app.py

"""
MineWatchAI - AI-Powered Mining Rehabilitation Monitoring
Main Streamlit Application

AI-powered environmental intelligence for measurable,
audit-ready mining rehabilitation.
"""

import streamlit as st
import json
import gc
from datetime import datetime, timedelta
from pathlib import Path

# Page configuration - MUST be first Streamlit command
st.set_page_config(
    page_title="MineWatchAI - Green Tech",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Show loading message while imports happen
with st.spinner("Loading MineWatchAI..."):
    # Lazy imports for faster initial load
    from streamlit_folium import st_folium
    import folium


def load_css():
    """Load custom CSS styling."""
    css_path = Path(__file__).parent / "assets" / "style.css"
    if css_path.exists():
        with open(css_path) as f:
            st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


@st.cache_data
def load_examples():
    """Load example mines data from JSON file."""
    json_path = Path(__file__).parent / "data" / "examples.json"
    if json_path.exists():
        with open(json_path) as f:
            return json.load(f)
    return {"examples": [], "default_location": {"center": [-28.0, 121.0], "zoom": 6}}


def get_bbox_from_example(example: dict) -> tuple:
    """Get bounding box from example mine data."""
    geom = example.get("geometry", {})
    coords = geom.get("coordinates", [])
    if len(coords) == 4:
        return tuple(coords)
    else:
        center = example.get("center", [-28.0, 121.0])
        lat, lon = center
        return (lon - 0.05, lat - 0.05, lon + 0.05, lat + 0.05)


def create_simple_map(center, zoom=10, bbox=None):
    """Create a simple Folium map."""
    m = folium.Map(location=center, zoom_start=zoom)

    # Add satellite imagery
    folium.TileLayer(
        tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
        attr='Esri',
        name='Satellite',
        overlay=False
    ).add_to(m)

    if bbox is not None:
        min_lon, min_lat, max_lon, max_lat = bbox
        boundary_coords = [
            [min_lat, min_lon],
            [min_lat, max_lon],
            [max_lat, max_lon],
            [max_lat, min_lon],
            [min_lat, min_lon]
        ]
        folium.Polygon(
            locations=boundary_coords,
            color='#1B5E20',
            weight=3,
            fill=True,
            fillColor='#2E7D32',
            fillOpacity=0.2,
            popup='Analysis Area'
        ).add_to(m)

    folium.LayerControl().add_to(m)
    return m


def run_analysis(bbox, mine_info, date_before, date_after):
    """Run the comprehensive vegetation change analysis."""
    # Import heavy modules only when needed
    from src.stac_utils import get_bbox_center
    from src.analysis import (
        analyze_vegetation_change,
        analyze_terrain,
        analyze_land_cover,
        calculate_reference_ndvi,
        calculate_rehab_score,
        calculate_comprehensive_rehab_score,
        generate_interpretation
    )

    progress = st.progress(0, text="Initializing analysis...")

    try:
        progress.progress(10, text="Searching for satellite imagery...")

        # Run vegetation change analysis with all indices
        results = analyze_vegetation_change(
            bbox,
            date_before,
            date_after,
            window_days=15,
            cloud_threshold=30
        )

        progress.progress(50, text="Analyzing terrain...")

        # Run terrain analysis
        bsi_after = results.get('indices_after', {}).get('bsi')
        terrain_results = analyze_terrain(bbox, bsi=bsi_after)
        terrain_stats = terrain_results.get('stats', {})

        progress.progress(70, text="Analyzing land cover...")

        # Run land cover analysis
        year_before = int(date_before[:4])
        year_after = int(date_after[:4])
        # Clamp years to available data range (2017-2023)
        year_before = max(2017, min(2023, year_before))
        year_after = max(2017, min(2023, year_after))

        land_cover_results = analyze_land_cover(bbox, year_before, year_after)
        land_cover_stats = land_cover_results.get('stats', {})

        progress.progress(85, text="Calculating rehabilitation metrics...")

