83 lines
3.5 KiB
Python
83 lines
3.5 KiB
Python
import base64
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from typing import Any, Dict, Optional, Union
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from huggingface_hub.hf_api import InferenceProviderMapping
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from huggingface_hub.inference._common import RequestParameters, _as_dict
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from huggingface_hub.inference._providers._common import (
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BaseConversationalTask,
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BaseTextGenerationTask,
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TaskProviderHelper,
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filter_none,
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)
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class NebiusTextGenerationTask(BaseTextGenerationTask):
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def __init__(self):
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super().__init__(provider="nebius", base_url="https://api.studio.nebius.ai")
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def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
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output = _as_dict(response)["choices"][0]
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return {
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"generated_text": output["text"],
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"details": {
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"finish_reason": output.get("finish_reason"),
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"seed": output.get("seed"),
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},
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}
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class NebiusConversationalTask(BaseConversationalTask):
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def __init__(self):
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super().__init__(provider="nebius", base_url="https://api.studio.nebius.ai")
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def _prepare_payload_as_dict(
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self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
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) -> Optional[Dict]:
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payload = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)
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response_format = parameters.get("response_format")
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if isinstance(response_format, dict) and response_format.get("type") == "json_schema":
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json_schema_details = response_format.get("json_schema")
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if isinstance(json_schema_details, dict) and "schema" in json_schema_details:
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payload["guided_json"] = json_schema_details["schema"] # type: ignore [index]
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return payload
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class NebiusTextToImageTask(TaskProviderHelper):
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def __init__(self):
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super().__init__(task="text-to-image", provider="nebius", base_url="https://api.studio.nebius.ai")
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def _prepare_route(self, mapped_model: str, api_key: str) -> str:
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return "/v1/images/generations"
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def _prepare_payload_as_dict(
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self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
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) -> Optional[Dict]:
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mapped_model = provider_mapping_info.provider_id
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parameters = filter_none(parameters)
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if "guidance_scale" in parameters:
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parameters.pop("guidance_scale")
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if parameters.get("response_format") not in ("b64_json", "url"):
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parameters["response_format"] = "b64_json"
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return {"prompt": inputs, **parameters, "model": mapped_model}
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def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
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response_dict = _as_dict(response)
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return base64.b64decode(response_dict["data"][0]["b64_json"])
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class NebiusFeatureExtractionTask(TaskProviderHelper):
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def __init__(self):
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super().__init__(task="feature-extraction", provider="nebius", base_url="https://api.studio.nebius.ai")
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def _prepare_route(self, mapped_model: str, api_key: str) -> str:
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return "/v1/embeddings"
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def _prepare_payload_as_dict(
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self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
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) -> Optional[Dict]:
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return {"input": inputs, "model": provider_mapping_info.provider_id}
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def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
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embeddings = _as_dict(response)["data"]
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return [embedding["embedding"] for embedding in embeddings]
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