import os import gradio as gr import numpy as np from .compute import run_model from .utils import load_ct_to_numpy 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" } # 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").style( full_width=False, size="lg" ) def set_class_name(self, value): print("Changed task to:", value) self.class_name = value def upload_file(self, files): return [f.name for f in files] def process(self, mesh_file_names): fixed_image_path = mesh_file_names[0].name moving_image_path = mesh_file_names[1].name output_path = "./" run_model(fixed_image_path, moving_image_path, output_path, self.class_names[self.class_name]) self.fixed_images = load_ct_to_numpy(fixed_image_path) self.moving_images = load_ct_to_numpy(moving_image_path) self.pred_images = load_ct_to_numpy(output_path + "pred_image.nii.gz") return None 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-fixed { height: 512px; margin: auto; } #model-2d-moving { height: 512px; margin: auto; } #model-2d-pred { height: 512px; margin: auto; } #upload { height: 120px; } """ with gr.Blocks(css=css) as demo: with gr.Row(): file_output = gr.File(file_count="multiple", elem_id="upload") file_output.upload(self.upload_file, file_output, file_output) model_selector = gr.Dropdown( list(self.class_names.keys()), label="Task", info="Which task to perform image-to-registration on", multiselect=False, size="sm", ) model_selector.input( fn=lambda x: self.set_class_name(x), inputs=model_selector, outputs=None, ) self.run_btn.render() """ with gr.Row(): gr.Examples( examples=[ os.path.join(self.cwd, "ixi_image.nii.gz"), os.path.join(self.cwd, "ixi_image2.nii.gz"), ], inputs=file_output, outputs=file_output, fn=self.upload_file, cache_examples=True, ) """ 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-fixed", 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-moving", 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): visibility = True if i == 1 else False t = gr.Image( visible=visibility, elem_id="model-2d-pred", label="predicted fixed image", show_label=True, ).style( height=512, width=512, ) pred_images.append(t) self.run_btn.click( fn=lambda x: self.process(x), inputs=file_output, outputs=None, ) 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 )