import gradio as gr
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.express as px
import plotly.graph_objects as go
import folium
from folium.plugins import MarkerCluster, HeatMap
import re
import numpy as np
from urllib.parse import urljoin
import time
import json
import os
from geopy.distance import geodesic
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

# Function to convert degrees, minutes, seconds to decimal degrees
def dms_to_decimal(degrees, minutes, seconds, direction):
    decimal = float(degrees) + float(minutes)/60 + float(seconds)/3600
    if direction in ['S', 'W', '-']:
        decimal = -decimal
    return decimal

# Function to parse DMS coordinates from text
def parse_dms_coordinates(text):
    if not text:
        return None, None
    
    # Clean up the text
    text = text.replace('**', '').replace('\n', ' ').strip()
    
    # Look for DMS format
    lat_pattern = r'(\d+)°\s*(\d+)\'\s*(\d+\.?\d*)\'?\'\s*(?:Latitude|[NS])'
    lon_pattern = r'(-?\d+)°\s*(\d+)\'\s*(\d+\.?\d*)\'?\'\s*(?:Longitude|[EW])'
    
    lat_match = re.search(lat_pattern, text)
    lon_match = re.search(lon_pattern, text)
    
    latitude = None
    longitude = None
    
    if lat_match:
        lat_deg, lat_min, lat_sec = lat_match.groups()
        # Determine direction (N positive, S negative)
        lat_dir = 'N'
        if 'S' in text:
            lat_dir = 'S'
        latitude = dms_to_decimal(lat_deg, lat_min, lat_sec, lat_dir)
    
    if lon_match:
        lon_deg, lon_min, lon_sec = lon_match.groups()
        # Determine direction (E positive, W negative)
        lon_dir = 'E'
        if 'W' in text or '-' in lon_deg:
            lon_dir = 'W'
        longitude = dms_to_decimal(lon_deg.replace('-', ''), lon_min, lon_sec, lon_dir)
    
    return latitude, longitude

# Function to fetch NASA FIRMS data
def fetch_firms_data():
    """
    Fetch NASA FIRMS VIIRS active fire data for the last 24 hours
    Filters for USA only and returns relevant fire hotspot data
    """
    firms_url = "https://firms.modaps.eosdis.nasa.gov/data/active_fire/viirs/csv/J1_VIIRS_C2_Global_24h.csv"
    
    try:
        print("Fetching NASA FIRMS data...")
        response = requests.get(firms_url, timeout=60)
        response.raise_for_status()
        
        # Read CSV data
        from io import StringIO
        firms_df = pd.read_csv(StringIO(response.text))
        
        print(f"Retrieved {len(firms_df)} global fire hotspots")
        
        # Filter for USA coordinates (approximate bounding box)
        # Continental US, Alaska, Hawaii
        usa_firms = firms_df[
            (
                # Continental US
                ((firms_df['latitude'] >= 24.5) & (firms_df['latitude'] <= 49.0) & 
                 (firms_df['longitude'] >= -125.0) & (firms_df['longitude'] <= -66.0)) |
                # Alaska
                ((firms_df['latitude'] >= 54.0) & (firms_df['latitude'] <= 72.0) & 
                 (firms_df['longitude'] >= -180.0) & (firms_df['longitude'] <= -130.0)) |
                # Hawaii
                ((firms_df['latitude'] >= 18.0) & (firms_df['latitude'] <= 23.0) & 
                 (firms_df['longitude'] >= -162.0) & (firms_df['longitude'] <= -154.0))
            )
        ].copy()
        
        print(f"Filtered to {len(usa_firms)} USA fire hotspots")
        
        # Add datetime column for easier processing
        usa_firms['datetime'] = pd.to_datetime(usa_firms['acq_date'] + ' ' + usa_firms['acq_time'].astype(str).str.zfill(4), 
                                             format='%Y-%m-%d %H%M')
        
        # Sort by acquisition time (most recent first)
        usa_firms = usa_firms.sort_values('datetime', ascending=False)
        
        return usa_firms
        
    except Exception as e:
        print(f"Error fetching FIRMS data: {e}")
        return pd.DataFrame()

# Function to match FIRMS hotspots with InciWeb incidents
def match_firms_to_inciweb(inciweb_df, firms_df, max_distance_km=50):
    """
    Match FIRMS hotspots to InciWeb incidents based on geographic proximity
    
    Args:
        inciweb_df: DataFrame with InciWeb incident data (must have latitude/longitude)
        firms_df: DataFrame with FIRMS hotspot data
        max_distance_km: Maximum distance in km to consider a match
    
    Returns:
        Enhanced inciweb_df with FIRMS data and activity status
    """
    if firms_df.empty or inciweb_df.empty:
        return inciweb_df
    
    print(f"Matching {len(firms_df)} FIRMS hotspots to {len(inciweb_df)} InciWeb incidents...")
    
