app.py

import gradio as gr
import requests
import pandas as pd
import folium
from folium import plugins
import json
from datetime import datetime, timedelta
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

class AirQualityMonitor:
    def __init__(self):
        self.base_url = "https://www.airnowapi.org/aq"
        # You'll need to get an API key from https://docs.airnowapi.org/
        self.api_key = "YOUR_API_KEY_HERE"  # Replace with actual API key
        
    def get_current_observations(self, state_code=None, bbox=None):
        """Get current air quality observations"""
        if not self.api_key or self.api_key == "YOUR_API_KEY_HERE":
            # Return sample data for demo purposes
            return self._get_sample_data()
            
        url = f"{self.base_url}/observation/zipCode/current/"
        params = {
            'format': 'application/json',
            'API_KEY': self.api_key,
            'distance': 100
        }
        
        if state_code:
            # Get observations for a specific state
            params['zipCode'] = self._get_state_zip(state_code)
        
        try:
            response = requests.get(url, params=params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            print(f"API Error: {e}")
            return self._get_sample_data()
    
    def _get_state_zip(self, state_code):
        """Get a representative zip code for a state"""
        state_zips = {
            'CA': '90210', 'NY': '10001', 'TX': '73301', 'FL': '33101',
            'IL': '60601', 'PA': '19101', 'OH': '43215', 'GA': '30301',
            'NC': '27601', 'MI': '48201', 'NJ': '07001', 'VA': '23219',
            'WA': '98101', 'AZ': '85001', 'MA': '02101', 'TN': '37201',
            'IN': '46201', 'MO': '63101', 'MD': '21201', 'WI': '53201'
        }
        return state_zips.get(state_code, '90210')
    
    def _get_sample_data(self):
        """Return sample data for demonstration"""
        return [
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "PST",
                "ReportingArea": "Los Angeles-South Coast Air Basin",
                "StateCode": "CA",
                "Latitude": 34.0522,
                "Longitude": -118.2437,
                "ParameterName": "PM2.5",
                "AQI": 65,
                "Category": {"Number": 2, "Name": "Moderate"}
            },
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "EST",
                "ReportingArea": "New York",
                "StateCode": "NY",
                "Latitude": 40.7128,
                "Longitude": -74.0060,
                "ParameterName": "Ozone",
                "AQI": 45,
                "Category": {"Number": 1, "Name": "Good"}
            },
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "CST",
                "ReportingArea": "Chicago",
                "StateCode": "IL",
                "Latitude": 41.8781,
                "Longitude": -87.6298,
                "ParameterName": "PM2.5",
                "AQI": 85,
                "Category": {"Number": 3, "Name": "Unhealthy for Sensitive Groups"}
            },
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "MST",
                "ReportingArea": "Phoenix",
                "StateCode": "AZ",
                "Latitude": 33.4484,
                "Longitude": -112.0740,
                "ParameterName": "Ozone",
                "AQI": 95,
                "Category": {"Number": 3, "Name": "Unhealthy for Sensitive Groups"}
            },
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "PST",
                "ReportingArea": "Seattle",
                "StateCode": "WA",
                "Latitude": 47.6062,
                "Longitude": -122.3321,
                "ParameterName": "PM2.5",
                "AQI": 25,
                "Category": {"Number": 1, "Name": "Good"}
            },
            {
                "DateObserved": "2025-06-29",
                "HourObserved": 14,
                "LocalTimeZone": "EST",
                "ReportingArea": "Miami",
                "StateCode": "FL",
                "Latitude": 25.7617,
                "Longitude": -80.1918,
                "ParameterName": "Ozone",
                "AQI": 55,
                "Category": {"Number": 2, "Name": "Moderate"}
            }
        ]

def get_aqi_color(aqi):
    """Return color based on AQI value"""
    if aqi <= 50:
        return "#00E400"  # Green
    elif aqi <= 100:
        return "#FFFF00"  # Yellow
    elif aqi <= 150:
        return "#FF7E00"  # Orange
    elif aqi <= 200:
        return "#FF0000"  # Red
    elif aqi <= 300:
        return "#8F3F97"  # Purple
    else:
        return "#7E0023"  # Maroon

def create_air_quality_map(data):
    """Create an interactive map with air quality data"""
    if not data:
        # Create empty map centered on US
        m = folium.Map(location=[39.8283, -98.5795], zoom_start=4)
        folium.Marker(
            [39.8283, -98.5795],
            popup="No data available. Please add your AirNow API key.",
            icon=folium.Icon(color='red')
        ).add_to(m)
        return m._repr_html_()
    
    # Calculate map center
    lats = [float(station['Latitude']) for station in data]
    lons = [float(station['Longitude']) for station in data]
    center_lat = sum(lats) / len(lats)
    center_lon = sum(lons) / len(lons)
    
    # Create map
    m = folium.Map(location=[center_lat, center_lon], zoom_start=4)
    
    # Add markers for each monitoring station
    for station in data:
        lat = float(station['Latitude'])
        lon = float(station['Longitude'])
        aqi = station.get('AQI', 0)
        parameter = station.get('ParameterName', 'Unknown')
        area = station.get('ReportingArea', 'Unknown Area')
        category = station.get('Category', {}).get('Name', 'Unknown')
        
        color = get_aqi_color(aqi)
        
        popup_html = f"""
        <div style="width: 200px;">
            <h4>{area}</h4>
            <p><strong>AQI:</strong> {aqi}</p>
            <p><strong>Parameter:</strong> {parameter}</p>
            <p><strong>Category:</strong> {category}</p>
            <p><strong>Date:</strong> {station.get('DateObserved', 'N/A')}</p>
            <p><strong>Time:</strong> {station.get('HourObserved', 'N/A')}:00 {station.get('LocalTimeZone', '')}</p>
        </div>
        """
        
        folium.CircleMarker(
            location=[lat, lon],
            radius=8,
            popup=folium.Popup(popup_html, max_width=250),
            color='black',
            weight=1,
            fill=True,
            fillColor=color,
            fillOpacity=0.8,
            tooltip=f"{area}: AQI {aqi}"
        ).add_to(m)
    
