renew pip url

.gitignore

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

Dockerfile

@@ -12,16 +12,11 @@ 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 （以下两行，可以删除）
+# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下三行，可以删除）
 COPY requirements.txt ./
 RUN pip3 install -r requirements.txt
 

README.md

@@ -1,8 +1,20 @@
-> [!IMPORTANT]
-> 2024.6.1: 版本3.80加入插件二级菜单功能（详见wiki）  
-> 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)  
-> 2024.3.11: 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。
+---
+title: GPT-Academic
+emoji: 😻
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+sdk_version: 3.32.0
+app_file: app.py
+pinned: false
+---
+
+# ChatGPT 学术优化
+> **Note**
+> 
+> 2023.11.12: 某些依赖包尚不兼容python 3.12，推荐python 3.11。
+> 
+> 2023.12.26: 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 +79,7 @@ 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.English.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_EN.md)了吗？就是出自他的手笔
 [PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
 [Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
 Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
@@ -87,10 +99,6 @@ 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">
@@ -257,7 +265,8 @@ 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）
 
-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：
+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。）
+例如
 
 ```python
 "超级英译中": {

app.py

@@ -0,0 +1,412 @@
+import os; 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),
+感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
+</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki),
+如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
+</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
+</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
+</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
+</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端
+</br></br>如何保存对话: 点击保存当前的对话按钮
+</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 main():
+    import subprocess, sys
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'https://public.agent-matrix.com/publish/gradio-3.32.8-py3-none-any.whl'])
+    import gradio as gr
+    if gr.__version__ not in ['3.32.8']:
+        raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
+    from request_llms.bridge_all import predict
+    from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
+    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
+    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, 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')
+    INIT_SYS_PROMPT = get_conf('INIT_SYS_PROMPT')
+
+    # 如果WEB_PORT是-1, 则随机选取WEB端口
+    PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
+    from check_proxy import get_current_version
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, 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_toggle_darkmode, js_code_for_persistent_cookie_init
+    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
+    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）
+    import logging, uuid
+    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
+    functional = get_core_functions()
+
+    # 高级函数插件
+    from crazy_functional import get_crazy_functions
+    DEFAULT_FN_GROUPS = get_conf('DEFAULT_FN_GROUPS')
+    plugins = get_crazy_functions()
+    all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
+    match_group = lambda tags, groups: any([g in groups for g in tags.split('|')])
+
+    # 处理markdown文本格式的转变
+    gr.Chatbot.postprocess = format_io
+
+    # 做一些外观色彩上的调整
+    set_theme = adjust_theme()
+
+    # 代理与自动更新
+    from check_proxy import check_proxy, auto_update, warm_up_modules
+    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, min_width=400)
+    if LAYOUT == "TOP-DOWN":
+        gr_L1 = lambda: DummyWith()
+        gr_L2 = lambda scale, elem_id: gr.Row()
+        CHATBOT_HEIGHT /= 2
+
+    cancel_handles = []
+    customize_btns = {}
+    predefined_btns = {}
+    with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
+        gr.HTML(title_html)
+        gr.HTML('''<center><a href="https://huggingface.co/spaces/qingxu98/gpt-academic?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>请您打开此页面后务必点击上方的“复制空间”（Duplicate Space）按钮！<font color="#FF00FF">使用时，先在输入框填入API-KEY然后回车。</font><br/>切忌在“复制空间”（Duplicate Space）之前填入API_KEY或进行提问，否则您的API_KEY将极可能被空间所有者攫取！<br/>支持任意数量的OpenAI的密钥和API2D的密钥共存，例如输入"OpenAI密钥1,API2D密钥2"，然后提交，即可同时使用两种模型接口。</center>''')
+        secret_css, dark_mode, py_pickle_cookie = gr.Textbox(visible=False), gr.Textbox(DARK_MODE, visible=False), gr.Textbox(visible=False)
+        cookies = gr.State(load_chat_cookies())
+        with gr_L1():
+            with gr_L2(scale=2, elem_id="gpt-chat"):
+                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([])
+            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():
+                        txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥，输入多个密钥时，用英文逗号间隔。支持多个OpenAI密钥共存。").style(container=False)
+                    with gr.Row():
+                        submitBtn = gr.Button("提交", elem_id="elem_submit", variant="primary")
+                    with gr.Row():
+                        resetBtn = gr.Button("重置", elem_id="elem_reset", variant="secondary"); resetBtn.style(size="sm")
+                        stopBtn = gr.Button("停止", elem_id="elem_stop", variant="secondary"); stopBtn.style(size="sm")
+                        clearBtn = gr.Button("清除", elem_id="elem_clear", variant="secondary", visible=False); clearBtn.style(size="sm")
+                    if ENABLE_AUDIO:
+                        with gr.Row():
+                            audio_mic = gr.Audio(source="microphone", type="numpy", elem_id="elem_audio", streaming=True, show_label=False).style(container=False)
+                    with gr.Row():
+                        status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}", elem_id="state-panel")
+
+                with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
+                    with gr.Row():
+                        for k in range(NUM_CUSTOM_BASIC_BTN):
+                            customize_btn = gr.Button("自定义按钮" + str(k+1), visible=False, variant="secondary", info_str=f'基础功能区: 自定义按钮')
+                            customize_btn.style(size="sm")
+                            customize_btns.update({"自定义按钮" + str(k+1): customize_btn})
+                        for k in functional:
+                            if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
+                            variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
+                            functional[k]["Button"] = gr.Button(k, variant=variant, info_str=f'基础功能区: {k}')
+                            functional[k]["Button"].style(size="sm")
+                            predefined_btns.update({k: functional[k]["Button"]})
+                with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
+                    with gr.Row():
+                        gr.Markdown("插件可读取“输入区”文本/路径作为参数（上传文件自动修正路径）")
+                    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)
+                    with gr.Row():
+                        for k, plugin in plugins.items():
+                            if not plugin.get("AsButton", True): continue
+                            visible = True if match_group(plugin['Group'], DEFAULT_FN_GROUPS) else False
+                            variant = plugins[k]["Color"] if "Color" in plugin else "secondary"
+                            info = plugins[k].get("Info", k)
+                            plugin['Button'] = plugins[k]['Button'] = gr.Button(k, variant=variant,
+                                visible=visible, info_str=f'函数插件区: {info}').style(size="sm")
+                    with gr.Row():
+                        with gr.Accordion("更多函数插件", open=True):
+                            dropdown_fn_list = []
+                            for k, plugin in plugins.items():
+                                if not match_group(plugin['Group'], DEFAULT_FN_GROUPS): continue
+                                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)
+                            with gr.Row():
+                                plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False,
+                                                                 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")
+                    with gr.Row():
+                        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")
+
+        with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden", elem_id="tooltip"):
+            with gr.Row():
+                with gr.Tab("上传文件", elem_id="interact-panel"):
+                    gr.Markdown("请上传本地文件/压缩包供“函数插件区”功能调用。请注意: 上传文件后会自动把输入区修改为相应路径。")
+                    file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload_float")
+
+                with gr.Tab("更换模型", elem_id="interact-panel"):
+                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
+                    top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
+                    temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
+                    max_length_sl = gr.Slider(minimum=256, maximum=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)
+                    opt = ["自定义菜单"]
+                    value=[]
+                    if ADD_WAIFU: opt += ["添加Live2D形象"]; value += ["添加Live2D形象"]
+                    checkboxes_2 = gr.CheckboxGroup(opt, value=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", elem_id="elem_clear2", 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_clean   = gr.Button("恢复默认", variant="primary"); basic_fn_clean.style(size="sm")
+                        def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
+                            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({py_pickle_cookie: persistent_cookie_})             # write persistent cookie
+                            return ret
+
+                        # update btn
+                        h = basic_fn_confirm.click(assign_btn, [py_pickle_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix],
+                                                   [py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h.then(None, [py_pickle_cookie], None, _js="""(py_pickle_cookie)=>{setCookie("py_pickle_cookie", py_pickle_cookie, 365);}""")
+                        # clean up btn
+                        h2 = basic_fn_clean.click(assign_btn, [py_pickle_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix, gr.State(True)],
+                                                   [py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h2.then(None, [py_pickle_cookie], None, _js="""(py_pickle_cookie)=>{setCookie("py_pickle_cookie", py_pickle_cookie, 365);}""")
+
+                        def persistent_cookie_reload(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
+
+        # 功能区显示开关与功能区的互动
+        def fn_area_visibility(a):
+            ret = {}
+            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({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, 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):
+            ret = {}
+            ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
+            return ret
+        checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
+        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]
+        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
+        cancel_handles.append(txt.submit(**predict_args))
+        cancel_handles.append(txt2.submit(**predict_args))
+        cancel_handles.append(submitBtn.click(**predict_args))
+        cancel_handles.append(submitBtn2.click(**predict_args))
+        resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset)   # 先在前端快速清除chatbot&status
+        resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset)  # 先在前端快速清除chatbot&status
+        resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])    # 再在后端清除history
+        resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])   # 再在后端清除history
+        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(None, None, [txt, txt2], _js=js_code_clear)
+            submitBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
+            txt.submit(None, None, [txt, txt2], _js=js_code_clear)
+            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
+            click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
+            cancel_handles.append(click_handle)
+        for btn in customize_btns.values():
+            click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
+            cancel_handles.append(click_handle)
+        # 文件上传区，接收文件后与chatbot的互动
+        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None,   _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
+        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 not plugins[k].get("AsButton", True): continue
+            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])
+            cancel_handles.append(click_handle)
+        # 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"
+            info = plugins[k].get("Info", k)
+            ret = {switchy_bt: gr.update(value=k, variant=variant, info_str=f'函数插件区: {info}')}
+            if plugins[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
+                ret.update({plugin_advanced_arg: gr.update(visible=True,  label=f"插件[{k}]的高级参数说明：" + plugins[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
+            else:
+                ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")})
+            return ret
+        dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] )
+
+        def on_md_dropdown_changed(k):
+            return {chatbot: gr.update(label="当前模型："+k)}
+        md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
+
+        def on_theme_dropdown_changed(theme, secret_css):
+            adjust_theme, css_part1, _, adjust_dynamic_theme = load_dynamic_theme(theme)
+            if adjust_dynamic_theme:
+                css_part2 = adjust_dynamic_theme._get_theme_css()
+            else:
+                css_part2 = adjust_theme()._get_theme_css()
+            return css_part2 + css_part1
+
+        theme_handle = theme_dropdown.select(on_theme_dropdown_changed, [theme_dropdown, secret_css], [secret_css])
+        theme_handle.then(
+            None,
+            [secret_css],
+            None,
+            _js=js_code_for_css_changing
+        )
+        # 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
+            yield from ArgsGeneralWrapper(plugins[k]["Function"])(request, *args, **kwargs)
+        click_handle = switchy_bt.click(route,[switchy_bt, *input_combo], output_combo)
+        click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
+        cancel_handles.append(click_handle)
+        # 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)
+        plugins_as_btn = {name:plugin for name, plugin in plugins.items() if plugin.get('Button', None)}
+        def on_group_change(group_list):
+            btn_list = []
+            fns_list = []
+            if not group_list: # 处理特殊情况：没有选择任何插件组
+                return [*[plugin['Button'].update(visible=False) for _, plugin in plugins_as_btn.items()], gr.Dropdown.update(choices=[])]
+            for k, plugin in plugins.items():
+                if plugin.get("AsButton", True):
+                    btn_list.append(plugin['Button'].update(visible=match_group(plugin['Group'], group_list))) # 刷新按钮
+                    if 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
+                elif match_group(plugin['Group'], group_list): fns_list.append(k) # 刷新下拉列表
+            return [*btn_list, gr.Dropdown.update(choices=fns_list)]
+        plugin_group_sel.select(fn=on_group_change, inputs=[plugin_group_sel], outputs=[*[plugin['Button'] for name, plugin in plugins_as_btn.items()], dropdown])
+        if ENABLE_AUDIO:
+            from crazy_functions.live_audio.audio_io import RealtimeAudioDistribution
+            rad = RealtimeAudioDistribution()
+            def deal_audio(audio, cookies):
+                rad.feed(cookies['uuid'].hex, audio)
+            audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
+
+
+        demo.load(init_cookie, inputs=[cookies], outputs=[cookies])
+        demo.load(persistent_cookie_reload, inputs = [py_pickle_cookie, cookies],
+            outputs = [py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
+        demo.load(None, inputs=[dark_mode], outputs=None, _js="""(dark_mode)=>{apply_cookie_for_checkbox(dark_mode);}""")    # 配置暗色主题或亮色主题
+        demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
+
+    # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
+    def run_delayed_tasks():
+        import threading, webbrowser, time
+        print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
+        if DARK_MODE:   print(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
+        else:           print(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
+
+        def auto_updates(): time.sleep(0); auto_update()
+        def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
+        def warm_up_mods(): time.sleep(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
+
+    run_delayed_tasks()
+    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
+
+
+    # 如果需要在二级路径下运行
+    # CUSTOM_PATH = get_conf('CUSTOM_PATH')
+    # if CUSTOM_PATH != "/":
+    #     from toolbox import run_gradio_in_subpath
+    #     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
+    # else:
+    #     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png",
+    #                 blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
+
+if __name__ == "__main__":
+    main()

check_proxy.py

@@ -1,44 +1,33 @@
 
-def check_proxy(proxies, return_ip=False):
+def check_proxy(proxies):
     import requests
     proxies_https = proxies['https'] if proxies is not None else '无'
-    ip = None
     try:
         response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
         data = response.json()
         if 'country_name' in data:
             country = data['country_name']
             result = f"代理配置 {proxies_https}, 代理所在地：{country}"
-            if 'ip' in data: ip = data['ip']
         elif 'error' in data:
-            alternative, ip = _check_with_backup_source(proxies)
+            alternative = _check_with_backup_source(proxies)
             if alternative is None:
                 result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
             else:
                 result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
         else:
             result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
-        if not return_ip:
         print(result)
         return result
-        else:
-            return ip
     except:
         result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
-        if not return_ip:
         print(result)
         return result
-        else:
-            return ip
 
 def _check_with_backup_source(proxies):
     import random, string, requests
     random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
-    try:
-        res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()
-        return res_json['dns']['geo'], res_json['dns']['ip']
-    except:
-        return None, None
+    try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
+    except: return None
 
 def backup_and_download(current_version, remote_version):
     """
@@ -58,7 +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.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
+    except: r = requests.get('https://public.gpt-academic.top/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)
@@ -82,7 +71,7 @@ def patch_and_restart(path):
     import sys
     import time
     import glob
-    from shared_utils.colorful import print亮黄, print亮绿, print亮红
+    from colorful import print亮黄, print亮绿, print亮红
     # if not using config_private, move origin config.py as config_private.py
     if not os.path.exists('config_private.py'):
         print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
@@ -124,7 +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.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
+        except: response = requests.get("https://public.gpt-academic.top/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"]:
@@ -135,7 +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 shared_utils.colorful import print亮黄
+            from colorful import print亮黄
             print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
             print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
             user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')

config.py

@@ -11,6 +11,10 @@
 API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
 
 
+# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
+API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
+
+
 # [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
 USE_PROXY = False
 if USE_PROXY:
@@ -30,40 +34,11 @@ if USE_PROXY:
 else:
     proxies = None
 
-# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
-                    "gpt-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", "sparkv4",
-#   "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 ---------------
+# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
 
 # 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
 # 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
+# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
 API_URL_REDIRECT = {}
 
 
@@ -74,7 +49,7 @@ DEFAULT_WORKER_NUM = 3
 
 # 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
 # 更多主题, 请查阅Gradio主题商店: https://huggingface.co/spaces/gradio/theme-gallery 可选 ["Gstaff/Xkcd", "NoCrypt/Miku", ...]
-THEME = "Default"
+THEME = "Chuanhu-Small-and-Beautiful"
 AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
 
 
@@ -95,7 +70,7 @@ LAYOUT = "LEFT-RIGHT"   # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下
 
 
 # 暗色模式 / 亮色模式
-DARK_MODE = True
+DARK_MODE = False
 
 
 # 发送请求到OpenAI后，等待多久判定为超时
@@ -106,18 +81,31 @@ 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
 
+# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
+LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo", "spark", "azure-gpt-3.5"]
 
 # 插件分类默认选项
 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-16k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+                    "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-3-turbo",
+                    "gemini-pro", "chatglm3", "claude-2"]
+# 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"
 
@@ -135,7 +123,7 @@ 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", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
+BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
 
 
 # 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
@@ -146,7 +134,6 @@ 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
 
@@ -156,7 +143,7 @@ AUTO_CLEAR_TXT = False
 
 
 # 加一个live2d装饰
-ADD_WAIFU = False
+ADD_WAIFU = True
 
 
 # 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
@@ -164,8 +151,7 @@ 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!!）
-# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
+# 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!）
 CUSTOM_PATH = "/"
 
 
@@ -193,8 +179,14 @@ AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.
 AZURE_CFG_ARRAY = {}
 
 
-# 阿里云实时语音识别 配置难度较高
-# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
+# 使用Newbing (不推荐使用，未来将删除)
+NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
+NEWBING_COOKIES = """
+put your new bing cookies here
+"""
+
+
+# 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
@@ -202,12 +194,6 @@ 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"
@@ -219,35 +205,21 @@ ZHIPUAI_API_KEY = ""
 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 = ""
-
-
-# 紫东太初大模型 https://ai-maas.wair.ac.cn
-TAICHU_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 = ""
 
@@ -269,10 +241,6 @@ GROBID_URLS = [
 ]
 
 
-# Searxng互联网检索服务
-SEARXNG_URL = "https://cloud-1.agent-matrix.com/"
-
-
 # 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
 ALLOW_RESET_CONFIG = False
 
@@ -281,23 +249,23 @@ ALLOW_RESET_CONFIG = False
 AUTOGEN_USE_DOCKER = False
 
 
-# 临时的上传文件夹位置，请尽量不要修改
+# 临时的上传文件夹位置，请勿修改
 PATH_PRIVATE_UPLOAD = "private_upload"
 
 
-# 日志文件夹的位置，请尽量不要修改
+# 日志文件夹的位置，请勿修改
 PATH_LOGGING = "gpt_log"
 
 
-# 存储翻译好的arxiv论文的路径，请尽量不要修改
-ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
-
-
-# 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
+# 除了连接OpenAI之外，还有哪些场合允许使用代理，请勿修改
 WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
                      "Warmup_Modules", "Nougat_Download", "AutoGen"]
 
 
+# *实验性功能*: 自动检测并屏蔽失效的KEY，请勿使用
+BLOCK_INVALID_APIKEY = False
+
+
 # 启用插件热加载
 PLUGIN_HOT_RELOAD = False
 
@@ -305,11 +273,7 @@ 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
@@ -333,7 +297,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── XFYUN_API_SECRET
 │   └── XFYUN_API_KEY
 │
-├── "claude-3-opus-20240229" 等claude模型
+├── "claude-1-100k" 等claude模型
 │   └── ANTHROPIC_API_KEY
 │
 ├── "stack-claude"
@@ -348,19 +312,15 @@ NUM_CUSTOM_BASIC_BTN = 4
 ├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
 │   └── ZHIPUAI_API_KEY
 │
-├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
-│   └── YIMODEL_API_KEY
-│
 ├── "qwen-turbo" 等通义千问大模型
 │   └──  DASHSCOPE_API_KEY
 │
 ├── "Gemini"
 │   └──  GEMINI_API_KEY
 │
-└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
-    ├── AVAIL_LLM_MODELS
-    ├── API_KEY
-    └── API_URL_REDIRECT
+└── "newbing" Newbing接口不再稳定，不推荐使用
+    ├── NEWBING_STYLE
+    └── NEWBING_COOKIES
 
 
 本地大模型示意图
@@ -394,9 +354,6 @@ NUM_CUSTOM_BASIC_BTN = 4
 
 插件在线服务配置依赖关系示意图
 │
-├── 互联网检索
-│   └── SEARXNG_URL
-│
 ├── 语音功能
 │   ├── ENABLE_AUDIO
 │   ├── ALIYUN_TOKEN

core_functional.py

@@ -33,19 +33,17 @@ 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":   '''"""\n\n''',
+            "Prefix":   r"",
             # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
             "Suffix":
                 # dedent() 函数用于去除多行字符串的缩进
-                dedent("\n\n"+r'''
-                    """
+                dedent("\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：
 
@@ -59,7 +57,7 @@ def get_core_functions():
                         C --> |"箭头名2"| F["节点名6"]
                     ```
 
-                    注意：
+                    警告：
                     （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

@@ -15,43 +15,32 @@ 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.Conversation_To_File 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.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.对话历史存档 import 对话历史存档
+    from crazy_functions.对话历史存档 import 载入对话历史存档
+    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
     from crazy_functions.辅助功能 import 清除缓存
-    from crazy_functions.Markdown_Translate import Markdown英译中
+    from crazy_functions.批量Markdown翻译 import Markdown英译中
     from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.PDF_Translate import 批量翻译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.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.Markdown_Translate import Markdown中译英
+    from crazy_functions.批量Markdown翻译 import Markdown中译英
     from crazy_functions.虚空终端 import 虚空终端
-    from crazy_functions.生成多种Mermaid图表 import 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
-    from crazy_functions.Internet_GPT import 连接网络回答问题
-    from crazy_functions.Internet_GPT_Wrap import NetworkGPT_Wrap
+    from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表
 
     function_plugins = {
         "虚空终端": {
             "Group": "对话|编程|学术|智能体",
             "Color": "stop",
             "AsButton": True,
-            "Info": "使用自然语言实现您的想法",
             "Function": HotReload(虚空终端),
         },
         "解析整个Python项目": {
@@ -86,21 +75,14 @@ def get_crazy_functions():
             "Color": "stop",
             "AsButton": False,
             "Info" : "基于当前对话或文件生成多种Mermaid图表,图表类型由模型判断",
-            "Function": None,
-            "Class": Mermaid_Gen
-        },
-        "Arxiv论文翻译": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,
-            "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
+            "Function": HotReload(生成多种Mermaid图表),
+            "AdvancedArgs": True,
+            "ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
         },
         "批量总结Word文档": {
             "Group": "学术",
             "Color": "stop",
-            "AsButton": False,
+            "AsButton": True,
             "Info": "批量总结word文档 | 输入参数为路径",
             "Function": HotReload(总结word文档),
         },
@@ -206,42 +188,28 @@ def get_crazy_functions():
         },
         "保存当前的对话": {
             "Group": "对话",
-            "Color": "stop",
             "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),    # 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
+            "Function": HotReload(对话历史存档),
         },
         "[多线程Demo]解析此项目本身（源码自译解）": {
             "Group": "对话|编程",
-            "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "多线程解析并翻译此项目的源码 | 不需要输入参数",
             "Function": HotReload(解析项目本身),
         },
-        "查互联网后回答": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": True,  # 加入下拉菜单中
-            # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-            "Function": HotReload(连接网络回答问题),
-            "Class": NetworkGPT_Wrap     # 新一代插件需要注册Class
-        },
         "历史上的今天": {
             "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,
+            "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": None,
-            "Class": Demo_Wrap, # 新一代插件需要注册Class
+            "Function": HotReload(高阶功能模板函数),
         },
         "精准翻译PDF论文": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": True,
             "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档), # 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
+            "Function": HotReload(批量翻译PDF文档),
         },
         "询问多个GPT模型": {
