修改代码格式

ChuanhuChatbot.py

@@ -7,12 +7,15 @@ import argparse
 from utils import *
 from presets import *
 
-logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
+)
 
-my_api_key = ""    # 在这里输入你的 API 密钥
+my_api_key = ""  # 在这里输入你的 API 密钥
 
-#if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
+# if we are running in Docker
+if os.environ.get("dockerrun") == "yes":
     dockerflag = True
 else:
     dockerflag = False
@@ -20,17 +23,21 @@ else:
 authflag = False
 
 if dockerflag:
-    my_api_key = os.environ.get('my_api_key')
+    my_api_key = os.environ.get("my_api_key")
     if my_api_key == "empty":
         logging.error("Please give a api key!")
         sys.exit(1)
-    #auth
-    username = os.environ.get('USERNAME')
-    password = os.environ.get('PASSWORD')
+    # auth
+    username = os.environ.get("USERNAME")
+    password = os.environ.get("PASSWORD")
     if not (isinstance(username, type(None)) or isinstance(password, type(None))):
         authflag = True
 else:
-    if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"):
+    if (
+        not my_api_key
+        and os.path.exists("api_key.txt")
+        and os.path.getsize("api_key.txt")
+    ):
         with open("api_key.txt", "r") as f:
             my_api_key = f.read().strip()
     if os.path.exists("auth.json"):
@@ -58,44 +65,91 @@ with gr.Blocks(css=customCSS) as demo:
     with gr.Row(scale=1).style(equal_height=True):
         with gr.Column(scale=5):
             with gr.Row(scale=1):
-                chatbot = gr.Chatbot().style(height=600)  # .style(color_map=("#1D51EE", "#585A5B"))
+                chatbot = gr.Chatbot().style(
+                    height=600
+                )  # .style(color_map=("#1D51EE", "#585A5B"))
             with gr.Row(scale=1):
                 with gr.Column(scale=12):
-                    user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                        container=False)
+                    user_input = gr.Textbox(
+                        show_label=False, placeholder="在这里输入"
+                    ).style(container=False)
                 with gr.Column(min_width=50, scale=1):
                     submitBtn = gr.Button("🚀", variant="primary")
             with gr.Row(scale=1):
-                emptyBtn = gr.Button("🧹 新的对话",)
+                emptyBtn = gr.Button(
+                    "🧹 新的对话",
+                )
                 retryBtn = gr.Button("🔄 重新生成")
                 delLastBtn = gr.Button("🗑️ 删除一条对话")
                 reduceTokenBtn = gr.Button("♻️ 总结对话")
 
         with gr.Column():
-            with gr.Column(min_width=50,scale=1):
+            with gr.Column(min_width=50, scale=1):
                 with gr.Tab(label="ChatGPT"):
-                    keyTxt = gr.Textbox(show_label=True, placeholder=f"OpenAI API-key...",value=my_api_key, type="password", visible=not HIDE_MY_KEY, label="API-Key")
-                    model_select_dropdown = gr.Dropdown(label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0])
+                    keyTxt = gr.Textbox(
+                        show_label=True,
+                        placeholder=f"OpenAI API-key...",
+                        value=my_api_key,
+                        type="password",
+                        visible=not HIDE_MY_KEY,
+                        label="API-Key",
+                    )
+                    model_select_dropdown = gr.Dropdown(
+                        label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
+                    )
                     with gr.Accordion("参数", open=False):
-                        top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05,
-                                        interactive=True, label="Top-p (nucleus sampling)",)
-                        temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0,
-                                                step=0.1, interactive=True, label="Temperature",)
-                    use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option)
+                        top_p = gr.Slider(
+                            minimum=-0,
+                            maximum=1.0,
+                            value=1.0,
+                            step=0.05,
+                            interactive=True,
+                            label="Top-p (nucleus sampling)",
+                        )
+                        temperature = gr.Slider(
+                            minimum=-0,
+                            maximum=2.0,
+                            value=1.0,
+                            step=0.1,
+                            interactive=True,
+                            label="Temperature",
+                        )
+                    use_streaming_checkbox = gr.Checkbox(
+                        label="实时传输回答", value=True, visible=enable_streaming_option
+                    )
                     use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
 
