Update main.py

main.py

@@ -105,13 +105,13 @@ def process_data(input_data: CoachingCodeInput):
         # Step 4: Merge all data
         merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
         merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
-
+        print("all data merged")
         # Step 5: Process Data
         df = pd.DataFrame(merged_data_cleaned)
         academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
         non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}
         max_weighted_panic_score = sum([max(academic_weights.values()) * 3, max(non_academic_weights.values()) * 3])
-
+        print("step 5 : data processing done")
         def calculate_potential_score(row):
             test_score_normalized = 0
             if row['test_scores']:
@@ -131,10 +131,11 @@ def process_data(input_data: CoachingCodeInput):
 
             total_potential_score = test_score_normalized + panic_score + journal_score
             return total_potential_score
-
+        
         merged_df = df.groupby('user_id').apply(lambda group: pd.Series({
             'potential_score': calculate_potential_score(group)
         })).reset_index()
+        print("step 6 : data merged_df")
 
         merged_df['potential_score'] = merged_df['potential_score'].round(2)
         sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)