             "Group": "对话",
@@ -316,51 +284,7 @@ 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:
@@ -381,36 +305,36 @@ def get_crazy_functions():
         print(trimmed_format_exc())
         print("Load function plugin failed")
 
-    # try:
-    #     from crazy_functions.联网的ChatGPT import 连接网络回答问题
+    try:
+        from crazy_functions.联网的ChatGPT import 连接网络回答问题
 
-    #     function_plugins.update(
-    #         {
-    #             "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
-    #                 "Group": "对话",
-    #                 "Color": "stop",
-    #                 "AsButton": False,  # 加入下拉菜单中
-    #                 # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-    #                 "Function": HotReload(连接网络回答问题),
-    #             }
-    #         }
-    #     )
-    #     from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
+        function_plugins.update(
+            {
+                "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,  # 加入下拉菜单中
+                    # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
+                    "Function": HotReload(连接网络回答问题),
+                }
+            }
+        )
+        from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
 
-    #     function_plugins.update(
-    #         {
-    #             "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
-    #                 "Group": "对话",
-    #                 "Color": "stop",
-    #                 "AsButton": False,  # 加入下拉菜单中
-    #                 "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
-    #                 "Function": HotReload(连接bing搜索回答问题),
-    #             }
-    #         }
-    #     )
-    # except:
-    #     print(trimmed_format_exc())
-    #     print("Load function plugin failed")
+        function_plugins.update(
+            {
+                "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,  # 加入下拉菜单中
+                    "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
+                    "Function": HotReload(连接bing搜索回答问题),
+                }
+            }
+        )
+    except:
+        print(trimmed_format_exc())
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.解析项目源代码 import 解析任意code项目
@@ -439,7 +363,7 @@ def get_crazy_functions():
                 "询问多个GPT模型（手动指定询问哪些模型）": {
                     "Group": "对话",
                     "Color": "stop",
-                    "AsButton": True,
+                    "AsButton": False,
                     "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
                     "ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&gpt-4",  # 高级参数输入区的显示提示
                     "Function": HotReload(同时问询_指定模型),
@@ -534,7 +458,7 @@ def get_crazy_functions():
         print("Load function plugin failed")
 
     try:
-        from crazy_functions.Markdown_Translate import Markdown翻译指定语言
+        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
 
         function_plugins.update(
             {
@@ -607,6 +531,59 @@ def get_crazy_functions():
         print(trimmed_format_exc())
         print("Load function plugin failed")
 
+    try:
+        from crazy_functions.Latex输出PDF import Latex英文纠错加PDF对比
+        from crazy_functions.Latex输出PDF import Latex翻译中文并重新编译PDF
+        from crazy_functions.Latex输出PDF import PDF翻译中文并重新编译PDF
+
+        function_plugins.update(
+            {
+                "Latex英文纠错+高亮修正位置 [需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
+                    "Function": HotReload(Latex英文纠错加PDF对比),
+                },
+                "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+                    "Function": HotReload(Latex翻译中文并重新编译PDF),
+                },
+                "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "本地Latex论文精细翻译 | 输入参数是路径",
+                    "Function": HotReload(Latex翻译中文并重新编译PDF),
+                },
+                "PDF翻译中文并重新编译PDF（上传PDF）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
+                    "Function": HotReload(PDF翻译中文并重新编译PDF)
+                }
+            }
+        )
+    except:
+        print(trimmed_format_exc())
+        print("Load function plugin failed")
 
     try:
         from toolbox import get_conf

crazy_functions/Internet_GPT.py

@@ -1,142 +0,0 @@
-from toolbox import CatchException, update_ui, get_conf
-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
-import random
-from functools import lru_cache
-from check_proxy import check_proxy
-
-@lru_cache
-def get_auth_ip():
-    ip = check_proxy(None, return_ip=True)
-    if ip is None:
-        return '114.114.114.' + str(random.randint(1, 10))
-    return ip
-
-def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
-    if searxng_url is None:
-        url = get_conf("SEARXNG_URL")
-    else:
-        url = searxng_url
-
-    if engines is None:
-        engines = 'bing'
-
-    if categories == 'general':
-        params = {
-            'q': query,         # 搜索查询
-            'format': 'json',   # 输出格式为JSON
-            'language': 'zh',   # 搜索语言
-            'engines': engines,
-        }
-    elif categories == 'science':
-        params = {
-            'q': query,         # 搜索查询
-            'format': 'json',   # 输出格式为JSON
-            'language': 'zh',   # 搜索语言
-            'categories': 'science'
-        }
-    else:
-        raise ValueError('不支持的检索类型')
-
-    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, timeout=30)
-    if response.status_code == 200:
-        json_result = response.json()
-        for result in json_result['results']:
-            item = {
-                "title": result.get("title", ""),
-                "source": result.get("engines", "unknown"),
-                "content": result.get("content", ""),
-                "link": result["url"],
-            }
-            results.append(item)
-        return results
-    else:
-        if response.status_code == 429:
-            raise ValueError("Searxng（在线搜索服务）当前使用人数太多，请稍后。")
-        else:
-            raise ValueError("在线搜索失败，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):
-
-    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}", "检索中..."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
-    from toolbox import get_conf
-    proxies = get_conf('proxies')
-    categories = plugin_kwargs.get('categories', 'general')
-    searxng_url = plugin_kwargs.get('searxng_url', None)
-    engines = plugin_kwargs.get('engine', None)
-    urls = searxng_request(txt, proxies, categories, searxng_url, engines=engines)
-    history = []
-    if len(urls) == 0:
-        chatbot.append((f"结论：{txt}",
-                        "[Local Message] 受到限制，无法从searxng获取信息！请尝试更换搜索引擎。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        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
-    chatbot.append([f"联网检索中 ...", None])
-    for index, url in enumerate(urls[:max_search_result]):
-        res = scrape_text(url['link'], proxies)
-        prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
-        history.extend([prefix, res])
-        res_squeeze = res.replace('\n', '...')
-        chatbot[-1] = [prefix + "\n\n" + res_squeeze[:500] + "......", None]
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # ------------- < 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=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
-    )
-    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/Internet_GPT_Wrap.py

@@ -1,44 +0,0 @@
-
-from toolbox import get_conf
-from crazy_functions.Internet_GPT import 连接网络回答问题
-from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
-
-
-class NetworkGPT_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`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="输入问题", description="待通过互联网检索的问题", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
-            "categories":
-                ArgProperty(title="搜索分类", options=["网页", "学术论文"], default_value="网页", description="无", type="dropdown").model_dump_json(),
-            "engine":
-                ArgProperty(title="选择搜索引擎", options=["bing", "google", "duckduckgo"], default_value="bing", description="无", type="dropdown").model_dump_json(),
-            "searxng_url":
-                ArgProperty(title="Searxng服务地址", description="输入Searxng的地址", default_value=get_conf("SEARXNG_URL"), 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
-        """
-        if plugin_kwargs["categories"] == "网页": plugin_kwargs["categories"] = "general"
-        if plugin_kwargs["categories"] == "学术论文": plugin_kwargs["categories"] = "science"
-
-        yield from 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-

crazy_functions/Latex_Function_Wrap.py

@@ -1,78 +0,0 @@
-
-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 = [r"Below is a section from an academic paper, polish this section to meet the academic standard, " +
-                            r"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
+            inputs_array = ["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:" + 
                             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 = [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：" +
+            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：" + 
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         else:
-            inputs_array = [r"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
+            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：" + 
                             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.py

@@ -1,10 +1,10 @@
-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 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, json, tarfile
 
 pj = os.path.join
-ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
+ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
 
 
 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
@@ -107,10 +107,6 @@ def arxiv_download(chatbot, history, txt, allow_cache=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
@@ -125,7 +121,6 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
     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
@@ -158,8 +153,7 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
     return extract_dst, arxiv_id
 
 
-def pdf2tex_project(pdf_file_path, plugin_kwargs):
-    if plugin_kwargs["method"] == "MATHPIX":
+def pdf2tex_project(pdf_file_path):
     # Mathpix API credentials
     app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
     headers = {"app_id": app_id, "app_key": app_key}
@@ -218,12 +212,6 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
     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 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
@@ -233,7 +221,7 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
 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。"])
+                    "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 ------------->
@@ -271,8 +259,6 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
     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 ------------->
@@ -296,7 +282,7 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
         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 ...'))
+                        '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)
@@ -312,7 +298,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
     # <-------------- 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。"])
+        "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 ------------->
@@ -367,8 +353,6 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
     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 ------------->
@@ -408,7 +392,7 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
     # <-------------- 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。"])
+        "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 ------------->
@@ -448,55 +432,16 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
         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
+    project_folder = pdf2tex_project(file_manifest[0])
 
-    # <-------------- 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)
+    # Translate English Latex to Chinese Latex, and compile it
     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}")
@@ -507,16 +452,8 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
     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,
@@ -524,7 +461,6 @@ def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, h
                                        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,

crazy_functions/PDF_Translate.py

@@ -1,83 +0,0 @@
-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

@@ -1,33 +0,0 @@
-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

@@ -1,20 +1,9 @@
 from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
-from shared_utils.char_visual_effect import scolling_visual_effect
 import threading
 import os
 import logging
 
 def input_clipping(inputs, history, max_token_limit):
-    """
-    当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
-    输入：
-        - inputs 本次请求
-        - history 历史上下文
-        - max_token_limit 最大token限制
-    输出:
-        - inputs 本次请求（经过clip）
-        - history 历史上下文（经过clip）
-    """
     import numpy as np
     from request_llms.bridge_all import model_info
     enc = model_info["gpt-3.5-turbo"]['tokenizer']
@@ -146,11 +135,7 @@ 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) -> bool:
-    from request_llms.bridge_all import model_info
-
-    def default_condition(llm) -> bool:
-        # legacy condition
+def can_multi_process(llm):
     if llm.startswith('gpt-'): return True
     if llm.startswith('api2d-'): return True
     if llm.startswith('azure-'): return True
@@ -158,18 +143,10 @@ def can_multi_process(llm) -> bool:
     if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
     return False
 
-    if llm in model_info:
-        if 'can_multi_thread' in model_info[llm]:
-            return model_info[llm]['can_multi_thread']
-        else:
-            return default_condition(llm)
-    else:
-        return default_condition(llm)
-
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array, inputs_show_user_array, llm_kwargs,
         chatbot, history_array, sys_prompt_array,
-        refresh_interval=0.2, max_workers=-1, scroller_max_len=75,
+        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
         handle_token_exceed=True, show_user_at_complete=False,
         retry_times_at_unknown_error=2,
         ):
@@ -294,8 +271,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
         range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
     cnt = 0
-
-
     while True:
         # yield一次以刷新前端页面
         time.sleep(refresh_interval)
@@ -308,7 +283,8 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             mutable[thread_index][1] = time.time()
         # 在前端打印些好玩的东西
         for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = f"[ ...`{scolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
+            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
+                replace('\n', '').replace('`', '.').replace(' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
             observe_win.append(print_something_really_funny)
         # 在前端打印些好玩的东西
         stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
@@ -361,7 +337,7 @@ def read_and_clean_pdf_text(fp):
     import fitz, copy
     import re
     import numpy as np
-    from shared_utils.colorful import print亮黄, print亮绿
+    from colorful import print亮黄, print亮绿
     fc = 0  # Index 0 文本
     fs = 1  # Index 1 字体
     fb = 2  # Index 2 框框
@@ -580,7 +556,7 @@ 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=False, cwd=cwd, env=os.environ)
+            process = subprocess.Popen(command, shell=True, cwd=cwd, env=os.environ)
         try:
             stdout, stderr = process.communicate(timeout=timeout)
         except subprocess.TimeoutExpired:
@@ -604,8 +580,7 @@ class nougat_interface():
 
         yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
                                          chatbot=chatbot, history=history, delay=0)
-        command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
-        self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
+        self.nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}"', 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.
-        schema_str = json.dumps(reduced_schema)
         if self.example_instruction:
+            schema_str = json.dumps(reduced_schema)
             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

@@ -1,11 +1,10 @@
 from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
-from toolbox import get_conf, promote_file_to_downloadzone
+from toolbox import get_conf, objdump, objload, 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

@@ -1,46 +0,0 @@
-import pickle
-
-
-class SafeUnpickler(pickle.Unpickler):
-
-    def get_safe_classes(self):
-        from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
-        from crazy_functions.latex_fns.latex_toolbox import LinkedListNode
-        # 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,
-            'LinkedListNode': LinkedListNode,
-        }
-        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()
-        match_class_name = None
-        for class_name in self.safe_classes.keys():
-            if (class_name in f'{module}.{name}'):
-                match_class_name = class_name
-        if module == 'numpy' or module.startswith('numpy.'):
-            return super().find_class(module, name)
-        if match_class_name is not None:
-            return self.safe_classes[match_class_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 shared_utils.colorful import *
+from 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, plugin_kwargs={}):
+def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
     from crazy_functions.pdf_fns.report_gen_html import construct_html
     from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
     from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@@ -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。" + plugin_kwargs.get("additional_prompt", "") 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。" 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

@@ -1,26 +0,0 @@
-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_via_doc2x.py

@@ -1,213 +0,0 @@
-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)
-            if 'limit exceeded' in decoded_json.get('status', ''):
-                raise RuntimeError("Doc2x API 页数受限，请联系 Doc2x 方面，并更换新的 API 秘钥。")
-        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/report_template_v2.html

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

crazy_functions/plugin_template/plugin_class_template.py

@@ -1,52 +0,0 @@
-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) and ('Function' in v)}
+    plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
     plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}

crazy_functions/对话历史存档.py

@@ -1,5 +1,4 @@
 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'
@@ -10,61 +9,27 @@ 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 textwrap import dedent
-        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 = ""
+        from themes.theme import advanced_css
+        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
         for i, contents in enumerate(chatbot):
-            question, answer = contents[0], contents[1]
-            if question is None: question = ""
-            try: question = str(question)
-            except: question = ""
-            if answer is None: answer = ""
-            try: answer = str(answer)
-            except: answer = ""
-            CHAT_PREVIEW_BUF += qa_from.format(QUESTION=question, ANSWER=answer)
-
-        HISTORY_PREVIEW_BUF = ""
+            for j, content in enumerate(contents):
+                try:    # 这个bug没找到触发条件，暂时先这样顶一下
+                    if type(content) != str: content = str(content)
+                except:
+                    continue
+                f.write(content)
+                if j == 0:
+                    f.write('<hr style="border-top: dotted 3px #ccc;">')
+            f.write('<hr color="red"> \n\n')
+        f.write('<hr color="blue"> \n\n raw chat context:\n')
+        f.write('<code>')
         for h in history:
-            HISTORY_PREVIEW_BUF += history_from.format(ENTRY=h)
-        html_content = form.format(CHAT_PREVIEW=CHAT_PREVIEW_BUF, HISTORY_PREVIEW=HISTORY_PREVIEW_BUF, CSS=advanced_css)
-        f.write(html_content)
-
+            f.write("\n>>>" + h)
+        f.write('</code>')
     promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
     return '对话历史写入：' + fp
 
@@ -75,7 +40,7 @@ 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 对话数据 (无渲染):\n')
+        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>>>")
@@ -86,25 +51,21 @@ 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()
-    from bs4 import BeautifulSoup
-    soup = BeautifulSoup(file_content, 'lxml')
-    # 提取QaBox信息
+    # pattern to match the text between <head> and </head>
+    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
+    file_content = re.sub(pattern, '', file_content)
+    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
+    history = history.strip('<code>')
+    history = history.strip('</code>')
+    history = history.split("\n>>>")
+    history = list(filter(lambda x:x!="", history))
+    html = html.split('<hr color="red"> \n\n')
+    html = list(filter(lambda x:x!="", html))
     chatbot.clear()
-    qa_box_list = []
-    qa_boxes = soup.find_all("div", class_="QaBox")
-    for box in qa_boxes:
-        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。"])
+    for i, h in enumerate(html):
+        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
+        chatbot.append([i_say, gpt_say])
+    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
     return chatbot, history    
 
 @CatchException
@@ -118,42 +79,11 @@ 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((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。"))
+    chatbot.append(("保存当前对话", 
+        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
 
-
-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()
@@ -218,3 +148,5 @@ def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot
     chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
     return
+
+

crazy_functions/批量Markdown翻译.py

@@ -1,5 +1,5 @@
-import glob, shutil, os, re, logging
-from toolbox import update_ui, trimmed_format_exc, gen_time_str
+import glob, time, os, re, logging
+from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
 from toolbox import CatchException, report_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=2048):
+    def run_file_split(self, max_token_limit=1900):
         """
         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=2048)
+    pfg.run_file_split(max_token_limit=1500)
     n_split = len(pfg.sp_file_contents)
 
     #  <-------- 多线程翻译开始 ----------> 
     if language == 'en->zh':
-        inputs_array = ["This is a Markdown file, translate it into Chinese, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
+        inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" + 
                         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." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
     elif language == 'zh->en':
-        inputs_array = [f"This is a Markdown file, translate it into English, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
+        inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" + 
                         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." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
     else:
-        inputs_array = [f"This is a Markdown file, translate it into {language}, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
+        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:" + 
                         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." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
 
     gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array=inputs_array,
@@ -99,12 +99,7 @@ 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()
-        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)
+        pfg.write_result(language)
     except:
         logging.error(trimmed_format_exc())
 
@@ -164,6 +159,7 @@ 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:
@@ -203,6 +199,7 @@ 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:
@@ -235,6 +232,7 @@ 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/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 shared_utils.colorful import *
+from colorful import *
 import copy
 import os
 import math

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,15 +1,83 @@
-from toolbox import get_log_folder
-from toolbox import update_ui, promote_file_to_downloadzone
+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
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from crazy_functions.crazy_utils import read_and_clean_pdf_text
-from shared_utils.colorful import *
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from .crazy_utils import read_and_clean_pdf_text
+from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
+from colorful import *
 import 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):
     """
-    注意：此函数已经弃用！！新函数位于：crazy_functions/pdf_fns/parse_pdf.py
+    此函数已经弃用
     """
     import copy
     TOKEN_LIMIT_PER_FRAGMENT = 1024
@@ -48,8 +116,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
             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。" + plugin_kwargs.get("additional_prompt", "")
-                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。" 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/生成多种Mermaid图表.py

@@ -1,11 +1,8 @@
 from toolbox import CatchException, update_ui, report_exception
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.plugin_template.plugin_class_template import (
-    GptAcademicPluginTemplate,
-)
-from crazy_functions.plugin_template.plugin_class_template import ArgProperty
+import datetime
 
-# 以下是每类图表的PROMPT
+#以下是每类图表的PROMPT
 SELECT_PROMPT = """
 “{subject}”
 =============
@@ -20,24 +17,22 @@ SELECT_PROMPT = """
 8 象限提示图
 不需要解释原因，仅需要输出单个不带任何标点符号的数字。
 """
-# 没有思维导图!!!测试发现模型始终会优先选择思维导图
-# 流程图
+#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，注意需要使用双引号将内容括起来。
-mermaid语法举例：
+请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
 ```mermaid
 graph TD
-    P("编程") --> L1("Python")
-    P("编程") --> L2("C")
-    P("编程") --> L3("C++")
-    P("编程") --> L4("Javascipt")
-    P("编程") --> L5("PHP")
+    P(编程) --> L1(Python)
+    P(编程) --> L2(C)
+    P(编程) --> L3(C++)
+    P(编程) --> L4(Javascipt)
+    P(编程) --> L5(PHP)
 ```
 """
-# 序列图
+#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
@@ -48,10 +43,9 @@ sequenceDiagram
     B->>A: 返回数据
 ```
 """
-# 类图
+#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
@@ -69,10 +63,9 @@ classDiagram
     Class08 <--> C2: Cool label
 ```
 """
-# 饼图
+#饼图
 PROMPT_4 = """
-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：
+请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例：
 ```mermaid
 pie title Pets adopted by volunteers
     "狗" : 386
@@ -80,41 +73,38 @@ pie title Pets adopted by volunteers
     "兔子" : 15
 ```
 """
-# 甘特图
+#甘特图
 PROMPT_5 = """
-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：
+请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例：
 ```mermaid
 gantt
-    title "项目开发流程"
+    title 项目开发流程
     dateFormat  YYYY-MM-DD
-    section "设计"
-    "需求分析" :done, des1, 2024-01-06,2024-01-08
-    "原型设计" :active, des2, 2024-01-09, 3d
-    "UI设计" : des3, after des2, 5d
-    section "开发"
-    "前端开发" :2024-01-20, 10d
-    "后端开发" :2024-01-20, 10d
+    section 设计
+    需求分析 :done, des1, 2024-01-06,2024-01-08
+    原型设计 :active, des2, 2024-01-09, 3d
+    UI设计 : des3, after des2, 5d
+    section 开发
+    前端开发 :2024-01-20, 10d
+    后端开发 :2024-01-20, 10d
 ```
 """
-# 状态图
+#状态图
 PROMPT_6 = """
-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：
+请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例：
 ```mermaid
 stateDiagram-v2
-   [*] --> "Still"
-    "Still" --> [*]
-    "Still" --> "Moving"
-    "Moving" --> "Still"
-    "Moving" --> "Crash"
-    "Crash" --> [*]
+   [*] --> Still
+    Still --> [*]
+    Still --> Moving
+    Moving --> Still
+    Moving --> Crash
+    Crash --> [*]
 ```
 """
-# 实体关系图
+#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
@@ -134,173 +124,118 @@ erDiagram
     }
 ```
 """
-# 象限提示图
+#象限提示图
 PROMPT_8 = """
-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：
+请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例：
 ```mermaid
 graph LR
-    A["Hard skill"] --> B("Programming")
-    A["Hard skill"] --> C("Design")
-    D["Soft skill"] --> E("Coordination")
-    D["Soft skill"] --> F("Communication")
+    A[Hard skill] --> B(Programming)
+    A[Hard skill] --> C(Design)
+    D[Soft skill] --> E(Coordination)
+    D[Soft skill] --> F(Communication)
 ```
 """
-# 思维导图
+#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，注意需要使用双引号将内容括起来。
-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，mermaid语法举例：
 ```mermaid
 mindmap
   root((mindmap))
-    ("Origins")
-      ("Long history")
+    Origins
+      Long history
       ::icon(fa fa-book)
-      ("Popularisation")
-        ("British popular psychology author Tony Buzan")
-        ::icon(fa fa-user)
-    ("Research")
-      ("On effectiveness<br/>and features")
-      ::icon(fa fa-search)
-      ("On Automatic creation")
-      ::icon(fa fa-robot)
-        ("Uses")
-            ("Creative techniques")
-            ::icon(fa fa-lightbulb-o)
-            ("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)
+      Popularisation
+        British popular psychology author Tony Buzan
+    Research
+      On effectiveness<br/>and features
+      On Automatic creation
+        Uses
+            Creative techniques
+            Strategic planning
+            Argument mapping
+    Tools
+      Pen and paper
+      Mermaid
 ```
 """
 
