Update main.py

main.py

@@ -304,30 +304,30 @@ async def get_sorted_potential_scores(coaching_code: str = Query(..., descriptio
         file_paths = get_file_paths(coaching_code)
         if not file_paths:
             raise HTTPException(status_code=400, detail="Invalid coaching code")
-
+        print("A");
         # Open Google Sheets using the URLs
         journal_file = client.open_by_url(file_paths['journal']).worksheet('Sheet1')
         panic_button_file = client.open_by_url(file_paths['panic_button']).worksheet('Sheet1')
         test_file = client.open_by_url(file_paths['test']).worksheet('Sheet1')
-
+        print("B");
         # Convert the sheets into Pandas DataFrames
         journal_df = pd.DataFrame(journal_file.get_all_values())
         panic_button_df = pd.DataFrame(panic_button_file.get_all_values())
         test_df = pd.DataFrame(test_file.get_all_values())
-
+        print("C");
         # Label the columns manually since there are no headers
         journal_df.columns = ['user_id', 'productivity_yes_no', 'productivity_rate']
         panic_button_df.columns = ['user_id', 'panic_button']
-
+        print("D")
         # Initialize a list for the merged data
         merged_data = []
 
         # Group panic buttons by user_id and combine into a single comma-separated string
         panic_button_grouped = panic_button_df.groupby('user_id')['panic_button'].apply(lambda x: ','.join(x)).reset_index()
-
+        print("E")
         # Merge journal and panic button data
         merged_journal_panic = pd.merge(journal_df, panic_button_grouped, on='user_id', how='outer')
-
+        print("F")
         # Process the test data
         test_data = []
         for index, row in test_df.iterrows():
@@ -339,19 +339,19 @@ async def get_sorted_potential_scores(coaching_code: str = Query(..., descriptio
                 if pd.notna(score):
                     test_data.append({'user_id': user_id, 'test_chapter': chapter, 'test_score': score})
                 i += 2
-
+        print("G")
         # Convert the processed test data into a DataFrame
         test_df_processed = pd.DataFrame(test_data)
-
+        print("H")
         # Merge the journal+panic button data with the test data
         merged_data = pd.merge(merged_journal_panic, test_df_processed, on='user_id', how='outer')
-
+        print("I")
         # Drop rows where all data (except user_id and test_chapter) is missing
         merged_data_cleaned = merged_data.dropna(subset=['productivity_yes_no', 'productivity_rate', 'panic_button', 'test_chapter'], how='all')
-
+        print("J")
         # Group the merged DataFrame by user_id
         df = pd.DataFrame(merged_data_cleaned)
-
+        print("K")
         # Function to process panic button counts and test scores
         def process_group(group):
             # Panic button counts
@@ -374,12 +374,12 @@ async def get_sorted_potential_scores(coaching_code: str = Query(..., descriptio
 
         # Apply the group processing function
         merged_df = df.groupby('user_id').apply(process_group).reset_index()
-
+        print("L")
         # Calculate potential scores and sort
         merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
         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)
-
+        print("M")
         result = sorted_df.to_dict(orient="records")
         return {"sorted_scores": result}
     except Exception as e: