/
_client.py
2354 lines (2093 loc) · 102 KB
/
_client.py
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# coding=utf-8
# Copyright 2023-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Related resources:
# https://huggingface.co/tasks
# https://huggingface.co/docs/huggingface.js/inference/README
# https://github.com/huggingface/huggingface.js/tree/main/packages/inference/src
# https://github.com/huggingface/text-generation-inference/tree/main/clients/python
# https://github.com/huggingface/text-generation-inference/blob/main/clients/python/text_generation/client.py
# https://huggingface.slack.com/archives/C03E4DQ9LAJ/p1680169099087869
# https://github.com/huggingface/unity-api#tasks
#
# Some TODO:
# - add all tasks
#
# NOTE: the philosophy of this client is "let's make it as easy as possible to use it, even if less optimized". Some
# examples of how it translates:
# - Timeout / Server unavailable is handled by the client in a single "timeout" parameter.
# - Files can be provided as bytes, file paths, or URLs and the client will try to "guess" the type.
# - Images are parsed as PIL.Image for easier manipulation.
# - Provides a "recommended model" for each task => suboptimal but user-wise quicker to get a first script running.
# - Only the main parameters are publicly exposed. Power users can always read the docs for more options.
import base64
import logging
import time
import warnings
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Literal,
Optional,
Union,
overload,
)
from requests import HTTPError
from requests.structures import CaseInsensitiveDict
from huggingface_hub.constants import ALL_INFERENCE_API_FRAMEWORKS, INFERENCE_ENDPOINT, MAIN_INFERENCE_API_FRAMEWORKS
from huggingface_hub.errors import InferenceTimeoutError
from huggingface_hub.inference._common import (
TASKS_EXPECTING_IMAGES,
ContentT,
ModelStatus,
_b64_encode,
_b64_to_image,
_bytes_to_dict,
_bytes_to_image,
_bytes_to_list,
_fetch_recommended_models,
_import_numpy,
_is_chat_completion_server,
_is_tgi_server,
_open_as_binary,
_set_as_non_chat_completion_server,
_set_as_non_tgi,
_stream_chat_completion_response_from_bytes,
_stream_chat_completion_response_from_text_generation,
_stream_text_generation_response,
raise_text_generation_error,
)
from huggingface_hub.inference._generated.types import (
AudioClassificationOutputElement,
AudioToAudioOutputElement,
AutomaticSpeechRecognitionOutput,
ChatCompletionOutput,
ChatCompletionOutputChoice,
ChatCompletionOutputChoiceMessage,
ChatCompletionStreamOutput,
DocumentQuestionAnsweringOutputElement,
FillMaskOutputElement,
ImageClassificationOutputElement,
ImageSegmentationOutputElement,
ImageToTextOutput,
ObjectDetectionOutputElement,
QuestionAnsweringOutputElement,
SummarizationOutput,
TableQuestionAnsweringOutputElement,
TextClassificationOutputElement,
TextGenerationOutput,
TextGenerationStreamOutput,
TokenClassificationOutputElement,
TranslationOutput,
VisualQuestionAnsweringOutputElement,
ZeroShotClassificationOutputElement,
ZeroShotImageClassificationOutputElement,
)
from huggingface_hub.inference._templating import render_chat_prompt
from huggingface_hub.inference._types import (
ConversationalOutput, # soon to be removed
)
from huggingface_hub.utils import (
BadRequestError,
build_hf_headers,
get_session,
hf_raise_for_status,
)
if TYPE_CHECKING:
import numpy as np
from PIL import Image
logger = logging.getLogger(__name__)
class InferenceClient:
"""
Initialize a new Inference Client.
[`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used
seamlessly with either the (free) Inference API or self-hosted Inference Endpoints.
Args:
model (`str`, `optional`):
The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `bigcode/starcoder`
or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
automatically selected for the task.
token (`str` or `bool`, *optional*):
Hugging Face token. Will default to the locally saved token if not provided.
Pass `token=False` if you don't want to send your token to the server.
timeout (`float`, `optional`):
The maximum number of seconds to wait for a response from the server. Loading a new model in Inference
API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.
headers (`Dict[str, str]`, `optional`):
Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
Values in this dictionary will override the default values.
cookies (`Dict[str, str]`, `optional`):
Additional cookies to send to the server.
