Update app.py

app.py

@@ -46,7 +46,7 @@ data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.jo
 labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]
 
 # Function to predict personality and log the predictions
-def predict_and_log(data, prediction_column, process_text_column, custom_labels=None):
+def predict_and_log(data, prediction_column, process_text_column, true_label_column=None, custom_labels=None):
     for index, row in data.iterrows():
         processed_text = row[process_text_column]
         if custom_labels:
@@ -55,12 +55,14 @@ def predict_and_log(data, prediction_column, process_text_column, custom_labels=
             result = classifier(processed_text, labels)
         highest_score_label = result['labels'][0]
         data.at[index, prediction_column] = highest_score_label
-        logging.info(f"Row {index}: Predicted - {highest_score_label}")
+        true_label = row[true_label_column] if true_label_column else 'Not available'
+        data_id = row['id']
+        logging.info(f"Row {data_id}: True Label - {true_label}, Predicted - {highest_score_label}")
 
 # Predict and log results for each DataFrame
-predict_and_log(data, 'Predicted', 'processed_text', ['MAX1', 'MAX2', 'MAX3'])
-predict_and_log(data_open, 'Predicted_F', 'processed_text_open')
-predict_and_log(data_open, 'Predicted_M', 'processed_text_mopen')
+predict_and_log(data, 'Predicted', 'processed_text', true_label_column='True_label', custom_labels=['MAX1', 'MAX2', 'MAX3'])
+predict_and_log(data_open, 'Predicted_F', 'processed_text_open', true_label_column='True_label')
+predict_and_log(data_open, 'Predicted_M', 'processed_text_mopen', true_label_column='True_label')
 
 # Optionally display a confirmation message
 st.write("Predictions have been logged. Check your logs for details.")