app1.py

import pandas as pd
import streamlit as st
import plotly.graph_objects as go

# Sample AQI data (replace with your actual data source)
data = {
    'Timestamp': pd.to_datetime(['2024-07-26 10:00:00', '2024-07-26 11:00:00', '2024-07-26 12:00:00', '2024-07-26 13:00:00', '2024-07-26 14:00:00']),
    'AQI': [80, 120, 150, 90, 60],
    'PM2.5': [30, 50, 65, 35, 20],
    'O3': [40, 60, 80, 50, 30],
    'CO': [5, 8, 10, 6, 3],
    'SO2': [10, 15, 20, 12, 8],
    'NO2': [15, 25, 30, 20, 12]
}
df = pd.DataFrame(data)

# AQI Categories and Health Recommendations
aqi_categories = {
    (0, 50): {'label': 'Good', 'color': 'green', 'recommendations': {
        'heart_patients': 'Enjoy your normal activities.',
        'old_age': 'Enjoy your normal activities.',
        'mid_age': 'Enjoy your normal activities.',
        'young_age': 'Enjoy your normal activities.',
        'general': 'Air quality is satisfactory.'
    }},
    (51, 100): {'label': 'Moderate', 'color': 'yellow', 'recommendations': {
        'heart_patients': 'Consider limiting prolonged or heavy exertion.',
        'old_age': 'Consider limiting prolonged or heavy exertion.',
        'mid_age': 'Generally acceptable for most.',
        'young_age': 'Generally acceptable for most.',
        'general': 'Air quality is acceptable; however, unusually sensitive people should consider limiting their outdoor exertion.'
    }},
    (101, 150): {'label': 'Unhealthy for Sensitive Groups', 'color': 'orange', 'recommendations': {
        'heart_patients': 'Avoid prolonged or heavy exertion.  Consult your doctor if you experience symptoms.',
        'old_age': 'Avoid prolonged or heavy exertion.  Consult your doctor if you experience symptoms.',
        'mid_age': 'Limit prolonged or heavy exertion.',
        'young_age': 'Limit prolonged or heavy exertion.',
        'general': 'Members of sensitive groups may experience health effects. The general public is not likely to be affected.'
    }},
    (151, 200): {'label': 'Unhealthy', 'color': 'red', 'recommendations': {
        'heart_patients': 'Avoid all outdoor exertion. Stay indoors as much as possible. Consult your doctor if you experience symptoms.',
        'old_age': 'Avoid all outdoor exertion. Stay indoors as much as possible. Consult your doctor if you experience symptoms.',
        'mid_age': 'Avoid all outdoor exertion. Stay indoors as much as possible.',
        'young_age': 'Avoid all outdoor exertion. Stay indoors as much as possible.',
        'general': 'Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.'
    }},
    (201, 300): {'label': 'Very Unhealthy', 'color': 'purple', 'recommendations': {
        'heart_patients': 'Remain indoors. Avoid all exertion. Consult your doctor immediately if you experience symptoms.',
        'old_age': 'Remain indoors. Avoid all exertion. Consult your doctor immediately if you experience symptoms.',
        'mid_age': 'Remain indoors. Avoid all exertion.',
        'young_age': 'Remain indoors. Avoid all exertion.',
        'general': 'Health alert: everyone may experience more serious health effects.'
    }},
    (301, 500): {'label': 'Hazardous', 'color': 'maroon', 'recommendations': {
        'heart_patients': 'Seek immediate medical attention.',
        'old_age': 'Seek immediate medical attention.',
        'mid_age': 'Seek immediate medical attention.',
        'young_age': 'Seek immediate medical attention.',
        'general': 'Health emergency: a health alert indicates that everyone may experience more serious health effects.'
    }}
}

# Streamlit app
st.title('Air Quality Dashboard')

# Current AQI and Category
current_aqi = df['AQI'].iloc[-1]
current_category = None
for aqi_range, category_data in aqi_categories.items():
    if aqi_range[0] <= current_aqi <= aqi_range[1]:
        current_category = category_data
        break

# Display AQI with color
st.markdown(
    f"<h2 style='color: {current_category['color']}'>Current AQI: {current_aqi} ({current_category['label']})</h2>",
    unsafe_allow_html=True
)

# Gauge chart for AQI
fig_gauge = go.Figure(go.Indicator(
    mode="gauge+number",
    value=current_aqi,
    title={'text': "Air Quality Index"},
    domain={'x': [0, 1], 'y': [0, 1]},
    gauge={
        'axis': {'range': [None, 500]},
        'steps': [{'range': [0, 50], 'color': "green"}, {'range': [50, 100], 'color': "yellow"}, {'range': [100, 150], 'color': "orange"}, {'range': [150, 200], 'color': "red"}, {'range': [200, 300], 'color': "purple"}, {'range': [300, 500], 'color': "maroon"}],
        'threshold': {
            'line': {'color': "black", 'width': 4},
            'thickness': 0.75,
            'value': current_aqi
        }
    }
))
st.plotly_chart(fig_gauge)

# Detailed Pollutant Levels
st.subheader('Detailed Pollutant Levels')
st.line_chart(df[['PM2.5', 'O3', 'CO', 'SO2', 'NO2']])

# Health Recommendations
st.subheader('Health Recommendations')

st.write(f"**General:** {current_category['recommendations']['general']}")
st.write(f"**Heart Patients:** {current_category['recommendations']['heart_patients']}")
st.write(f"**Old Age:** {current_category['recommendations']['old_age']}")
st.write(f"**Mid Age:** {current_category['recommendations']['mid_age']}")
st.write(f"**Young Age:** {current_category['recommendations']['young_age']}")

# Add more visualizations (e.g., bar charts for pollutant comparison) as needed.