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app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load pre-trained models and scalers
+scaler_initial = joblib.load("scaler_initial.pkl")
+scaler_with_cluster = joblib.load("scaler_with_cluster.pkl")
+kmeans = joblib.load("kmeans.pkl")
+linear_model = joblib.load("linear_model.pkl")
+poly_model = joblib.load("poly_model.pkl")
+ridge_model = joblib.load("ridge_model.pkl")
+rf_regressor = joblib.load("rf_regressor.pkl")
+logistic_model = joblib.load("logistic_model.pkl")
+rf_classifier = joblib.load("rf_classifier.pkl")
+
+
+# Prediction function
+def predict_aqi(pm25, pm10, no2, co, temp, humidity):
+    # Create input dataframe with initial features
+    input_data = pd.DataFrame([[pm25, pm10, no2, co, temp, humidity]],
+                              columns=["PM2.5", "PM10", "NO2", "CO", "Temperature", "Humidity"])
+
+    # Scale initial features for clustering
+    input_scaled_initial = scaler_initial.transform(input_data)
+
+    # Apply K-means clustering
+    cluster = kmeans.predict(input_scaled_initial)[0]
+    input_data['Cluster'] = cluster
+
+    # Scale data with Cluster feature
+    input_scaled_with_cluster = scaler_with_cluster.transform(input_data)
+
+    # Regression predictions
+    linear_pred = linear_model.predict(input_scaled_with_cluster)[0]
+    poly_pred = poly_model.predict(input_scaled_with_cluster)[0]
+    ridge_pred = ridge_model.predict(input_scaled_with_cluster)[0]
+    rf_pred = rf_regressor.predict(input_scaled_with_cluster)[0]
+
+    # Classification predictions
+    logistic_class = logistic_model.predict(input_scaled_with_cluster)[0]
+    rf_class = rf_classifier.predict(input_scaled_with_cluster)[0]
+
+    # Create performance plot
+    models = ["Linear", "Polynomial", "Ridge", "Random Forest"]
+    predictions = [linear_pred, poly_pred, ridge_pred, rf_pred]
+    plt.figure(figsize=(8, 4))
+    sns.barplot(x=models, y=predictions)
+    plt.title("AQI Predictions by Model")
+    plt.ylabel("Predicted AQI")
+    plt.savefig("aqi_plot.png")
+    plt.close()
+
+    output_text = (
+        f"Linear Regression AQI: {linear_pred:.2f}\n"
+        f"Polynomial Regression AQI: {poly_pred:.2f}\n"
+        f"Ridge Regression AQI: {ridge_pred:.2f}\n"
+        f"Random Forest AQI: {rf_pred:.2f}\n"
+        f"Logistic Classification: {'Safe' if logistic_class == 0 else 'Unsafe'}\n"
+        f"Random Forest Classification: {'Safe' if rf_class == 0 else 'Unsafe'}"
+    )
+    return output_text, "aqi_plot.png"
+
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_aqi,
+    inputs=[
+        gr.Slider(0, 200, label="PM2.5 (µg/m³)", value=50),
+        gr.Slider(0, 300, label="PM10 (µg/m³)", value=80),
+        gr.Slider(0, 100, label="NO2 (µg/m³)", value=20),
+        gr.Slider(0, 10, label="CO (mg/m³)", value=1),
+        gr.Slider(-10, 40, label="Temperature (°C)", value=20),
+        gr.Slider(0, 100, label="Humidity (%)", value=50)
+    ],
+    outputs=[
+        gr.Textbox(label="Predictions"),
+        gr.Image(label="Model Comparison Plot")
+    ],
+    title="Air Quality Prediction and Classification",
+    description="Enter pollutant levels and weather conditions to predict AQI and classify air quality. Built with multiple machine learning models to address urban air pollution."
+)
+
+if __name__ == "__main__":
+    iface.launch()

kmeans.pkl

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linear_model.pkl

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logistic_model.pkl

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poly_model.pkl

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rf_classifier.pkl

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rf_regressor.pkl

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ridge_model.pkl

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scaler.pkl

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scaler_initial.pkl

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scaler_with_cluster.pkl

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