    # Add legend
    legend_html = '''
    <div style="position: fixed; 
                top: 10px; right: 10px; width: 160px; height: 140px; 
                background-color: white; border:2px solid grey; z-index:9999; 
                font-size:14px; padding: 10px">
    <h4>AQI Legend</h4>
    <p><i class="fa fa-circle" style="color:#00E400"></i> Good (0-50)</p>
    <p><i class="fa fa-circle" style="color:#FFFF00"></i> Moderate (51-100)</p>
    <p><i class="fa fa-circle" style="color:#FF7E00"></i> Unhealthy for Sensitive (101-150)</p>
    <p><i class="fa fa-circle" style="color:#FF0000"></i> Unhealthy (151-200)</p>
    <p><i class="fa fa-circle" style="color:#8F3F97"></i> Very Unhealthy (201-300)</p>
    </div>
    '''
    m.get_root().html.add_child(folium.Element(legend_html))
    
    return m._repr_html_()

def create_aqi_chart(data):
    """Create a chart showing AQI levels by location"""
    if not data:
        return None
    
    df = pd.DataFrame(data)
    df['AQI'] = pd.to_numeric(df['AQI'], errors='coerce')
    
    # Create bar chart
    fig = px.bar(df, 
                 x='ReportingArea', 
                 y='AQI',
                 color='AQI',
                 color_continuous_scale='RdYlGn_r',
                 title='Air Quality Index by Location',
                 labels={'AQI': 'Air Quality Index', 'ReportingArea': 'Location'})
    
    fig.update_layout(xaxis_tickangle=-45, height=500)
    fig.update_traces(hovertemplate='<b>%{x}</b><br>AQI: %{y}<extra></extra>')
    
    return fig

def get_air_quality_summary(data):
    """Generate a summary of air quality data"""
    if not data:
        return "No air quality data available."
    
    df = pd.DataFrame(data)
    df['AQI'] = pd.to_numeric(df['AQI'], errors='coerce')
    
    total_stations = len(df)
    avg_aqi = df['AQI'].mean()
    max_aqi = df['AQI'].max()
    min_aqi = df['AQI'].min()
    
    # Count by category
    categories = df['Category'].apply(lambda x: x.get('Name', 'Unknown') if isinstance(x, dict) else 'Unknown')
    category_counts = categories.value_counts()
    
    summary = f"""
    ## Air Quality Summary
    
    **Total Monitoring Stations:** {total_stations}
    **Average AQI:** {avg_aqi:.1f}
    **Highest AQI:** {max_aqi}
    **Lowest AQI:** {min_aqi}
    
    ### Air Quality Categories:
    """
    
    for category, count in category_counts.items():
        percentage = (count / total_stations) * 100
        summary += f"- **{category}:** {count} stations ({percentage:.1f}%)\n"
    
    return summary

def update_data():
    """Main function to fetch and display air quality data"""
    monitor = AirQualityMonitor()
    data = monitor.get_current_observations()
    
    map_html = create_air_quality_map(data)
    chart = create_aqi_chart(data)
    summary = get_air_quality_summary(data)
    
    return map_html, chart, summary

# Create Gradio interface
with gr.Blocks(title="Air Quality Monitor", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🌬️ Real-Time Air Quality Monitor")
    gr.Markdown("Monitor air quality data from NOAA/EPA AirNow stations across the United States")
    
    with gr.Row():
        gr.Markdown("""
        This application displays real-time air quality data from over 2,000 monitoring stations 
        across the United States. The data includes Air Quality Index (AQI) values for various 
        pollutants like PM2.5, PM10, Ozone, and more.
        
        **Note:** To access live data, you need to:
        1. Get a free API key from [AirNow API](https://docs.airnowapi.org/)
        2. Replace `YOUR_API_KEY_HERE` in the code with your actual API key
        
        Currently showing sample data for demonstration.
        """)
    
    with gr.Row():
        update_btn = gr.Button("🔄 Update Air Quality Data", variant="primary", size="lg")
    
    with gr.Row():
        with gr.Column(scale=2):
            map_output = gr.HTML(label="Air Quality Map")
        with gr.Column(scale=1):
            summary_output = gr.Markdown(label="Summary")
    
    with gr.Row():
        chart_output = gr.Plot(label="AQI Chart")
    
    # Add information section
    with gr.Row():
        gr.Markdown("""
        ## About Air Quality Index (AQI)
        
        The AQI is an index for reporting daily air quality. It tells you how clean or polluted your air is:
        
        - **Good (0-50)**: 🟢 Air quality is satisfactory
        - **Moderate (51-100)**: 🟡 Acceptable for most people
        - **Unhealthy for Sensitive Groups (101-150)**: 🟠 May cause problems for sensitive individuals
        - **Unhealthy (151-200)**: 🔴 May cause health problems for everyone
        - **Very Unhealthy (201-300)**: 🟣 Health alert for everyone
        - **Hazardous (301+)**: 🟤 Health emergency
        
        **Data Source:** EPA AirNow - Real-time air quality observations from government monitoring stations
        """)
    
    # Load initial data
    demo.load(update_data, outputs=[map_output, chart_output, summary_output])
    update_btn.click(update_data, outputs=[map_output, chart_output, summary_output])

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