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backend/app.py

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+from flask import Flask, request, jsonify, render_template, send_from_directory
+from flask_cors import CORS
+import os
+from llama3 import LlaMa3  # 导入您的 LlaMa3 类
+
+app = Flask(__name__)
+CORS(app) 
+
+# 实例化 LlaMa3 模型
+llama3_model = LlaMa3()
+
+@app.route('/')
+def index():
+    # 返回 HTML 页面
+    return render_template('index.html')
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    # 获取前端发送的用户消息
+    user_message = request.json.get('message', '')
+
+    if not user_message.strip():
+        return jsonify({"response": "请输入有效内容！"}), 400
+
+    try:
+        # 构造聊天上下文
+        messages = [{"role": "user", "content": user_message}]
+        
+        # 调用 LlaMa3 的 chat 方法生成回复
+        ai_response = llama3_model.chat(messages)
+        
+        # 返回 AI 的回复
+        return jsonify({"response": ai_response})
+    except Exception as e:
+        print(f"Error during llama3 call: {e}")
+        return jsonify({"response": "发生错误，请稍后重试！"}), 500
+
+@app.route('/favicon.ico')
+def favicon():
+    return send_from_directory(os.path.join(app.root_path, 'static'),
+                               'favicon.ico', mimetype='image/vnd.microsoft.icon')
+
+if __name__ == '__main__':
+    app.run(debug=True, host='127.0.0.1', port=5000)

backend/llama3.py

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+from openai import OpenAI
+class LlaMa3:
+    def __init__(self) -> None:
+        self.client = OpenAI(
+            base_url="https://integrate.api.nvidia.com/v1",
+            api_key="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"
+        )
+        self.name = "Llama3"
+        
+        # Initial greeting and request for decision topic
+        self.initial_prompt = """
+        Hello! I can assist you in making a decision. What decision would you like to make today? 
+        Please describe the decision and provide any relevant details to help me understand.
+        """
+
+    def chat(self, messages):
+        # If this is the first message, we use the initial prompt to greet and ask for the decision topic
+        if len(messages) == 0:  # Initial conversation step
+            messages.append({"role": "system", "content": self.initial_prompt})
+
+        # Call the API to get the model's response
+        completion = self.client.chat.completions.create(
+            model="nvidia/llama-3.1-nemotron-70b-instruct",
+            messages=messages,  
+            temperature=0.5,
+            top_p=1,
+            max_tokens=1024,
+            stream=True
+        )
+        
+        response = ""
+        for chunk in completion:
+            if chunk.choices[0].delta.content is not None:
+                response += chunk.choices[0].delta.content
+
+        return response