version 3.75 (#1702)

* Update version to 3.74

* Add support for Yi Model API (#1635)

* 更新以支持零一万物模型

* 删除newbing

* 修改config

---------

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

* Refactor function signatures in bridge files

* fix qwen api change

* rename and ref functions

* rename and move some cookie functions

* 增加haiku模型，新增endpoint配置说明 (#1626)

* haiku added

* 新增haiku，新增endpoint配置说明

* Haiku added

* 将说明同步至最新Endpoint

---------

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

* private_upload目录下进行文件鉴权 (#1596)

* private_upload目录下进行文件鉴权

* minor fastapi adjustment

* Add logging functionality to enable saving
conversation records

* waiting to fix username retrieve

* support 2rd web path

* allow accessing default user dir

---------

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

* remove yaml deps

* fix favicon

* fix abs path auth problem

* forget to write a return

* add `dashscope` to deps

* fix GHSA-v9q9-xj86-953p

* 用户名重叠越权访问patch (#1681)

* add cohere model api access

* cohere + can_multi_thread

* fix block user access(fail)

* fix fastapi bug

* change cohere api endpoint

* explain version

* # fix com_zhipuglm.py illegal temperature problem (#1687)

* Update com_zhipuglm.py

# fix 用户在使用 zhipuai 界面时遇到了关于温度参数的非法参数错误

* allow store lm model dropdown

* add a btn to reverse previous reset

* remove extra fns

* Add support for glm-4v model (#1700)

* 修改chatglm3量化加载方式 (#1688)

Co-authored-by: zym9804 <ren990603@gmail.com>

* save chat stage 1

* consider null cookie situation

* 在点击复制按钮时激活语音

* miss some parts

* move all to js

* done first stage

* add edge tts

* bug fix

* bug fix

* remove console log

* bug fix

* bug fix

* bug fix

* audio switch

* update tts readme

* remove tempfile when done

* disable auto audio follow

* avoid play queue update after shut up

* feat: minimizing common.js

* improve tts functionality

* deterine whether the cached model is in choices

* Add support for Ollama (#1740)

* print err when doc2x not successful

* add icon

* adjust url for doc2x key version

* prepare merge

---------

Co-authored-by: Menghuan1918 <menghuan2003@outlook.com>
Co-authored-by: Skyzayre <120616113+Skyzayre@users.noreply.github.com>
Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com>
Co-authored-by: Yuki <903728862@qq.com>
Co-authored-by: zyren123 <91042213+zyren123@users.noreply.github.com>
Co-authored-by: zym9804 <ren990603@gmail.com>

.gitignore

@@ -153,3 +153,4 @@ media
 flagged
 request_llms/ChatGLM-6b-onnx-u8s8
 .pre-commit-config.yaml
+themes/common.js.min.*.js

config.py

@@ -34,7 +34,7 @@ else:
 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-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-3-turbo",
+                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
                     "gemini-pro", "chatglm3"
                     ]
 # --- --- --- ---
@@ -50,9 +50,9 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
 #   "yi-34b-chat-0205", "yi-34b-chat-200k"
 # ]
 # --- --- --- ---
-# 此外，为了更灵活地接入one-api多模型管理界面，您还可以在接入one-api时，
+# 此外，您还可以在接入one-api/vllm/ollama时，
-# 使用"one-api-*"前缀直接使用非标准方式接入的模型，例如
+# 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)"]
+# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)"]
 # --- --- --- ---
 
 
@@ -60,7 +60,7 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
 
 # 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
 # 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
+# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
 API_URL_REDIRECT = {}
 
 
@@ -195,6 +195,12 @@ ALIYUN_ACCESSKEY="" # （无需填写）
 ALIYUN_SECRET=""    # （无需填写）
 
 
+# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
+TTS_TYPE = "DISABLE" # LOCAL / 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"

crazy_functions/PDF批量翻译.py

@@ -74,8 +74,10 @@ def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, cha
         import requests, json, os
         markdown_dir = get_log_folder(plugin_name="pdf_ocr")
         doc2x_api_key = DOC2X_API_KEY
-        # url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
+        if doc2x_api_key.startswith('sk-'):
-        url = "https://api.doc2x.noedgeai.com/api/platform/pdf"
+            url = "https://api.doc2x.noedgeai.com/api/v1/pdf"
+        else:
+            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

docs/use_tts.md

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

main.py

@@ -1,4 +1,4 @@
-import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
+import os, json; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
 
 help_menu_description = \
 """Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic),
@@ -14,7 +14,7 @@ help_menu_description = \
 </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, uuid
+    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")
@@ -29,13 +29,14 @@ def main():
     if gr.__version__ not in ['3.32.9']:
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
     from request_llms.bridge_all import predict
-    from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
+    from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
     # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
     ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME, 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 = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU')
+    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
@@ -79,15 +80,18 @@ def main():
     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, web_cookie_cache = gr.Textbox(visible=False), gr.Textbox(visible=False)
+        secret_css = gr.Textbox(visible=False, elem_id="secret_css")
-        cookies = gr.State(load_chat_cookies())
+
+
+        cookies, web_cookie_cache = make_cookie_cache() # 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
         with gr_L1():
             with gr_L2(scale=2, elem_id="gpt-chat"):
                 chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}", elem_id="gpt-chatbot")
                 if LAYOUT == "TOP-DOWN":  chatbot.style(height=CHATBOT_HEIGHT)
-                history = gr.State([])
+                history, history_cache, history_cache_update = make_history_cache() # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
             with gr_L2(scale=1, elem_id="gpt-panel"):
                 with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
                     with gr.Row():
@@ -155,7 +159,7 @@ def main():
                     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)
+                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, elem_id="elem_model_sel", label="更换LLM模型/请求源").style(container=False)
                     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", elem_id="elem_temperature")
                     max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
@@ -164,6 +168,8 @@ def main():
                         _js="""(temperature)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_temperature_cookie", temperature)""")
                     system_prompt.change(None, inputs=[system_prompt], outputs=None,
                         _js="""(system_prompt)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_system_prompt_cookie", system_prompt)""")
+                    md_dropdown.change(None, inputs=[md_dropdown], outputs=None,
+                        _js="""(md_dropdown)=>gpt_academic_gradio_saveload("save", "elem_model_sel", "js_md_dropdown_cookie", md_dropdown)""")
 
