Update app.py

app.py

@@ -9,7 +9,6 @@ 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
@@ -18,12 +17,12 @@ st.set_page_config(layout="wide", page_title="Pakistan Climate & Disaster Monito
 class DataCollector:
     @staticmethod
     def fetch_usgs_earthquake_data():
-        """Fetch earthquake data from USGS website."""
+        """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)
-            response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
             data = response.json()
+            # Filter for Pakistan region
             pakistan_data = {
                 "type": "FeatureCollection",
                 "features": [
@@ -33,93 +32,153 @@ class DataCollector:
                 ]
             }
             return pakistan_data
-        except requests.exceptions.RequestException as e:
+        except Exception as e:
             st.error(f"Error fetching earthquake data: {e}")
             return None
 
     @staticmethod
-    def fetch_world_bank_climate_data():
-        """Fetch climate data from World Bank Climate Change Knowledge Portal."""
-        url = "https://climateknowledgeportal.worldbank.org/api/data/get-download-data"
-        params = {
-            "region": "South Asia",
-            "country": "Pakistan"
+    def fetch_weather_data():
+        """Fetch weather data from OpenMeteo (free, no API required)"""
+        # Multiple cities in Pakistan
+        cities = {
+            'Islamabad': {'lat': 33.7294, 'lon': 73.0931},
+            'Karachi': {'lat': 24.8607, 'lon': 67.0011},
+            'Lahore': {'lat': 31.5204, 'lon': 74.3587},
+            'Peshawar': {'lat': 34.0151, 'lon': 71.5249},
+            'Quetta': {'lat': 30.1798, 'lon': 66.9750}
         }
-        try:
-            response = requests.post(url, data=params)
-            response.raise_for_status()
-            # Use StringIO and specify encoding to handle potential issues
-            df = pd.read_csv(StringIO(response.text), encoding='utf-8')
-            return df
-        except requests.exceptions.RequestException as e:
-            st.error(f"Error fetching climate data: {e}")
-            return None
-        except pd.errors.ParserError as e:
-            st.error(f"Error parsing climate data (CSV): {e}. Check the data format.")
-            st.error(f"Response Text: {response.text}") # Print response for debugging
-            return None
-        except Exception as e:
-            st.error(f"An unexpected error occurred: {e}")
-            return None
+        
+        weather_data = []
+        for city, coords in cities.items():
+            url = f"https://api.open-meteo.com/v1/forecast?latitude={coords['lat']}&longitude={coords['lon']}&daily=temperature_2m_max,temperature_2m_min,precipitation_sum&timezone=auto"
+            try:
+                response = requests.get(url)
+                data = response.json()
+                df = pd.DataFrame({
+                    'Date': pd.to_datetime(data['daily']['time']),
+                    'Temperature_Max': data['daily']['temperature_2m_max'],
+                    'Temperature_Min': data['daily']['temperature_2m_min'],
+                    'Precipitation': data['daily']['precipitation_sum'],
+                    'City': city
+                })
+                weather_data.append(df)
+            except Exception as e:
+                st.error(f"Error fetching data for {city}: {e}")
+                continue
+        
+        if weather_data:
+            return pd.concat(weather_data, ignore_index=True)
+        return None
 
-
-def create_dashboard():
-    st.title("Pakistan Climate & Disaster Monitoring System")
-
-    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()
+    @staticmethod
+    def fetch_air_quality_data():
+        """Fetch air quality data from OpenMeteo Air Quality API (free)"""
+        cities = {
+            'Islamabad': {'lat': 33.7294, 'lon': 73.0931},
+            'Karachi': {'lat': 24.8607, 'lon': 67.0011},
+            'Lahore': {'lat': 31.5204, 'lon': 74.3587}
+        }
+        
+        aqi_data = []
+        for city, coords in cities.items():
+            url = f"https://air-quality-api.open-meteo.com/v1/air-quality?latitude={coords['lat']}&longitude={coords['lon']}&hourly=pm10,pm2_5,carbon_monoxide&timezone=auto"
+            try:
+                response = requests.get(url)
+                data = response.json()
+                df = pd.DataFrame({
+                    'Time': pd.to_datetime(data['hourly']['time']),
+                    'PM10': data['hourly']['pm10'],
+                    'PM2.5': data['hourly']['pm2_5'],
+                    'CO': data['hourly']['carbon_monoxide'],
+                    'City': city
+                })
+                aqi_data.append(df)
+            except Exception as e:
+                st.error(f"Error fetching AQI data for {city}: {e}")
+                continue
+        
+        if aqi_data:
+            return pd.concat(aqi_data, ignore_index=True)
+        return None
 
