Update app.py

app.py

@@ -36,6 +36,7 @@ def preprocess_text(text):
 question_columns = [f'Q{i}' for i in range(1, 37)]  # Adjust range if needed
 data['combined_text'] = data[['CV/Resume'] + question_columns].agg(' '.join, axis=1)
 data['processed_text'] = data['combined_text'].apply(preprocess_text)
+labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]
 
 # Prediction confidence threshold
 confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)
@@ -45,10 +46,10 @@ if st.button("Predict Personality"):
     def get_predictions(row):
         custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']]  # Get labels from each row
         processed_text = row['processed_text']
-        result = classifier(processed_text, custom_labels)
+        result = classifier(processed_text, labels)
         highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
         return highest_score_label
 
     # Apply predictions across all rows
     data['Predicted'] = data.apply(get_predictions, axis=1)
-    st.dataframe(data[['True_label', 'Predicted']])
+    st.dataframe(data[['True_label','MAX1','MAX2','MAX3', 'Predicted']])