                 with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...", label="System prompt", value=initial_prompt).style(container=True)
+                    systemPromptTxt = gr.Textbox(
+                        show_label=True,
+                        placeholder=f"在这里输入System Prompt...",
+                        label="System prompt",
+                        value=initial_prompt,
+                    ).style(container=True)
                     with gr.Accordion(label="加载Prompt模板", open=True):
                         with gr.Column():
                             with gr.Row():
                                 with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0])
+                                    templateFileSelectDropdown = gr.Dropdown(
+                                        label="选择Prompt模板集合文件",
+                                        choices=get_template_names(plain=True),
+                                        multiselect=False,
+                                        value=get_template_names(plain=True)[0],
+                                    )
                                 with gr.Column(scale=1):
                                     templateRefreshBtn = gr.Button("🔄 刷新")
                             with gr.Row():
                                 with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False, value=load_template(get_template_names(plain=True)[0], mode=1)[0])
+                                    templateSelectDropdown = gr.Dropdown(
+                                        label="从Prompt模板中加载",
+                                        choices=load_template(
+                                            get_template_names(plain=True)[0], mode=1
+                                        ),
+                                        multiselect=False,
+                                        value=load_template(
+                                            get_template_names(plain=True)[0], mode=1
+                                        )[0],
+                                    )
 
                 with gr.Tab(label="保存/加载"):
                     with gr.Accordion(label="保存/加载对话历史记录", open=True):
@@ -104,13 +158,22 @@ with gr.Blocks(css=customCSS) as demo:
                             with gr.Row():
                                 with gr.Column(scale=6):
                                     saveFileName = gr.Textbox(
-                                        show_label=True, placeholder=f"设置文件名: 默认为.json，可选为.md", label="设置保存文件名", value="对话历史记录").style(container=True)
+                                        show_label=True,
+                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
+                                        label="设置保存文件名",
+                                        value="对话历史记录",
+                                    ).style(container=True)
                                 with gr.Column(scale=1):
                                     saveHistoryBtn = gr.Button("💾 保存对话")
                                     exportMarkdownBtn = gr.Button("📝 导出为Markdown")
                             with gr.Row():
                                 with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False, value=get_history_names(plain=True)[0])
+                                    historyFileSelectDropdown = gr.Dropdown(
+                                        label="从列表中加载对话",
+                                        choices=get_history_names(plain=True),
+                                        multiselect=False,
+                                        value=get_history_names(plain=True)[0],
+                                    )
                                 with gr.Column(scale=1):
                                     historyRefreshBtn = gr.Button("🔄 刷新")
                             with gr.Row():
@@ -120,52 +183,159 @@ with gr.Blocks(css=customCSS) as demo:
     gr.Markdown(description)
 
     # Chatbot
-    user_input.submit(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox, model_select_dropdown, use_websearch_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
+    user_input.submit(
+        predict,
+        [
+            keyTxt,
+            systemPromptTxt,
+            history,
+            user_input,
+            chatbot,
+            token_count,
+            top_p,
+            temperature,
+            use_streaming_checkbox,
+            model_select_dropdown,
+            use_websearch_checkbox,
+        ],
+        [chatbot, history, status_display, token_count],
+        show_progress=True,
+    )
     user_input.submit(reset_textbox, [], [user_input])
 
-    submitBtn.click(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox, model_select_dropdown, use_websearch_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
+    submitBtn.click(
+        predict,
+        [
+            keyTxt,
+            systemPromptTxt,
+            history,
+            user_input,
+            chatbot,
+            token_count,
+            top_p,
+            temperature,
+            use_streaming_checkbox,
+            model_select_dropdown,
+            use_websearch_checkbox,
+        ],
+        [chatbot, history, status_display, token_count],
+        show_progress=True,
+    )
     submitBtn.click(reset_textbox, [], [user_input])
 
-    emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True)
+    emptyBtn.click(
+        reset_state,
+        outputs=[chatbot, history, token_count, status_display],
+        show_progress=True,
+    )
 
