Create app.py

app.py

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+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())
+
+    # 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
+
+    # 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)
+
+    return assistant_response
+
+#示例
+session_id = "session_1"
+
+
+def gradio_chat_gpt(input_text):
+    session_id = "gradio_session"
+    return chat_gpt(session_id, input_text)
+
+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")
+
+iface.launch()