HighOrderFunctionTemplateDiagram",
+    "载入对话历史存档": "LoadChatHistoryArchive",
+    "对话历史存档": "ChatHistoryArchive",
+    "解析PDF_DOC2X_转Latex": "ParsePDF_DOC2X_toLatex",
+    "解析PDF_基于DOC2X": "ParsePDF_basedDOC2X",
+    "解析PDF_简单拆解": "ParsePDF_simpleDecomposition",
+    "解析PDF_DOC2X_单文件": "ParsePDF_DOC2X_singleFile"
 }

docs/translate_traditionalchinese.json

@@ -35,15 +35,15 @@
     "总结word文档": "SummarizeWordDocument",
     "解析ipynb文件": "ParseIpynbFile",
     "解析JupyterNotebook": "ParseJupyterNotebook",
-    "对话历史存档": "ConversationHistoryArchive",
+    "Conversation_To_File": "ConversationHistoryArchive",
-    "载入对话历史存档": "LoadConversationHistoryArchive",
+    "载入Conversation_To_File": "LoadConversationHistoryArchive",
     "删除所有本地对话历史记录": "DeleteAllLocalConversationHistoryRecords",
     "Markdown英译中": "MarkdownEnglishToChinese",
-    "批量Markdown翻译": "BatchMarkdownTranslation",
+    "Markdown_Translate": "BatchMarkdownTranslation",
     "批量总结PDF文档": "BatchSummarizePDFDocuments",
     "批量总结PDF文档pdfminer": "BatchSummarizePDFDocumentsPdfminer",
     "批量翻译PDF文档": "BatchTranslatePDFDocuments",
-    "批量翻译PDF文档_多线程": "BatchTranslatePdfDocumentsMultithreaded",
+    "PDF_Translate": "BatchTranslatePdfDocumentsMultithreaded",
     "谷歌检索小助手": "GoogleSearchAssistant",
     "理解PDF文档内容标准文件输入": "StandardFileInputForUnderstandingPdfDocumentContent",
     "理解PDF文档内容": "UnderstandingPdfDocumentContent",
@@ -1468,7 +1468,7 @@
     "交互功能模板函数": "InteractiveFunctionTemplateFunctions",
     "交互功能函数模板": "InteractiveFunctionFunctionTemplates",
     "Latex英文纠错加PDF对比": "LatexEnglishCorrectionWithPDFComparison",
-    "Latex输出PDF结果": "OutputPDFFromLatex",
+    "Latex_Function": "OutputPDFFromLatex",
     "Latex翻译中文并重新编译PDF": "TranslateLatexToChineseAndRecompilePDF",
     "语音助手": "VoiceAssistant",
     "微调数据集生成": "FineTuneDatasetGeneration",

docs/use_audio.md

@@ -3,7 +3,7 @@
 
 ## 1. 安装额外依赖
 ```
-pip install --upgrade pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+pip install --upgrade pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
 ```
 
 如果因为特色网络问题导致上述命令无法执行：

docs/use_tts.md

@@ -0,0 +1,58 @@
+# 使用TTS文字转语音
+
+
+## 1. 使用EDGE-TTS（简单）
+
+将本项目配置项修改如下即可
+
+```
+TTS_TYPE = "EDGE_TTS"
+EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
+```
+
+## 2. 使用SoVITS（需要有显卡）
+
+使用以下docker-compose.yml文件，先启动SoVITS服务API
+
+  1. 创建以下文件夹结构
+      ```shell
+      .
+      ├── docker-compose.yml
+      └── reference
+          ├── clone_target_txt.txt
+          └── clone_target_wave.mp3
+      ```
+  2. 其中`docker-compose.yml`为
+      ```yaml
+      version: '3.8'
+      services:
+        gpt-sovits:
+          image: fuqingxu/sovits_gptac_trim:latest
+          container_name: sovits_gptac_container
+          working_dir: /workspace/gpt_sovits_demo
+          environment:
+            - is_half=False
+            - is_share=False
+          volumes:
+            - ./reference:/reference
+          ports:
+            - "19880:9880"  # 19880 为 sovits api 的暴露端口，记住它
+          shm_size: 16G
+          deploy:
+            resources:
+              reservations:
+                devices:
+                - driver: nvidia
+                  count: "all"
+                  capabilities: [gpu]
+          command: bash -c "python3 api.py"
+      ```
+  3. 其中`clone_target_wave.mp3`为需要克隆的角色音频，`clone_target_txt.txt`为该音频对应的文字文本（ https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2%E8%AF%AD%E9%9F%B3 ）
+  4. 运行`docker-compose up`
+  5. 将本项目配置项修改如下即可
+      (19880 为 sovits api 的暴露端口，与docker-compose.yml中的端口对应)
+      ```
+      TTS_TYPE = "LOCAL_SOVITS_API"
+      GPT_SOVITS_URL = "http://127.0.0.1:19880"
+      ```
+  6. 启动本项目

docs/use_vllm.md

@@ -0,0 +1,46 @@
+# 使用VLLM
+
+
+## 1. 首先启动 VLLM，自行选择模型
+
+```
+python -m vllm.entrypoints.openai.api_server --model /home/hmp/llm/cache/Qwen1___5-32B-Chat --tensor-parallel-size 2 --dtype=half
+```
+
+这里使用了存储在 `/home/hmp/llm/cache/Qwen1___5-32B-Chat` 的本地模型，可以根据自己的需求更改。
+
+## 2. 测试 VLLM
+
+```
+curl http://localhost:8000/v1/chat/completions \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "/home/hmp/llm/cache/Qwen1___5-32B-Chat",
+  "messages": [
+  {"role": "system", "content": "You are a helpful assistant."},
+  {"role": "user", "content": "怎么实现一个去中心化的控制器?"}
+  ]
+}'
+```
+
+## 3. 配置本项目
+
+```
+API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"
+LLM_MODEL = "vllm-/home/hmp/llm/cache/Qwen1___5-32B-Chat(max_token=4096)"
+API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "http://localhost:8000/v1/chat/completions"}
+```
+
+```
+"vllm-/home/hmp/llm/cache/Qwen1___5-32B-Chat(max_token=4096)"
+其中
+  "vllm-"                                     是前缀（必要）
+  "/home/hmp/llm/cache/Qwen1___5-32B-Chat"    是模型名（必要）
+  "(max_token=6666)"                          是配置（非必要）
+```
+
+## 4. 启动！
+
+```
+python main.py
+```

docs/waifu_plugin/autoload.js

@@ -1,30 +0,0 @@
-try {
-    $("<link>").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head');
-    $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
-    $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
-        $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
-            /* 可直接修改部分参数 */
-            live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
-            live2d_settings['modelId'] = 5;                  // 默认模型 ID
-            live2d_settings['modelTexturesId'] = 1;          // 默认材质 ID
-            live2d_settings['modelStorage'] = false;         // 不储存模型 ID
-            live2d_settings['waifuSize']            = '210x187';
-            live2d_settings['waifuTipsSize']        = '187x52';
-            live2d_settings['canSwitchModel']       = true;
-            live2d_settings['canSwitchTextures']    = true;
-            live2d_settings['canSwitchHitokoto']    = false;
-            live2d_settings['canTakeScreenshot']    = false;
-            live2d_settings['canTurnToHomePage']    = false;
-            live2d_settings['canTurnToAboutPage']   = false;
-            live2d_settings['showHitokoto']         = false;         // 显示一言
-            live2d_settings['showF12Status']        = false;         // 显示加载状态
-            live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-            live2d_settings['showF12OpenMsg']       = false;         // 显示控制台打开提示
-            live2d_settings['showCopyMessage']      = false;         // 显示 复制内容 提示
-            live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-            /* 在 initModel 前添加 */
-            initModel("file=docs/waifu_plugin/waifu-tips.json");
-        }});
-    }});
-} catch(err) { console.log("[Error] JQuery is not defined.") }

main.py

@@ -1,4 +1,4 @@
-import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
+import os, json; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
 
 help_menu_description = \
 """Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic),
@@ -13,35 +13,41 @@ help_menu_description = \
 </br></br>如何语音对话: 请阅读Wiki
 </br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
 
+def enable_log(PATH_LOGGING):
+    import logging
+    admin_log_path = os.path.join(PATH_LOGGING, "admin")
+    os.makedirs(admin_log_path, exist_ok=True)
+    log_dir = os.path.join(admin_log_path, "chat_secrets.log")
+    try:logging.basicConfig(filename=log_dir, level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    except:logging.basicConfig(filename=log_dir, level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    # Disable logging output from the 'httpx' logger
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    print(f"所有对话记录将自动保存在本地目录{log_dir}, 请注意自我隐私保护哦！")
+
 def main():
     import gradio as gr
-    if gr.__version__ not in ['3.32.6', '3.32.7']: 
+    if gr.__version__ not in ['3.32.9', '3.32.10']:
         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
+    from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
     # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
     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')
+    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
-    DARK_MODE, NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('DARK_MODE', 'NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
+    NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
-    INIT_SYS_PROMPT = get_conf('INIT_SYS_PROMPT')
+    DARK_MODE, INIT_SYS_PROMPT, ADD_WAIFU, TTS_TYPE = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU', 'TTS_TYPE')
+    if LLM_MODEL not in AVAIL_LLM_MODELS: AVAIL_LLM_MODELS += [LLM_MODEL]
 
     # 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
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_reset, js_code_show_or_hide, js_code_show_or_hide_group2
-    from themes.theme import js_code_for_css_changing, js_code_for_darkmode_init, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
+    from themes.theme import js_code_for_css_changing, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
-    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
+    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
 
-    # 问询记录, python 版本建议3.9+（越新越好）
+    # 对话、日志记录
-    import logging, uuid
+    enable_log(PATH_LOGGING)
-    os.makedirs(PATH_LOGGING, exist_ok=True)
-    try:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    except:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    # Disable logging output from the 'httpx' logger
-    logging.getLogger("httpx").setLevel(logging.WARNING)
-    print(f"所有问询记录将自动保存在本地目录./{PATH_LOGGING}/chat_secrets.log, 请注意自我隐私保护哦！")
 
     # 一些普通功能模块
     from core_functional import get_core_functions
@@ -65,7 +71,7 @@ def main():
     proxy_info = check_proxy(proxies)
 
     gr_L1 = lambda: gr.Row().style()
-    gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id)
+    gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id, min_width=400)
     if LAYOUT == "TOP-DOWN":
         gr_L1 = lambda: DummyWith()
         gr_L2 = lambda scale, elem_id: gr.Row()
@@ -74,15 +80,18 @@ def main():
     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:
+    from shared_utils.cookie_manager import make_cookie_cache, make_history_cache
+    with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as app_block:
         gr.HTML(title_html)
-        secret_css, dark_mode, persistent_cookie = gr.Textbox(visible=False), gr.Textbox(DARK_MODE, visible=False), gr.Textbox(visible=False)
+        secret_css = gr.Textbox(visible=False, elem_id="secret_css")
-        cookies = gr.State(load_chat_cookies())
+        register_advanced_plugin_init_code_arr = ""
+
+        cookies, web_cookie_cache = make_cookie_cache() # 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
         with gr_L1():
             with gr_L2(scale=2, elem_id="gpt-chat"):
                 chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}", elem_id="gpt-chatbot")
                 if LAYOUT == "TOP-DOWN":  chatbot.style(height=CHATBOT_HEIGHT)
-                history = gr.State([])
+                history, history_cache, history_cache_update = make_history_cache() # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
             with gr_L2(scale=1, elem_id="gpt-panel"):
                 with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
                     with gr.Row():
@@ -98,6 +107,7 @@ def main():
                             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):
@@ -112,7 +122,7 @@ def main():
                             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）")
+                        gr.Markdown("<small>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）</small>")
                     with gr.Row(elem_id="input-plugin-group"):
                         plugin_group_sel = gr.Dropdown(choices=all_plugin_groups, label='', show_label=False, value=DEFAULT_FN_GROUPS,
                                                       multiselect=True, interactive=True, elem_classes='normal_mut_select').style(container=False)
@@ -132,9 +142,9 @@ def main():
                                 if not plugin.get("AsButton", True): dropdown_fn_list.append(k)     # 排除已经是按钮的插件
                                 elif plugin.get('AdvancedArgs', False): dropdown_fn_list.append(k)  # 对于需要高级参数的插件，亦在下拉菜单中显示
                             with gr.Row():
-                                dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="", show_label=False).style(container=False)
+                                dropdown = gr.Dropdown(dropdown_fn_list, value=r"点击这里搜索插件列表", label="", show_label=False).style(container=False)
                             with gr.Row():
-                                plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False, 
+                                plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False, elem_id="advance_arg_input_legacy",
                                                                  placeholder="这里是特殊函数插件的高级参数输入区").style(container=False)
                             with gr.Row():
                                 switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary").style(size="sm")
@@ -142,119 +152,28 @@ def main():
                         with gr.Accordion("点击展开“文件下载区”。", open=False) as area_file_up:
                             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")
 
+        from themes.gui_toolbar import define_gui_toolbar
+        checkboxes, checkboxes_2, max_length_sl, theme_dropdown, system_prompt, file_upload_2, md_dropdown, top_p, temperature = \
+            define_gui_toolbar(AVAIL_LLM_MODELS, LLM_MODEL, INIT_SYS_PROMPT, THEME, AVAIL_THEMES, ADD_WAIFU, help_menu_description, js_code_for_toggle_darkmode)
 
-        with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden", elem_id="tooltip"):
+        from themes.gui_floating_menu import define_gui_floating_menu
-            with gr.Row():
+        area_input_secondary, txt2, area_customize, submitBtn2, resetBtn2, clearBtn2, stopBtn2 = \
-                with gr.Tab("上传文件", elem_id="interact-panel"):
+            define_gui_floating_menu(customize_btns, functional, predefined_btns, cookies, web_cookie_cache)
-                    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", elem_id="elem_upload_float")
 
-                with gr.Tab("更换模型", elem_id="interact-panel"):
+        from themes.gui_advanced_plugin_class import define_gui_advanced_plugin_class
-                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
+        new_plugin_callback, route_switchy_bt_with_arg, usr_confirmed_arg = \
-                    top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
+            define_gui_advanced_plugin_class(plugins)
-                    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=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
-                    system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=INIT_SYS_PROMPT)
-
-                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='cbsc').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=js_code_for_toggle_darkmode)
-                with gr.Tab("帮助", elem_id="interact-panel"):
-                    gr.Markdown(help_menu_description)
-
-        with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_input_secondary:
-            with gr.Accordion("浮动输入区", open=True, elem_id="input-panel2"):
-                with gr.Row() as row:
-                    row.style(equal_height=True)
-                    with gr.Column(scale=10):
-                        txt2 = gr.Textbox(show_label=False, placeholder="Input question here.",
-                                          elem_id='user_input_float', 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")
-
-
-        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()])
-                        # save persistent cookie
-                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""")
 
         # 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=("浮动输入区" not 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 "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] )
+        checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, plugin_advanced_arg] )
+        checkboxes.select(None, [checkboxes], None, _js=js_code_show_or_hide)
 
         # 功能区显示开关与功能区的互动
         def fn_area_visibility_2(a):
@@ -262,9 +181,11 @@ def main():
             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] )
+        checkboxes_2.select(None, [checkboxes_2], None, _js=js_code_show_or_hide_group2)
 
         # 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]
+        input_combo_order = ["cookies", "max_length_sl", "md_dropdown", "txt", "txt2", "top_p", "temperature", "chatbot", "history", "system_prompt", "plugin_advanced_arg"]
         output_combo = [cookies, chatbot, history, status]
         predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True)], outputs=output_combo)
         # 提交按钮、重置按钮
@@ -272,15 +193,18 @@ def main():
         cancel_handles.append(txt2.submit(**predict_args))
         cancel_handles.append(submitBtn.click(**predict_args))
         cancel_handles.append(submitBtn2.click(**predict_args))
-        resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
+        resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset)   # 先在前端快速清除chatbot&status
-        resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
+        resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset)  # 先在前端快速清除chatbot&status
-        clearBtn.click(lambda: ("",""), None, [txt, txt2])
+        reset_server_side_args = (lambda history: ([], [], "已重置", json.dumps(history)), [history], [chatbot, history, status, history_cache])
-        clearBtn2.click(lambda: ("",""), None, [txt, txt2])
+        resetBtn.click(*reset_server_side_args)    # 再在后端清除history，把history转存history_cache备用
+        resetBtn2.click(*reset_server_side_args)   # 再在后端清除history，把history转存history_cache备用
+        clearBtn.click(None, None, [txt, txt2], _js=js_code_clear)
+        clearBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
         if AUTO_CLEAR_TXT:
-            submitBtn.click(lambda: ("",""), None, [txt, txt2])
+            submitBtn.click(None, None, [txt, txt2], _js=js_code_clear)
-            submitBtn2.click(lambda: ("",""), None, [txt, txt2])
+            submitBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
-            txt.submit(lambda: ("",""), None, [txt, txt2])
+            txt.submit(None, None, [txt, txt2], _js=js_code_clear)
-            txt2.submit(lambda: ("",""), None, [txt, txt2])
+            txt2.submit(None, None, [txt, txt2], _js=js_code_clear)
         # 基础功能区的回调函数注册
         for k in functional:
             if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
@@ -294,10 +218,18 @@ def main():
         file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None, _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
         # 函数插件-固定按钮区
         for k in plugins:
+            if plugins[k].get("Class", None):
+                plugins[k]["JsMenu"] = plugins[k]["Class"]().get_js_code_for_generating_menu(k)
+                register_advanced_plugin_init_code_arr += """register_advanced_plugin_init_code("{k}","{gui_js}");""".format(k=k, gui_js=plugins[k]["JsMenu"])
             if not plugins[k].get("AsButton", True): continue
+            if plugins[k].get("Class", None) is None:
+                assert plugins[k].get("Function", None) is not None
                 click_handle = plugins[k]["Button"].click(ArgsGeneralWrapper(plugins[k]["Function"]), [*input_combo], output_combo)
-            click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
+                click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [plugins[k]["Button"]], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
                 cancel_handles.append(click_handle)
+            else:
+                click_handle = plugins[k]["Button"].click(None, inputs=[], outputs=None, _js=f"""()=>run_advanced_plugin_launch_code("{k}")""")
+
         # 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"
@@ -329,13 +261,27 @@ def main():
             None,
             _js=js_code_for_css_changing
         )
+
+
+        switchy_bt.click(None, [switchy_bt], None, _js="(switchy_bt)=>on_flex_button_click(switchy_bt)")
         # 随变按钮的回调函数注册
         def route(request: gr.Request, k, *args, **kwargs):
-            if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
+            if k not in [r"点击这里搜索插件列表", r"请先从插件列表中选择"]:
+                if plugins[k].get("Class", None) is None:
+                    assert plugins[k].get("Function", None) is not None
                     yield from ArgsGeneralWrapper(plugins[k]["Function"])(request, *args, **kwargs)
-        click_handle = switchy_bt.click(route,[switchy_bt, *input_combo], output_combo)
+        # 旧插件的高级参数区确认按钮（隐藏）
-        click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
+        old_plugin_callback = gr.Button(r"未选定任何插件", variant="secondary", visible=False, elem_id="old_callback_btn_for_plugin_exe")
-        cancel_handles.append(click_handle)
+        click_handle_ng = old_plugin_callback.click(route, [switchy_bt, *input_combo], output_combo)
+        click_handle_ng.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [switchy_bt], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
+        cancel_handles.append(click_handle_ng)
+        # 新一代插件的高级参数区确认按钮（隐藏）
+        click_handle_ng = new_plugin_callback.click(route_switchy_bt_with_arg, [
+                gr.State(["new_plugin_callback", "usr_confirmed_arg"] + input_combo_order),
+                new_plugin_callback, usr_confirmed_arg, *input_combo
+            ], output_combo)
+        click_handle_ng.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [switchy_bt], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
+        cancel_handles.append(click_handle_ng)
         # 终止按钮的回调函数注册
         stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
         stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
@@ -360,11 +306,15 @@ def main():
             audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
 
