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Dockerfile

@@ -6,5 +6,5 @@ COPY . .
 
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "backend_files.app:sales_price_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_sales_api"]
 

app.py

@@ -28,10 +28,8 @@ def predict_sales():
     Expects JSON payload with product + store details.
     Returns the predicted store sales as JSON.
     """
-    # Get the JSON data from the request body
     data = request.get_json()
 
-    # Extract relevant features from the JSON data
     sample = {
         'Product_Id': data['Product_Id'],
         'Product_Weight': data['Product_Weight'],
@@ -46,16 +44,10 @@ def predict_sales():
         'Store_Type': data['Store_Type']
     }
 
-    # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
-
-    # Make prediction
     predicted_sales = model.predict(input_data)[0]
-
-    # Convert to Python float (avoid NumPy serialization issues)
     predicted_sales = round(float(predicted_sales), 2)
 
-    # Return the predicted sales as JSON
     return jsonify({'Predicted Store Sales': predicted_sales})
 
 # Define an endpoint for batch predictions (CSV upload)
@@ -66,24 +58,18 @@ def predict_sales_batch():
     Expects a CSV file with product + store details.
     Returns predicted sales for each row as JSON dictionary.
     """
-    # Get uploaded CSV file
     file = request.files['file']
-
-    # Read into DataFrame
     input_data = pd.read_csv(file)
 
-    # Make predictions
     predictions = model.predict(input_data).tolist()
-
-    # Round predictions
     predictions = [round(float(p), 2) for p in predictions]
 
-    # Use (Product_Id, Store_Id) tuple as unique key
     ids = list(zip(input_data['Product_Id'], input_data['Store_Id']))
     output = {str(k): v for k, v in zip(ids, predictions)}
 
     return jsonify(output)
 
-# Run the Flask application
+# Run the Flask application (for local debugging)
 if __name__ == '__main__':
-    superkart_sales_api.run(debug=True)
+    superkart_sales_api.run(debug=True, host="0.0.0.0", port=7860)
+