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Add files for gradio space
Browse files- README.md +3 -3
- app.py +50 -0
- examples/spleen_46.nii.gz +3 -0
- requirements.txt +2 -0
README.md
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---
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title: Spleen Segmentation
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sdk: gradio
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sdk_version: 3.1.1
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app_file: app.py
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---
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title: Spleen Segmentation
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emoji: 👀
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colorFrom: indigo
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colorTo: red
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sdk: gradio
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sdk_version: 3.1.1
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app_file: app.py
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app.py
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import os
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import gradio as gr
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import torch
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from monai import bundle
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BUNDLE_NAME = 'spleen_ct_segmentation_v0.1.0'
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BUNDLE_PATH = os.path.join(torch.hub.get_dir(), 'bundle', BUNDLE_NAME)
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examples = ['examples/spleen_46.nii.gz']
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model, _, _ = bundle.load(
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name = BUNDLE_NAME,
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source = 'hf_hub',
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repo = 'katielink/spleen_ct_segmentation_v0.1.0',
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load_ts_module=True,
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)
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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parser = bundle.load_bundle_config(BUNDLE_PATH, 'inference.json')
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preproc_transforms = parser.get_parsed_content('preprocessing', lazy=True, eval_expr=True, instantiate=True)
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inferer = parser.get_parsed_content('inferer', lazy=True, eval_expr=True, instantiate=True)
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def predict(input_file, z_axis, model=model, device=device):
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data = {'image': [input_file.name]}
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data = preproc_transforms(data)
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model.to(device)
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model.eval()
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with torch.no_grad():
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inputs = data['image'].to(device)[None,...]
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data['pred'] = inferer(inputs=inputs, network=model)
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input_image = data['image'].numpy()
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pred_image = torch.argmax(data['pred'], dim=1).cpu().detach().numpy()
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return input_image[0, :, :, z_axis], pred_image[0, :, :, z_axis]*255
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iface = gr.Interface(
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fn=predict,
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inputs=[
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gr.File(label='Nifti file'),
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gr.Slider(0, 200, label='z-axis', value=50)
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],
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outputs=['image', 'image'],
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title='Segment the Spleen from a CT Scan using MONAI',
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examples=examples,
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)
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iface.launch()
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examples/spleen_46.nii.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:1dc87026596e782c25de81ab171cd2b456a8e2c76e304949e9318451406c8edf
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size 28610147
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requirements.txt
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git+https://github.com/katielink/MONAI.git@4042-download-hf-hub-bundle
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huggingface_hub
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