Update app.py

app.py

@@ -136,6 +136,7 @@ rl_envs = [
 ]
 
 def restart():
+    print("RESTART")
     api.restart_space(repo_id="huggingface-projects/Deep-Reinforcement-Learning-Leaderboard")
 
 def get_metadata(model_id):
@@ -219,7 +220,6 @@ def update_leaderboard_dataset(rl_env, path):
 
 def download_leaderboard_dataset():
     path = snapshot_download(repo_id=DATASET_REPO_ID, repo_type="dataset")
-    print("PATH DOWNLOAD", path)
     return path
 
 def get_data(rl_env, path) -> pd.DataFrame:
@@ -270,11 +270,7 @@ def run_update_dataset():
     commit_message="Update dataset")
 
 def filter_data(rl_env, path, user_id):
-    print("RL ENV", rl_env)
-    print("PATH", path)
-    print("USER ID", user_id)
     data_df = get_data_no_html(rl_env, path)
-    print(data_df)
     models = []
     models = data_df[data_df["User"] == user_id]
 
@@ -285,7 +281,6 @@ def filter_data(rl_env, path, user_id):
         models.loc[index, "Model"] = make_clickable_model(model_id)
         
 
-    print(models)
     return models
 
 run_update_dataset()
@@ -337,7 +332,6 @@ with block:
                 env = gr.Variable(rl_env["rl_env"])
                 grpath = gr.Variable(path_)
             with gr.Row():
-                print("PATH USED", path_)
                 gr_dataframe = gr.components.Dataframe(value=get_data(rl_env["rl_env"], path_), headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"],  row_count=(100, 'fixed'))
              
             with gr.Row():