app.py

import gradio as gr
import utils
import Model_Class 
import Model_Seg

import SimpleITK as sitk
import torch
from numpy import uint8
import spaces

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

image_base64 = utils.image_to_base64("anatomy_aware_pipeline.png")
article_html = f"<img src='data:image/png;base64,{image_base64}' alt='Anatomical pipeline illustration' style='width:100%;'>"

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:** Please ensure data privacy and don't upload any sensitive patient information to this tool. 
"""

description_html = """
<div style="background-color: #0b0f1a; color: white; padding: 10px; border-radius: 5px; box-shadow: 0 0 10px rgba(11,15,26,1); display: inline-flex; flex-direction: column; justify-content: center; align-items: center; margin: auto;">
    <ul style="background-color: #1e2936; border-radius: 5px; color: white; padding: 10px; box-shadow: 0 0 10px rgba(0,0,0,0.3); padding-left: 20px; text-align: left; list-style-position: inside;">
        <li>This tool combines a U-Net Segmentation Model with a ResNet-50 for Classification.</li>
        <li><strong>Usage:</strong> Just drag a pelvic x-ray into the box and hit run.</li>
        <li><strong>Process:</strong> The input image will be segmented and cropped to the SIJ before classification.</li>
        <li><strong>Please Note:</strong> This tool is intended for research purposes only.</li>
        <li><strong>Privacy:</strong> Please ensure data privacy and don't upload any sensitive patient information to this tool.</li>
    </ul>
</div>
"""

css = """

h1 {
    text-align: center;
    display: block;
}

.gradio-container .styled-block {
    background-color: #0b0f1a; 
    color: white !important;   
    padding: 10px;             
    border-radius: 5px;        
    box-shadow: 0 0 10px rgba(11,15,26,1);
    display: inline-flex;      
    flex-direction: column;
    justify-content: center;   
    align-items: center;       
    margin: auto;              
}

.gradio-container .styled-block ul, 
.gradio-container .styled-block ol {
    background-color: #1e2936;
    border-radius: 5px;
    color: white !important;    
    padding: 10px;
    box-shadow: 0 0 10px rgba(0,0,0,0.3);
    padding-left: 20px;         
    text-align: left;           
    list-style-position: inside;
}

footer {
    display: none !important;
}
"""

@spaces.GPU(duration=20)
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, device)

    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, device)


    gradcam = Model_Class.make_GradCAM(image_transformed, device)
    
    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)
    gr.HTML(description_html)


    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()