import os from constants import EVAL_REQUESTS_PATH from pathlib import Path from huggingface_hub import HfApi, Repository TOKEN_HUB = os.environ.get("TOKEN_HUB", None) QUEUE_REPO = os.environ.get("QUEUE_REPO", None) QUEUE_PATH = os.environ.get("QUEUE_PATH", None) hf_api = HfApi( endpoint="https://huggingface.co", token=TOKEN_HUB, ) def load_all_info_from_dataset_hub(): eval_queue_repo = None requested_models = None passed = True if TOKEN_HUB is None: passed = False else: print("Pulling evaluation requests and results.") eval_queue_repo = Repository( local_dir=QUEUE_PATH, clone_from=QUEUE_REPO, use_auth_token=TOKEN_HUB, repo_type="dataset", ) eval_queue_repo.git_pull() # Local directory where dataset repo is cloned + folder with eval requests directory = QUEUE_PATH / EVAL_REQUESTS_PATH requested_models = get_all_requested_models(directory) requested_models = [p.stem for p in requested_models] # Local directory where dataset repo is cloned csv_results = get_csv_with_results(QUEUE_PATH) if csv_results is None: passed = False if not passed: raise ValueError("No Hugging Face token provided. Skipping evaluation requests and results.") return eval_queue_repo, requested_models, csv_results def upload_file(requested_model_name, path_or_fileobj): dest_repo_file = Path(EVAL_REQUESTS_PATH) / path_or_fileobj.name dest_repo_file = str(dest_repo_file) hf_api.upload_file( path_or_fileobj=path_or_fileobj, path_in_repo=str(dest_repo_file), repo_id=QUEUE_REPO, token=TOKEN_HUB, repo_type="dataset", commit_message=f"Add {requested_model_name} to eval queue") def get_all_requested_models(directory): directory = Path(directory) all_requested_models = list(directory.glob("*.txt")) return all_requested_models def get_csv_with_results(directory): directory = Path(directory) all_csv_files = list(directory.glob("*.csv")) latest = [f for f in all_csv_files if f.stem.endswith("latest")] if len(latest) != 1: return None return latest[0] def is_model_on_hub(model_name, revision="main") -> bool: try: model_name = model_name.replace(" ","") author = model_name.split("/")[0] model_id = model_name.split("/")[1] if len(author) == 0 or len(model_id) == 0: return False, "is not a valid model name. Please use the format `author/model_name`." except Exception as e: return False, "is not a valid model name. Please use the format `author/model_name`." try: models = list(hf_api.list_models(author=author, search=model_id)) matched = [model_name for m in models if m.modelId == model_name] if len(matched) != 1: return False, "was not found on the hub!" else: return True, None except Exception as e: print(f"Could not get the model from the hub.: {e}") return False, "was not found on hub!"