                 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)
@@ -247,8 +253,10 @@ def main():
         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
+        reset_server_side_args = (lambda history: ([], [], "已重置", json.dumps(history)),
-        resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])   # 再在后端清除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:
@@ -271,7 +279,7 @@ def main():
         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])
+            click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [plugins[k]["Button"]], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
             cancel_handles.append(click_handle)
         # 函数插件-下拉菜单与随变按钮的互动
         def on_dropdown_changed(k):
@@ -309,7 +317,7 @@ def main():
             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])
+        click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot]).then(None, [switchy_bt], None, _js=r"(fn)=>on_plugin_exe_complete(fn)")
         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)
@@ -342,7 +350,7 @@ def main():
         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}")""")    # 配置暗色主题或亮色主题
+        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
 
     # 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():

request_llms/bridge_all.py

@@ -67,7 +67,8 @@ newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
 gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
 claude_endpoint = "https://api.anthropic.com/v1/messages"
 yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
-cohere_endpoint = 'https://api.cohere.ai/v1/chat'
+cohere_endpoint = "https://api.cohere.ai/v1/chat"
+ollama_endpoint = "http://localhost:11434/api/chat"
 
 if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
 azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
@@ -87,6 +88,7 @@ if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemin
 if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
 if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
 if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
+if ollama_endpoint in API_URL_REDIRECT: ollama_endpoint = API_URL_REDIRECT[ollama_endpoint]
 
 # 获取tokenizer
 tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
@@ -266,6 +268,14 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "glm-4v": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 1000,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
     "glm-3-turbo": {
         "fn_with_ui": zhipu_ui,
         "fn_without_ui": zhipu_noui,
@@ -827,7 +837,32 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("vllm-")]:
             "token_cnt": get_token_num_gpt35,
         },
     })
-
+# -=-=-=-=-=-=- ollama 对齐支持 -=-=-=-=-=-=-
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
+    from .bridge_ollama import predict_no_ui_long_connection as ollama_noui
+    from .bridge_ollama import predict as ollama_ui
+    break
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
+    # 为了更灵活地接入ollama多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["ollama-phi3(max_token=6666)"]
+    # 其中
+    #   "ollama-"           是前缀（必要）
+    #   "phi3"            是模型名（必要）
+    #   "(max_token=6666)"  是配置（非必要）
+    try:
+        _, max_token_tmp = read_one_api_model_name(model)
+    except:
+        print(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        continue
+    model_info.update({
+        model: {
+            "fn_with_ui": ollama_ui,
+            "fn_without_ui": ollama_noui,
+            "endpoint": ollama_endpoint,
+            "max_token": max_token_tmp,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
 
 # -=-=-=-=-=-=- azure模型对齐支持 -=-=-=-=-=-=-
 AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY") # <-- 用于定义和切换多个azure模型 -->

request_llms/bridge_chatglm3.py

@@ -6,7 +6,6 @@ from toolbox import get_conf, ProxyNetworkActivate
 from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
-
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
 # ------------------------------------------------------------------------------------------------------------------------
@@ -23,20 +22,45 @@ class GetGLM3Handle(LocalLLMHandle):
         import os, glob
         import os
         import platform
-        LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
 
-        if LOCAL_MODEL_QUANT == "INT4":         # INT4
+        LOCAL_MODEL_QUANT, device = get_conf("LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
-            _model_name_ = "THUDM/chatglm3-6b-int4"
+        _model_name_ = "THUDM/chatglm3-6b"
-        elif LOCAL_MODEL_QUANT == "INT8":       # INT8
+        # if LOCAL_MODEL_QUANT == "INT4":  # INT4
-            _model_name_ = "THUDM/chatglm3-6b-int8"
+        #     _model_name_ = "THUDM/chatglm3-6b-int4"
-        else:
+        # elif LOCAL_MODEL_QUANT == "INT8":  # INT8
-            _model_name_ = "THUDM/chatglm3-6b"  # FP16
+        #     _model_name_ = "THUDM/chatglm3-6b-int8"
-        with ProxyNetworkActivate('Download_LLM'):
+        # else:
-            chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
+        #     _model_name_ = "THUDM/chatglm3-6b"  # FP16
-            if device=='cpu':
+        with ProxyNetworkActivate("Download_LLM"):
-                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cpu').float()
+            chatglm_tokenizer = AutoTokenizer.from_pretrained(
+                _model_name_, trust_remote_code=True
+            )
+            if device == "cpu":
+                chatglm_model = AutoModel.from_pretrained(
+                    _model_name_,
+                    trust_remote_code=True,
+                    device="cpu",
+                ).float()
+            elif LOCAL_MODEL_QUANT == "INT4":  # INT4
+                chatglm_model = AutoModel.from_pretrained(
+                    pretrained_model_name_or_path=_model_name_,
+                    trust_remote_code=True,
+                    device="cuda",
+                    load_in_4bit=True,
+                )
+            elif LOCAL_MODEL_QUANT == "INT8":  # INT8
+                chatglm_model = AutoModel.from_pretrained(
+                    pretrained_model_name_or_path=_model_name_,
+                    trust_remote_code=True,
+                    device="cuda",
+                    load_in_8bit=True,
+                )
             else:
-                chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True, device='cuda')
+                chatglm_model = AutoModel.from_pretrained(
+                    pretrained_model_name_or_path=_model_name_,
+                    trust_remote_code=True,
+                    device="cuda",
+                )
             chatglm_model = chatglm_model.eval()
 
         self._model = chatglm_model
@@ -46,32 +70,36 @@ class GetGLM3Handle(LocalLLMHandle):
     def llm_stream_generator(self, **kwargs):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         def adaptor(kwargs):
-            query = kwargs['query']
+            query = kwargs["query"]
-            max_length = kwargs['max_length']
+            max_length = kwargs["max_length"]
-            top_p = kwargs['top_p']
+            top_p = kwargs["top_p"]
-            temperature = kwargs['temperature']
+            temperature = kwargs["temperature"]
-            history = kwargs['history']
+            history = kwargs["history"]
             return query, max_length, top_p, temperature, history
 
         query, max_length, top_p, temperature, history = adaptor(kwargs)
 