"""
def __init__(
self,
model: Optional[str] = None,
token: Union[str, bool, None] = None,
timeout: Optional[float] = None,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
) -> None:
self.model: Optional[str] = model
self.token: Union[str, bool, None] = token
self.headers = CaseInsensitiveDict(build_hf_headers(token=token)) # contains 'authorization' + 'user-agent'
if headers is not None:
self.headers.update(headers)
self.cookies = cookies
self.timeout = timeout
def __repr__(self):
return f"<InferenceClient(model='{self.model if self.model else ''}', timeout={self.timeout})>"
@overload
def post( # type: ignore[misc]
self,
*,
json: Optional[Union[str, Dict, List]] = None,
data: Optional[ContentT] = None,
model: Optional[str] = None,
task: Optional[str] = None,
stream: Literal[False] = ...,
) -> bytes: ...
@overload
def post( # type: ignore[misc]
self,
*,
json: Optional[Union[str, Dict, List]] = None,
data: Optional[ContentT] = None,
model: Optional[str] = None,
task: Optional[str] = None,
stream: Literal[True] = ...,
) -> Iterable[bytes]: ...
@overload
def post(
self,
*,
json: Optional[Union[str, Dict, List]] = None,
data: Optional[ContentT] = None,
model: Optional[str] = None,
task: Optional[str] = None,
stream: bool = False,
) -> Union[bytes, Iterable[bytes]]: ...
def post(
self,
*,
json: Optional[Union[str, Dict, List]] = None,
data: Optional[ContentT] = None,
model: Optional[str] = None,
task: Optional[str] = None,
stream: bool = False,
) -> Union[bytes, Iterable[bytes]]:
"""
Make a POST request to the inference server.
Args:
json (`Union[str, Dict, List]`, *optional*):
The JSON data to send in the request body, specific to each task. Defaults to None.
data (`Union[str, Path, bytes, BinaryIO]`, *optional*):
The content to send in the request body, specific to each task.
It can be raw bytes, a pointer to an opened file, a local file path,
or a URL to an online resource (image, audio file,...). If both `json` and `data` are passed,
`data` will take precedence. At least `json` or `data` must be provided. Defaults to None.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. Will override the model defined at the instance level. Defaults to None.
task (`str`, *optional*):
The task to perform on the inference. All available tasks can be found
[here](https://huggingface.co/tasks). Used only to default to a recommended model if `model` is not
provided. At least `model` or `task` must be provided. Defaults to None.
stream (`bool`, *optional*):
Whether to iterate over streaming APIs.
Returns:
bytes: The raw bytes returned by the server.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
"""
url = self._resolve_url(model, task)
if data is not None and json is not None:
warnings.warn("Ignoring `json` as `data` is passed as binary.")
# Set Accept header if relevant
headers = self.headers.copy()
if task in TASKS_EXPECTING_IMAGES and "Accept" not in headers:
headers["Accept"] = "image/png"
t0 = time.time()
timeout = self.timeout
while True:
with _open_as_binary(data) as data_as_binary:
try:
response = get_session().post(
url,
json=json,
data=data_as_binary,
headers=headers,
cookies=self.cookies,
timeout=self.timeout,
stream=stream,
)
except TimeoutError as error:
# Convert any `TimeoutError` to a `InferenceTimeoutError`
raise InferenceTimeoutError(f"Inference call timed out: {url}") from error # type: ignore
try:
hf_raise_for_status(response)
return response.iter_lines() if stream else response.content
except HTTPError as error:
if error.response.status_code == 422 and task is not None:
error.args = (
f"{error.args[0]}\nMake sure '{task}' task is supported by the model.",
) + error.args[1:]
if error.response.status_code == 503:
# If Model is unavailable, either raise a TimeoutError...
if timeout is not None and time.time() - t0 > timeout:
raise InferenceTimeoutError(
f"Model not loaded on the server: {url}. Please retry with a higher timeout (current:"
f" {self.timeout}).",
request=error.request,
response=error.response,
) from error
# ...or wait 1s and retry
logger.info(f"Waiting for model to be loaded on the server: {error}")
time.sleep(1)
if timeout is not None:
timeout = max(self.timeout - (time.time() - t0), 1) # type: ignore
continue
raise
def audio_classification(
self,
audio: ContentT,
*,
model: Optional[str] = None,
) -> List[AudioClassificationOutputElement]:
"""
Perform audio classification on the provided audio content.
Args:
audio (Union[str, Path, bytes, BinaryIO]):
The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.
model (`str`, *optional*):
The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio classification will be used.
