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	* fix(deps): update machine-learning * updated ruff command * use isinstance --------- Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
		
			
				
	
	
		
			46 lines
		
	
	
		
			938 B
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			46 lines
		
	
	
		
			938 B
		
	
	
	
		
			Python
		
	
	
	
	
	
| from enum import StrEnum
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| from typing import Any, Protocol, TypeAlias, TypedDict, TypeGuard
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| 
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| import numpy as np
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| from pydantic import BaseModel
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| 
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| ndarray_f32: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
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| ndarray_i64: TypeAlias = np.ndarray[int, np.dtype[np.int64]]
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| ndarray_i32: TypeAlias = np.ndarray[int, np.dtype[np.int32]]
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| 
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| 
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| class TextResponse(BaseModel):
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|     __root__: str
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| 
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| 
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| class MessageResponse(BaseModel):
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|     message: str
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| 
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| 
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| class BoundingBox(TypedDict):
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|     x1: int
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|     y1: int
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|     x2: int
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|     y2: int
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| 
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| 
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| class ModelType(StrEnum):
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|     CLIP = "clip"
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|     FACIAL_RECOGNITION = "facial-recognition"
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| 
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| 
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| class HasProfiling(Protocol):
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|     profiling: dict[str, float]
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| 
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| 
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| class Face(TypedDict):
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|     boundingBox: BoundingBox
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|     embedding: ndarray_f32
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|     imageWidth: int
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|     imageHeight: int
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|     score: float
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| 
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| 
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| def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
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|     return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
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