    # Initialize new columns
    inciweb_df = inciweb_df.copy()
    inciweb_df['firms_hotspots'] = 0
    inciweb_df['total_frp'] = 0.0  # Fire Radiative Power
    inciweb_df['avg_confidence'] = 0.0
    inciweb_df['latest_hotspot'] = None
    inciweb_df['is_active'] = False
    inciweb_df['hotspot_coords'] = None
    inciweb_df['activity_level'] = 'Unknown'
    
    # Only process incidents that have coordinates
    incidents_with_coords = inciweb_df[
        (inciweb_df['latitude'].notna()) & (inciweb_df['longitude'].notna())
    ].copy()
    
    print(f"Processing {len(incidents_with_coords)} incidents with coordinates...")
    
    for idx, incident in incidents_with_coords.iterrows():
        incident_coords = (incident['latitude'], incident['longitude'])
        
        # Find FIRMS hotspots within the specified distance
        hotspot_distances = []
        matched_hotspots = []
        
        for _, hotspot in firms_df.iterrows():
            hotspot_coords = (hotspot['latitude'], hotspot['longitude'])
            
            try:
                distance = geodesic(incident_coords, hotspot_coords).kilometers
                if distance <= max_distance_km:
                    hotspot_distances.append(distance)
                    matched_hotspots.append(hotspot)
            except Exception as e:
                continue  # Skip invalid coordinates
        
        if matched_hotspots:
            matched_df = pd.DataFrame(matched_hotspots)
            
            # Calculate aggregated metrics
            num_hotspots = len(matched_hotspots)
            total_frp = matched_df['frp'].sum() if 'frp' in matched_df.columns else 0
            avg_confidence = matched_df['confidence'].mean() if 'confidence' in matched_df.columns else 0
            latest_hotspot = matched_df['datetime'].max() if 'datetime' in matched_df.columns else None
            
            # Determine activity level based on hotspot count and FRP
            if num_hotspots >= 20 and total_frp >= 100:
                activity_level = 'Very High'
            elif num_hotspots >= 10 and total_frp >= 50:
                activity_level = 'High'
            elif num_hotspots >= 5 and total_frp >= 20:
                activity_level = 'Medium'
            elif num_hotspots >= 1:
                activity_level = 'Low'
            else:
                activity_level = 'Minimal'
            
            # Update the incident data
            inciweb_df.at[idx, 'firms_hotspots'] = num_hotspots
            inciweb_df.at[idx, 'total_frp'] = total_frp
            inciweb_df.at[idx, 'avg_confidence'] = avg_confidence
            inciweb_df.at[idx, 'latest_hotspot'] = latest_hotspot
            inciweb_df.at[idx, 'is_active'] = True
            inciweb_df.at[idx, 'activity_level'] = activity_level
            
            # Store hotspot coordinates for visualization
            hotspot_coords = [(hs['latitude'], hs['longitude'], hs.get('frp', 1)) 
                            for hs in matched_hotspots]
            inciweb_df.at[idx, 'hotspot_coords'] = hotspot_coords
            
            print(f"  {incident['name']}: {num_hotspots} hotspots, {total_frp:.1f} FRP, {activity_level} activity")
    
    # Mark incidents without recent hotspots as potentially inactive
    active_count = (inciweb_df['is_active'] == True).sum()
    total_with_coords = len(incidents_with_coords)
    
    print(f"Found {active_count} active incidents out of {total_with_coords} with coordinates")
    
    return inciweb_df

# Function to scrape InciWeb data from the accessible view page
def fetch_inciweb_data():
    base_url = "https://inciweb.wildfire.gov"
    accessible_url = urljoin(base_url, "/accessible-view")
    
    try:
        print(f"Fetching data from: {accessible_url}")
        response = requests.get(accessible_url, timeout=30)
        response.raise_for_status()
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data from InciWeb: {e}")
        return pd.DataFrame()
    
    soup = BeautifulSoup(response.content, "html.parser")
    
    incidents = []
    
