import gradio as gr
import requests
import folium
import os
import json
from datetime import datetime, timedelta
import pandas as pd

# Get API key from environment
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

def create_map(lat=40.7128, lon=-74.0060):
    """Create a Folium map centered at given coordinates"""
    m = folium.Map(
        location=[lat, lon],
        zoom_start=10,
        tiles="OpenStreetMap"
    )
    
    # Add a marker for the selected location
    folium.Marker(
        [lat, lon],
        popup=f"Selected Location: {lat:.4f}, {lon:.4f}",
        tooltip="Selected location for pollen forecast",
        icon=folium.Icon(color='red', icon='info-sign')
    ).add_to(m)
    
    return m._repr_html_()

def geocode_address(address):
    """Convert address to coordinates using a free geocoding service"""
    try:
        # Using Nominatim (OpenStreetMap) for free geocoding
        url = "https://nominatim.openstreetmap.org/search"
        params = {
            'q': address,
            'format': 'json',
            'limit': 1
        }
        headers = {'User-Agent': 'PollenForecastApp/1.0'}
        
        response = requests.get(url, params=params, headers=headers)
        response.raise_for_status()
        data = response.json()
        
        if data:
            lat = float(data[0]['lat'])
            lon = float(data[0]['lon'])
            display_name = data[0]['display_name']
            return lat, lon, display_name
        else:
            return None, None, "Address not found"
    except Exception as e:
        return None, None, f"Geocoding error: {str(e)}"

def get_pollen_category_from_index(index_value):
    """Convert index value to category when API doesn't provide it"""
    if not isinstance(index_value, (int, float)):
        return "UNKNOWN"
    
    if index_value <= 2.4:
        return "VERY_LOW"
    elif index_value <= 4.8:
        return "LOW"
    elif index_value <= 7.2:
        return "MEDIUM"
    elif index_value <= 9.6:
        return "HIGH"
    else:
        return "VERY_HIGH"

def get_pollen_data(lat, lon, days=5):
    """Fetch pollen data from Google Pollen API"""
    if not GOOGLE_API_KEY:
        return "Error: Google API key not found. Please set GOOGLE_API_KEY as a secret."
    
    # Google Pollen API endpoint
    url = "https://pollen.googleapis.com/v1/forecast:lookup"
    
    # Calculate date range
    start_date = datetime.now()
    end_date = start_date + timedelta(days=days-1)
    
    params = {
        "key": GOOGLE_API_KEY,
        "location.longitude": lon,
        "location.latitude": lat,
        "days": days,
        "plantsDescription": True
    }
    
    try:
        response = requests.get(url, params=params)
        response.raise_for_status()
        data = response.json()
        
        # Debug: Print the structure to understand the API response
        print(f"API Response keys: {data.keys()}")
        if "dailyInfo" in data and len(data["dailyInfo"]) > 0:
            print(f"First day info keys: {data['dailyInfo'][0].keys()}")
            print(f"Date structure: {data['dailyInfo'][0].get('date', 'No date field')}")
            
            # Debug pollen info structure
            if "pollenTypeInfo" in data["dailyInfo"][0]:
                for pollen in data["dailyInfo"][0]["pollenTypeInfo"]:
                    print(f"Pollen type: {pollen.get('code', 'Unknown')}")
                    if "indexInfo" in pollen:
                        index_info = pollen["indexInfo"]
                        print(f"  Index info: {index_info}")
                        print(f"  Category: '{index_info.get('category', 'Missing')}'")
                        print(f"  Value: {index_info.get('value', 'Missing')}")
        
        return data
    except requests.exceptions.RequestException as e:
        error_msg = f"Error fetching pollen data: {str(e)}"
        if hasattr(e, 'response') and e.response is not None:
            error_msg += f" (Status: {e.response.status_code})"
        return error_msg
    except json.JSONDecodeError as e:
        return f"Error parsing response: {str(e)}"
    except Exception as e:
        return f"Unexpected error: {str(e)}"

def format_pollen_data(data):
    """Format pollen data for display in user-friendly terms"""
    if isinstance(data, str):  # Error message
        return data, None
    
    if "dailyInfo" not in data:
        return "No pollen data available for this location.", None
    
    # Create formatted output
    output = []
    output.append("# 🌸 Pollen Forecast Report\n")
    
    # Location info if available
    if "regionInfo" in data:
        region = data["regionInfo"]
        output.append(f"**📍 Location:** {region.get('displayName', 'Unknown')}\n")
    
    # Add explanation of what pollen index means with precise measurements
    output.append("## 📊 Understanding Your Pollen Forecast\n")
    output.append("**The Pollen Index** measures the actual concentration of pollen grains in the air:")
    output.append(f"- **Index 0-2 (0-50 grains/m³):** Very Low - Minimal pollen particles in the air")
    output.append(f"- **Index 3-5 (51-150 grains/m³):** Low - Light pollen concentration")  
    output.append(f"- **Index 6-7 (151-500 grains/m³):** Medium - Moderate pollen density")
    output.append(f"- **Index 8-9 (501-1000 grains/m³):** High - Heavy pollen concentration")
    output.append(f"- **Index 10+ (1000+ grains/m³):** Very High - Extreme pollen density")
    output.append("")
    output.append("**🔬 What This Means Scientifically:**")
    output.append("Each number represents grains of pollen per cubic meter of air over 24 hours.")
    output.append("To put this in perspective:")
    output.append("- **50 grains/m³** = About 50 microscopic pollen particles in a space the size of a large refrigerator")
    output.append("- **500 grains/m³** = 500 pollen particles in that same space - enough to trigger most allergies")
    output.append("- **1000+ grains/m³** = Over 1,000 particles - like breathing through a cloud of pollen")
    output.append("")
    output.append("**⚗️ Technical Details:**")
    output.append("Measured using specialized air samplers that collect particles on microscope slides.")
    output.append("Each pollen grain is 15-200 micrometers (invisible to naked eye, 400x magnification needed).\n")
    
    # Daily pollen data
    daily_data = []
    for day_info in data["dailyInfo"]:
        # Handle different date formats from the API
        date_info = day_info.get("date", {})
        
        if isinstance(date_info, dict):
            # If date is a dict, try to extract the date string
            if "year" in date_info and "month" in date_info and "day" in date_info:
                year = date_info["year"]
                month = date_info["month"]
                day = date_info["day"]
                date_obj = datetime(year, month, day)
            else:
                # Fallback to today's date if we can't parse
                date_obj = datetime.now()
        elif isinstance(date_info, str):
            # If it's already a string, parse it
            try:
                date_obj = datetime.strptime(date_info, "%Y-%m-%d")
            except ValueError:
                date_obj = datetime.now()
        else:
            # Fallback
            date_obj = datetime.now()
            
        formatted_date = date_obj.strftime("%B %d, %Y")
        day_of_week = date_obj.strftime("%A")
        
        output.append(f"## 📅 {day_of_week}, {formatted_date}\n")
        
        if "pollenTypeInfo" in day_info:
            pollen_types = day_info["pollenTypeInfo"]
            
            # Create a row for the dataframe
            row_data = {"Date": f"{day_of_week}, {formatted_date}"}
            
            # Calculate overall day severity
            max_index = 0
            total_types = 0
            
            for pollen in pollen_types:
                pollen_type = pollen["code"].replace("_", " ").title()
                index_info = pollen.get("indexInfo", {})
                index_value = index_info.get("value", 0)
                api_category = index_info.get("category", "")
                
                # Debug: Print what we're getting from the API
                print(f"Processing {pollen_type}: index={index_value}, api_category='{api_category}'")
                
                # Use fallback if API category is missing or unknown
                if api_category and api_category in ["VERY_LOW", "LOW", "MEDIUM", "HIGH", "VERY_HIGH"]:
                    category = api_category
                else:
                    # Use our fallback function
                    category = get_pollen_category_from_index(index_value)
                    print(f"  Using fallback category: {category}")
                
                if isinstance(index_value, (int, float)) and index_value > max_index:
                    max_index = index_value
                total_types += 1
                
                # Convert technical terms to user-friendly descriptions
                severity_map = {
                    "VERY_LOW": {"emoji": "🟢", "description": "Excellent", "advice": "Great day to be outdoors!"},
                    "LOW": {"emoji": "🟡", "description": "Good", "advice": "Most people will be fine outdoors"},
                    "MEDIUM": {"emoji": "🟠", "description": "Moderate", "advice": "Consider taking allergy medication"},
                    "HIGH": {"emoji": "🔴", "description": "Poor", "advice": "Limit outdoor activities if allergic"},
                    "VERY_HIGH": {"emoji": "🟣", "description": "Very Poor", "advice": "Stay indoors if possible"},
                    "UNKNOWN": {"emoji": "⚪", "description": "Data Unavailable", "advice": "Monitor symptoms and check again later"}
                }
                
                severity = severity_map.get(category, severity_map["UNKNOWN"])
                
                # More specific pollen type descriptions
                pollen_descriptions = {
                    "Tree": "🌳 Tree pollen (oak, birch, cedar, etc.)",
                    "Grass": "🌱 Grass pollen (timothy, bermuda, etc.)", 
                    "Weed": "🌿 Weed pollen (ragweed, sagebrush, etc.)"
                }
                
                pollen_desc = pollen_descriptions.get(pollen_type, f"🌸 {pollen_type}")
                
