Update app.py

app.py

@@ -49,11 +49,15 @@ if resume_text:
     highest_score_label = resume_prediction['labels'][0]
     st.write("Predicted Personality for the given resume:", highest_score_label)
 
-# Automatic prediction for each row in DataFrame
-for index, row in data.iterrows():
-    result = classifier(row['processed_text'], labels)
-    data.at[index, 'Predicted'] = result['labels'][0]
-st.dataframe(data[['True_label', 'Predicted']])
+# Automatic prediction for each row in DataFrame using custom labels
+def predict_personality(row):
+    custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']]
+    processed_text = row['processed_text']
+    result = classifier(processed_text, custom_labels)
+    return result['labels'][0]  # Assumes the labels are sorted by score, highest first
+
+data['Predicted'] = data.apply(predict_personality, axis=1)
+st.dataframe(data[['True_label', 'MAX1', 'MAX2', 'MAX3', 'Predicted']])
 
 # Separate predictions for Female and Male questions
 for index, row in data_open.iterrows():