Update app.py

app.py

@@ -1,43 +1,231 @@
-import pickle
 import streamlit as st
-import numpy as np
-import os
+import pandas as pd
+from air_pollution_predictor import AirPollutionPredictor
 
-# Load the regression model
-with open("regression_model.pkl", "rb") as f:
-    regression_model = pickle.load(f)
+# Configure page
+st.set_page_config(
+    page_title="🌍 Air Pollution Prediction System",
+    page_icon="🌍",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
 
-print("Regression model loaded successfully!")
+# Initialize the predictor
+@st.cache_resource
+def load_predictor():
+    """Load the air pollution predictor with caching"""
+    try:
+        return AirPollutionPredictor()
+    except Exception as e:
+        st.error(f"Error loading models: {str(e)}")
+        st.stop()
 
-# Load the classification model
-with open("classification_model.pkl", "rb") as f:
-    classification_model = pickle.load(f)
+predictor = load_predictor()
 
-print("Model loaded successfully!")
-
-# Streamlit UI
+# Main title and description
 st.title("🌍 Air Pollution Prediction System")
+st.markdown("""
+This system predicts **PM2.5 levels** and **Air Quality** based on various environmental factors.
+Enter the environmental parameters below to get predictions.
+""")
+
+# Create columns for better layout
+col1, col2 = st.columns([2, 1])
+
+with col1:
+    st.header("📊 Environmental Parameters")
+    
+    # Create sub-columns for input fields
+    input_col1, input_col2 = st.columns(2)
+    
+    with input_col1:
+        st.subheader("🌡️ Weather Conditions")
+        temperature = st.number_input(
+            "Temperature (°C)", 
+            min_value=-10.0, 
+            max_value=50.0, 
+            value=25.0,
+            help="Ambient temperature in Celsius"
+        )
+        
+        humidity = st.number_input(
+            "Humidity (%)", 
+            min_value=0.0, 
+            max_value=100.0, 
+            value=50.0,
+            help="Relative humidity percentage"
+        )
+        
+        st.subheader("🏭 Pollutant Levels")
+        pm10 = st.number_input(
+            "PM10 Level (μg/m³)", 
+            min_value=0.0, 
+            max_value=500.0, 
+            value=20.0,
+            help="Particulate Matter 10 micrometers"
+        )
+        
+        no2 = st.number_input(
+            "NO2 Level (μg/m³)", 
+            min_value=0.0, 
+            max_value=500.0, 
+            value=15.0,
+            help="Nitrogen Dioxide level"
+        )
+    
+    with input_col2:
+        st.subheader("💨 Gas Concentrations")
+        so2 = st.number_input(
+            "SO2 Level (μg/m³)", 
+            min_value=0.0, 
+            max_value=500.0, 
+            value=10.0,
+            help="Sulfur Dioxide level"
+        )
+        
+        co = st.number_input(
+            "CO Level (mg/m³)", 
+            min_value=0.0, 
+            max_value=10.0, 
+            value=1.0,
+            help="Carbon Monoxide level"
+        )
+        
+        st.subheader("🏘️ Location Factors")
+        industrial_proximity = st.slider(
+            "Proximity to Industrial Areas", 
+            0.0, 
+            10.0, 
+            5.0,
+            help="Scale: 0 (far) to 10 (very close)"
+        )
+        
+        population_density = st.number_input(
+            "Population Density (people/km²)", 
+            min_value=0, 
+            max_value=10000, 
+            value=500,
+            help="Number of people per square kilometer"
+        )
+    
+    # Prediction buttons
+    st.header("🔮 Predictions")
+    
+    prediction_col1, prediction_col2, prediction_col3 = st.columns(3)
+    
+    with prediction_col1:
+        predict_pm25 = st.button("🔍 Predict PM2.5 Level", type="primary", use_container_width=True)
+    
+    with prediction_col2:
+        predict_quality = st.button("🌬️ Predict Air Quality", type="primary", use_container_width=True)
+    
+    with prediction_col3:
+        predict_both = st.button("📈 Predict Both", type="secondary", use_container_width=True)
+
+with col2:
+    st.header("📈 Results")
+    
+    # Handle predictions
+    if predict_pm25 or predict_both:
+        try:
+            features = predictor.prepare_features(
+                temperature, humidity, pm10, no2, so2, co, 
+                industrial_proximity, population_density
+            )
+            pm25_result = predictor.predict_pm25(features)
+            
+            st.success("PM2.5 Prediction Complete!")
+            st.metric(
+                label="Predicted PM2.5 Level",
+                value=f"{pm25_result:.2f} μg/m³",
+                delta=None
+            )
+            
+            # PM2.5 health information
+            if pm25_result <= 12:
+                st.success("🟢 Good air quality")
+            elif pm25_result <= 35:
+                st.warning("🟡 Moderate air quality")
+            elif pm25_result <= 55:
+                st.warning("🟠 Unhealthy for sensitive groups")
+            else:
+                st.error("🔴 Unhealthy air quality")
+                
+        except Exception as e:
+            st.error(f"Error in PM2.5 prediction: {str(e)}")
+    
+    if predict_quality or predict_both:
+        try:
+            features = predictor.prepare_features(
+                temperature, humidity, pm10, no2, so2, co, 
+                industrial_proximity, population_density
+            )
+            quality_index, quality_label = predictor.predict_air_quality(features)
+            
+            st.success("Air Quality Prediction Complete!")
+            st.metric(
+                label="Predicted Air Quality",
+                value=quality_label,
+                delta=None
+            )
+            
+            # Get and display description
+            description = predictor.get_quality_description(quality_label)
+            st.info(description)
+            
+            # Color coding for quality levels
+            if quality_label == "Good":
+                st.success("🟢 " + quality_label)
+            elif quality_label == "Moderate":
+                st.warning("🟡 " + quality_label)
+            elif quality_label == "Unhealthy":
+                st.warning("🟠 " + quality_label)
+            else:
+                st.error("🔴 " + quality_label)
+                
+        except Exception as e:
+            st.error(f"Error in air quality prediction: {str(e)}")
+
+# Additional information section
+st.markdown("---")
+st.header("ℹ️ About the System")
+
+info_col1, info_col2 = st.columns(2)
+
+with info_col1:
+    st.subheader("📋 Input Parameters")
+    st.markdown("""
+    - **Temperature**: Ambient air temperature
+    - **Humidity**: Relative humidity percentage
+    - **PM10**: Particulate matter ≤ 10μm
+    - **NO2**: Nitrogen dioxide concentration
+    - **SO2**: Sulfur dioxide concentration
+    - **CO**: Carbon monoxide concentration
+    - **Industrial Proximity**: Distance to industrial areas
+    - **Population Density**: People per square kilometer
+    """)
+
+with info_col2:
+    st.subheader("🎯 Prediction Outputs")
+    st.markdown("""
+    - **PM2.5 Level**: Predicted particulate matter ≤ 2.5μm concentration
+    - **Air Quality**: Classification into Good, Moderate, Unhealthy, or Hazardous
+    
+    **Air Quality Standards:**
+    - 🟢 Good: Minimal health impact
+    - 🟡 Moderate: Acceptable for most people
+    - 🟠 Unhealthy: Risk for sensitive groups
+    - 🔴 Hazardous: Health risk for everyone
+    """)
 
