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Jan 20, 2025
Merged

Handle hf_inference in single file #2766

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36 changes: 14 additions & 22 deletions src/huggingface_hub/inference/_client.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -404,11 +404,7 @@ def audio_classification(
parameters = {"function_to_apply": function_to_apply, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="audio-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(
audio,
parameters=parameters,
expect_binary=True,
)
payload = provider_helper.prepare_payload(audio, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="audio-classification")
return AudioClassificationOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1008,7 +1004,7 @@ def document_question_answering(
}
provider_helper = get_provider_helper(self.provider, task="document-question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="document-question-answering")
return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1075,7 +1071,7 @@ def feature_extraction(
}
provider_helper = get_provider_helper(self.provider, task="feature-extraction")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="feature-extraction")
np = _import_numpy()
return np.array(_bytes_to_dict(response), dtype="float32")
Expand Down Expand Up @@ -1127,7 +1123,7 @@ def fill_mask(
parameters = {"targets": targets, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="fill-mask")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="fill-mask")
return FillMaskOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1172,7 +1168,7 @@ def image_classification(
parameters = {"function_to_apply": function_to_apply, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="image-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="image-classification")
return ImageClassificationOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1234,7 +1230,7 @@ def image_segmentation(
}
provider_helper = get_provider_helper(self.provider, task="image-segmentation")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="image-segmentation")
output = ImageSegmentationOutputElement.parse_obj_as_list(response)
for item in output:
Expand Down Expand Up @@ -1308,7 +1304,7 @@ def image_to_image(
}
provider_helper = get_provider_helper(self.provider, task="image-to-image")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="image-to-image")
return _bytes_to_image(response)

Expand Down Expand Up @@ -1478,7 +1474,7 @@ def object_detection(
}
provider_helper = get_provider_helper(self.provider, task="object-detection")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="object-detection")
return ObjectDetectionOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1556,7 +1552,7 @@ def question_answering(
inputs: Dict[str, Any] = {"question": question, "context": context}
provider_helper = get_provider_helper(self.provider, task="question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="question-answering")
# Parse the response as a single `QuestionAnsweringOutputElement` when top_k is 1 or not provided, or a list of `QuestionAnsweringOutputElement` to ensure backward compatibility.
output = QuestionAnsweringOutputElement.parse_obj(response)
Expand Down Expand Up @@ -1672,7 +1668,7 @@ def summarization(
}
provider_helper = get_provider_helper(self.provider, task="summarization")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = self.post(**payload, model=model, task="summarization")
return SummarizationOutput.parse_obj_as_list(response)[0]

Expand Down Expand Up @@ -1737,7 +1733,7 @@ def table_question_answering(
}
provider_helper = get_provider_helper(self.provider, task="table-question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = self.post(
**payload,
model=model,
Expand Down Expand Up @@ -1887,7 +1883,7 @@ def text_classification(
}
provider_helper = get_provider_helper(self.provider, task="text-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = self.post(
**payload,
model=model,
Expand Down Expand Up @@ -2482,11 +2478,7 @@ def text_to_image(

provider_helper = get_provider_helper(self.provider, task="text-to-image")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(
prompt,
parameters=parameters,
model=model,
)
payload = provider_helper.prepare_payload(prompt, parameters=parameters, model=model)