-    retryBtn.click(retry, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox, model_select_dropdown], [chatbot, history, status_display, token_count], show_progress=True)
+    retryBtn.click(
+        retry,
+        [
+            keyTxt,
+            systemPromptTxt,
+            history,
+            chatbot,
+            token_count,
+            top_p,
+            temperature,
+            use_streaming_checkbox,
+            model_select_dropdown,
+        ],
+        [chatbot, history, status_display, token_count],
+        show_progress=True,
+    )
 
-    delLastBtn.click(delete_last_conversation, [chatbot, history, token_count], [
-                     chatbot, history, token_count, status_display], show_progress=True)
+    delLastBtn.click(
+        delete_last_conversation,
+        [chatbot, history, token_count],
+        [chatbot, history, token_count, status_display],
+        show_progress=True,
+    )
 
-    reduceTokenBtn.click(reduce_token_size, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox, model_select_dropdown], [chatbot, history, status_display, token_count], show_progress=True)
+    reduceTokenBtn.click(
+        reduce_token_size,
+        [
+            keyTxt,
+            systemPromptTxt,
+            history,
+            chatbot,
+            token_count,
+            top_p,
+            temperature,
+            use_streaming_checkbox,
+            model_select_dropdown,
+        ],
+        [chatbot, history, status_display, token_count],
+        show_progress=True,
+    )
 
     # Template
     templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(load_template, [templateFileSelectDropdown],  [promptTemplates, templateSelectDropdown], show_progress=True)
-    templateSelectDropdown.change(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],  [systemPromptTxt], show_progress=True)
+    templateFileSelectDropdown.change(
+        load_template,
+        [templateFileSelectDropdown],
+        [promptTemplates, templateSelectDropdown],
+        show_progress=True,
+    )
+    templateSelectDropdown.change(
+        get_template_content,
+        [promptTemplates, templateSelectDropdown, systemPromptTxt],
+        [systemPromptTxt],
+        show_progress=True,
+    )
 
     # S&L
-    saveHistoryBtn.click(save_chat_history, [saveFileName, systemPromptTxt, history, chatbot], downloadFile, show_progress=True)
+    saveHistoryBtn.click(
+        save_chat_history,
+        [saveFileName, systemPromptTxt, history, chatbot],
+        downloadFile,
+        show_progress=True,
+    )
     saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(export_markdown, [saveFileName, systemPromptTxt, history, chatbot], downloadFile, show_progress=True)
+    exportMarkdownBtn.click(
+        export_markdown,
+        [saveFileName, systemPromptTxt, history, chatbot],
+        downloadFile,
+        show_progress=True,
+    )
     historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot],  [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
-    downloadFile.change(load_chat_history, [downloadFile, systemPromptTxt, history, chatbot],  [saveFileName, systemPromptTxt, history, chatbot])
+    historyFileSelectDropdown.change(
+        load_chat_history,
+        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
+        [saveFileName, systemPromptTxt, history, chatbot],
+        show_progress=True,
+    )
+    downloadFile.change(
+        load_chat_history,
+        [downloadFile, systemPromptTxt, history, chatbot],
+        [saveFileName, systemPromptTxt, history, chatbot],
+    )
 
 
-logging.info(colorama.Back.GREEN + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面" + colorama.Style.RESET_ALL)
+logging.info(
+    colorama.Back.GREEN
+    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
+    + colorama.Style.RESET_ALL
+)
 # 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
 demo.title = "川虎ChatGPT 🚀"
 
 if __name__ == "__main__":
-    #if running in Docker
+    # if running in Docker
     if dockerflag:
         if authflag:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
+            demo.queue().launch(
+                server_name="0.0.0.0", server_port=7860, auth=(username, password)
+            )
         else:
             demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
-    #if not running in Docker
+    # if not running in Docker
     else:
         if authflag:
             demo.queue().launch(share=False, auth=(username, password))
         else:
-            demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
+            demo.queue().launch(share=False)  # 改为 share=True 可以创建公开分享链接
+        # demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
+        # demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
+        # demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理

presets.py

@@ -62,8 +62,15 @@ pre code {
 }
 """
 
