structure saas with tools
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Davidson Gomes
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import io
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from typing import Any, Dict, List, Optional, Union
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from . import constants
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from .hf_api import HfApi
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from .utils import build_hf_headers, get_session, is_pillow_available, logging, validate_hf_hub_args
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from .utils._deprecation import _deprecate_method
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logger = logging.get_logger(__name__)
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ALL_TASKS = [
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# NLP
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"text-classification",
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"token-classification",
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"table-question-answering",
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"question-answering",
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"zero-shot-classification",
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"translation",
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"summarization",
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"conversational",
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"feature-extraction",
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"text-generation",
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"text2text-generation",
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"fill-mask",
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"sentence-similarity",
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# Audio
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"text-to-speech",
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"automatic-speech-recognition",
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"audio-to-audio",
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"audio-classification",
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"voice-activity-detection",
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# Computer vision
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"image-classification",
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"object-detection",
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"image-segmentation",
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"text-to-image",
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"image-to-image",
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# Others
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"tabular-classification",
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"tabular-regression",
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]
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class InferenceApi:
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"""Client to configure requests and make calls to the HuggingFace Inference API.
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Example:
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```python
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>>> from huggingface_hub.inference_api import InferenceApi
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>>> # Mask-fill example
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>>> inference = InferenceApi("bert-base-uncased")
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>>> inference(inputs="The goal of life is [MASK].")
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[{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
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>>> # Question Answering example
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>>> inference = InferenceApi("deepset/roberta-base-squad2")
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>>> inputs = {
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... "question": "What's my name?",
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... "context": "My name is Clara and I live in Berkeley.",
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... }
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>>> inference(inputs)
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{'score': 0.9326569437980652, 'start': 11, 'end': 16, 'answer': 'Clara'}
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>>> # Zero-shot example
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>>> inference = InferenceApi("typeform/distilbert-base-uncased-mnli")
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>>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
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>>> params = {"candidate_labels": ["refund", "legal", "faq"]}
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>>> inference(inputs, params)
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{'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
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>>> # Overriding configured task
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>>> inference = InferenceApi("bert-base-uncased", task="feature-extraction")
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>>> # Text-to-image
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>>> inference = InferenceApi("stabilityai/stable-diffusion-2-1")
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>>> inference("cat")
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<PIL.PngImagePlugin.PngImageFile image (...)>
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>>> # Return as raw response to parse the output yourself
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>>> inference = InferenceApi("mio/amadeus")
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>>> response = inference("hello world", raw_response=True)
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>>> response.headers
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{"Content-Type": "audio/flac", ...}
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>>> response.content # raw bytes from server
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b'(...)'
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```
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"""
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@validate_hf_hub_args
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@_deprecate_method(
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version="1.0",
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message=(
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"`InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out"
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" this guide to learn how to convert your script to use it:"
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" https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client."
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),
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)
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def __init__(
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self,
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repo_id: str,
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task: Optional[str] = None,
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token: Optional[str] = None,
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gpu: bool = False,
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):
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"""Inits headers and API call information.
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Args:
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repo_id (``str``):
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Id of repository (e.g. `user/bert-base-uncased`).
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task (``str``, `optional`, defaults ``None``):
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Whether to force a task instead of using task specified in the
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repository.
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token (`str`, `optional`):
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The API token to use as HTTP bearer authorization. This is not
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the authentication token. You can find the token in
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https://huggingface.co/settings/token. Alternatively, you can
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find both your organizations and personal API tokens using
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`HfApi().whoami(token)`.
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gpu (`bool`, `optional`, defaults `False`):
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Whether to use GPU instead of CPU for inference(requires Startup
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plan at least).
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"""
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self.options = {"wait_for_model": True, "use_gpu": gpu}
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self.headers = build_hf_headers(token=token)
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# Configure task
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model_info = HfApi(token=token).model_info(repo_id=repo_id)
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if not model_info.pipeline_tag and not task:
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raise ValueError(
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"Task not specified in the repository. Please add it to the model card"
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" using pipeline_tag"
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" (https://huggingface.co/docs#how-is-a-models-type-of-inference-api-and-widget-determined)"
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)
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if task and task != model_info.pipeline_tag:
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if task not in ALL_TASKS:
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raise ValueError(f"Invalid task {task}. Make sure it's valid.")
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logger.warning(
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"You're using a different task than the one specified in the"
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" repository. Be sure to know what you're doing :)"
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)
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self.task = task
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else:
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assert model_info.pipeline_tag is not None, "Pipeline tag cannot be None"
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self.task = model_info.pipeline_tag
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self.api_url = f"{constants.INFERENCE_ENDPOINT}/pipeline/{self.task}/{repo_id}"
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def __repr__(self):
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# Do not add headers to repr to avoid leaking token.
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return f"InferenceAPI(api_url='{self.api_url}', task='{self.task}', options={self.options})"
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def __call__(
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self,
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inputs: Optional[Union[str, Dict, List[str], List[List[str]]]] = None,
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params: Optional[Dict] = None,
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data: Optional[bytes] = None,
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raw_response: bool = False,
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) -> Any:
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"""Make a call to the Inference API.
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Args:
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inputs (`str` or `Dict` or `List[str]` or `List[List[str]]`, *optional*):
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Inputs for the prediction.
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params (`Dict`, *optional*):
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Additional parameters for the models. Will be sent as `parameters` in the
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payload.
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data (`bytes`, *optional*):
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Bytes content of the request. In this case, leave `inputs` and `params` empty.
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raw_response (`bool`, defaults to `False`):
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If `True`, the raw `Response` object is returned. You can parse its content
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as preferred. By default, the content is parsed into a more practical format
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(json dictionary or PIL Image for example).
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"""
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# Build payload
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payload: Dict[str, Any] = {
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"options": self.options,
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}
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if inputs:
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payload["inputs"] = inputs
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if params:
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payload["parameters"] = params
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# Make API call
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response = get_session().post(self.api_url, headers=self.headers, json=payload, data=data)
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# Let the user handle the response
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if raw_response:
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return response
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# By default, parse the response for the user.
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content_type = response.headers.get("Content-Type") or ""
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if content_type.startswith("image"):
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if not is_pillow_available():
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raise ImportError(
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f"Task '{self.task}' returned as image but Pillow is not installed."
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" Please install it (`pip install Pillow`) or pass"
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" `raw_response=True` to get the raw `Response` object and parse"
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" the image by yourself."
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)
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from PIL import Image
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return Image.open(io.BytesIO(response.content))
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elif content_type == "application/json":
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return response.json()
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else:
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raise NotImplementedError(
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f"{content_type} output type is not implemented yet. You can pass"
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" `raw_response=True` to get the raw `Response` object and parse the"
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" output by yourself."
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)
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