    # Find all incident links and process them
    incident_links = soup.find_all("a", href=re.compile(r"/incident-information/"))
    
    for link in incident_links:
        try:
            incident = {}
            
            # Extract incident name and ID from link
            incident["name"] = link.text.strip()
            incident["link"] = urljoin(base_url, link.get("href"))
            incident["id"] = link.get("href").split("/")[-1]
            
            # Navigate through the structure to get incident details
            row = link.parent
            if row and row.name == "td":
                row_cells = row.parent.find_all("td")
                
                # Parse the row cells to extract information
                if len(row_cells) >= 5:
                    incident_type_cell = row_cells[1] if len(row_cells) > 1 else None
                    if incident_type_cell:
                        incident["type"] = incident_type_cell.text.strip()
                    
                    location_cell = row_cells[2] if len(row_cells) > 2 else None
                    if location_cell:
                        incident["location"] = location_cell.text.strip()
                        state_match = re.search(r'([A-Z]{2})', incident["location"])
                        if state_match:
                            incident["state"] = state_match.group(1)
                        else:
                            state_parts = incident["location"].split(',')
                            if state_parts:
                                incident["state"] = state_parts[0].strip()
                            else:
                                incident["state"] = None
                    
                    size_cell = row_cells[3] if len(row_cells) > 3 else None
                    if size_cell:
                        size_text = size_cell.text.strip()
                        size_match = re.search(r'(\d+(?:,\d+)*)', size_text)
                        if size_match:
                            incident["size"] = int(size_match.group(1).replace(',', ''))
                        else:
                            incident["size"] = None
                    
                    updated_cell = row_cells[4] if len(row_cells) > 4 else None
                    if updated_cell:
                        incident["updated"] = updated_cell.text.strip()
                
                incidents.append(incident)
        except Exception as e:
            print(f"Error processing incident: {e}")
            continue
    
    df = pd.DataFrame(incidents)
    
    # Ensure all expected columns exist
    for col in ["size", "type", "location", "state", "updated"]:
        if col not in df.columns:
            df[col] = None
    
    df["size"] = pd.to_numeric(df["size"], errors="coerce")
    
    print(f"Fetched {len(df)} incidents")
    return df

# Simplified coordinate extraction function (focusing on key incidents for demo)
def get_incident_coordinates_basic(incident_url):
    """Simplified coordinate extraction for demo purposes"""
    try:
        response = requests.get(incident_url, timeout=15)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, "html.parser")
        
        # Look for script tags with map data
        script_tags = soup.find_all("script")
        for script in script_tags:
            if not script.string:
                continue
            
            script_text = script.string
            
            # Look for map initialization patterns
            if "L.map" in script_text or "leaflet" in script_text.lower():
                setview_match = re.search(r'setView\s*\(\s*\[\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*\]', 
                                        script_text, re.IGNORECASE)
                if setview_match:
                    return float(setview_match.group(1)), float(setview_match.group(2))
            
            # Look for direct coordinate assignments
            lat_match = re.search(r'(?:lat|latitude)\s*[=:]\s*(-?\d+\.?\d*)', script_text, re.IGNORECASE)
            lon_match = re.search(r'(?:lon|lng|longitude)\s*[=:]\s*(-?\d+\.?\d*)', script_text, re.IGNORECASE)
            
            if lat_match and lon_match:
                return float(lat_match.group(1)), float(lon_match.group(1))
        
        return None, None
        
    except Exception as e:
        print(f"Error extracting coordinates: {e}")
        return None, None

# Function to get coordinates for a subset of incidents (for demo efficiency)
def add_coordinates_to_incidents(df, max_incidents=20):
    """Add coordinates to a subset of incidents for demo purposes"""
    df = df.copy()
    df['latitude'] = None
    df['longitude'] = None
    
    # Focus on wildfires first, then take others
    wildfires = df[df['type'].str.contains('Wildfire', na=False)].head(max_incidents // 2)
    others = df[~df['type'].str.contains('Wildfire', na=False)].head(max_incidents // 2)
    sample_df = pd.concat([wildfires, others]).head(max_incidents)
    
    print(f"Getting coordinates for {len(sample_df)} incidents...")
    