-summarize_prompt = "你是谁？我们刚才聊了什么？" # 总结对话时的 prompt
-MODELS = ["gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-4","gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"] # 可选的模型
+summarize_prompt = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
+MODELS = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+]  # 可选的模型
 websearch_prompt = """Web search results:
 
 {web_results}
@@ -74,17 +81,17 @@ Query: {query}
 Reply in 中文"""
 
 # 错误信息
-standard_error_msg = "☹️发生了错误：" # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。" # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。" # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。" # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。" # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。" # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。" # API key 长度不足 51 位
+standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
+error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
+connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
+read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
+proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
+ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
+no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
 
-max_token_streaming = 3500 # 流式对话时的最大 token 数
-timeout_streaming = 30 # 流式对话时的超时时间
-max_token_all = 3500 # 非流式对话时的最大 token 数
-timeout_all = 200 # 非流式对话时的超时时间
+max_token_streaming = 3500  # 流式对话时的最大 token 数
+timeout_streaming = 30  # 流式对话时的超时时间
+max_token_all = 3500  # 非流式对话时的最大 token 数
+timeout_all = 200  # 非流式对话时的超时时间
 enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
+HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True

utils.py

@@ -4,10 +4,12 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
 import logging
 import json
 import gradio as gr
+
 # import openai
 import os
 import traceback
 import requests
+
 # import markdown
 import csv
 import mdtex2html
@@ -28,30 +30,33 @@ if TYPE_CHECKING:
         headers: List[str]
         data: List[List[str | int | bool]]
 
+
 initial_prompt = "You are a helpful assistant."
 API_URL = "https://api.openai.com/v1/chat/completions"
 HISTORY_DIR = "history"
 TEMPLATES_DIR = "templates"
 
+
 def postprocess(
-        self, y: List[Tuple[str | None, str | None]]
-    ) -> List[Tuple[str | None, str | None]]:
-        """
-        Parameters:
-            y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
-        Returns:
-            List of tuples representing the message and response. Each message and response will be a string of HTML.
-        """
-        if y is None:
-            return []
-        for i, (message, response) in enumerate(y):
-            y[i] = (
-                # None if message is None else markdown.markdown(message),
-                # None if response is None else markdown.markdown(response),
-                None if message is None else message,
-                None if response is None else mdtex2html.convert(response),
-            )
-        return y
+    self, y: List[Tuple[str | None, str | None]]
+) -> List[Tuple[str | None, str | None]]:
+    """
+    Parameters:
+        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
+    Returns:
+        List of tuples representing the message and response. Each message and response will be a string of HTML.
+    """
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            # None if message is None else markdown.markdown(message),
+            # None if response is None else markdown.markdown(response),
+            None if message is None else message,
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
 
 def count_token(message):
     encoding = tiktoken.get_encoding("cl100k_base")
@@ -59,6 +64,7 @@ def count_token(message):
     length = len(encoding.encode(input_str))
     return length
 
+
 def parse_text(text):
     lines = text.split("\n")
     lines = [line for line in lines if line != ""]
@@ -66,11 +72,11 @@ def parse_text(text):
     for i, line in enumerate(lines):
         if "```" in line:
             count += 1
-            items = line.split('`')
+            items = line.split("`")
             if count % 2 == 1:
                 lines[i] = f'<pre><code class="language-{items[-1]}">'
             else:
-                lines[i] = f'<br></code></pre>'
+                lines[i] = f"<br></code></pre>"
         else:
             if i > 0:
                 if count % 2 == 1:
@@ -86,29 +92,37 @@ def parse_text(text):
                     line = line.replace("(", "&#40;")
                     line = line.replace(")", "&#41;")
                     line = line.replace("$", "&#36;")
-                lines[i] = "<br>"+line
+                lines[i] = "<br>" + line
     text = "".join(lines)
     return text
 
+
 def construct_text(role, text):
     return {"role": role, "content": text}
 
+
 def construct_user(text):
     return construct_text("user", text)
 