Returns:
`List[AudioClassificationOutputElement]`: List of [`AudioClassificationOutputElement`] items containing the predicted labels and their confidence.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.audio_classification("audio.flac")
[
AudioClassificationOutputElement(score=0.4976358711719513, label='hap'),
AudioClassificationOutputElement(score=0.3677836060523987, label='neu'),
...
]
```
"""
response = self.post(data=audio, model=model, task="audio-classification")
return AudioClassificationOutputElement.parse_obj_as_list(response)
def audio_to_audio(
self,
audio: ContentT,
*,
model: Optional[str] = None,
) -> List[AudioToAudioOutputElement]:
"""
Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation).
Args:
audio (Union[str, Path, bytes, BinaryIO]):
The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.
model (`str`, *optional*):
The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio_to_audio will be used.
Returns:
`List[AudioToAudioOutputElement]`: A list of [`AudioToAudioOutputElement`] items containing audios label, content-type, and audio content in blob.
Raises:
`InferenceTimeoutError`:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> audio_output = client.audio_to_audio("audio.flac")
>>> for i, item in enumerate(audio_output):
>>> with open(f"output_{i}.flac", "wb") as f:
f.write(item.blob)
```
"""
response = self.post(data=audio, model=model, task="audio-to-audio")
audio_output = AudioToAudioOutputElement.parse_obj_as_list(response)
for item in audio_output:
item.blob = base64.b64decode(item.blob)
return audio_output
def automatic_speech_recognition(
self,
audio: ContentT,
*,
model: Optional[str] = None,
) -> AutomaticSpeechRecognitionOutput:
"""
Perform automatic speech recognition (ASR or audio-to-text) on the given audio content.
Args:
audio (Union[str, Path, bytes, BinaryIO]):
The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file.
model (`str`, *optional*):
The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended model for ASR will be used.
Returns:
[`AutomaticSpeechRecognitionOutput`]: An item containing the transcribed text and optionally the timestamp chunks.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.automatic_speech_recognition("hello_world.flac").text
"hello world"
```
"""
response = self.post(data=audio, model=model, task="automatic-speech-recognition")
return AutomaticSpeechRecognitionOutput.parse_obj_as_instance(response)
@overload
def chat_completion( # type: ignore
self,
messages: List[Dict[str, str]],
*,
model: Optional[str] = None,
stream: Literal[False] = False,
max_tokens: int = 20,
seed: Optional[int] = None,
stop: Optional[Union[List[str], str]] = None,
temperature: float = 1.0,
top_p: Optional[float] = None,
) -> ChatCompletionOutput: ...
@overload
def chat_completion( # type: ignore
self,
messages: List[Dict[str, str]],
*,
model: Optional[str] = None,
stream: Literal[True] = True,
max_tokens: int = 20,
seed: Optional[int] = None,
stop: Optional[Union[List[str], str]] = None,
temperature: float = 1.0,
top_p: Optional[float] = None,
) -> Iterable[ChatCompletionStreamOutput]: ...
@overload
def chat_completion(
self,
messages: List[Dict[str, str]],
*,
model: Optional[str] = None,
stream: bool = False,
max_tokens: int = 20,
seed: Optional[int] = None,
stop: Optional[Union[List[str], str]] = None,
temperature: float = 1.0,
top_p: Optional[float] = None,
) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]: ...
def chat_completion(
self,
messages: List[Dict[str, str]],
*,
model: Optional[str] = None,
stream: bool = False,
max_tokens: int = 20,
seed: Optional[int] = None,
stop: Optional[Union[List[str], str]] = None,
temperature: float = 1.0,
top_p: Optional[float] = None,
) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]:
"""
A method for completing conversations using a specified language model.
<Tip>
If the model is served by a server supporting chat-completion, the method will directly call the server's
`/v1/chat/completions` endpoint. If the server does not support chat-completion, the method will render the
chat template client-side based on the information fetched from the Hub API. In this case, you will need to
have `minijinja` template engine installed. Run `pip install "huggingface_hub[inference]"` or `pip install minijinja`
to install it.
</Tip>
Args:
messages (List[Union[`SystemMessage`, `UserMessage`, `AssistantMessage`]]):
Conversation history consisting of roles and content pairs.
model (`str`, *optional*):
The model to use for chat-completion. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended model for chat-based text-generation will be used.