    for idx, row in sample_df.iterrows():
        if pd.notna(row.get("link")):
            try:
                lat, lon = get_incident_coordinates_basic(row["link"])
                if lat is not None and lon is not None:
                    df.at[idx, 'latitude'] = lat
                    df.at[idx, 'longitude'] = lon
                    print(f"  Got coordinates for {row['name']}: {lat:.4f}, {lon:.4f}")
                
                time.sleep(0.5)  # Rate limiting
            except Exception as e:
                print(f"  Error getting coordinates for {row['name']}: {e}")
                continue
    
    return df

# Enhanced map generation with FIRMS data
def generate_enhanced_map(df, firms_df):
    """Generate map with both InciWeb incidents and FIRMS hotspots"""
    if df.empty:
        return "<div style='padding: 20px; text-align: center;'>No data available to generate map.</div>"
    
    # Create map centered on the US
    m = folium.Map(location=[39.8283, -98.5795], zoom_start=4)
    
    # Add incident markers
    incident_cluster = MarkerCluster(name="InciWeb Incidents").add_to(m)
    
    # Track statistics
    active_incidents = 0
    inactive_incidents = 0
    
    for _, row in df.iterrows():
        if pd.notna(row.get('latitude')) and pd.notna(row.get('longitude')):
            lat, lon = row['latitude'], row['longitude']
            
            # Determine marker color based on activity and type
            if row.get('is_active', False):
                active_incidents += 1
                activity_level = row.get('activity_level', 'Unknown')
                if activity_level == 'Very High':
                    color = 'red'
                    icon = 'fire'
                elif activity_level == 'High':
                    color = 'orange'
                    icon = 'fire'
                elif activity_level == 'Medium':
                    color = 'yellow'
                    icon = 'fire'
                else:
                    color = 'lightred'
                    icon = 'fire'
            else:
                inactive_incidents += 1
                color = 'gray'
                icon = 'pause'
            
            # Create detailed popup
            popup_content = f"""
            <div style="width: 300px;">
                <h4>{row.get('name', 'Unknown')}</h4>
                <b>Type:</b> {row.get('type', 'N/A')}<br>
                <b>Location:</b> {row.get('location', 'N/A')}<br>
                <b>Size:</b> {row.get('size', 'N/A')} acres<br>
                <b>Last Updated:</b> {row.get('updated', 'N/A')}<br>
                
                <hr style="margin: 10px 0;">
                <h5>🔥 Fire Activity (NASA FIRMS)</h5>
                <b>Status:</b> {'🔴 ACTIVE' if row.get('is_active', False) else '⚫ Inactive'}<br>
                <b>Activity Level:</b> {row.get('activity_level', 'Unknown')}<br>
                <b>Hotspots (24h):</b> {row.get('firms_hotspots', 0)}<br>
                <b>Total Fire Power:</b> {row.get('total_frp', 0):.1f} MW<br>
                <b>Avg Confidence:</b> {row.get('avg_confidence', 0):.1f}%<br>
                
                <a href="{row.get('link', '#')}" target="_blank">📋 More Details</a>
            </div>
            """
            
            folium.Marker(
                location=[lat, lon],
                popup=folium.Popup(popup_content, max_width=350),
                icon=folium.Icon(color=color, icon=icon, prefix='fa')
            ).add_to(incident_cluster)
            
            # Add hotspot visualization for active incidents
            if row.get('is_active', False) and row.get('hotspot_coords'):
                hotspot_coords = row.get('hotspot_coords', [])
                if hotspot_coords:
                    # Create heat map data for this incident
                    heat_data = [[coord[0], coord[1], min(coord[2], 100)] for coord in hotspot_coords]
                    
                    # Add individual hotspot markers (smaller, less intrusive)
                    for coord in hotspot_coords[:20]:  # Limit to 20 hotspots per incident
                        folium.CircleMarker(
                            location=[coord[0], coord[1]],
                            radius=3 + min(coord[2] / 20, 10),  # Size based on FRP
                            popup=f"🔥 Hotspot<br>FRP: {coord[2]:.1f} MW",
                            color='red',
                            fillColor='orange',
                            fillOpacity=0.7
                        ).add_to(m)
    
    # Add FIRMS heat map layer for all USA hotspots
    if not firms_df.empty:
        heat_data = [[row['latitude'], row['longitude'], min(row.get('frp', 1), 100)] 
                    for _, row in firms_df.iterrows()]
        
        if heat_data:
            HeatMap(
                heat_data,
                name="Fire Intensity Heatmap",
                radius=15,
                blur=10,
                max_zoom=1,
                gradient={0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 1: 'red'}
            ).add_to(m)
    