        # Calculate reference NDVI
        reference_ndvi = calculate_reference_ndvi(
            bbox,
            date_after,
            window_days=15,
            buffer_deg=0.01
        )

        # Calculate comprehensive rehabilitation scores
        rehab_scores = calculate_comprehensive_rehab_score(
            results['stats'],
            terrain_stats=terrain_stats,
            land_cover_stats=land_cover_stats,
            reference_ndvi=reference_ndvi if reference_ndvi > 0 else 0.5
        )

        # Legacy score for backwards compatibility
        site_ndvi = results['stats']['ndvi_after_mean']
        rehab_score = calculate_rehab_score(site_ndvi, reference_ndvi)

        progress.progress(95, text="Generating interpretation...")

        # Generate comprehensive interpretation
        interpretation = generate_interpretation(
            results['stats'],
            rehab_score,
            terrain_stats=terrain_stats,
            land_cover_stats=land_cover_stats
        )

        # Get center coordinates
        center = get_bbox_center(bbox)

        progress.progress(100, text="Analysis complete!")

        # Store only necessary results (exclude large arrays to save memory)
        st.session_state.analysis_results = {
            'ndvi_before': results['ndvi_before'],
            'ndvi_after': results['ndvi_after'],
            'ndvi_change': results['ndvi_change'],
            'indices_after': results.get('indices_after', {}),
            'index_changes': results.get('index_changes', {}),
            'stats': results['stats'],
            'date_before': results['date_before'],
            'date_after': results['date_after'],
            'bbox': results['bbox'],
            'terrain_results': terrain_results,
            'terrain_stats': terrain_stats,
            'land_cover_results': land_cover_results,
            'land_cover_stats': land_cover_stats,
            'rehab_score': rehab_score,
            'rehab_scores': rehab_scores,
            'reference_ndvi': reference_ndvi,
            'interpretation': interpretation,
            'mine_info': mine_info,
            'center': center
        }

        # Clean up memory
        del results
        gc.collect()

        return True

    except ValueError as e:
        st.error(f"Analysis Error: {str(e)}")
        return False
    except Exception as e:
        st.error(f"Unexpected error: {str(e)}")
        st.info("Try adjusting the date range or selecting a different site.")
        return False


def display_results():
    """Display comprehensive analysis results."""
    # Import visualization only when showing results
    from src.visualization import (
        create_comparison_map,
        create_multi_index_map,
        create_terrain_map,
        create_land_cover_map,
        create_comprehensive_stats_display,
        create_area_breakdown_chart,
        create_ndvi_comparison_chart,
        create_multi_index_chart,
        create_terrain_stats_chart,
        create_land_cover_chart,
        create_vegetation_health_chart,
        create_environmental_indicators_chart,
        create_statistics_table,
        create_time_series_chart
    )
    from src.report import generate_pdf_report, stats_to_csv

    results = st.session_state.analysis_results
    stats = results['stats']
    rehab_score = results['rehab_score']
    rehab_scores = results.get('rehab_scores', {})
    interpretation = results['interpretation']
    mine_info = results.get('mine_info', {})
    terrain_stats = results.get('terrain_stats', {})
    land_cover_stats = results.get('land_cover_stats', {})
    terrain_results = results.get('terrain_results', {})
    land_cover_results = results.get('land_cover_results', {})

    mine_name = mine_info.get('name', 'Selected Site')
    tenement_id = mine_info.get('tenement_id', 'N/A')

    st.markdown(f"### Analysis Results: {mine_name}")
    st.markdown(f"*Tenement: {tenement_id} | Period: {results['date_before']} to {results['date_after']}*")

    if st.button("🔄 New Analysis", type="secondary"):
        st.session_state.analysis_results = None
        st.rerun()

    st.markdown("---")

    # Map section with tabs for different views
    st.markdown("### Maps")
    map_tab1, map_tab2, map_tab3, map_tab4 = st.tabs([
        "Vegetation Change", "Multi-Index", "Terrain", "Land Cover"
    ])

    with map_tab1:
        with st.spinner("Rendering vegetation change map..."):
            comparison_map = create_comparison_map(
                bbox=results['bbox'],
                ndvi_before=results['ndvi_before'],
                ndvi_after=results['ndvi_after'],
                ndvi_change=results['ndvi_change'],
                center_coords=results['center'],
                zoom=mine_info.get('zoom', 13)
            )
            st_folium(comparison_map, width=None, height=450, returned_objects=[])
        st.caption("Green = improvement, Red = decline. Use layer control to toggle views.")

