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app.py

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+
+# Initialize Flask app
+sales_forecast_api = Flask("SuperKart Sales Forecast API")
+
+# Load trained model (includes preprocessing pipeline)
+model = joblib.load("model/best_random_forest.pkl")
+
+# Home route
+@sales_forecast_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Forecast API!"
+
+# Predict for a single input
+@sales_forecast_api.post('/v1/predict')
+def predict_sales():
+    data = request.get_json()
+
+    # Convert JSON into DataFrame (1 row)
+    input_df = pd.DataFrame([data])
+
+    # Model prediction
+    prediction = model.predict(input_df).tolist()[0]
+
+    return jsonify({"Predicted_Sales_Total": prediction})
+
+# Predict for a batch (CSV upload)
+@sales_forecast_api.post('/v1/predict_batch')
+def predict_sales_batch():
+    file = request.files['file']
+    input_data = pd.read_csv(file)
+
+    preds = model.predict(input_data).tolist()
+
+    # Map row index → prediction
+    results = {i: pred for i, pred in enumerate(preds)}
+
+    return jsonify(results)
+
+# Run app
+if __name__ == "__main__":
+    sales_forecast_api.run(debug=True)

requirements.txt

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+
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+joblib==1.4.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+huggingface_hub==0.24.0