Spaces:
Running
Running
File size: 3,291 Bytes
a415f27 |
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 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import os
import pandas as pd
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
results_csv_path = 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)
# csv_results = pd.read_json(QUEUE_PATH, lines=True)
if csv_results is None:
passed = False
if not passed:
print("No HuggingFace token or result path 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!" |