+
 def construct_system(text):
     return construct_text("system", text)
 
+
 def construct_assistant(text):
     return construct_text("assistant", text)
 
+
 def construct_token_message(token, stream=False):
     return f"Token 计数: {token}"
 
-def get_response(openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model):
+
+def get_response(
+    openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
+):
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}"
+        "Authorization": f"Bearer {openai_api_key}",
     }
 
     history = [construct_system(system_prompt), *history]
@@ -127,10 +141,23 @@ def get_response(openai_api_key, system_prompt, history, temperature, top_p, str
         timeout = timeout_streaming
     else:
         timeout = timeout_all
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True, timeout=timeout)
+    response = requests.post(
+        API_URL, headers=headers, json=payload, stream=True, timeout=timeout
+    )
     return response
 
-def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model):
+
+def stream_predict(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    selected_model,
+):
     def get_return_value():
         return chatbot, history, status_text, all_token_counts
 
@@ -144,16 +171,28 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
     user_token_count = 0
     if len(all_token_counts) == 0:
         system_prompt_token_count = count_token(construct_system(system_prompt))
-        user_token_count = count_token(construct_user(inputs)) + system_prompt_token_count
+        user_token_count = (
+            count_token(construct_user(inputs)) + system_prompt_token_count
+        )
     else:
         user_token_count = count_token(construct_user(inputs))
     all_token_counts.append(user_token_count)
     logging.info(f"输入token计数: {user_token_count}")
     yield get_return_value()
     try:
-        response = get_response(openai_api_key, system_prompt, history, temperature, top_p, True, selected_model)
+        response = get_response(
+            openai_api_key,
+            system_prompt,
+            history,
+            temperature,
+            top_p,
+            True,
+            selected_model,
+        )
     except requests.exceptions.ConnectTimeout:
-        status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        status_text = (
+            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        )
         yield get_return_value()
         return
     except requests.exceptions.ReadTimeout:
@@ -182,16 +221,24 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
                 yield get_return_value()
                 continue
             # decode each line as response data is in bytes
-            if chunklength > 6 and "delta" in chunk['choices'][0]:
-                finish_reason = chunk['choices'][0]['finish_reason']
-                status_text = construct_token_message(sum(all_token_counts), stream=True)
+            if chunklength > 6 and "delta" in chunk["choices"][0]:
+                finish_reason = chunk["choices"][0]["finish_reason"]
+                status_text = construct_token_message(
+                    sum(all_token_counts), stream=True
+                )
                 if finish_reason == "stop":
                     yield get_return_value()
                     break
                 try:
-                    partial_words = partial_words + chunk['choices'][0]["delta"]["content"]
+                    partial_words = (
+                        partial_words + chunk["choices"][0]["delta"]["content"]
+                    )
                 except KeyError:
-                    status_text = standard_error_msg + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: " + str(sum(all_token_counts))
+                    status_text = (
+                        standard_error_msg
+                        + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
+                        + str(sum(all_token_counts))
+                    )
                     yield get_return_value()
                     break
                 history[-1] = construct_assistant(partial_words)
@@ -200,16 +247,36 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
                 yield get_return_value()
 
 
-def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model):
+def predict_all(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    selected_model,
+):
     logging.info("一次性回答模式")
     history.append(construct_user(inputs))
     history.append(construct_assistant(""))
     chatbot.append((parse_text(inputs), ""))
     all_token_counts.append(count_token(construct_user(inputs)))
     try:
-        response = get_response(openai_api_key, system_prompt, history, temperature, top_p, False, selected_model)
+        response = get_response(
+            openai_api_key,
+            system_prompt,
+            history,
+            temperature,
+            top_p,
+            False,
+            selected_model,
+        )
     except requests.exceptions.ConnectTimeout:
-        status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        status_text = (
+            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        )
         return chatbot, history, status_text, all_token_counts
     except requests.exceptions.ProxyError:
         status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
@@ -227,8 +294,21 @@ def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_tok
     return chatbot, history, status_text, all_token_counts
 