See https://huggingface.co/tasks/text-generation for more details.
frequency_penalty (`float`, optional):
Penalizes new tokens based on their existing frequency
in the text so far. Range: [-2.0, 2.0]. Defaults to 0.0.
max_tokens (`int`, optional):
Maximum number of tokens allowed in the response. Defaults to 20.
seed (Optional[`int`], optional):
Seed for reproducible control flow. Defaults to None.
stop (Optional[`str`], optional):
Up to four strings which trigger the end of the response.
Defaults to None.
stream (`bool`, optional):
Enable realtime streaming of responses. Defaults to False.
temperature (`float`, optional):
Controls randomness of the generations. Lower values ensure
less random completions. Range: [0, 2]. Defaults to 1.0.
top_p (`float`, optional):
Fraction of the most likely next words to sample from.
Must be between 0 and 1. Defaults to 1.0.
Returns:
`Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]`:
Generated text returned from the server:
- if `stream=False`, the generated text is returned as a [`ChatCompletionOutput`] (default).
- if `stream=True`, the generated text is returned token by token as a sequence of [`ChatCompletionStreamOutput`].
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> messages = [{"role": "user", "content": "What is the capital of France?"}]
>>> client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
>>> client.chat_completion(messages, max_tokens=100)
ChatCompletionOutput(
choices=[
ChatCompletionOutputChoice(
finish_reason='eos_token',
index=0,
message=ChatCompletionOutputChoiceMessage(
content='The capital of France is Paris. The official name of the city is "Ville de Paris" (City of Paris) and the name of the country\'s governing body, which is located in Paris, is "La République française" (The French Republic). \nI hope that helps! Let me know if you need any further information.'
)
)
],
created=1710498360
)
>>> for token in client.chat_completion(messages, max_tokens=10, stream=True):
... print(token)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content='The', role='assistant'), index=0, finish_reason=None)], created=1710498504)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' capital', role='assistant'), index=0, finish_reason=None)], created=1710498504)
(...)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' may', role='assistant'), index=0, finish_reason=None)], created=1710498504)
ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=None, role=None), index=0, finish_reason='length')], created=1710498504)
```
"""
# determine model
model = model or self.model or self.get_recommended_model("text-generation")
if _is_chat_completion_server(model):
# First, let's consider the server has a `/v1/chat/completions` endpoint.
# If that's the case, we don't have to render the chat template client-side.
model_url = self._resolve_url(model)
if not model_url.endswith("/chat/completions"):
model_url += "/v1/chat/completions"
try:
data = self.post(
model=model_url,
json=dict(
model="tgi", # random string
messages=messages,
max_tokens=max_tokens,
seed=seed,
stop=stop,
temperature=temperature,
top_p=top_p,
stream=stream,
),
stream=stream,
)
except HTTPError:
# Let's consider the server is not a chat completion server.
# Then we call again `chat_completion` which will render the chat template client side.
# (can be HTTP 500, HTTP 400, HTTP 404 depending on the server)
_set_as_non_chat_completion_server(model)
return self.chat_completion(
messages=messages,
model=model,
stream=stream,
max_tokens=max_tokens,
seed=seed,
stop=stop,
temperature=temperature,
top_p=top_p,
)
if stream:
return _stream_chat_completion_response_from_bytes(data) # type: ignore[arg-type]
return ChatCompletionOutput.parse_obj_as_instance(data) # type: ignore[arg-type]
# At this point, we know the server is not a chat completion server.
# We need to render the chat template client side based on the information we can fetch from
# the Hub API.
model_id = None
if model.startswith(("http://", "https://")):
# If URL, we need to know which model is served. This is not always possible.
# A workaround is to list the user Inference Endpoints and check if one of them correspond to the model URL.
# If not, we raise an error.
# TODO: fix when we have a proper API for this (at least for Inference Endpoints)
# TODO: what if Sagemaker URL?
# TODO: what if Azure URL?
from ..hf_api import HfApi
for endpoint in HfApi(token=self.token).list_inference_endpoints():
if endpoint.url == model:
model_id = endpoint.repository
break
else:
model_id = model
if model_id is None:
# If we don't have the model ID, we can't fetch the chat template.
# We raise an error.
raise ValueError(
"Request can't be processed as the model ID can't be inferred from model URL. "
"This is needed to fetch the chat template from the Hub since the model is not "
"served with a Chat-completion API."