    # Add custom legend
    legend_html = f'''
    <div style="position: fixed; 
                bottom: 50px; left: 50px; width: 220px; height: 280px; 
                border:2px solid grey; z-index:9999; font-size:12px;
                background-color:white; padding: 10px;
                border-radius: 5px; font-family: Arial;">
        <div style="font-weight: bold; margin-bottom: 8px; font-size: 14px;">🔥 Wildfire Activity Status</div>
        
        <div style="margin-bottom: 8px;"><b>InciWeb Incidents:</b></div>
        <div style="display: flex; align-items: center; margin-bottom: 3px;">
            <div style="background-color: red; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>Very High Activity</div>
        </div>
        <div style="display: flex; align-items: center; margin-bottom: 3px;">
            <div style="background-color: orange; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>High Activity</div>
        </div>
        <div style="display: flex; align-items: center; margin-bottom: 3px;">
            <div style="background-color: yellow; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>Medium Activity</div>
        </div>
        <div style="display: flex; align-items: center; margin-bottom: 3px;">
            <div style="background-color: lightcoral; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>Low Activity</div>
        </div>
        <div style="display: flex; align-items: center; margin-bottom: 8px;">
            <div style="background-color: gray; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>Inactive/No Data</div>
        </div>
        
        <div style="margin-bottom: 5px;"><b>NASA FIRMS Data:</b></div>
        <div style="display: flex; align-items: center; margin-bottom: 3px;">
            <div style="background-color: orange; width: 12px; height: 12px; margin-right: 5px; border-radius: 50%;"></div>
            <div>Fire Hotspots (24h)</div>
        </div>
        <div style="margin-bottom: 8px; font-style: italic;">Heat map shows fire intensity</div>
        
        <div style="font-size: 11px; margin-top: 10px; padding-top: 5px; border-top: 1px solid #ccc;">
            <b>Statistics:</b><br>
            🔴 Active: {active_incidents}<br>
            ⚫ Inactive: {inactive_incidents}<br>
            🌡️ Total Hotspots: {len(firms_df) if not firms_df.empty else 0}
        </div>
    </div>
    '''
    
    # Add layer control
    folium.LayerControl().add_to(m)
    
    # Get map HTML and add legend
    map_html = m._repr_html_()
    map_with_legend = map_html.replace('</body>', legend_html + '</body>')
    
    return map_with_legend

# Enhanced visualization functions
def generate_enhanced_visualizations(df, firms_df):
    """Generate enhanced visualizations with FIRMS data integration"""
    figures = []
    
    if df.empty:
        return [px.bar(title="No data available")]
    
    # 1. Activity Status Overview
    if 'is_active' in df.columns:
        activity_summary = df['is_active'].value_counts().reset_index()
        activity_summary.columns = ['is_active', 'count']
        activity_summary['status'] = activity_summary['is_active'].map({True: 'Active (FIRMS detected)', False: 'Inactive/Unknown'})
        
        fig1 = px.pie(
            activity_summary,
            values='count',
            names='status',
            title="🔥 Wildfire Activity Status (Based on NASA FIRMS Data)",
            color_discrete_map={'Active (FIRMS detected)': 'red', 'Inactive/Unknown': 'gray'}
        )
        fig1.update_traces(textinfo='label+percent+value')
    else:
        fig1 = px.bar(title="Activity status data not available")
    figures.append(fig1)
    
    # 2. Activity Level Distribution
    if 'activity_level' in df.columns and df['activity_level'].notna().any():
        activity_levels = df[df['activity_level'] != 'Unknown']['activity_level'].value_counts().reset_index()
        activity_levels.columns = ['activity_level', 'count']
        
        # Define order and colors
        level_order = ['Very High', 'High', 'Medium', 'Low', 'Minimal']
        color_map = {'Very High': 'darkred', 'High': 'red', 'Medium': 'orange', 'Low': 'yellow', 'Minimal': 'lightblue'}
        
        fig2 = px.bar(
            activity_levels,
            x='activity_level',
            y='count',
            title="📊 Fire Activity Levels (NASA FIRMS Intensity)",
            labels={'activity_level': 'Activity Level', 'count': 'Number of Incidents'},
            color='activity_level',
            color_discrete_map=color_map,
            category_orders={'activity_level': level_order}
        )
    else:
        fig2 = px.bar(title="Activity level data not available")
    figures.append(fig2)
    
