Create app纯单模型.py

app纯单模型.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from flask import Flask, request, make_response
+import hashlib
+import time
+import xml.etree.ElementTree as ET
+import os
+from openai import OpenAI
+from dotenv import load_dotenv
+
+# 加载环境变量
+load_dotenv()
+
+app = Flask(__name__)
+
+# 配置
+TOKEN = os.getenv('TOKEN')
+API_KEY = os.getenv("API_KEY")
+BASE_URL = os.getenv("OPENAI_BASE_URL")
+client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+# 存储用户会话信息
+user_sessions = {}
+
+def verify_wechat(request):
+    # 获取微信服务器发送过来的参数
+    data = request.args
+    signature = data.get('signature')
+    timestamp = data.get('timestamp')
+    nonce = data.get('nonce')
+    echostr = data.get('echostr')
+    
+    # 对参数进行字典排序，拼接字符串
+    temp = [timestamp, nonce, TOKEN]
+    temp.sort()
+    temp = ''.join(temp)
+    
+    # 加密
+    if (hashlib.sha1(temp.encode('utf8')).hexdigest() == signature):
+        return echostr
+    else:
+        return 'error', 403
+
+def getUserMessageContentFromXML(xml_content):
+    # 解析XML字符串
+    root = ET.fromstring(xml_content)
+    # 提取数据
+    content = root.find('Content').text
+    from_user_name = root.find('FromUserName').text
+    to_user_name = root.find('ToUserName').text
+    return content, from_user_name, to_user_name
+
+def generate_response_xml(from_user_name, to_user_name, output_content):
+    output_xml = '''
+    <xml>
+        <ToUserName><![CDATA[%s]]></ToUserName>
+        <FromUserName><![CDATA[%s]]></FromUserName>
+        <CreateTime>%s</CreateTime>
+        <MsgType><![CDATA[text]]></MsgType>
+        <Content><![CDATA[%s]]></Content>
+    </xml>'''
+    
+    response = make_response(output_xml % (from_user_name, to_user_name, str(int(time.time())), output_content))
+    response.content_type = 'application/xml'
+    return response
+
+def get_openai_response(messages):
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"调用OpenAI API时出错: {str(e)}")
+        return "抱歉，我遇到了一些问题，无法回答您的问题。"
+
+def split_message(message, max_length=500):
+    return [message[i:i+max_length] for i in range(0, len(message), max_length)]
+
+@app.route('/api/wx', methods=['GET', 'POST'])
+def wechatai():
+    if request.method == 'GET':
+        return verify_wechat(request)
+    else:
+        # 处理POST请求
+        print("user request data: ", request.data)
+        user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(request.data)
+        print("user message content: ", user_message_content)
+
+        if user_message_content.lower() == '继续':
+            if from_user_name in user_sessions and user_sessions[from_user_name]['pending_response']:
+                response_content = user_sessions[from_user_name]['pending_response'].pop(0)
+                if user_sessions[from_user_name]['pending_response']:
+                    response_content += '\n\n回复"继续"获取下一部分。'
+                else:
+                    response_content += '\n\n回复结束。'
+            else:
+                response_content = "没有待发送的消息。"
+        else:
+            if from_user_name not in user_sessions:
+                user_sessions[from_user_name] = {'messages': [], 'pending_response': []}
+
+            session = user_sessions[from_user_name]
+            session['messages'].append({"role": "user", "content": user_message_content})
+            
+            gpt_response = get_openai_response(session['messages'])
+            session['messages'].append({"role": "assistant", "content": gpt_response})
+
+            response_parts = split_message(gpt_response)
+            
+            if len(response_parts) > 1:
+                response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
+                session['pending_response'] = response_parts[1:]
+            else:
+                response_content = response_parts[0]
+
+        return generate_response_xml(from_user_name, to_user_name, response_content)
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860, debug=True)