 
-        demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
+        app_block.load(assign_user_uuid, inputs=[cookies], outputs=[cookies])
-        darkmode_js = js_code_for_darkmode_init
+
-        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=js_code_for_persistent_cookie_init)
+        from shared_utils.cookie_manager import load_web_cookie_cache__fn_builder
-        demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js)    # 配置暗色主题或亮色主题
+        load_web_cookie_cache = load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)
-        demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
+        app_block.load(load_web_cookie_cache, inputs = [web_cookie_cache, cookies],
+            outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
+
+        app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}","{TTS_TYPE}")""")    # 配置暗色主题或亮色主题
+        app_block.load(None, inputs=[], outputs=None, _js="""()=>{REP}""".replace("REP", register_advanced_plugin_init_code_arr))
 
     # 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 run_delayed_tasks():
@@ -378,29 +328,17 @@ def main():
         def warm_up_mods(): time.sleep(6); 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
+        if get_conf('AUTO_OPEN_BROWSER'):
+            threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面
 
+    # 运行一些异步任务：自动更新、打开浏览器页面、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=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",f"{PATH_LOGGING}/admin"])
 
-    # 如果需要在二级路径下运行
+    # 最后，正式开始服务
-    # CUSTOM_PATH = get_conf('CUSTOM_PATH')
+    from shared_utils.fastapi_server import start_app
-    # if CUSTOM_PATH != "/": 
+    start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE)
-    #     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",f"{PATH_LOGGING}/admin"])
 
 if __name__ == "__main__":
     main()

request_llms/bridge_all.py

@@ -8,10 +8,10 @@
     具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
     2. predict_no_ui_long_connection(...)
 """
-import tiktoken, copy
+import tiktoken, copy, re
 from functools import lru_cache
 from concurrent.futures import ThreadPoolExecutor
-from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask
+from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
 
 from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
 from .bridge_chatgpt import predict as chatgpt_ui
@@ -31,6 +31,14 @@ from .bridge_qianfan import predict as qianfan_ui
 from .bridge_google_gemini import predict as genai_ui
 from .bridge_google_gemini import predict_no_ui_long_connection  as genai_noui
 
+from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
+from .bridge_zhipu import predict as zhipu_ui
+
+from .bridge_cohere import predict as cohere_ui
+from .bridge_cohere import predict_no_ui_long_connection as cohere_noui
+
+from .oai_std_model_template import get_predict_function
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
 
 class LazyloadTiktoken(object):
@@ -58,6 +66,13 @@ API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "A
 openai_endpoint = "https://api.openai.com/v1/chat/completions"
 api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
 newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
+gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
+claude_endpoint = "https://api.anthropic.com/v1/messages"
+cohere_endpoint = "https://api.cohere.ai/v1/chat"
+ollama_endpoint = "http://localhost:11434/api/chat"
+yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
+deepseekapi_endpoint = "https://api.deepseek.com/v1/chat/completions"
+
 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
@@ -72,7 +87,12 @@ except:
 if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
 if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
 if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
-
+if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemini_endpoint]
+if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
+if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
+if ollama_endpoint in API_URL_REDIRECT: ollama_endpoint = API_URL_REDIRECT[ollama_endpoint]
+if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
+if deepseekapi_endpoint in API_URL_REDIRECT: deepseekapi_endpoint = API_URL_REDIRECT[deepseekapi_endpoint]
 
 # 获取tokenizer
 tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
@@ -91,7 +111,7 @@ model_info = {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
-        "max_token": 4096,
+        "max_token": 16385,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
@@ -132,6 +152,15 @@ model_info = {
         "token_cnt": get_token_num_gpt35,
     },
 
+    "gpt-3.5-turbo-0125": { #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,
+    },
+
     "gpt-4": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -150,6 +179,33 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    "gpt-4o": {
+        "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-4o-2024-05-13": {
+        "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-4-turbo-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-4-1106-preview": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -159,6 +215,34 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    "gpt-4-0125-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-4-turbo": {
+        "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-4-turbo-2024-04-09": {
+        "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,
@@ -197,16 +281,65 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
-    # 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)
+    # 智谱AI
-    "api2d-gpt-3.5-turbo": {
+    "glm-4": {
-        "fn_with_ui": chatgpt_ui,
+        "fn_with_ui": zhipu_ui,
-        "fn_without_ui": chatgpt_noui,
+        "fn_without_ui": zhipu_noui,
-        "endpoint": api2d_endpoint,
+        "endpoint": None,
-        "max_token": 4096,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-4-0520": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-4-air": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-4-airx": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-4-flash": {
+         "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,       
+    },
+    "glm-4v": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 1000,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-3-turbo": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 4,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
 
+    # 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-4": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -252,7 +385,7 @@ model_info = {
     "gemini-pro": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
-        "endpoint": None,
+        "endpoint": gemini_endpoint,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
@@ -260,13 +393,56 @@ model_info = {
     "gemini-pro-vision": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
+        "endpoint": gemini_endpoint,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+    # cohere
+    "cohere-command-r-plus": {
+        "fn_with_ui": cohere_ui,
+        "fn_without_ui": cohere_noui,
+        "can_multi_thread": True,
+        "endpoint": cohere_endpoint,
+        "max_token": 1024 * 4,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+}
+# -=-=-=-=-=-=- 月之暗面 -=-=-=-=-=-=-
+from request_llms.bridge_moonshot import predict as moonshot_ui
+from request_llms.bridge_moonshot import predict_no_ui_long_connection as moonshot_no_ui
+model_info.update({
+    "moonshot-v1-8k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
+        "endpoint": None,
+        "max_token": 1024 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "moonshot-v1-32k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
         "endpoint": None,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "moonshot-v1-128k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
+        "endpoint": None,
+        "max_token": 1024 * 128,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
     }
-
+})
 # -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
 for model in AVAIL_LLM_MODELS:
     if model.startswith('api2d-') and (model.replace('api2d-','') in model_info.keys()):
@@ -282,25 +458,67 @@ for model in AVAIL_LLM_MODELS:
         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:
+# claude家族
+claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229"]
+if any(item in claude_models for item in AVAIL_LLM_MODELS):
     from .bridge_claude import predict_no_ui_long_connection as claude_noui
     from .bridge_claude import predict as claude_ui
     model_info.update({
-        "claude-1-100k": {
+        "claude-instant-1.2": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": None,
+            "endpoint": claude_endpoint,
-            "max_token": 8196,
+            "max_token": 100000,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
     })
     model_info.update({
-        "claude-2": {
+        "claude-2.0": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": None,
+            "endpoint": claude_endpoint,
-            "max_token": 8196,
+            "max_token": 100000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-2.1": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-haiku-20240307": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-sonnet-20240229": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-opus-20240229": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
@@ -370,22 +588,6 @@ if "stack-claude" in AVAIL_LLM_MODELS:
             "token_cnt": get_token_num_gpt35,
         }
     })
-if "newbing-free" in AVAIL_LLM_MODELS:
-    try:
-        from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
-        from .bridge_newbingfree import predict as newbingfree_ui
-        model_info.update({
-            "newbing-free": {
-                "fn_with_ui": newbingfree_ui,
-                "fn_without_ui": newbingfree_noui,
-                "endpoint": newbing_endpoint,
-                "max_token": 4096,
-                "tokenizer": tokenizer_gpt35,
-                "token_cnt": get_token_num_gpt35,
-            }
-        })
-    except:
-        print(trimmed_format_exc())
 if "newbing" in AVAIL_LLM_MODELS:   # same with newbing-free
     try:
         from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
@@ -418,6 +620,7 @@ if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 上海AI-LAB书生大模型 -=-=-=-=-=-=-
 if "internlm" in AVAIL_LLM_MODELS:
     try:
         from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
@@ -450,6 +653,7 @@ if "chatglm_onnx" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 通义-本地模型 -=-=-=-=-=-=-
 if "qwen-local" in AVAIL_LLM_MODELS:
     try:
         from .bridge_qwen_local import predict_no_ui_long_connection as qwen_local_noui
@@ -458,6 +662,7 @@ if "qwen-local" in AVAIL_LLM_MODELS:
             "qwen-local": {
                 "fn_with_ui": qwen_local_ui,
                 "fn_without_ui": qwen_local_noui,
+                "can_multi_thread": False,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -466,6 +671,7 @@ if "qwen-local" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 通义-在线模型 -=-=-=-=-=-=-
 if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS:   # zhipuai
     try:
         from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
@@ -474,6 +680,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-turbo": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 6144,
                 "tokenizer": tokenizer_gpt35,
@@ -482,6 +689,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-plus": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 30720,
                 "tokenizer": tokenizer_gpt35,
@@ -490,6 +698,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-max": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 28672,
                 "tokenizer": tokenizer_gpt35,
@@ -498,7 +707,88 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
         })
     except:
         print(trimmed_format_exc())
-if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+# -=-=-=-=-=-=- 零一万物模型 -=-=-=-=-=-=-
+yi_models = ["yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview"]
+if any(item in yi_models for item in AVAIL_LLM_MODELS):
+    try:
+        yimodel_4k_noui, yimodel_4k_ui = get_predict_function(
+            api_key_conf_name="YIMODEL_API_KEY", max_output_token=600, disable_proxy=False
+            )
+        yimodel_16k_noui, yimodel_16k_ui = get_predict_function(
+            api_key_conf_name="YIMODEL_API_KEY", max_output_token=4000, disable_proxy=False
+            )
+        yimodel_200k_noui, yimodel_200k_ui = get_predict_function(
+            api_key_conf_name="YIMODEL_API_KEY", max_output_token=4096, disable_proxy=False
+            )
+        model_info.update({
+            "yi-34b-chat-0205": {
+                "fn_with_ui": yimodel_4k_ui,
+                "fn_without_ui": yimodel_4k_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 4000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-34b-chat-200k": {
+                "fn_with_ui": yimodel_200k_ui,
+                "fn_without_ui": yimodel_200k_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 200000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-large": {
+                "fn_with_ui": yimodel_16k_ui,
+                "fn_without_ui": yimodel_16k_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-medium": {
+                "fn_with_ui": yimodel_16k_ui,
+                "fn_without_ui": yimodel_16k_noui,
+                "can_multi_thread": True,  # 这个并发量稍微大一点
+                "endpoint": yimodel_endpoint,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-spark": {
+                "fn_with_ui": yimodel_16k_ui,
+                "fn_without_ui": yimodel_16k_noui,
+                "can_multi_thread": True,  # 这个并发量稍微大一点
+                "endpoint": yimodel_endpoint,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-large-turbo": {
+                "fn_with_ui": yimodel_16k_ui,
+                "fn_without_ui": yimodel_16k_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-large-preview": {
+                "fn_with_ui": yimodel_16k_ui,
+                "fn_without_ui": yimodel_16k_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+        })
+    except:
+        print(trimmed_format_exc())
+# -=-=-=-=-=-=- 讯飞星火认知大模型 -=-=-=-=-=-=-
+if "spark" in AVAIL_LLM_MODELS:
     try:
         from .bridge_spark import predict_no_ui_long_connection as spark_noui
         from .bridge_spark import predict as spark_ui
@@ -506,6 +796,7 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "spark": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -522,6 +813,7 @@ if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "sparkv2": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -530,7 +822,7 @@ 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
+if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" 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
@@ -538,6 +830,16 @@ if "sparkv3" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "sparkv3": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "sparkv3.5": {
+                "fn_with_ui": spark_ui,
+                "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -562,22 +864,22 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
         })
     except:
         print(trimmed_format_exc())
-if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai
+# -=-=-=-=-=-=- 智谱 -=-=-=-=-=-=-
+if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容配置
     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,
+                "max_token": 10124 * 8,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
-            }
+            },
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:
         from .bridge_deepseekcoder import predict_no_ui_long_connection as deepseekcoder_noui
@@ -594,26 +896,109 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
         })
     except:
         print(trimmed_format_exc())
-# if "skylark" in AVAIL_LLM_MODELS:
+# -=-=-=-=-=-=- 幻方-深度求索大模型在线API -=-=-=-=-=-=-
-#     try:
+if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
-#         from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
+    try:
-#         from .bridge_skylark2 import predict as skylark_ui
+        deepseekapi_noui, deepseekapi_ui = get_predict_function(
-#         model_info.update({
+            api_key_conf_name="DEEPSEEK_API_KEY", max_output_token=4096, disable_proxy=False
-#             "skylark": {
+            )
-#                 "fn_with_ui": skylark_ui,
+        model_info.update({
-#                 "fn_without_ui": skylark_noui,
+            "deepseek-chat":{
-#                 "endpoint": None,
+                "fn_with_ui": deepseekapi_ui,
-#                 "max_token": 4096,
+                "fn_without_ui": deepseekapi_noui,
-#                 "tokenizer": tokenizer_gpt35,
+                "endpoint": deepseekapi_endpoint,
-#                 "token_cnt": get_token_num_gpt35,
+                "can_multi_thread": True,
-#             }
+                "max_token": 32000,
-#         })
+                "tokenizer": tokenizer_gpt35,
-#     except:
+                "token_cnt": get_token_num_gpt35,
-#         print(trimmed_format_exc())
+            },
+            "deepseek-coder":{
+                "fn_with_ui": deepseekapi_ui,
+                "fn_without_ui": deepseekapi_noui,
+                "endpoint": deepseekapi_endpoint,
+                "can_multi_thread": True,
+                "max_token": 16000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+        })
+    except:
+        print(trimmed_format_exc())
+# -=-=-=-=-=-=- one-api 对齐支持 -=-=-=-=-=-=-
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
+    # 为了更灵活地接入one-api多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["one-api-mixtral-8x7b(max_token=6666)"]
+    # 其中
+    #   "one-api-"          是前缀（必要）
+    #   "mixtral-8x7b"      是模型名（必要）
+    #   "(max_token=6666)"  是配置（非必要）
+    try:
+        _, max_token_tmp = read_one_api_model_name(model)
+    except:
+        print(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        continue
+    model_info.update({
+        model: {
+            "fn_with_ui": chatgpt_ui,
+            "fn_without_ui": chatgpt_noui,
+            "can_multi_thread": True,
+            "endpoint": openai_endpoint,
+            "max_token": max_token_tmp,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+# -=-=-=-=-=-=- vllm 对齐支持 -=-=-=-=-=-=-
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("vllm-")]:
+    # 为了更灵活地接入vllm多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["vllm-/home/hmp/llm/cache/Qwen1___5-32B-Chat(max_token=6666)"]
+    # 其中
+    #   "vllm-"             是前缀（必要）
+    #   "mixtral-8x7b"      是模型名（必要）
+    #   "(max_token=6666)"  是配置（非必要）
+    try:
+        _, max_token_tmp = read_one_api_model_name(model)
+    except:
+        print(f"vllm模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        continue
+    model_info.update({
+        model: {
+            "fn_with_ui": chatgpt_ui,
+            "fn_without_ui": chatgpt_noui,
+            "can_multi_thread": True,
+            "endpoint": openai_endpoint,
+            "max_token": max_token_tmp,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+# -=-=-=-=-=-=- ollama 对齐支持 -=-=-=-=-=-=-
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
+    from .bridge_ollama import predict_no_ui_long_connection as ollama_noui
+    from .bridge_ollama import predict as ollama_ui
+    break
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
+    # 为了更灵活地接入ollama多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["ollama-phi3(max_token=6666)"]
+    # 其中
+    #   "ollama-"           是前缀（必要）
+    #   "phi3"            是模型名（必要）
+    #   "(max_token=6666)"  是配置（非必要）
+    try:
+        _, max_token_tmp = read_one_api_model_name(model)
+    except:
+        print(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        continue
+    model_info.update({
+        model: {
+            "fn_with_ui": ollama_ui,
+            "fn_without_ui": ollama_noui,
+            "endpoint": ollama_endpoint,
+            "max_token": max_token_tmp,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
 
-
+# -=-=-=-=-=-=- azure模型对齐支持 -=-=-=-=-=-=-
-# <-- 用于定义和切换多个azure模型 -->
+AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY") # <-- 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
@@ -636,13 +1021,20 @@ if len(AZURE_CFG_ARRAY) > 0:
             AVAIL_LLM_MODELS += [azure_model_name]
 
 
+# -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
+# -=-=-=-=-=-=-=-=-=- ☝️ 以上是模型路由 -=-=-=-=-=-=-=-=-=
+# -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
+
+# -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
+# -=-=-=-=-=-=-= 👇 以下是多模型路由切换函数 -=-=-=-=-=-=-=
+# -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
 
 
 def LLM_CATCH_EXCEPTION(f):
     """
     装饰器函数，将错误显示出来
     """
-    def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
+    def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
         try:
             return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
         except Exception as e:
@@ -652,9 +1044,9 @@ 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:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=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。
+    发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）用stream的方法避免中途网线被掐。
     inputs：
         是本次问询的输入
     sys_prompt:
@@ -672,14 +1064,11 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
     model = llm_kwargs['llm_model']
     n_model = 1
     if '&' not in model:
-        assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
+        # 如果只询问“一个”大语言模型（多数情况）：
-
-        # 如果只询问1个大语言模型：
         method = model_info[model]["fn_without_ui"]
         return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
     else:
-
+        # 如果同时询问“多个”大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
-        # 如果同时询问多个大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
         executor = ThreadPoolExecutor(max_workers=4)
         models = model.split('&')
         n_model = len(models)
@@ -707,7 +1096,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
                 # 观察窗（window）
                 chat_string = []
                 for i in range(n_model):
-                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
+                    color = colors[i%len(colors)]
+                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {window_mutex[i][0]} </font>" )
                 res = '<br/><br/>\n\n---\n\n'.join(chat_string)
                 # # # # # # # # # # #
                 observe_window[0] = res
@@ -724,25 +1114,56 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
             time.sleep(1)
 
         for i, future in enumerate(futures):  # wait and get
-            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
+            color = colors[i%len(colors)]
+            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {future.result()} </font>" )
 
         window_mutex[-1] = False # stop mutex thread
         res = '<br/><br/>\n\n---\n\n'.join(return_string_collect)
         return res
 
+# 根据基础功能区 ModelOverride 参数调整模型类型，用于 `predict` 中
+import importlib
+import core_functional
+def execute_model_override(llm_kwargs, additional_fn, method):
+    functional = core_functional.get_core_functions()
+    if (additional_fn in functional) and 'ModelOverride' in functional[additional_fn]:
+        # 热更新Prompt & ModelOverride
+        importlib.reload(core_functional)
+        functional = core_functional.get_core_functions()
+        model_override = functional[additional_fn]['ModelOverride']
+        if model_override not in model_info:
+            raise ValueError(f"模型覆盖参数 '{model_override}' 指向一个暂不支持的模型，请检查配置文件。")
+        method = model_info[model_override]["fn_with_ui"]
+        llm_kwargs['llm_model'] = model_override
+        return llm_kwargs, additional_fn, method
+    # 默认返回原参数
+    return llm_kwargs, additional_fn, method
 
