init.py

from constants import EVAL_REQUESTS_PATH
from pathlib import Path
from huggingface_hub import HfApi
from dotenv import load_dotenv
import git
import os

load_dotenv()

# Hub to access the dataset repo
TOKEN_HUB = os.environ.get("TOKEN_HUB_V2", None)
# Name of the repo where the dataset is stored user/repo_name
QUEUE_REPO = os.environ.get("QUEUE_REPO", None)
# Local path where the repo is cloned to
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
    csv_results = None
    requested_models = None

    if TOKEN_HUB is None:
        print(
            "No HuggingFace token provided. Skipping evaluation requests and results."
        )
        return eval_queue_repo, requested_models, csv_results
    else:
        print("Pulling evaluation requests and results.")

        # Pull the dataset repo
        user_name = QUEUE_REPO.split("/")[0]
        repo_url = (
            f"https://{user_name}:{TOKEN_HUB}@huggingface.co/datasets/{QUEUE_REPO}"
        )
        git.Repo.clone_from(repo_url, QUEUE_PATH)

        # 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)

    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:
        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!"