 
-def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model = MODELS[0], use_websearch_checkbox = False, should_check_token_count = True):  # repetition_penalty, top_k
-    logging.info("输入为：" +colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
+def predict(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+    use_websearch_checkbox=False,
+    should_check_token_count=True,
+):  # repetition_penalty, top_k
+    logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
     if use_websearch_checkbox:
         results = ddg(inputs, max_results=3)
         web_results = []
@@ -237,7 +317,11 @@ def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_c
             web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
         web_results = "\n\n".join(web_results)
         today = datetime.datetime.today().strftime("%Y-%m-%d")
-        inputs = websearch_prompt.replace("{current_date}", today).replace("{query}", inputs).replace("{web_results}", web_results)
+        inputs = (
+            websearch_prompt.replace("{current_date}", today)
+            .replace("{query}", inputs)
+            .replace("{web_results}", web_results)
+        )
     if len(openai_api_key) != 51:
         status_text = standard_error_msg + no_apikey_msg
         logging.info(status_text)
@@ -254,16 +338,41 @@ def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_c
         yield chatbot, history, "开始生成回答……", all_token_counts
     if stream:
         logging.info("使用流式传输")
-        iter = stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
+        iter = stream_predict(
+            openai_api_key,
+            system_prompt,
+            history,
+            inputs,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            selected_model,
+        )
         for chatbot, history, status_text, all_token_counts in iter:
             yield chatbot, history, status_text, all_token_counts
     else:
         logging.info("不使用流式传输")
-        chatbot, history, status_text, all_token_counts = predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
+        chatbot, history, status_text, all_token_counts = predict_all(
+            openai_api_key,
+            system_prompt,
+            history,
+            inputs,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            selected_model,
+        )
         yield chatbot, history, status_text, all_token_counts
     logging.info(f"传输完毕。当前token计数为{all_token_counts}")
-    if len(history) > 1 and history[-1]['content'] != inputs:
-        logging.info("回答为：" +colorama.Fore.BLUE + f"{history[-1]['content']}" + colorama.Style.RESET_ALL)
+    if len(history) > 1 and history[-1]["content"] != inputs:
+        logging.info(
+            "回答为："
+            + colorama.Fore.BLUE
+            + f"{history[-1]['content']}"
+            + colorama.Style.RESET_ALL
+        )
     if stream:
         max_token = max_token_streaming
     else:
@@ -272,13 +381,34 @@ def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_c
         status_text = f"精简token中{all_token_counts}/{max_token}"
         logging.info(status_text)
         yield chatbot, history, status_text, all_token_counts
-        iter = reduce_token_size(openai_api_key, system_prompt, history, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model=selected_model, hidden=True)
+        iter = reduce_token_size(
+            openai_api_key,
+            system_prompt,
+            history,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            stream=False,
+            selected_model=selected_model,
+            hidden=True,
+        )
         for chatbot, history, status_text, all_token_counts in iter:
             status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
             yield chatbot, history, status_text, all_token_counts
 
 
-def retry(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0]):
+def retry(
+    openai_api_key,
+    system_prompt,
+    history,
+    chatbot,
+    token_count,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+):
     logging.info("重试中……")
     if len(history) == 0:
         yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
@@ -286,22 +416,58 @@ def retry(openai_api_key, system_prompt, history, chatbot, token_count, top_p, t
     history.pop()
     inputs = history.pop()["content"]
     token_count.pop()
-    iter = predict(openai_api_key, system_prompt, history, inputs, chatbot, token_count, top_p, temperature, stream=stream, selected_model=selected_model)
+    iter = predict(
+        openai_api_key,
+        system_prompt,
+        history,
+        inputs,
+        chatbot,
+        token_count,
+        top_p,
+        temperature,
+        stream=stream,
+        selected_model=selected_model,
+    )
     logging.info("重试完毕")
     for x in iter:
         yield x
 