-def predict(inputs, llm_kwargs, *args, **kwargs):
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
     发送至LLM，流式获取输出。
     用于基础的对话功能。
-    inputs 是本次问询的输入
+
-    top_p, temperature是LLM的内部调优参数
+    完整参数列表：
-    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+        predict(
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+            inputs:str,                     # 是本次问询的输入
-    additional_fn代表点击的哪个按钮，按钮见functional.py
+            llm_kwargs:dict,                # 是LLM的内部调优参数
+            plugin_kwargs:dict,             # 是插件的内部参数
+            chatbot:ChatBotWithCookies,     # 原样传递，负责向用户前端展示对话，兼顾前端状态的功能
+            history:list=[],                # 是之前的对话列表
+            system_prompt:str='',           # 系统静默prompt
+            stream:bool=True,               # 是否流式输出（已弃用）
+            additional_fn:str=None          # 基础功能区按钮的附加功能
+        ):
     """
 
     inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")
-    method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]  # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
+
-    yield from method(inputs, llm_kwargs, *args, **kwargs)
+    method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]  # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
+
+    if additional_fn: # 根据基础功能区 ModelOverride 参数调整模型类型
+        llm_kwargs, additional_fn, method = execute_model_override(llm_kwargs, additional_fn, method)
+
+    yield from method(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn)
 

request_llms/bridge_chatglm3.py

@@ -6,7 +6,6 @@ from toolbox import get_conf, ProxyNetworkActivate
 from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
-
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
@@ -23,20 +22,45 @@ class GetGLM3Handle(LocalLLMHandle):
         import os, glob
         import os
         import platform
-        LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
 
-        if LOCAL_MODEL_QUANT == "INT4":         # INT4
+        LOCAL_MODEL_QUANT, device = get_conf("LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
-            _model_name_ = "THUDM/chatglm3-6b-int4"
+        _model_name_ = "THUDM/chatglm3-6b"
+        # if LOCAL_MODEL_QUANT == "INT4":  # INT4
+        #     _model_name_ = "THUDM/chatglm3-6b-int4"
+        # elif LOCAL_MODEL_QUANT == "INT8":  # INT8
+        #     _model_name_ = "THUDM/chatglm3-6b-int8"
+        # else:
+        #     _model_name_ = "THUDM/chatglm3-6b"  # FP16
+        with ProxyNetworkActivate("Download_LLM"):
+            chatglm_tokenizer = AutoTokenizer.from_pretrained(
+                _model_name_, trust_remote_code=True
+            )
+            if device == "cpu":
+                chatglm_model = AutoModel.from_pretrained(
+                    _model_name_,
+                    trust_remote_code=True,
+                    device="cpu",
+                ).float()
+            elif LOCAL_MODEL_QUANT == "INT4":  # INT4
+                chatglm_model = AutoModel.from_pretrained(
+                    pretrained_model_name_or_path=_model_name_,
+                    trust_remote_code=True,
+                    device="cuda",
+                    load_in_4bit=True,
+                )
             elif LOCAL_MODEL_QUANT == "INT8":  # INT8
-            _model_name_ = "THUDM/chatglm3-6b-int8"
+                chatglm_model = AutoModel.from_pretrained(
+                    pretrained_model_name_or_path=_model_name_,
+                    trust_remote_code=True,
+                    device="cuda",
+                    load_in_8bit=True,
+                )
             else:
-            _model_name_ = "THUDM/chatglm3-6b"  # FP16
+                chatglm_model = AutoModel.from_pretrained(
-        with ProxyNetworkActivate('Download_LLM'):
+                    pretrained_model_name_or_path=_model_name_,
-            chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
+                    trust_remote_code=True,
-            if device=='cpu':
+                    device="cuda",
-                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
@@ -46,16 +70,17 @@ class GetGLM3Handle(LocalLLMHandle):
     def llm_stream_generator(self, **kwargs):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         def adaptor(kwargs):
-            query = kwargs['query']
+            query = kwargs["query"]
-            max_length = kwargs['max_length']
+            max_length = kwargs["max_length"]
-            top_p = kwargs['top_p']
+            top_p = kwargs["top_p"]
-            temperature = kwargs['temperature']
+            temperature = kwargs["temperature"]
-            history = kwargs['history']
+            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, 
+        for response, history in self._model.stream_chat(
+            self._tokenizer,
             query,
             history,
             max_length=max_length,
@@ -68,10 +93,13 @@ class GetGLM3Handle(LocalLLMHandle):
         # import something that will raise error if the user does not install requirement_*.txt
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
         import importlib
+
         # importlib.import_module('modelscope')
 
 
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 GPT-Academic Interface
 # ------------------------------------------------------------------------------------------------------------------------
-predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetGLM3Handle, model_name, history_format='chatglm3')
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(
+    GetGLM3Handle, model_name, history_format="chatglm3"
+)

request_llms/bridge_chatglmft.py

@@ -137,7 +137,8 @@ class GetGLMFTHandle(Process):
 global glmft_handle
 glmft_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_chatgpt.py

@@ -21,7 +21,9 @@ 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
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
+from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
+from toolbox import ChatBotWithCookies
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
     get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
 
@@ -68,7 +70,7 @@ def verify_endpoint(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):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -113,6 +115,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             error_msg = get_full_error(chunk, stream_response).decode()
             if "reduce the length" in error_msg:
                 raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
+            elif """type":"upstream_error","param":"307""" in error_msg:
+                raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
             else:
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
         if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
@@ -123,8 +127,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
         json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
         if len(delta) == 0: break
-        if "role" in delta: continue
+        if (not has_content) and has_role: continue
-        if "content" in delta:
+        if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
+        if has_content: # has_role = True/False
             result += delta["content"]
             if not console_slience: print(delta["content"], end='')
             if observe_window is not None:
@@ -143,7 +148,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     return result
 
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
     发送至chatGPT，流式获取输出。
     用于基础的对话功能。
@@ -168,8 +174,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
@@ -250,7 +254,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     # 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(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}')
+                        # logging.info(f'[response] {gpt_replying_buffer}')
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -262,7 +267,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         # 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
+                        # 至此已经超出了正常接口应该进入的范围，一些垃圾第三方接口会出现这样的错误
+                        if chunkjson['choices'][0]["delta"]["content"] is None: continue # 一些垃圾第三方接口出现这样的错误，兼容一下吧
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
 
                     history[-1] = gpt_replying_buffer
@@ -315,6 +321,9 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     if not is_any_api_key(llm_kwargs['api_key']):
         raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
 
+    if llm_kwargs['llm_model'].startswith('vllm-'):
+        api_key = 'no-api-key'
+    else:
         api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
 
     headers = {
@@ -354,7 +363,12 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     model = llm_kwargs['llm_model']
     if llm_kwargs['llm_model'].startswith('api2d-'):
         model = llm_kwargs['llm_model'][len('api2d-'):]
-
+    if llm_kwargs['llm_model'].startswith('one-api-'):
+        model = llm_kwargs['llm_model'][len('one-api-'):]
+        model, _ = read_one_api_model_name(model)
+    if llm_kwargs['llm_model'].startswith('vllm-'):
+        model = llm_kwargs['llm_model'][len('vllm-'):]
+        model, _ = read_one_api_model_name(model)
     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",
@@ -373,8 +387,6 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         "top_p": llm_kwargs['top_p'],  # 1.0,
         "n": 1,
         "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
     }
     try:
         print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")

request_llms/bridge_claude.py

@@ -9,15 +9,15 @@
     具备多线程调用能力的函数
     2. predict_no_ui_long_connection：支持多线程
 """
-
-import os
-import json
-import time
-import gradio as gr
 import logging
+import os
+import time
 import traceback
+import json
 import requests
-import importlib
+from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path, log_chat
+picture_system_prompt = "\n当回复图像时,必须说明正在回复哪张图像。所有图像仅在最后一个问题中提供,即使它们在历史记录中被提及。请使用'这是第X张图像:'的格式来指明您正在描述的是哪张图像。"
+Claude_3_Models = ["claude-3-haiku-20240307", "claude-3-sonnet-20240229", "claude-3-opus-20240229"]
 
 # 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
@@ -39,6 +39,34 @@ 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
+    is_last_chunk = False
+    need_to_pass = False
+    if chunk_decoded.startswith('data:'):
+        try:
+            chunkjson = json.loads(chunk_decoded[6:])
+        except:
+            need_to_pass = True
+            pass
+    elif chunk_decoded.startswith('event:'):
+        try:
+            event_type = chunk_decoded.split(':')[1].strip()
+            if event_type == 'content_block_stop' or event_type == 'message_stop':
+                is_last_chunk = True
+            elif event_type == 'content_block_start' or event_type == 'message_start':
+                need_to_pass = True
+                pass
+        except:
+            need_to_pass = True
+            pass
+    else:
+        need_to_pass = True
+        pass
+    return need_to_pass, chunkjson, is_last_chunk
+
 
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
     """
@@ -54,50 +82,67 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     observe_window = None：
         用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
     """
-    from anthropic import Anthropic
     watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
-    retry = 0
     if len(ANTHROPIC_API_KEY) == 0:
         raise RuntimeError("没有设置ANTHROPIC_API_KEY选项")
+    if inputs == "":     inputs = "空空如也的输入栏"
+    headers, message = generate_payload(inputs, llm_kwargs, history, sys_prompt, image_paths=None)
+    retry = 0
+
 
     while True:
         try:
             # make a POST request to the API endpoint, stream=False
             from .bridge_all import model_info
-            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, json=message,
-            # with ProxyNetworkActivate()
+                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
-            stream = anthropic.completions.create(
+        except requests.exceptions.ReadTimeout as e:
-                prompt=prompt,
-                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
-                model=llm_kwargs['llm_model'],
-                stream=True,
-                temperature = llm_kwargs['temperature']
-            )
-            break
-        except Exception as e:
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
             if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+    stream_response = response.iter_lines()
     result = ''
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        need_to_pass, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if chunk:
             try:
-        for completion in stream:
+                if need_to_pass:
-            result += completion.completion
+                    pass
-            if not console_slience: print(completion.completion, end='')
+                elif is_last_chunk:
+                    # logging.info(f'[response] {result}')
+                    break
+                else:
+                    if chunkjson and chunkjson['type'] == 'content_block_delta':
+                        result += chunkjson['delta']['text']
+                        print(chunkjson['delta']['text'], end='')
                         if observe_window is not None:
                             # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += completion.completion
+                            if len(observe_window) >= 1:
+                                observe_window[0] += chunkjson['delta']['text']
                             # 看门狗，如果超过期限没有喂狗，则终止
                             if len(observe_window) >= 2:
                                 if (time.time()-observe_window[1]) > watch_dog_patience:
                                     raise RuntimeError("用户取消了程序。")
             except Exception as e:
-        traceback.print_exc()
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                print(error_msg)
+                raise RuntimeError("Json解析不合常规")
 
     return result
 
+def make_media_input(history,inputs,image_paths):
+    for image_path in image_paths:
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
+    return inputs
 
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
@@ -109,7 +154,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
-    from anthropic import Anthropic
+    if inputs == "":     inputs = "空空如也的输入栏"
     if len(ANTHROPIC_API_KEY) == 0:
         chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY"))
         yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
@@ -119,13 +164,23 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    raw_input = inputs
+    have_recent_file, image_paths = every_image_file_in_path(chatbot)
-    logging.info(f'[raw_input] {raw_input}')
+    if len(image_paths) > 20:
+        chatbot.append((inputs, "图片数量超过api上限(20张)"))
+        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应")
+        return
+
+    if any([llm_kwargs['llm_model'] == model for model in Claude_3_Models]) and have_recent_file:
+        if inputs == "" or inputs == "空空如也的输入栏":     inputs = "请描述给出的图片"
+        system_prompt += picture_system_prompt  # 由于没有单独的参数保存包含图片的历史，所以只能通过提示词对第几张图片进行定位
+        chatbot.append((make_media_input(history,inputs, image_paths), ""))
+        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+    else:
         chatbot.append((inputs, ""))
         yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
     try:
-        prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+        headers, message = generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths)
     except RuntimeError as e:
         chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
         yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
@@ -138,91 +193,117 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         try:
             # make a POST request to the API endpoint, stream=True
             from .bridge_all import model_info
-            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, json=message,
-            # with ProxyNetworkActivate()
+                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
-            stream = anthropic.completions.create(
+        except requests.exceptions.ReadTimeout as e:
-                prompt=prompt,
-                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
-                model=llm_kwargs['llm_model'],
-                stream=True,
-                temperature = llm_kwargs['temperature']
-            )
-            
-            break
-        except:
             retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            traceback.print_exc()
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
             if retry > MAX_RETRY: raise TimeoutError
-
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+    stream_response = response.iter_lines()
     gpt_replying_buffer = ""
 
-    for completion in stream:
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        need_to_pass, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if chunk:
             try:
-            gpt_replying_buffer = gpt_replying_buffer + completion.completion
+                if need_to_pass:
+                    pass
+                elif is_last_chunk:
+                    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                    # logging.info(f'[response] {gpt_replying_buffer}')
+                    break
+                else:
+                    if chunkjson and chunkjson['type'] == 'content_block_delta':
+                        gpt_replying_buffer += chunkjson['delta']['text']
                         history[-1] = gpt_replying_buffer
                         chatbot[-1] = (history[-2], history[-1])
                         yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面
 
             except Exception as e:
-            from toolbox import regular_txt_to_markdown
+                chunk = get_full_error(chunk, stream_response)
-            tb_str = '```\n' + trimmed_format_exc() + '```'
+                chunk_decoded = chunk.decode()
-            chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str}")
+                error_msg = chunk_decoded
-            yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
+                print(error_msg)
-            return
+                raise RuntimeError("Json解析不合常规")
 
+def multiple_picture_types(image_paths):
+    """
+    根据图片类型返回image/jpeg, image/png, image/gif, image/webp，无法判断则返回image/jpeg
+    """
+    for image_path in image_paths:
+        if image_path.endswith('.jpeg') or image_path.endswith('.jpg'):
+            return 'image/jpeg'
+        elif image_path.endswith('.png'):
+            return 'image/png'
+        elif image_path.endswith('.gif'):
+            return 'image/gif'
+        elif image_path.endswith('.webp'):
+            return 'image/webp'
+    return 'image/jpeg'
 
-
+def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
-
-# https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py
-def convert_messages_to_prompt(messages):
-    prompt = ""
-    role_map = {
-        "system": "Human",
-        "user": "Human",
-        "assistant": "Assistant",
-    }
-    for message in messages:
-        role = message["role"]
-        content = message["content"]
-        transformed_role = role_map[role]
-        prompt += f"\n\n{transformed_role.capitalize()}: {content}"
-    prompt += "\n\nAssistant: "
-    return prompt
-
-def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     """
     整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
     """
-    from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
 
     conversation_cnt = len(history) // 2
 
-    messages = [{"role": "system", "content": system_prompt}]
+    messages = []
+
     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_i_have_asked["content"] = [{"type": "text", "text": history[index]}]
             what_gpt_answer = {}
             what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
+            what_gpt_answer["content"] = [{"type": "text", "text": history[index+1]}]
-            if what_i_have_asked["content"] != "":
+            if what_i_have_asked["content"][0]["text"] != "":
-                if what_gpt_answer["content"] == "": continue
+                if what_i_have_asked["content"][0]["text"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                if what_i_have_asked["content"][0]["text"] == timeout_bot_msg: continue
                 messages.append(what_i_have_asked)
                 messages.append(what_gpt_answer)
             else:
-                messages[-1]['content'] = what_gpt_answer['content']
+                messages[-1]['content'][0]['text'] = what_gpt_answer['content'][0]['text']
 
+    if any([llm_kwargs['llm_model'] == model for model in Claude_3_Models]) and image_paths:
         what_i_ask_now = {}
         what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
+        what_i_ask_now["content"] = []
+        for image_path in image_paths:
+            what_i_ask_now["content"].append({
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": multiple_picture_types(image_paths),
+                    "data": encode_image(image_path),
+                }
+            })
+        what_i_ask_now["content"].append({"type": "text", "text": inputs})
+    else:
+        what_i_ask_now = {}
+        what_i_ask_now["role"] = "user"
+        what_i_ask_now["content"] = [{"type": "text", "text": inputs}]
     messages.append(what_i_ask_now)
-    prompt = convert_messages_to_prompt(messages)
+    # 开始整理headers与message
-
+    headers = {
-    return prompt
+        'x-api-key': ANTHROPIC_API_KEY,
-
+        'anthropic-version': '2023-06-01',
-
+        'content-type': 'application/json'
+    }
+    payload = {
+        'model': llm_kwargs['llm_model'],
+        'max_tokens': 4096,
+        'messages': messages,
+        'temperature': llm_kwargs['temperature'],
+        'stream': True,
+        'system': system_prompt
+    }
+    return headers, payload

request_llms/bridge_cohere.py

@@ -0,0 +1,328 @@
+# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
+
+"""
+    该文件中主要包含三个函数
+
+    不具备多线程能力的函数：
+    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
+
+    具备多线程调用能力的函数
+    2. predict_no_ui_long_connection：支持多线程
+"""
+
+import json
+import time
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+import random
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
+from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
+from toolbox import ChatBotWithCookies
+proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
+    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。'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Cohere返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def decode_chunk(chunk):
+    # 提前读取一些信息 （用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    has_choices = False
+    choice_valid = False
+    has_content = False
+    has_role = False
+    try:
+        chunkjson = json.loads(chunk_decoded)
+        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_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
+        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:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+    """
+    发送，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    inputs：
+        是本次问询的输入
+    sys_prompt:
+        系统静默prompt
+    llm_kwargs：
+        内部调优参数
+    history：
+        是之前的对话列表
+    observe_window = None：
+        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            from .bridge_all import model_info
+            endpoint = 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:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    stream_response = response.iter_lines()
+    result = ''
+    json_data = None
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+        if chunkjson['event_type'] == 'stream-start': continue
+        if chunkjson['event_type'] == 'text-generation':
+            result += chunkjson["text"]
+            if not console_slience: print(chunkjson["text"], end='')
+            if observe_window is not None:
+                # 观测窗，把已经获取的数据显示出去
+                if len(observe_window) >= 1:
+                    observe_window[0] += chunkjson["text"]
+                # 看门狗，如果超过期限没有喂狗，则终止
+                if len(observe_window) >= 2:
+                    if (time.time()-observe_window[1]) > watch_dog_patience:
+                        raise RuntimeError("用户取消了程序。")
+        if chunkjson['event_type'] == 'stream-end': break
+    return result
+
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    """
+    发送至chatGPT，流式获取输出。
+    用于基础的对话功能。
+    inputs 是本次问询的输入
+    top_p, temperature是chatGPT的内部调优参数
+    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    # if is_any_api_key(inputs):
+    #     chatbot._cookies['api_key'] = inputs
+    #     chatbot.append(("输入已识别为Cohere的api_key", what_keys(inputs)))
+    #     yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
+    #     return
+    # elif not is_any_api_key(chatbot._cookies['api_key']):
+    #     chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
+    #     yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
+    #     return
+
+    user_input = inputs
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    raw_input = inputs
+    # logging.info(f'[raw_input] {raw_input}')
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
+    try:
+        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+    except RuntimeError as e:
+        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
+        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
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            if retry > MAX_RETRY: raise TimeoutError
+
+    gpt_replying_buffer = ""
+
+    is_head_of_the_stream = True
+    if stream:
+        stream_response =  response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                # 非Cohere官方接口的出现这样的报错，Cohere和API2D不会走这里
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                # 其他情况，直接返回报错
+                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                yield from update_ui(chatbot=chatbot, history=history, msg="非Cohere官方接口返回了错误:" + chunk.decode()) # 刷新界面
+                return
+
+            # 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 chunkjson:
+                try:
+                    if chunkjson['event_type'] == 'stream-start':
+                        continue
+                    if chunkjson['event_type'] == 'text-generation':
+                        gpt_replying_buffer = gpt_replying_buffer + chunkjson["text"]
+                        history[-1] = gpt_replying_buffer
+                        chatbot[-1] = (history[-2], history[-1])
+                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+                    if chunkjson['event_type'] == 'stream-end':
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        history[-1] = gpt_replying_buffer
+                        chatbot[-1] = (history[-2], history[-1])
+                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+                        break
+                except Exception as e:
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
+                    chunk = get_full_error(chunk, stream_response)
+                    chunk_decoded = chunk.decode()
+                    error_msg = chunk_decoded
+                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    print(error_msg)
+                    return
+
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+    from .bridge_all import model_info
+    Cohere_website = ' 请登录Cohere查看详情 https://platform.Cohere.com/signup'
+    if "reduce the length" in error_msg:
+        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
+        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
+                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
+    elif "does not exist" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
+    elif "Incorrect API key" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. Cohere以提供了不正确的API_KEY为由, 拒绝服务. " + Cohere_website)
+    elif "exceeded your current quota" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. Cohere以账户额度不足为由, 拒绝服务." + Cohere_website)
+    elif "account is not active" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
+    elif "associated with a deactivated account" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
+    elif "API key has been deactivated" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. Cohere以账户失效为由, 拒绝服务." + Cohere_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:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
+    else:
+        from toolbox import regular_txt_to_markdown
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
+    return chatbot, history
+
+def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    # if not is_any_api_key(llm_kwargs['api_key']):
+    #     raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
+
+    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
+    if API_ORG.startswith('org-'): headers.update({"Cohere-Organization": API_ORG})
+    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
+
+    messages = [{"role": "SYSTEM", "message": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "USER"
+            what_i_have_asked["message"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "CHATBOT"
+            what_gpt_answer["message"] = history[index+1]
+            if what_i_have_asked["message"] != "":
+                if what_gpt_answer["message"] == "": continue
+                if what_gpt_answer["message"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['message'] = what_gpt_answer['message']
+
+    model = llm_kwargs['llm_model']
+    if model.startswith('cohere-'): model = model[len('cohere-'):]
+    payload = {
+        "model": model,
+        "message": inputs,
+        "chat_history": messages,
+        "temperature": llm_kwargs['temperature'],  # 1.0,
+        "top_p": llm_kwargs['top_p'],  # 1.0,
+        "n": 1,
+        "stream": stream,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+    }
+
+    return headers,payload
+
+