                # Convert index to approximate grains per cubic meter
                grains_per_m3 = "Unknown"
                if isinstance(index_value, (int, float)):
                    if index_value <= 2:
                        grains_per_m3 = f"~{int(index_value * 25)} grains/m³"
                    elif index_value <= 5:
                        grains_per_m3 = f"~{int(50 + (index_value-2) * 33)} grains/m³"
                    elif index_value <= 7:
                        grains_per_m3 = f"~{int(150 + (index_value-5) * 175)} grains/m³"
                    elif index_value <= 9:
                        grains_per_m3 = f"~{int(500 + (index_value-7) * 250)} grains/m³"
                    else:
                        grains_per_m3 = f"~{int(1000 + (index_value-10) * 500)}+ grains/m³"
                
                output.append(f"### {pollen_desc}")
                output.append(f"**Level:** {severity['emoji']} {severity['description']} (Index: {index_value}/10)")
                output.append(f"**Scientific Measurement:** {grains_per_m3}")
                output.append(f"**What this means:** {severity['advice']}")
                
                row_data[pollen_type] = f"{severity['description']} ({index_value}) - {grains_per_m3}"
                
                # Add plant descriptions if available
                if "plantDescription" in pollen:
                    plants = pollen["plantDescription"]
                    if "plants" in plants:
                        plant_list = [plant["displayName"] for plant in plants["plants"][:3]]  # Show top 3
                        output.append(f"**Main sources:** {', '.join(plant_list)}")
                
                output.append("")
            
            # Add overall day assessment
            if max_index > 0:
                if max_index <= 2:
                    day_rating = "🟢 **Excellent day** for outdoor activities!"
                elif max_index <= 5:
                    day_rating = "🟡 **Good day** - most people will be comfortable outside"
                elif max_index <= 7:
                    day_rating = "🟠 **Moderate day** - allergy sufferers should prepare"
                elif max_index <= 9:
                    day_rating = "🔴 **Challenging day** - limit outdoor exposure if allergic"
                else:
                    day_rating = "🟣 **Very difficult day** - stay indoors if you have allergies"
                
                output.append(f"**Overall Assessment:** {day_rating}\n")
            
            daily_data.append(row_data)
        
        output.append("---\n")
    
    # Create DataFrame for tabular view
    df = None
    if daily_data:
        df = pd.DataFrame(daily_data)
    
    # Add comprehensive advice section
    output.append("\n## 💡 Practical Tips for Pollen Season")
    
    output.append("### 🔬 **Understanding the Numbers:**")
    output.append("- **Under 100 grains/m³:** Like having a few specks of dust in a large room")
    output.append("- **100-500 grains/m³:** Similar to light sawdust in the air - noticeable to sensitive people")
    output.append("- **500-1000 grains/m³:** Like fine powder in the air - most people will feel it")
    output.append("- **Over 1000 grains/m³:** Heavy particulate load - breathing through a pollen cloud")
    
    output.append("\n### 🏠 **Indoor Protection:**")
    output.append("- Keep windows and doors closed during high pollen days (500+ grains/m³)")
    output.append("- Use air conditioning with clean HEPA filters")
    output.append("- Consider an air purifier for your bedroom")
    
    output.append("\n### 🚶 **When Going Outside:**")
    output.append("- Check this forecast before planning outdoor activities")
    output.append("- Wear wraparound sunglasses to protect your eyes")
    output.append("- Consider wearing a mask during very high pollen days (1000+ grains/m³)")
    output.append("- Plan outdoor activities for late evening when pollen counts are lower")
    
    output.append("\n### 🚿 **After Being Outdoors:**")
    output.append("- Shower and change clothes to remove pollen")
    output.append("- Wash your hair before bed to avoid pollen on your pillow")
    output.append("- Keep pets indoors or wipe them down after walks")
    
    output.append("\n### 💊 **Medication Timing:**")
    output.append("- Start allergy medications BEFORE symptoms begin")
    output.append("- Take antihistamines in the evening for next-day protection")
    output.append("- Consult your doctor about prescription options for severe allergies")
    
    return "\n".join(output), df

def update_location(lat, lon):
    """Update the map and fetch pollen data for new location"""
    if lat is None or lon is None:
        return create_map(), "Please select a location on the map.", None
    
    # Create new map
    new_map = create_map(lat, lon)
    
    # Get pollen data
    pollen_data = get_pollen_data(lat, lon)
    formatted_data, df = format_pollen_data(pollen_data)
    
    return new_map, formatted_data, df

# Create the Gradio interface
with gr.Blocks(title="🌸 Pollen Forecast Map", theme=gr.themes.Soft()) as app:
    gr.HTML("""
    <div style="text-align: center; padding: 20px;">
        <h1>🌸 Pollen Forecast Map</h1>
        <p>Get detailed, scientifically-accurate pollen forecasts with actual particle concentrations!</p>
        <div style="background-color: #e8f5e8; padding: 15px; border-radius: 10px; margin: 15px 0;">
            <strong>🎯 How to Use:</strong>
            <br>• <strong>Search by Address:</strong> Type any city, address, or landmark in the search box
            <br>• <strong>Use Quick Cities:</strong> Click preset buttons for major cities worldwide  
            <br>• <strong>Enter Coordinates:</strong> Input precise latitude/longitude values
            <br>• <strong>Map Note:</strong> The map displays your location but doesn't support clicking to select new points
        </div>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.HTML("<h3>📍 Location Selection</h3>")
            
