Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import requests
+from bs4 import BeautifulSoup
+import folium
+from streamlit_folium import folium_static
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import json
+import csv
+from io import StringIO
+
+# Page configuration
+st.set_page_config(layout="wide", page_title="Pakistan Climate & Disaster Monitor")
+
+class DataCollector:
+    @staticmethod
+    def fetch_usgs_earthquake_data():
+        """Fetch earthquake data from USGS website (free, no API key needed)"""
+        url = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_month.geojson"
+        try:
+            response = requests.get(url)
+            data = response.json()
+            # Filter for Pakistan region
+            pakistan_data = {
+                "type": "FeatureCollection",
+                "features": [
+                    feature for feature in data["features"]
+                    if 60.878 <= feature["geometry"]["coordinates"][0] <= 77.840
+                    and 23.692 <= feature["geometry"]["coordinates"][1] <= 37.097
+                ]
+            }
+            return pakistan_data
+        except Exception as e:
+            st.error(f"Error fetching earthquake data: {e}")
+            return None
+
+    @staticmethod
+    def fetch_weather_data():
+        """Fetch weather data from WorldBank Climate Data (free dataset)"""
+        url = "https://climateknowledgeportal.worldbank.org/api/data/get-download-data"
+        params = {
+            "region": "South Asia",
+            "country": "Pakistan"
+        }
+        try:
+            response = requests.post(url, data=params)
+            df = pd.read_csv(StringIO(response.text))
+            return df
+        except Exception as e:
+            st.error(f"Error fetching weather data: {e}")
+            return None
+
+def create_dashboard():
+    st.title("Pakistan Climate & Disaster Monitoring System")
+    
+    # Sidebar for navigation
+    page = st.sidebar.selectbox(
+        "Select Module",
+        ["Climate Analysis", "Disaster Monitor", "Risk Assessment", "Environmental Data"]
+    )
+    
+    if page == "Climate Analysis":
+        show_climate_analysis()
+    elif page == "Disaster Monitor":
+        show_disaster_monitor()
+    elif page == "Risk Assessment":
+        show_risk_assessment()
+    elif page == "Environmental Data":
+        show_environmental_data()
+
+def show_climate_analysis():
+    st.header("Climate Analysis")
+    
+    # Historical temperature data from World Bank dataset
+    data_collector = DataCollector()
+    climate_data = data_collector.fetch_weather_data()
+    
+    if climate_data is not None:
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            # Temperature trends
+            fig = px.line(climate_data, 
+                         x='Year', 
+                         y='Temperature',
+                         title='Historical Temperature Trends')
+            st.plotly_chart(fig)
+        
+        with col2:
+            # Precipitation patterns
+            fig = px.bar(climate_data, 
+                        x='Year', 
+                        y='Precipitation',
+                        title='Annual Precipitation')
+            st.plotly_chart(fig)
+        
+        # Climate indicators
+        st.subheader("Climate Indicators")
+        cols = st.columns(3)
+        
+        with cols[0]:
+            avg_temp = climate_data['Temperature'].mean()
+            st.metric("Average Temperature", f"{avg_temp:.1f}°C")
+        
+        with cols[1]:
+            avg_precip = climate_data['Precipitation'].mean()
+            st.metric("Average Precipitation", f"{avg_precip:.1f}mm")
+        
+        with cols[2]:
+            temp_trend = climate_data['Temperature'].diff().mean()
+            st.metric("Temperature Trend", f"{temp_trend:+.2f}°C/year")
+
+def show_disaster_monitor():
+    st.header("Disaster Monitoring")
+    
+    # Fetch earthquake data
+    data_collector = DataCollector()
+    earthquake_data = data_collector.fetch_usgs_earthquake_data()
+    
+    if earthquake_data:
+        # Create map
+        m = folium.Map(location=[30.3753, 69.3451], zoom_start=5)
+        
+        for eq in earthquake_data['features']:
+            coords = eq['geometry']['coordinates']
+            mag = eq['properties']['mag']
+            
+            folium.CircleMarker(
+                location=[coords[1], coords[0]],
+                radius=mag * 3,
+                color='red',
+                fill=True,
+                popup=f"Magnitude: {mag}",
+            ).add_to(m)
+        
+        st.subheader("Recent Earthquakes Map")
+        folium_static(m)
+        
+        # Recent earthquakes list
+        st.subheader("Recent Earthquakes")
+        eq_df = pd.DataFrame([
+            {
+                'Time': datetime.fromtimestamp(eq['properties']['time']/1000),
+                'Magnitude': eq['properties']['mag'],
+                'Location': eq['properties']['place']
+            }
+            for eq in earthquake_data['features']
+        ])
+        st.dataframe(eq_df)
+
+def show_risk_assessment():
+    st.header("Risk Assessment")
+    
+    # Sample risk data (could be enhanced with real historical data)
+    risk_data = pd.DataFrame({
+        'Region': ['Punjab', 'Sindh', 'KPK', 'Balochistan', 'Gilgit-Baltistan'],
+        'Flood_Risk': np.random.uniform(0, 1, 5),
+        'Drought_Risk': np.random.uniform(0, 1, 5),
+        'Earthquake_Risk': np.random.uniform(0, 1, 5)
+    })
+    
+    # Risk heatmap
+    fig = go.Figure(data=go.Heatmap(
+        z=[risk_data[col] for col in ['Flood_Risk', 'Drought_Risk', 'Earthquake_Risk']],
+        x=risk_data['Region'],
+        y=['Flood', 'Drought', 'Earthquake'],
+        colorscale='RdYlBu_r'
+    ))
+    fig.update_layout(title='Regional Risk Assessment Heatmap')
+    st.plotly_chart(fig)
+    
+    # Risk analysis
+    st.subheader("Risk Analysis by Region")
+    selected_region = st.selectbox("Select Region", risk_data['Region'])
+    region_data = risk_data[risk_data['Region'] == selected_region].iloc[0]
+    
+    cols = st.columns(3)
+    with cols[0]:
+        st.metric("Flood Risk", f"{region_data['Flood_Risk']:.2%}")
+    with cols[1]:
+        st.metric("Drought Risk", f"{region_data['Drought_Risk']:.2%}")
+    with cols[2]:
+        st.metric("Earthquake Risk", f"{region_data['Earthquake_Risk']:.2%}")
+
+def show_environmental_data():
+    st.header("Environmental Data")
+    
+    # Sample environmental data (could be enhanced with real data sources)
+    dates = pd.date_range(start='2023-01-01', periods=365, freq='D')
+    env_data = pd.DataFrame({
+        'Date': dates,
+        'Temperature': np.random.normal(25, 5, 365),
+        'Humidity': np.random.normal(60, 10, 365),
+        'Air_Quality': np.random.normal(50, 20, 365)
+    })
+    
+    # Environmental indicators
+    st.subheader("Environmental Indicators")
+    metric = st.selectbox("Select Indicator", ['Temperature', 'Humidity', 'Air_Quality'])
+    
+    fig = px.line(env_data, x='Date', y=metric,
+                  title=f'{metric} Over Time')
+    st.plotly_chart(fig)
+    
+    # Current conditions
+    st.subheader("Current Conditions")
+    cols = st.columns(3)
+    with cols[0]:
+        st.metric("Temperature", f"{env_data['Temperature'].iloc[-1]:.1f}°C")
+    with cols[1]:
+        st.metric("Humidity", f"{env_data['Humidity'].iloc[-1]:.1f}%")
+    with cols[2]:
+        st.metric("Air Quality Index", f"{env_data['Air_Quality'].iloc[-1]:.0f}")
+
+if __name__ == "__main__":
+    create_dashboard()