Update app.py

app.py

@@ -1,106 +1,106 @@
-from flask import Flask, render_template, request, Response
-from openai import OpenAI
-from dotenv import load_dotenv
-import os
-import json
-from datetime import datetime
-
-load_dotenv()
-
-app = Flask(__name__)
-client = OpenAI(
-    api_key=os.getenv('OPENAI_API_KEY'),
-    base_url=os.getenv('OPENAI_BASE_URL')
-)
-
-class ChatBot:
-    def __init__(self):
-        self.messages = []
-        # 加载系统提示词
-        self.system_prompt = self.load_system_prompt()
-        
-    def load_system_prompt(self):
-        # 从文件加载系统提示词
-        try:
-            with open('system_prompt.txt', 'r', encoding='utf-8') as file:
-                return file.read()
-        except Exception as e:
-            print(f"Error loading system prompt: {e}")
-            return ""
-    
-    def format_birth_info(self, birth_info):
-        """格式化出生信息用于分析"""
-        try:
-            birth_date = datetime.strptime(birth_info['date'], '%Y年%m月%d日')
-            formatted_date = birth_date.strftime('%Y年%m月%d日')
-            return f"{formatted_date} {birth_info['time']}"
-        except Exception as e:
-            print(f"Error formatting birth info: {e}")
-            return birth_info['raw']
-    
-    def prepare_analysis_prompt(self, user_data):
-        """准备用于分析的完整提示词"""
-        birth_info = self.format_birth_info(user_data['birth_info'])
-        
-        analysis_prompt = f"""根据以下信息进行命理分析：
-出生信息：{birth_info}
-咨询问题：{user_data['question']}
-
-请基于此信息，结合八字、紫微斗数等进行分析。"""
-        
-        return analysis_prompt
-
-    def get_stream_response(self, user_input):
-        """处理用户输入并生成流式响应"""
-        try:
-            # 解析用户输入的JSON数据
-            user_data = json.loads(user_input)
-            
-            # 如果是新的对话，添加系统提示词
-            if not self.messages:
-                self.messages.append({"role": "system", "content": self.system_prompt})
-            
-            # 准备分析提示词
-            analysis_prompt = self.prepare_analysis_prompt(user_data)
-            self.messages.append({"role": "user", "content": analysis_prompt})
-
-            # 创建流式响应
-            response = client.chat.completions.create(
-                model=os.getenv('OPENAI_MODEL'),
-                messages=self.messages,
-                stream=True,
-                temperature=0.7,
-                max_tokens=2000
-            )
-
-            for chunk in response:
-                if chunk.choices[0].delta.content:
-                    yield f"data: {json.dumps({'content': chunk.choices[0].delta.content})}\n\n"
-
-            # 保存完整对话到历史记录
-            full_response = ''.join(chunk.choices[0].delta.content or '' 
-                                  for chunk in response)
-            self.messages.append({"role": "assistant", "content": full_response})
-
-        except Exception as e:
-            yield f"data: {json.dumps({'error': str(e)})}\n\n"
-
-chatbot = ChatBot()
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    try:
-        user_message = request.get_json()
-        return Response(
-            chatbot.get_stream_response(json.dumps(user_message)),
-            mimetype='text/event-stream'
-        )
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, render_template, request, Response
+from openai import OpenAI
+from dotenv import load_dotenv
+import os
+import json
+from datetime import datetime
+
+load_dotenv()
+
+app = Flask(__name__)
+client = OpenAI(
+    api_key=os.getenv('OPENAI_API_KEY'),
+    base_url=os.getenv('OPENAI_BASE_URL')
+)
+
+class ChatBot:
+    def __init__(self):
+        self.messages = []
+        # 加载系统提示词
+        self.system_prompt = self.load_system_prompt()
+        
+    def load_system_prompt(self):
+        # 从文件加载系统提示词
+        try:
+            with open('system_prompt.txt', 'r', encoding='utf-8') as file:
+                return file.read()
+        except Exception as e:
+            print(f"Error loading system prompt: {e}")
+            return ""
+    
+    def format_birth_info(self, birth_info):
+        """格式化出生信息用于分析"""
+        try:
+            birth_date = datetime.strptime(birth_info['date'], '%Y年%m月%d日')
+            formatted_date = birth_date.strftime('%Y年%m月%d日')
+            return f"{formatted_date} {birth_info['time']}"
+        except Exception as e:
+            print(f"Error formatting birth info: {e}")
+            return birth_info['raw']
+    
+    def prepare_analysis_prompt(self, user_data):
+        """准备用于分析的完整提示词"""
+        birth_info = self.format_birth_info(user_data['birth_info'])
+        
+        analysis_prompt = f"""根据以下信息进行命理分析：
+出生信息：{birth_info}
+咨询问题：{user_data['question']}
+
+请基于此信息，结合八字、紫微斗数等进行分析。"""
+        
+        return analysis_prompt
+
+    def get_stream_response(self, user_input):
+        """处理用户输入并生成流式响应"""
+        try:
+            # 解析用户输入的JSON数据
+            user_data = json.loads(user_input)
+            
+            # 如果是新的对话，添加系统提示词
+            if not self.messages:
+                self.messages.append({"role": "system", "content": self.system_prompt})
+            
+            # 准备分析提示词
+            analysis_prompt = self.prepare_analysis_prompt(user_data)
+            self.messages.append({"role": "user", "content": analysis_prompt})
+
+            # 创建流式响应
+            response = client.chat.completions.create(
+                model=os.getenv('OPENAI_MODEL'),
+                messages=self.messages,
+                stream=True,
+                temperature=0.7,
+                max_tokens=2000
+            )
+
+            for chunk in response:
+                if chunk.choices[0].delta.content:
+                    yield f"data: {json.dumps({'content': chunk.choices[0].delta.content})}\n\n"
+
+            # 保存完整对话到历史记录
+            full_response = ''.join(chunk.choices[0].delta.content or '' 
+                                  for chunk in response)
+            self.messages.append({"role": "assistant", "content": full_response})
+
+        except Exception as e:
+            yield f"data: {json.dumps({'error': str(e)})}\n\n"
+
+chatbot = ChatBot()
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        user_message = request.get_json()
+        return Response(
+            chatbot.get_stream_response(json.dumps(user_message)),
+            mimetype='text/event-stream'
+        )
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860, debug=False)