-        for response, history in self._model.stream_chat(self._tokenizer,
+        for response, history in self._model.stream_chat(
-                                                         query,
+            self._tokenizer,
-                                                         history,
+            query,
-                                                         max_length=max_length,
+            history,
-                                                         top_p=top_p,
+            max_length=max_length,
-                                                         temperature=temperature,
+            top_p=top_p,
-                                                         ):
+            temperature=temperature,
+        ):
             yield response
 
     def try_to_import_special_deps(self, **kwargs):
         # import something that will raise error if the user does not install requirement_*.txt
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
         import importlib
+
         # importlib.import_module('modelscope')
 
 
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 GPT-Academic Interface
 # ------------------------------------------------------------------------------------------------------------------------
-predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetGLM3Handle, model_name, history_format='chatglm3')
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(
+    GetGLM3Handle, model_name, history_format="chatglm3"
+)

request_llms/bridge_ollama.py

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

request_llms/bridge_zhipu.py

@@ -75,6 +75,10 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         llm_kwargs["llm_model"] = zhipuai_default_model
 
     if llm_kwargs["llm_model"] in ["glm-4v"]:
+        if (len(inputs) + sum(len(temp) for temp in history) + 1047) > 2000:
+            chatbot.append((inputs, "上下文长度超过glm-4v上限2000tokens，注意图片大约占用1,047个tokens"))
+            yield from update_ui(chatbot=chatbot, history=history)
+            return
         have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
         if not have_recent_file:
             chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))

request_llms/com_zhipuglm.py

@@ -36,8 +36,14 @@ class ZhipuChatInit:
             what_i_have_asked = {"role": "user", "content": []}
             what_i_have_asked['content'].append({"type": 'text', "text": user_input})
             if encode_img:
+                if len(encode_img) > 1:
+                    logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
+                    print("glm-4v只支持一张图片,将只取第一张图片进行处理")
                 img_d = {"type": "image_url",
-                         "image_url": {'url': encode_img}}
+                            "image_url": {
+                                "url": encode_img[0]['data']
+                            }
+                        }
                 what_i_have_asked['content'].append(img_d)
             return what_i_have_asked
 

requirements.txt

@@ -24,6 +24,7 @@ Markdown
 pygments
 pymupdf
 openai
+rjsmin
 arxiv
 numpy
 rich

shared_utils/cookie_manager.py

@@ -1,4 +1,6 @@
+import json
 from typing import Callable
+
 def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)->Callable:
     def load_web_cookie_cache(persistent_cookie_, cookies_):
         import gradio as gr
@@ -22,7 +24,6 @@ def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)-
         return ret
     return load_web_cookie_cache
 
-
 def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)->Callable:
     def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
         import gradio as gr
@@ -59,3 +60,29 @@ def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_
         return ret
     return assign_btn
 
+# cookies, web_cookie_cache = make_cookie_cache()
+def make_cookie_cache():
+    # 定义 后端state（cookies）、前端（web_cookie_cache）两兄弟
+    import gradio as gr
+    from toolbox import load_chat_cookies
+    # 定义cookies的后端state
+    cookies = gr.State(load_chat_cookies())
+    # 定义cookies的一个孪生的前端存储区（隐藏）
+    web_cookie_cache = gr.Textbox(visible=False, elem_id="web_cookie_cache")
+    return cookies, web_cookie_cache
+
+# history, history_cache, history_cache_update = make_history_cache()
+def make_history_cache():
+    # 定义 后端state（history）、前端（history_cache）、后端setter（history_cache_update）三兄弟
+    import gradio as gr
+    # 定义history的后端state
+    history = gr.State([])
+    # 定义history的一个孪生的前端存储区（隐藏）
+    history_cache = gr.Textbox(visible=False, elem_id="history_cache")
+    # 定义history_cache->history的更新方法（隐藏）。在触发这个按钮时，会先执行js代码更新history_cache，然后再执行python代码更新history
+    def process_history_cache(history_cache):
+        return json.loads(history_cache)
+    # 另一种更简单的setter方法
+    history_cache_update = gr.Button("", elem_id="elem_update_history", visible=False).click(
+        process_history_cache, inputs=[history_cache], outputs=[history])
+    return history, history_cache, history_cache_update

shared_utils/fastapi_server.py

@@ -137,6 +137,47 @@ def start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SS
                     return "越权访问!"
             return await endpoint(path_or_url, request)
 
