Update app.py

app.py

@@ -142,10 +142,10 @@ def predict_sales(input_data, input_df):
     numeric_columns = [i for i in columns if i not in categoric_columns]
     scaled_num = scaler.fit_transform(input_df[numeric_columns])
     encoded_cat = encoder.transform(input_df[categoric_columns])
-    scaled_num_df = pd.DataFrame(scaled_num, columns=numeric_columns)
-    encoded_cat_df = pd.DataFrame(encoded_cat, columns=categoric_columns)
-    input_data = pd.concat([scaled_num_df, encoded_cat_df], axis=1)
-    # input_data = pd.concat([scaled_num, encoded_cat], axis=1)
+    # scaled_num_df = pd.DataFrame(scaled_num, columns=numeric_columns)
+    # encoded_cat_df = pd.DataFrame(encoded_cat, columns=categoric_columns)
+    # input_data = pd.concat([scaled_num_df, encoded_cat_df], axis=1)
+    input_data = pd.concat([scaled_num, encoded_cat], axis=1)
     # convert input_data to a numpy array before flattening to convert it back to a 2D array
     input_data = input_data.to_numpy()
     prediction = model.predict(input_data.flatten().reshape(1, -1))