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app.py

@@ -70,48 +70,33 @@ def predict_Product_Sales():
 
 
 # Define an endpoint for batch prediction (POST request)
+
 @product_sales_predictor_api.post('/v1/ProductBatchSales')
 def predict_sales_batch():
-    """
-    This function handles POST requests to the '/v1/ProductBatchSales' endpoint.
-    It expects a CSV file containing details for multiple products
-    and returns the predicted  product sales prices as a dictionary in the JSON response.
-    """
-
     print(">>> Batch endpoint invoked!", flush=True)
-    product_sales_predictor_api.logger.info('Batch endpoint invoked')
-
-    # Get the uploaded CSV file from the request
-    file = request.files['file']
-
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
-
-    # Save Product_Id for mapping in output
-    product_ids = input_data['Product_Id'].tolist()
-
-    # Columns that your model does NOT need
-    drop_cols = [
-        'Product_Id',
-        'Store_Id',
-        'Store_Establishment_Year',
-        'Product_Store_Sales_Total'  # target column
-    ]
+    try:
+        file = request.files.get('file')
+        print(">>> File received:", file is not None, flush=True)
 
-     # Drop only columns that exist in the CSV
-    input_data = input_data.drop(columns=[c for c in drop_cols if c in input_data.columns])
+        input_data = pd.read_csv(file)
+        print(">>> CSV loaded. Columns:", list(input_data.columns), flush=True)
 
-    # Now df contains ONLY the features used for training
-    predictions = model.predict(input_data)
+        drop_cols = [
+            'Product_Id',
+            'Store_Id',
+            'Store_Establishment_Year',
+            'Product_Store_Sales_Total'
+        ]
+        input_data = input_data.drop(columns=[c for c in drop_cols if c in input_data.columns])
+        print(">>> After column drop:", list(input_data.columns), flush=True)
 
-    # Convert NumPy types → Python float
-    predictions = [float(p) for p in predictions]
+        predictions = model.predict(input_data)
+        predictions = [float(p) for p in predictions]
 
-    # Prepare output with Product_Id as keys
-    output = dict(zip(product_ids, predictions))
+        print(">>> Predictions completed", flush=True)
 
-    return jsonify(output)
+        return jsonify({"predictions": predictions})
 
-# Run the Flask application in debug mode if this script is executed directly
-if __name__ == '__main__':
-    product_sales_predictor_api.run(debug=True)
+    except Exception as e:
+        print(">>> ERROR:", str(e), flush=True)
+        return jsonify({"error": str(e)}), 500