+    TTS_TYPE = get_conf("TTS_TYPE")
+    if TTS_TYPE != "DISABLE":
+        # audio generation functionality
+        import httpx
+        from fastapi import FastAPI, Request, HTTPException
+        from starlette.responses import Response
+        async def forward_request(request: Request, method: str) -> Response:
+            async with httpx.AsyncClient() as client:
+                try:
+                    # Forward the request to the target service
+                    if TTS_TYPE == "EDGE_TTS":
+                        import tempfile
+                        import edge_tts
+                        import wave
+                        import uuid
+                        from pydub import AudioSegment
+                        json = await request.json()
+                        voice = get_conf("EDGE_TTS_VOICE")
+                        tts = edge_tts.Communicate(text=json['text'], voice=voice)
+                        temp_folder = tempfile.gettempdir()
+                        temp_file_name = str(uuid.uuid4().hex)
+                        temp_file = os.path.join(temp_folder, f'{temp_file_name}.mp3')
+                        await tts.save(temp_file)
+                        mp3_audio = AudioSegment.from_file(temp_file, format="mp3")
+                        mp3_audio.export(temp_file, format="wav")
+                        with open(temp_file, 'rb') as wav_file: t = wav_file.read()
+                        os.remove(temp_file)
+                        return Response(content=t)
+                    if TTS_TYPE == "LOCAL_SOVITS_API":
+                        # Forward the request to the target service
+                        TARGET_URL = get_conf("GPT_SOVITS_URL")
+                        body = await request.body()
+                        resp = await client.post(TARGET_URL, content=body, timeout=60)
+                        # Return the response from the target service
+                        return Response(content=resp.content, status_code=resp.status_code, headers=dict(resp.headers))
+                except httpx.RequestError as e:
+                    raise HTTPException(status_code=400, detail=f"Request to the target service failed: {str(e)}")
+        @gradio_app.post("/vits")
+        async def forward_post_request(request: Request):
+            return await forward_request(request, "POST")
+
     # --- --- app_lifespan --- ---
     from contextlib import asynccontextmanager
     @asynccontextmanager

themes/common.css

@@ -38,6 +38,7 @@
     left: calc(100% + 3px);
     top: 0;
     display: flex;
+    flex-direction: column;
     justify-content: space-between;
 }
 /* .message-btn-row-leading, .message-btn-row-trailing {

themes/common.js

@@ -7,6 +7,9 @@ function push_data_to_gradio_component(DAT, ELEM_ID, TYPE) {
     if (TYPE == "str") {
         // convert dat to string: do nothing
     }
+    else if (TYPE == "obj") {
+        // convert dat to string: do nothing
+    }
     else if (TYPE == "no_conversion") {
         // no nothing
     }
@@ -254,11 +257,22 @@ function cancel_loading_status() {
 //  第 2 部分: 复制按钮
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 
-function addCopyButton(botElement) {
+
+var allow_auto_read_continously = true;
+var allow_auto_read_tts_flag = false;
+function addCopyButton(botElement, index, is_last_in_arr) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     // Copy bot button
     const copiedIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
     const copyIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
+    // const audioIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
+    const audioIcon = '<span><svg t="1713628577799" fill="currentColor" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4587" width=".9em" height=".9em"><path d="M113.7664 540.4672c0-219.9552 178.2784-398.2336 398.2336-398.2336S910.2336 320.512 910.2336 540.4672v284.4672c0 31.4368-25.4976 56.9344-56.9344 56.9344h-56.9344c-31.4368 0-56.9344-25.4976-56.9344-56.9344V597.2992c0-31.4368 25.4976-56.9344 56.9344-56.9344h56.9344c0-188.5184-152.7808-341.2992-341.2992-341.2992S170.7008 351.9488 170.7008 540.4672h56.9344c31.4368 0 56.9344 25.4976 56.9344 56.9344v227.5328c0 31.4368-25.4976 56.9344-56.9344 56.9344h-56.9344c-31.4368 0-56.9344-25.4976-56.9344-56.9344V540.4672z" p-id="4588"></path></svg></span>';
+    // const cancelAudioIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
+
+    // 此功能没准备好
+    if (allow_auto_read_continously && is_last_in_arr && allow_auto_read_tts_flag) {
+        process_latest_text_output(botElement.innerText, index);
+    }
 
     const messageBtnColumnElement = botElement.querySelector('.message-btn-row');
     if (messageBtnColumnElement) {
@@ -273,6 +287,7 @@ function addCopyButton(botElement) {
     copyButton.addEventListener('click', async () => {
         const textToCopy = botElement.innerText;
         try {
+            // push_text_to_audio(textToCopy).catch(console.error);
             if ("clipboard" in navigator) {
                 await navigator.clipboard.writeText(textToCopy);
                 copyButton.innerHTML = copiedIcon;
@@ -299,9 +314,35 @@ function addCopyButton(botElement) {
             console.error("Copy failed: ", error);
         }
     });
+
+    if (enable_tts){
+        var audioButton = document.createElement('button');
+        audioButton.classList.add('audio-toggle-btn');
+        audioButton.innerHTML = audioIcon;
+        audioButton.addEventListener('click', async () => {
+            if (audioPlayer.isPlaying) {
+                allow_auto_read_tts_flag = false;
+                toast_push('自动朗读已禁用。', 3000);
+                audioPlayer.stop();
+                setCookie("js_auto_read_cookie", "False", 365);
+
+            } else {
+                allow_auto_read_tts_flag = true;
+                toast_push('正在合成语音 & 自动朗读已开启 (再次点击此按钮可禁用自动朗读)。', 3000);
+                // toast_push('正在合成语音', 3000);
+                const readText = botElement.innerText;
+                push_text_to_audio(readText);
+                setCookie("js_auto_read_cookie", "True", 365);
+            }
+        });
+    }
+
     var messageBtnColumn = document.createElement('div');
     messageBtnColumn.classList.add('message-btn-row');
     messageBtnColumn.appendChild(copyButton);
+    if (enable_tts){
+        messageBtnColumn.appendChild(audioButton);
+    }
     botElement.appendChild(messageBtnColumn);
 }
 
