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import torch
import gradio as gr
from src.model import DRModel
from torchvision import transforms as T
CHECKPOINT_PATH = "artifacts/dr-model.ckpt"
model = DRModel.load_from_checkpoint(CHECKPOINT_PATH)
model.eval()
labels = {
0: "No DR",
1: "Mild",
2: "Moderate",
3: "Severe",
4: "Proliferative DR",
}
transform = T.Compose(
[
T.Resize((192, 192)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
# Define the prediction function
def predict(input_img):
input_img = transform(input_img).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(input_img)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in labels}
return confidences
# Set up the Gradio app interface
dr_app = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(),
title="Diabetic Retinopathy Detection App",
description="Welcome to our Diabetic Retinopathy Detection App! \
This app utilizes deep learning models to detect diabetic retinopathy in retinal images.\
Diabetic retinopathy is a common complication of diabetes and early detection is crucial for effective treatment.",
examples=[
"data/sample/10_left.jpeg",
"data/sample/10_right.jpeg",
"data/sample/15_left.jpeg",
"data/sample/16_right.jpeg",
],
)
# Run the Gradio app
if __name__ == "__main__":
dr_app.launch()
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