)
# fetch chat template + tokens
prompt = render_chat_prompt(model_id=model_id, token=self.token, messages=messages)
# generate response
stop_sequences = [stop] if isinstance(stop, str) else stop
text_generation_output = self.text_generation(
prompt=prompt,
details=True,
stream=stream,
model=model,
max_new_tokens=max_tokens,
seed=seed,
stop_sequences=stop_sequences,
temperature=temperature,
top_p=top_p,
)
created = int(time.time())
if stream:
return _stream_chat_completion_response_from_text_generation(text_generation_output) # type: ignore [arg-type]
if isinstance(text_generation_output, TextGenerationOutput):
# General use case => format ChatCompletionOutput from text generation details
content: str = text_generation_output.generated_text
finish_reason: str = text_generation_output.details.finish_reason # type: ignore[union-attr]
else:
# Corner case: if server doesn't support details (e.g. if not a TGI server), we only receive an output string.
# In such a case, `finish_reason` is set to `"unk"`.
content = text_generation_output # type: ignore[assignment]
finish_reason = "unk"
return ChatCompletionOutput(
created=created,
choices=[
ChatCompletionOutputChoice(
finish_reason=finish_reason, # type: ignore
index=0,
message=ChatCompletionOutputChoiceMessage(
content=content,
role="assistant",
),
)
],
)
def conversational(
self,
text: str,
generated_responses: Optional[List[str]] = None,
past_user_inputs: Optional[List[str]] = None,
*,
parameters: Optional[Dict[str, Any]] = None,
model: Optional[str] = None,
) -> ConversationalOutput:
"""
Generate conversational responses based on the given input text (i.e. chat with the API).
<Tip warning={true}>
[`InferenceClient.conversational`] API is deprecated and will be removed in a future release. Please use
[`InferenceClient.chat_completion`] instead.
</Tip>
Args:
text (`str`):
The last input from the user in the conversation.
generated_responses (`List[str]`, *optional*):
A list of strings corresponding to the earlier replies from the model. Defaults to None.
past_user_inputs (`List[str]`, *optional*):
A list of strings corresponding to the earlier replies from the user. Should be the same length as
`generated_responses`. Defaults to None.
parameters (`Dict[str, Any]`, *optional*):
Additional parameters for the conversational task. Defaults to None. For more details about the available
parameters, please refer to [this page](https://huggingface.co/docs/api-inference/detailed_parameters#conversational-task)
model (`str`, *optional*):
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.
Returns:
`Dict`: The generated conversational output.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> output = client.conversational("Hi, who are you?")
>>> output
{'generated_text': 'I am the one who knocks.', 'conversation': {'generated_responses': ['I am the one who knocks.'], 'past_user_inputs': ['Hi, who are you?']}, 'warnings': ['Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.']}
>>> client.conversational(
... "Wow, that's scary!",
... generated_responses=output["conversation"]["generated_responses"],
... past_user_inputs=output["conversation"]["past_user_inputs"],
... )
```
"""
warnings.warn(
"'InferenceClient.conversational' is deprecated and will be removed starting from huggingface_hub>=0.25. "
"Please use the more appropriate 'InferenceClient.chat_completion' API instead.",
FutureWarning,
)
payload: Dict[str, Any] = {"inputs": {"text": text}}
if generated_responses is not None:
payload["inputs"]["generated_responses"] = generated_responses
if past_user_inputs is not None:
payload["inputs"]["past_user_inputs"] = past_user_inputs
if parameters is not None:
payload["parameters"] = parameters
response = self.post(json=payload, model=model, task="conversational")
return _bytes_to_dict(response) # type: ignore
def document_question_answering(
self,
image: ContentT,
question: str,
*,
model: Optional[str] = None,
) -> List[DocumentQuestionAnsweringOutputElement]:
"""
Answer questions on document images.
Args:
image (`Union[str, Path, bytes, BinaryIO]`):
The input image for the context. It can be raw bytes, an image file, or a URL to an online image.
question (`str`):
Question to be answered.
model (`str`, *optional*):
The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used.
Defaults to None.
Returns:
`List[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.document_question_answering(image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", question="What is the invoice number?")
[DocumentQuestionAnsweringOutputElement(score=0.42515629529953003, answer='us-001', start=16, end=16)]
```
"""
payload: Dict[str, Any] = {"question": question, "image": _b64_encode(image)}
response = self.post(json=payload, model=model, task="document-question-answering")
return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)
def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.ndarray":
"""
Generate embeddings for a given text.
Args:
text (`str`):
The text to embed.
model (`str`, *optional*):
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.