    with map_tab2:
        with st.spinner("Rendering multi-index map..."):
            indices_after = results.get('indices_after', {})
            index_changes = results.get('index_changes', {})
            if indices_after:
                multi_map = create_multi_index_map(
                    bbox=results['bbox'],
                    indices_after=indices_after,
                    index_changes=index_changes,
                    center_coords=results['center'],
                    zoom=mine_info.get('zoom', 13)
                )
                st_folium(multi_map, width=None, height=450, returned_objects=[])
                st.caption("Toggle layers to view different indices: NDVI, SAVI, EVI, BSI, NDWI, NDMI")
            else:
                st.info("Multi-index data not available")

    with map_tab3:
        if terrain_results and 'slope' in terrain_results:
            with st.spinner("Rendering terrain map..."):
                terrain_map = create_terrain_map(
                    bbox=results['bbox'],
                    slope=terrain_results['slope'],
                    aspect=terrain_results.get('aspect'),
                    erosion_risk=terrain_results.get('erosion_risk'),
                    center_coords=results['center'],
                    zoom=mine_info.get('zoom', 13)
                )
                st_folium(terrain_map, width=None, height=450, returned_objects=[])
                st.caption("Slope analysis from Copernicus DEM GLO-30")
        else:
            st.info("Terrain data not available for this location")

    with map_tab4:
        if land_cover_results and 'lulc_after' in land_cover_results:
            with st.spinner("Rendering land cover map..."):
                lulc_map = create_land_cover_map(
                    bbox=results['bbox'],
                    lulc=land_cover_results['lulc_after'],
                    center_coords=results['center'],
                    zoom=mine_info.get('zoom', 13),
                    year=land_cover_stats.get('year_after', 2023)
                )
                st_folium(lulc_map, width=None, height=450, returned_objects=[])
                st.caption("IO-LULC land cover classification")
        else:
            st.info("Land cover data not available for this location")

    st.markdown("---")

    # Main analysis tabs
    tab1, tab2, tab3, tab4, tab5 = st.tabs([
        "Summary", "All Indices", "Terrain & Land Cover", "Time Series", "Export"
    ])

    with tab1:
        # Use comprehensive stats display
        create_comprehensive_stats_display(
            stats, rehab_score,
            terrain_stats=terrain_stats,
            land_cover_stats=land_cover_stats
        )

        st.markdown("---")
        st.markdown("### Interpretation")
        st.info(interpretation)

        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(create_area_breakdown_chart(stats), use_container_width=True)
        with col2:
            st.plotly_chart(create_vegetation_health_chart(stats), use_container_width=True)

        # Environmental indicators radar chart
        st.plotly_chart(create_environmental_indicators_chart(stats), use_container_width=True)

    with tab2:
        st.markdown("### Multi-Index Analysis")
        st.markdown("""
        Multiple vegetation and soil indices provide a comprehensive view:
        - **NDVI**: Overall vegetation health
        - **SAVI**: Better for sparse vegetation (soil-adjusted)
        - **EVI**: Better for dense vegetation
        - **NDWI**: Water presence
        - **NDMI**: Vegetation moisture content
        - **BSI**: Bare soil extent
        """)

        st.plotly_chart(create_multi_index_chart(stats), use_container_width=True)

        # Detailed stats table
        st.markdown("### Detailed Statistics")
        create_statistics_table(stats)

    with tab3:
        col1, col2 = st.columns(2)

        with col1:
            st.markdown("### Terrain Analysis")
            if terrain_stats:
                st.plotly_chart(create_terrain_stats_chart(terrain_stats), use_container_width=True)

                st.markdown("**Terrain Metrics:**")
                st.write(f"- Mean Slope: {terrain_stats.get('slope_mean', 0):.1f}°")
                st.write(f"- Max Slope: {terrain_stats.get('slope_max', 0):.1f}°")
                st.write(f"- Elevation Range: {terrain_stats.get('elevation_min', 0):.0f}m - {terrain_stats.get('elevation_max', 0):.0f}m")
                if 'percent_high_erosion_risk' in terrain_stats:
                    st.write(f"- High Erosion Risk: {terrain_stats['percent_high_erosion_risk']:.1f}%")
            else:
                st.info("Terrain analysis not available")