request_llms/bridge_google_gemini.py

@@ -7,7 +7,8 @@ import re
 import os
 import time
 from request_llms.com_google import GoogleChatInit
-from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
+from toolbox import ChatBotWithCookies
+from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
 timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
@@ -20,7 +21,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     if get_conf("GEMINI_API_KEY") == "":
         raise ValueError(f"请配置 GEMINI_API_KEY。")
 
-    genai = GoogleChatInit()
+    genai = GoogleChatInit(llm_kwargs)
     watch_dog_patience = 5  # 看门狗的耐心, 设置5秒即可
     gpt_replying_buffer = ''
     stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
@@ -44,7 +45,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     return gpt_replying_buffer
 
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
         yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
@@ -57,6 +59,10 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
     if "vision" in llm_kwargs["llm_model"]:
         have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
+        if not have_recent_file:
+            chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
+            yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
+            return
         def make_media_input(inputs, image_paths):
             for image_path in image_paths:
                 inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
@@ -66,7 +72,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history)
-    genai = GoogleChatInit()
+    genai = GoogleChatInit(llm_kwargs)
     retry = 0
     while True:
         try:
@@ -93,6 +99,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             gpt_replying_buffer += paraphrase['text']    # 使用 json 解析库进行处理
             chatbot[-1] = (inputs, gpt_replying_buffer)
             history[-1] = gpt_replying_buffer
+            log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
             yield from update_ui(chatbot=chatbot, history=history)
         if error_match:
             history = history[-2]  # 错误的不纳入对话

request_llms/bridge_jittorllms_llama.py

@@ -1,10 +1,10 @@
 
-from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
 from toolbox import update_ui, get_conf
 from multiprocessing import Process, Pipe
+from transformers import AutoModel, AutoTokenizer
 
 load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global llama_glm_handle
 llama_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_pangualpha.py

@@ -1,10 +1,10 @@
 
-from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
 from toolbox import update_ui, get_conf
 from multiprocessing import Process, Pipe
+from transformers import AutoModel, AutoTokenizer
 
 load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global pangu_glm_handle
 pangu_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_rwkv.py

@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global rwkv_glm_handle
 rwkv_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_moonshot.py

@@ -0,0 +1,197 @@
+# encoding: utf-8
+# @Time   : 2024/3/3
+# @Author : Spike
+# @Descr   :
+import json
+import os
+import time
+import logging
+
+from toolbox import get_conf, update_ui, log_chat
+from toolbox import ChatBotWithCookies
+
+import requests
+
+
+class MoonShotInit:
+
+    def __init__(self):
+        self.llm_model = None
+        self.url = 'https://api.moonshot.cn/v1/chat/completions'
+        self.api_key = get_conf('MOONSHOT_API_KEY')
+
+    def __converter_file(self, user_input: str):
+        what_ask = []
+        for f in user_input.splitlines():
+            if os.path.exists(f):
+                files = []
+                if os.path.isdir(f):
+                    file_list = os.listdir(f)
+                    files.extend([os.path.join(f, file) for file in file_list])
+                else:
+                    files.append(f)
+                for file in files:
+                    if file.split('.')[-1] in ['pdf']:
+                        with open(file, 'r') as fp:
+                            from crazy_functions.crazy_utils import read_and_clean_pdf_text
+                            file_content, _ = read_and_clean_pdf_text(fp)
+                        what_ask.append({"role": "system", "content": file_content})
+        return what_ask
+
+    def __converter_user(self, user_input: str):
+        what_i_ask_now = {"role": "user", "content": user_input}
+        return what_i_ask_now
+
+    def __conversation_history(self, history):
+        conversation_cnt = len(history) // 2
+        messages = []
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = {
+                    "role": "user",
+                    "content": str(history[index])
+                }
+                what_gpt_answer = {
+                    "role": "assistant",
+                    "content": str(history[index + 1])
+                }
+                if what_i_have_asked["content"] != "":
+                    if what_gpt_answer["content"] == "": continue
+                    messages.append(what_i_have_asked)
+                    messages.append(what_gpt_answer)
+                else:
+                    messages[-1]['content'] = what_gpt_answer['content']
+        return messages
+
+    def _analysis_content(self, chuck):
+        chunk_decoded = chuck.decode("utf-8")
+        chunk_json = {}
+        content = ""
+        try:
+            chunk_json = json.loads(chunk_decoded[6:])
+            content = chunk_json['choices'][0]["delta"].get("content", "")
+        except:
+            pass
+        return chunk_decoded, chunk_json, content
+
+    def generate_payload(self, inputs, llm_kwargs, history, system_prompt, stream):
+        self.llm_model = llm_kwargs['llm_model']
+        llm_kwargs.update({'use-key': self.api_key})
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.extend(self.__converter_file(inputs))
+        for i in history[0::2]:    # 历史文件继续上传
+            messages.extend(self.__converter_file(i))
+        messages.extend(self.__conversation_history(history))
+        messages.append(self.__converter_user(inputs))
+        header = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+        }
+        payload = {
+            "model": self.llm_model,
+            "messages": messages,
+            "temperature": llm_kwargs.get('temperature', 0.3),  # 1.0,
+            "top_p": llm_kwargs.get('top_p', 1.0),  # 1.0,
+            "n": llm_kwargs.get('n_choices', 1),
+            "stream": stream
+        }
+        return payload, header
+
+    def generate_messages(self, inputs, llm_kwargs, history, system_prompt, stream):
+        payload, headers = self.generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+        response = requests.post(self.url, headers=headers, json=payload, stream=stream)
+
+        chunk_content = ""
+        gpt_bro_result = ""
+        for chuck in response.iter_lines():
+            chunk_decoded, check_json, content = self._analysis_content(chuck)
+            chunk_content += chunk_decoded
+            if content:
+                gpt_bro_result += content
+                yield content, gpt_bro_result, ''
+            else:
+                error_msg = msg_handle_error(llm_kwargs, chunk_decoded)
+                if error_msg:
+                    yield error_msg, gpt_bro_result, error_msg
+                    break
+
+
+def msg_handle_error(llm_kwargs, chunk_decoded):
+    use_ket = llm_kwargs.get('use-key', '')
+    api_key_encryption = use_ket[:8] + '****' + use_ket[-5:]
+    openai_website = f' 请登录OpenAI查看详情 https://platform.openai.com/signup  api-key: `{api_key_encryption}`'
+    error_msg = ''
+    if "does not exist" in chunk_decoded:
+        error_msg = f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格."
+    elif "Incorrect API key" in chunk_decoded:
+        error_msg = f"[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务." + openai_website
+    elif "exceeded your current quota" in chunk_decoded:
+        error_msg = "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website
+    elif "account is not active" in chunk_decoded:
+        error_msg = "[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 chunk_decoded:
+        error_msg = "[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 chunk_decoded:
+        error_msg = "[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 chunk_decoded:
+        error_msg = "[Local Message] Bad forward key. API2D账户额度不足."
+    elif "Not enough point" in chunk_decoded:
+        error_msg = "[Local Message] Not enough point. API2D账户点数不足."
+    elif 'error' in str(chunk_decoded).lower():
+        try:
+            error_msg = json.dumps(json.loads(chunk_decoded[:6]), indent=4, ensure_ascii=False)
+        except:
+            error_msg = chunk_decoded
+    return error_msg
+
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    chatbot.append([inputs, ""])
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应")  # 刷新界面
+    gpt_bro_init = MoonShotInit()
+    history.extend([inputs, ''])
+    stream_response = gpt_bro_init.generate_messages(inputs, llm_kwargs, history, system_prompt, stream)
+    for content, gpt_bro_result, error_bro_meg in stream_response:
+        chatbot[-1] = [inputs, gpt_bro_result]
+        history[-1] = gpt_bro_result
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        if error_bro_meg:
+            chatbot[-1] = [inputs, error_bro_meg]
+            history = history[:-2]
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            break
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result)
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
+                                  console_slience=False):
+    gpt_bro_init = MoonShotInit()
+    watch_dog_patience = 60  # 看门狗的耐心, 设置10秒即可
+    stream_response = gpt_bro_init.generate_messages(inputs, llm_kwargs, history, sys_prompt, True)
+    moonshot_bro_result = ''
+    for content, moonshot_bro_result, error_bro_meg in stream_response:
+        moonshot_bro_result = moonshot_bro_result
+        if error_bro_meg:
+            if len(observe_window) >= 3:
+                observe_window[2] = error_bro_meg
+            return f'{moonshot_bro_result} 对话错误'
+            # 观测窗
+        if len(observe_window) >= 1:
+            observe_window[0] = moonshot_bro_result
+        if len(observe_window) >= 2:
+            if (time.time() - observe_window[1]) > watch_dog_patience:
+                observe_window[2] = "请求超时，程序终止。"
+                raise RuntimeError(f"{moonshot_bro_result} 程序终止。")
+    return moonshot_bro_result
+
+if __name__ == '__main__':
+    moon_ai = MoonShotInit()
+    for g in moon_ai.generate_messages('hello', {'llm_model': 'moonshot-v1-8k'},
+                                       [], '', True):
+        print(g)

request_llms/bridge_moss.py

@@ -171,7 +171,8 @@ class GetGLMHandle(Process):
 global moss_handle
 moss_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_ollama.py

@@ -0,0 +1,272 @@
+# 借鉴自同目录下的bridge_chatgpt.py
+
+"""
+    该文件中主要包含三个函数
+
+    不具备多线程能力的函数：
+    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
+
+    具备多线程调用能力的函数
+    2. predict_no_ui_long_connection：支持多线程
+"""
+
+import json
+import time
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+import random
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, trimmed_format_exc, is_the_upload_folder, read_one_api_model_name
+proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
+    "proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
+)
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
+                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Openai返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def decode_chunk(chunk):
+    # 提前读取一些信息（用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    is_last_chunk = False
+    try:
+        chunkjson = json.loads(chunk_decoded)
+        is_last_chunk = chunkjson.get("done", False)
+    except:
+        pass
+    return chunk_decoded, chunkjson, is_last_chunk
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+    """
+    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    inputs：
+        是本次问询的输入
+    sys_prompt:
+        系统静默prompt
+    llm_kwargs：
+        chatGPT的内部调优参数
+    history：
+        是之前的对话列表
+    observe_window = None：
+        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+    if inputs == "":     inputs = "空空如也的输入栏"
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            from .bridge_all import model_info
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
+        except requests.exceptions.ReadTimeout as e:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    stream_response = response.iter_lines()
+    result = ''
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if chunk:
+            try:
+                if is_last_chunk:
+                    # 判定为数据流的结束，gpt_replying_buffer也写完了
+                    logging.info(f'[response] {result}')
+                    break
+                result += chunkjson['message']["content"]
+                if not console_slience: print(chunkjson['message']["content"], end='')
+                if observe_window is not None:
+                    # 观测窗，把已经获取的数据显示出去
+                    if len(observe_window) >= 1:
+                        observe_window[0] += chunkjson['message']["content"]
+                    # 看门狗，如果超过期限没有喂狗，则终止
+                    if len(observe_window) >= 2:
+                        if (time.time()-observe_window[1]) > watch_dog_patience:
+                            raise RuntimeError("用户取消了程序。")
+            except Exception as e:
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                print(error_msg)
+                raise RuntimeError("Json解析不合常规")
+    return result
+
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+    发送至chatGPT，流式获取输出。
+    用于基础的对话功能。
+    inputs 是本次问询的输入
+    top_p, temperature是chatGPT的内部调优参数
+    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    if inputs == "":     inputs = "空空如也的输入栏"
+    user_input = inputs
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    raw_input = inputs
+    logging.info(f'[raw_input] {raw_input}')
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+
+    from .bridge_all import model_info
+    endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+
+    history.append(inputs); history.append("")
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            if retry > MAX_RETRY: raise TimeoutError
+
+    gpt_replying_buffer = ""
+
+    if stream:
+        stream_response =  response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                break
+            except requests.exceptions.ConnectionError:
+                chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+
+            # 提前读取一些信息 （用于判断异常）
+            chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
+
+            if chunk:
+                try:
+                    if is_last_chunk:
+                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                        logging.info(f'[response] {gpt_replying_buffer}')
+                        break
+                    # 处理数据流的主体
+                    try:
+                        status_text = f"finish_reason: {chunkjson['error'].get('message', 'null')}"
+                    except:
+                        status_text = "finish_reason: null"
+                    gpt_replying_buffer = gpt_replying_buffer + chunkjson['message']["content"]
+                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    history[-1] = gpt_replying_buffer
+                    chatbot[-1] = (history[-2], history[-1])
+                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
+                except Exception as e:
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
+                    chunk = get_full_error(chunk, stream_response)
+                    chunk_decoded = chunk.decode()
+                    error_msg = chunk_decoded
+                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    print(error_msg)
+                    return
+
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+    from .bridge_all import model_info
+    if "bad_request" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 已经超过了模型的最大上下文或是模型格式错误,请尝试削减单次输入的文本量。")
+    elif "authentication_error" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. 请确保API key有效。")
+    elif "not_found" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] {llm_kwargs['llm_model']} 无效，请确保使用小写的模型名称。")
+    elif "rate_limit" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 遇到了控制请求速率限制，请一分钟后重试。")
+    elif "system_busy" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 系统繁忙，请一分钟后重试。")
+    else:
+        from toolbox import regular_txt_to_markdown
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
+    return chatbot, history
+
+def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+
+    headers = {
+        "Content-Type": "application/json",
+    }
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    model = llm_kwargs['llm_model']
+    if llm_kwargs['llm_model'].startswith('ollama-'):
+        model = llm_kwargs['llm_model'][len('ollama-'):]
+        model, _ = read_one_api_model_name(model)
+    options = {"temperature": llm_kwargs['temperature']}
+    payload = {
+        "model": model,
+        "messages": messages,
+        "options": options,
+    }
+    try:
+        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
+    except:
+        print('输入中可能存在乱码。')
+    return headers,payload

request_llms/bridge_qianfan.py

@@ -82,6 +82,9 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
         "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",
+        "ERNIE-Speed-128K":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k",
+        "ERNIE-Speed-8K":       "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed",
+        "ERNIE-Lite-8K":        "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k",
 
         "Llama-2-70B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b",
         "Llama-2-13B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_13b",
@@ -117,7 +120,8 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
                 raise RuntimeError(dec['error_msg'])
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -146,9 +150,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     yield from update_ui(chatbot=chatbot, history=history)
     # 开始接收回复
     try:
+        response = f"[Local Message] 等待{model_name}响应中 ..."
         for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
             chatbot[-1] = (inputs, response)
             yield from update_ui(chatbot=chatbot, history=history)
+        history.extend([inputs, response])
+        yield from update_ui(chatbot=chatbot, history=history)
     except ConnectionAbortedError as e:
         from .bridge_all import model_info
         if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
@@ -157,10 +164,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
         yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
         return
-    
+    except RuntimeError as e:
-    # 总结输出
+        tb_str = '```\n' + trimmed_format_exc() + '```'
-    response = f"[Local Message] {model_name}响应异常 ..."
+        chatbot[-1] = (chatbot[-1][0], tb_str)
-    if response == f"[Local Message] 等待{model_name}响应中 ...":
+        yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
-        response = f"[Local Message] {model_name}响应异常 ..."
+        return
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_qwen.py

@@ -5,7 +5,8 @@ from toolbox import check_packages, report_exception
 
 model_name = 'Qwen'
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -47,10 +48,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     if additional_fn is not None:
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        chatbot[-1] = (inputs, "")
+        yield from update_ui(chatbot=chatbot, history=history)
 
     # 开始接收回复
     from .com_qwenapi import QwenRequestInstance
     sri = QwenRequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_skylark2.py

@@ -9,7 +9,8 @@ def validate_key():
     if YUNQUE_SECRET_KEY == '': return False
     return True
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐ 多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -56,6 +57,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     # 开始接收回复
     from .com_skylark2api import YUNQUERequestInstance
     sri = YUNQUERequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_spark.py

@@ -13,7 +13,8 @@ def validate_key():
         return False
     return True
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -52,6 +53,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     # 开始接收回复
     from .com_sparkapi import SparkRequestInstance
     sri = SparkRequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt, use_image_api=True):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_zhipu.py

@@ -1,16 +1,24 @@
-
 import time
-from toolbox import update_ui, get_conf, update_ui_lastest_msg
+import os
-from toolbox import check_packages, report_exception
+from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
+from toolbox import ChatBotWithCookies
 
 model_name = '智谱AI大模型'
+zhipuai_default_model = 'glm-4'
 
 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):
+def make_media_input(inputs, image_paths):
+    for image_path in image_paths:
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
+    return inputs
+
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -18,31 +26,38 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     watch_dog_patience = 5
     response = ""
 
+    if llm_kwargs["llm_model"] == "zhipuai":
+        llm_kwargs["llm_model"] = zhipuai_default_model
+
     if validate_key() is False:
         raise RuntimeError('请配置ZHIPUAI_API_KEY')
 