@@ -337,7 +378,15 @@ function chatbotContentChanged(attempt = 1, force = false) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     for (var i = 0; i < attempt; i++) {
         setTimeout(() => {
-            gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
+            const messages = gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot');
+            messages.forEach((message, index, arr) => {
+                // Check if the current message is the last in the array
+                const is_last_in_arr = index === arr.length - 1;
+
+                // Now pass both the message element and the is_last_in_arr boolean to addCopyButton
+                addCopyButton(message, index, is_last_in_arr);
+            });
+            // gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
         }, i === 0 ? 0 : 200);
     }
     // we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0
@@ -621,16 +670,16 @@ function monitoring_input_box() {
 
     if (elem_input_main) {
         if (elem_input_main.querySelector("textarea")) {
-            register_func_paste(elem_input_main.querySelector("textarea"))
+            register_func_paste(elem_input_main.querySelector("textarea"));
         }
     }
     if (elem_input_float) {
         if (elem_input_float.querySelector("textarea")) {
-            register_func_paste(elem_input_float.querySelector("textarea"))
+            register_func_paste(elem_input_float.querySelector("textarea"));
         }
     }
     if (elem_chatbot) {
-        register_func_drag(elem_chatbot)
+        register_func_drag(elem_chatbot);
     }
 
 }
@@ -737,7 +786,7 @@ function minor_ui_adjustment() {
     }
 
     setInterval(function () {
-        auto_hide_toolbar()
+        auto_hide_toolbar();
     }, 200); // 每50毫秒执行一次
 }
 
@@ -857,8 +906,8 @@ function gpt_academic_gradio_saveload(
     }
 }
 
-
+enable_tts = false;
-async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
+async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
     // 第一部分，布局初始化
     audio_fn_init();
     minor_ui_adjustment();
@@ -873,7 +922,6 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
     // 第二部分，读取Cookie，初始话界面
     let searchString = "";
     let bool_value = "";
-
     //  darkmode 深色模式
     if (getCookie("js_darkmode_cookie")) {
         dark = getCookie("js_darkmode_cookie")
@@ -889,11 +937,39 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
         }
     }
 
+    //  自动朗读
+    if (tts != "DISABLE"){
+        enable_tts = true;
+        if (getCookie("js_auto_read_cookie")) {
+            auto_read_tts = getCookie("js_auto_read_cookie")
+            auto_read_tts = auto_read_tts == "True";
+            if (auto_read_tts) {
+                allow_auto_read_tts_flag = true;
+            }
+        }
+    }
+
     // SysPrompt 系统静默提示词
     gpt_academic_gradio_saveload("load", "elem_prompt", "js_system_prompt_cookie", null, "str");
-
     // Temperature 大模型温度参数
     gpt_academic_gradio_saveload("load", "elem_temperature", "js_temperature_cookie", null, "float");
+    // md_dropdown 大模型类型选择
+    if (getCookie("js_md_dropdown_cookie")) {
+        const cached_model = getCookie("js_md_dropdown_cookie");
+        var model_sel = await get_gradio_component("elem_model_sel");
+        // deterine whether the cached model is in the choices
+        if (model_sel.props.choices.includes(cached_model)){
+            // change dropdown
+            gpt_academic_gradio_saveload("load", "elem_model_sel", "js_md_dropdown_cookie", null, "str");
+            // 连锁修改chatbot的label
+            push_data_to_gradio_component({
+                label: '当前模型：' + getCookie("js_md_dropdown_cookie"),
+                __type__: 'update'
+            }, "gpt-chatbot", "obj")
+        }
+    }
+
+
 
     // clearButton 自动清除按钮
     if (getCookie("js_clearbtn_show_cookie")) {
@@ -953,4 +1029,510 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
         }
     }
 
