Print statements to help explain what's happening

app.py

@@ -53,6 +53,7 @@ except Exception:
 raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
 leaderboard_df = original_df.copy()
 
+
 (
     finished_eval_queue_df,
     running_eval_queue_df,

src/leaderboard/read_evals.py

@@ -157,7 +157,8 @@ def get_request_file_for_model(requests_path, model_name, precision):
 def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
     """From the path of the results folder root, extract all needed info for results"""
     model_result_filepaths = []
-
+    print('looking in results_path: %s' % results_path)
+    print('looking in requests_path: %s' % requests_path)
     for root, _, files in os.walk(results_path):
         # We should only have json files in model results
         if len(files) == 0 or any([not f.endswith(".json") for f in files]):
@@ -184,13 +185,15 @@ def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResu
             eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
         else:
             eval_results[eval_name] = eval_result
+    print("eval results is")
+    print(eval_results)
 
     results = []
     for v in eval_results.values():
-        try:
-            v.to_dict() # we test if the dict version is complete
-            results.append(v)
-        except KeyError:  # not all eval values present
-            continue
+        #try:
+        v.to_dict() # we test if the dict version is complete
+        results.append(v)
+        #except KeyError:  # not all eval values present
+        #    continue
 
     return results

src/populate.py

@@ -11,9 +11,12 @@ 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:
     """Creates a dataframe from all the individual experiment results"""
     raw_data = get_raw_eval_results(results_path, requests_path)
+    print(raw_data)
     all_data_json = [v.to_dict() for v in raw_data]
 
     df = pd.DataFrame.from_records(all_data_json)
+    print('df is')
+    print(df)
     df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
     df = df[cols].round(decimals=2)