update scripts

app.py

@@ -74,36 +74,6 @@ def process_pending_evals():
         print(f"Finished evaluation for model: {eval_request['model']}")
 
 
-# 在初始化完成后调用
-original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
-process_pending_evals()
-
-# try:
-#     print(envs.EVAL_REQUESTS_PATH)
-#     snapshot_download(
-#         repo_id=envs.QUEUE_REPO, local_dir=envs.EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
-#     )
-# except Exception:
-#     restart_space()
-# try:
-#     print(envs.EVAL_RESULTS_PATH)
-#     snapshot_download(
-#         repo_id=envs.RESULTS_REPO, local_dir=envs.EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
-#     )
-# except Exception:
-#     restart_space()
-
-# raw_data, original_df = populate.get_leaderboard_df(envs.RESULTS_REPO, envs.QUEUE_REPO, utils.COLS, utils.BENCHMARK_COLS)
-
-
-leaderboard_df = original_df.copy()
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = populate.get_evaluation_queue_df(envs.EVAL_REQUESTS_PATH, utils.EVAL_COLS)
-
 
 # Searching and filtering
 def update_table(
@@ -392,3 +362,33 @@ scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
 demo.queue(default_concurrency_limit=40).launch()
+# 在初始化完成后调用
+original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
+process_pending_evals()
+
+# try:
+#     print(envs.EVAL_REQUESTS_PATH)
+#     snapshot_download(
+#         repo_id=envs.QUEUE_REPO, local_dir=envs.EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
+#     )
+# except Exception:
+#     restart_space()
+# try:
+#     print(envs.EVAL_RESULTS_PATH)
+#     snapshot_download(
+#         repo_id=envs.RESULTS_REPO, local_dir=envs.EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
+#     )
+# except Exception:
+#     restart_space()
+
+# raw_data, original_df = populate.get_leaderboard_df(envs.RESULTS_REPO, envs.QUEUE_REPO, utils.COLS, utils.BENCHMARK_COLS)
+
+
+leaderboard_df = original_df.copy()
+
+(
+    finished_eval_queue_df,
+    running_eval_queue_df,
+    pending_eval_queue_df,
+) = populate.get_evaluation_queue_df(envs.EVAL_REQUESTS_PATH, utils.EVAL_COLS)
+