Update app.py

app.py

@@ -39,9 +39,9 @@ def preprocess_text(text):
     return ' '.join(tokens)
 
 # Prepare the data for prediction
-data['processed_text'] = data[['CV/Resume', 'True_label'] + [f'Q{i}' for i in range(1, 37)]].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
-data_open['processed_text_open'] = data_open[['Demo_F', 'Question', 'True_label']].agg(' '.join, axis=1).apply(preprocess_text)
-data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question', 'True_label']].agg(' '.join, axis=1).apply(preprocess_text)
+data['processed_text'] = data[['CV/Resume'] + [f'Q{i}' for i in range(1, 37)]].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
+data_open['processed_text_open'] = data_open[['Demo_F', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
+data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
 
 labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]