Drop conda support + bump v1.5.0

.github/workflows/build.yml

@@ -79,45 +79,3 @@ jobs:
       - name: Test inference .nii
         run: |
           livermask --input samples --output results --verbose --vessels
-
-  conda:
-    needs: build
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ windows-2019, ubuntu-20.04 ] #, macos-11 ]
-        python-version: [ 3.7 ]
-    steps:
-      - uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          python-version: 3.7
-
-      - name: Download artifact
-        uses: actions/download-artifact@master
-        with:
-          name: "Python wheel"
-
-      - name: Install wheel
-        if: runner.os == 'Windows'
-        run: |
-              $TARGET = Resolve-Path "${{github.workspace}}/livermask-*.whl" | Select -ExpandProperty Path
-              echo $TARGET
-              pip3 install --find-links=${{github.workspace}} $TARGET
-
-      - name: Install wheel
-        if: runner.os != 'Windows'
-        run: pip3 install --find-links=${{github.workspace}} livermask-*.whl
-
-      - name: Download test sample
-        run: |
-          pip3 install gdown==4.4.0
-          mkdir samples
-          mkdir results
-          cd samples
-          gdown https://drive.google.com/uc?id=1shjSrFjS4PHE5sTku30PZTLPZpGu24o3
-          gdown https://drive.google.com/uc?id=1bNmls5o0Rxw5HvBF1IYnEzmpysYJaywN
-
-      - name: Test inference
-        run: |
-          livermask --input samples --output results --verbose --vessels

demo/app.py

@@ -40,7 +40,7 @@ def load_mesh(mesh_file_name):
 if __name__ == "__main__":
     demo = gr.Interface(
         fn=load_mesh,
-        inputs=gr.UploadButton(label="Click to Upload a File", file_type=[".nii", ".nii.nz"], file_count="single"),
+        inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
         outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
         title="livermask: Automatic Liver Parenchyma segmentation in CT",
         description="Using pretrained deep learning model trained on the LiTS17 dataset",

setup.py

@@ -10,7 +10,7 @@ with open('requirements.txt', 'r', encoding='utf-16', errors='ignore') as ff:
 
 setup(
     name='livermask',
-    version='1.4.2',
+    version='1.5.0',
     author="André Pedersen and Javier Pérez de Frutos",
     author_email="[email protected]",
     license='MIT',