readding update functionality

app.py

@@ -9,9 +9,11 @@ from huggingface_hub import HfApi, hf_hub_download, snapshot_download
 from huggingface_hub.repocard import metadata_load
 from apscheduler.schedulers.background import BackgroundScheduler
 
+
 from tqdm.contrib.concurrent import thread_map
 
-from utils import *
+from utils import make_clickable_model
+from utils import make_clickable_user
 
 DATASET_REPO_URL = "https://huggingface.co/datasets/pkalkman/drlc-leaderboard-data"
 DATASET_REPO_ID = "pkalkman/drlc-leaderboard-data"
@@ -24,6 +26,153 @@ api = HfApi(token=HF_TOKEN)
 with open('envs.json', 'r') as f:
     rl_envs = json.load(f)
 
+def get_metadata(model_id):
+    try:
+        readme_path = hf_hub_download(model_id, filename="README.md", etag_timeout=180)
+        return metadata_load(readme_path)
+    except requests.exceptions.HTTPError:
+        # 404 README.md not found
+        return None
+        
+def parse_metrics_accuracy(meta):
+    if "model-index" not in meta:
+        return None
+    result = meta["model-index"][0]["results"]
+    metrics = result[0]["metrics"]
+    accuracy = metrics[0]["value"]
+    return accuracy
+
+# We keep the worst case episode
+def parse_rewards(accuracy):
+    default_std = -1000
+    default_reward=-1000
+    if accuracy !=  None:
+        accuracy = str(accuracy)
+        parsed =  accuracy.split('+/-')
+        if len(parsed)>1:
+            mean_reward = float(parsed[0].strip())
+            std_reward =  float(parsed[1].strip())
+        elif len(parsed)==1: #only mean reward
+            mean_reward = float(parsed[0].strip())
+            std_reward =  float(0) 
+        else: 
+            mean_reward = float(default_std)
+            std_reward = float(default_reward)
+
+    else:
+        mean_reward = float(default_std)
+        std_reward = float(default_reward)
+    return mean_reward, std_reward
+
+
+def get_model_ids(rl_env):
+    api = HfApi()
+    models = api.list_models(filter=rl_env)
+    model_ids = [x.modelId for x in models]
+    return model_ids
+
+# Parralelized version
+def update_leaderboard_dataset_parallel(rl_env, path):
+    # Get model ids associated with rl_env
+    model_ids = get_model_ids(rl_env)
+
+    def process_model(model_id):
+        meta = get_metadata(model_id)
+        #LOADED_MODEL_METADATA[model_id] = meta if meta is not None else ''
+        if meta is None:
+            return None
+        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
+        return row
+
+    data = list(thread_map(process_model, model_ids, desc="Processing models"))
+
+    # Filter out None results (models with no metadata)
+    data = [row for row in data if row is not None]
+
+    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 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"] = make_clickable_user(user_id)
+        row["Model"] = make_clickable_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 get_data_no_html(rl_env, path) -> pd.DataFrame:
+    """
+    Get data from rl_env
+    :return: data as a pandas DataFrame
+    """
+    csv_path = path + "/" + rl_env + ".csv"
+    data = pd.read_csv(csv_path)
+
+    return data
+    
+
+def rank_dataframe(dataframe):
+    dataframe = dataframe.sort_values(by=['Results', 'User', 'Model'], ascending=False)
+    if not 'Ranking' in dataframe.columns:
+        dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)])
+    else:
+        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="pkalkman/drlc-leaderboard-data",
+        repo_type="dataset",
+        commit_message="Update dataset")
+
 
 def download_leaderboard_dataset():
     # Download the dataset from the Hugging Face Hub