            # Address search
            gr.HTML("<h4>🔍 Search by Address</h4>")
            address_input = gr.Textbox(
                label="Enter Address or City", 
                placeholder="e.g., Central Park, New York or Paris, France",
                info="Enter any address, city, or landmark"
            )
            search_btn = gr.Button("🔍 Search Address", variant="primary")
            
            gr.HTML("<h4>📐 Or Use Coordinates</h4>")
            # Coordinate inputs
            lat_input = gr.Number(
                label="Latitude", 
                value=40.7128, 
                precision=6,
                info="Enter latitude (-90 to 90)"
            )
            lon_input = gr.Number(
                label="Longitude", 
                value=-74.0060, 
                precision=6,
                info="Enter longitude (-180 to 180)"
            )
            
            update_btn = gr.Button("🔄 Update Location", variant="secondary")
            
            # Preset locations
            gr.HTML("<h4>🏙️ Popular Cities</h4>")
            with gr.Row():
                nyc_btn = gr.Button("🗽 New York", size="sm")
                la_btn = gr.Button("🌴 Los Angeles", size="sm") 
            with gr.Row():
                chicago_btn = gr.Button("🌬️ Chicago", size="sm")
                miami_btn = gr.Button("🏖️ Miami", size="sm")
            with gr.Row():
                london_btn = gr.Button("🇬🇧 London", size="sm")
                tokyo_btn = gr.Button("🇯🇵 Tokyo", size="sm")
        
        with gr.Column(scale=2):
            # Map display
            gr.HTML("<h3>🗺️ Location Map</h3>")
            gr.HTML("""
            <div style="background-color: #f0f8ff; padding: 10px; border-radius: 5px; margin-bottom: 10px;">
                <strong>📍 Note:</strong> The map shows your selected location but doesn't support clicking to select new points. 
                Use the address search or coordinate inputs on the left to change locations.
            </div>
            """)
            
            map_html = gr.HTML(
                value=create_map(),
                label="Current Location"
            )
            
            # Location info display
            location_info = gr.Textbox(
                label="📍 Selected Location",
                value="New York City, NY, USA",
                interactive=False
            )
    
    with gr.Row():
        with gr.Column():
            # Pollen data output
            pollen_output = gr.Markdown(
                value="Select a location to view pollen forecast.",
                label="Pollen Forecast"
            )
            
            # Data table
            pollen_table = gr.Dataframe(
                label="Pollen Data Summary",
                visible=False
            )
    
    # Event handlers
    def search_address(address):
        """Search for address and return coordinates"""
        if not address.strip():
            return 40.7128, -74.0060, "Please enter an address to search"
        
        lat, lon, display_name = geocode_address(address)
        if lat is not None and lon is not None:
            return lat, lon, display_name
        else:
            return 40.7128, -74.0060, display_name  # display_name contains error message
    
    def set_nyc():
        return 40.7128, -74.0060, "New York City, NY, USA"
    
    def set_la():
        return 34.0522, -118.2437, "Los Angeles, CA, USA"
    
    def set_chicago():
        return 41.8781, -87.6298, "Chicago, IL, USA"
    
    def set_miami():
        return 25.7617, -80.1918, "Miami, FL, USA"
        
    def set_london():
        return 51.5074, -0.1278, "London, England, UK"
        
    def set_tokyo():
        return 35.6762, 139.6503, "Tokyo, Japan"
    
    # Button events
    search_btn.click(
        fn=search_address,
        inputs=[address_input],
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    update_btn.click(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    nyc_btn.click(
        fn=set_nyc,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    la_btn.click(
        fn=set_la,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    chicago_btn.click(
        fn=set_chicago,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    miami_btn.click(
        fn=set_miami,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    london_btn.click(
        fn=set_london,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    tokyo_btn.click(
        fn=set_tokyo,
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    # Auto-update when coordinates change
    lat_input.change(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    lon_input.change(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )
    
    # Load initial data
    app.load(
        fn=lambda: (40.7128, -74.0060, "New York City, NY, USA"),
        outputs=[lat_input, lon_input, location_info]
    ).then(
        fn=update_location,
        inputs=[lat_input, lon_input],
        outputs=[map_html, pollen_output, pollen_table]
    )

if __name__ == "__main__":
    app.launch()