3. Serve an algorithm

Any algorithm can be served as a web API via a built-in FastAPI server. Once served, the algorithm can be used from Napari, QuPath, or Python through HTTP requests, and from the same or another machine.

Serving an algorithm

Pass the algorithm to sk.serve(). Save the following code as a Python script, for example threshold_server.py:

import imaging_server_kit as sk
import skimage.data

@sk.algorithm(
    name="Intensity threshold",
    parameters={"threshold": sk.Integer(name="Threshold", min=0, max=255, default=128)},
    samples=[{"image": skimage.data.coins()}],
)
def threshold_algo(image, threshold):
    mask = image > threshold
    return sk.Mask(mask, name="Binary mask")

if __name__ == "__main__":
    sk.serve(threshold_algo)  # <- Serve the algorithm

Then run it from a terminal:

python threshold_server.py

sk.serve() starts a FastAPI server that exposes your algorithm through a set of predefined routes (see HTTP endpoints).

By default, the server listens on port 8000 on all network interfaces. Open http://localhost:8000 in a browser to see the algorithm's documentation page. FastAPI also generates an interactive page at http://localhost:8000/docs that documents every route.

Connecting from Napari

In Napari, open Plugins > Imaging Server Kit > Connect to server. Under Server URL, enter http://localhost:8000 and press Connect. Your threshold algorithm appears in the Algorithm dropdown, with the same features as in the local case: loading samples, running the algorithm, and opening its documentation.

Algorithm servers can also be used from QuPath (see Usage with QuPath), and from Python, which will be the topic of the next step of this tutorial.

Summary

• Use sk.serve() in a Python script to expose an algorithm as a web service.
• By default, the server is reachable at http://localhost:8000.
• Connect to algorithm servers from Napari with Plugins > Imaging Server Kit > Connect to server.

To make a server reachable from other machines, see Serve an algorithm.

Next steps

Next, we will cover how to use algorithms from Python, locally and through a server.