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import os
import gradio as gr
from .inference import run_model
from .utils import load_pred_volume_to_numpy
from .utils import load_to_numpy
from .utils import nifti_to_glb
class WebUI:
def __init__(
self,
model_name: str = None,
cwd: str = "/home/user/app/",
share: int = 1,
):
# global states
self.images = []
self.pred_images = []
# @TODO: This should be dynamically set based on chosen volume size
self.nb_slider_items = 512
self.model_name = model_name
self.cwd = cwd
self.share = share
self.class_name = "meningioma" # default
self.class_names = {
"meningioma": "MRI_Meningioma",
"lower-grade-glioma": "MRI_LGGlioma",
"metastasis": "MRI_Metastasis",
"glioblastoma": "MRI_GBM",
"brain": "MRI_Brain",
}
self.result_names = {
"meningioma": "Tumor",
"lower-grade-glioma": "Tumor",
"metastasis": "Tumor",
"glioblastoma": "Tumor",
"brain": "Brain",
}
# define widgets not to be rendered immediately, but later on
self.slider = gr.Slider(
minimum=1,
maximum=self.nb_slider_items,
value=1,
step=1,
label="Which 2D slice to show",
interactive=True,
)
self.volume_renderer = gr.Model3D(
clear_color=[0.0, 0.0, 0.0, 0.0],
label="3D Model",
visible=True,
elem_id="model-3d",
).style(height=512)
def set_class_name(self, value):
print("Changed task to:", value)
self.class_name = value
def combine_ct_and_seg(self, img, pred):
return (img, [(pred, self.class_name)])
def upload_file(self, file):
return file.name
def process(self, mesh_file_name):
path = mesh_file_name.name
run_model(
path,
model_path=os.path.join(self.cwd, "resources/models/"),
task=self.class_names[self.class_name],
name=self.result_names[self.class_name],
)
nifti_to_glb("prediction.nii.gz")
self.images = load_to_numpy(path)
# @TODO. Dynamic update of the slider does not seem to work like this
# self.nb_slider_items = len(self.images)
# self.slider.update(value=int(self.nb_slider_items/2), maximum=self.nb_slider_items)
self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
return "./prediction.obj"
def get_img_pred_pair(self, k):
k = int(k) - 1
# @TODO. Will the duplicate the last slice to fill up, since slider not adjustable right now
if k >= len(self.images):
k = len(self.images) - 1
out = [gr.AnnotatedImage.update(visible=False)] * self.nb_slider_items
out[k] = gr.AnnotatedImage.update(
self.combine_ct_and_seg(self.images[k], self.pred_images[k]),
visible=True,
)
return out
def run(self):
css = """
#model-3d {
height: 512px;
}
#model-2d {
height: 512px;
margin: auto;
}
#upload {
height: 120px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
file_output = gr.File(file_count="single", elem_id="upload")
file_output.upload(self.upload_file, file_output, file_output)
model_selector = gr.Dropdown(
list(self.class_names.keys()),
label="Segmentation task",
info="Select the preoperative segmentation model to run",
multiselect=False,
size="sm",
)
model_selector.input(
fn=lambda x: self.set_class_name(x),
inputs=model_selector,
outputs=None,
)
run_btn = gr.Button("Run segmentation").style(
full_width=False, size="lg"
)
run_btn.click(
fn=lambda x: self.process(x),
inputs=file_output,
outputs=self.volume_renderer,
)
with gr.Row():
gr.Examples(
examples=[
os.path.join(self.cwd, "t1gd.nii.gz"),
],
inputs=file_output,
outputs=file_output,
fn=self.upload_file,
cache_examples=True,
)
with gr.Row():
with gr.Box():
with gr.Column():
image_boxes = []
for i in range(self.nb_slider_items):
visibility = True if i == 1 else False
t = gr.AnnotatedImage(
visible=visibility, elem_id="model-2d"
).style(
color_map={self.class_name: "#ffae00"},
height=512,
width=512,
)
image_boxes.append(t)
self.slider.input(
self.get_img_pred_pair, self.slider, image_boxes
)
self.slider.render()
with gr.Box():
self.volume_renderer.render()
# sharing app publicly -> share=True:
# https://gradio.app/sharing-your-app/
# inference times > 60 seconds -> need queue():
# https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
demo.queue().launch(
server_name="0.0.0.0", server_port=7860, share=self.share
)
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