-
-def 解析历史输入(history, llm_kwargs, file_manifest, chatbot, plugin_kwargs):
+def 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs):
     ############################## <第 0 步，切割输入> ##################################
     # 借用PDF切割中的函数对文本进行切割
     TOKEN_LIMIT_PER_FRAGMENT = 2500
-    txt = (
-        str(history).encode("utf-8", "ignore").decode()
-    )  # avoid reading non-utf8 chars
-    from crazy_functions.pdf_fns.breakdown_txt import (
-        breakdown_text_to_satisfy_token_limit,
-    )
-
-    txt = breakdown_text_to_satisfy_token_limit(
-        txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs["llm_model"]
-    )
+    txt = str(history).encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+    txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
     ############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
     results = []
     MAX_WORD_TOTAL = 4096
     n_txt = len(txt)
     last_iteration_result = "从以下文本中提取摘要。"
-    if n_txt >= 20:
-        print("文章极长，不能达到预期效果")
+    if n_txt >= 20: print('文章极长，不能达到预期效果')
     for i in range(n_txt):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
         i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
         i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i][:200]} ...."
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            i_say,
-            i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-            llm_kwargs,
-            chatbot,
-            history=[
-                "The main content of the previous section is?",
-                last_iteration_result,
-            ],  # 迭代上一次的结果
-            sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese.",  # 提示
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
+                                                                           llm_kwargs, chatbot, 
+                                                                           history=["The main content of the previous section is?", last_iteration_result], # 迭代上一次的结果
+                                                                           sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese."  # 提示
                                                                         ) 
         results.append(gpt_say)
         last_iteration_result = gpt_say
     ############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
-    gpt_say = str(plugin_kwargs)  # 将图表类型参数赋值为插件参数
-    results_txt = "\n".join(results)  # 合并摘要
-    if gpt_say not in [
-        "1",
-        "2",
-        "3",
-        "4",
-        "5",
-        "6",
-        "7",
-        "8",
-        "9",
-    ]:  # 如插件参数不正确则使用对话模型判断
-        i_say_show_user = (
-            f"接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制"
-        )
-        gpt_say = "[Local Message] 收到。"  # 用户提示
-        chatbot.append([i_say_show_user, gpt_say])
-        yield from update_ui(chatbot=chatbot, history=[])  # 更新UI
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    gpt_say = plugin_kwargs.get("advanced_arg", "")     #将图表类型参数赋值为插件参数  
+    results_txt = '\n'.join(results)    #合并摘要
+    if gpt_say not in ['1','2','3','4','5','6','7','8','9']:    #如插件参数不正确则使用对话模型判断
+        i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; gpt_say = "[Local Message] 收到。"   # 用户提示
+        chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
         i_say = SELECT_PROMPT.format(subject=results_txt)
         i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。'
         for i in range(3):
             gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
                 inputs=i_say,
                 inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt="",
+                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+                sys_prompt=""
             )
-            if gpt_say in [
-                "1",
-                "2",
-                "3",
-                "4",
-                "5",
-                "6",
-                "7",
-                "8",
-                "9",
-            ]:  # 判断返回是否正确
+            if gpt_say in ['1','2','3','4','5','6','7','8','9']:     #判断返回是否正确
                 break
-        if gpt_say not in ["1", "2", "3", "4", "5", "6", "7", "8", "9"]:
-            gpt_say = "1"
+        if gpt_say not in ['1','2','3','4','5','6','7','8','9']:
+            gpt_say = '1'
     ############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
-    if gpt_say == "1":
+    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":
-        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 == '7':
+        i_say = PROMPT_7.replace("{subject}", results_txt)      #由于实体关系图用到了{}符号
+    elif gpt_say == '8':
         i_say = PROMPT_8.format(subject=results_txt)
-    elif gpt_say == "9":
+    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=[],
-        sys_prompt="",
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+        sys_prompt=""
     )
     history.append(gpt_say)
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
     
