Dockerized livermask and app

README.md

@@ -1,13 +1,3 @@
----
-title: 'livermask: Automatic Liver Parenchyma and vessel segmentation in CT'
-colorFrom: indigo
-sdk: gradio
-sdk_version: 3.32.0
-emoji: 🚀
-pinned: false
-license: mit
-app_file: demo/app.py
----
 <div align="center">
 <h1 align="center">livermask</h1>
 <h3 align="center">Automatic liver parenchyma and vessel segmentation in CT using deep learning</h3>

demo/Dockerfile

@@ -0,0 +1,31 @@
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.7
+
+WORKDIR /code
+
+# install dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# resolve issue with tf==2.4 and gradio versioning issue
+RUN python3 -m pip install --force-reinstall typing_extensions==4.0.0
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["python3", "app.py"]

demo/README.md

@@ -0,0 +1,13 @@
+---
+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

demo/app.py

@@ -27,7 +27,17 @@ def nifti_to_glb(path):
 
 
 def run_model(input_path):
-    sp.check_call(["livermask", "--input", input_path, "--output", "prediction", "--verbose"])
+    from livermask.utils.run import run_analysis
+    
+    run_analysis(cpu=False, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=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):
@@ -38,6 +48,7 @@ def load_mesh(mesh_file_name):
 
 
 if __name__ == "__main__":
+    print("Launching demo...")
     demo = gr.Interface(
         fn=load_mesh,
         inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
@@ -45,4 +56,4 @@ if __name__ == "__main__":
         title="livermask: Automatic Liver Parenchyma segmentation in CT",
         description="Using pretrained deep learning model trained on the LiTS17 dataset",
     )
-    demo.launch()
+    demo.launch(server_name="0.0.0.0", server_port=7860)

demo/requirements.txt

@@ -0,0 +1,2 @@
+livermask @ git+https://github.com/andreped/livermask.git
+gradio==3.32.0