"""
AI Personas for Urban Planning - Web UI

A beautiful web interface for interacting with urban planning stakeholder personas.

Usage:
    streamlit run web_app.py
"""

import streamlit as st
import sys
import os
from pathlib import Path
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Add current directory and src to path for imports
current_dir = Path(__file__).parent.resolve()
sys.path.insert(0, str(current_dir))
sys.path.insert(0, str(current_dir / "src"))

# Debug: Print path information (will appear in logs)
print(f"Current directory: {current_dir}")
print(f"Python path: {sys.path[:3]}")
print(f"Contents: {list(current_dir.iterdir())[:10]}")
print(f"ANTHROPIC_API_KEY present: {bool(os.getenv('ANTHROPIC_API_KEY'))}")

from src.pipeline.query_engine import QueryEngine
from src.llm.anthropic_client import AnthropicClient
from src.llm.local_model_client import LocalModelClient


# Page configuration
st.set_page_config(
    page_title="AI Personas - Urban Planning",
    page_icon="🏙️",
    layout="wide",
    initial_sidebar_state="collapsed"
)

# Custom CSS for better styling
st.markdown("""
<style>
    .persona-card {
        padding: 1rem;
        border-radius: 10px;
        margin-bottom: 0.5rem;
        cursor: pointer;
        transition: all 0.3s ease;
        border: 2px solid transparent;
    }
    .persona-card:hover {
        transform: translateY(-2px);
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }
    .persona-card.selected {
        border: 2px solid #1f77b4;
        background-color: #e7f3ff;
    }
    .persona-avatar {
        font-size: 3rem;
        text-align: center;
        margin-bottom: 0.5rem;
    }
    .persona-name {
        font-weight: bold;
        font-size: 1.1rem;
        text-align: center;
        margin-bottom: 0.3rem;
    }
    .persona-role {
        font-size: 0.9rem;
        color: #666;
        text-align: center;
    }
    .chat-message {
        padding: 1rem;
        border-radius: 10px;
        margin-bottom: 1rem;
    }
    .user-message {
        background-color: #e3f2fd;
        border-left: 4px solid #2196f3;
    }
    .assistant-message {
        background-color: #f5f5f5;
        border-left: 4px solid #4caf50;
    }
    .stButton>button {
        width: 100%;
        border-radius: 5px;
        height: 3rem;
        font-weight: bold;
    }
</style>
""", unsafe_allow_html=True)


# Initialize session state
if "selected_persona" not in st.session_state:
    st.session_state.selected_persona = None

if "conversation_history" not in st.session_state:
    st.session_state.conversation_history = []

if "current_question" not in st.session_state:
    st.session_state.current_question = ""

# Sidebar - Model Selection
llm_client = None  # Initialize to None to avoid NameError
initialization_error = None  # Store any error that occurs

with st.sidebar:
    st.title("🤖 LLM Model")

    # Diagnostics (collapsed by default)
    with st.expander("🔍 System Diagnostics", expanded=False):
        api_key = os.getenv("ANTHROPIC_API_KEY")
        st.write(f"**API Key Status:** {'✓ Found' if api_key else '✗ Missing'}")
        if api_key:
            st.write(f"**API Key Preview:** `{api_key[:15]}...`")
            st.write(f"**API Key Length:** {len(api_key)} chars")

        # Check if we're on HF Spaces
        is_hf_space = os.path.exists('/home/user/app')
        st.write(f"**Environment:** {'🤗 HF Spaces' if is_hf_space else '💻 Local'}")

        # Show package versions
        try:
            import anthropic
            st.write(f"**Anthropic SDK:** v{anthropic.__version__}")
        except Exception as e:
            st.write(f"**Anthropic SDK:** ❌ Error: {e}")

        try:
            import torch
            st.write(f"**PyTorch:** v{torch.__version__}")
            st.write(f"**Device:** {torch.device('mps' if torch.backends.mps.is_available() else 'cpu')}")
        except Exception as e:
            st.write(f"**PyTorch:** Not installed ({e})")

        st.write(f"**Python:** {sys.version.split()[0]}")
        st.write(f"**Streamlit:** v{st.__version__}")

    # Check API key availability
    api_key_available = bool(os.getenv("ANTHROPIC_API_KEY"))
    if not api_key_available:
        st.error("🚨 **No Anthropic API key detected!**")
        st.info("**To use Anthropic Claude:**")
        if os.path.exists('/home/user/app'):
            st.info("1. Go to Space Settings")
            st.info("2. Add secret: `ANTHROPIC_API_KEY`")
            st.info("3. Restart the Space")
        else:
            st.info("Set `ANTHROPIC_API_KEY` in your `.env` file")
        st.info("**Alternative:** Use 'Local Be.FM' model (no API key needed)")

    model_choice = st.radio(
        "Select Model:",
        ["Anthropic Claude", "Local Be.FM"],
        index=0 if api_key_available else 1,  # Default to Local Be.FM if no API key
        help="""
        **Anthropic Claude**: Use Claude API (requires API key)

