Update app.py

app.py

@@ -1,89 +1,37 @@
 import openai
-import os
-
-
 import gradio as gr
-import openai
-import backoff  # for exponential backoff
-from reportlab.lib.pagesizes import letter
-from reportlab.lib import colors
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
-from reportlab.lib.styles import getSampleStyleSheet
-from reportlab.lib.enums import TA_CENTER
-
-openai.api_key = os.environ['chat_key']
-
-class SessionManager:
-    def __init__(self):
-        self.sessions = {}
-
-    def add_message(self, session_id, message):
-        if session_id not in self.sessions:
-            self.sessions[session_id] = []
-        self.sessions[session_id].append(message)
-
-    def get_messages(self, session_id):
-        return self.sessions.get(session_id, [])
-
-# 自定义一个函数，用于将对话消息整理为简短概要
-def summarize_message(message):
-    # 在这个示例中，我们简单地返回原始消息。
-    # 你可以根据需要替换为更复杂的逻辑，以提取关键信息并生成简短概要。
-    return message
-
-session_manager = SessionManager()
-def chat_gpt(session_id, user_message, model="gpt-3.5-turbo", max_tokens=4096):
-    # 将用户消息添加到对应的会话中
-    summarized_message = summarize_message(f"User: {user_message}")
-    session_manager.add_message(session_id, summarized_message)
-
-    # 获取当前会话的消息列表
-    message_list = session_manager.get_messages(session_id)
-
-    # 组合聊天历史
-    conversation = "\n\n".join(message_list)
-
-    # 检查对话历史是否超过模型的token限制
-    tokens_length = len(conversation.split())
 
-    if tokens_length > max_tokens:
-        # 移除早期消息，直到满足token限制
-        while tokens_length > max_tokens:
-            message_list.pop(0)
-            conversation = "\n\n".join(message_list)
-            tokens_length = len(conversation.split())
+# 设置您的API密钥
+openai.api_key = "your_openai_api_key_here"
 
-    # 生成聊天回复
-    response = openai.ChatCompletion.create(
-        model= model,
-        messages=[{"role": "system", "content": "你好，请问有什么问题我可以帮您解答吗？"}] +
-                 [{"role": "user", "content": msg} for msg in message_list],
-        # system 中 定义回答问题的具体类型等
-        temperature=0.5,
-        max_tokens=150,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0,
-        stop=["\n\n"],
-    )
-    assistant_response = response.choices[0].message.content
+# 存储用户对话历史的字典
+user_dialogue_histories = {}
+max_tokens_per_user = 1000  # 您可以根据需要设置此值
 
-    # 将助手回复添加到对应的会话中
-    summarized_message = summarize_message(f"Assistant: {assistant_response}")
-    session_manager.add_message(session_id, summarized_message)
+def get_total_tokens(dialogue_history):
+    total_tokens = 0
+    for message in dialogue_history:
+        total_tokens += len(message["content"])
+    return total_tokens
 
-    return assistant_response
+def remove_earliest_messages(user_id, tokens_to_remove):
+    while tokens_to_remove > 0 and user_dialogue_histories[user_id]:
+        removed_message = user_dialogue_histories[user_id].pop(0)
+        tokens_to_remove -= len(removed_message["content"])
 
-#示例
-session_id = "session_1"
+def chat_with_chatgpt(user_id, user_message):
+    # ... 保持与之前相同的代码 ...
 
+# Gradio界面
+def gradio_interface(user_id, user_message):
+    response = chat_with_chatgpt(user_id, user_message)
+    return response
 
-def gradio_chat_gpt(input_text):
-    session_id = "gradio_session"
-    return chat_gpt(session_id, input_text)
+inputs = [
+    gr.inputs.Textbox(label="User ID", placeholder="Enter user ID here"),
+    gr.inputs.Textbox(label="Message", placeholder="Enter your message here"),
+]
 
-input_text = gr.inputs.Textbox(lines=5, placeholder="请输入你的问题...")
-output_text = gr.outputs.Textbox()
-iface = gr.Interface(fn=gradio_chat_gpt, inputs=input_text, outputs=output_text, title="ChatGPT", description="与GPT模型聊天")
+output = gr.outputs.Textbox(label="ChatGPT Response")
 
-iface.launch()
+gr.Interface(fn=gradio_interface, inputs=inputs, outputs=output, title="Chat with ChatGPT").launch()