-    from .com_zhipuapi import ZhipuRequestInstance
+    # 开始接收回复
-    sri = ZhipuRequestInstance()
+    from .com_zhipuglm import ZhipuChatInit
-    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+    zhipu_bro_init = ZhipuChatInit()
+    for chunk, response in zhipu_bro_init.generate_chat(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。")
+            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):
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
         ⭐单线程方法
         函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, ""))
+    chatbot.append([inputs, ""])
     yield from update_ui(chatbot=chatbot, history=history)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
         check_packages(["zhipuai"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install zhipuai==1.0.7```。",
+        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
             chatbot=chatbot, history=history, delay=0)
         return
 
@@ -53,16 +68,34 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     if additional_fn is not None:
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        chatbot[-1] = [inputs, ""]
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    if llm_kwargs["llm_model"] == "zhipuai":
+        llm_kwargs["llm_model"] = zhipuai_default_model
+
+    if llm_kwargs["llm_model"] in ["glm-4v"]:
+        if (len(inputs) + sum(len(temp) for temp in history) + 1047) > 2000:
+            chatbot.append((inputs, "上下文长度超过glm-4v上限2000tokens，注意图片大约占用1,047个tokens"))
+            yield from update_ui(chatbot=chatbot, history=history)
+            return
+        have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
+        if not have_recent_file:
+            chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
+            yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
+            return
+        if have_recent_file:
+            inputs = make_media_input(inputs, image_paths)
+            chatbot[-1] = [inputs, ""]
+            yield from update_ui(chatbot=chatbot, history=history)
+
 
     # 开始接收回复
-    from .com_zhipuapi import ZhipuRequestInstance
+    from .com_zhipuglm import ZhipuChatInit
-    sri = ZhipuRequestInstance()
+    zhipu_bro_init = ZhipuChatInit()
-    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+    for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
-        chatbot[-1] = (inputs, response)
+        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])
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/com_google.py

@@ -7,7 +7,7 @@ import os
 import re
 import requests
 from typing import List, Dict, Tuple
-from toolbox import get_conf, encode_image, get_pictures_list
+from toolbox import get_conf, encode_image, get_pictures_list, to_markdown_tabs
 
 proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS")
 
@@ -112,38 +112,12 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr
     return a
 
 
-def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False):
-    """
-    Args:
-        head: 表头：[]
-        tabs: 表值：[[列1], [列2], [列3], [列4]]
-        alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
-        column: True to keep data in columns, False to keep data in rows (default).
-    Returns:
-        A string representation of the markdown table.
-    """
-    if column:
-        transposed_tabs = list(map(list, zip(*tabs)))
-    else:
-        transposed_tabs = tabs
-    # Find the maximum length among the columns
-    max_len = max(len(column) for column in transposed_tabs)
-
-    tab_format = "| %s "
-    tabs_list = "".join([tab_format % i for i in head]) + "|\n"
-    tabs_list += "".join([tab_format % alignment for i in head]) + "|\n"
-
-    for i in range(max_len):
-        row_data = [tab[i] if i < len(tab) else "" for tab in transposed_tabs]
-        row_data = file_manifest_filter_html(row_data, filter_=None)
-        tabs_list += "".join([tab_format % i for i in row_data]) + "|\n"
-
-    return tabs_list
-
 
 class GoogleChatInit:
-    def __init__(self):
+    def __init__(self, llm_kwargs):
-        self.url_gemini = "https://generativelanguage.googleapis.com/v1beta/models/%m:streamGenerateContent?key=%k"
+        from .bridge_all import model_info
+        endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+        self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k"
 
     def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
         headers, payload = self.generate_message_payload(

request_llms/com_qwenapi.py

@@ -48,6 +48,10 @@ class QwenRequestInstance():
         for response in responses:
             if response.status_code == HTTPStatus.OK:
                 if response.output.choices[0].finish_reason == 'stop':
+                    try:
+                        self.result_buf += response.output.choices[0].message.content
+                    except:
+                        pass
                     yield self.result_buf
                     break
                 elif response.output.choices[0].finish_reason == 'length':

request_llms/com_sparkapi.py

@@ -65,6 +65,7 @@ class SparkRequestInstance():
         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.gpt_url_v35 = "wss://spark-api.xf-yun.com/v3.5/chat"
         self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
 
         self.time_to_yield_event = threading.Event()
@@ -91,6 +92,8 @@ class SparkRequestInstance():
             gpt_url = self.gpt_url_v2
         elif llm_kwargs['llm_model'] == 'sparkv3':
             gpt_url = self.gpt_url_v3
+        elif llm_kwargs['llm_model'] == 'sparkv3.5':
+            gpt_url = self.gpt_url_v35
         else:
             gpt_url = self.gpt_url
         file_manifest = []
@@ -190,6 +193,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest)
         "spark": "general",
         "sparkv2": "generalv2",
         "sparkv3": "generalv3",
+        "sparkv3.5": "generalv3.5",
     }
     domains_select = domains[llm_kwargs['llm_model']]
     if file_manifest: domains_select = 'image'

request_llms/com_zhipuapi.py

@@ -1,70 +0,0 @@
-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']*0.7,              # 智谱的API抽风，手动*0.7给做个线性变换
-            temperature=llm_kwargs['temperature']*0.95, # 智谱的API抽风，手动*0.7给做个线性变换
-        )
-        for event in response.events():
-            if event.event == "add":
-                # if self.result_buf == "" and event.data.startswith(" "):
-                #     event.data = event.data.lstrip(" ") # 每次智谱为啥都要带个空格开头呢？
-                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))
-        if self.result_buf == "":
-            yield "智谱没有返回任何数据, 请检查ZHIPUAI_API_KEY和ZHIPUAI_MODEL是否填写正确."
-        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/com_zhipuglm.py

@@ -0,0 +1,135 @@
+# encoding: utf-8
+# @Time   : 2024/1/22
+# @Author : Kilig947 & binary husky
+# @Descr   : 兼容最新的智谱Ai
+from toolbox import get_conf
+from zhipuai import ZhipuAI
+from toolbox import get_conf, encode_image, get_pictures_list
+import logging, os
+
+
+def input_encode_handler(inputs:str, llm_kwargs:dict):
+    if llm_kwargs["most_recent_uploaded"].get("path"):
+        image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
+    md_encode = []
+    for md_path in image_paths:
+        type_ = os.path.splitext(md_path)[1].replace(".", "")
+        type_ = "jpeg" if type_ == "jpg" else type_
+        md_encode.append({"data": encode_image(md_path), "type": type_})
+    return inputs, md_encode
+
+
+class ZhipuChatInit:
+
+    def __init__(self):
+        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
+        if len(ZHIPUAI_MODEL) > 0:
+            logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
+        self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
+        self.model = ''
+
+    def __conversation_user(self, user_input: str, llm_kwargs:dict):
+        if self.model not in ["glm-4v"]:
+            return {"role": "user", "content": user_input}
+        else:
+            input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
+            what_i_have_asked = {"role": "user", "content": []}
+            what_i_have_asked['content'].append({"type": 'text', "text": user_input})
+            if encode_img:
+                if len(encode_img) > 1:
+                    logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
+                    print("glm-4v只支持一张图片,将只取第一张图片进行处理")
+                img_d = {"type": "image_url",
+                            "image_url": {
+                                "url": encode_img[0]['data']
+                            }
+                        }
+                what_i_have_asked['content'].append(img_d)
+            return what_i_have_asked
+
+    def __conversation_history(self, history:list, llm_kwargs:dict):
+        messages = []
+        conversation_cnt = len(history) // 2
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
+                what_gpt_answer = {
+                    "role": "assistant",
+                    "content": history[index + 1]
+                }
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+        return messages
+
+    @staticmethod
+    def preprocess_param(param, default=0.95, min_val=0.01, max_val=0.99):
+        """预处理参数，保证其在允许范围内，并处理精度问题"""
+        try:
+            param = float(param)
+        except ValueError:
+            return default
+
+        if param <= min_val:
+            return min_val
+        elif param >= max_val:
+            return max_val
+        else:
+            return round(param, 2)  # 可挑选精度，目前是两位小数
+
+    def __conversation_message_payload(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        self.model = llm_kwargs['llm_model']
+        messages.extend(self.__conversation_history(history, llm_kwargs))  # 处理 history
+        if inputs.strip() == "": # 处理空输入导致报错的问题 https://github.com/binary-husky/gpt_academic/issues/1640 提示 {"error":{"code":"1214","message":"messages[1]:content和tool_calls 字段不能同时为空"}
+            inputs = "."    # 空格、换行、空字符串都会报错，所以用最没有意义的一个点代替
+        messages.append(self.__conversation_user(inputs, llm_kwargs))  # 处理用户对话
+        """
+        采样温度，控制输出的随机性，必须为正数
+        取值范围是：(0.0, 1.0)，不能等于 0，默认值为 0.95，
+        值越大，会使输出更随机，更具创造性；
+        值越小，输出会更加稳定或确定
+        建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数
+        """
+        temperature = self.preprocess_param(
+            param=llm_kwargs.get('temperature', 0.95),
+            default=0.95,
+            min_val=0.01,
+            max_val=0.99
+        )
+        """
+        用温度取样的另一种方法，称为核取样
+        取值范围是：(0.0, 1.0) 开区间，
+        不能等于 0 或 1，默认值为 0.7
+        模型考虑具有 top_p 概率质量 tokens 的结果
+        例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens
+        建议您根据应用场景调整 top_p 或 temperature 参数，
+        但不要同时调整两个参数
+        """
+        top_p = self.preprocess_param(
+            param=llm_kwargs.get('top_p', 0.70),
+            default=0.70,
+            min_val=0.01,
+            max_val=0.99
+        )
+        response = self.zhipu_bro.chat.completions.create(
+            model=self.model, messages=messages, stream=True,
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=llm_kwargs.get('max_tokens', 1024 * 4),
+        )
+        return response
+
+    def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
+        self.model = llm_kwargs['llm_model']
+        response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
+        bro_results = ''
+        for chunk in response:
+            bro_results += chunk.choices[0].delta.content
+            yield chunk.choices[0].delta.content, bro_results
+
+
+if __name__ == '__main__':
+    zhipu = ZhipuChatInit()
+    zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')

request_llms/local_llm_class.py

@@ -1,6 +1,7 @@
 import time
 import threading
 from toolbox import update_ui, Singleton
+from toolbox import ChatBotWithCookies
 from multiprocessing import Process, Pipe
 from contextlib import redirect_stdout
 from request_llms.queued_pipe import create_queue_pipe
@@ -214,7 +215,7 @@ class LocalLLMHandle(Process):
 def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
     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):
+    def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False):
         """
             refer to request_llms/bridge_all.py
         """
@@ -260,7 +261,8 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
                     raise RuntimeError("程序终止。")
         return response
 
-    def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+    def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+                history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
         """
             refer to request_llms/bridge_all.py
         """

request_llms/oai_std_model_template.py

@@ -0,0 +1,401 @@
+import json
+import time
+import logging
+import traceback
+import requests
+
+# 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_the_upload_folder,
+)
+
+proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
+    "proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
+)
+
+timeout_bot_msg = (
+    "[Local Message] Request timeout. Network error. Please check proxy settings in config.py."
+    + "网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。"
+)
+
+
+def get_full_error(chunk, stream_response):
+    """
+    尝试获取完整的错误信息
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+
+def decode_chunk(chunk):
+    """
+    用于解读"content"和"finish_reason"的内容
+    """
+    chunk = chunk.decode()
+    respose = ""
+    finish_reason = "False"
+    try:
+        chunk = json.loads(chunk[6:])
+    except:
+        finish_reason = "JSON_ERROR"
+    # 错误处理部分
+    if "error" in chunk:
+        respose = "API_ERROR"
+        try:
+            chunk = json.loads(chunk)
+            finish_reason = chunk["error"]["code"]
+        except:
+            finish_reason = "API_ERROR"
+        return respose, finish_reason
+
+    try:
+        respose = chunk["choices"][0]["delta"]["content"]
+    except:
+        pass
+    try:
+        finish_reason = chunk["choices"][0]["finish_reason"]
+    except:
+        pass
+    return respose, finish_reason
+
+
+def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    api_key = f"Bearer {key}"
+
+    headers = {"Content-Type": "application/json", "Authorization": api_key}
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2 * conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index + 1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "":
+                    continue
+                if what_gpt_answer["content"] == timeout_bot_msg:
+                    continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]["content"] = what_gpt_answer["content"]
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = input
+    messages.append(what_i_ask_now)
+    playload = {
+        "model": model,
+        "messages": messages,
+        "temperature": temperature,
+        "stream": True,
+        "max_tokens": max_output_token,
+    }
+    try:
+        print(f" {model} : {conversation_cnt} : {input[:100]} ..........")
+    except:
+        print("输入中可能存在乱码。")
+    return headers, playload
+
+
+def get_predict_function(
+        api_key_conf_name,
+        max_output_token,
+        disable_proxy = False
+    ):
+    """
+    为openai格式的API生成响应函数，其中传入参数：
+    api_key_conf_name：
+        `config.py`中此模型的APIKEY的名字，例如"YIMODEL_API_KEY"
+    max_output_token：
+        每次请求的最大token数量，例如对于01万物的yi-34b-chat-200k，其最大请求数为4096
+        ⚠️请不要与模型的最大token数量相混淆。
+    disable_proxy：
+        是否使用代理，True为不使用，False为使用。
+    """
+
+    APIKEY = get_conf(api_key_conf_name)
+
+    def predict_no_ui_long_connection(
+        inputs,
+        llm_kwargs,
+        history=[],
+        sys_prompt="",
+        observe_window=None,
+        console_slience=False,
+    ):
+        """
+        发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+        inputs：
+            是本次问询的输入
+        sys_prompt:
+            系统静默prompt
+        llm_kwargs：
+            chatGPT的内部调优参数
+        history：
+            是之前的对话列表
+        observe_window = None：
+            用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+        """
+        watch_dog_patience = 5  # 看门狗的耐心，设置5秒不准咬人(咬的也不是人
+        if len(APIKEY) == 0:
+            raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
+        if inputs == "":
+            inputs = "你好👋"
+        headers, playload = generate_message(
+            input=inputs,
+            model=llm_kwargs["llm_model"],
+            key=APIKEY,
+            history=history,
+            max_output_token=max_output_token,
+            system_prompt=sys_prompt,
+            temperature=llm_kwargs["temperature"],
+        )
+        retry = 0
+        while True:
+            try:
+                from .bridge_all import model_info
+
+                endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
+                if not disable_proxy:
+                    response = requests.post(
+                        endpoint,
+                        headers=headers,
+                        proxies=proxies,
+                        json=playload,
+                        stream=True,
+                        timeout=TIMEOUT_SECONDS,
+                    )
+                else:
+                    response = requests.post(
+                        endpoint,
+                        headers=headers,
+                        json=playload,
+                        stream=True,
+                        timeout=TIMEOUT_SECONDS,
+                    )
+                break
+            except:
+                retry += 1
+                traceback.print_exc()
+                if retry > MAX_RETRY:
+                    raise TimeoutError
+                if MAX_RETRY != 0:
+                    print(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
+
+        stream_response = response.iter_lines()
+        result = ""
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                break
+            except requests.exceptions.ConnectionError:
+                chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
+            response_text, finish_reason = decode_chunk(chunk)
+            # 返回的数据流第一次为空，继续等待
+            if response_text == "" and finish_reason != "False":
+                continue
+            if response_text == "API_ERROR" and (
+                finish_reason != "False" or finish_reason != "stop"
+            ):
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                print(chunk_decoded)
+                raise RuntimeError(
+                    f"API异常,请检测终端输出。可能的原因是:{finish_reason}"
+                )
+            if chunk:
+                try:
+                    if finish_reason == "stop":
+                        logging.info(f"[response] {result}")
+                        break
+                    result += response_text
+                    if not console_slience:
+                        print(response_text, end="")
+                    if observe_window is not None:
+                        # 观测窗，把已经获取的数据显示出去
+                        if len(observe_window) >= 1:
+                            observe_window[0] += response_text
+                        # 看门狗，如果超过期限没有喂狗，则终止
+                        if len(observe_window) >= 2:
+                            if (time.time() - observe_window[1]) > watch_dog_patience:
+                                raise RuntimeError("用户取消了程序。")
+                except Exception as e:
+                    chunk = get_full_error(chunk, stream_response)
+                    chunk_decoded = chunk.decode()
+                    error_msg = chunk_decoded
+                    print(error_msg)
+                    raise RuntimeError("Json解析不合常规")
+        return result
+
+    def predict(
+        inputs,
+        llm_kwargs,
+        plugin_kwargs,
+        chatbot,
+        history=[],
+        system_prompt="",
+        stream=True,
+        additional_fn=None,
+    ):
+        """
+        发送至chatGPT，流式获取输出。
+        用于基础的对话功能。
+        inputs 是本次问询的输入
+        top_p, temperature是chatGPT的内部调优参数
+        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+        additional_fn代表点击的哪个按钮，按钮见functional.py
+        """
+        if len(APIKEY) == 0:
+            raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
+        if inputs == "":
+            inputs = "你好👋"
+        if additional_fn is not None:
+            from core_functional import handle_core_functionality
+
+            inputs, history = handle_core_functionality(
+                additional_fn, inputs, history, chatbot
+            )
+        logging.info(f"[raw_input] {inputs}")
+        chatbot.append((inputs, ""))
+        yield from update_ui(
+            chatbot=chatbot, history=history, msg="等待响应"
+        )  # 刷新界面
+
+        # check mis-behavior
+        if is_the_upload_folder(inputs):
+            chatbot[-1] = (
+                inputs,
+                f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。",
+            )
+            yield from update_ui(
+                chatbot=chatbot, history=history, msg="正常"
+            )  # 刷新界面
+            time.sleep(2)
+
+        headers, playload = generate_message(
+            input=inputs,
+            model=llm_kwargs["llm_model"],
+            key=APIKEY,
+            history=history,
+            max_output_token=max_output_token,
+            system_prompt=system_prompt,
+            temperature=llm_kwargs["temperature"],
+        )
+
+        history.append(inputs)
+        history.append("")
+        retry = 0
+        while True:
+            try:
+                from .bridge_all import model_info
+
+                endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
+                if not disable_proxy:
+                    response = requests.post(
+                        endpoint,
+                        headers=headers,
+                        proxies=proxies,
+                        json=playload,
+                        stream=True,
+                        timeout=TIMEOUT_SECONDS,
+                    )
+                else:
+                    response = requests.post(
+                        endpoint,
+                        headers=headers,
+                        json=playload,
+                        stream=True,
+                        timeout=TIMEOUT_SECONDS,
+                    )
+                break
+            except:
+                retry += 1
+                chatbot[-1] = (chatbot[-1][0], timeout_bot_msg)
+                retry_msg = (
+                    f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+                )
+                yield from update_ui(
+                    chatbot=chatbot, history=history, msg="请求超时" + retry_msg
+                )  # 刷新界面
+                if retry > MAX_RETRY:
+                    raise TimeoutError
+
+        gpt_replying_buffer = ""
+
+        stream_response = response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                break
+            except requests.exceptions.ConnectionError:
+                chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
+            response_text, finish_reason = decode_chunk(chunk)
+            # 返回的数据流第一次为空，继续等待
+            if response_text == "" and finish_reason != "False":
+                continue
+            if chunk:
+                try:
+                    if response_text == "API_ERROR" and (
+                        finish_reason != "False" or finish_reason != "stop"
+                    ):
+                        chunk = get_full_error(chunk, stream_response)
+                        chunk_decoded = chunk.decode()
+                        chatbot[-1] = (
+                            chatbot[-1][0],
+                            "[Local Message] {finish_reason},获得以下报错信息：\n"
+                            + chunk_decoded,
+                        )
+                        yield from update_ui(
+                            chatbot=chatbot,
+                            history=history,
+                            msg="API异常:" + chunk_decoded,
+                        )  # 刷新界面
+                        print(chunk_decoded)
+                        return
+
+                    if finish_reason == "stop":
+                        logging.info(f"[response] {gpt_replying_buffer}")
+                        break
+                    status_text = f"finish_reason: {finish_reason}"
+                    gpt_replying_buffer += response_text
+                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    history[-1] = gpt_replying_buffer
+                    chatbot[-1] = (history[-2], history[-1])
+                    yield from update_ui(
+                        chatbot=chatbot, history=history, msg=status_text
+                    )  # 刷新界面
+                except Exception as e:
+                    yield from update_ui(
+                        chatbot=chatbot, history=history, msg="Json解析不合常规"
+                    )  # 刷新界面
+                    chunk = get_full_error(chunk, stream_response)
+                    chunk_decoded = chunk.decode()
+                    chatbot[-1] = (
+                        chatbot[-1][0],
+                        "[Local Message] 解析错误,获得以下报错信息：\n" + chunk_decoded,
+                    )
+                    yield from update_ui(
+                        chatbot=chatbot, history=history, msg="Json异常" + chunk_decoded
+                    )  # 刷新界面
+                    print(chunk_decoded)
+                    return
+
+    return predict_no_ui_long_connection, predict

