src/envs.py

import os

from huggingface_hub import HfApi

# Info to change for your repository
# ----------------------------------
TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org


#OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
#REPO_ID = f"{OWNER}/leaderboard"
#QUEUE_REPO = f"{OWNER}/requests"
#RESULTS_REPO = f"{OWNER}/results"
#DYNAMIC_INFO_REPO = f"{OWNER}/dynamic_model_information"

REPO_ID = "BAAI/EmbodiedVerse"
QUEUE_REPO = "open-cn-llm-leaderboard/EmbodiedVerse_requests"
DYNAMIC_INFO_REPO = "open-cn-llm-leaderboard/EmbodiedVerse_dynamic_model_information"
RESULTS_REPO = "open-cn-llm-leaderboard/EmbodiedVerse_results"

IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
# If you setup a cache later, just change HF_HOME
CACHE_PATH=os.getenv("HF_HOME", ".")

# Local caches
EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
DYNAMIC_INFO_PATH = os.path.join(CACHE_PATH, "dynamic-info")
DYNAMIC_INFO_FILE_PATH = os.path.join(DYNAMIC_INFO_PATH, "model_infos.json")

PATH_TO_COLLECTION = "open-cn-llm-leaderboard/flageval-vlm-leaderboard-best-models-677e51cdc44f8123e02cbda1"

# Rate limit variables
RATE_LIMIT_PERIOD = 7
RATE_LIMIT_QUOTA = 5
HAS_HIGHER_RATE_LIMIT = ["TheBloke"]

API = HfApi(token=TOKEN)