Update src/populate.py

src/populate.py

@@ -8,23 +8,27 @@ from src.display.utils import AutoEvalColumn, EvalQueueColumn
 from src.leaderboard.read_evals import get_raw_eval_results
 
 def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
-    print("get_leaderboard_df: Starting to process leaderboard data.")
+    print("before get_raw_eval_results") # blz
     raw_data = get_raw_eval_results(results_path, requests_path)
-    print("get_leaderboard_df: Raw eval results obtained.")
+    print("after get_raw_eval_results") # blz
 
     all_data_json = [v.to_dict() for v in raw_data]
-    print(f"get_leaderboard_df: Converted raw data to JSON. Number of entries: {len(all_data_json)}")
 
     df = pd.DataFrame.from_records(all_data_json)
-    print("get_leaderboard_df: DataFrame created from records.")
+
+    # Print the name of the average field from AutoEvalColumn
+    print("Name of the average field in AutoEvalColumn:", AutoEvalColumn.average.name)
+
+    # Print DataFrame column names
+    print("DataFrame column names:", df.columns)
 
     df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
     df = df[cols].round(decimals=2)
-    print("get_leaderboard_df: DataFrame sorted and columns rounded.")
+
+    print("after df things") # blz
 
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
-    print("get_leaderboard_df: DataFrame filtered for NaN values in benchmarks.")
     return raw_data, df
 
 def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]: