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| import torch | |
| from monai.networks.nets import DenseNet121 | |
| import gradio as gr | |
| #from PIL import Image | |
| model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=6) | |
| model.load_state_dict(torch.load('weights/mednist_model.pth', map_location=torch.device('cpu'))) | |
| from monai.transforms import ( | |
| EnsureChannelFirst, | |
| Compose, | |
| LoadImage, | |
| ScaleIntensity, | |
| ) | |
| test_transforms = Compose( | |
| [LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity()] | |
| ) | |
| class_names = [ | |
| 'AbdomenCT', 'BreastMRI', 'CXR', 'ChestCT', 'Hand', 'HeadCT' | |
| ] | |
| import os, glob | |
| #examples_dir = './samples' | |
| #example_files = glob.glob(os.path.join(examples_dir, '*.jpg')) | |
| def classify_image(image_filepath): | |
| input = test_transforms(image_filepath) | |
| model.eval() | |
| with torch.no_grad(): | |
| pred = model(input.unsqueeze(dim=0)) | |
| prob = torch.nn.functional.softmax(pred[0], dim=0) | |
| confidences = {class_names[i]: float(prob[i]) for i in range(6)} | |
| print(confidences) | |
| return confidences | |
| with gr.Blocks(title="Medical Image Classification- ClassCat", | |
| css=".gradio-container {background:mintcream;}" | |
| ) as demo: | |
| gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">Medical Image Classification with MONAI</div>""") | |
| with gr.Row(): | |
| input_image = gr.Image(type="filepath", image_mode="L", shape=(64, 64)) | |
| output_label=gr.Label(label="Probabilities", num_top_classes=3) | |
| send_btn = gr.Button("Infer") | |
| send_btn.click(fn=classify_image, inputs=input_image, outputs=output_label) | |
| with gr.Row(): | |
| gr.Examples(['./samples/mednist_AbdomenCT00.png'], label='Sample images : AbdomenCT', inputs=input_image) | |
| gr.Examples(['./samples/mednist_CXR02.png'], label='CXR', inputs=input_image) | |
| gr.Examples(['./samples/mednist_ChestCT08.png'], label='ChestCT', inputs=input_image) | |
| gr.Examples(['./samples/mednist_Hand01.png'], label='Hand', inputs=input_image) | |
| gr.Examples(['./samples/mednist_HeadCT07.png'], label='HeadCT', inputs=input_image) | |
| #demo.queue(concurrency_count=3) | |
| demo.launch(debug=True) | |