import streamlit as st
import json
import time

# Page configuration
st.set_page_config(
    page_title="Manyue's Portfolio Chatbot",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="collapsed"
)

# Custom CSS for better UI
st.markdown("""
<style>
    .chat-message {
        padding: 1.5rem;
        border-radius: 0.5rem;
        margin-bottom: 1rem;
    }
    .user-message {
        background-color: #e9ecef;
    }
    .bot-message {
        background-color: #f8f9fa;
    }
    .stButton>button {
        width: 100%;
    }
</style>
""", unsafe_allow_html=True)

# Initialize session state
if 'messages' not in st.session_state:
    st.session_state.messages = []
if 'knowledge_base' not in st.session_state:
    st.session_state.knowledge_base = None

def load_knowledge_base():
    """Load the knowledge base from JSON file"""
    if st.session_state.knowledge_base is None:
        try:
            with open('knowledge_base.json', 'r', encoding='utf-8') as f:
                st.session_state.knowledge_base = json.load(f)
        except Exception as e:
            st.error(f"Error loading knowledge base: {str(e)}")
            st.session_state.knowledge_base = {}
    return st.session_state.knowledge_base

def get_enhanced_context(query: str, knowledge_base: dict) -> dict:
    """Get relevant context with improved retrieval"""
    query_lower = query.lower()
    context = {
        "type": "general",
        "content": [],
        "relevant_sections": []
    }
    
    # Project-related queries
    if any(word in query_lower for word in ["project", "build", "develop", "create", "make", "portfolio"]):
        context["type"] = "project"
        if "projects" in knowledge_base:
            for name, project in knowledge_base["projects"].items():
                context["content"].append({
                    "title": name,
                    "description": project.get("description", ""),
                    "skills_used": project.get("skills_used", []),
                    "status": project.get("status", "")
                })
    
    # Skills and experience
    elif any(word in query_lower for word in ["skill", "experience", "know", "capable", "ability", "expert"]):
        context["type"] = "skill"
        if "skills" in knowledge_base.get("personal_details", {}):
            context["content"] = knowledge_base["personal_details"]["skills"]
    
    # Educational background
    elif any(word in query_lower for word in ["education", "study", "learn", "degree", "college", "university"]):
        context["type"] = "education"
        context["content"] = knowledge_base.get("education", {})
    
    # Career and goals
    elif any(word in query_lower for word in ["goal", "plan", "future", "career", "aspiration"]):
        context["type"] = "career"
        context["content"] = {
            "short_term": knowledge_base.get("goals_and_aspirations", {}).get("short_term", []),
            "long_term": knowledge_base.get("goals_and_aspirations", {}).get("long_term", [])
        }
    
    # Personal background
    elif any(word in query_lower for word in ["background", "journey", "story", "transition"]):
        context["type"] = "background"
        context["content"] = knowledge_base.get("personal_journey", {})
        
    # Add FAQ matches if available
    for qa in knowledge_base.get("frequently_asked_questions", []):
        if any(word in qa["question"].lower() for word in query_lower.split()):
            context["relevant_sections"].append({
                "type": "faq",
                "question": qa["question"],
                "answer": qa["answer"]
            })
    
    return context

def generate_response(query: str, context: dict) -> str:
    """Generate natural response based on context"""
    response_parts = []
    
    # Handle different types of queries
    if context["type"] == "project":
        response_parts.append("Let me tell you about my projects.")
        for project in context["content"]:
            response_parts.append(f"\n\n**{project['title']}**")
            response_parts.append(f"{project['description']}")
            if project['skills_used']:
                response_parts.append(f"\nSkills used: {', '.join(project['skills_used'])}")
            if project['status']:
                response_parts.append(f"\nStatus: {project['status']}")
    
    elif context["type"] == "skill":
        response_parts.append("Here are my key skills and experiences:")
        for skill, desc in context["content"].items():
            response_parts.append(f"\n\n**{skill}**:\n{desc}")
    
    elif context["type"] == "education":
        response_parts.append("Regarding my educational background:")
        if "academic_background" in context["content"]:
            response_parts.append(context["content"]["academic_background"])
        if "academic_achievements" in context["content"]:
            response_parts.append("\n\nAchievements:")
            for achievement in context["content"]["academic_achievements"]:
                response_parts.append(f"- {achievement}")
    
    elif context["type"] == "career":
        response_parts.append("Let me share my career goals:")
        response_parts.append("\n\n**Short-term goals:**")
        for goal in context["content"]["short_term"]:
            response_parts.append(f"- {goal}")
        response_parts.append("\n\n**Long-term goals:**")
        for goal in context["content"]["long_term"]:
            response_parts.append(f"- {goal}")
    
    elif context["type"] == "background":
        response_parts.append(context["content"].get("mindset", ""))
        response_parts.append("\n\n" + context["content"].get("motto_or_vision", ""))
    
    # Add any relevant FAQ information
    if context["relevant_sections"]:
        for section in context["relevant_sections"]:
            if section["type"] == "faq":
                response_parts.append(f"\n\n{section['answer']}")
    
    # Default response if no specific context matched
    if not response_parts:
        response_parts = ["I am Manyue, an aspiring AI/ML engineer. I can tell you about my projects, skills, education, or career goals. What would you like to know?"]
    
    return "\n".join(response_parts)

def main():
    st.title("💬 Chat with Manyue's Portfolio")
    st.write("Ask me about my skills, projects, education, or career goals!")
    
    # Load knowledge base
    knowledge_base = load_knowledge_base()
    
    # Create two columns for layout
    col1, col2 = st.columns([3, 1])
    
    with col1:
        # Display chat messages
        for message in st.session_state.messages:
            with st.chat_message(message["role"]):
                st.markdown(message["content"])
        
        # 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})
            
            # Get context and generate response
            context = get_enhanced_context(prompt, knowledge_base)
            response = generate_response(prompt, context)
            
            # Display response with typing effect
            with st.chat_message("assistant"):
                st.markdown(response)
            
            # Add assistant response to history
            st.session_state.messages.append({"role": "assistant", "content": response})
    
    with col2:
        st.subheader("Quick Questions")
        if st.button("📊 Tell me about your projects"):
            prompt = "What projects have you worked on?"
            st.session_state.messages.append({"role": "user", "content": prompt})
            st.experimental_rerun()
            
        if st.button("💻 What are your technical skills?"):
            prompt = "What are your main technical skills?"
            st.session_state.messages.append({"role": "user", "content": prompt})
            st.experimental_rerun()
            
        if st.button("🎓 Educational background?"):
            prompt = "Tell me about your education"
            st.session_state.messages.append({"role": "user", "content": prompt})
            st.experimental_rerun()
            
        if st.button("🎯 What are your career goals?"):
            prompt = "What are your career goals?"
            st.session_state.messages.append({"role": "user", "content": prompt})
            st.experimental_rerun()
        
        st.markdown("---")
        if st.button("🗑️ Clear Chat"):
            st.session_state.messages = []
            st.experimental_rerun()

if __name__ == "__main__":
    main()