 def show_climate_analysis():
     st.header("Climate Analysis")
-
+    
     data_collector = DataCollector()
-    climate_data = data_collector.fetch_world_bank_climate_data()
-
-    if climate_data is not None:
-        if 'Temperature - (Celsius)' in climate_data.columns:
-            climate_data = climate_data.rename(columns={'Temperature - (Celsius)': 'Temperature'})
-        if 'Annual average precipitation in mm' in climate_data.columns:
-            climate_data = climate_data.rename(columns={'Annual average precipitation in mm': 'Precipitation'})
-
+    weather_data = data_collector.fetch_weather_data()
+    
+    if weather_data is not None:
+        # City selector
+        selected_city = st.selectbox("Select City", weather_data['City'].unique())
+        city_data = weather_data[weather_data['City'] == selected_city]
+        
         col1, col2 = st.columns(2)
-
+        
         with col1:
-            fig = px.line(climate_data, x='Year', y='Temperature', title='Historical Temperature Trends')
+            # Temperature trends
+            fig = px.line(city_data, 
+                         x='Date', 
+                         y=['Temperature_Max', 'Temperature_Min'],
+                         title=f'Temperature Trends - {selected_city}')
             st.plotly_chart(fig)
-
+        
         with col2:
-            fig = px.bar(climate_data, x='Year', y='Precipitation', title='Annual Precipitation')
+            # Precipitation patterns
+            fig = px.bar(city_data, 
+                        x='Date', 
+                        y='Precipitation',
+                        title=f'Daily Precipitation - {selected_city}')
             st.plotly_chart(fig)
-
-        st.subheader("Climate Indicators")
+        
+        # Climate indicators
+        st.subheader("Current Climate Indicators")
+        latest_data = city_data.iloc[-1]
         cols = st.columns(3)
-
+        
         with cols[0]:
-            avg_temp = climate_data['Temperature'].mean()
-            st.metric("Average Temperature", f"{avg_temp:.1f}°C")
-
+            st.metric("Max Temperature", f"{latest_data['Temperature_Max']:.1f}°C")
         with cols[1]:
-            avg_precip = climate_data['Precipitation'].mean()
-            st.metric("Average Precipitation", f"{avg_precip:.1f}mm")
+            st.metric("Min Temperature", f"{latest_data['Temperature_Min']:.1f}°C")
+        with cols[2]:
+            st.metric("Precipitation", f"{latest_data['Precipitation']:.1f}mm")
 
+def show_environmental_data():
+    st.header("Environmental Data")
+    
+    data_collector = DataCollector()
+    aqi_data = data_collector.fetch_air_quality_data()
+    
+    if aqi_data is not None:
+        # City selector
+        selected_city = st.selectbox("Select City", aqi_data['City'].unique())
+        city_data = aqi_data[aqi_data['City'] == selected_city]
+        
+        # Latest 24 hours of data
+        recent_data = city_data.tail(24)
+        
+        # AQI time series
+        st.subheader("Air Quality Indicators")
+        metric = st.selectbox("Select Pollutant", ['PM10', 'PM2.5', 'CO'])
+        
+        fig = px.line(recent_data, 
+                     x='Time', 
+                     y=metric,
+                     title=f'{metric} Levels - {selected_city} (Last 24 Hours)')
+        st.plotly_chart(fig)
+        
+        # Current conditions
+        st.subheader("Current Air Quality")
+        latest_data = city_data.iloc[-1]
+        cols = st.columns(3)
+        
+        with cols[0]:
+            st.metric("PM10", f"{latest_data['PM10']:.1f} µg/m³")
+        with cols[1]:
+            st.metric("PM2.5", f"{latest_data['PM2.5']:.1f} µg/m³")
         with cols[2]:
-            temp_trend = climate_data['Temperature'].diff().mean()
-            st.metric("Temperature Trend", f"{temp_trend:+.2f}°C/year")
-    else:
-        st.warning("Could not retrieve climate data. Please check the data source.")
+            st.metric("Carbon Monoxide", f"{latest_data['CO']:.1f} µg/m³")
 
-# ... (rest of the functions: show_disaster_monitor, show_risk_assessment, show_environmental_data) - No changes needed here
+# [Previous show_disaster_monitor and show_risk_assessment functions remain the same]
 
 if __name__ == "__main__":
     create_dashboard()