app.py

import streamlit as st
import json
from huggingface_hub import InferenceClient
import time

# Initialize Streamlit page configuration
st.set_page_config(
    page_title="Manyue's Portfolio Chatbot",
    page_icon="🤖",
    layout="wide"
)

# Secret management
def get_hf_api_token():
    # In production, use Streamlit secrets
    return st.secrets["HUGGINGFACE_API_TOKEN"]

# Initialize HF client
@st.cache_resource
def get_hf_client():
    client = InferenceClient(
        model="meta-llama/Llama-2-7b-chat-hf",
        token=get_hf_api_token()
    )
    return client

# Load and cache knowledge base
@st.cache_data
def load_knowledge_base():
    try:
        with open('knowledge_base.json', 'r', encoding='utf-8') as f:
            return json.load(f)
    except Exception as e:
        st.error(f"Error loading knowledge base: {str(e)}")
        return {}

def get_context(query: str, knowledge_base: dict) -> str:
    """Get relevant context for the query"""
    query_lower = query.lower()
    contexts = []
    
    # Project-related queries
    if any(word in query_lower for word in ["project", "build", "develop", "create"]):
        if "projects" in knowledge_base:
            for name, details in knowledge_base["projects"].items():
                contexts.append(f"Project - {name}: {details.get('description', '')}")
    
    # Skills and expertise
    elif any(word in query_lower for word in ["skill", "know", "experience", "expert"]):
        if "skills" in knowledge_base.get("personal_details", {}):
            contexts.extend([
                f"Skill - {skill}: {desc}"
                for skill, desc in knowledge_base["personal_details"]["skills"].items()
            ])
    
    # Role fit and career
    elif any(word in query_lower for word in ["role", "fit", "job", "position", "career"]):
        contexts.append(knowledge_base.get("professional_journey", {}).get("mindset", ""))
        contexts.extend(knowledge_base.get("goals_and_aspirations", {}).get("short_term", []))
        
    # Background and journey
    elif any(word in query_lower for word in ["background", "journey", "story"]):
        faq = knowledge_base.get("frequently_asked_questions", [])
        for qa in faq:
            if "background" in qa["question"].lower():
                contexts.append(qa["answer"])
    
    # Default context
    if not contexts:
        contexts = [
            f"I am {knowledge_base.get('personal_details', {}).get('full_name', 'Manyue')}",
            knowledge_base.get('personal_details', {}).get('professional_summary', '')
        ]
    
    return "\n".join(contexts)

def generate_llama_prompt(query: str, context: str) -> str:
    """Generate prompt for Llama model"""
    return f"""[INST] You are Manyue's AI assistant. Use this context to answer questions about Manyue:

Context:
{context}

Question: {query}

Instructions:
- Use information from the context
- Speak in first person as Manyue
- Be specific about technical details and achievements
- Keep responses concise but informative
- Focus on relevant experience and skills
- Maintain a professional tone

Your response: [/INST]"""

def get_chat_response(query: str, knowledge_base: dict) -> str:
    """Get response from Llama model via HF API"""
    try:
        # Get context
        context = get_context(query, knowledge_base)
        
        # Generate prompt
        prompt = generate_llama_prompt(query, context)
        
        # Get client
        client = get_hf_client()
        
        # Generate response
        response = client.text_generation(
            prompt,
            max_new_tokens=200,
            temperature=0.7,
            top_p=0.95,
            repetition_penalty=1.1
        )
        
        # Clean response
        response = response.strip()
        response = response.split("[/INST]")[-1].strip()
        
        return response
        
    except Exception as e:
        st.error(f"Error generating response: {str(e)}")
        return "I apologize, but I encountered an error. Please try asking again."

def initialize_session_state():
    """Initialize session state variables"""
    if "messages" not in st.session_state:
        st.session_state.messages = []
    if "knowledge_base" not in st.session_state:
        st.session_state.knowledge_base = load_knowledge_base()

def display_chat_interface():
    """Display main chat interface"""
    # Display chat messages
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

def main():
    st.title("💬 Chat with Manyue's Portfolio")
    st.write("Ask me about my skills, projects, experience, or career goals!")
    
    # Initialize session state
    initialize_session_state()
    
    # Create two columns
    col1, col2 = st.columns([3, 1])
    
    with col1:
        # Display chat interface
        display_chat_interface()
        
        # Chat input
        if prompt := st.chat_input("What would you like to know?"):
            # Add user message
            st.session_state.messages.append({"role": "user", "content": prompt})
            
            # Display user message
            with st.chat_message("user"):
                st.markdown(prompt)
            
            # Generate and display response
            with st.chat_message("assistant"):
                with st.spinner("Thinking..."):
                    response = get_chat_response(prompt, st.session_state.knowledge_base)
                st.markdown(response)
            
            # Add assistant response to history
            st.session_state.messages.append({"role": "assistant", "content": response})
    
    with col2:
        st.subheader("Quick Questions")
        example_questions = [
            "Tell me about your ML projects",
            "What are your technical skills?",
            "Why are you suitable for ML roles?",
            "What is your educational background?"
        ]
        
        for question in example_questions:
            if st.button(question):
                st.session_state.messages.append({"role": "user", "content": question})
                st.experimental_rerun()
        
        st.markdown("---")
        if st.button("Clear Chat"):
            st.session_state.messages = []
            st.experimental_rerun()

if __name__ == "__main__":
    main()