-
 @CatchException
-def 生成多种Mermaid图表(
-    txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
-):
+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
@@ -313,21 +248,15 @@ def 生成多种Mermaid图表(
     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
     
-    if os.path.exists(txt):  # 如输入区无内容则直接解析历史记录
+    if os.path.exists(txt):     #如输入区无内容则直接解析历史记录
         from crazy_functions.pdf_fns.parse_word import extract_text_from_files
-
-        file_exist, final_result, page_one, file_manifest, excption = (
-            extract_text_from_files(txt, chatbot, history)
-        )
+        file_exist, final_result, page_one, file_manifest, excption = extract_text_from_files(txt, chatbot, history)
     else:
         file_exist = False
         excption = ""
@@ -335,104 +264,33 @@ def 生成多种Mermaid图表(
 
     if excption != "":
         if excption == "word":
-            report_exception(
-                chatbot,
-                history,
-                a=f"解析项目: {txt}",
-                b=f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。",
-            )
+            report_exception(chatbot, history, 
+                a = f"解析项目: {txt}", 
+                b = f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。")
             
         elif excption == "pdf":
-            report_exception(
-                chatbot,
-                history,
-                a=f"解析项目: {txt}",
-                b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。",
-            )
+            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,
+                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```。",
-            )
+                    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
-            )
+            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
+            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,
-        )
+                yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs)

crazy_functions/解析项目源代码.py

@@ -1,7 +1,6 @@
 from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
 from toolbox import CatchException, report_exception, write_history_to_file
-from shared_utils.fastapi_server import validate_path_safety
-from crazy_functions.crazy_utils import input_clipping
+from .crazy_utils import input_clipping
 
 def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import os, copy
@@ -129,7 +128,6 @@ 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}")
@@ -148,7 +146,6 @@ 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}")
@@ -167,7 +164,6 @@ 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}")
@@ -188,7 +184,6 @@ 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}")
@@ -211,7 +206,6 @@ 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}")
@@ -234,7 +228,6 @@ 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}")
@@ -264,7 +257,6 @@ 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}")
@@ -286,7 +278,6 @@ 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}")
@@ -307,7 +298,6 @@ 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}")
@@ -330,7 +320,6 @@ 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}")
@@ -356,19 +345,15 @@ 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(".", 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
-                           ]
+    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
     # 生成正则表达式
-    pattern_except = r'/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
+    pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
     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/辅助回答.py

@@ -0,0 +1,28 @@
+# encoding: utf-8
+# @Time   : 2023/4/19
+# @Author : Spike
+# @Descr   :
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+
+@CatchException
+def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    if txt:
+        show_say = txt
+        prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
+    else:
+        prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
+        show_say = '分析上述回答，再列出用户可能提出的三个问题。'
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt,
+        inputs_show_user=show_say,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history=history,
+        sys_prompt=system_prompt
+    )
+    chatbot[-1] = (show_say, gpt_say)
+    history.extend([show_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

crazy_functions/高级功能函数模板.py

@@ -2,10 +2,6 @@ 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
@@ -30,7 +26,7 @@ flowchart TD
 """
 
 @CatchException
-def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, num_day=5):
+def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
     """
     # 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
 
@@ -47,7 +43,7 @@ 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(int(num_day)):
+    for i in range(5):
         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。'
@@ -63,56 +59,6 @@ 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
 
@@ -75,8 +75,10 @@ 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:
@@ -95,16 +97,16 @@ services:
       # DEFAULT_WORKER_NUM:       '    10                                                                                             '
       # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
 
-    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
+    # 与宿主的网络融合
     network_mode: "host"
 
-    # 启动命令
+    # 不使用代理网络拉取最新代码
     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:
@@ -128,10 +130,8 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
+    # 与宿主的网络融合
     network_mode: "host"
-
-    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
@@ -139,9 +139,8 @@ 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:
@@ -165,16 +164,16 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
+    # 与宿主的网络融合
     network_mode: "host"
 
-    # 启动命令
+    # 不使用代理网络拉取最新代码
     command: >
       python3 -u main.py
 
 
 ## ===================================================
-## 「方案四」 ChatGPT + Latex
+## 【方案四】 ChatGPT + Latex
 ## ===================================================
 version: '3'
 services:
@@ -191,16 +190,16 @@ services:
       DEFAULT_WORKER_NUM:       '    10                                                                               '
       WEB_PORT:                 '    12303                                                                            '
 
-    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
+    # 与宿主的网络融合
     network_mode: "host"
 
-    # 启动命令
+    # 不使用代理网络拉取最新代码
     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:
@@ -224,9 +223,9 @@ services:
       # (无需填写) ALIYUN_ACCESSKEY:         '    LTAI5q6BrFUzoRXVGUWnekh1                                         '
       # (无需填写) ALIYUN_SECRET:            '    eHmI20AVWIaQZ0CiTD2bGQVsaP9i68                                   '
 
-    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
+    # 与宿主的网络融合
     network_mode: "host"
 
-    # 启动命令
+    # 不使用代理网络拉取最新代码
     command: >
       bash -c "python3 -u main.py"

docs/GithubAction+AllCapacity

@@ -3,9 +3,6 @@
 # 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
 FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
 
-# edge-tts需要的依赖，某些pip包所需的依赖
-RUN apt update && apt install ffmpeg build-essential -y
-
 # use python3 as the system default python
 WORKDIR /gpt
 RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8

docs/GithubAction+AllCapacityBeta

@@ -5,9 +5,6 @@
 # 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
 FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
 
-# edge-tts需要的依赖，某些pip包所需的依赖
-RUN apt update && apt install ffmpeg build-essential -y
-
 # use python3 as the system default python
 WORKDIR /gpt
 RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
@@ -39,7 +36,6 @@ 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
 
-
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 

docs/GithubAction+ChatGLM+Moss

@@ -5,8 +5,6 @@ RUN apt-get update
 RUN apt-get install -y curl proxychains curl gcc
 RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
 
-# edge-tts需要的依赖，某些pip包所需的依赖
-RUN apt update && apt install ffmpeg build-essential -y
 
 # use python3 as the system default python
 RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
@@ -24,6 +22,7 @@ RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llms/requirements_newbing.txt
 
 
+
 # 预热Tiktoken模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 

docs/GithubAction+JittorLLMs

@@ -23,9 +23,6 @@ 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

@@ -12,8 +12,6 @@ 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

@@ -15,9 +15,6 @@ RUN pip3 install -r requirements.txt
 # 安装语音插件的额外依赖
 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

@@ -10,6 +10,9 @@ ENV PATH "$PATH:/usr/local/texlive/2024/bin/x86_64-linux"
 ENV PATH "$PATH:/usr/local/texlive/2025/bin/x86_64-linux"
 ENV PATH "$PATH:/usr/local/texlive/2026/bin/x86_64-linux"
 
+# 删除文档文件以节约空间
+RUN rm -rf /usr/local/texlive/2023/texmf-dist/doc
+
 # 指定路径
 WORKDIR /gpt
 
@@ -25,9 +28,6 @@ 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

@@ -19,9 +19,6 @@ 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

@@ -1,189 +0,0 @@
-# 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\Conversation_To_File.py | 将每次对话记录写入Markdown格式的文件中 |
+| 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\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\Markdown_Translate.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| 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\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\PDF_Translate.py | 将指定目录下的PDF文件进行中英文翻译 |
+| 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\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\Conversation_To_File.py
+## [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
 
-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：
+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：
 
 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. 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。
+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。
 
 ## [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\Markdown_Translate.py
+## [21/48] 请对下面的程序文件做一个概述: crazy_functions\批量Markdown翻译.py
 
-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。
+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。
 
 ## [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\PDF_Translate.py
+## [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
 
-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。
+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。
 
 ## [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\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。
+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。
 
 | 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 |
-| Conversation_To_File.py | 将每次对话记录写入Markdown格式的文件中 |
+| 对话历史存档.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 |
-| Markdown_Translate.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| 批量Markdown翻译.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 |
-| PDF_Translate.py | 将指定目录下的PDF文件进行中英文翻译 |
+| 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 |
 | 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",
-    "Conversation_To_File": "ConversationHistoryArchive",
-    "载入Conversation_To_File": "LoadConversationHistoryArchive",
+    "对话历史存档": "ConversationHistoryArchive",
+    "载入对话历史存档": "LoadConversationHistoryArchive",
     "删除所有本地对话历史记录": "DeleteAllLocalChatHistory",
     "Markdown英译中": "MarkdownTranslateFromEngToChi",
-    "Markdown_Translate": "BatchTranslateMarkdown",
+    "批量Markdown翻译": "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",
-    "PDF_Translate": "BatchTranslatePDFDocumentsUsingMultiThreading",
+    "批量翻译PDF文档_多线程": "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_Function": "LatexOutputPDFResult",
+    "Latex输出PDF": "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

