Upload 3 files

app.py

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+from flask import Flask, request, jsonify
+import numpy as np
+import tensorflow as tf
+import joblib
+
+app = Flask(__name__)
+
+# Load the saved model and scaler
+model = tf.keras.models.load_model('aqi_model.h5')
+scaler = joblib.load('scaler.pkl')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get the input features from the JSON request
+        data = request.get_json()
+        features = [
+            data['PM10'],
+            data['PM2.5'],
+            data['NO2'],
+            data['O3'],
+            data['CO'],
+            data['SO2'],
+            data['NH3']
+        ]
+
+        # Convert to numpy array and reshape for a single prediction
+        features_array = np.array(features).reshape(1, -1)
+
+        # Scale the input features using the loaded scaler
+        features_scaled = scaler.transform(features_array)
+
+        # Make prediction using the loaded model
+        prediction = model.predict(features_scaled)
+        predicted_aqi = prediction[0][0]
+
+        # Convert the result to a standard Python float
+        predicted_aqi = float(predicted_aqi)
+
+        # Return the predicted AQI
+        return jsonify({'predicted_aqi': predicted_aqi})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=8080)
+

requirements.txt

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+Flask==2.3.2
+numpy==1.23.5
+tensorflow==2.17.0
+scikit-learn==1.0.2
+joblib==1.2.0

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a08b0155563bdc6a5b681384119c8b5c2cbe1a468308845e9daf0ce168c6079
+size 1135