Add tokens

src/envs.py

@@ -16,6 +16,7 @@ PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
 IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
 
 HF_HOME = os.getenv("HF_HOME", ".")
+HF_TOKEN_PRIVATE = os.environ.get("HF_TOKEN_PRIVATE")
 
 # Check HF_HOME write access
 print(f"Initial HF_HOME set to: {HF_HOME}")

src/gen/show_result.py

@@ -12,6 +12,8 @@ from sklearn.linear_model import LogisticRegression
 from tqdm import tqdm
 from utils import load_model_answers
 
+from src.envs import HF_TOKEN_PRIVATE
+
 
 def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
     models = pd.concat([df["model_a"], df["model_b"]]).unique()
@@ -265,6 +267,7 @@ if __name__ == "__main__":
             path_in_repo="leaderboard.json",
             repo_id="Vikhrmodels/arena-leaderboard-metainfo",
             repo_type="space",
+            token=HF_TOKEN_PRIVATE,
         )
 
         huggingface_hub.HfApi().upload_file(
@@ -272,4 +275,5 @@ if __name__ == "__main__":
             path_in_repo=f"leaderboard_logs/{json_file_name}",
             repo_id="Vikhrmodels/arena-leaderboard-metainfo",
             repo_type="dataset",
+            token=HF_TOKEN_PRIVATE,
         )

src/leaderboard/build_leaderboard.py

@@ -6,7 +6,7 @@ import time
 import pandas as pd
 from huggingface_hub import snapshot_download
 
-from src.envs import DATA_ARENA_PATH, DATA_PATH
+from src.envs import DATA_ARENA_PATH, DATA_PATH, HF_TOKEN_PRIVATE
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -37,7 +37,7 @@ def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, ba
                 local_dir=local_dir,
                 repo_type=repo_type,
                 tqdm_class=None,
-                token=os.environ.get("HF_TOKEN_PRIVATE"),
+                token=HF_TOKEN_PRIVATE,
                 etag_timeout=30,
                 max_workers=8,
             )