-}
+}
+
+
+function reset_conversation(a, b) {
+    console.log("js_code_reset");
+    a = btoa(unescape(encodeURIComponent(JSON.stringify(a))));
+    setCookie("js_previous_chat_cookie", a, 1);
+    gen_restore_btn();
+    return [[], [], "已重置"];
+}
+
+// clear -> 将 history 缓存至 history_cache -> 点击复原 -> restore_previous_chat() -> 触发elem_update_history -> 读取 history_cache
+function restore_previous_chat() {
+    console.log("restore_previous_chat");
+    let chat = getCookie("js_previous_chat_cookie");
+    chat = JSON.parse(decodeURIComponent(escape(atob(chat))));
+    push_data_to_gradio_component(chat, "gpt-chatbot", "obj");
+    document.querySelector("#elem_update_history").click(); // in order to call set_history_gr_state, and send history state to server
+}
+
+function gen_restore_btn() {
+
+
+    // 创建按钮元素
+    const button = document.createElement('div');
+    // const recvIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
+    const rec_svg = '<svg t="1714361184567" style="transform:translate(1px, 2.5px)" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4389" width="35" height="35"><path d="M320 512h384v64H320zM320 384h384v64H320zM320 640h192v64H320z" p-id="4390" fill="#ffffff"></path><path d="M863.7 544c-1.9 44-11.4 86.8-28.5 127.2-18.5 43.8-45.1 83.2-78.9 117-33.8 33.8-73.2 60.4-117 78.9C593.9 886.3 545.7 896 496 896s-97.9-9.7-143.2-28.9c-43.8-18.5-83.2-45.1-117-78.9-33.8-33.8-60.4-73.2-78.9-117C137.7 625.9 128 577.7 128 528s9.7-97.9 28.9-143.2c18.5-43.8 45.1-83.2 78.9-117s73.2-60.4 117-78.9C398.1 169.7 446.3 160 496 160s97.9 9.7 143.2 28.9c23.5 9.9 45.8 22.2 66.5 36.7l-119.7 20 9.9 59.4 161.6-27 59.4-9.9-9.9-59.4-27-161.5-59.4 9.9 19 114.2C670.3 123.8 586.4 96 496 96 257.4 96 64 289.4 64 528s193.4 432 432 432c233.2 0 423.3-184.8 431.7-416h-64z" p-id="4391" fill="#ffffff"></path></svg>'
+    const recvIcon = '<span>' + rec_svg + '</span>';
+
+    // 设置按钮的样式和属性
+    button.id = 'floatingButton';
+    button.className = 'glow';
+    button.style.textAlign = 'center';
+    button.style.position = 'fixed';
+    button.style.bottom = '10px';
+    button.style.left = '10px';
+    button.style.width = '50px';
+    button.style.height = '50px';
+    button.style.borderRadius = '50%';
+    button.style.backgroundColor = '#007bff';
+    button.style.color = 'white';
+    button.style.display = 'flex';
+    button.style.alignItems = 'center';
+    button.style.justifyContent = 'center';
+    button.style.cursor = 'pointer';
+    button.style.transition = 'all 0.3s ease';
+    button.style.boxShadow = '0 0 10px rgba(0,0,0,0.2)';
+
+    button.innerHTML = recvIcon;
+
+    // 添加发光动画的关键帧
+    const styleSheet = document.createElement('style');
+    styleSheet.id = 'floatingButtonStyle';
+    styleSheet.innerText = `
+    @keyframes glow {
+        from {
+        box-shadow: 0 0 10px rgba(0,0,0,0.2);
+        }
+        to {
+        box-shadow: 0 0 13px rgba(0,0,0,0.5);
+        }
+    }
+    #floatingButton.glow {
+        animation: glow 1s infinite alternate;
+    }
+    #floatingButton:hover {
+        transform: scale(1.2);
+        box-shadow: 0 0 20px rgba(0,0,0,0.4);
+    }
+    #floatingButton.disappearing {
+        animation: shrinkAndDisappear 0.5s forwards;
+    }
+    `;
+
+    // only add when not exist
+    if (!document.getElementById('recvButtonStyle'))
+    {
+        document.head.appendChild(styleSheet);
+    }
+
+    // 鼠标悬停和移开的事件监听器
+    button.addEventListener('mouseover', function () {
+        this.textContent = "还原\n对话";
+    });
+
+    button.addEventListener('mouseout', function () {
+        this.innerHTML = recvIcon;
+    });
+
+    // 点击事件监听器
+    button.addEventListener('click', function () {
+        // 添加一个类来触发缩小和消失的动画
+        restore_previous_chat();
+        this.classList.add('disappearing');
+        // 在动画结束后移除按钮
+        document.body.removeChild(this);
+    });
+    // only add when not exist
+    if (!document.getElementById('recvButton'))
+    {
+        document.body.appendChild(button);
+    }
+
+    // 将按钮添加到页面中
+
+}
+
+async function on_plugin_exe_complete(fn_name) {
+    console.log(fn_name);
+    if (fn_name === "保存当前的对话") {
+        // get chat profile path
+        let chatbot = await get_data_from_gradio_component('gpt-chatbot');
+        let may_have_chat_profile_info = chatbot[chatbot.length - 1][1];
+
+        function get_href(htmlString) {
+            const parser = new DOMParser();
+            const doc = parser.parseFromString(htmlString, 'text/html');
+            const anchor = doc.querySelector('a');
+
+            if (anchor) {
+                return anchor.getAttribute('href');
+            } else {
+                return null;
+            }
+        }
+        let href = get_href(may_have_chat_profile_info);
+        if (href) {
+            const cleanedHref = href.replace('file=', ''); // /home/fuqingxu/chatgpt_academic/gpt_log/default_user/chat_history/GPT-Academic对话存档2024-04-12-00-35-06.html
+            console.log(cleanedHref);
+        }
+
+    }
+}
+
+
+
+
+
+
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 8 部分: TTS语音生成函数
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+class AudioPlayer {
+    constructor() {
+        this.audioCtx = new (window.AudioContext || window.webkitAudioContext)();
+        this.queue = [];
+        this.isPlaying = false;
+        this.currentSource = null; // 添加属性来保存当前播放的源
+    }
+
+    // Base64 编码的字符串转换为 ArrayBuffer
+    base64ToArrayBuffer(base64) {
+        const binaryString = window.atob(base64);
+        const len = binaryString.length;
+        const bytes = new Uint8Array(len);
+        for (let i = 0; i < len; i++) {
+            bytes[i] = binaryString.charCodeAt(i);
+        }
+        return bytes.buffer;
+    }
+
+    // 检查音频播放队列并播放音频
+    checkQueue() {
+        if (!this.isPlaying && this.queue.length > 0) {
+            this.isPlaying = true;
+            const nextAudio = this.queue.shift();
+            this.play_wave(nextAudio);
+        }
+    }
+
+    // 将音频添加到播放队列
+    enqueueAudio(audio_buf_wave) {
+        if (allow_auto_read_tts_flag) {
+            this.queue.push(audio_buf_wave);
+            this.checkQueue();
+        }
+    }
+
+    // 播放音频
+    async play_wave(encodedAudio) {
+        //const audioData = this.base64ToArrayBuffer(encodedAudio);
+        const audioData = encodedAudio;
+        try {
+            const buffer = await this.audioCtx.decodeAudioData(audioData);
+            const source = this.audioCtx.createBufferSource();
+            source.buffer = buffer;
+            source.connect(this.audioCtx.destination);
+            source.onended = () => {
+                if (allow_auto_read_tts_flag) {
+                    this.isPlaying = false;
+                    this.currentSource = null; // 播放结束后清空当前源
+                    this.checkQueue();
+                }
+            };
+            this.currentSource = source; // 保存当前播放的源
+            source.start();
+        } catch (e) {
+            console.log("Audio error!", e);
+            this.isPlaying = false;
+            this.currentSource = null; // 出错时也应清空当前源
+            this.checkQueue();
+        }
+    }
+
+    // 新增：立即停止播放音频的方法
+    stop() {
+        if (this.currentSource) {
+            this.queue = []; // 清空队列
+            this.currentSource.stop(); // 停止当前源
+            this.currentSource = null; // 清空当前源
+            this.isPlaying = false; // 更新播放状态
+            // 关闭音频上下文可能会导致无法再次播放音频，因此仅停止当前源
+            // this.audioCtx.close(); // 可选：如果需要可以关闭音频上下文
+        }
+    }
+}
+
+const audioPlayer = new AudioPlayer();
+
+class FIFOLock {
+    constructor() {
+        this.queue = [];
+        this.currentTaskExecuting = false;
+    }
+
+    lock() {
+        let resolveLock;
+        const lock = new Promise(resolve => {
+            resolveLock = resolve;
+        });
+
+        this.queue.push(resolveLock);
+
+        if (!this.currentTaskExecuting) {
+            this._dequeueNext();
+        }
+
+        return lock;
+    }
+
+    _dequeueNext() {
+        if (this.queue.length === 0) {
+            this.currentTaskExecuting = false;
+            return;
+        }
+        this.currentTaskExecuting = true;
+        const resolveLock = this.queue.shift();
+        resolveLock();
+    }
+
+    unlock() {
+        this.currentTaskExecuting = false;
+        this._dequeueNext();
+    }
+}
+
+
+
+
+
+
+
+
+function delay(ms) {
+    return new Promise(resolve => setTimeout(resolve, ms));
+}
+
+// Define the trigger function with delay parameter T in milliseconds
+function trigger(T, fire) {
+    // Variable to keep track of the timer ID
