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app.py

@@ -28,11 +28,13 @@ def predict_sales():
     It expects a JSON payload containing store details and returns
     the predicted sales as a JSON response.
     """
-    # Get the JSON data from the request body
-    data = request.get_json()
 
-    # Extract relevant features from the JSON data
-    sample = {
+    try:
+      # Get the JSON data from the request body
+      data = request.get_json()
+
+      # Extract relevant features from the JSON data
+      sample = {
         'Product_Weight': data['Product_Weight'],
         'Product_Sugar_Content': data['Product_Sugar_Content'],
         'Product_Allocated_Area': data['Product_Allocated_Area'],
@@ -40,19 +42,24 @@ def predict_sales():
         'Store_Size': data['Store_Size'],
         'Store_Location_City_Type': data['Store_Location_City_Type'],
         'Store_Type': data['Store_Type'],
-        'Product_Id_char': data['Product_Id_char'],
-        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Code': data['Product_Code'],
+        'Store_Age': data['Store_Age'],
         'Product_Category': data['Product_Category']
-    }
+      }
+
+
+      # Convert the extracted data into a Pandas DataFrame
+      input_data = pd.DataFrame([sample])
 
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
+      # Make prediction (get log_price)
+      sales_prediction = xgb_model.predict(input_data)[0]
 
-    # Make prediction (get log_price)
-    predicted_sales = rf_model.predict(input_data)[0]
+      # Return the prediction
+      return jsonify({'Sales': sales_prediction.tolist()})
+    except Exception as e:
+      print(f"Error in prediction: {e}")
+      return jsonify({'error': str(e)})
 
-    # Return the prediction
-    return jsonify({'Predicted Sales': predicted_sales})
 
 
 # Run the Flask application in debug mode if this script is executed directly