requirements.txt

@@ -1,12 +1,15 @@
-https://fastly.jsdelivr.net/gh/binary-husky/gradio-fix@gpt-academic/release/gradio-3.32.7-py3-none-any.whl
+https://public.agent-matrix.com/publish/gradio-3.32.10-py3-none-any.whl
+fastapi==0.110
+gradio-client==0.8
 pypdf2==2.12.1
-zhipuai<2
+zhipuai==2.0.1
 tiktoken>=0.3.3
 requests[socks]
-pydantic==1.10.11
+pydantic==2.5.2
 protobuf==3.18
 transformers>=4.27.1
 scipdf_parser>=0.52
+anthropic>=0.18.1
 python-markdown-math
 pymdown-extensions
 websocket-client
@@ -15,13 +18,15 @@ prompt_toolkit
 latex2mathml
 python-docx
 mdtex2html
-anthropic
+dashscope
 pyautogen
 colorama
 Markdown
 pygments
+edge-tts
 pymupdf
 openai
+rjsmin
 arxiv
 numpy
 rich

shared_utils/advanced_markdown_format.py

@@ -207,6 +207,53 @@ def fix_code_segment_indent(txt):
         return txt
 
 
+def markdown_convertion_for_file(txt):
+    """
+    将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
+    """
+    from themes.theme import advanced_css
+    pre = f"""
+    <!DOCTYPE html><head><meta charset="utf-8"><title>PDF文档翻译</title><style>{advanced_css}</style></head>
+    <body>
+    <div class="test_temp1" style="width:10%; height: 500px; float:left;"></div>
+    <div class="test_temp2" style="width:80%;padding: 40px;float:left;padding-left: 20px;padding-right: 20px;box-shadow: rgba(0, 0, 0, 0.2) 0px 0px 8px 8px;border-radius: 10px;">
+        <div class="markdown-body">
+    """
+    suf = """
+        </div>
+    </div>
+    <div class="test_temp3" style="width:10%; height: 500px; float:left;"></div>
+    </body>
+    """
+
+    if txt.startswith(pre) and txt.endswith(suf):
+        # print('警告，输入了已经经过转化的字符串，二次转化可能出问题')
+        return txt  # 已经被转化过，不需要再次转化
+
+    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
+    txt = fix_markdown_indent(txt)
+    # convert everything to html format
+    split = markdown.markdown(text="---")
+    convert_stage_1 = markdown.markdown(
+        text=txt,
+        extensions=[
+            "sane_lists",
+            "tables",
+            "mdx_math",
+            "pymdownx.superfences",
+            "pymdownx.highlight",
+        ],
+        extension_configs={**markdown_extension_configs, **code_highlight_configs},
+    )
+    convert_stage_1 = markdown_bug_hunt(convert_stage_1)
+
+    # 2. convert to rendered equation
+    convert_stage_2_2, n = re.subn(
+        find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL
+    )
+    # cat them together
+    return pre + convert_stage_2_2 + suf
+
 @lru_cache(maxsize=128)  # 使用 lru缓存 加快转换速度
 def markdown_convertion(txt):
     """

shared_utils/config_loader.py

@@ -2,7 +2,7 @@ import importlib
 import time
 import os
 from functools import lru_cache
-from colorful import print亮红, print亮绿, print亮蓝
+from shared_utils.colorful import print亮红, print亮绿, print亮蓝
 
 pj = os.path.join
 default_user_name = 'default_user'

shared_utils/connect_void_terminal.py

@@ -15,13 +15,13 @@ import os
 
 def get_plugin_handle(plugin_name):
     """
-    e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'
+    e.g. plugin_name = 'crazy_functions.Markdown_Translate->Markdown翻译指定语言'
     """
     import importlib
 
     assert (
         "->" in plugin_name
-    ), "Example of plugin_name: crazy_functions.批量Markdown翻译->Markdown翻译指定语言"
+    ), "Example of plugin_name: crazy_functions.Markdown_Translate->Markdown翻译指定语言"
     module, fn_name = plugin_name.split("->")
     f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
     return f_hot_reload

shared_utils/cookie_manager.py

@@ -0,0 +1,144 @@
+import json
+import base64
+from typing import Callable
+
+def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)->Callable:
+    def load_web_cookie_cache(persistent_cookie_, cookies_):
+        import gradio as gr
+        from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
+
+        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
+    return load_web_cookie_cache
+
+def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)->Callable:
+    def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
+        import gradio as gr
+        from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
+        ret = {}
+        # 读取之前的自定义按钮
+        customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
+        # 更新新的自定义按钮
+        customize_fn_overwrite_.update({
+            basic_btn_dropdown_:
+                {
+                    "Title":basic_fn_title,
+                    "Prefix":basic_fn_prefix,
+                    "Suffix":basic_fn_suffix,
+                }
+            }
+        )
+        if clean_up:
+            customize_fn_overwrite_ = {}
+        cookies_.update(customize_fn_overwrite_)    # 更新cookie
+        visible = (not clean_up) and (basic_fn_title != "")
+        if basic_btn_dropdown_ in customize_btns:
+            # 是自定义按钮，不是预定义按钮
+            ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
+        else:
+            # 是预定义按钮
+            ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=visible, 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({web_cookie_cache: persistent_cookie_})             # write persistent cookie
+        return ret
+    return assign_btn
+
+# cookies, web_cookie_cache = make_cookie_cache()
+def make_cookie_cache():
+    # 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
+    import gradio as gr
+    from toolbox import load_chat_cookies
+    # 定义cookies的后端state
+    cookies = gr.State(load_chat_cookies())
+    # 定义cookies的一个孪生的前端存储区（隐藏）
+    web_cookie_cache = gr.Textbox(visible=False, elem_id="web_cookie_cache")
+    return cookies, web_cookie_cache
+
+# history, history_cache, history_cache_update = make_history_cache()
+def make_history_cache():
+    # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
+    import gradio as gr
+    # 定义history的后端state
+    history = gr.State([])
+    # 定义history的一个孪生的前端存储区（隐藏）
+    history_cache = gr.Textbox(visible=False, elem_id="history_cache")
+    # 定义history_cache->history的更新方法（隐藏）。在触发这个按钮时，会先执行js代码更新history_cache，然后再执行python代码更新history
+    def process_history_cache(history_cache):
+        return json.loads(history_cache)
+    # 另一种更简单的setter方法
+    history_cache_update = gr.Button("", elem_id="elem_update_history", visible=False).click(
+        process_history_cache, inputs=[history_cache], outputs=[history])
+    return history, history_cache, history_cache_update
+
+
+
+# """
+# with gr.Row():
+#     txt = gr.Textbox(show_label=False, placeholder="Input question here.", elem_id='user_input_main').style(container=False)
+#     txtx = gr.Textbox(show_label=False, placeholder="Input question here.", elem_id='user_input_main').style(container=False)
+# with gr.Row():
+#     btn_value = "Test"
+#     elem_id = "TestCase"
+#     variant = "primary"
+#     input_list = [txt, txtx]
+#     output_list = [txt, txtx]
+#     input_name_list = ["txt(input)", "txtx(input)"]
+#     output_name_list = ["txt", "txtx"]
+#     js_callback = """(txt, txtx)=>{console.log(txt); console.log(txtx);}"""
+#     def function(txt, txtx):
+#         return "booo", "goooo"
+#     create_button_with_javascript_callback(btn_value, elem_id, variant, js_callback, input_list, output_list, function, input_name_list, output_name_list)
+# """
+def create_button_with_javascript_callback(btn_value, elem_id, variant, js_callback, input_list, output_list, function, input_name_list, output_name_list):
+    import gradio as gr
+    middle_ware_component = gr.Textbox(visible=False, elem_id=elem_id+'_buffer')
+    def get_fn_wrap():
+        def fn_wrap(*args):
+            summary_dict = {}
+            for name, value in zip(input_name_list, args):
+                summary_dict.update({name: value})
+
+            res = function(*args)
+
+            for name, value in zip(output_name_list, res):
+                summary_dict.update({name: value})
+
+            summary = base64.b64encode(json.dumps(summary_dict).encode('utf8')).decode("utf-8")
+            return (*res, summary)
+        return fn_wrap
+
+    btn = gr.Button(btn_value, elem_id=elem_id, variant=variant)
+    call_args = ""
+    for name in output_name_list:
+        call_args += f"""Data["{name}"],"""
+    call_args = call_args.rstrip(",")
+    _js_callback = """
+        (base64MiddleString)=>{
+            console.log('hello')
+            const stringData = atob(base64MiddleString);
+            let Data = JSON.parse(stringData);
+            call = JS_CALLBACK_GEN;
+            call(CALL_ARGS);
+        }
+    """.replace("JS_CALLBACK_GEN", js_callback).replace("CALL_ARGS", call_args)
+
+    btn.click(get_fn_wrap(), input_list, output_list+[middle_ware_component]).then(None, [middle_ware_component], None, _js=_js_callback)
+    return btn

shared_utils/fastapi_server.py

@@ -0,0 +1,277 @@
+"""
+Tests:
+
+- custom_path false / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block __pycache__ access(yes)
+        -- rel (yes)
+        -- abs (yes)
+    -- block user access(fail) http://localhost:45013/file=gpt_log/admin/chat_secrets.log
+        -- fix(commit f6bf05048c08f5cd84593f7fdc01e64dec1f584a)-> block successful
+
+- custom_path yes("/cc/gptac") / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block __pycache__ access(yes)
+    -- block user access(yes)
+
+- custom_path yes("/cc/gptac/") / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+
+- custom_path yes("/cc/gptac/") / + user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+    -- block user-wise access (yes)
+
+- custom_path no + user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+    -- block user-wise access (yes)
+
+queue cocurrent effectiveness
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+"""
+
+import os, requests, threading, time
+import uvicorn
+
+def validate_path_safety(path_or_url, user):
+    from toolbox import get_conf, default_user_name
+    from toolbox import FriendlyException
+    PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
+    sensitive_path = None
+    path_or_url = os.path.relpath(path_or_url)
+    if path_or_url.startswith(PATH_LOGGING):    # 日志文件（按用户划分）
+        sensitive_path = PATH_LOGGING
+    elif path_or_url.startswith(PATH_PRIVATE_UPLOAD):   # 用户的上传目录（按用户划分）
+        sensitive_path = PATH_PRIVATE_UPLOAD
+    elif path_or_url.startswith('tests'):   # 一个常用的测试目录
+        return True
+    else:
+        raise FriendlyException(f"输入文件的路径 ({path_or_url}) 存在，但位置非法。请将文件上传后再执行该任务。") # return False
+    if sensitive_path:
+        allowed_users = [user, 'autogen', default_user_name]  # three user path that can be accessed
+        for user_allowed in allowed_users:
+            if f"{os.sep}".join(path_or_url.split(os.sep)[:2]) == os.path.join(sensitive_path, user_allowed):
+                return True
+        raise FriendlyException(f"输入文件的路径 ({path_or_url}) 存在，但属于其他用户。请将文件上传后再执行该任务。") # return False
+    return True
+
+def _authorize_user(path_or_url, request, gradio_app):
+    from toolbox import get_conf, default_user_name
+    PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
+    sensitive_path = None
+    path_or_url = os.path.relpath(path_or_url)
+    if path_or_url.startswith(PATH_LOGGING):
+        sensitive_path = PATH_LOGGING
+    if path_or_url.startswith(PATH_PRIVATE_UPLOAD):
+        sensitive_path = PATH_PRIVATE_UPLOAD
+    if sensitive_path:
+        token = request.cookies.get("access-token") or request.cookies.get("access-token-unsecure")
+        user = gradio_app.tokens.get(token)  # get user
+        allowed_users = [user, 'autogen', default_user_name]  # three user path that can be accessed
+        for user_allowed in allowed_users:
+            # exact match
+            if f"{os.sep}".join(path_or_url.split(os.sep)[:2]) == os.path.join(sensitive_path, user_allowed):
+                return True
+        return False # "越权访问!"
+    return True
+
+
+class Server(uvicorn.Server):
+    # A server that runs in a separate thread
+    def install_signal_handlers(self):
+        pass
+
+    def run_in_thread(self):
+        self.thread = threading.Thread(target=self.run, daemon=True)
+        self.thread.start()
+        while not self.started:
+            time.sleep(1e-3)
+
+    def close(self):
+        self.should_exit = True
+        self.thread.join()
+
+
+def start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE):
+    import uvicorn
+    import fastapi
+    import gradio as gr
+    from fastapi import FastAPI
+    from gradio.routes import App
+    from toolbox import get_conf
+    CUSTOM_PATH, PATH_LOGGING = get_conf('CUSTOM_PATH', 'PATH_LOGGING')
+
+    # --- --- configurate gradio app block --- ---
+    app_block:gr.Blocks
+    app_block.ssl_verify = False
+    app_block.auth_message = '请登录'
+    app_block.favicon_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "docs/logo.png")
+    app_block.auth = AUTHENTICATION if len(AUTHENTICATION) != 0 else None
+    app_block.blocked_paths = ["config.py", "__pycache__", "config_private.py", "docker-compose.yml", "Dockerfile", f"{PATH_LOGGING}/admin"]
+    app_block.dev_mode = False
+    app_block.config = app_block.get_config_file()
+    app_block.enable_queue = True
+    app_block.queue(concurrency_count=CONCURRENT_COUNT)
+    app_block.validate_queue_settings()
+    app_block.show_api = False
+    app_block.config = app_block.get_config_file()
+    max_threads = 40
+    app_block.max_threads = max(
+        app_block._queue.max_thread_count if app_block.enable_queue else 0, max_threads
+    )
+    app_block.is_colab = False
+    app_block.is_kaggle = False
+    app_block.is_sagemaker = False
+
+    gradio_app = App.create_app(app_block)
+
+    # --- --- replace gradio endpoint to forbid access to sensitive files --- ---
+    if len(AUTHENTICATION) > 0:
+        dependencies = []
+        endpoint = None
+        for route in list(gradio_app.router.routes):
+            if route.path == "/file/{path:path}":
+                gradio_app.router.routes.remove(route)
+            if route.path == "/file={path_or_url:path}":
+                dependencies = route.dependencies
+                endpoint = route.endpoint
+                gradio_app.router.routes.remove(route)
+        @gradio_app.get("/file/{path:path}", dependencies=dependencies)
+        @gradio_app.head("/file={path_or_url:path}", dependencies=dependencies)
+        @gradio_app.get("/file={path_or_url:path}", dependencies=dependencies)
+        async def file(path_or_url: str, request: fastapi.Request):
+            if len(AUTHENTICATION) > 0:
+                if not _authorize_user(path_or_url, request, gradio_app):
+                    return "越权访问!"
+            return await endpoint(path_or_url, request)
+
+    TTS_TYPE = get_conf("TTS_TYPE")
+    if TTS_TYPE != "DISABLE":
+        # audio generation functionality
+        import httpx
+        from fastapi import FastAPI, Request, HTTPException
+        from starlette.responses import Response
+        async def forward_request(request: Request, method: str) -> Response:
+            async with httpx.AsyncClient() as client:
+                try:
+                    # Forward the request to the target service
+                    if TTS_TYPE == "EDGE_TTS":
+                        import tempfile
+                        import edge_tts
+                        import wave
+                        import uuid
+                        from pydub import AudioSegment
+                        json = await request.json()
+                        voice = get_conf("EDGE_TTS_VOICE")
+                        tts = edge_tts.Communicate(text=json['text'], voice=voice)
+                        temp_folder = tempfile.gettempdir()
+                        temp_file_name = str(uuid.uuid4().hex)
+                        temp_file = os.path.join(temp_folder, f'{temp_file_name}.mp3')
+                        await tts.save(temp_file)
+                        try:
+                            mp3_audio = AudioSegment.from_file(temp_file, format="mp3")
+                            mp3_audio.export(temp_file, format="wav")
+                            with open(temp_file, 'rb') as wav_file: t = wav_file.read()
+                            os.remove(temp_file)
+                            return Response(content=t)
+                        except:
+                            raise RuntimeError("ffmpeg未安装，无法处理EdgeTTS音频。安装方法见`https://github.com/jiaaro/pydub#getting-ffmpeg-set-up`")
+                    if TTS_TYPE == "LOCAL_SOVITS_API":
+                        # Forward the request to the target service
+                        TARGET_URL = get_conf("GPT_SOVITS_URL")
+                        body = await request.body()
+                        resp = await client.post(TARGET_URL, content=body, timeout=60)
+                        # Return the response from the target service
+                        return Response(content=resp.content, status_code=resp.status_code, headers=dict(resp.headers))
+                except httpx.RequestError as e:
+                    raise HTTPException(status_code=400, detail=f"Request to the target service failed: {str(e)}")
+        @gradio_app.post("/vits")
+        async def forward_post_request(request: Request):
+            return await forward_request(request, "POST")
+
+    # --- --- app_lifespan --- ---
+    from contextlib import asynccontextmanager
+    @asynccontextmanager
+    async def app_lifespan(app):
+        async def startup_gradio_app():
+            if gradio_app.get_blocks().enable_queue:
+                gradio_app.get_blocks().startup_events()
+        async def shutdown_gradio_app():
+            pass
+        await startup_gradio_app() # startup logic here
+        yield  # The application will serve requests after this point
+        await shutdown_gradio_app() # cleanup/shutdown logic here
+
+    # --- --- FastAPI --- ---
+    fastapi_app = FastAPI(lifespan=app_lifespan)
+    fastapi_app.mount(CUSTOM_PATH, gradio_app)
+
+    # --- --- favicon --- ---
+    if CUSTOM_PATH != '/':
+        from fastapi.responses import FileResponse
+        @fastapi_app.get("/favicon.ico")
+        async def favicon():
+            return FileResponse(app_block.favicon_path)
+
+    # --- --- uvicorn.Config --- ---
+    ssl_keyfile = None if SSL_KEYFILE == "" else SSL_KEYFILE
+    ssl_certfile = None if SSL_CERTFILE == "" else SSL_CERTFILE
+    server_name = "0.0.0.0"
+    config = uvicorn.Config(
+        fastapi_app,
+        host=server_name,
+        port=PORT,
+        reload=False,
+        log_level="warning",
+        ssl_keyfile=ssl_keyfile,
+        ssl_certfile=ssl_certfile,
+    )
+    server = Server(config)
+    url_host_name = "localhost" if server_name == "0.0.0.0" else server_name
+    if ssl_keyfile is not None:
+        if ssl_certfile is None:
+            raise ValueError(
+                "ssl_certfile must be provided if ssl_keyfile is provided."
+            )
+        path_to_local_server = f"https://{url_host_name}:{PORT}/"
+    else:
+        path_to_local_server = f"http://{url_host_name}:{PORT}/"
+    if CUSTOM_PATH != '/':
+        path_to_local_server += CUSTOM_PATH.lstrip('/').rstrip('/') + '/'
+    # --- --- begin  --- ---
+    server.run_in_thread()
+
+    # --- --- after server launch --- ---
+    app_block.server = server
+    app_block.server_name = server_name
+    app_block.local_url = path_to_local_server
+    app_block.protocol = (
+        "https"
+        if app_block.local_url.startswith("https") or app_block.is_colab
+        else "http"
+    )
+
+    if app_block.enable_queue:
+        app_block._queue.set_url(path_to_local_server)
+
+    forbid_proxies = {
+        "http": "",
+        "https": "",
+    }
+    requests.get(f"{app_block.local_url}startup-events", verify=app_block.ssl_verify, proxies=forbid_proxies)
+    app_block.is_running = True
+    app_block.block_thread()

shared_utils/handle_upload.py

@@ -0,0 +1,145 @@
+import importlib
+import time
+import inspect
+import re
+import os
+import base64
+import gradio
+import shutil
+import glob
+from shared_utils.config_loader import get_conf
+
+def html_local_file(file):
+    base_path = os.path.dirname(__file__)  # 项目目录
+    if os.path.exists(str(file)):
+        file = f'file={file.replace(base_path, ".")}'
+    return file
+
+
+def html_local_img(__file, layout="left", max_width=None, max_height=None, md=True):
+    style = ""
+    if max_width is not None:
+        style += f"max-width: {max_width};"
+    if max_height is not None:
+        style += f"max-height: {max_height};"
+    __file = html_local_file(__file)
+    a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
+    if md:
+        a = f"![{__file}]({__file})"
+    return a
+
+
+def file_manifest_filter_type(file_list, filter_: list = None):
+    new_list = []
+    if not filter_:
+        filter_ = ["png", "jpg", "jpeg"]
+    for file in file_list:
+        if str(os.path.basename(file)).split(".")[-1] in filter_:
+            new_list.append(html_local_img(file, md=False))
+        else:
+            new_list.append(file)
+    return new_list
+
+
+def zip_extract_member_new(self, member, targetpath, pwd):
+    # 修复中文乱码的问题
+    """Extract the ZipInfo object 'member' to a physical
+        file on the path targetpath.
+    """
+    import zipfile
+    if not isinstance(member, zipfile.ZipInfo):
+        member = self.getinfo(member)
+
+    # build the destination pathname, replacing
+    # forward slashes to platform specific separators.
+    arcname = member.filename.replace('/', os.path.sep)
+    arcname = arcname.encode('cp437', errors='replace').decode('gbk', errors='replace')
+
+    if os.path.altsep:
+        arcname = arcname.replace(os.path.altsep, os.path.sep)
+    # interpret absolute pathname as relative, remove drive letter or
+    # UNC path, redundant separators, "." and ".." components.
+    arcname = os.path.splitdrive(arcname)[1]
+    invalid_path_parts = ('', os.path.curdir, os.path.pardir)
+    arcname = os.path.sep.join(x for x in arcname.split(os.path.sep)
+                                if x not in invalid_path_parts)
+    if os.path.sep == '\\':
+        # filter illegal characters on Windows
+        arcname = self._sanitize_windows_name(arcname, os.path.sep)
+
+    targetpath = os.path.join(targetpath, arcname)
+    targetpath = os.path.normpath(targetpath)
+
+    # Create all upper directories if necessary.
+    upperdirs = os.path.dirname(targetpath)
+    if upperdirs and not os.path.exists(upperdirs):
+        os.makedirs(upperdirs)
+
+    if member.is_dir():
+        if not os.path.isdir(targetpath):
+            os.mkdir(targetpath)
+        return targetpath
+
+    with self.open(member, pwd=pwd) as source, \
+            open(targetpath, "wb") as target:
+        shutil.copyfileobj(source, target)
+
+    return targetpath
+
+
+def extract_archive(file_path, dest_dir):
+    import zipfile
+    import tarfile
+    import os
+
+    # Get the file extension of the input file
+    file_extension = os.path.splitext(file_path)[1]
+
+    # Extract the archive based on its extension
+    if file_extension == ".zip":
+        with zipfile.ZipFile(file_path, "r") as zipobj:
+            zipobj._extract_member = lambda a,b,c: zip_extract_member_new(zipobj, a,b,c)    # 修复中文乱码的问题
+            zipobj.extractall(path=dest_dir)
+            print("Successfully extracted zip archive to {}".format(dest_dir))
+
+    elif file_extension in [".tar", ".gz", ".bz2"]:
+        with tarfile.open(file_path, "r:*") as tarobj:
+            # 清理提取路径，移除任何不安全的元素
+            for member in tarobj.getmembers():
+                member_path = os.path.normpath(member.name)
+                full_path = os.path.join(dest_dir, member_path)
+                full_path = os.path.abspath(full_path)
+                if not full_path.startswith(os.path.abspath(dest_dir) + os.sep):
+                    raise Exception(f"Attempted Path Traversal in {member.name}")
+
+            tarobj.extractall(path=dest_dir)
+            print("Successfully extracted tar archive to {}".format(dest_dir))
+
+    # 第三方库，需要预先pip install rarfile
+    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
+    elif file_extension == ".rar":
+        try:
+            import rarfile
+
+            with rarfile.RarFile(file_path) as rf:
+                rf.extractall(path=dest_dir)
+                print("Successfully extracted rar archive to {}".format(dest_dir))
+        except:
+            print("Rar format requires additional dependencies to install")
+            return "\n\n解压失败! 需要安装pip install rarfile来解压rar文件。建议：使用zip压缩格式。"
+
+    # 第三方库，需要预先pip install py7zr
+    elif file_extension == ".7z":
+        try:
+            import py7zr
+
+            with py7zr.SevenZipFile(file_path, mode="r") as f:
+                f.extractall(path=dest_dir)
+                print("Successfully extracted 7z archive to {}".format(dest_dir))
+        except:
+            print("7z format requires additional dependencies to install")
+            return "\n\n解压失败! 需要安装pip install py7zr来解压7z文件"
+    else:
+        return ""
+    return ""
+

shared_utils/key_pattern_manager.py

@@ -14,7 +14,7 @@ def is_openai_api_key(key):
     if len(CUSTOM_API_KEY_PATTERN) != 0:
         API_MATCH_ORIGINAL = re.match(CUSTOM_API_KEY_PATTERN, key)
     else:
-        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
+        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$|sk-proj-[a-zA-Z0-9]{48}$|sess-[a-zA-Z0-9]{40}$", key)
     return bool(API_MATCH_ORIGINAL)
 
 
@@ -28,6 +28,11 @@ def is_api2d_key(key):
     return bool(API_MATCH_API2D)
 
 
+def is_cohere_api_key(key):
+    API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{40}$", key)
+    return bool(API_MATCH_AZURE)
+
+
 def is_any_api_key(key):
     if ',' in key:
         keys = key.split(',')
@@ -35,7 +40,7 @@ def is_any_api_key(key):
             if is_any_api_key(k): return True
         return False
     else:
-        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
+        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key) or is_cohere_api_key(key)
 
 
 def what_keys(keys):
@@ -62,7 +67,7 @@ def select_api_key(keys, llm_model):
     avail_key_list = []
     key_list = keys.split(',')
 
-    if llm_model.startswith('gpt-'):
+    if llm_model.startswith('gpt-') or llm_model.startswith('one-api-'):
         for k in key_list:
             if is_openai_api_key(k): avail_key_list.append(k)
 
@@ -74,8 +79,12 @@ def select_api_key(keys, llm_model):
         for k in key_list:
             if is_azure_api_key(k): avail_key_list.append(k)
 
+    if llm_model.startswith('cohere-'):
+        for k in key_list:
+            if is_cohere_api_key(k): avail_key_list.append(k)
+
     if len(avail_key_list) == 0:
-        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
+        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（左上角更换模型菜单中可切换openai,azure,claude,cohere等请求源）。")
 
     api_key = random.choice(avail_key_list) # 随机负载均衡
     return api_key

shared_utils/map_names.py

@@ -0,0 +1,34 @@
+import re
+mapping_dic = {
+    # "qianfan": "qianfan（文心一言大模型）",
+    # "zhipuai": "zhipuai（智谱GLM4超级模型🔥）",
+    # "gpt-4-1106-preview": "gpt-4-1106-preview（新调优版本GPT-4🔥）",
+    # "gpt-4-vision-preview": "gpt-4-vision-preview（识图模型GPT-4V）",
+}
+
+rev_mapping_dic = {}
+for k, v in mapping_dic.items():
+    rev_mapping_dic[v] = k
+
+def map_model_to_friendly_names(m):
+    if m in mapping_dic:
+        return mapping_dic[m]
+    return m
+
+def map_friendly_names_to_model(m):
+    if m in rev_mapping_dic:
+        return rev_mapping_dic[m]
+    return m
+
+def read_one_api_model_name(model: str):
+    """return real model name and max_token.
+    """
+    max_token_pattern = r"\(max_token=(\d+)\)"
+    match = re.search(max_token_pattern, model)
+    if match:
+        max_token_tmp = match.group(1)  # 获取 max_token 的值
+        max_token_tmp = int(max_token_tmp)
+        model = re.sub(max_token_pattern, "", model)  # 从原字符串中删除 "(max_token=...)"
+    else:
+        max_token_tmp = 4096
+    return model, max_token_tmp

shared_utils/text_mask.py

@@ -26,6 +26,8 @@ def apply_gpt_academic_string_mask(string, mode="show_all"):
     当字符串中有掩码tag时（<gpt_academic_string_mask><show_...>），根据字符串要给谁看（大模型，还是web渲染），对字符串进行处理，返回处理后的字符串
     示意图：https://mermaid.live/edit#pako:eNqlkUtLw0AUhf9KuOta0iaTplkIPlpduFJwoZEwJGNbzItpita2O6tF8QGKogXFtwu7cSHiq3-mk_oznFR8IYLgrGbuOd9hDrcCpmcR0GDW9ubNPKaBMDauuwI_A9M6YN-3y0bODwxsYos4BdMoBrTg5gwHF-d0mBH6-vqFQe58ed5m9XPW2uteX3Tubrj0ljLYcwxxR3h1zB43WeMs3G19yEM9uapDMe_NG9i2dagKw1Fee4c1D9nGEbtc-5n6HbNtJ8IyHOs8tbs7V2HrlDX2w2Y7XD_5haHEtQiNsOwfMVa_7TzsvrWIuJGo02qTrdwLk9gukQylHv3Afv1ML270s-HZUndrmW1tdA-WfvbM_jMFYuAQ6uCCxVdciTJ1CPLEITpo_GphypeouzXuw6XAmyi7JmgBLZEYlHwLB2S4gHMUO-9DH7tTnvf1CVoFFkBLSOk4QmlRTqpIlaWUHINyNFXjaQWpCYRURUKiWovBYo8X4ymEJFlECQUpqaQkJmuvWygPpg
     """
+    if not string:
+        return string
     if "<gpt_academic_string_mask>" not in string: # No need to process
         return string
 

tests/test_key_pattern_manager.py

@@ -0,0 +1,41 @@
+import unittest
+
+def validate_path():
+    import os, sys
+
+    os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(os.path.dirname(__file__) + "/..")
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+
+
+validate_path()  # validate path so you can run from base directory
+
+from shared_utils.key_pattern_manager import is_openai_api_key
+
+class TestKeyPatternManager(unittest.TestCase):
+    def test_is_openai_api_key_with_valid_key(self):
+        key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+        self.assertTrue(is_openai_api_key(key))
+
+        key = "sx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+        self.assertFalse(is_openai_api_key(key))
+
+        key = "sess-wg61ZafYHpNz7FFwIH7HGZlbVqUVaeV5tatHCWpl"
+        self.assertTrue(is_openai_api_key(key))
+
+        key = "sess-wg61ZafYHpNz7FFwIH7HGZlbVqUVa5tatHCWpl"
+        self.assertFalse(is_openai_api_key(key))
+
+
+    def test_is_openai_api_key_with_invalid_key(self):
+        key = "invalid_key"
+        self.assertFalse(is_openai_api_key(key))
+
+    def test_is_openai_api_key_with_custom_pattern(self):
+        # Assuming you have set a custom pattern in your configuration
+        key = "custom-pattern-key"
+        self.assertFalse(is_openai_api_key(key))
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_llms.py

@@ -11,6 +11,28 @@ def validate_path():
 
 
 validate_path()  # validate path so you can run from base directory
+
+if "在线模型":
+    if __name__ == "__main__":
+        from request_llms.bridge_cohere import predict_no_ui_long_connection
+        # from request_llms.bridge_spark import predict_no_ui_long_connection
+        # from request_llms.bridge_zhipu import predict_no_ui_long_connection
+        # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
+        llm_kwargs = {
+            "llm_model": "command-r-plus",
+            "max_length": 4096,
+            "top_p": 1,
+            "temperature": 1,
+        }
+
+        result = predict_no_ui_long_connection(
+            inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt="系统"
+        )
+        print("final result:", result)
+        print("final result:", result)
+
+
+if "本地模型":
     if __name__ == "__main__":
         # from request_llms.bridge_newbingfree import predict_no_ui_long_connection
         # from request_llms.bridge_moss import predict_no_ui_long_connection
@@ -20,19 +42,14 @@ if __name__ == "__main__":
         # from request_llms.bridge_internlm import predict_no_ui_long_connection
         # from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
         # from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
-    from request_llms.bridge_qwen_local import predict_no_ui_long_connection
+        # from request_llms.bridge_qwen_local import predict_no_ui_long_connection
-
-    # from request_llms.bridge_spark import predict_no_ui_long_connection
-    # from request_llms.bridge_zhipu import predict_no_ui_long_connection
-    # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
-
         llm_kwargs = {
             "max_length": 4096,
             "top_p": 1,
             "temperature": 1,
         }
-
         result = predict_no_ui_long_connection(
             inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt=""
         )
         print("final result:", result)
+

tests/test_markdown.py

@@ -43,8 +43,10 @@ def validate_path():
 
 validate_path()  # validate path so you can run from base directory
 from toolbox import markdown_convertion
-
+from shared_utils.advanced_markdown_format import markdown_convertion_for_file
-html = markdown_convertion(md)
+with open("gpt_log/default_user/shared/2024-04-22-01-27-43.zip.extract/translated_markdown.md", "r", encoding="utf-8") as f:
+    md = f.read()
+html = markdown_convertion_for_file(md)
 # print(html)
 with open("test.html", "w", encoding="utf-8") as f:
     f.write(html)

tests/test_plugins.py

@@ -18,14 +18,18 @@ validate_path()  # 返回项目根路径
 if __name__ == "__main__":
     from tests.test_utils import plugin_test
 
+    plugin_test(plugin='crazy_functions.Internet_GPT->连接网络回答问题', main_input="谁是应急食品？")
+
     # plugin_test(plugin='crazy_functions.函数动态生成->函数动态生成', main_input='交换图像的蓝色通道和红色通道', advanced_arg={"file_path_arg": "./build/ants.jpg"})
 
-    # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2307.07522")
+    # plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2307.07522")
 
-    plugin_test(
+    # plugin_test(plugin='crazy_functions.PDF_Translate->批量翻译PDF文档', main_input='build/pdf/t1.pdf')
-        plugin="crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF",
+
-        main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix",
+    # plugin_test(
-    )
+    #     plugin="crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF",
+    #     main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix",
+    # )
 
     # plugin_test(plugin='crazy_functions.虚空终端->虚空终端', main_input='修改api-key为sk-jhoejriotherjep')
 
@@ -41,9 +45,9 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.Latex全文润色->Latex英文润色', main_input="crazy_functions/test_project/latex/attention")
 
-    # plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown中译英', main_input="README.md")
+    # plugin_test(plugin='crazy_functions.Markdown_Translate->Markdown中译英', main_input="README.md")
 
-    # plugin_test(plugin='crazy_functions.批量翻译PDF文档_多线程->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf')
+    # plugin_test(plugin='crazy_functions.PDF_Translate->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf')
 
     # plugin_test(plugin='crazy_functions.谷歌检索小助手->谷歌检索小助手', main_input="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=")
 
@@ -58,7 +62,7 @@ if __name__ == "__main__":
     # plugin_test(plugin='crazy_functions.数学动画生成manim->动画生成', main_input="A ball split into 2, and then split into 4, and finally split into 8.")
 
