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- from enum import StrEnum
- from typing import TypeAlias
- import numpy as np
- from pydantic import BaseModel
- def to_lower_camel(string: str) -> str:
- tokens = [token.capitalize() if i > 0 else token for i, token in enumerate(string.split("_"))]
- return "".join(tokens)
- class TextModelRequest(BaseModel):
- text: str
- class TextResponse(BaseModel):
- __root__: str
- class MessageResponse(BaseModel):
- message: str
- class BoundingBox(BaseModel):
- x1: int
- y1: int
- x2: int
- y2: int
- class ModelType(StrEnum):
- IMAGE_CLASSIFICATION = "image-classification"
- CLIP = "clip"
- FACIAL_RECOGNITION = "facial-recognition"
- 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]]
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