src/tabs/analytics_tab.py

"""
Dynamic Analytical Brief tab for the Sri Lanka Disaster Dashboard.

Provides AI-powered trend analysis using LLM (DeepSeek/OpenAI).
"""

import streamlit as st
from datetime import datetime
from pathlib import Path


def render_analytics_tab(sitreps_dir: Path, floods_dir: Path, landslide_dir: Path, generate_trend_summary_func):
    """
    Render the Dynamic Analytical Brief tab with AI-powered analysis.
    
    Args:
        sitreps_dir: Path to the sitreps data directory
        floods_dir: Path to the floods data directory
        landslide_dir: Path to the landslide data directory
        generate_trend_summary_func: Function to call for generating trend summary
    """
    st.header("🤖 Dynamic Analytical Brief")
    st.markdown("""
    AI-powered descriptive analysis of the current disaster situation in Sri Lanka.
    The analysis covers:
    - **Impact on People and Infrastructure** - Latest situation report data
    - **Hazards Overview** - Current flood and landslide warnings
    - **Trend Analysis** - Evolution of the situation over time
    """)
    
    st.divider()
    
    # Initialize session state for trend summary
    if "trend_summary" not in st.session_state:
        st.session_state.trend_summary = None
    if "trend_summary_error" not in st.session_state:
        st.session_state.trend_summary_error = None
    
    # Controls section
    col_btn, col_warning = st.columns([1, 3])
    
    with col_btn:
        generate_clicked = st.button(
            "🤖 Generate AI Analysis",
            use_container_width=True,
            type="primary",
            help="Generates a comprehensive trend analysis using AI"
        )
    
    with col_warning:
        st.info(
            "⚠️ **Please use responsibly:** Only regenerate after several new situation reports "
            "have been published to avoid unnecessary API usage."
        )
    
    if generate_clicked:
        with st.spinner("🔄 Analyzing situation reports, flood and landslide data..."):
            result = generate_trend_summary_func(sitreps_dir, floods_dir, landslide_dir)
            
            if result["success"]:
                st.session_state.trend_summary = result["summary"]
                st.session_state.trend_summary_meta = {
                    "provider": result.get("provider", "Unknown"),
                    "num_reports": result.get("num_reports", 0),
                    "date_range": result.get("date_range", {}),
                    "sitrep_date": result.get("sitrep_date", ""),
                    "flood_date": result.get("flood_date", ""),
                    "landslide_date": result.get("landslide_date", ""),
                    "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M")
                }
                st.session_state.trend_summary_error = None
            else:
                st.session_state.trend_summary = None
                st.session_state.trend_summary_error = result.get("error", "Unknown error occurred")
    
    st.divider()
    
    # Display the summary or placeholder
    if st.session_state.trend_summary:
        meta = st.session_state.get("trend_summary_meta", {})
        
        # Metadata row - Data sources
        st.subheader("📅 Data Sources")
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("Sitrep Date", meta.get('sitrep_date', 'N/A')[:20] if meta.get('sitrep_date') else 'N/A')
        with col2:
            st.metric("Flood Report", meta.get('flood_date', 'N/A')[:20] if meta.get('flood_date') else 'N/A')
        with col3:
            st.metric("Landslide Report", meta.get('landslide_date', 'N/A')[:20] if meta.get('landslide_date') else 'N/A')
        with col4:
            st.metric("Sitreps for Trends", meta.get('num_reports', '?'))
        
        # AI Info row
        col_ai, col_gen = st.columns(2)
        with col_ai:
            st.caption(f"🤖 AI Provider: {meta.get('provider', 'Unknown')}")
        with col_gen:
            st.caption(f"🕐 Generated: {meta.get('generated_at', 'Unknown')}")
        
        st.divider()
        
        # Display the summary in a styled container
        st.subheader("📋 Analysis Report")
        with st.container():
            st.markdown(st.session_state.trend_summary)
    
    elif st.session_state.trend_summary_error:
        st.error(f"❌ {st.session_state.trend_summary_error}")
    
    else:
        # Placeholder when no analysis has been generated
        st.markdown(
            """
            <div style="padding: 40px; background-color: #f0f2f6; border-radius: 10px; text-align: center; margin-top: 20px;">
                <h3 style="color: #333; margin-bottom: 10px;">📊 No Analysis Generated Yet</h3>
                <p style="color: #666; margin: 0;">
                    Click <strong>"Generate AI Analysis"</strong> above to generate a comprehensive analytical brief.
                </p>
                <p style="color: #888; font-size: 14px; margin-top: 15px;">
                    The analysis includes:
                </p>
                <ul style="color: #888; font-size: 14px; text-align: left; display: inline-block;">
                    <li>🌍 Situation and Hazards overview (floods + landslides)</li>
                    <li>👥 Impact on Population (affected, deaths, displaced)</li>
                    <li>🏠 Impact on Infrastructure (house damage)</li>
                    <li>📈 Trend Analysis across all available reports</li>
                </ul>
            </div>
            """,
            unsafe_allow_html=True
        )
    
    # Data sources info
    st.divider()
    with st.expander("ℹ️ About This Analysis"):
        st.markdown("""
        ### Analysis Structure
        
        **🌍 SITUATION and HAZARDS**
        - Overall context of the disaster situation
        - Current flood alerts (Major/Minor/Alert/Normal levels)
        - Active landslide warnings (Level 1/2/3 by district)
        
        **👥 IMPACT ON POPULATION**
        - People affected with changes from previous report
        - Deaths and missing persons
        - Displaced populations and top affected districts
        
        **🏠 IMPACT ON INFRASTRUCTURE**  
        - Houses fully destroyed and partially damaged
        - Most affected districts for infrastructure damage
        
        **📈 TREND ANALYSIS**
        - Evolution of the situation over time
        - Whether conditions are improving, worsening, or stabilizing
        - Key metric changes between reports
        
        ### Data Sources
        - **Situation Reports**: Official DMC situation reports (PDF extracted)
        - **Flood Data**: Water level and rainfall reports
        - **Landslide Data**: Early warning bulletins from NBRO
        
        ### AI Providers
        - **Primary**: DeepSeek AI (cost-effective, high quality)
        - **Fallback**: OpenAI GPT-4o-mini (if DeepSeek is unavailable)
        """)