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Dockerfile

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+
+FROM python:3.9-slim
+
+WORKDIR/app
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["gunicorn","-w","4","-b","0.0.0.0:7860","app:app"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+app = Flask('SuperKart Sales Predictor')
+
+# Load the saved model
+saved_model = joblib.load(saved_model_path)
+
+@app.get('/')
+def home():
+  return 'Welcome to SuperKart Sales Predictor'
+
+@app.post('/predict')
+def predict_sales():
+  store_data = request.get_json()
+
+  sample = {
+      'Product_Weight' :	store_data['Product_Weight'],
+      'Product_Sugar_Content' : store_data['Product_Sugar_Content'],	
+      'Product_Allocated_Area'	: store_data['Product_Allocated_Area'],
+      'Product_Type'	: store_data['Product_Type'],
+      'Product_MRP'	: store_data['Product_MRP'],
+      'Store_Id'	: store_data['Store_Id'],
+      'Store_Size' : store_data['Store_Size'],	
+      'Store_Location_City_Type' : store_data['Store_Location_City_Type'],	
+      'Store_Type' : 	store_data['Store_Type'],
+      'Store_Age' : store_data['Store_Age']
+  }
+
+  input_data = pd.DataFrame([sample])
+
+  predictions = saved_model.predict(input_data).tolist()[0]
+
+  return jsonify({'prediction': predictions})
+
+@app.post('/predict_batch')
+def predict_batch():
+  file = request.files['file']
+  input_data = pd.read_csv(file)
+
+  predictions = saved_model.predict(input_data).tolist()
+
+  store_id_list = input_data.storeID.values.tolist()
+  output_dict = dict(zip(store_id_list, predictions))
+
+  return output_dict
+
+
+if __name__ == '__main__':
+  app.run(debug=True)
+

requirements.txt

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+
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+flask==2.2.2
+joblib==1.4.2
+Werkzeug==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]