+    let timeoutID = null;
+    // Variable to store the latest arguments
+    let lastArgs = null;
+
+    return function (...args) {
+        // Update lastArgs with the latest arguments
+        lastArgs = args;
+        // Clear the existing timer if the function is called again
+        if (timeoutID !== null) {
+            clearTimeout(timeoutID);
+        }
+        // Set a new timer that calls the `fire` function with the latest arguments after T milliseconds
+        timeoutID = setTimeout(() => {
+            fire(...lastArgs);
+        }, T);
+    };
+}
+
+
+prev_text = "";
+prev_text_already_pushed = "";
+prev_chatbot_index = -1;
+const delay_live_text_update = trigger(3000, on_live_stream_terminate);
+
+function on_live_stream_terminate(latest_text) {
+    // remove `prev_text_already_pushed` from `latest_text`
+    console.log("on_live_stream_terminate", latest_text)
+    remaining_text = latest_text.slice(prev_text_already_pushed.length);
+    if ((!isEmptyOrWhitespaceOnly(remaining_text)) && remaining_text.length != 0) {
+        prev_text_already_pushed = latest_text;
+        push_text_to_audio(remaining_text);
+    }
+}
+function is_continue_from_prev(text, prev_text) {
+    abl = 5
+    if (text.length < prev_text.length - abl) {
+        return false;
+    }
+    if (prev_text.length > 10) {
+        return text.startsWith(prev_text.slice(0, Math.min(prev_text.length - abl, 100)));
+    } else {
+        return text.startsWith(prev_text);
+    }
+}
+function isEmptyOrWhitespaceOnly(remaining_text) {
+    // Replace \n and 。 with empty strings
+    let textWithoutSpecifiedCharacters = remaining_text.replace(/[\n。]/g, '');
+    // Check if the remaining string is empty
+    return textWithoutSpecifiedCharacters.trim().length === 0;
+}
+function process_increased_text(remaining_text) {
+    // console.log('[is continue], remaining_text: ', remaining_text)
+    // remaining_text starts with \n or 。, then move these chars into prev_text_already_pushed
+    while (remaining_text.startsWith('\n') || remaining_text.startsWith('。')) {
+        prev_text_already_pushed = prev_text_already_pushed + remaining_text[0];
+        remaining_text = remaining_text.slice(1);
+    }
+    if (remaining_text.includes('\n') || remaining_text.includes('。')) { // determine remaining_text contain \n or 。
+        // new message begin!
+        index_of_last_sep = Math.max(remaining_text.lastIndexOf('\n'), remaining_text.lastIndexOf('。'));
+        // break the text into two parts
+        tobe_pushed = remaining_text.slice(0, index_of_last_sep + 1);
+        prev_text_already_pushed = prev_text_already_pushed + tobe_pushed;
+        // console.log('[is continue], push: ', tobe_pushed)
+        // console.log('[is continue], update prev_text_already_pushed: ', prev_text_already_pushed)
+        if (!isEmptyOrWhitespaceOnly(tobe_pushed)) {
+            // console.log('[is continue], remaining_text is empty')
+            push_text_to_audio(tobe_pushed);
+        }
+    }
+}
+function process_latest_text_output(text, chatbot_index) {
+    if (text.length == 0) {
+        prev_text = text;
+        prev_text_mask = text;
+        // console.log('empty text')
+        return;
+    }
+    if (text == prev_text) {
+        // console.log('[nothing changed]')
+        return;
+    }
+
+    var is_continue = is_continue_from_prev(text, prev_text_already_pushed);
+    if (chatbot_index == prev_chatbot_index && is_continue) {
+        // on_text_continue_grow
+        remaining_text = text.slice(prev_text_already_pushed.length);
+        process_increased_text(remaining_text);
+        delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
+    }
+    else if (chatbot_index == prev_chatbot_index && !is_continue) {
+        console.log('---------------------')
+        console.log('text twisting!')
+        console.log('[new message begin]', 'text', text, 'prev_text_already_pushed', prev_text_already_pushed)
+        console.log('---------------------')
+        prev_text_already_pushed = "";
+        delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
+    }
+    else {
+        // on_new_message_begin, we have to clear `prev_text_already_pushed`
+        console.log('---------------------')
+        console.log('new message begin!')
+        console.log('[new message begin]', 'text', text, 'prev_text_already_pushed', prev_text_already_pushed)
+        console.log('---------------------')
+        prev_text_already_pushed = "";
+        process_increased_text(text);
+        delay_live_text_update(text); // in case of no \n or 。 in the text, this timer will finally commit
+    }
+    prev_text = text;
+    prev_chatbot_index = chatbot_index;
+}
+
+const audio_push_lock = new FIFOLock();
+async function push_text_to_audio(text) {
+    if (!allow_auto_read_tts_flag) {
+        return;
+    }
+    await audio_push_lock.lock();
+    var lines = text.split(/[\n。]/);
+    for (const audio_buf_text of lines) {
+        if (audio_buf_text) {
+            // Append '/vits' to the current URL to form the target endpoint
+            const url = `${window.location.href}vits`;
+            // Define the payload to be sent in the POST request
+            const payload = {
+                text: audio_buf_text, // Ensure 'audio_buf_text' is defined with valid data
+                text_language: "zh"
+            };
+            // Call the async postData function and log the response
+            post_text(url, payload, send_index);
+            send_index = send_index + 1;
+            console.log(send_index, audio_buf_text)
+            // sleep 2 seconds
+            if (allow_auto_read_tts_flag) {
+                await delay(3000);
+            }
+        }
+    }
+    audio_push_lock.unlock();
+}
+
+
+send_index = 0;
+recv_index = 0;
+to_be_processed = [];
+async function UpdatePlayQueue(cnt, audio_buf_wave) {
+    if (cnt != recv_index) {
+        to_be_processed.push([cnt, audio_buf_wave]);
+        console.log('cache', cnt);
+    }
+    else {
+        console.log('processing', cnt);
+        recv_index = recv_index + 1;
+        if (audio_buf_wave) {
+            audioPlayer.enqueueAudio(audio_buf_wave);
+        }
+        // deal with other cached audio
+        while (true) {
+            find_any = false;
+            for (i = to_be_processed.length - 1; i >= 0; i--) {
+                if (to_be_processed[i][0] == recv_index) {
+                    console.log('processing cached', recv_index);
+                    if (to_be_processed[i][1]) {
+                        audioPlayer.enqueueAudio(to_be_processed[i][1]);
+                    }
+                    to_be_processed.pop(i);
+                    find_any = true;
+                    recv_index = recv_index + 1;
+                }
+            }
+            if (!find_any) { break; }
+        }
+    }
+}
+
+function post_text(url, payload, cnt) {
+    if (allow_auto_read_tts_flag) {
+        postData(url, payload, cnt)
+        .then(data => {
+            UpdatePlayQueue(cnt, data);
+            return;
+        });
+    } else {
+        UpdatePlayQueue(cnt, null);
+        return;
+    }
+}
+
+notify_user_error = false
+// Create an async function to perform the POST request
+async function postData(url = '', data = {}) {
+    try {
+        // Use the Fetch API with await
+        const response = await fetch(url, {
+            method: 'POST', // Specify the request method
+            body: JSON.stringify(data), // Convert the JavaScript object to a JSON string
+        });
+        // Check if the response is ok (status in the range 200-299)
+        if (!response.ok) {
+            // If not OK, throw an error
+            console.info('There was a problem during audio generation requests:', response.status);
+            // if (!notify_user_error){
+            //     notify_user_error = true;
+            //     alert('There was a problem during audio generation requests:', response.status);
+            // }
+            return null;
+        }
+        // If OK, parse and return the JSON response
+        return await response.arrayBuffer();
+    } catch (error) {
+        // Log any errors that occur during the fetch operation
+        console.info('There was a problem during audio generation requests:', error);
+        // if (!notify_user_error){
+        //     notify_user_error = true;
+        //     alert('There was a problem during audio generation requests:', error);
+        // }
+        return null;
+    }
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

