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app.py

@@ -40,14 +40,15 @@ def predict_sales():
         'Store_Size': sales_data['Store_Size'],
         'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
         'Store_Type': sales_data['Store_Type'],
-        'Store_Age': sales_data['Store_Age']
+        'Store_Age': sales_data['Store_Age'],
+        'Store_id': sales_data['Store_id']
     }
 
     # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
 
     # Make prediction (get log_sales)
-    predictions = np.log(model.predict(input_data)[0])
+    predictions = np.exp(model.predict(input_data)[0])
 
     # Round predictions
     predicted_sales = round(float(predictions), 2)
@@ -79,13 +80,13 @@ def sales_price_batch():
         store_ids = input_data_batch['Store_Id'].tolist()
 
         # Drop unused columns
-        input_data_batch = input_data_batch.drop(['Product_Id', 'Store_Id', 'Store_Establishment_Year'], axis=1)
+        input_data_batch = input_data_batch.drop(['Product_Id', 'Store_Establishment_Year'], axis=1)
 
         # Apply preprocessing if needed
         # input_data_transformed = preprocessor.transform(input_data_batch)
         # predictions = model.predict(input_data_transformed)
 
-        predictions = np.log(model.predict(input_data_batch))  # Assuming already preprocessed or numeric
+        predictions = np.exp(model.predict(input_data_batch))  # Assuming already preprocessed or numeric
 
         # Round predictions
         predicted_sales = [round(float(x), 2) for x in predictions]