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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
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
COHERE_API_KEY = os.environ.get("COHERE_API_KEY")
# Change to your org - don't forget to create a results and request dataset, with the correct format!
OWNER = "qanta-challenge"
REPO_ID = f"{OWNER}/quizbowl-submission"
QUEUE_REPO = f"{OWNER}/advcal-requests"
RESULTS_REPO = f"{OWNER}/advcal-results"
LLM_CACHE_REPO = f"{OWNER}/advcal-llm-cache"
USERS_REPO = f"{OWNER}/registered-users"
EVAL_SPLITS = ["tiny_eval"]
DOCS_REPO_URL = "https://github.com/qanta-challenge/QANTA25"
DOCS_REPO_BRANCH = "main"
EXAMPLES_PATH = "examples"
PLAYGROUND_DATASET_NAMES = {
"tossup": f"{OWNER}/acf-co24-tossups",
"bonus": f"{OWNER}/acf-co24-bonuses",
}
# ----------------------------------
# If you setup a cache later, just change HF_HOME
CACHE_PATH = os.getenv("HF_HOME", ".")
# Local caches
LLM_CACHE_PATH = os.path.join(CACHE_PATH, "llm-cache")
USERS_PATH = os.path.join(CACHE_PATH, "registered-users")
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")
LLM_CACHE_REFRESH_INTERVAL = 600 # seconds (30 minutes)
SERVER_RESTART_INTERVAL = 2 * 24 * 60 * 60 # seconds (2 days)
LEADERBOARD_REFRESH_INTERVAL = 600 # seconds (10 minutes)
API = HfApi(token=TOKEN)
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