import os

from huggingface_hub import HfApi


H4_TOKEN = os.environ.get("HF_SECRET", None) 

# REPO_ID = "pminervini/hallucinations-leaderboard"
REPO_ID = "openlifescienceai/open_medical_llm_leaderboard" 

QUEUE_REPO = "openlifescienceai/test_requests"
RESULTS_REPO = "openlifescienceai/test_results"

# have not created these repos yet
PRIVATE_QUEUE_REPO = "openlifescienceai/test_private-requests"
PRIVATE_RESULTS_REPO = "openlifescienceai/test_private-results"

IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))

# CACHE_PATH = "/Users/chaeeunlee/Documents/VSC_workspaces/test_leaderboard" # 
CACHE_PATH = os.getenv("HF_HOME", ".") 

print(f"CACHE_PATH = {CACHE_PATH}")

EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")

EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"

# PATH_TO_COLLECTION = "hallucinations-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03" # ??

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

API = HfApi(token=H4_TOKEN)
# API = HfApi()