Spaces:
Running
Running
File size: 1,392 Bytes
9ab539a c198bfa fbd403a 9ab539a fbd403a 9ab539a c198bfa 9ab539a c198bfa 9ab539a bccaf50 9ab539a fbd403a 9ab539a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
import os
from huggingface_hub import HfApi
# Dynamically determine if we're running in local mode
def is_local_mode():
if os.environ.get("SPACE_AUTHOR_NAME") and os.environ.get("SPACE_REPO_NAME") and os.environ.get("HF_TOKEN") and os.environ.get("SPACE_ID"):
return False
return True
LOCAL_MODE = is_local_mode()
# Info to change for your repository
# ----------------------------------
# Get token from environment or use None in local mode
TOKEN = os.environ.get("HF_TOKEN") if not LOCAL_MODE else None
OWNER = "holistic-ai" # Change to your org - don't forget to create a results and request dataset, with the correct format!
# ----------------------------------
REPO_ID = f"{OWNER}/LibVulnWatch"
QUEUE_REPO = REPO_ID # Use the same repository
RESULTS_REPO = REPO_ID # Use the same repository
if not LOCAL_MODE:
REPO_ID = str(os.environ.get("SPACE_ID"))
QUEUE_REPO = REPO_ID
RESULTS_REPO = REPO_ID
# 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, "assessment-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "assessment-results")
EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "assessment-queue-bk")
EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "assessment-results-bk")
# Initialize API with token if available
API = HfApi(token=TOKEN)
|