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| from typing import Dict, Union | |
| import requests | |
| from huggingface_hub import DatasetFilter, 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]: | |
| filt = DatasetFilter(dataset_name=dataset_name) | |
| data = api.list_datasets(filter=filt, full=True) | |
| if data[0].cardData is not None and "train-eval-index" in data[0].cardData.keys(): | |
| return data[0].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] | |