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Zero
Running
on
Zero
File size: 1,918 Bytes
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from typing import List, Optional, Union
from pydantic import Field, validator
from inference.core.entities.common import ApiKey
from inference.core.entities.requests.inference import (
BaseRequest,
InferenceRequestImage,
)
from inference.core.env import GAZE_VERSION_ID
class GazeDetectionInferenceRequest(BaseRequest):
"""Request for gaze detection inference.
Attributes:
api_key (Optional[str]): Roboflow API Key.
gaze_version_id (Optional[str]): The version ID of Gaze to be used for this request.
do_run_face_detection (Optional[bool]): If true, face detection will be applied; if false, face detection will be ignored and the whole input image will be used for gaze detection.
image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) for inference.
"""
gaze_version_id: Optional[str] = Field(
default=GAZE_VERSION_ID,
examples=["l2cs"],
description="The version ID of Gaze to be used for this request. Must be one of l2cs.",
)
do_run_face_detection: Optional[bool] = Field(
default=True,
examples=[False],
description="If true, face detection will be applied; if false, face detection will be ignored and the whole input image will be used for gaze detection",
)
image: Union[List[InferenceRequestImage], InferenceRequestImage]
model_id: Optional[str] = Field(None)
# TODO[pydantic]: We couldn't refactor the `validator`, please replace it by `field_validator` manually.
# Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-validators for more information.
@validator("model_id", always=True, allow_reuse=True)
def validate_model_id(cls, value, values):
if value is not None:
return value
if values.get("gaze_version_id") is None:
return None
return f"gaze/{values['gaze_version_id']}"
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