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| import gradio as gr | |
| import torch | |
| from utils import * | |
| import os | |
| model = torch.load('AlzheimerModelCPU.pth') | |
| model.eval() | |
| def reset(): | |
| return None, None, None, None, None | |
| with gr.Blocks() as demo: | |
| gr.HTML(""" | |
| <h1 style="text-align: center; font-size: 50px;"> | |
| Alzheimer Detection with Explainable AI (XAI) | |
| </h1> | |
| <p style="text-align: center;"> | |
| Early Detection of Alzheimer's Disease: A Deep Learning Approach for Accurate Diagnosis. | |
| </p> | |
| <h3 style="text-align: left;"> | |
| To use the demo, please follow the steps below. | |
| </h3> | |
| <ul> | |
| <li style="text-align: left; padding: 0px 0px 0px 30px;"> | |
| If you want to try examples, click one of <span style="font-weight: bold;">Examples</span> images below. Then, click | |
| <span style="font-weight: bold;">Submit</span>. | |
| </li> | |
| <li style="text-align: left; padding: 0px 0px 0px 30px;"> | |
| if you don't want to try examples, upload an image and click <span style="font-weight: bold;">Submit</span>. | |
| </li> | |
| <li style="text-align: left; padding: 0px 0px 0px 30px;"> | |
| You can adjust the <span style="font-weight: bold;">Target</span> for your desire visualization and | |
| <span style="font-weight: bold;">Plot Type</span> between <span style="font-weight: bold;">withmask</span> and <span style="font-weight: bold;">withoutmask</span> | |
| to plot original images with or without Grad-CAM. | |
| </li> | |
| <li style="text-align: left; padding: 0px 0px 0px 30px;"> | |
| If you want to reset all components, click <span style="font-weight: bold;">Reset All Components</span> button. | |
| </li> | |
| </ul> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_area = gr.Image(sources=["upload"], type="pil", scale=2, label="Upload Image") | |
| with gr.Row(): | |
| choosen_plottype =gr.Radio(choices=["withmask", "withoutmask"], value="withmask", label="Plot Type", scale=1, interactive=True) | |
| choosen_target = gr.Slider(minimum=0, maximum=3, step=1, value=2, label="Classifier OutputTarget", scale=1, interactive=True) | |
| submit_btn = gr.Button("Submit", variant='primary', scale=1) | |
| with gr.Column(): | |
| gr.HTML(""" | |
| <h2 style="text-align: center;"> | |
| </h2> | |
| <h2 style="text-align: center;"> | |
| Prediction and Grad-CAM | |
| </h2> | |
| <p style="text-align: center;"> | |
| Alzheimer detection result with Grad-CAM for 3 images. Original, White Matter and Gray Matter image. | |
| </p> | |
| """ | |
| ) | |
| text_area = gr.Textbox(label="Prediction Result") | |
| plotarea_original = gr.Plot(label="Original image") | |
| plotarea_white = gr.Plot(label="White Matter image") | |
| plotarea_gray = gr.Plot(label="Gray Matter image") | |
| reset_btn = gr.Button("Reset All Components", variant='stop', scale=1) | |
| gr.HTML(""" | |
| <h2 style="text-align: left;"> | |
| Examples | |
| </h2> | |
| <p style="text-align: left;"> | |
| You can select 1 image from the examples and click "Submit". | |
| </p> | |
| <p style="text-align: left;"> | |
| These images represent 4 different stages of Alzheimer's disease in the following order:<span style="font-weight: bold;"> | |
| Non Demented, Very Mild Demented, Mild Demented and Moderate Demented.</span> | |
| </p> | |
| """ | |
| ) | |
| examples = gr.Examples(examples=[ os.path.join(os.getcwd(), "non.jpg"), | |
| os.path.join(os.getcwd(),"verymild.jpg"), | |
| os.path.join(os.getcwd(), "mild.jpg"), | |
| os.path.join(os.getcwd(), "moderate.jpg")], | |
| inputs=image_area, | |
| outputs=image_area, | |
| label="Examples", | |
| ) | |
| submit_btn.click(lambda x, target, plot_type: predict_and_gradcam(x, model=model, target=target, plot_type=plot_type), inputs=[image_area, choosen_target, choosen_plottype], outputs=[text_area, plotarea_original, plotarea_white, plotarea_gray]) | |
| reset_btn.click(reset, outputs=[image_area, text_area, plotarea_original, plotarea_white, plotarea_gray]) | |
| demo.launch() |