    # 3. Fire Radiative Power vs Incident Size
    if 'total_frp' in df.columns and 'size' in df.columns:
        active_df = df[(df['is_active'] == True) & (df['total_frp'] > 0) & (df['size'].notna())].copy()
        
        if not active_df.empty:
            fig3 = px.scatter(
                active_df,
                x='size',
                y='total_frp',
                size='firms_hotspots',
                color='activity_level',
                hover_data=['name', 'state', 'firms_hotspots'],
                title="🔥 Fire Intensity vs Incident Size (Active Fires Only)",
                labels={'size': 'Incident Size (acres)', 'total_frp': 'Total Fire Radiative Power (MW)'},
                color_discrete_map={'Very High': 'darkred', 'High': 'red', 'Medium': 'orange', 'Low': 'yellow'}
            )
            fig3.update_layout(xaxis_type="log", yaxis_type="log")
        else:
            fig3 = px.bar(title="No active fires with size and intensity data")
    else:
        fig3 = px.bar(title="Fire intensity vs size data not available")
    figures.append(fig3)
    
    # 4. Hotspot Detection Over Time (if FIRMS data available)
    if not firms_df.empty and 'datetime' in firms_df.columns:
        # Group by hour to show detection pattern
        firms_df['hour'] = firms_df['datetime'].dt.floor('H')
        hourly_detections = firms_df.groupby('hour').size().reset_index(name='detections')
        
        fig4 = px.line(
            hourly_detections,
            x='hour',
            y='detections',
            title="🕐 Fire Hotspot Detections Over Time (Last 24 Hours)",
            labels={'hour': 'Time', 'detections': 'Number of Hotspots Detected'}
        )
        fig4.update_traces(line_color='red')
    else:
        fig4 = px.bar(title="FIRMS temporal data not available")
    figures.append(fig4)
    
    # 5. State-wise Active vs Inactive Breakdown
    if 'state' in df.columns and 'is_active' in df.columns:
        state_activity = df.groupby(['state', 'is_active']).size().reset_index(name='count')
        state_activity['status'] = state_activity['is_active'].map({True: 'Active', False: 'Inactive'})
        
        # Get top 10 states by total incidents
        top_states = df['state'].value_counts().head(10).index.tolist()
        state_activity_filtered = state_activity[state_activity['state'].isin(top_states)]
        
        if not state_activity_filtered.empty:
            fig5 = px.bar(
                state_activity_filtered,
                x='state',
                y='count',
                color='status',
                title="🗺️ Active vs Inactive Incidents by State (Top 10)",
                labels={'state': 'State', 'count': 'Number of Incidents'},
                color_discrete_map={'Active': 'red', 'Inactive': 'gray'}
            )
        else:
            fig5 = px.bar(title="State activity data not available")
    else:
        fig5 = px.bar(title="State activity data not available")
    figures.append(fig5)
    
    return figures

# Main application function
def create_enhanced_wildfire_app():
    """Create the enhanced Gradio application"""
    
    with gr.Blocks(title="Enhanced InciWeb + NASA FIRMS Wildfire Tracker", theme=gr.themes.Soft()) as app:
        gr.Markdown("""
        # 🔥 Enhanced Wildfire Tracker
        ## InciWeb Incidents + NASA FIRMS Real-Time Fire Detection
        
        This application combines wildfire incident reports from InciWeb with real-time satellite fire detection data from NASA FIRMS to provide:
        - **Active fire status** based on satellite hotspot detection
        - **Fire intensity metrics** using Fire Radiative Power (FRP)
        - **Real-time hotspot mapping** from the last 24 hours
        - **Enhanced situational awareness** for wildfire management
        """)
        
        with gr.Row():
            fetch_btn = gr.Button("🚀 Fetch Latest Data (InciWeb + NASA FIRMS)", variant="primary", size="lg")
            status_text = gr.Textbox(label="Status", interactive=False, value="Ready to fetch data...")
        
