Quickstart

from bioptimus.models.backbones import Backbone
from bioptimus.models.types import Models

model = Backbone(Models.M_OPTIMUS, backend="remote", base_url="http://localhost:8080")

from bioptimus.models.backbones import Backbone
from bioptimus.models.types import Models

model = Backbone(Models.M_OPTIMUS, backend="aws",
                 endpoint_name="m-optimus", region_name="us-east-1")

import numpy as np
from PIL import Image
from bioptimus.inference.schemas import ModelRequest

# Replace the blank tile with a real 224×224 H&E tile at 0.5 µm/px.
tile = Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8))
request = ModelRequest(image_data=tile, slide_name="demo",
                       x=0, y=0, width=224, height=224, patch_idx=0)

response = model.embed(request)
print(len(response.output))   # 1536

import numpy as np
from PIL import Image
from bioptimus.inference.schemas import ModelRequest

# Replace the blank tile with a real 224×224 H&E tile at 0.5 µm/px.
tile = Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8))
request = ModelRequest(image_data=tile, slide_name="demo",
                       x=0, y=0, width=224, height=224, patch_idx=0)

response = model.predict(request)   # spatial gene expression for the tile
print(len(response.output))         # one value per output gene