        **Local Be.FM**: Run Stanford's Be.FM model locally (GPU-accelerated)
        """
    )

    # Initialize LLM client based on selection
    st.markdown("---")
    st.markdown("### Initialization Status")

    try:
        if model_choice == "Anthropic Claude":
            st.info("🔄 Initializing Anthropic Claude...")
            llm_client = AnthropicClient()
            st.success("✅ Anthropic Claude initialized successfully!")
        else:
            st.info("🔄 Initializing Local Be.FM...")
            llm_client = LocalModelClient()
            st.success("✅ Local Be.FM initialized successfully!")
            st.caption("💡 First run will download the model (~16GB)")
    except ValueError as e:
        # Handle missing API key specifically
        error_msg = str(e)
        initialization_error = error_msg
        st.error(f"❌ **Configuration Error**")
        st.error(error_msg)

        if "API key" in error_msg:
            st.markdown("**📋 Fix this by:**")
            if os.path.exists('/home/user/app'):
                st.markdown("1. Go to your HF Space Settings")
                st.markdown("2. Variables and secrets → Add a secret")
                st.markdown("3. Name: `ANTHROPIC_API_KEY`")
                st.markdown("4. Value: Your API key (starts with `sk-ant-`)")
                st.markdown("5. Save and restart Space")
            else:
                st.markdown("1. Create/update `.env` file")
                st.markdown("2. Add: `ANTHROPIC_API_KEY=sk-ant-...`")
                st.markdown("3. Restart the app")
        llm_client = None
    except Exception as e:
        # Handle other errors
        error_msg = f"{type(e).__name__}: {e}"
        initialization_error = error_msg
        st.error(f"❌ **Initialization Failed**")
        st.error(error_msg)

        # Show detailed error for debugging
        import traceback
        error_trace = traceback.format_exc()
        with st.expander("🔍 Full Error Details (for debugging)"):
            st.code(error_trace)
        llm_client = None

# Only proceed if LLM client was successfully initialized
if llm_client is None:
    st.error("## ❌ Failed to initialize LLM client")

    if initialization_error:
        st.error(f"**Error:** {initialization_error}")

    st.warning("### 👈 Please check the sidebar for detailed error information and setup instructions")

    # Show helpful tips in main area
    st.markdown("---")
    st.markdown("### 🔧 Quick Troubleshooting")

    api_key = os.getenv("ANTHROPIC_API_KEY")
    if not api_key:
        st.markdown("**Issue:** No API key found")
        if os.path.exists('/home/user/app'):
            st.markdown("""
            **Solution for HF Spaces:**
            1. Go to your Space Settings (⚙️)
            2. Click on "Variables and secrets"
            3. Add a new secret with name `ANTHROPIC_API_KEY`
            4. Paste your Anthropic API key (get one from https://console.anthropic.com/)
            5. Save and restart your Space
            """)
        else:
            st.markdown("""
            **Solution for Local:**
            1. Create a `.env` file in the project root
            2. Add: `ANTHROPIC_API_KEY=your-key-here`
            3. Get your API key from https://console.anthropic.com/
            4. Restart the app
            """)

        st.info("**Alternative:** Select 'Local Be.FM' model in the sidebar (no API key needed)")
    else:
        st.markdown(f"**API Key Detected:** Yes ({len(api_key)} chars)")
        st.markdown("**Issue:** Initialization failed despite having an API key")
        st.markdown("Check the sidebar for the specific error details.")

    st.stop()

# Initialize QueryEngine with selected LLM client
# Use .get() to safely check llm_client without raising KeyError
if "engine" not in st.session_state or st.session_state.get("llm_client") is not llm_client:
    with st.spinner("🔧 Initializing AI Personas system..."):
        st.session_state.engine = QueryEngine(llm_client=llm_client)
        st.session_state.llm_client = llm_client
        st.session_state.engine.test_system()


# Persona definitions with avatars and colors
PERSONAS = {
    "sarah_chen": {
        "name": "Sarah Chen",
        "role": "Urban Planner",
        "avatar": "🌱",
        "color": "#4CAF50",
        "tagline": "Progressive, sustainability-focused"
    },
    "marcus_thompson": {
        "name": "Marcus Thompson",
        "role": "Business Owner",
        "avatar": "🏪",
        "color": "#FF9800",
        "tagline": "Pragmatic, economy-focused"
    },
    "elena_rodriguez": {
        "name": "Dr. Elena Rodriguez",
        "role": "Transportation Engineer",
        "avatar": "🚇",
        "color": "#2196F3",
        "tagline": "Data-driven, safety-first"
    },
    "james_obrien": {
        "name": "James O'Brien",
        "role": "Long-time Resident",
        "avatar": "🏡",
        "color": "#795548",
        "tagline": "Traditional, community-focused"
    },
    "priya_patel": {
        "name": "Priya Patel",
        "role": "Housing Advocate",
        "avatar": "✊",
        "color": "#E91E63",
        "tagline": "Activist, equity-focused"
    },
    "david_kim": {
        "name": "David Kim",
        "role": "Real Estate Developer",
        "avatar": "🏢",
        "color": "#607D8B",
        "tagline": "Market-driven, growth-oriented"
    }
}


