AI Portfolio Assistant

# FastAPI endpoint with mode detection
@app.post("/api/chat")
async def chat(request: ChatRequest):
    user_message = request.message
    history = request.history
    
    # Detect response mode based on question patterns
    mode = detect_mode(user_message)
    
    # Build context-aware prompt
    system_prompt = get_system_prompt(mode)
    context = build_context(history)
    
    # Call Gemini API
    start_time = time.time()
    response = await gemini_client.generate_content(
        model="gemini-1.5-flash",
        contents=[
            {"role": "system", "parts": [system_prompt]},
            {"role": "user", "parts": [context + user_message]}
        ]
    )
    
    # Log interaction
    log_interaction(
        user_message=user_message,
        mode=mode,
        response_time=time.time() - start_time,
        tokens=response.usage_metadata.total_tokens
    )
    
    return {"response": response.text, "mode": mode}

// ChatWidget component with session persistence
const [messages, setMessages] = useState([]);

// Load conversation on mount
useEffect(() => {
  const savedMessages = localStorage.getItem('chat_history');
  if (savedMessages) {
    setMessages(JSON.parse(savedMessages));
  }
}, []);

// Save after each message
useEffect(() => {
  localStorage.setItem('chat_history', JSON.stringify(messages));
}, [messages]);

// Send message with full context
const sendMessage = async (userMessage) => {
  const newMessage = { role: 'user', content: userMessage };
  setMessages(prev => [...prev, newMessage]);
  
  // Include conversation history for context
  const response = await fetch('/api/chat', {
    method: 'POST',
    body: JSON.stringify({
      message: userMessage,
      history: messages // Full context sent to backend
    })
  });
  
  const { response: aiResponse, mode } = await response.json();
  setMessages(prev => [...prev, { 
    role: 'assistant', 
    content: aiResponse, 
    mode 
  }]);
};