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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_REPO_WHISPER = os.environ.get("QUEUE_REPO_WHISPER", None)
QUEUE_PATH = os.environ.get("QUEUE_PATH", None)
QUEUE_PATH_WHISPER = os.environ.get("QUEUE_PATH_WHISPER", 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()
whisper_eval_queue_repo = Repository(
local_dir=QUEUE_PATH_WHISPER,
clone_from=QUEUE_REPO_WHISPER,
use_auth_token=TOKEN_HUB,
repo_type="dataset",
)
whisper_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
whisper_csv_results = get_csv_with_results(QUEUE_PATH_WHISPER)
if whisper_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, whisper_eval_queue_repo, whisper_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!" |