        with col2:
            st.markdown("### Land Cover Change")
            if land_cover_stats and 'class_changes' in land_cover_stats:
                st.plotly_chart(create_land_cover_chart(land_cover_stats), use_container_width=True)

                st.markdown("**Land Cover Metrics:**")
                st.write(f"- Vegetation Cover: {land_cover_stats.get('vegetation_cover_after', 0):.1f}%")
                st.write(f"- Vegetation Change: {land_cover_stats.get('vegetation_cover_change', 0):+.1f}%")
                st.write(f"- Bare Ground: {land_cover_stats.get('bare_ground_after', 0):.1f}%")
                st.write(f"- Bare Ground Change: {land_cover_stats.get('bare_ground_change', 0):+.1f}%")
            else:
                st.info("Land cover analysis not available")

    with tab4:
        st.markdown("### Time Series")
        st.info("Click button below to load historical NDVI data (may take a few minutes)")

        if st.button("Load Time Series"):
            from src.analysis import get_monthly_ndvi_timeseries

            date_before = datetime.strptime(results['date_before'], '%Y-%m-%d')
            date_after = datetime.strptime(results['date_after'], '%Y-%m-%d')

            with st.spinner("Loading time series..."):
                try:
                    timeseries = get_monthly_ndvi_timeseries(
                        results['bbox'],
                        date_before.year,
                        date_after.year
                    )
                    if timeseries:
                        fig = create_time_series_chart(timeseries)
                        st.plotly_chart(fig, use_container_width=True)
                    else:
                        st.warning("No time series data available.")
                except Exception as e:
                    st.error(f"Error: {e}")

    with tab5:
        st.markdown("### Export Results")

        col1, col2 = st.columns(2)

        with col1:
            st.markdown("#### PDF Report")
            pdf_bytes = generate_pdf_report(
                tenement_id=tenement_id,
                stats=stats,
                rehab_score=rehab_score,
                interpretation=interpretation,
                date_before=results['date_before'],
                date_after=results['date_after'],
                mine_name=mine_name
            )
            st.download_button(
                "Download PDF",
                data=pdf_bytes,
                file_name=f"rehabwatch_{tenement_id.replace(' ', '_')}.pdf",
                mime="application/pdf",
                use_container_width=True
            )

        with col2:
            st.markdown("#### CSV Data")
            csv_data = stats_to_csv(
                stats=stats,
                tenement_id=tenement_id,
                rehab_score=rehab_score,
                date_before=results['date_before'],
                date_after=results['date_after'],
                mine_name=mine_name
            )
            st.download_button(
                "Download CSV",
                data=csv_data,
                file_name=f"rehabwatch_{tenement_id.replace(' ', '_')}.csv",
                mime="text/csv",
                use_container_width=True
            )


def main():
    """Main application function."""
    load_css()

    examples_data = load_examples()
    examples = examples_data.get("examples", [])
    default_location = examples_data.get("default_location", {"center": [-28.0, 121.0], "zoom": 6})

    # Initialize session state
    if "analysis_results" not in st.session_state:
        st.session_state.analysis_results = None
    if "selected_bbox" not in st.session_state:
        st.session_state.selected_bbox = None
    if "selected_mine" not in st.session_state:
        st.session_state.selected_mine = None
    if "analyzing" not in st.session_state:
        st.session_state.analyzing = False

    # Sidebar
    with st.sidebar:
        st.markdown("## 🌱 Analysis Hub")
        st.markdown("**MineWatchAI**")
        st.markdown("---")

        st.markdown("### Select Mining Site")

        example_names = ["Select a mine..."] + [e["name"] for e in examples]
        selected_name = st.selectbox("Choose a mine:", example_names)

        if selected_name != "Select a mine...":
            for example in examples:
                if example["name"] == selected_name:
                    st.session_state.selected_mine = example
                    base_bbox = get_bbox_from_example(example)

