test

src/populate.py

@@ -12,51 +12,14 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     """Creates a dataframe from all the individual experiment results"""
     raw_data = get_raw_eval_results(results_path, requests_path)
 
-    # raw_data示例
-    """raw_data = [
-        EvalResult(
-            model_name="org1/model1",
-            model_dtype="float32",
-            model_sha="commit_hash1",
-            results={
-                "task1": {"metric1": 0.85, "metric2": 0.90},
-                "task2": {"metric1": 0.75, "metric2": 0.80}
-            },
-            model_type="Pretrained",
-            weight_type="Original",
-            license="MIT",
-            likes=100,
-            params=123456789,
-            submitted_time="2025-02-28T12:34:56Z",
-            status="FINISHED",
-            precision="float32"
-        ),
-        EvalResult(
-            model_name="org2/model2",
-            model_dtype="float32",
-            model_sha="commit_hash2",
-            results={
-                "task1": {"metric1": 0.88, "metric2": 0.92},
-                "task2": {"metric1": 0.78, "metric2": 0.82}
-            },
-            model_type="Fine-tuned",
-            weight_type="Adapter",
-            license="Apache-2.0",
-            likes=200,
-            params=987654321,
-            submitted_time="2025-02-28T12:34:56Z",
-            status="FINISHED",
-            precision="float32"
-        )
-    ]
-    """
     all_data_json = [v.to_dict() for v in raw_data]
 
     df = pd.DataFrame.from_records(all_data_json)
 
     df = df.sort_values(by=[AutoEvalColumn.task0.name], ascending=False)
     df = df[cols].round(decimals=2)
-
+    print(df)
+    assert 0
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
     return df