Resolve TypeError when processing evaluation queue data

src/populate.py

@@ -12,7 +12,7 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     print(f"Getting raw eval results from {results_path} and {requests_path}")
     raw_data = get_raw_eval_results(results_path, requests_path)
     print(f"Got {len(raw_data)} raw eval results")
-
+ 
     if not raw_data:
         print("No raw data found!")
         return pd.DataFrame(columns=cols)
@@ -65,17 +65,15 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
                 with open(file_path) as fp:
                     data = json.load(fp)
 
-                # Create a new dict with the required column names
-                formatted_data = {
-                    "model": make_clickable_model(data["model"]),
-                    "revision": data.get("revision", "main"),
-                    "private": data.get("private", False),
-                    "precision": data.get("precision", ""),
-                    "weight_type": data.get("weight_type", ""),
-                    "status": data.get("status", "")
-                }
-
-                all_evals.append(formatted_data)
+                # Check if data is a list (multiple requests in one file)
+                if isinstance(data, list):
+                    for item in data:
+                        formatted_data = format_eval_data(item)
+                        all_evals.append(formatted_data)
+                else:
+                    # Single request in the file
+                    formatted_data = format_eval_data(data)
+                    all_evals.append(formatted_data)
 
     print(f"Found {len(all_evals)} total eval requests")
     pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
@@ -88,3 +86,14 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
     df_running = pd.DataFrame.from_records(running_list, columns=cols)
     df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
     return df_finished[cols], df_running[cols], df_pending[cols]
+
+def format_eval_data(data: dict) -> dict:
+    """Format the evaluation data into the required structure"""
+    return {
+        "model": make_clickable_model(data.get("model", "")),
+        "revision": data.get("revision", "main"),
+        "private": data.get("private", False),
+        "precision": data.get("precision", ""),
+        "weight_type": data.get("weight_type", ""),
+        "status": data.get("status", "")
+    }