@@ -6,14 +6,17 @@
     "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": "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",
@@ -43,7 +46,7 @@
     "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Conversation_To_File": "LoadConversationHistoryArchive",
+    "载入对话历史存档": "LoadConversationHistoryArchive",
     "Latex中译英": "LatexChineseToEnglish",
     "Latex英译中": "LatexEnglishToChinese",
     "连接网络回答问题": "ConnectToNetworkToAnswerQuestions",
@@ -67,6 +70,7 @@
     "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",
@@ -100,11 +104,5 @@
     "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"
+    "高阶功能模板函数示意图": "HighOrderFunctionTemplateDiagram"
 }

docs/translate_traditionalchinese.json

@@ -35,15 +35,15 @@
     "总结word文档": "SummarizeWordDocument",
     "解析ipynb文件": "ParseIpynbFile",
     "解析JupyterNotebook": "ParseJupyterNotebook",
-    "Conversation_To_File": "ConversationHistoryArchive",
-    "载入Conversation_To_File": "LoadConversationHistoryArchive",
+    "对话历史存档": "ConversationHistoryArchive",
+    "载入对话历史存档": "LoadConversationHistoryArchive",
     "删除所有本地对话历史记录": "DeleteAllLocalConversationHistoryRecords",
     "Markdown英译中": "MarkdownEnglishToChinese",
-    "Markdown_Translate": "BatchMarkdownTranslation",
+    "批量Markdown翻译": "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",
-    "PDF_Translate": "BatchTranslatePdfDocumentsMultithreaded",
+    "批量翻译PDF文档_多线程": "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_Function": "OutputPDFFromLatex",
+    "Latex输出PDF": "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_tts.md

@@ -1,58 +0,0 @@
-# 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

@@ -1,46 +0,0 @@
-# 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
-```

main.py

@@ -1,354 +0,0 @@
-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),
-感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
-</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki),
-如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
-</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
-</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
-</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
-</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端
-</br></br>如何保存对话: 点击保存当前的对话按钮
-</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 ['3.32.9', '3.32.10', '3.32.11']:
-        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, DummyWith
-
-    # 读取配置
-    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, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
-    NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
-    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, js_code_clear, js_code_reset, js_code_show_or_hide, js_code_show_or_hide_group2
-    from themes.theme import js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
-    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}"
-
-    # 对话、日志记录
-    enable_log(PATH_LOGGING)
-
-    # 一些普通功能模块
-    from core_functional import get_core_functions
-    functional = get_core_functions()
-
-    # 高级函数插件
-    from crazy_functional import get_crazy_functions
-    DEFAULT_FN_GROUPS = get_conf('DEFAULT_FN_GROUPS')
-    plugins = get_crazy_functions()
-    all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
-    match_group = lambda tags, groups: any([g in groups for g in tags.split('|')])
-
-    # 处理markdown文本格式的转变
-    gr.Chatbot.postprocess = format_io
-
-    # 做一些外观色彩上的调整
-    set_theme = adjust_theme()
-
-    # 代理与自动更新
-    from check_proxy import check_proxy, auto_update, warm_up_modules
-    proxy_info = check_proxy(proxies)
-
-    # 切换布局
-    gr_L1 = lambda: gr.Row().style()
-    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()
-        CHATBOT_HEIGHT /= 2
-
-    cancel_handles = []
-    customize_btns = {}
-    predefined_btns = {}
-    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 = gr.Textbox(visible=False, elem_id="secret_css")
-        register_advanced_plugin_init_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, 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():
-                        txt = gr.Textbox(show_label=False, placeholder="Input question here.", elem_id='user_input_main').style(container=False)
-                    with gr.Row():
-                        submitBtn = gr.Button("提交", elem_id="elem_submit", variant="primary")
-                    with gr.Row():
-                        resetBtn = gr.Button("重置", elem_id="elem_reset", variant="secondary"); resetBtn.style(size="sm")
-                        stopBtn = gr.Button("停止", elem_id="elem_stop", variant="secondary"); stopBtn.style(size="sm")
-                        clearBtn = gr.Button("清除", elem_id="elem_clear", variant="secondary", visible=False); clearBtn.style(size="sm")
-                    if ENABLE_AUDIO:
-                        with gr.Row():
-                            audio_mic = gr.Audio(source="microphone", type="numpy", 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。支持将文件直接粘贴到输入区。", elem_id="state-panel")
-
-                with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
-                    with gr.Row():
-                        for k in range(NUM_CUSTOM_BASIC_BTN):
-                            customize_btn = gr.Button("自定义按钮" + str(k+1), visible=False, variant="secondary", info_str=f'基础功能区: 自定义按钮')
-                            customize_btn.style(size="sm")
-                            customize_btns.update({"自定义按钮" + str(k+1): customize_btn})
-                        for k in functional:
-                            if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
-                            variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
-                            functional[k]["Button"] = gr.Button(k, variant=variant, info_str=f'基础功能区: {k}')
-                            functional[k]["Button"].style(size="sm")
-                            predefined_btns.update({k: functional[k]["Button"]})
-                with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
-                    with gr.Row():
-                        gr.Markdown("<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)
-                    with gr.Row():
-                        for index, (k, plugin) in enumerate(plugins.items()):
-                            if not plugin.get("AsButton", True): continue
-                            visible = True if match_group(plugin['Group'], DEFAULT_FN_GROUPS) else False
-                            variant = plugins[k]["Color"] if "Color" in plugin else "secondary"
-                            info = plugins[k].get("Info", k)
-                            btn_elem_id = f"plugin_btn_{index}"
-                            plugin['Button'] = plugins[k]['Button'] = gr.Button(k, variant=variant,
-                                visible=visible, info_str=f'函数插件区: {info}', elem_id=btn_elem_id).style(size="sm")
-                            plugin['ButtonElemId'] = btn_elem_id
-                    with gr.Row():
-                        with gr.Accordion("更多函数插件", open=True):
-                            dropdown_fn_list = []
-                            for k, plugin in plugins.items():
-                                if not match_group(plugin['Group'], DEFAULT_FN_GROUPS): continue
-                                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"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, 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", elem_id="elem_switchy_bt").style(size="sm")
-                    with gr.Row():
-                        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")
-
-        # 左上角工具栏定义
-        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)
-
-        # 浮动菜单定义
-        from themes.gui_floating_menu import define_gui_floating_menu
-        area_input_secondary, txt2, area_customize, submitBtn2, resetBtn2, clearBtn2, stopBtn2 = \
-            define_gui_floating_menu(customize_btns, functional, predefined_btns, cookies, web_cookie_cache)
-
-        # 插件二级菜单的实现
-        from themes.gui_advanced_plugin_class import define_gui_advanced_plugin_class
-        new_plugin_callback, route_switchy_bt_with_arg, usr_confirmed_arg = \
-            define_gui_advanced_plugin_class(plugins)
-
-        # 功能区显示开关与功能区的互动
-        def fn_area_visibility(a):
-            ret = {}
-            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({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, 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):
-            ret = {}
-            ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
-            return ret
-        checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
-        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
-        cancel_handles.append(txt.submit(**predict_args))
-        cancel_handles.append(txt2.submit(**predict_args))
-        cancel_handles.append(submitBtn.click(**predict_args))
-        cancel_handles.append(submitBtn2.click(**predict_args))
-        resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset)   # 先在前端快速清除chatbot&status
-        resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset)  # 先在前端快速清除chatbot&status
-        reset_server_side_args = (lambda history: ([], [], "已重置", json.dumps(history)), [history], [chatbot, history, status, history_cache])
-        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(None, None, [txt, txt2], _js=js_code_clear)
-            submitBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
-            txt.submit(None, None, [txt, txt2], _js=js_code_clear)
-            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
-            click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
-            cancel_handles.append(click_handle)
-        for btn in customize_btns.values():
-            click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
-            cancel_handles.append(click_handle)
-        # 文件上传区，接收文件后与chatbot的互动
-        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None,   _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
-        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
-        def encode_plugin_info(k, plugin)->str:
-            import copy
-            from themes.theme import to_cookie_str
-            plugin_ = copy.copy(plugin)
-            plugin_.pop("Function", None)
-            plugin_.pop("Class", None)
-            plugin_.pop("Button", None)
-            plugin_["Info"] = plugin.get("Info", k)
-            if plugin.get("AdvancedArgs", False):
-                plugin_["Label"] = f"插件[{k}]的高级参数说明：" + plugin.get("ArgsReminder", f"没有提供高级参数功能说明")
-            else:
-                plugin_["Label"] = f"插件[{k}]不需要高级参数。"
-            return to_cookie_str(plugin_)
-
-        # 插件的注册（前端代码注册）
-        for k in plugins:
-            register_advanced_plugin_init_arr += f"""register_plugin_init("{k}","{encode_plugin_info(k, plugins[k])}");"""
-            if plugins[k].get("Class", None):
-                plugins[k]["JsMenu"] = plugins[k]["Class"]().get_js_code_for_generating_menu(k)
-                register_advanced_plugin_init_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(None, inputs=[], outputs=None, _js=f"""()=>run_classic_plugin_via_id("{plugins[k]["ButtonElemId"]}")""")
-            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（新版-更流畅）
-        dropdown.select(None, [dropdown], None, _js=f"""(dropdown)=>run_dropdown_shift(dropdown)""")
-
-        # 模型切换时的回调
-        def on_md_dropdown_changed(k):
-            return {chatbot: gr.update(label="当前模型："+k)}
-        md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot])
-
-        # 主题修改
-        def on_theme_dropdown_changed(theme, secret_css):
-            adjust_theme, css_part1, _, adjust_dynamic_theme = load_dynamic_theme(theme)
-            if adjust_dynamic_theme:
-                css_part2 = adjust_dynamic_theme._get_theme_css()
-            else:
-                css_part2 = adjust_theme()._get_theme_css()
-            return css_part2 + css_part1
-        theme_handle = theme_dropdown.select(on_theme_dropdown_changed, [theme_dropdown, secret_css], [secret_css]) # , _js="""change_theme_prepare""")
-        theme_handle.then(None, [theme_dropdown, secret_css], None, _js="""change_theme""")
-
-        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 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)
-        # 旧插件的高级参数区确认按钮（隐藏）
-        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")
-        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), # 第一个参数: 指定了后续参数的名称
-                new_plugin_callback, usr_confirmed_arg, *input_combo                        # 后续参数: 真正的参数
-            ], 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)
-        plugins_as_btn = {name:plugin for name, plugin in plugins.items() if plugin.get('Button', None)}
-        def on_group_change(group_list):
-            btn_list = []
-            fns_list = []
-            if not group_list: # 处理特殊情况：没有选择任何插件组
-                return [*[plugin['Button'].update(visible=False) for _, plugin in plugins_as_btn.items()], gr.Dropdown.update(choices=[])]
-            for k, plugin in plugins.items():
-                if plugin.get("AsButton", True):
-                    btn_list.append(plugin['Button'].update(visible=match_group(plugin['Group'], group_list))) # 刷新按钮
-                    if 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
-                elif match_group(plugin['Group'], group_list): fns_list.append(k) # 刷新下拉列表
-            return [*btn_list, gr.Dropdown.update(choices=fns_list)]
-        plugin_group_sel.select(fn=on_group_change, inputs=[plugin_group_sel], outputs=[*[plugin['Button'] for name, plugin in plugins_as_btn.items()], dropdown])
-
-        # 是否启动语音输入功能
-        if ENABLE_AUDIO:
-            from crazy_functions.live_audio.audio_io import RealtimeAudioDistribution
-            rad = RealtimeAudioDistribution()
-            def deal_audio(audio, cookies):
-                rad.feed(cookies['uuid'].hex, audio)
-            audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
-
-        # 生成当前浏览器窗口的uuid（刷新失效）
-        app_block.load(assign_user_uuid, inputs=[cookies], outputs=[cookies])
-
-        # 初始化（前端）
-        from shared_utils.cookie_manager import load_web_cookie_cache__fn_builder
-        load_web_cookie_cache = load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)
-        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_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():
-        import threading, webbrowser, time
-        print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
-        if DARK_MODE:   print(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
-        else:           print(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
-
-        def auto_updates(): time.sleep(0); auto_update()
-        def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
-        def warm_up_mods(): time.sleep(6); warm_up_modules()
-
-        threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
-        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()
-
-    # 最后，正式开始服务
-    from shared_utils.fastapi_server import start_app
-    start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE)
-
-
-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, re
+import tiktoken, copy
 from functools import lru_cache
 from concurrent.futures import ThreadPoolExecutor
-from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
+from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask
 
 from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
 from .bridge_chatgpt import predict as chatgpt_ui
@@ -34,14 +34,6 @@ 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_taichu import predict_no_ui_long_connection as taichu_noui
-from .bridge_taichu import predict as taichu_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):
@@ -69,13 +61,6 @@ 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
@@ -90,12 +75,7 @@ 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")
@@ -114,15 +94,6 @@ model_info = {
         "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,
-    },
-
-    "taichu": {
-        "fn_with_ui": taichu_ui,
-        "fn_without_ui": taichu_noui,
-        "endpoint": openai_endpoint,
         "max_token": 4096,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
@@ -155,16 +126,7 @@ model_info = {
         "token_cnt": get_token_num_gpt35,
     },
 
-    "gpt-3.5-turbo-1106": { #16k
-        "fn_with_ui": chatgpt_ui,
-        "fn_without_ui": chatgpt_noui,
-        "endpoint": openai_endpoint,
-        "max_token": 16385,
-        "tokenizer": tokenizer_gpt35,
-        "token_cnt": get_token_num_gpt35,
-    },
-
-    "gpt-3.5-turbo-0125": { #16k
+    "gpt-3.5-turbo-1106": {#16k
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
@@ -191,26 +153,6 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
-    "gpt-4o": {
-        "fn_with_ui": chatgpt_ui,
-        "fn_without_ui": chatgpt_noui,
-        "endpoint": openai_endpoint,
-        "has_multimodal_capacity": True,
-        "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,
-        "has_multimodal_capacity": True,
-        "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,
@@ -238,27 +180,6 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
-    "gpt-4-turbo": {
-        "fn_with_ui": chatgpt_ui,
-        "fn_without_ui": chatgpt_noui,
-        "has_multimodal_capacity": True,
-        "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,
-        "has_multimodal_capacity": True,
-        "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,
@@ -306,46 +227,6 @@ model_info = {
         "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,
@@ -401,7 +282,7 @@ model_info = {
     "gemini-pro": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
-        "endpoint": gemini_endpoint,
+        "endpoint": None,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
@@ -409,56 +290,13 @@ model_info = {
     "gemini-pro-vision": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
-        "endpoint": gemini_endpoint,
+        "endpoint": None,
         "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()):
@@ -474,67 +312,25 @@ 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 -=-=-=-=-=-=-
-# 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):
+if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
     from .bridge_claude import predict_no_ui_long_connection as claude_noui
     from .bridge_claude import predict as claude_ui
     model_info.update({
-        "claude-instant-1.2": {
+        "claude-1-100k": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": claude_endpoint,
-            "max_token": 100000,
+            "endpoint": None,
+            "max_token": 8196,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
     })
     model_info.update({
-        "claude-2.0": {
+        "claude-2": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": claude_endpoint,
-            "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,
+            "endpoint": None,
+            "max_token": 8196,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
@@ -604,6 +400,22 @@ 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
@@ -636,7 +448,6 @@ 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
@@ -669,7 +480,6 @@ 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
@@ -678,7 +488,6 @@ 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,
@@ -687,7 +496,6 @@ 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
@@ -696,7 +504,6 @@ 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,
@@ -705,7 +512,6 @@ 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,
@@ -714,7 +520,6 @@ 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,
@@ -723,88 +528,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
         })
     except:
         print(trimmed_format_exc())
-# -=-=-=-=-=-=- 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:
+if "spark" 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
@@ -812,7 +536,6 @@ if "spark" in AVAIL_LLM_MODELS:
             "spark": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
-                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -829,7 +552,6 @@ 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,
@@ -846,7 +568,6 @@ if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS:   # 讯飞
             "sparkv3": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
-                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -855,16 +576,6 @@ if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS:   # 讯飞
             "sparkv3.5": {
                 "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,
-            },
-            "sparkv4":{
-                "fn_with_ui": spark_ui,
-                "fn_without_ui": spark_noui,
-                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -889,7 +600,6 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
         })
     except:
         print(trimmed_format_exc())
-# -=-=-=-=-=-=- 智谱 -=-=-=-=-=-=-
 if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容配置
     try:
         model_info.update({
@@ -904,7 +614,6 @@ if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容
         })
     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
@@ -921,119 +630,26 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
         })
     except:
         print(trimmed_format_exc())
-# -=-=-=-=-=-=- 幻方-深度求索大模型在线API -=-=-=-=-=-=-
-if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
-    try:
-        deepseekapi_noui, deepseekapi_ui = get_predict_function(
-            api_key_conf_name="DEEPSEEK_API_KEY", max_output_token=4096, disable_proxy=False
-            )
-        model_info.update({
-            "deepseek-chat":{
-                "fn_with_ui": deepseekapi_ui,
-                "fn_without_ui": deepseekapi_noui,
-                "endpoint": deepseekapi_endpoint,
-                "can_multi_thread": True,
-                "max_token": 32000,
-                "tokenizer": tokenizer_gpt35,
-                "token_cnt": get_token_num_gpt35,
-            },
-            "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:
-        origin_model_name, max_token_tmp = read_one_api_model_name(model)
-        # 如果是已知模型，则尝试获取其信息
-        original_model_info = model_info.get(origin_model_name.replace("one-api-", "", 1), None)
-    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
-    this_model_info = {
-        "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,
-    }
+# if "skylark" in AVAIL_LLM_MODELS:
+#     try:
+#         from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
+#         from .bridge_skylark2 import predict as skylark_ui
+#         model_info.update({
+#             "skylark": {
+#                 "fn_with_ui": skylark_ui,
+#                 "fn_without_ui": skylark_noui,
+#                 "endpoint": None,
+#                 "max_token": 4096,
+#                 "tokenizer": tokenizer_gpt35,
+#                 "token_cnt": get_token_num_gpt35,
+#             }
+#         })
+#     except:
+#         print(trimmed_format_exc())
 
-    # 同步已知模型的其他信息
-    attribute = "has_multimodal_capacity"
-    if original_model_info is not None and original_model_info.get(attribute, None) is not None: this_model_info.update({attribute: original_model_info.get(attribute, None)})
-    # attribute = "attribute2"
-    # if original_model_info is not None and original_model_info.get(attribute, None) is not None: this_model_info.update({attribute: original_model_info.get(attribute, None)})
-    # attribute = "attribute3"
-    # if original_model_info is not None and original_model_info.get(attribute, None) is not None: this_model_info.update({attribute: original_model_info.get(attribute, None)})
-    model_info.update({model: this_model_info})
 
-# -=-=-=-=-=-=- 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 -=-=-=-=-=-=-
-AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY") # <-- 用于定义和切换多个azure模型 -->
+# <-- 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
@@ -1056,20 +672,13 @@ 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:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
+    def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
         try:
             return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
         except Exception as e:
@@ -1079,9 +688,9 @@ def LLM_CATCH_EXCEPTION(f):
     return decorated
 
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
     """
-    发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）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。但内部用stream的方法避免中途网线被掐。
     inputs：
         是本次问询的输入
     sys_prompt:
@@ -1099,11 +708,14 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
     model = llm_kwargs['llm_model']
     n_model = 1
     if '&' not in model:
-        # 如果只询问“一个”大语言模型（多数情况）：
+        assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
+
+        # 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)
@@ -1131,8 +743,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
                 # 观察窗（window）
                 chat_string = []
                 for i in range(n_model):
-                    color = colors[i%len(colors)]
-                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {window_mutex[i][0]} </font>" )
+                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
                 res = '<br/><br/>\n\n---\n\n'.join(chat_string)
                 # # # # # # # # # # #
                 observe_window[0] = res
@@ -1149,56 +760,25 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
             time.sleep(1)
 
         for i, future in enumerate(futures):  # wait and get
-            color = colors[i%len(colors)]
-            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {future.result()} </font>" )
+            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {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:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
-            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+def predict(inputs, llm_kwargs, *args, **kwargs):
     """
     发送至LLM，流式获取输出。
     用于基础的对话功能。
-
-    完整参数列表：
-        predict(
-            inputs:str,                     # 是本次问询的输入
-            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 是本次问询的输入
+    top_p, temperature是LLM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
     """
 
     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
-
-    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)
+    yield from method(inputs, llm_kwargs, *args, **kwargs)
 

request_llms/bridge_chatglm3.py

@@ -6,6 +6,7 @@ from toolbox import get_conf, ProxyNetworkActivate
 from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
+
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
@@ -22,45 +23,20 @@ class GetGLM3Handle(LocalLLMHandle):
         import os, glob
         import os
         import platform
+        LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
 
-        LOCAL_MODEL_QUANT, device = get_conf("LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
-        _model_name_ = "THUDM/chatglm3-6b"
-        # if LOCAL_MODEL_QUANT == "INT4":  # INT4
-        #     _model_name_ = "THUDM/chatglm3-6b-int4"
-        # elif LOCAL_MODEL_QUANT == "INT8":  # INT8
-        #     _model_name_ = "THUDM/chatglm3-6b-int8"
-        # else:
-        #     _model_name_ = "THUDM/chatglm3-6b"  # FP16
-        with ProxyNetworkActivate("Download_LLM"):
-            chatglm_tokenizer = AutoTokenizer.from_pretrained(
-                _model_name_, trust_remote_code=True
-            )
-            if device == "cpu":
-                chatglm_model = AutoModel.from_pretrained(
-                    _model_name_,
-                    trust_remote_code=True,
-                    device="cpu",
-                ).float()
-            elif LOCAL_MODEL_QUANT == "INT4":  # INT4
-                chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
-                    trust_remote_code=True,
-                    device="cuda",
-                    load_in_4bit=True,
-                )
+        if LOCAL_MODEL_QUANT == "INT4":         # INT4
+            _model_name_ = "THUDM/chatglm3-6b-int4"
         elif LOCAL_MODEL_QUANT == "INT8":       # INT8
-                chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
-                    trust_remote_code=True,
-                    device="cuda",
-                    load_in_8bit=True,
-                )
+            _model_name_ = "THUDM/chatglm3-6b-int8"
         else:
-                chatglm_model = AutoModel.from_pretrained(
-                    pretrained_model_name_or_path=_model_name_,
-                    trust_remote_code=True,
-                    device="cuda",
-                )
+            _model_name_ = "THUDM/chatglm3-6b"  # FP16
+        with ProxyNetworkActivate('Download_LLM'):
+            chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
+            if device=='cpu':
+                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cpu').float()
+            else:
+                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cuda')
             chatglm_model = chatglm_model.eval()
 
         self._model = chatglm_model
@@ -70,17 +46,16 @@ class GetGLM3Handle(LocalLLMHandle):
     def llm_stream_generator(self, **kwargs):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         def adaptor(kwargs):
-            query = kwargs["query"]
-            max_length = kwargs["max_length"]
-            top_p = kwargs["top_p"]
-            temperature = kwargs["temperature"]
-            history = kwargs["history"]
+            query = kwargs['query']
+            max_length = kwargs['max_length']
+            top_p = kwargs['top_p']
+            temperature = kwargs['temperature']
+            history = kwargs['history']
             return query, max_length, top_p, temperature, history
 
         query, max_length, top_p, temperature, history = adaptor(kwargs)
 
-        for response, history in self._model.stream_chat(
-            self._tokenizer,
+        for response, history in self._model.stream_chat(self._tokenizer, 
                                                          query, 
                                                          history, 
                                                          max_length=max_length,
@@ -93,13 +68,10 @@ 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

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

request_llms/bridge_chatgpt.py

@@ -1,3 +1,5 @@
+# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
+
 """
     该文件中主要包含三个函数
 
@@ -9,19 +11,17 @@
 """
 
 import json
-import os
-import re
 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, have_any_recent_upload_image_files, encode_image
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
     get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
 
@@ -39,57 +39,6 @@ def get_full_error(chunk, stream_response):
             break
     return chunk
 
-def make_multimodal_input(inputs, image_paths):
-    image_base64_array = []
-    for image_path in image_paths:
-        path = os.path.abspath(image_path)
-        base64 = encode_image(path)
-        inputs = inputs + f'<br/><br/><div align="center"><img src="file={path}" base64="{base64}"></div>'
-        image_base64_array.append(base64)
-    return inputs, image_base64_array
-
-def reverse_base64_from_input(inputs):
-    # 定义一个正则表达式来匹配 Base64 字符串（假设格式为 base64="<Base64编码>"）
-    # pattern = re.compile(r'base64="([^"]+)"></div>')
-    pattern = re.compile(r'<br/><br/><div align="center"><img[^<>]+base64="([^"]+)"></div>')
-    # 使用 findall 方法查找所有匹配的 Base64 字符串
-    base64_strings = pattern.findall(inputs)
-    # 返回反转后的 Base64 字符串列表
-    return base64_strings
-
-def contain_base64(inputs):
-    base64_strings = reverse_base64_from_input(inputs)
-    return len(base64_strings) > 0
-
-def append_image_if_contain_base64(inputs):
-    if not contain_base64(inputs):
-        return inputs
-    else:
-        image_base64_array = reverse_base64_from_input(inputs)
-        pattern = re.compile(r'<br/><br/><div align="center"><img[^><]+></div>')
-        inputs = re.sub(pattern, '', inputs)
-        res = []
-        res.append({
-            "type": "text",
-            "text": inputs
-        })
-        for image_base64 in image_base64_array:
-            res.append({
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{image_base64}"
-                }
-            })
-        return res
-
-def remove_image_if_contain_base64(inputs):
-    if not contain_base64(inputs):
-        return inputs
-    else:
-        pattern = re.compile(r'<br/><br/><div align="center"><img[^><]+></div>')
-        inputs = re.sub(pattern, '', inputs)
-        return inputs
-
 def decode_chunk(chunk):
     # 提前读取一些信息 （用于判断异常）
     chunk_decoded = chunk.decode()
@@ -119,7 +68,7 @@ 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:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+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：
@@ -176,9 +125,8 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
         if len(delta) == 0: break
-        if (not has_content) and has_role: continue
-        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
+        if "role" in delta: continue
+        if "content" in delta:
             result += delta["content"]
             if not console_slience: print(delta["content"], end='')
             if observe_window is not None: 
@@ -197,8 +145,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
     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):
+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。
@@ -208,7 +155,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     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 .bridge_all import model_info
     if is_any_api_key(inputs):
         chatbot._cookies['api_key'] = inputs
         chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
@@ -224,17 +170,9 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    # 多模态模型
-    has_multimodal_capacity = model_info[llm_kwargs['llm_model']].get('has_multimodal_capacity', False)
-    if has_multimodal_capacity:
-        has_recent_image_upload, image_paths = have_any_recent_upload_image_files(chatbot, pop=True)
-    else:
-        has_recent_image_upload, image_paths = False, []
-    if has_recent_image_upload:
-        _inputs, image_base64_array = make_multimodal_input(inputs, image_paths)
-    else:
-        _inputs, image_base64_array = inputs, []
-    chatbot.append((_inputs, ""))
+    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
@@ -244,7 +182,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         time.sleep(2)
 
     try:
-        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity, stream)
+        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
@@ -252,6 +190,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         
     # 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() + '```'
@@ -259,11 +198,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
         return
     
-    # 加入历史
-    if has_recent_image_upload:
-        history.extend([_inputs, ""])
-    else:
-        history.extend([inputs, ""])
+    history.append(inputs); history.append("")
 
     retry = 0
     while True:
@@ -317,8 +252,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     # 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}')
-                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        logging.info(f'[response] {gpt_replying_buffer}')
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -330,8 +264,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                         # 一些第三方接口的出现这样的错误，兼容一下吧
                         continue
                     else:
-                        # 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
+                        # 一些垃圾第三方接口的出现这样的错误
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
 
                     history[-1] = gpt_replying_buffer
@@ -377,16 +310,13 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
         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:str, llm_kwargs:dict, history:list, system_prompt:str, image_base64_array:list=[], has_multimodal_capacity:bool=False, stream:bool=True):
+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。")
 
-    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 = {
@@ -400,27 +330,17 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
             azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
             headers.update({"api-key": azure_api_key_unshared})
 
-    if has_multimodal_capacity:
-        # 当以下条件满足时，启用多模态能力：
-        # 1. 模型本身是多模态模型（has_multimodal_capacity）
-        # 2. 输入包含图像（len(image_base64_array) > 0）
-        # 3. 历史输入包含图像（ any([contain_base64(h) for h in history]) ）
-        enable_multimodal_capacity = (len(image_base64_array) > 0) or any([contain_base64(h) for h in history])
-    else:
-        enable_multimodal_capacity = False
-
-    if not enable_multimodal_capacity:
-        # 不使用多模态能力
     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"] = remove_image_if_contain_base64(history[index])
+            what_i_have_asked["content"] = history[index]
             what_gpt_answer = {}
             what_gpt_answer["role"] = "assistant"
-                what_gpt_answer["content"] = remove_image_if_contain_base64(history[index+1])
+            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
@@ -428,55 +348,15 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
                 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)
-    else:
-        # 多模态能力
-        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"] = append_image_if_contain_base64(history[index])
-                what_gpt_answer = {}
-                what_gpt_answer["role"] = "assistant"
-                what_gpt_answer["content"] = append_image_if_contain_base64(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"] = []
-        what_i_ask_now["content"].append({
-            "type": "text",
-            "text": inputs
-        })
-        for image_base64 in image_base64_array:
-            what_i_ask_now["content"].append({
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{image_base64}"
-                }
-            })
-        messages.append(what_i_ask_now)
-
-
     model = llm_kwargs['llm_model']
     if llm_kwargs['llm_model'].startswith('api2d-'):
         model = llm_kwargs['llm_model'][len('api2d-'):]
-    if 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", 
@@ -495,6 +375,8 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         "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_chatgpt_vision.py