def select_persona(persona_id):
    """Select a persona and clear conversation history"""
    st.session_state.selected_persona = persona_id
    st.session_state.conversation_history = []


def send_question():
    """Send question to selected persona"""
    if not st.session_state.current_question.strip():
        return

    if not st.session_state.selected_persona:
        st.error("Please select a persona first!")
        return

    question = st.session_state.current_question

    # Add question to history
    st.session_state.conversation_history.append({
        "role": "user",
        "content": question
    })

    # Get response
    with st.spinner(f"💭 {PERSONAS[st.session_state.selected_persona]['name']} is thinking..."):
        response = st.session_state.engine.query(
            persona_id=st.session_state.selected_persona,
            question=question,
            context_id="downtown_district"
        )

    # Add response to history
    st.session_state.conversation_history.append({
        "role": "assistant",
        "content": response.response
    })

    # Clear input
    st.session_state.current_question = ""


# Main layout
st.title("🏙️ AI Personas for Urban Planning")
st.markdown("### Explore diverse stakeholder perspectives on urban planning issues")

# Create two-column layout
left_col, right_col = st.columns([2, 1])

# LEFT COLUMN: Chat Interface
with left_col:
    st.markdown("### 💬 Conversation")

    # Show selected persona
    if st.session_state.selected_persona:
        persona = PERSONAS[st.session_state.selected_persona]
        st.info(f"**Currently talking with:** {persona['avatar']} {persona['name']} ({persona['role']})")
    else:
        st.warning("👉 **Select a persona from the right panel to start!**")

    # Initialize current_question if it doesn't exist
    if "current_question" not in st.session_state:
        st.session_state.current_question = ""

    # Check for example question from session state BEFORE creating widget
    if "example_question" in st.session_state:
        st.session_state.current_question = st.session_state.example_question
        del st.session_state.example_question

    # Input area
    col1, col2 = st.columns([5, 1])
    with col1:
        question = st.text_input(
            "Your question:",
            key="current_question",
            placeholder="e.g., What do you think about the bike lane proposal?",
            label_visibility="collapsed"
        )

    with col2:
        st.button("Send", on_click=send_question, type="primary", use_container_width=True)

    # Quick suggestions
    if not st.session_state.conversation_history:
        st.markdown("**💡 Try asking:**")
        suggestions = [
            "What's the most important issue facing downtown?",
            "Should we allow food trucks in the plaza?",
            "How can we make the city more sustainable?",
            "What do you think about the affordable housing crisis?"
        ]
        cols = st.columns(2)
        for i, suggestion in enumerate(suggestions):
            with cols[i % 2]:
                if st.button(suggestion, key=f"suggestion_{i}", use_container_width=True):
                    st.session_state.example_question = suggestion
                    # Streamlit auto-reruns on button click - no manual st.rerun() needed

    # Conversation history
    if st.session_state.conversation_history:
        st.markdown("---")
        st.markdown("### 📜 Conversation History")

        for msg in st.session_state.conversation_history:
            if msg["role"] == "user":
                st.markdown(f"""
                <div class="chat-message user-message">
                    <strong>🙋 You:</strong><br>
                    {msg["content"]}
                </div>
                """, unsafe_allow_html=True)
            else:
                persona = PERSONAS[st.session_state.selected_persona]
                st.markdown(f"""
                <div class="chat-message assistant-message">
                    <strong>{persona['avatar']} {persona['name']}:</strong><br>
                    {msg["content"]}
                </div>
                """, unsafe_allow_html=True)

        # Clear conversation button
        if st.button("🗑️ Clear Conversation", use_container_width=True):
            st.session_state.conversation_history = []
            st.rerun()


# RIGHT COLUMN: Persona Selection
with right_col:
    st.markdown("### 👥 Select a Persona")
    st.markdown("Click to start conversation")

    for persona_id, persona in PERSONAS.items():
        is_selected = st.session_state.selected_persona == persona_id

        # Create persona card
        if st.button(
            f"{persona['avatar']}\n\n**{persona['name']}**\n\n{persona['role']}\n\n_{persona['tagline']}_",
            key=f"persona_{persona_id}",
            use_container_width=True,
            type="primary" if is_selected else "secondary"
        ):
            select_persona(persona_id)
            st.rerun()


# Footer
st.markdown("---")
st.markdown("""
<div style='text-align: center; color: #666; padding: 1rem;'>
    <small>
        AI Personas for Urban Planning • Phase 1 •
        Powered by Claude 3 Haiku •
        <a href='https://github.com' target='_blank'>View Code</a>
    </small>
</div>
""", unsafe_allow_html=True)