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app.py

@@ -22,14 +22,15 @@ def predict_sales():
     # Extract relevant customer features from the input data. The order of the column names matters.
     sample = {
         'Product_Weight': data['Product_Weight'],
-        'Product_Sugar_Content': data['Product_Sugar_Content'],
         'Product_Allocated_Area': data['Product_Allocated_Area'],
-        'Product_Type_Category': data['Product_Type_Category'],
         'Product_MRP': data['Product_MRP'],
-        'Store_Size': data['Store_Size'],
-        'Store_Location_City_Type': data['Store_Location_City_Type'],
-        'Store_Type': data['Store_Type'],
-        'Store_Age_Years': data['Store_Age_Years'],
+        'Store_Establishment_Year': data['Store_Age_Years'],
+        'Product_Sugar_Content_Mapping': data['Product_Sugar_Content'],
+        'Store_Size_Mapping': data['Store_Size'],
+        'Store_Location_City_Type_Mapping': data['Store_Location_City_Type'],
+        'Product_Type_Mapping': data['Product_Type_Category'],
+        'Store_Id_Mapping': data['Store_Id'],
+        'Store_Type_Mapping': data['Store_Type'],
     }
 
     # Convert the extracted data into a DataFrame and preprocess it