Update app.py

app.py

@@ -151,7 +151,7 @@ rl_envs = [
 
 def restart():
     print("RESTART")
-    api.restart_space(repo_id="huggingface-projects/Deep-Reinforcement-Learning-Leaderboard")
+    api.restart_space(repo_id="homunculus/Deep-Reinforcement-Learning-Leaderboard")
 
 def get_metadata(model_id):
     try:
@@ -233,40 +233,6 @@ def update_leaderboard_dataset_parallel(rl_env, path):
 
     return ranked_dataframe
 
-
-def update_leaderboard_dataset(rl_env, path):
-    # Get model ids associated with rl_env
-    model_ids = get_model_ids(rl_env)
-    data = []
-    for model_id in model_ids:
-        """
-        readme_path = hf_hub_download(model_id, filename="README.md")
-        meta = metadata_load(readme_path)
-        """
-        meta = get_metadata(model_id)
-        #LOADED_MODEL_METADATA[model_id] = meta if meta is not None else ''
-        if meta is None:
-            continue
-        user_id = model_id.split('/')[0]
-        row = {}
-        row["User"] = user_id
-        row["Model"] = model_id
-        accuracy = parse_metrics_accuracy(meta)
-        mean_reward, std_reward = parse_rewards(accuracy)
-        mean_reward = mean_reward if not pd.isna(mean_reward) else 0
-        std_reward = std_reward if not pd.isna(std_reward) else 0
-        row["Results"] = mean_reward - std_reward
-        row["Mean Reward"] = mean_reward
-        row["Std Reward"] = std_reward
-        data.append(row)
-
-    ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data))
-    new_history = ranked_dataframe
-    file_path = path + "/" + rl_env + ".csv"
-    new_history.to_csv(file_path, index=False)
-
-    return ranked_dataframe
-
 def download_leaderboard_dataset():
     path = snapshot_download(repo_id=DATASET_REPO_ID, repo_type="dataset")
     return path
@@ -305,19 +271,6 @@ def rank_dataframe(dataframe):
         dataframe['Ranking'] =   [i for i in range(1,len(dataframe)+1)]
     return dataframe
 
-
-def run_update_dataset():
-    path_ = download_leaderboard_dataset()
-    for i in range(0, len(rl_envs)):
-        rl_env = rl_envs[i]
-        update_leaderboard_dataset_parallel(rl_env["rl_env"], path_)
-
-    api.upload_folder(
-    folder_path=path_,
-    repo_id="huggingface-projects/drlc-leaderboard-data",
-    repo_type="dataset",
-    commit_message="Update dataset")
-
 def filter_data(rl_env, path, user_id):
     data_df = get_data_no_html(rl_env, path)
     models = []