        with gr.Tabs():
            with gr.TabItem("🗺️ Enhanced Map"):
                map_display = gr.HTML(label="Interactive Map with Fire Activity")
                
            with gr.TabItem("📊 Enhanced Analytics"):
                with gr.Row():
                    plot_selector = gr.Dropdown(
                        choices=[
                            "Activity Status Overview",
                            "Fire Activity Levels", 
                            "Intensity vs Size Analysis",
                            "Hotspot Detection Timeline",
                            "State Activity Breakdown"
                        ],
                        label="Select Visualization",
                        value="Activity Status Overview"
                    )
                plot_display = gr.Plot(label="Enhanced Analytics")
                
            with gr.TabItem("📋 Data Tables"):
                with gr.Tabs():
                    with gr.TabItem("InciWeb Incidents"):
                        inciweb_table = gr.Dataframe(label="InciWeb Incidents with FIRMS Integration")
                    with gr.TabItem("NASA FIRMS Hotspots"):
                        firms_table = gr.Dataframe(label="NASA FIRMS Fire Hotspots (USA, 24h)")
                        
            with gr.TabItem("📁 Export Data"):
                gr.Markdown("### Download Enhanced Dataset")
                with gr.Row():
                    download_csv = gr.File(label="Download Enhanced CSV")
                    download_geojson = gr.File(label="Download GeoJSON")
        
        # Store data in state
        app_state = gr.State({})
        
        def fetch_and_process_data():
            """Main data processing function"""
            try:
                yield "📡 Fetching InciWeb incident data...", None, None, None, None, None, None
                
                # Fetch InciWeb data
                inciweb_df = fetch_inciweb_data()
                if inciweb_df.empty:
                    yield "❌ Failed to fetch InciWeb data", None, None, None, None, None, None
                    return
                
                yield f"✅ Found {len(inciweb_df)} InciWeb incidents. Getting coordinates...", None, None, None, None, None, None
                
                # Get coordinates for sample incidents
                inciweb_df = add_coordinates_to_incidents(inciweb_df, max_incidents=15)
                
                yield "🛰️ Fetching NASA FIRMS fire detection data...", None, None, None, None, None, None
                
                # Fetch FIRMS data
                firms_df = fetch_firms_data()
                if firms_df.empty:
                    yield "⚠️ FIRMS data unavailable, proceeding with InciWeb only", None, None, inciweb_df, firms_df, None, None
                    return
                
                yield f"✅ Found {len(firms_df)} USA fire hotspots. Matching with incidents...", None, None, None, None, None, None
                
                # Match FIRMS data to InciWeb incidents
                enhanced_df = match_firms_to_inciweb(inciweb_df, firms_df)
                
                yield "🗺️ Generating enhanced map...", None, None, None, None, None, None
                
                # Generate map and visualizations
                map_html = generate_enhanced_map(enhanced_df, firms_df)
                plots = generate_enhanced_visualizations(enhanced_df, firms_df)
                
                # Prepare export data
                csv_data = enhanced_df.to_csv(index=False)
                
                active_count = (enhanced_df.get('is_active', pd.Series([False])) == True).sum()
                total_hotspots = len(firms_df)
                
                final_status = f"✅ Complete! Found {active_count} active fires with {total_hotspots} total hotspots"
                
                yield (final_status, map_html, plots[0], enhanced_df, firms_df, csv_data, 
                       {"inciweb_df": enhanced_df, "firms_df": firms_df, "plots": plots})
                
            except Exception as e:
                yield f"❌ Error: {str(e)}", None, None, None, None, None, None
        
        def update_plot(plot_name, state_data):
            """Update plot based on selection"""
            if not state_data or "plots" not in state_data:
                return px.bar(title="No data available")
            
            plot_options = [
                "Activity Status Overview",
                "Fire Activity Levels", 
                "Intensity vs Size Analysis",
                "Hotspot Detection Timeline",
                "State Activity Breakdown"
            ]
            
            try:
                plot_index = plot_options.index(plot_name)
                return state_data["plots"][plot_index]
            except (ValueError, IndexError):
                return state_data["plots"][0] if state_data["plots"] else px.bar(title="Plot not available")
        
        # Wire up the interface
        fetch_btn.click(
            fetch_and_process_data,
            outputs=[status_text, map_display, plot_display, inciweb_table, firms_table, download_csv, app_state]
        )
        
        plot_selector.change(
            update_plot,
            inputs=[plot_selector, app_state],
            outputs=[plot_display]
        )
    
    return app

# Create and launch the application
if __name__ == "__main__":
    app = create_enhanced_wildfire_app()
    app.launch(share=True, debug=True)