-st.sidebar.header("Enter Environmental Factors:")
-temperature = st.sidebar.number_input("Temperature (°C)", min_value=-10.0, max_value=50.0, value=25.0)
-humidity = st.sidebar.number_input("Humidity (%)", min_value=0.0, max_value=100.0, value=50.0)
-pm10 = st.sidebar.number_input("PM10 Level", min_value=0.0, max_value=500.0, value=20.0)
-no2 = st.sidebar.number_input("NO2 Level", min_value=0.0, max_value=500.0, value=15.0)
-so2 = st.sidebar.number_input("SO2 Level", min_value=0.0, max_value=500.0, value=10.0)
-co = st.sidebar.number_input("CO Level", min_value=0.0, max_value=10.0, value=1.0)
-industrial_proximity = st.sidebar.slider("Proximity to Industrial Areas", 0.0, 10.0, 5.0)
-population_density = st.sidebar.number_input("Population Density", min_value=0, max_value=10000, value=500)
-
-# Collect input into an array
-features = np.array([[temperature, humidity, pm10, no2, so2, co, industrial_proximity, population_density]])
-
-# Predict PM2.5 Level (Regression)
-if st.sidebar.button("Predict PM2.5 Level"):
-    pm25_pred = regression_model.predict(features)
-    st.write(f"**Predicted PM2.5 Level:** {pm25_pred[0]:.2f}")
-
-# Predict Air Quality (Classification)
-if st.sidebar.button("Predict Air Quality Level"):
-    air_quality_pred = classification_model.predict(features)
-    quality_map = {0: "Good", 1: "Moderate", 2: "Unhealthy", 3: "Hazardous"}
-    st.write(f"**Predicted Air Quality Level:** {quality_map[air_quality_pred[0]]}")
+# Display input summary
+with st.expander("📊 Current Input Summary"):
+    input_data = {
+        'Parameter': ['Temperature', 'Humidity', 'PM10', 'NO2', 'SO2', 'CO', 'Industrial Proximity', 'Population Density'],
+        'Value': [f"{temperature}°C", f"{humidity}%", f"{pm10} μg/m³", f"{no2} μg/m³", 
+                 f"{so2} μg/m³", f"{co} mg/m³", f"{industrial_proximity}/10", f"{population_density} people/km²"],
+        'Unit': ['Celsius', 'Percentage', 'μg/m³', 'μg/m³', 'μg/m³', 'mg/m³', 'Scale 0-10', 'people/km²']
+    }
+    
+    df = pd.DataFrame(input_data)
+    st.dataframe(df, use_container_width=True, hide_index=True)