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from typing import Dict, Union
import requests
from huggingface_hub import HfApi, ModelFilter
AUTOTRAIN_TASK_TO_HUB_TASK = {
"binary_classification": "text-classification",
"multi_class_classification": "text-classification",
# "multi_label_classification": "text-classification", # Not fully supported in AutoTrain
"entity_extraction": "token-classification",
"extractive_question_answering": "question-answering",
"translation": "translation",
"summarization": "summarization",
# "single_column_regression": 10,
}
HUB_TASK_TO_AUTOTRAIN_TASK = {v: k for k, v in AUTOTRAIN_TASK_TO_HUB_TASK.items()}
api = HfApi()
def get_auth_headers(token: str, prefix: str = "autonlp"):
return {"Authorization": f"{prefix} {token}"}
def http_post(path: str, token: str, payload=None, domain: str = None, params=None) -> requests.Response:
"""HTTP POST request to the AutoNLP API, raises UnreachableAPIError if the API cannot be reached"""
try:
response = requests.post(
url=domain + path, json=payload, headers=get_auth_headers(token=token), allow_redirects=True, params=params
)
except requests.exceptions.ConnectionError:
print("❌ Failed to reach AutoNLP API, check your internet connection")
response.raise_for_status()
return response
def http_get(path: str, domain: str, token: str = None, params: dict = None) -> requests.Response:
"""HTTP POST request to the AutoNLP API, raises UnreachableAPIError if the API cannot be reached"""
try:
response = requests.get(
url=domain + path, headers=get_auth_headers(token=token), allow_redirects=True, params=params
)
except requests.exceptions.ConnectionError:
print("❌ Failed to reach AutoNLP API, check your internet connection")
response.raise_for_status()
return response
def get_metadata(dataset_name: str) -> Union[Dict, None]:
data = requests.get(f"https://huggingface.co/api/datasets/{dataset_name}").json()
if data["cardData"] is not None and "train-eval-index" in data["cardData"].keys():
return data["cardData"]["train-eval-index"]
else:
return None
def get_compatible_models(task, dataset_name):
# TODO: relax filter on PyTorch models once supported in AutoTrain
filt = ModelFilter(
task=AUTOTRAIN_TASK_TO_HUB_TASK[task], trained_dataset=dataset_name, library=["transformers", "pytorch"]
)
compatible_models = api.list_models(filter=filt)
return [model.modelId for model in compatible_models]
def get_key(col_mapping, val):
for key, value in col_mapping.items():
if val == value:
return key
return "key doesn't exist"
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