     # for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
-    #     plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang})
+    #     plugin_test(plugin='crazy_functions.Markdown_Translate->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang})
 
     # plugin_test(plugin='crazy_functions.知识库文件注入->知识库文件注入', main_input="./")
 
@@ -66,7 +70,7 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？")
 
-    # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2210.03629")
+    # plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2210.03629")
 
     # advanced_arg = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''" }
     # plugin_test(plugin='crazy_functions.chatglm微调工具->微调数据集生成', main_input='build/dev.json', advanced_arg=advanced_arg)

themes/base64.mjs

@@ -1,296 +1 @@
-/**
+// we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0
- *  base64.ts
- *
- *  Licensed under the BSD 3-Clause License.
- *    http://opensource.org/licenses/BSD-3-Clause
- *
- *  References:
- *    http://en.wikipedia.org/wiki/Base64
- *
- * @author Dan Kogai (https://github.com/dankogai)
- */
-const version = '3.7.2';
-/**
- * @deprecated use lowercase `version`.
- */
-const VERSION = version;
-const _hasatob = typeof atob === 'function';
-const _hasbtoa = typeof btoa === 'function';
-const _hasBuffer = typeof Buffer === 'function';
-const _TD = typeof TextDecoder === 'function' ? new TextDecoder() : undefined;
-const _TE = typeof TextEncoder === 'function' ? new TextEncoder() : undefined;
-const b64ch = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=';
-const b64chs = Array.prototype.slice.call(b64ch);
-const b64tab = ((a) => {
-    let tab = {};
-    a.forEach((c, i) => tab[c] = i);
-    return tab;
-})(b64chs);
-const b64re = /^(?:[A-Za-z\d+\/]{4})*?(?:[A-Za-z\d+\/]{2}(?:==)?|[A-Za-z\d+\/]{3}=?)?$/;
-const _fromCC = String.fromCharCode.bind(String);
-const _U8Afrom = typeof Uint8Array.from === 'function'
-    ? Uint8Array.from.bind(Uint8Array)
-    : (it, fn = (x) => x) => new Uint8Array(Array.prototype.slice.call(it, 0).map(fn));
-const _mkUriSafe = (src) => src
-    .replace(/=/g, '').replace(/[+\/]/g, (m0) => m0 == '+' ? '-' : '_');
-const _tidyB64 = (s) => s.replace(/[^A-Za-z0-9\+\/]/g, '');
-/**
- * polyfill version of `btoa`
- */
-const btoaPolyfill = (bin) => {
-    // console.log('polyfilled');
-    let u32, c0, c1, c2, asc = '';
-    const pad = bin.length % 3;
-    for (let i = 0; i < bin.length;) {
-        if ((c0 = bin.charCodeAt(i++)) > 255 ||
-            (c1 = bin.charCodeAt(i++)) > 255 ||
-            (c2 = bin.charCodeAt(i++)) > 255)
-            throw new TypeError('invalid character found');
-        u32 = (c0 << 16) | (c1 << 8) | c2;
-        asc += b64chs[u32 >> 18 & 63]
-            + b64chs[u32 >> 12 & 63]
-            + b64chs[u32 >> 6 & 63]
-            + b64chs[u32 & 63];
-    }
-    return pad ? asc.slice(0, pad - 3) + "===".substring(pad) : asc;
-};
-/**
- * does what `window.btoa` of web browsers do.
- * @param {String} bin binary string
- * @returns {string} Base64-encoded string
- */
-const _btoa = _hasbtoa ? (bin) => btoa(bin)
-    : _hasBuffer ? (bin) => Buffer.from(bin, 'binary').toString('base64')
-        : btoaPolyfill;
-const _fromUint8Array = _hasBuffer
-    ? (u8a) => Buffer.from(u8a).toString('base64')
-    : (u8a) => {
-        // cf. https://stackoverflow.com/questions/12710001/how-to-convert-uint8-array-to-base64-encoded-string/12713326#12713326
-        const maxargs = 0x1000;
-        let strs = [];
-        for (let i = 0, l = u8a.length; i < l; i += maxargs) {
-            strs.push(_fromCC.apply(null, u8a.subarray(i, i + maxargs)));
-        }
-        return _btoa(strs.join(''));
-    };
-/**
- * converts a Uint8Array to a Base64 string.
- * @param {boolean} [urlsafe] URL-and-filename-safe a la RFC4648 §5
- * @returns {string} Base64 string
- */
-const fromUint8Array = (u8a, urlsafe = false) => urlsafe ? _mkUriSafe(_fromUint8Array(u8a)) : _fromUint8Array(u8a);
-// This trick is found broken https://github.com/dankogai/js-base64/issues/130
-// const utob = (src: string) => unescape(encodeURIComponent(src));
-// reverting good old fationed regexp
-const cb_utob = (c) => {
-    if (c.length < 2) {
-        var cc = c.charCodeAt(0);
-        return cc < 0x80 ? c
-            : cc < 0x800 ? (_fromCC(0xc0 | (cc >>> 6))
-                + _fromCC(0x80 | (cc & 0x3f)))
-                : (_fromCC(0xe0 | ((cc >>> 12) & 0x0f))
-                    + _fromCC(0x80 | ((cc >>> 6) & 0x3f))
-                    + _fromCC(0x80 | (cc & 0x3f)));
-    }
-    else {
-        var cc = 0x10000
-            + (c.charCodeAt(0) - 0xD800) * 0x400
-            + (c.charCodeAt(1) - 0xDC00);
-        return (_fromCC(0xf0 | ((cc >>> 18) & 0x07))
-            + _fromCC(0x80 | ((cc >>> 12) & 0x3f))
-            + _fromCC(0x80 | ((cc >>> 6) & 0x3f))
-            + _fromCC(0x80 | (cc & 0x3f)));
-    }
-};
-const re_utob = /[\uD800-\uDBFF][\uDC00-\uDFFFF]|[^\x00-\x7F]/g;
-/**
- * @deprecated should have been internal use only.
- * @param {string} src UTF-8 string
- * @returns {string} UTF-16 string
- */
-const utob = (u) => u.replace(re_utob, cb_utob);
-//
-const _encode = _hasBuffer
-    ? (s) => Buffer.from(s, 'utf8').toString('base64')
-    : _TE
-        ? (s) => _fromUint8Array(_TE.encode(s))
-        : (s) => _btoa(utob(s));
-/**
- * converts a UTF-8-encoded string to a Base64 string.
- * @param {boolean} [urlsafe] if `true` make the result URL-safe
- * @returns {string} Base64 string
- */
-const encode = (src, urlsafe = false) => urlsafe
-    ? _mkUriSafe(_encode(src))
-    : _encode(src);
-/**
- * converts a UTF-8-encoded string to URL-safe Base64 RFC4648 §5.
- * @returns {string} Base64 string
- */
-const encodeURI = (src) => encode(src, true);
-// This trick is found broken https://github.com/dankogai/js-base64/issues/130
-// const btou = (src: string) => decodeURIComponent(escape(src));
-// reverting good old fationed regexp
-const re_btou = /[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF]{2}|[\xF0-\xF7][\x80-\xBF]{3}/g;
-const cb_btou = (cccc) => {
-    switch (cccc.length) {
-        case 4:
-            var cp = ((0x07 & cccc.charCodeAt(0)) << 18)
-                | ((0x3f & cccc.charCodeAt(1)) << 12)
-                | ((0x3f & cccc.charCodeAt(2)) << 6)
-                | (0x3f & cccc.charCodeAt(3)), offset = cp - 0x10000;
-            return (_fromCC((offset >>> 10) + 0xD800)
-                + _fromCC((offset & 0x3FF) + 0xDC00));
-        case 3:
-            return _fromCC(((0x0f & cccc.charCodeAt(0)) << 12)
-                | ((0x3f & cccc.charCodeAt(1)) << 6)
-                | (0x3f & cccc.charCodeAt(2)));
-        default:
-            return _fromCC(((0x1f & cccc.charCodeAt(0)) << 6)
-                | (0x3f & cccc.charCodeAt(1)));
-    }
-};
-/**
- * @deprecated should have been internal use only.
- * @param {string} src UTF-16 string
- * @returns {string} UTF-8 string
- */
-const btou = (b) => b.replace(re_btou, cb_btou);
-/**
- * polyfill version of `atob`
- */
-const atobPolyfill = (asc) => {
-    // console.log('polyfilled');
-    asc = asc.replace(/\s+/g, '');
-    if (!b64re.test(asc))
-        throw new TypeError('malformed base64.');
-    asc += '=='.slice(2 - (asc.length & 3));
-    let u24, bin = '', r1, r2;
-    for (let i = 0; i < asc.length;) {
-        u24 = b64tab[asc.charAt(i++)] << 18
-            | b64tab[asc.charAt(i++)] << 12
-            | (r1 = b64tab[asc.charAt(i++)]) << 6
-            | (r2 = b64tab[asc.charAt(i++)]);
-        bin += r1 === 64 ? _fromCC(u24 >> 16 & 255)
-            : r2 === 64 ? _fromCC(u24 >> 16 & 255, u24 >> 8 & 255)
-                : _fromCC(u24 >> 16 & 255, u24 >> 8 & 255, u24 & 255);
-    }
-    return bin;
-};
-/**
- * does what `window.atob` of web browsers do.
- * @param {String} asc Base64-encoded string
- * @returns {string} binary string
- */
-const _atob = _hasatob ? (asc) => atob(_tidyB64(asc))
-    : _hasBuffer ? (asc) => Buffer.from(asc, 'base64').toString('binary')
-        : atobPolyfill;
-//
-const _toUint8Array = _hasBuffer
-    ? (a) => _U8Afrom(Buffer.from(a, 'base64'))
-    : (a) => _U8Afrom(_atob(a), c => c.charCodeAt(0));
-/**
- * converts a Base64 string to a Uint8Array.
- */
-const toUint8Array = (a) => _toUint8Array(_unURI(a));
-//
-const _decode = _hasBuffer
-    ? (a) => Buffer.from(a, 'base64').toString('utf8')
-    : _TD
-        ? (a) => _TD.decode(_toUint8Array(a))
-        : (a) => btou(_atob(a));
-const _unURI = (a) => _tidyB64(a.replace(/[-_]/g, (m0) => m0 == '-' ? '+' : '/'));
-/**
- * converts a Base64 string to a UTF-8 string.
- * @param {String} src Base64 string.  Both normal and URL-safe are supported
- * @returns {string} UTF-8 string
- */
-const decode = (src) => _decode(_unURI(src));
-/**
- * check if a value is a valid Base64 string
- * @param {String} src a value to check
-  */
-const isValid = (src) => {
-    if (typeof src !== 'string')
-        return false;
-    const s = src.replace(/\s+/g, '').replace(/={0,2}$/, '');
-    return !/[^\s0-9a-zA-Z\+/]/.test(s) || !/[^\s0-9a-zA-Z\-_]/.test(s);
-};
-//
-const _noEnum = (v) => {
-    return {
-        value: v, enumerable: false, writable: true, configurable: true
-    };
-};
-/**
- * extend String.prototype with relevant methods
- */
-const extendString = function () {
-    const _add = (name, body) => Object.defineProperty(String.prototype, name, _noEnum(body));
-    _add('fromBase64', function () { return decode(this); });
-    _add('toBase64', function (urlsafe) { return encode(this, urlsafe); });
-    _add('toBase64URI', function () { return encode(this, true); });
-    _add('toBase64URL', function () { return encode(this, true); });
-    _add('toUint8Array', function () { return toUint8Array(this); });
-};
-/**
- * extend Uint8Array.prototype with relevant methods
- */
-const extendUint8Array = function () {
-    const _add = (name, body) => Object.defineProperty(Uint8Array.prototype, name, _noEnum(body));
-    _add('toBase64', function (urlsafe) { return fromUint8Array(this, urlsafe); });
-    _add('toBase64URI', function () { return fromUint8Array(this, true); });
-    _add('toBase64URL', function () { return fromUint8Array(this, true); });
-};
-/**
- * extend Builtin prototypes with relevant methods
- */
-const extendBuiltins = () => {
-    extendString();
-    extendUint8Array();
-};
-const gBase64 = {
-    version: version,
-    VERSION: VERSION,
-    atob: _atob,
-    atobPolyfill: atobPolyfill,
-    btoa: _btoa,
-    btoaPolyfill: btoaPolyfill,
-    fromBase64: decode,
-    toBase64: encode,
-    encode: encode,
-    encodeURI: encodeURI,
-    encodeURL: encodeURI,
-    utob: utob,
-    btou: btou,
-    decode: decode,
-    isValid: isValid,
-    fromUint8Array: fromUint8Array,
-    toUint8Array: toUint8Array,
-    extendString: extendString,
-    extendUint8Array: extendUint8Array,
-    extendBuiltins: extendBuiltins,
-};
-// makecjs:CUT //
-export { version };
-export { VERSION };
-export { _atob as atob };
-export { atobPolyfill };
-export { _btoa as btoa };
-export { btoaPolyfill };
-export { decode as fromBase64 };
-export { encode as toBase64 };
-export { utob };
-export { encode };
-export { encodeURI };
-export { encodeURI as encodeURL };
-export { btou };
-export { decode };
-export { isValid };
-export { fromUint8Array };
-export { toUint8Array };
-export { extendString };
-export { extendUint8Array };
-export { extendBuiltins };
-// and finally,
-export { gBase64 as Base64 };

themes/common.css

@@ -1,3 +1,9 @@
+#plugin_arg_menu {
+    transform: translate(-50%, -50%);
+    border: dashed;
+}
+
+
 /* hide remove all button */
 .remove-all.svelte-aqlk7e.svelte-aqlk7e.svelte-aqlk7e {
     visibility: hidden;
@@ -38,6 +44,7 @@
     left: calc(100% + 3px);
     top: 0;
     display: flex;
+    flex-direction: column;
     justify-content: space-between;
 }
 /* .message-btn-row-leading, .message-btn-row-trailing {
@@ -59,6 +66,7 @@
 
 /* Scrollbar Width */
 ::-webkit-scrollbar {
+    height: 12px;
     width: 12px;
 }
 
@@ -107,6 +115,7 @@
     border-width: thin;
     user-select: none;
     padding-left: 2%;
+    text-align: center;
 }
 
 .floating-component #input-panel2 {
@@ -116,3 +125,20 @@
     border-width: thin;
     border-top-width: 0;
 }
+
+.floating-component #plugin_arg_panel {
+    border-top-left-radius: 0px;
+    border-top-right-radius: 0px;
+    border: solid;
+    border-width: thin;
+    border-top-width: 0;
+}
+
+.floating-component #edit-panel {
+    border-top-left-radius: 0px;
+    border-top-right-radius: 0px;
+    border: solid;
+    border-width: thin;
+    border-top-width: 0;
+}
+

themes/common.py

@@ -1,21 +1,44 @@
 from toolbox import get_conf
 CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf("CODE_HIGHLIGHT", "ADD_WAIFU", "LAYOUT")
 
+def minimize_js(common_js_path):
+    try:
+        import rjsmin, hashlib, glob, os
+        # clean up old minimized js files, matching `common_js_path + '.min.*'`
+        for old_min_js in glob.glob(common_js_path + '.min.*.js'):
+            os.remove(old_min_js)
+        # use rjsmin to minimize `common_js_path`
+        c_jsmin = rjsmin.jsmin
+        with open(common_js_path, "r") as f:
+            js_content = f.read()
+        minimized_js_content = c_jsmin(js_content)
+        # compute sha256 hash of minimized js content
+        sha_hash = hashlib.sha256(minimized_js_content.encode()).hexdigest()[:8]
+        minimized_js_path = common_js_path + '.min.' + sha_hash + '.js'
+        # save to minimized js file
+        with open(minimized_js_path, "w") as f:
+            f.write(minimized_js_content)
+        # return minimized js file path
+        return minimized_js_path
+    except:
+        return common_js_path
+
 def get_common_html_javascript_code():
     js = "\n"
+    common_js_path = "themes/common.js"
+    minimized_js_path = minimize_js(common_js_path)
     for jsf in [
-        "file=themes/common.js",
+        f"file={minimized_js_path}",
-        "file=themes/mermaid.min.js",
-        "file=themes/mermaid_loader.js",
     ]:
         js += f"""<script src="{jsf}"></script>\n"""
 
     # 添加Live2D
     if ADD_WAIFU:
         for jsf in [
-            "file=docs/waifu_plugin/jquery.min.js",
+            "file=themes/waifu_plugin/jquery.min.js",
-            "file=docs/waifu_plugin/jquery-ui.min.js",
+            "file=themes/waifu_plugin/jquery-ui.min.js",
-            "file=docs/waifu_plugin/autoload.js",
         ]:
             js += f"""<script src="{jsf}"></script>\n"""
+    else:
+        js += """<script>window.loadLive2D = function(){};</script>\n"""
     return js

themes/gradios.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import gradio as gr
 from toolbox import get_conf, ProxyNetworkActivate
@@ -10,12 +9,15 @@ theme_dir = os.path.dirname(__file__)
 def dynamic_set_theme(THEME):
     set_theme = gr.themes.ThemeClass()
     with ProxyNetworkActivate("Download_Gradio_Theme"):
-        logging.info("正在下载Gradio主题，请稍等。")
+        print("正在下载Gradio主题，请稍等。")
+        try:
             if THEME.startswith("Huggingface-"):
                 THEME = THEME.lstrip("Huggingface-")
             if THEME.startswith("huggingface-"):
                 THEME = THEME.lstrip("huggingface-")
             set_theme = set_theme.from_hub(THEME.lower())
+        except:
+            print("下载Gradio主题时出现异常。")
     return set_theme
 
 
@@ -23,13 +25,16 @@ def adjust_theme():
     try:
         set_theme = gr.themes.ThemeClass()
         with ProxyNetworkActivate("Download_Gradio_Theme"):
-            logging.info("正在下载Gradio主题，请稍等。")
+            print("正在下载Gradio主题，请稍等。")
+            try:
                 THEME = get_conf("THEME")
                 if THEME.startswith("Huggingface-"):
                     THEME = THEME.lstrip("Huggingface-")
                 if THEME.startswith("huggingface-"):
                     THEME = THEME.lstrip("huggingface-")
                 set_theme = set_theme.from_hub(THEME.lower())
+            except:
+                print("下载Gradio主题时出现异常。")
 
         from themes.common import get_common_html_javascript_code
         js = get_common_html_javascript_code()
@@ -49,9 +54,7 @@ def adjust_theme():
         )
     except Exception:
         set_theme = None
-        from toolbox import trimmed_format_exc
+        print("gradio版本较旧, 不能自定义字体和颜色。")
-
-        logging.error("gradio版本较旧, 不能自定义字体和颜色:", trimmed_format_exc())
     return set_theme
 
 

themes/gui_advanced_plugin_class.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import json
+from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
+
+def define_gui_advanced_plugin_class(plugins):
+    # 定义新一代插件的高级参数区
+    with gr.Floating(init_x="50%", init_y="50%", visible=False, width="30%", drag="top", elem_id="plugin_arg_menu"):
+        with gr.Accordion("选择插件参数", open=True, elem_id="plugin_arg_panel"):
+            for u in range(8):
+                with gr.Row():
+                    gr.Textbox(show_label=True, label="T1", placeholder="请输入", lines=1, visible=False, elem_id=f"plugin_arg_txt_{u}").style(container=False)
+            for u in range(8):
+                with gr.Row(): # PLUGIN_ARG_MENU
+                    gr.Dropdown(label="T1", value="请选择", choices=[], visible=True, elem_id=f"plugin_arg_drop_{u}", interactive=True)
+
+            with gr.Row():
+                # 这个隐藏textbox负责装入当前弹出插件的属性
+                gr.Textbox(show_label=False, placeholder="请输入", lines=1, visible=False,
+                        elem_id=f"invisible_current_pop_up_plugin_arg").style(container=False)
+                usr_confirmed_arg = gr.Textbox(show_label=False, placeholder="请输入", lines=1, visible=False,
+                        elem_id=f"invisible_current_pop_up_plugin_arg_final").style(container=False)
+
+                arg_confirm_btn = gr.Button("确认参数并执行", variant="stop")
+                arg_confirm_btn.style(size="sm")
+
+                arg_cancel_btn = gr.Button("取消", variant="stop")
+                arg_cancel_btn.click(None, None, None, _js="""()=>close_current_pop_up_plugin()""")
+                arg_cancel_btn.style(size="sm")
+
+                arg_confirm_btn.click(None, None, None, _js="""()=>execute_current_pop_up_plugin()""")
+                invisible_callback_btn_for_plugin_exe = gr.Button(r"未选定任何插件", variant="secondary", visible=False, elem_id="invisible_callback_btn_for_plugin_exe").style(size="sm")
+                # 随变按钮的回调函数注册
+                def route_switchy_bt_with_arg(request: gr.Request, input_order, *arg):
+                    arguments = {k:v for k,v in zip(input_order, arg)}
+                    which_plugin = arguments.pop('new_plugin_callback')
+                    if which_plugin in [r"未选定任何插件"]: return
+                    usr_confirmed_arg = arguments.pop('usr_confirmed_arg')
+                    arg_confirm: dict = {}
+                    usr_confirmed_arg_dict = json.loads(usr_confirmed_arg)
+                    for arg_name in usr_confirmed_arg_dict:
+                        arg_confirm.update({arg_name: str(usr_confirmed_arg_dict[arg_name]['user_confirmed_value'])})
+                    plugin_obj = plugins[which_plugin]["Class"]
+                    arguments['plugin_advanced_arg'] = arg_confirm
+                    if arg_confirm.get('main_input', None) is not None:
+                        arguments['txt'] = arg_confirm['main_input']
+                    yield from ArgsGeneralWrapper(plugin_obj.execute)(request, *arguments.values())
+    return invisible_callback_btn_for_plugin_exe, route_switchy_bt_with_arg, usr_confirmed_arg
+