Upload app.py

app.py

@@ -131,13 +131,13 @@ def process_reviews(df, review_column, class_names):
     df_display = scores_to_percent(df_new.copy())
 
     # Get all columns excluding the created ones and the review_column
-    remaining_columns = [col for col in df.columns if col not in [review_column, 'raw_scores', 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star']]
+    remaining_columns = [col for col in df.columns if col not in [review_column, 'raw_scores', 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star', 'Highest Class']]
 
     # Reorder the dataframe with selected columns first, created columns next, then the remaining columns
-    df_new = df_new[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star', 'Highest Class'] + remaining_columns]
+    df_new = df_new[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star'] + class_names + ['Highest Class'] + remaining_columns]
 
     # Reorder df_display as well
-    df_display = df_display[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star', 'Highest Class'] + remaining_columns]
+    df_display = df_display[[review_column, 'Weighted Rating', 'Rating', 'Probability', '1 Star', '2 Star', '3 Star', '4 Star', '5 Star'] + class_names + ['Highest Class'] + remaining_columns]
 
     return df_new, df_display