response = self.post(**payload, model=model, task="text-to-image")
response = provider_helper.get_response(response)
Expand Down Expand Up @@ -3012,7 +3004,7 @@ def zero_shot_image_classification(
}
provider_helper = get_provider_helper(self.provider, task="zero-shot-image-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(image, parameters=parameters, model=model, expect_binary=True)
payload = provider_helper.prepare_payload(image, parameters=parameters, model=model)
response = self.post(
**payload,
model=model,
Expand Down
36 changes: 14 additions & 22 deletions src/huggingface_hub/inference/_generated/_async_client.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -438,11 +438,7 @@ async def audio_classification(
parameters = {"function_to_apply": function_to_apply, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="audio-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(
audio,
parameters=parameters,
expect_binary=True,
)
payload = provider_helper.prepare_payload(audio, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="audio-classification")
return AudioClassificationOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1051,7 +1047,7 @@ async def document_question_answering(
}
provider_helper = get_provider_helper(self.provider, task="document-question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="document-question-answering")
return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1119,7 +1115,7 @@ async def feature_extraction(
}
provider_helper = get_provider_helper(self.provider, task="feature-extraction")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="feature-extraction")
np = _import_numpy()
return np.array(_bytes_to_dict(response), dtype="float32")
Expand Down Expand Up @@ -1172,7 +1168,7 @@ async def fill_mask(
parameters = {"targets": targets, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="fill-mask")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="fill-mask")
return FillMaskOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1218,7 +1214,7 @@ async def image_classification(
parameters = {"function_to_apply": function_to_apply, "top_k": top_k}
provider_helper = get_provider_helper(self.provider, task="image-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="image-classification")
return ImageClassificationOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1281,7 +1277,7 @@ async def image_segmentation(
}
provider_helper = get_provider_helper(self.provider, task="image-segmentation")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="image-segmentation")
output = ImageSegmentationOutputElement.parse_obj_as_list(response)
for item in output:
Expand Down Expand Up @@ -1356,7 +1352,7 @@ async def image_to_image(
}
provider_helper = get_provider_helper(self.provider, task="image-to-image")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="image-to-image")
return _bytes_to_image(response)

Expand Down Expand Up @@ -1534,7 +1530,7 @@ async def object_detection(
}
provider_helper = get_provider_helper(self.provider, task="object-detection")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, expect_binary=True)
payload = provider_helper.prepare_payload(inputs=image, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="object-detection")
return ObjectDetectionOutputElement.parse_obj_as_list(response)

Expand Down Expand Up @@ -1613,7 +1609,7 @@ async def question_answering(
inputs: Dict[str, Any] = {"question": question, "context": context}
provider_helper = get_provider_helper(self.provider, task="question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="question-answering")
# Parse the response as a single `QuestionAnsweringOutputElement` when top_k is 1 or not provided, or a list of `QuestionAnsweringOutputElement` to ensure backward compatibility.
output = QuestionAnsweringOutputElement.parse_obj(response)
Expand Down Expand Up @@ -1731,7 +1727,7 @@ async def summarization(
}
provider_helper = get_provider_helper(self.provider, task="summarization")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = await self.post(**payload, model=model, task="summarization")
return SummarizationOutput.parse_obj_as_list(response)[0]

Expand Down Expand Up @@ -1797,7 +1793,7 @@ async def table_question_answering(
}
provider_helper = get_provider_helper(self.provider, task="table-question-answering")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=inputs, parameters=parameters, model=model)
response = await self.post(
**payload,
model=model,
Expand Down Expand Up @@ -1950,7 +1946,7 @@ async def text_classification(
}
provider_helper = get_provider_helper(self.provider, task="text-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters)
payload = provider_helper.prepare_payload(inputs=text, parameters=parameters, model=model)
response = await self.post(
**payload,
model=model,
Expand Down Expand Up @@ -2547,11 +2543,7 @@ async def text_to_image(

provider_helper = get_provider_helper(self.provider, task="text-to-image")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(
prompt,
parameters=parameters,
model=model,
)
payload = provider_helper.prepare_payload(prompt, parameters=parameters, model=model)

response = await self.post(**payload, model=model, task="text-to-image")
response = provider_helper.get_response(response)
Expand Down Expand Up @@ -3084,7 +3076,7 @@ async def zero_shot_image_classification(
}
provider_helper = get_provider_helper(self.provider, task="zero-shot-image-classification")
model = provider_helper.map_model(model=model or self.model)
payload = provider_helper.prepare_payload(image, parameters=parameters, model=model, expect_binary=True)
payload = provider_helper.prepare_payload(image, parameters=parameters, model=model)
response = await self.post(
**payload,
model=model,
Expand Down
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