                    # Area size adjustment
                    st.markdown("#### Adjust Analysis Area")
                    area_scale = st.slider(
                        "Area Size",
                        min_value=0.5,
                        max_value=2.0,
                        value=1.0,
                        step=0.1,
                        help="Adjust the analysis area: <1.0 = smaller, >1.0 = larger"
                    )

                    # Apply scaling to bbox
                    min_lon, min_lat, max_lon, max_lat = base_bbox
                    center_lon = (min_lon + max_lon) / 2
                    center_lat = (min_lat + max_lat) / 2
                    half_width = (max_lon - min_lon) / 2 * area_scale
                    half_height = (max_lat - min_lat) / 2 * area_scale

                    st.session_state.selected_bbox = (
                        center_lon - half_width,
                        center_lat - half_height,
                        center_lon + half_width,
                        center_lat + half_height
                    )

                    st.info(f"📌 {example['description']}")
                    break

        st.markdown("---")
        st.markdown("### Analysis Period")

        col1, col2, col3 = st.columns(3)
        today = datetime.now().date()

        with col1:
            if st.button("1Y", use_container_width=True):
                st.session_state.date_before = today - timedelta(days=365)
                st.session_state.date_after = today
        with col2:
            if st.button("2Y", use_container_width=True):
                st.session_state.date_before = today - timedelta(days=730)
                st.session_state.date_after = today
        with col3:
            if st.button("5Y", use_container_width=True):
                st.session_state.date_before = today - timedelta(days=1825)
                st.session_state.date_after = today

        default_before = st.session_state.get("date_before", today - timedelta(days=730))
        default_after = st.session_state.get("date_after", today)

        date_before = st.date_input("Start Date", value=default_before, max_value=today)
        date_after = st.date_input("End Date", value=default_after, max_value=today)

        if date_after <= date_before:
            st.error("End date must be after start date")

        st.markdown("---")

        analyze_disabled = st.session_state.selected_bbox is None or date_after <= date_before

        if st.button("🤖 AI-Analyze", type="primary", use_container_width=True, disabled=analyze_disabled):
            st.session_state.analyzing = True

        if analyze_disabled:
            st.caption("Select a mine and valid dates")

        st.markdown("---")

        with st.expander("ℹ️ About"):
            st.markdown("""
            **MineWatchAI** - AI-driven green technology
            for sustainable mining rehabilitation.

            ---

            Developed by [Ashkan Taghipour](https://ashkantaghipour.github.io/)

            ⚠️ *This is a research startup project currently
            under active development.*
            """)

    # Main area
    st.markdown("# 🤖 MineWatchAI")
    st.markdown("*AI-powered environmental intelligence for measurable, audit-ready mining rehabilitation*")

    # Handle analysis
    if st.session_state.analyzing:
        st.session_state.analyzing = False
        success = run_analysis(
            st.session_state.selected_bbox,
            st.session_state.selected_mine,
            str(date_before),
            str(date_after)
        )
        if success:
            st.rerun()

    # Display results or initial map
    if st.session_state.analysis_results is not None:
        display_results()
    else:
        st.markdown("### Select a Mining Site")

        if st.session_state.selected_bbox is not None:
            mine = st.session_state.selected_mine
            center = mine.get("center", default_location["center"])
            zoom = mine.get("zoom", 12)

            st.markdown(f"**Selected:** {mine['name']} ({mine['tenement_id']})")
            m = create_simple_map(tuple(center), zoom, st.session_state.selected_bbox)
            st_folium(m, width=None, height=400, returned_objects=[])
        else:
            st.info("👆 Select a mining site from the sidebar")
            m = create_simple_map(tuple(default_location["center"]), default_location["zoom"])
            st_folium(m, width=None, height=400, returned_objects=[])

            st.markdown("### Available Sites")
            cols = st.columns(len(examples))
            for i, ex in enumerate(examples):
                with cols[i]:
                    st.markdown(f"**{ex['name']}**")
                    st.caption(ex['tenement_id'])

    # Footer
    st.markdown("---")
    st.caption("MineWatchAI - AI-driven green technology for sustainable mining rehabilitation.")


if __name__ == "__main__":
    main()