import gradio as gr import utils import Model_Class import Model_Seg import SimpleITK as sitk import torch from numpy import uint8 import spaces image_base64 = utils.image_to_base64("anatomy_aware_pipeline.png") article_html = f"Anatomical pipeline illustration" description_markdown = """ - This tool combines a U-Net Segmentation Model with a ResNet-50 for Classification. - **Usage:** Just drag a pelvic x-ray into the box and hit run. - **Process:** The input image will be segmented and cropped to the SIJ before classification. - **Please Note:** This tool is intended for research purposes only. - **Privacy:** This tool runs completely locally, ensuring data privacy. """ css = """ h1 { text-align: center; display:block; } .markdown-block { background-color: #0b0f1a; /* Light gray background */ color: black; /* Black text */ padding: 10px; /* Padding around the text */ border-radius: 5px; /* Rounded corners */ box-shadow: 0 0 10px rgba(11,15,26,1); display: inline-flex; /* Use inline-flex to shrink to content size */ flex-direction: column; justify-content: center; /* Vertically center content */ align-items: center; /* Horizontally center items within */ margin: auto; /* Center the block */ } .markdown-block ul, .markdown-block ol { background-color: #1e2936; border-radius: 5px; padding: 10px; box-shadow: 0 0 10px rgba(0,0,0,0.3); padding-left: 20px; /* Adjust padding for bullet alignment */ text-align: left; /* Ensure text within list is left-aligned */ list-style-position: inside;/* Ensures bullets/numbers are inside the content flow */ } footer { display:none !important } """ @spaces.GPU def predict_image(input_image, input_file): if input_image is not None: image_path = input_image elif input_file is not None: image_path = input_file else: return None , None , "Please input an image before pressing run" , None , None image_mask = Model_Seg.load_and_segment_image(image_path) overlay_image_np, original_image_np = utils.overlay_mask(image_path, image_mask) image_mask_im = sitk.GetImageFromArray(image_mask[None, :, :].astype(uint8)) image_im = sitk.GetImageFromArray(original_image_np[None, :, :].astype(uint8)) cropped_boxed_im, _ = utils.mask_and_crop(image_im, image_mask_im) cropped_boxed_array = sitk.GetArrayFromImage(cropped_boxed_im) cropped_boxed_array_disp = cropped_boxed_array.squeeze() cropped_boxed_tensor = torch.Tensor(cropped_boxed_array) prediction, image_transformed = Model_Class.load_and_classify_image(cropped_boxed_tensor) gradcam = Model_Class.make_GradCAM(image_transformed) nr_axSpA_prob = float(prediction[0].item()) r_axSpA_prob = float(prediction[1].item()) # Decision based on the threshold considered = "be considered r-axSpA" if r_axSpA_prob > 0.59 else "not be considered r-axSpA" explanation = f"According to the pre-determined cut-off threshold of 0.59, the image should {considered}. This Tool is for research purposes only." pred_dict = {"nr-axSpA": nr_axSpA_prob, "r-axSpA": r_axSpA_prob} return overlay_image_np, pred_dict, explanation, gradcam, cropped_boxed_array_disp with gr.Blocks(css=css, title="Anatomy Aware axSpA") as iface: gr.Markdown("# Anatomy-Aware Image Classification for radiographic axSpA") gr.Markdown(description_markdown, elem_classes="markdown-block") with gr.Row(): with gr.Column(): with gr.Tab("PNG/JPG"): input_image = gr.Image(type='filepath', label="Upload an X-ray Image") with gr.Tab("NIfTI/DICOM"): input_file = gr.File(type='filepath', label="Upload an X-ray Image") with gr.Row(): submit_button = gr.Button("Run", variant="primary") clear_button = gr.ClearButton() with gr.Column(): overlay_image_np = gr.Image(label="Segmentation Mask") pred_dict = gr.Label(label="Prediction") explanation= gr.Textbox(label="Classification Decision") with gr.Accordion("Additional Information", open=False): gradcam = gr.Image(label="GradCAM") cropped_boxed_array_disp = gr.Image(label="Bounding Box") submit_button.click(predict_image, inputs = [input_image, input_file], outputs=[overlay_image_np, pred_dict, explanation, gradcam, cropped_boxed_array_disp]) clear_button.add([input_image,overlay_image_np, pred_dict, explanation, gradcam, cropped_boxed_array_disp]) gr.HTML(article_html) if __name__ == "__main__": iface.queue() iface.launch(server_name='0.0.0.0', server_port=8080)