@@ -27,8 +27,10 @@ timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check
 
 
 def report_invalid_key(key):
-    # 弃用功能
-    return
+    if get_conf("BLOCK_INVALID_APIKEY"): 
+        # 实验性功能，自动检测并屏蔽失效的KEY，请勿使用
+        from request_llms.key_manager import ApiKeyManager
+        api_key = ApiKeyManager().add_key_to_blacklist(key)
 
 def get_full_error(chunk, stream_response):
     """

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 logging
+
 import os
-import time
-import traceback
 import json
+import time
+import gradio as gr
+import logging
+import traceback
 import requests
-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"]
+import importlib
 
 # 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
@@ -39,34 +39,6 @@ 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):
     """
@@ -82,67 +54,50 @@ 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
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, json=message,
-                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
-        except requests.exceptions.ReadTimeout as e:
+            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            # with ProxyNetworkActivate()
+            stream = anthropic.completions.create(
+                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: 
-                if need_to_pass:
-                    pass
-                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='')
+        for completion in stream:
+            result += completion.completion
+            if not console_slience: print(completion.completion, 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['delta']['text']
+                if len(observe_window) >= 1: observe_window[0] += completion.completion
                 # 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")
+        traceback.print_exc()
 
     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):
     """
@@ -154,7 +109,7 @@ 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
     """
-    if inputs == "":     inputs = "空空如也的输入栏"
+    from anthropic import Anthropic
     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
@@ -164,23 +119,13 @@ 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)
 
-    have_recent_file, image_paths = every_image_file_in_path(chatbot)
-    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:
+    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
 
     try:
-        headers, message = generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths)
+        prompt = 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
@@ -193,117 +138,91 @@ 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
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, json=message,
-                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
-        except requests.exceptions.ReadTimeout as e:
+            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            # with ProxyNetworkActivate()
+            stream = anthropic.completions.create(
+                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
-            traceback.print_exc()
+            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
-            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 = ""
     
-    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:
+    for completion in stream:
         try:
-                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']
+            gpt_replying_buffer = gpt_replying_buffer + completion.completion
             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:
-                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")
+            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}")
+            yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
+            return
         
-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 = []
-
+    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"] = [{"type": "text", "text": history[index]}]
+            what_i_have_asked["content"] = history[index]
             what_gpt_answer = {}
             what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = [{"type": "text", "text": history[index+1]}]
-            if what_i_have_asked["content"][0]["text"] != "":
-                if what_i_have_asked["content"][0]["text"] == "": continue
-                if what_i_have_asked["content"][0]["text"] == timeout_bot_msg: continue
+            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'][0]['text'] = what_gpt_answer['content'][0]['text']
+                messages[-1]['content'] = what_gpt_answer['content']
 
-    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"] = []
-        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}]
+    what_i_ask_now["content"] = inputs
     messages.append(what_i_ask_now)
-    # 开始整理headers与message
-    headers = {
-        '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
+    prompt = convert_messages_to_prompt(messages)
+
+    return prompt
+
+

request_llms/bridge_cohere.py

@@ -1,328 +0,0 @@
-# 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

@@ -7,8 +7,7 @@ import re
 import os
 import time
 from request_llms.com_google import GoogleChatInit
-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
+from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
 
 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.' + \
@@ -21,7 +20,7 @@ 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(llm_kwargs)
+    genai = GoogleChatInit()
     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)
@@ -45,8 +44,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     return gpt_replying_buffer
 
 
-def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
-            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=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)
@@ -72,7 +70,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
 
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history)
-    genai = GoogleChatInit(llm_kwargs)
+    genai = GoogleChatInit()
     retry = 0
     while True:
         try:
@@ -99,7 +97,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
             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 ……"
 
@@ -106,8 +106,7 @@ class GetGLMHandle(Process):
 global llama_glm_handle
 llama_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

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

request_llms/bridge_jittorllms_rwkv.py

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

request_llms/bridge_moonshot.py

@@ -1,197 +0,0 @@
-# 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

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

request_llms/bridge_ollama.py

@@ -1,272 +0,0 @@
-# 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

@@ -82,9 +82,6 @@ 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",
@@ -120,8 +117,7 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
                 raise RuntimeError(dec['error_msg'])
 
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -164,8 +160,3 @@ 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:
-        tb_str = '```\n' + trimmed_format_exc() + '```'
-        chatbot[-1] = (chatbot[-1][0], tb_str)
-        yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
-        return

request_llms/bridge_qwen.py

@@ -1,12 +1,11 @@
 import time
 import os
 from toolbox import update_ui, get_conf, update_ui_lastest_msg
-from toolbox import check_packages, report_exception, log_chat
+from toolbox import check_packages, report_exception
 
 model_name = 'Qwen'
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -48,8 +47,6 @@ 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
@@ -59,7 +56,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
-    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
     # 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 ..."

request_llms/bridge_skylark2.py

@@ -9,8 +9,7 @@ def validate_key():
     if YUNQUE_SECRET_KEY == '': return False
     return True
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐ 多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_spark.py

@@ -13,8 +13,7 @@ def validate_key():
         return False
     return True
 
-def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
-                                  observe_window:list=[], console_slience:bool=False):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_taichu.py

@@ -1,72 +0,0 @@
-import time
-import os
-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 = 'Taichu-2.0'
-# taichu_default_model = 'taichu_llm'
-
-def validate_key():
-    TAICHU_API_KEY = get_conf("TAICHU_API_KEY")
-    if TAICHU_API_KEY == '': return False
-    return True
-
-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
-    """
-    watch_dog_patience = 5
-    response = ""
-
-    # if llm_kwargs["llm_model"] == "taichu":
-    #     llm_kwargs["llm_model"] = "taichu"
-
-    if validate_key() is False:
-        raise RuntimeError('请配置 TAICHU_API_KEY')
-
-    # 开始接收回复
-    from .com_taichu import TaichuChatInit
-    zhipu_bro_init = TaichuChatInit()
-    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。")
-    return response
-
-
-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, ""])
-    yield from update_ui(chatbot=chatbot, history=history)
-
-    if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
-        return
-
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-        chatbot[-1] = [inputs, ""]
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # if llm_kwargs["llm_model"] == "taichu":
-    #     llm_kwargs["llm_model"] = taichu_default_model
-
-    # 开始接收回复
-    from .com_taichu import TaichuChatInit
-    zhipu_bro_init = TaichuChatInit()
-    for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
-        chatbot[-1] = [inputs, response]
-        yield from update_ui(chatbot=chatbot, history=history)
-    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/bridge_zhipu.py

@@ -1,8 +1,7 @@
 import time
 import os
-from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
 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'
@@ -17,8 +16,7 @@ def make_media_input(inputs, 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):
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -44,8 +42,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
     return response
 
 
-def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
-            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
     """
         ⭐单线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -75,10 +72,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         llm_kwargs["llm_model"] = zhipuai_default_model
 
     if llm_kwargs["llm_model"] in ["glm-4v"]:
-        if (len(inputs) + sum(len(temp) for temp in history) + 1047) > 2000:
-            chatbot.append((inputs, "上下文长度超过glm-4v上限2000tokens，注意图片大约占用1,047个tokens"))
-            yield from update_ui(chatbot=chatbot, history=history)
-            return
         have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
         if not have_recent_file:
             chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
@@ -97,5 +90,4 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         chatbot[-1] = [inputs, response]
         yield from update_ui(chatbot=chatbot, history=history)
     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

@@ -114,10 +114,8 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr
 
 
 class GoogleChatInit:
-    def __init__(self, llm_kwargs):
-        from .bridge_all import model_info
-        endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-        self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k"
+    def __init__(self):
+        self.url_gemini = "https://generativelanguage.googleapis.com/v1beta/models/%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

@@ -48,10 +48,6 @@ 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':
@@ -65,12 +61,8 @@ class QwenRequestInstance():
                 self.result_buf += f"[Local Message] 请求错误：状态码：{response.status_code}，错误码:{response.code}，消息：{response.message}"
                 yield self.result_buf
                 break
-
-        # 耗尽generator避免报错
-        while True:
-            try: next(responses)
-            except: break
-
+        logging.info(f'[raw_input] {inputs}')
+        logging.info(f'[response] {self.result_buf}')
         return self.result_buf
 
 

request_llms/com_sparkapi.py

@@ -67,7 +67,6 @@ class SparkRequestInstance():
         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.gpt_url_v4 = "wss://spark-api.xf-yun.com/v4.0/chat"
 
         self.time_to_yield_event = threading.Event()
         self.time_to_exit_event = threading.Event()
@@ -95,8 +94,6 @@ class SparkRequestInstance():
             gpt_url = self.gpt_url_v3
         elif llm_kwargs['llm_model'] == 'sparkv3.5':
             gpt_url = self.gpt_url_v35
-        elif llm_kwargs['llm_model'] == 'sparkv4':
-            gpt_url = self.gpt_url_v4
         else:
             gpt_url = self.gpt_url
         file_manifest = []
@@ -197,7 +194,6 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest)
         "sparkv2": "generalv2",
         "sparkv3": "generalv3",
         "sparkv3.5": "generalv3.5",
-        "sparkv4": "4.0Ultra"
     }
     domains_select = domains[llm_kwargs['llm_model']]
     if file_manifest: domains_select = 'image'

request_llms/com_taichu.py

@@ -1,55 +0,0 @@
-# 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 toolbox import get_conf, encode_image, get_pictures_list
-import logging, os, requests
-import json
-class TaichuChatInit:
-    def __init__(self): ...
-
-    def __conversation_user(self, user_input: str, llm_kwargs:dict):
-        return {"role": "user", "content": user_input}
-
-    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
-
-    def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
-        TAICHU_API_KEY = get_conf("TAICHU_API_KEY")
-        params = {
-            'api_key': TAICHU_API_KEY,
-            'model_code': 'taichu_llm',
-            'question': '\n\n'.join(history) + inputs,
-            'prefix': system_prompt,
-            'temperature': llm_kwargs.get('temperature', 0.95),
-            'stream_format': 'json'
-        }
-
-        api = 'https://ai-maas.wair.ac.cn/maas/v1/model_api/invoke'
-        response = requests.post(api, json=params, stream=True)
-        results = ""
-        if response.status_code == 200:
-            response.encoding = 'utf-8'
-            for line in response.iter_lines(decode_unicode=True):
-                delta = json.loads(line)['choices'][0]['text']
-                results += delta
-                yield delta, results
-        else:
-            raise ValueError
-
-
-if __name__ == '__main__':
-    zhipu = TaichuChatInit()
-    zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')

request_llms/com_zhipuglm.py

@@ -8,7 +8,7 @@ from toolbox import get_conf, encode_image, get_pictures_list
 import logging, os
 
 
-def input_encode_handler(inputs:str, llm_kwargs:dict):
+def input_encode_handler(inputs, llm_kwargs):        
     if llm_kwargs["most_recent_uploaded"].get("path"):
         image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
     md_encode = []
@@ -28,7 +28,7 @@ class ZhipuChatInit:
         self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
         self.model = ''
 
-    def __conversation_user(self, user_input: str, llm_kwargs:dict):
+    def __conversation_user(self, user_input: str, llm_kwargs):
         if self.model not in ["glm-4v"]:
             return {"role": "user", "content": user_input}
         else:
@@ -36,18 +36,12 @@ class ZhipuChatInit:
             what_i_have_asked = {"role": "user", "content": []}
             what_i_have_asked['content'].append({"type": 'text', "text": user_input})
             if encode_img:
-                if len(encode_img) > 1:
-                    logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
-                    print("glm-4v只支持一张图片,将只取第一张图片进行处理")
                 img_d = {"type": "image_url",
-                            "image_url": {
-                                "url": encode_img[0]['data']
-                            }
-                        }
+                         "image_url": {'url': encode_img}}
                 what_i_have_asked['content'].append(img_d)
             return what_i_have_asked
 
-    def __conversation_history(self, history:list, llm_kwargs:dict):
+    def __conversation_history(self, history, llm_kwargs):
         messages = []
         conversation_cnt = len(history) // 2
         if conversation_cnt:
@@ -61,67 +55,22 @@ class ZhipuChatInit:
                 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):
+    def __conversation_message_payload(self, inputs, llm_kwargs, history, system_prompt):
         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),
+            temperature=llm_kwargs.get('temperature', 0.95) * 0.95,  # 只能传默认的 temperature 和 top_p
+            top_p=llm_kwargs.get('top_p', 0.7) * 0.7,
+            max_tokens=llm_kwargs.get('max_tokens', 1024 * 4),  # 最大输出模型的一半
         )
         return response
 
-    def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
+    def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
         self.model = llm_kwargs['llm_model']
         response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
         bro_results = ''

request_llms/local_llm_class.py

@@ -1,7 +1,6 @@
 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
@@ -215,7 +214,7 @@ 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:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False):
+    def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
         """
             refer to request_llms/bridge_all.py
         """
@@ -261,8 +260,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
                     raise RuntimeError("程序终止。")
         return response
 
-    def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
-                history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
         """
             refer to request_llms/bridge_all.py
         """

request_llms/oai_std_model_template.py

@@ -1,402 +0,0 @@
-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:
-        respose = "API_ERROR"
-        finish_reason = chunk
-    # 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

