remove sync

app.py

@@ -56,21 +56,14 @@ def restart_space():
     API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
 
 
-def init_space(update_model_type_with_open_llm=True):
+def init_space():
     dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv')
 
     if socket.gethostname() not in {'neuromancer'}:
         # sync model_type with open-llm-leaderboard
         ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
         ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
-    # if EVAL_REQUESTS_PATH_OPEN_LLM == '' then we will not update model_type with open-llm-leaderbaord
-    if update_model_type_with_open_llm:
-        from src.envs import EVAL_REQUESTS_PATH_OPEN_LLM, QUEUE_REPO_OPEN_LLM
-        ui_snapshot_download(repo_id=QUEUE_REPO_OPEN_LLM, local_dir=EVAL_REQUESTS_PATH_OPEN_LLM, repo_type="dataset", tqdm_class=None, etag_timeout=30)
-    else:
-        EVAL_REQUESTS_PATH_OPEN_LLM = ""
-
-    raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, EVAL_REQUESTS_PATH_OPEN_LLM, COLS, BENCHMARK_COLS)
+    raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, "", COLS, BENCHMARK_COLS)
 
     finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
     return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df

src/populate.py

@@ -21,8 +21,8 @@ def get_leaderboard_df(results_path: str,
                        is_backend: bool = False) -> tuple[list[EvalResult], pd.DataFrame]:
     # Returns a list of EvalResult
     raw_data: list[EvalResult] = get_raw_eval_results(results_path, requests_path, requests_path_open_llm)
-    for result_idx in tqdm(range(len(raw_data)), desc="updating model type with open llm leaderboard"):
-        if requests_path_open_llm:
+    if requests_path_open_llm != "":
+        for result_idx in tqdm(range(len(raw_data)), desc="updating model type with open llm leaderboard"):
             raw_data[result_idx] = update_model_type_with_open_llm_request_file(raw_data[result_idx], requests_path_open_llm)
 
     all_data_json_ = [v.to_dict() for v in raw_data if v.is_complete()]