Uploaded the app files

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
+import os
+
+SAVED_MODEL_DIR = ""
+
+# Load the pre-trained models and scalers
+scalar = joblib.load(os.path.join(SAVED_MODEL_DIR, "scaler.pkl"))
+linear_model = joblib.load(os.path.join(SAVED_MODEL_DIR, "linear_model.pkl"))
+poly_model = joblib.load(os.path.join(SAVED_MODEL_DIR, "poly_model.pkl"))
+logistic_model = joblib.load(os.path.join(SAVED_MODEL_DIR, "logistic_model.pkl"))
+rf_classifier = joblib.load(os.path.join(SAVED_MODEL_DIR, "rf_classifier.pkl"))
+
+
+def predict_aqi(pm25, pm10, no2, co, temp, humidity):
+    input_data = pd.DataFrame([[pm25, pm10, no2, co, temp, humidity]], columns=["PM2.5", "PM10", "NO2", "CO", "Temperature", "Humidity"])
+    input_scaled = scalar.transform(input_data)
+
+    linear_pred = linear_model.predict(input_scaled)[0]
+    poly_pred = poly_model.predict(input_scaled)[0]
+
+    logistic_class = logistic_model.predict(input_scaled)[0]
+    rf_class = rf_classifier.predict(input_scaled)[0]
+
+    # Create performance plot
+    models = ["Linear", "Polynomial"]
+    predictions = [linear_pred, poly_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"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"
+
+
+if __name__ == '__main__':
+
+    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."
+    )
+
+    iface.launch()

linear_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:890576fa25a8490b5c0524045c0ee8cd1a8e63bbb73091ea4f143137932b2ec9
+size 648

logistic_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d64fd0601891d0f3922b419c14405fa428a2208efe1ba1ea50f0750c776d44aa
+size 911

poly_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:890576fa25a8490b5c0524045c0ee8cd1a8e63bbb73091ea4f143137932b2ec9
+size 648

requirements.txt

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+gradio
+scikit-learn
+pandas
+numpy
+matplotlib
+seaborn
+joblib

rf_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d64fd0601891d0f3922b419c14405fa428a2208efe1ba1ea50f0750c776d44aa
+size 911

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ac5b8a1b45308d11ae0dbef2e5d5cd209777f89f5de59c7ec4bd6635346c144
+size 1127