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import os
import gradio as gr
from PIL import Image
import logging
from zipfile import ZipFile
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,
):
self.file_output = None
self.model_selector = None
self.stripped_cb = None
self.registered_cb = None
self.run_btn = None
self.slider = None
self.download_file = None
# global states
self.images = []
self.pred_images = []
self.image_boxes = []
self.model_name = model_name
self.cwd = cwd
self.share = share
self.class_name = "tumorcore" # default
self.class_names = {
"tumorcore": "MRI_TumorCore",
"NETC": "MRI_Necrosis",
"residual-tumor": "MRI_TumorCE_Postop",
"cavity": "MRI_Cavity",
"brain": "MRI_Brain",
}
self.result_names = {
"tumorcore": "Tumor",
"NETC": "NETC",
"residual-tumor": "Tumor",
"cavity": "Cavity",
"brain": "Brain",
}
self.volume_renderer = gr.Model3D(
clear_color=[0.0, 0.0, 0.0, 0.0],
label="3D Model",
visible=True,
elem_id="model-3d",
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, stripped_inputs_status:bool=False):
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],
stripped_inputs_status=stripped_inputs_status,
)
nifti_to_glb("prediction.nii.gz")
self.images = load_to_numpy(path)
self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
slider = gr.Slider(
minimum=0,
maximum=len(self.images) - 1,
value=int(len(self.images) / 2),
step=1,
label="Which 2D slice to show",
interactive=True,
)
return "./prediction.obj", slider
def get_img_pred_pair(self, k):
img = self.images[k]
img_pil = Image.fromarray(img)
seg_list = []
seg_list.append((self.pred_images[k], self.class_name))
return img_pil, seg_list
def setup_interface_inputs(self):
with gr.Row():
with gr.Column():
self.file_output = gr.File(file_count="single", elem_id="upload")
with gr.Column():
self.model_selector = gr.Dropdown(
list(self.class_names.keys()),
label="Segmentation task",
info="Select the segmentation model to run",
multiselect=False,
# size="sm",
)
with gr.Column():
with gr.Row():
self.stripped_cb = gr.Checkbox(label="Stripped inputs")
self.registered_cb = gr.Checkbox(label="Co-registered inputs")
with gr.Row():
self.run_btn = gr.Button("Run segmentation", scale=1)
def setup_interface_outputs(self):
with gr.Row():
with gr.Group():
with gr.Column():
t = gr.AnnotatedImage(
visible=True,
elem_id="model-2d",
color_map={self.class_name: "#ffae00"},
height=512,
width=512,
)
self.slider = gr.Slider(
minimum=0,
maximum=1,
value=0,
step=1,
label="Which 2D slice to show",
interactive=True,
)
self.slider.change(fn=self.get_img_pred_pair, inputs=self.slider, outputs=t)
with gr.Group():
self.volume_renderer.render()
self.download_btn = gr.DownloadButton(label="Download results", visible=False)
self.download_file = gr.File(label="Download Zip", interactive=True, visible=False)
def package_results(self):
"""Generates text files and zips them."""
output_dir = "temp_output"
os.makedirs(output_dir, exist_ok=True)
zip_filename = os.path.join(output_dir, "generated_files.zip")
with ZipFile(zip_filename, 'w') as zf:
zf.write("./prediction.nii.gz")
return zip_filename
def run(self):
css = """
#model-3d {
height: 512px;
}
#model-2d {
height: 512px;
margin: auto;
}
#upload {
height: 120px;
}
"""
with gr.Blocks(css=css) as demo:
# Define the interface components first
self.setup_interface_inputs()
with gr.Row():
gr.Examples(
examples=[
os.path.join(self.cwd, "t1gd.nii.gz"),
],
inputs=self.file_output,
outputs=self.file_output,
fn=self.upload_file,
cache_examples=True,
)
self.setup_interface_outputs()
# Define the signals/slots
self.file_output.upload(self.upload_file, self.file_output, self.file_output)
self.model_selector.input(fn=lambda x: self.set_class_name(x), inputs=self.model_selector, outputs=None)
self.run_btn.click(fn=self.process, inputs=[self.file_output, self.stripped_cb],
outputs=[self.volume_renderer, self.slider]).then(fn=lambda:
gr.DownloadButton(visible=True), inputs=None, outputs=self.download_btn)
self.download_btn.click(fn=self.package_results, inputs=[], outputs=self.download_file).then(fn=lambda
file_path: gr.File(label="Download Zip", visible=True, value=file_path), inputs=self.download_file,
outputs=self.download_file)
# 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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