import streamlit as st
import json
from typing import Dict, List, Any

def format_project_response(project: dict) -> str:
    """Format project details with clear separation"""
    response = [f"\n• {project['name']}:"]
    response.append(f"  Description: {project['description']}")
    
    if 'skills_used' in project:
        response.append(f"\n  Technologies Used:")
        response.append(f"  {', '.join(project['skills_used'])}")
    
    if 'status' in project:
        response.append(f"\n  Current Status: {project['status']}")
        if 'confidentiality_note' in project:
            response.append(f"  Note: {project['confidentiality_note']}")
    
    return '\n'.join(response) + '\n'

def get_philosophical_response(query: str, knowledge_base: dict) -> str:
    """Handle philosophical or market-related queries"""
    query_lower = query.lower()
    
    # Market-related response
    if any(word in query_lower for word in ['market', 'job', 'opportunity', 'down']):
        return """I believe success in any market comes down to quality of effort and preparation. While the market may have cycles, I focus on:

• Continuous Skill Development:
  - Building practical projects that solve real problems
  - Staying updated with latest ML/AI trends
  - Enhancing my technical portfolio

• Value Proposition:
  - Unique combination of business and technical skills
  - Focus on practical implementation
  - Strong problem-solving approach

I see this period as an opportunity to strengthen my skills and build more impactful projects."""

    # Off-topic response
    if any(word in query_lower for word in ['weather', 'temperature', 'climate']):
        return """I'm focused on discussing my ML/AI journey and projects. For weather information, I'd recommend checking local weather services.

Would you like to know about:
• My ML projects and technical skills?
• My journey from commerce to tech?
• My approach to the current job market?"""

    return None  # Return None if not a philosophical query

def generate_response(query: str, knowledge_base: dict) -> str:
    """Enhanced response generation with better handling of various queries"""
    query_lower = query.lower()
    
    # First check for philosophical/off-topic queries
    philosophical_response = get_philosophical_response(query, knowledge_base)
    if philosophical_response:
        return philosophical_response
    
    # Handle project listing requests
    if any(word in query_lower for word in ['list', 'project', 'portfolio', 'built', 'created', 'developed']):
        response_parts = ["Here are my key projects:"]
        
        # Major Projects
        response_parts.append("\nMajor Projects (In Development):")
        for project in knowledge_base['projects']['major_projects']:
            response_parts.append(format_project_response(project))
        
        # Algorithm Projects
        response_parts.append("\nCompleted Algorithm Implementation Projects:")
        for project in knowledge_base['projects']['algorithm_practice_projects']:
            response_parts.append(format_project_response(project))
        
        response = '\n'.join(response_parts)
        
        # Add relevant links
        if 'online_presence' in knowledge_base.get('personal_details', {}):
            response += f"\n\nView my complete portfolio: {knowledge_base['personal_details']['online_presence']['portfolio']}"
        
        return response
    
    # [Rest of your existing response handlers]

def main():
    st.title("💬 Chat with Manyue's Portfolio")
    
    # Initialize session state
    if "messages" not in st.session_state:
        st.session_state.messages = []
    if "knowledge_base" not in st.session_state:
        try:
            with open('knowledge_base.json', 'r', encoding='utf-8') as f:
                st.session_state.knowledge_base = json.load(f)
        except FileNotFoundError:
            st.error("Knowledge base file not found.")
            return
    
    # Display welcome message
    if "displayed_welcome" not in st.session_state:
        st.write("""
        Hi! I'm Manyue's AI assistant. I can tell you about:
        - My journey from commerce to ML/AI
        - My technical skills and projects
        - My fit for ML/AI roles
        - You can also paste job descriptions to see how my profile matches!
        """)
        st.session_state.displayed_welcome = True

    # Create two columns with proper sizing
    col1, col2 = st.columns([3, 1])
    
    with col1:
        # Chat container for better scrolling
        chat_container = st.container()
        with chat_container:
            for message in st.session_state.messages:
                with st.chat_message(message["role"]):
                    st.markdown(message["content"])
        
        # Chat input
        if prompt := st.chat_input("Ask me anything or paste a job description...", key="chat_input"):
            # Add user message
            with st.chat_message("user"):
                st.markdown(prompt)
            st.session_state.messages.append({"role": "user", "content": prompt})
            
            # Generate and display response
            with st.chat_message("assistant"):
                try:
                    response = generate_response(prompt, st.session_state.knowledge_base)
                    st.markdown(response)
                    st.session_state.messages.append({"role": "assistant", "content": response})
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
    
    with col2:
        st.subheader("Quick Questions")
        example_questions = [
            "Tell me about your ML projects",
            "What are your technical skills?",
            "What makes you stand out?",
            "What's your journey into ML?",
            "Your view on the current market?"
        ]
        
        # Handle quick questions with proper keys
        for i, question in enumerate(example_questions):
            if st.button(question, key=f"btn_{i}"):
                with st.chat_message("user"):
                    st.markdown(question)
                    st.session_state.messages.append({"role": "user", "content": question})
                
                with st.chat_message("assistant"):
                    try:
                        response = generate_response(question, st.session_state.knowledge_base)
                        st.markdown(response)
                        st.session_state.messages.append({"role": "assistant", "content": response})
                    except Exception as e:
                        st.error(f"An error occurred: {str(e)}")
                st.rerun()
        
        st.markdown("---")
        if st.button("Clear Chat", key="clear_chat"):
            st.session_state.messages = []
            st.rerun()

if __name__ == "__main__":
    main()