handle video not in repo and count the number of models

app.py

@@ -180,8 +180,12 @@ def refresh_video(df, env_id):
         model_id = env_df.iloc[0]["model_id"]
         model_sha = env_df.iloc[0]["model_sha"]
         repo_id = f"{user_id}/{model_id}"
-        video_path = API.hf_hub_download(repo_id=repo_id, filename="replay.mp4", revision=model_sha, repo_type="model")
-        return video_path
+        try:
+            video_path = API.hf_hub_download(repo_id=repo_id, filename="replay.mp4", revision=model_sha, repo_type="model")
+            return video_path
+        except Exception as e:
+            logger.error(f"Error while downloading video for {env_id}: {e}")
+            return None
     else:
         return None
 
@@ -211,6 +215,9 @@ This leaderboard is quite empty... 😢
 Be the first to submit your model!
 Check the tab "🚀 Getting my agent evaluated"
 """
+    
+def refresh_num_models(df):
+    return f"The leaderboard currently contains {len(df):,} models."
 
 
 css = """
@@ -228,25 +235,27 @@ h3 {
 
 
 def update_globals():
-    global dataframes, winner_texts, video_pathes, df
+    global dataframes, winner_texts, video_pathes, num_models_str, df
     df = get_leaderboard_df()
     all_env_ids = [env_id for env_ids in ALL_ENV_IDS.values() for env_id in env_ids]
     dataframes = {env_id: format_df(select_env(df, env_id)) for env_id in all_env_ids}
     winner_texts = {env_id: refresh_winner(df, env_id) for env_id in all_env_ids}
     video_pathes = {env_id: refresh_video(df, env_id) for env_id in all_env_ids}
+    num_models_str = refresh_num_models(df)
 
 
 update_globals()
 
 
 def refresh():
-    global dataframes, winner_texts, video_pathes
-    return list(dataframes.values()) + list(winner_texts.values()) + [list(video_pathes.values())[0]]
+    global dataframes, winner_texts, num_models_str
+    return list(dataframes.values()) + list(winner_texts.values()) + [num_models_str]
 
 
 with gr.Blocks(css=css) as demo:
     with open("texts/heading.md") as fp:
         gr.Markdown(fp.read())
+        num_models_md = gr.Markdown()
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏅 Leaderboard"):
             all_gr_dfs = {}
@@ -295,7 +304,7 @@ with gr.Blocks(css=css) as demo:
             with open("texts/about.md") as fp:
                 gr.Markdown(fp.read())
 
-    demo.load(refresh, outputs=list(all_gr_dfs.values()) + list(all_gr_winners.values()))
+    demo.load(refresh, outputs=list(all_gr_dfs.values()) + list(all_gr_winners.values()) + [num_models_md])
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(func=backend_routine, trigger="interval", seconds=REFRESH_RATE, max_instances=1)