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Request and response schemas for model inference. These Pydantic models define the JSON payloads exchanged between clients and a deployed model endpoint (e.g. SageMaker or a local server). Each request/response pair corresponds to a single tile.

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ModelRequest

class ModelRequest(BaseModel)
Payload sent to the model endpoint for a single tile. The tile image is base64-encoded for JSON transport. Bulk RNA counts can optionally be included for multimodal models.
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image_data
str
required
Base64-encoded tile image (PNG).
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bulk_rna
list[float] | None
Optional bulk RNA counts vector.
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slide_name
str
required
Stem of the source slide filename.
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x
int
required
Tile x-coordinate in the slide.
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y
int
required
Tile y-coordinate in the slide.
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width
int
required
Tile width in pixels.
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height
int
required
Tile height in pixels.
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tissue_ratio
float | None
Fraction of tissue in the tile.
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patch_idx
int
required
Index of this tile in the extraction plan.

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image

@property
def image() -> Image.Image
Decodes and returns the tile image as PIL.

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ModelResponse

class ModelResponse(BaseModel)
Payload returned by the model endpoint for a single tile. Carries the model output together with the full tile metadata from the originating request, so that downstream writers can persist everything without needing a separate schema.
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output
list[float]
Model output vector for this tile.
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slide_name
str
Stem of the source slide filename.
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x
int
Tile x-coordinate in the slide.
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y
int
Tile y-coordinate in the slide.
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width
int
Tile width in pixels.
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height
int
Tile height in pixels.
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tissue_ratio
float | None
Fraction of tissue in the tile.
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patch_idx
int
Index of this tile in the extraction plan.
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resolution
float | None
Extraction resolution (MPP) if available.

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SageMakerRequest

class SageMakerRequest(ModelRequest)
Extended request for the SageMaker /invocations endpoint. Adds a model_name field so the server can dispatch to the correct model and a mode field to select the scheduler ("prediction" or "embedding").