@@ -1,15 +1,13 @@
-https://public.agent-matrix.com/publish/gradio-3.32.10-py3-none-any.whl
-fastapi==0.110
+https://public.agent-matrix.com/publish/gradio-3.32.8-py3-none-any.whl
 gradio-client==0.8
 pypdf2==2.12.1
-zhipuai==2.0.1
+zhipuai>=2
 tiktoken>=0.3.3
 requests[socks]
 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
@@ -18,15 +16,13 @@ prompt_toolkit
 latex2mathml
 python-docx
 mdtex2html
-dashscope
+anthropic
 pyautogen
 colorama
 Markdown
 pygments
-edge-tts
 pymupdf
 openai
-rjsmin
 arxiv
 numpy
 rich

shared_utils/advanced_markdown_format.py

@@ -46,16 +46,6 @@ code_highlight_configs_block_mermaid = {
     },
 }
 
-
-mathpatterns = {
-    r"(?<!\\|\$)(\$)([^\$]+)(\$)": {"allow_multi_lines": False},  #  $...$
-    r"(?<!\\)(\$\$)([^\$]+)(\$\$)": {"allow_multi_lines": True},  # $$...$$
-    r"(?<!\\)(\\\[)(.+?)(\\\])": {"allow_multi_lines": False},  # \[...\]
-    r'(?<!\\)(\\\()(.+?)(\\\))': {'allow_multi_lines': False},                       # \(...\)
-    # r'(?<!\\)(\\begin{([a-z]+?\*?)})(.+?)(\\end{\2})': {'allow_multi_lines': True},  # \begin...\end
-    # r'(?<!\\)(\$`)([^`]+)(`\$)': {'allow_multi_lines': False},                       # $`...`$
-}
-
 def tex2mathml_catch_exception(content, *args, **kwargs):
     try:
         content = tex2mathml(content, *args, **kwargs)
@@ -106,7 +96,14 @@ def is_equation(txt):
         return False
     if "$" not in txt and "\\[" not in txt:
         return False
-
+    mathpatterns = {
+        r"(?<!\\|\$)(\$)([^\$]+)(\$)": {"allow_multi_lines": False},  #  $...$
+        r"(?<!\\)(\$\$)([^\$]+)(\$\$)": {"allow_multi_lines": True},  # $$...$$
+        r"(?<!\\)(\\\[)(.+?)(\\\])": {"allow_multi_lines": False},  # \[...\]
+        # r'(?<!\\)(\\\()(.+?)(\\\))': {'allow_multi_lines': False},                       # \(...\)
+        # r'(?<!\\)(\\begin{([a-z]+?\*?)})(.+?)(\\end{\2})': {'allow_multi_lines': True},  # \begin...\end
+        # r'(?<!\\)(\$`)([^`]+)(`\$)': {'allow_multi_lines': False},                       # $`...`$
+    }
     matches = []
     for pattern, property in mathpatterns.items():
         flags = re.ASCII | re.DOTALL if property["allow_multi_lines"] else re.ASCII
@@ -210,118 +207,6 @@ def fix_code_segment_indent(txt):
         return txt
 
 
-def fix_dollar_sticking_bug(txt):
-    """
-    修复不标准的dollar公式符号的问题
-    """
-    txt_result = ""
-    single_stack_height = 0
-    double_stack_height = 0
-    while True:
-        while True:
-            index = txt.find('$')
-
-            if index == -1:
-                txt_result += txt
-                return txt_result
-
-            if single_stack_height > 0:
-                if txt[:(index+1)].find('\n') > 0 or txt[:(index+1)].find('<td>') > 0 or txt[:(index+1)].find('</td>') > 0:
-                    print('公式之中出现了异常 (Unexpect element in equation)')
-                    single_stack_height = 0
-                    txt_result += ' $'
-                    continue
-
-            if double_stack_height > 0:
-                if txt[:(index+1)].find('\n\n') > 0:
-                    print('公式之中出现了异常 (Unexpect element in equation)')
-                    double_stack_height = 0
-                    txt_result += '$$'
-                    continue
-
-            is_double = (txt[index+1] == '$')
-            if is_double:
-                if single_stack_height != 0:
-                    # add a padding
-                    txt = txt[:(index+1)] + " " + txt[(index+1):]
-                    continue
-                if double_stack_height == 0:
-                    double_stack_height = 1
-                else:
-                    double_stack_height = 0
-                txt_result += txt[:(index+2)]
-                txt = txt[(index+2):]
-            else:
-                if double_stack_height != 0:
-                    # print(txt[:(index)])
-                    print('发现异常嵌套公式')
-                if single_stack_height == 0:
-                    single_stack_height = 1
-                else:
-                    single_stack_height = 0
-                    # print(txt[:(index)])
-                txt_result += txt[:(index+1)]
-                txt = txt[(index+1):]
-            break
-
-
-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_stage_1 = fix_dollar_sticking_bug(txt)
-    # convert everything to html format
-    convert_stage_2 = markdown.markdown(
-        text=convert_stage_1,
-        extensions=[
-            "sane_lists",
-            "tables",
-            "mdx_math",
-            "pymdownx.superfences",
-            "pymdownx.highlight",
-        ],
-        extension_configs={**markdown_extension_configs, **code_highlight_configs},
-    )
-
-
-    def repl_fn(match):
-        content = match.group(2)
-        return f'<script type="math/tex">{content}</script>'
-
-    pattern = "|".join([pattern for pattern, property in mathpatterns.items() if not property["allow_multi_lines"]])
-    pattern = re.compile(pattern, flags=re.ASCII)
-    convert_stage_3 = pattern.sub(repl_fn, convert_stage_2)
-
-    convert_stage_4 = markdown_bug_hunt(convert_stage_3)
-
-    # 2. convert to rendered equation
-    convert_stage_5, n = re.subn(
-        find_equation_pattern, replace_math_render, convert_stage_4, flags=re.DOTALL
-    )
-    # cat them together
-    return pre + convert_stage_5 + suf
-
 @lru_cache(maxsize=128)  # 使用 lru缓存 加快转换速度
 def markdown_convertion(txt):
     """

shared_utils/char_visual_effect.py

@@ -1,25 +0,0 @@
-def is_full_width_char(ch):
-    """判断给定的单个字符是否是全角字符"""
-    if '\u4e00' <= ch <= '\u9fff':
-        return True  # 中文字符
-    if '\uff01' <= ch <= '\uff5e':
-        return True  # 全角符号
-    if '\u3000' <= ch <= '\u303f':
-        return True  # CJK标点符号
-    return False
-
-def scolling_visual_effect(text, scroller_max_len):
-    text = text.\
-            replace('\n', '').replace('`', '.').replace(' ', '.').replace('<br/>', '.....').replace('$', '.')
-    place_take_cnt = 0
-    pointer = len(text) - 1
-
-    if len(text) < scroller_max_len:
-        return text
-
-    while place_take_cnt < scroller_max_len and pointer > 0:
-        if is_full_width_char(text[pointer]): place_take_cnt += 2
-        else: place_take_cnt += 1
-        pointer -= 1
-
-    return text[pointer:]

shared_utils/config_loader.py

@@ -2,7 +2,7 @@ import importlib
 import time
 import os
 from functools import lru_cache
-from shared_utils.colorful import print亮红, print亮绿, print亮蓝
+from 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.Markdown_Translate->Markdown翻译指定语言'
+    e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'
     """
     import importlib
 
     assert (
         "->" in plugin_name
-    ), "Example of plugin_name: crazy_functions.Markdown_Translate->Markdown翻译指定语言"
+    ), "Example of plugin_name: crazy_functions.批量Markdown翻译->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

@@ -1,144 +0,0 @@
-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

@@ -1,296 +0,0 @@
-"""
-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', 'arxiv_cache', 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', 'arxiv_cache', 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(5e-2)
-
-    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)
-
-        from fastapi import Request, status
-        from fastapi.responses import FileResponse, RedirectResponse
-        @gradio_app.get("/academic_logout")
-        async def logout():
-            response = RedirectResponse(url=CUSTOM_PATH, status_code=status.HTTP_302_FOUND)
-            response.delete_cookie('access-token')
-            response.delete_cookie('access-token-unsecure')
-            return response
-
-    # --- --- enable TTS (text-to-speech) functionality --- ---
-    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 and block fastapi api reference routes --- ---
-    from starlette.responses import JSONResponse
-    if CUSTOM_PATH != '/':
-        from fastapi.responses import FileResponse
-        @fastapi_app.get("/favicon.ico")
-        async def favicon():
-            return FileResponse(app_block.favicon_path)
-
-        @fastapi_app.middleware("http")
-        async def middleware(request: Request, call_next):
-            if request.scope['path'] in ["/docs", "/redoc", "/openapi.json"]:
-                return JSONResponse(status_code=404, content={"message": "Not Found"})
-            response = await call_next(request)
-            return response
-
-
-    # --- --- 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

@@ -104,14 +104,6 @@ def extract_archive(file_path, 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))
 

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}$|sk-proj-[a-zA-Z0-9]{48}$|sess-[a-zA-Z0-9]{40}$", key)
+        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$|sess-[a-zA-Z0-9]{40}$", key)
     return bool(API_MATCH_ORIGINAL)
 
 
@@ -28,11 +28,6 @@ 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(',')
@@ -40,7 +35,7 @@ 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) or is_cohere_api_key(key)
+        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
 
 
 def what_keys(keys):
@@ -67,7 +62,7 @@ def select_api_key(keys, llm_model):
     avail_key_list = []
     key_list = keys.split(',')
 
-    if llm_model.startswith('gpt-') or llm_model.startswith('one-api-'):
+    if llm_model.startswith('gpt-'):
         for k in key_list:
             if is_openai_api_key(k): avail_key_list.append(k)
 
@@ -79,12 +74,8 @@ 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（左上角更换模型菜单中可切换openai,azure,claude,cohere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（右下角更换模型菜单中可切换openai,azure,claude,api2d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

@@ -1,34 +0,0 @@
-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

@@ -26,8 +26,6 @@ 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_latex_auto_correct.py

@@ -1,22 +0,0 @@
-"""
-对项目中的各个插件进行测试。运行方法：直接运行 python tests/test_plugins.py
-"""
-
-
-import os, sys, importlib
-
-
-def validate_path():
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(dir_name + "/..")
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-
-
-validate_path()  # 返回项目根路径
-
-if __name__ == "__main__":
-    plugin_test = importlib.import_module('test_utils').plugin_test
-
-
-    plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2203.01927")

tests/test_llms.py

@@ -11,30 +11,7 @@ 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_taichu import predict_no_ui_long_connection
-        # 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": "taichu",
-            "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__":
+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
     # from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
@@ -43,14 +20,19 @@ if "本地模型":
     # 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

@@ -43,10 +43,8 @@ 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
-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)
+
+html = markdown_convertion(md)
 # print(html)
 with open("test.html", "w", encoding="utf-8") as f:
     f.write(html)

tests/test_plugins.py

@@ -18,18 +18,14 @@ 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_Function->Latex翻译中文并重新编译PDF', main_input="2307.07522")
+    # plugin_test(plugin='crazy_functions.Latex输出PDF->Latex翻译中文并重新编译PDF', main_input="2307.07522")
 
-    # plugin_test(plugin='crazy_functions.PDF_Translate->批量翻译PDF文档', main_input='build/pdf/t1.pdf')
-
-    # 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.Latex输出PDF->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')
 
@@ -45,9 +41,9 @@ 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.Markdown_Translate->Markdown中译英', main_input="README.md")
+    # plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown中译英', main_input="README.md")
 
-    # 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.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.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=")
 
@@ -62,7 +58,7 @@ 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.Markdown_Translate->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang})
+    #     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.知识库文件注入->知识库文件注入', main_input="./")
 
@@ -70,7 +66,7 @@ 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_Function->Latex翻译中文并重新编译PDF', main_input="2210.03629")
+    # plugin_test(plugin='crazy_functions.Latex输出PDF->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)

tests/test_safe_pickle.py

@@ -1,17 +0,0 @@
-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 crazy_functions.latex_fns.latex_pickle_io import objdump, objload
-from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
-pfg = LatexPaperFileGroup()
-pfg.get_token_num = None
-pfg.target = "target_elem"
-x = objdump(pfg)
-t = objload()
-
-print(t.target)

tests/test_save_chat_to_html.py

@@ -1,102 +0,0 @@
-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
-
-def write_chat_to_file(chatbot, history=None, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    from themes.theme import advanced_css
-    # debug
-    import pickle
-    # 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:
-            return pickle.load(f)
-    # objdump((chatbot, history))
-    chatbot, history = objload()
-
-    with open("test.html", 'w', encoding='utf8') as f:
-        from textwrap import dedent
-        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):
-            question, answer = contents[0], contents[1]
-            if question is None: question = ""
-            try: question = str(question)
-            except: question = ""
-            if answer is None: answer = ""
-            try: answer = str(answer)
-            except: answer = ""
-            CHAT_PREVIEW_BUF += qa_from.format(QUESTION=question, ANSWER=answer)
-
-        HISTORY_PREVIEW_BUF = ""
-        for h in history:
-            HISTORY_PREVIEW_BUF += history_from.format(ENTRY=h)
-        html_content = form.format(CHAT_PREVIEW=CHAT_PREVIEW_BUF, HISTORY_PREVIEW=HISTORY_PREVIEW_BUF, CSS=advanced_css)
-
-
-    from bs4 import BeautifulSoup
-    soup = BeautifulSoup(html_content, 'lxml')
-
-    # 提取QaBox信息
-    qa_box_list = []
-    qa_boxes = soup.find_all("div", class_="QaBox")
-    for box in qa_boxes:
-        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})
-
-    # 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)
-
-    print('')
-
-
-write_chat_to_file(None, None, None)

tests/test_searxng.py

@@ -1,58 +0,0 @@
-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 toolbox import get_conf
-import requests
-
-def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
-    url = 'http://localhost:50001/'
-
-    if engines is None:
-        engine = 'bing,'
-    if categories == 'general':
-        params = {
-            'q': query,         # 搜索查询
-            'format': 'json',   # 输出格式为JSON
-            'language': 'zh',   # 搜索语言
-            'engines': engine,
-        }
-    elif categories == 'science':
-        params = {
-            'q': query,         # 搜索查询
-            'format': 'json',   # 输出格式为JSON
-            'language': 'zh',   # 搜索语言
-            'categories': 'science'
-        }
-    else:
-        raise ValueError('不支持的检索类型')
-    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': '112.112.112.112',
-        'X-Real-IP': '112.112.112.112'
-    }
-    results = []
-    response = requests.post(url, params=params, headers=headers, proxies=proxies, timeout=30)
-    if response.status_code == 200:
-        json_result = response.json()
-        for result in json_result['results']:
-            item = {
-                "title": result.get("title", ""),
-                "content": result.get("content", ""),
-                "link": result["url"],
-            }
-            print(result['engines'])
-            results.append(item)
-        return results
-    else:
-        if response.status_code == 429:
-            raise ValueError("Searxng（在线搜索服务）当前使用人数太多，请稍后。")
-        else:
-            raise ValueError("在线搜索失败，状态码: " + str(response.status_code) + '\t' + response.content.decode('utf-8'))
-res = searxng_request("vr environment", None, categories='science', searxng_url=None, engines=None)
-print(res)

themes/common.css

@@ -1,9 +1,3 @@
-#plugin_arg_menu {
-    transform: translate(-50%, -50%);
-    border: dashed;
-}
-
-
 /* hide remove all button */
 .remove-all.svelte-aqlk7e.svelte-aqlk7e.svelte-aqlk7e {
     visibility: hidden;
@@ -44,7 +38,6 @@
     left: calc(100% + 3px);
     top: 0;
     display: flex;
-    flex-direction: column;
     justify-content: space-between;
 }
 /* .message-btn-row-leading, .message-btn-row-trailing {
@@ -115,7 +108,6 @@
     border-width: thin;
     user-select: none;
     padding-left: 2%;
-    text-align: center;
 }
 
 .floating-component #input-panel2 {
@@ -125,20 +117,3 @@
     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;
-}
-