Refactored + updated gradio app README

README.md

@@ -4,7 +4,7 @@ colorFrom: indigo
 colorTo: indigo
 sdk: docker
 app_port: 7860
-emoji: 🚀
+emoji: 🔎
 pinned: false
 license: mit
 app_file: demo/app.py

demo/README.md

@@ -11,3 +11,33 @@ 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.
+
+Natural future TODOs include:
+- [ ] Add gallery widget to enable scrolling through 2D slices
+- [ ] Render segmentation for individual 2D slices as overlays

demo/app.py

@@ -30,14 +30,6 @@ def run_model(input_path):
     from livermask.utils.run import run_analysis
     
     run_analysis(cpu=True, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=False, name="/home/user/app/model.h5", mp_enabled=False)
-    
-    #cmd_docker = ["python3", "-m", "livermask.livermask", "--input", input_path, "--output", "prediction", "--verbose"]
-    #sp.check_call(cmd_docker, shell=True)  # @FIXME: shell=True here is not optimal -> starts a shell after calling script
-    
-    #p = sp.Popen(cmd_docker, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    #stdout, stderr = p.communicate()
-    #print("stdout:", stdout)
-    #print("stderr:", stderr)
 
 
 def load_mesh(mesh_file_name):