 
-def reduce_token_size(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0], hidden=False):
+def reduce_token_size(
+    openai_api_key,
+    system_prompt,
+    history,
+    chatbot,
+    token_count,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+    hidden=False,
+):
     logging.info("开始减少token数量……")
-    iter = predict(openai_api_key, system_prompt, history, summarize_prompt, chatbot, token_count, top_p, temperature, stream=stream, selected_model = selected_model, should_check_token_count=False)
+    iter = predict(
+        openai_api_key,
+        system_prompt,
+        history,
+        summarize_prompt,
+        chatbot,
+        token_count,
+        top_p,
+        temperature,
+        stream=stream,
+        selected_model=selected_model,
+        should_check_token_count=False,
+    )
     logging.info(f"chatbot: {chatbot}")
     for chatbot, history, status_text, previous_token_count in iter:
         history = history[-2:]
         token_count = previous_token_count[-1:]
         if hidden:
             chatbot.pop()
-        yield chatbot, history, construct_token_message(sum(token_count), stream=stream), token_count
+        yield chatbot, history, construct_token_message(
+            sum(token_count), stream=stream
+        ), token_count
     logging.info("减少token数量完毕")
 
 
@@ -320,7 +486,12 @@ def delete_last_conversation(chatbot, history, previous_token_count):
     if len(previous_token_count) > 0:
         logging.info("删除了一组对话的token计数记录")
         previous_token_count.pop()
-    return chatbot, history, previous_token_count, construct_token_message(sum(previous_token_count))
+    return (
+        chatbot,
+        history,
+        previous_token_count,
+        construct_token_message(sum(previous_token_count)),
+    )
 
 
 def save_file(filename, system, history, chatbot):
@@ -340,6 +511,7 @@ def save_file(filename, system, history, chatbot):
     logging.info("保存对话历史完毕")
     return os.path.join(HISTORY_DIR, filename)
 
+
 def save_chat_history(filename, system, history, chatbot):
     if filename == "":
         return
@@ -347,6 +519,7 @@ def save_chat_history(filename, system, history, chatbot):
         filename += ".json"
     return save_file(filename, system, history, chatbot)
 
+
 def export_markdown(filename, system, history, chatbot):
     if filename == "":
         return
@@ -382,9 +555,11 @@ def load_chat_history(filename, system, history, chatbot):
         logging.info("没有找到对话历史文件，不执行任何操作")
         return filename, system, history, chatbot
 
+
 def sorted_by_pinyin(list):
     return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
 
+
 def get_file_names(dir, plain=False, filetypes=[".json"]):
     logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
     files = []
@@ -401,10 +576,12 @@ def get_file_names(dir, plain=False, filetypes=[".json"]):
     else:
         return gr.Dropdown.update(choices=files)
 
+
 def get_history_names(plain=False):
     logging.info("获取历史记录文件名列表")
     return get_file_names(HISTORY_DIR, plain)
 
+
 def load_template(filename, mode=0):
     logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
     lines = []
@@ -414,22 +591,28 @@ def load_template(filename, mode=0):
             lines = json.load(f)
         lines = [[i["act"], i["prompt"]] for i in lines]
     else:
-        with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as csvfile:
+        with open(
+            os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
+        ) as csvfile:
             reader = csv.reader(csvfile)
             lines = list(reader)
         lines = lines[1:]
     if mode == 1:
         return sorted_by_pinyin([row[0] for row in lines])
     elif mode == 2:
-        return {row[0]:row[1] for row in lines}
+        return {row[0]: row[1] for row in lines}
     else:
         choices = sorted_by_pinyin([row[0] for row in lines])
-        return {row[0]:row[1] for row in lines}, gr.Dropdown.update(choices=choices, value=choices[0])
+        return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
+            choices=choices, value=choices[0]
+        )
+
 
 def get_template_names(plain=False):
     logging.info("获取模板文件名列表")
     return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
 
+
 def get_template_content(templates, selection, original_system_prompt):
     logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
     try:
@@ -437,9 +620,11 @@ def get_template_content(templates, selection, original_system_prompt):
     except:
         return original_system_prompt
 
+
 def reset_state():
     logging.info("重置状态")
     return [], [], [], construct_token_message(0)
 
+
 def reset_textbox():
-    return gr.update(value='')
+    return gr.update(value="")