themes/common.py

@@ -1,10 +1,34 @@
 from toolbox import get_conf
 CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf("CODE_HIGHLIGHT", "ADD_WAIFU", "LAYOUT")
 
+def minimize_js(common_js_path):
+    try:
+        import rjsmin, hashlib, glob, os
+        # clean up old minimized js files, matching `common_js_path + '.min.*'`
+        for old_min_js in glob.glob(common_js_path + '.min.*.js'):
+            os.remove(old_min_js)
+        # use rjsmin to minimize `common_js_path`
+        c_jsmin = rjsmin.jsmin
+        with open(common_js_path, "r") as f:
+            js_content = f.read()
+        minimized_js_content = c_jsmin(js_content)
+        # compute sha256 hash of minimized js content
+        sha_hash = hashlib.sha256(minimized_js_content.encode()).hexdigest()[:8]
+        minimized_js_path = common_js_path + '.min.' + sha_hash + '.js'
+        # save to minimized js file
+        with open(minimized_js_path, "w") as f:
+            f.write(minimized_js_content)
+        # return minimized js file path
+        return minimized_js_path
+    except:
+        return common_js_path
+
 def get_common_html_javascript_code():
     js = "\n"
+    common_js_path = "themes/common.js"
+    minimized_js_path = minimize_js(common_js_path)
     for jsf in [
-        "file=themes/common.js",
+        f"file={minimized_js_path}",
     ]:
         js += f"""<script src="{jsf}"></script>\n"""
 

themes/theme.py

@@ -111,10 +111,10 @@ js_code_for_persistent_cookie_init = """(web_cookie_cache, cookie) => {
 }
 """
 
-
+# 详见 themes/common.js
 js_code_reset = """
 (a,b,c)=>{
-    return [[], [], "已重置"];
+    return reset_conversation(a,b);
 }
 """
 

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.74,
+  "version": 3.75,
   "show_feature": true,
-  "new_feature": "增加多用户文件鉴权验证提高安全性 <-> 优化oneapi接入方法 <-> 接入Cohere和月之暗面模型 <-> 简化挂载二级目录的步骤 <-> 支持Mermaid绘图库（让大模型绘制脑图）"
+  "new_feature": "添加TTS语音输出（EdgeTTS和SoVits语音克隆） <-> Doc2x PDF翻译 <-> 添加回溯对话按钮"
 }