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- from enum import StrEnum
- from typing import Any, Protocol, TypeAlias, TypedDict, TypeGuard
- import numpy as np
- from pydantic import BaseModel
- ndarray_f32: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
- ndarray_i64: TypeAlias = np.ndarray[int, np.dtype[np.int64]]
- ndarray_i32: TypeAlias = np.ndarray[int, np.dtype[np.int32]]
- class TextResponse(BaseModel):
- __root__: str
- class MessageResponse(BaseModel):
- message: str
- class BoundingBox(TypedDict):
- x1: int
- y1: int
- x2: int
- y2: int
- class ModelType(StrEnum):
- IMAGE_CLASSIFICATION = "image-classification"
- CLIP = "clip"
- FACIAL_RECOGNITION = "facial-recognition"
- class HasProfiling(Protocol):
- profiling: dict[str, float]
- class Face(TypedDict):
- boundingBox: BoundingBox
- embedding: ndarray_f32
- imageWidth: int
- imageHeight: int
- score: float
- def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
- return hasattr(obj, "profiling") and type(obj.profiling) == dict
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