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app.py

@@ -73,42 +73,5 @@ def predict_sales():
     return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})
 
 
-# @super_kart_api.post('/v1/salesbatch')
-# def predict_sales_batch():
-#     """
-#     This function handles POST requests to the '/v1/salesbatch' endpoint.
-#     It expects a CSV file containing product details for multiple products
-#     and returns the predicted sales totals as a dictionary in the JSON response.
-#     """
-#     if 'file' not in request.files:
-#         return jsonify({'error': 'No file part in the request'}), 400
-#     file = request.files['file']
-#     try:
-#         data = pd.read_csv(file)
-#     except Exception as e:
-#         return jsonify({'error': f'Failed to read CSV: {str(e)}'}), 400
-    
-#     # Apply one-hot encoding
-#     data = pd.get_dummies(data, columns=['Product_Type', 'Store_Type'], drop_first=True)
-    
-#     # Apply ordinal encoding
-#     sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
-#     size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
-#     city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
-#     data['Product_Sugar_Content'] = data['Product_Sugar_Content'].map(sugar_mapping)
-#     data['Store_Size'] = data['Store_Size'].map(size_mapping)
-#     data['Store_Location_City_Type'] = data['Store_Location_City_Type'].map(city_mapping)
-    
-#     # Align with expected columns
-#     data = data.reindex(columns=EXPECTED_COLUMNS, fill_value=0)
-    
-#     # Make predictions
-#     preds = model.predict(data)
-#     results = preds.round(2).tolist()
-#     response = {str(i): float(val) for i, val in enumerate(results)}
-    
-#     return jsonify(response)
-
-
 if __name__ == '__main__':
     super_kart_api.run(debug=True)