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import gradio as gr
from .compute import run_model
from .logger import setup_logger, read_logs
from .utils import load_ct_to_numpy
# setup logging
LOGGER = setup_logger()
class WebUI:
def __init__(
self,
model_name: str = None,
cwd: str = "/home/user/app/",
share: int = 1,
):
# global states
self.fixed_images = []
self.moving_images = []
self.pred_images = []
# @TODO: This should be dynamically set based on chosen volume size
self.nb_slider_items = 128
self.model_name = model_name
self.cwd = cwd
self.share = share
self.class_names = {
"Brain": "B",
"Liver": "L"
}
self.class_name = "Brain"
self.fixed_image_path = None
self.moving_image_path = None
self.fixed_seg_path = None
self.moving_seg_path = None
# define widgets not to be rendered immediantly, but later on
self.slider = gr.Slider(
1,
self.nb_slider_items,
value=1,
step=1,
label="Which 2D slice to show",
)
self.run_btn = gr.Button("Run analysis", show_progress="full", elem_id="button").style(
full_width=False, size="lg"
)
def set_class_name(self, value):
LOGGER.info(f"Changed task to: {value}")
self.class_name = value
def upload_file(self, files):
return [f.name for f in files]
def update_fixed(self, cfile):
self.fixed_image_path = cfile.name
return self.fixed_image_path
def update_moving(self, cfile):
self.moving_image_path = cfile.name
return self.moving_image_path
def update_fixed_seg(self, cfile):
self.fixed_seg_path = cfile.name
return self.fixed_seg_path
def update_moving_seg(self, cfile):
self.moving_seg_path = cfile.name
return self.moving_seg_path
def process(self):
if (self.fixed_image_path is None) or (self.moving_image_path is None):
raise ValueError("Please, select both a fixed and moving image before running inference.")
output_path = self.cwd
run_model(self.fixed_image_path, self.moving_image_path, self.fixed_seg_path, self.moving_seg_path, output_path, self.class_names[self.class_name])
# reset - to avoid using these segmentations again for new images
self.fixed_seg_path = None
self.moving_seg_path = None
self.fixed_images = load_ct_to_numpy(self.fixed_image_path)
self.moving_images = load_ct_to_numpy(self.moving_image_path)
self.pred_images = load_ct_to_numpy(output_path + "pred_image.nii.gz")
return self.pred_images[0]
def get_fixed_image(self, k):
k = int(k) - 1
out = [gr.Image.update(visible=False)] * self.nb_slider_items
out[k] = gr.Image.update(
self.fixed_images[k],
visible=True,
)
return out
def get_moving_image(self, k):
k = int(k) - 1
out = [gr.Image.update(visible=False)] * self.nb_slider_items
out[k] = gr.Image.update(
self.moving_images[k],
visible=True,
)
return out
def get_pred_image(self, k):
k = int(k) - 1
out = [gr.Image.update(visible=False)] * self.nb_slider_items
out[k] = gr.Image.update(
self.pred_images[k],
visible=True,
)
return out
def run(self):
css = """
#model-2d {
height: 512px;
margin: auto;
}
#upload {
height: 120px;
}
#button {
height: 120px;
}
#dropdown {
height: 120px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column():
file_fixed = gr.File(file_count="single", elem_id="upload", label="Select Fixed Image", show_label=True)
file_fixed.upload(self.update_fixed, file_fixed, file_fixed)
file_moving = gr.File(file_count="single", elem_id="upload", label="Select Moving Image", show_label=True)
file_moving.upload(self.update_moving, file_moving, file_moving)
#with gr.Group():
with gr.Column():
file_fixed_seg = gr.File(file_count="single", elem_id="upload", label="Select Fixed Seg Image", show_label=True)
file_fixed_seg.upload(self.update_fixed_seg, file_fixed_seg, file_fixed_seg)
file_moving_seg = gr.File(file_count="single", elem_id="upload", label="Select Moving Seg Image", show_label=True)
file_moving_seg.upload(self.update_moving_seg, file_moving_seg, file_moving_seg)
with gr.Column():
model_selector = gr.Dropdown(
list(self.class_names.keys()),
label="Task",
info="Which task to perform image-to-registration on",
multiselect=False,
size="sm",
default="Brain",
elem_id="dropdown",
)
model_selector.input(
fn=lambda x: self.set_class_name(x),
inputs=model_selector,
outputs=None,
)
self.run_btn.render()
logs = gr.Textbox(label="Logs", info="Verbose from inference will be displayed below.", max_lines=8, autoscroll=True)
demo.load(read_logs, None, logs, every=1)
with gr.Row():
with gr.Box():
with gr.Column():
with gr.Row():
fixed_images = []
for i in range(self.nb_slider_items):
visibility = True if i == 1 else False
t = gr.Image(
visible=visibility, elem_id="model-2d", label="fixed image", show_label=True,
).style(
height=512,
width=512,
)
fixed_images.append(t)
moving_images = []
for i in range(self.nb_slider_items):
visibility = True if i == 1 else False
t = gr.Image(
visible=visibility, elem_id="model-2d", label="moving image", show_label=True,
).style(
height=512,
width=512,
)
moving_images.append(t)
pred_images = []
for i in range(self.nb_slider_items):
if i == 0:
first_pred_component = t
visibility = True if i == 1 else False
t = gr.Image(
visible=visibility, elem_id="model-2d", label="predicted fixed image", show_label=True,
).style(
height=512,
width=512,
)
pred_images.append(t)
self.run_btn.click(
fn=self.process,
inputs=None,
outputs=first_pred_component,
)
self.slider.input(
self.get_fixed_image, self.slider, fixed_images
)
self.slider.input(
self.get_moving_image, self.slider, moving_images
)
self.slider.input(
self.get_pred_image, self.slider, pred_images
)
self.slider.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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