Returns:
`np.ndarray`: The embedding representing the input text as a float32 numpy array.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.feature_extraction("Hi, who are you?")
array([[ 2.424802 , 2.93384 , 1.1750331 , ..., 1.240499, -0.13776633, -0.7889173 ],
[-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ],
...,
[ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32)
```
"""
response = self.post(json={"inputs": text}, model=model, task="feature-extraction")
np = _import_numpy()
return np.array(_bytes_to_dict(response), dtype="float32")
def fill_mask(self, text: str, *, model: Optional[str] = None) -> List[FillMaskOutputElement]:
"""
Fill in a hole with a missing word (token to be precise).
Args:
text (`str`):
a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask).
model (`str`, *optional*):
The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used.
Defaults to None.
Returns:
`List[FillMaskOutputElement]`: a list of [`FillMaskOutputElement`] items containing the predicted label, associated
probability, token reference, and completed text.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.fill_mask("The goal of life is <mask>.")
[
FillMaskOutputElement(score=0.06897063553333282, token=11098, token_str=' happiness', sequence='The goal of life is happiness.'),
FillMaskOutputElement(score=0.06554922461509705, token=45075, token_str=' immortality', sequence='The goal of life is immortality.')
]
```
"""
response = self.post(json={"inputs": text}, model=model, task="fill-mask")
return FillMaskOutputElement.parse_obj_as_list(response)
def image_classification(
self,
image: ContentT,
*,
model: Optional[str] = None,
) -> List[ImageClassificationOutputElement]:
"""
Perform image classification on the given image using the specified model.
Args:
image (`Union[str, Path, bytes, BinaryIO]`):
The image to classify. It can be raw bytes, an image file, or a URL to an online image.
model (`str`, *optional*):
The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used.
Returns:
`List[ImageClassificationOutputElement]`: a list of [`ImageClassificationOutputElement`] items containing the predicted label and associated probability.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.image_classification("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
[ImageClassificationOutputElement(score=0.9779096841812134, label='Blenheim spaniel'), ...]
```
"""
response = self.post(data=image, model=model, task="image-classification")
return ImageClassificationOutputElement.parse_obj_as_list(response)
def image_segmentation(
self,
image: ContentT,
*,
model: Optional[str] = None,
) -> List[ImageSegmentationOutputElement]:
"""
Perform image segmentation on the given image using the specified model.
<Tip warning={true}>
You must have `PIL` installed if you want to work with images (`pip install Pillow`).
</Tip>
Args:
image (`Union[str, Path, bytes, BinaryIO]`):
The image to segment. It can be raw bytes, an image file, or a URL to an online image.
model (`str`, *optional*):
The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used.
Returns:
`List[ImageSegmentationOutputElement]`: A list of [`ImageSegmentationOutputElement`] items containing the segmented masks and associated attributes.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.image_segmentation("cat.jpg"):
[ImageSegmentationOutputElement(score=0.989008, label='LABEL_184', mask=<PIL.PngImagePlugin.PngImageFile image mode=L size=400x300 at 0x7FDD2B129CC0>), ...]
```
"""
response = self.post(data=image, model=model, task="image-segmentation")
output = ImageSegmentationOutputElement.parse_obj_as_list(response)
for item in output:
item.mask = _b64_to_image(item.mask)
return output
def image_to_image(
self,
image: ContentT,
prompt: Optional[str] = None,
*,
negative_prompt: Optional[str] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: Optional[int] = None,
guidance_scale: Optional[float] = None,
model: Optional[str] = None,
**kwargs,
) -> "Image":
"""
Perform image-to-image translation using a specified model.
<Tip warning={true}>
You must have `PIL` installed if you want to work with images (`pip install Pillow`).
</Tip>
Args:
image (`Union[str, Path, bytes, BinaryIO]`):
The input image for translation. It can be raw bytes, an image file, or a URL to an online image.
prompt (`str`, *optional*):
The text prompt to guide the image generation.
negative_prompt (`str`, *optional*):
A negative prompt to guide the translation process.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*):
Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
Returns:
`Image`: The translated image.
Raises:
[`InferenceTimeoutError`]:
If the model is unavailable or the request times out.
`HTTPError`:
If the request fails with an HTTP error status code other than HTTP 503.
Example:
```py
>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> image = client.image_to_image("cat.jpg", prompt="turn the cat into a tiger")
>>> image.save("tiger.jpg")
```
"""
parameters = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"height": height,
"width": width,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale,