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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)
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