title: 'livermask: Automatic Liver Parenchyma and vessel segmentation in CT'
colorFrom: indigo
colorTo: indigo
sdk: docker
app_port: 7860
emoji: 🚀
pinned: false
license: mit
app_file: demo/app.py
livermask Hugging Face demo - through docker SDK
Deploying simple models in a gradio-based web interface in Hugging Face spaces is easy. For any other custom pipeline, with various dependencies and challenging behaviour, it might be necessary to use Docker containers instead.
Deployment through a custom Docker image over the existing Gradio image was
necessary in this case due to tensorflow and gradio having colliding
versions. As livermask depends on tf, the only way to get around it was
fixing the broken dependency, which was handled by reinstalling and changing
the typing_extensions with a version that gradio required for the widgets
we used. Luckily, this did not break anything in tf, even though tf has a
very strict versioning criteria for this dependency.
Anyways, everything works as intended now. For every new push to the main branch,
continuous deployment to the Hugging Face livermask space is performed through
GitHub Actions.
When the space is updated, the Docker image is rebuilt/updated (caching if possible). Then when finished, the end users can test the app as they please.
Right now, the functionality of the app is extremely limited, only offering a widget
for uploading a NIfTI file (.nii or .nii.gz) and visualizing the produced surface
of the predicted liver parenchyma 3D volume when finished processing.
Analysis process can be monitored from the Logs tab next to the Running button
in the Hugging Face livermask space.
It is also possible to build the app as a docker image and deploy it. To do so follow these steps:
docker build -t livermask ..
docker run -it -p 7860:7860 livermask
Then open http://127.0.0.1:7860 in your favourite internet browser to view the demo.
TODOs:
- Add gallery widget to enable scrolling through 2D slices
- Render segmentation for individual 2D slices as overlays