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