Added argparse to support setting cwd and disabling public link sharing
Browse files- README.md +25 -1
- app.py +14 -4
- neukit/gui.py +7 -5
README.md
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@@ -10,7 +10,7 @@ license: mit
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app_file: app.py
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---
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<div align="center">
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<h1 align="center">neukit</h1>
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<h3 align="center">Automatic brain extraction and preoperative tumor segmentation from MRI</h3>
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@@ -36,6 +36,8 @@ To access the live demo, click on the `Hugging Face` badge above. Below is a sna
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## Development
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Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it:
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```
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Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo.
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## Citation
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If you found the tool useful in your research, please, cite the corresponding software paper:
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app_file: app.py
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---
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<div align="center">M
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<h1 align="center">neukit</h1>
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<h3 align="center">Automatic brain extraction and preoperative tumor segmentation from MRI</h3>
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## Development
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### Docker
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Alternatively, you can deploy the software locally. Note that this is only relevant for development purposes. Simply dockerize the app and run it:
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```
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Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo.
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### Python
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It is also possible to run the app locally without Docker. Just setup a virtual environment and run the app.
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Note that the current working directory would need to be adjusted based on where `neukit` is located on disk.
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```
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git clone https://github.com/andreped/neukit.git
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cd neukit/
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virtualenv -ppython3 venv --clear
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source venv/bin/activate
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pip install -r requirements.txt
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python app.py --cwd ./
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```
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## Troubleshooting
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Note that due to `share=True` being enabled by default when launching the app,
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internet access is required for the app to be launched. This can disabled by setting
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the argument to `--share 0`.
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## Citation
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If you found the tool useful in your research, please, cite the corresponding software paper:
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app.py
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from neukit.gui import WebUI
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def main():
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-
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-
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-
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# initialize and run app
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app.run()
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from neukit.gui import WebUI
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from argparse import ArgumentParser
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import os
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def main():
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parser = ArgumentParser()
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parser.add_argument("--cwd", type=str, default="/home/user/app/", help="Set current working directory (path to app.py).")
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parser.add_argument("--share", type=int, default=1, help="Whether to enable the app to be accessible online -> setups a public link which requires internet access.")
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args = parser.parse_args()
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print("Current working directory:", args.cwd)
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if not os.path.exists(args.cwd):
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raise ValueError("Chosen 'cwd' is not a valid path!")
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if not args.share in [0, 1]:
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raise ValueError("The 'share' argument can only be set to 0 or 1 (boolean), but was:", args.share)
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# initialize and run app
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print("Launching demo...")
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app = WebUI(cwd=args.cwd, share=args.share)
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app.run()
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neukit/gui.py
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import gradio as gr
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from .inference import run_model
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from .utils import load_ct_to_numpy
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@@ -7,7 +8,7 @@ from .utils import nifti_to_glb
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class WebUI:
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def __init__(self, model_name: str = None, cwd: str = "/home/user/app/"):
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# global states
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self.images = []
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self.pred_images = []
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self.model_name = model_name
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self.cwd = cwd
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self.class_name = "meningioma" # default
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self.class_names = {
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path = mesh_file_name.name
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run_model(
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path,
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model_path=self.cwd
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task=self.class_names[self.class_name],
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name=self.result_names[self.class_name],
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)
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with gr.Row():
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gr.Examples(
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examples=[
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self.cwd
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self.cwd
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],
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inputs=file_output,
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outputs=file_output,
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# https://gradio.app/sharing-your-app/
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# inference times > 60 seconds -> need queue():
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# https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
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demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=
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import gradio as gr
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import os
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from .inference import run_model
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from .utils import load_ct_to_numpy
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class WebUI:
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def __init__(self, model_name: str = None, cwd: str = "/home/user/app/", share: int = 1):
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# global states
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self.images = []
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self.pred_images = []
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self.model_name = model_name
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self.cwd = cwd
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self.share = share
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self.class_name = "meningioma" # default
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self.class_names = {
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path = mesh_file_name.name
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run_model(
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path,
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model_path=os.path.join(self.cwd, "resources/models/"),
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task=self.class_names[self.class_name],
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name=self.result_names[self.class_name],
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)
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with gr.Row():
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gr.Examples(
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examples=[
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os.path.join(self.cwd, "RegLib_C01_1.nii"),
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os.path.join(self.cwd, "RegLib_C01_2.nii"),
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],
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inputs=file_output,
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outputs=file_output,
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# https://gradio.app/sharing-your-app/
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# inference times > 60 seconds -> need queue():
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# https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
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demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=self.share)
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