Update app.py
Browse files
app.py
CHANGED
@@ -8,7 +8,11 @@ from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
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from pytorch_grad_cam.utils.image import show_cam_on_image
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device = torch.device("cpu")
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# Load model
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@@ -22,7 +26,7 @@ model.eval()
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target_layer = model.layer4[-1]
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cam = GradCAM(model=model, target_layers=[target_layer])
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# Image
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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@@ -30,6 +34,23 @@ transform = transforms.Compose([
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[0.229, 0.224, 0.225])
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])
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def predict_retinopathy(image):
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img = image.convert("RGB").resize((224, 224))
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img_tensor = transform(img).unsqueeze(0).to(device)
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@@ -48,10 +69,14 @@ def predict_retinopathy(image):
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grayscale_cam = cam(input_tensor=img_tensor, targets=[ClassifierOutputTarget(pred)])[0]
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cam_image = show_cam_on_image(rgb_img_np, grayscale_cam, use_rgb=True)
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cam_pil = Image.fromarray(cam_image)
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return cam_pil, f"{label} (Confidence: {confidence:.2f})"
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# Gradio
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gr.Interface(
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fn=predict_retinopathy,
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inputs=gr.Image(type="pil"),
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@@ -60,5 +85,5 @@ gr.Interface(
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gr.Text(label="Prediction")
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],
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title="Diabetic Retinopathy Detection",
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description="Upload a retinal image to classify DR and view Grad-CAM heatmap."
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).launch()
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from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
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from pytorch_grad_cam.utils.image import show_cam_on_image
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import csv
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import datetime
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import os
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# Set device
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device = torch.device("cpu")
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# Load model
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target_layer = model.layer4[-1]
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cam = GradCAM(model=model, target_layers=[target_layer])
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# Image preprocessing
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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[0.229, 0.224, 0.225])
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])
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# Logging setup
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log_path = "prediction_logs.csv"
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def log_prediction(filename, prediction, confidence):
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timestamp = datetime.datetime.now().isoformat()
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row = [timestamp, filename, prediction, f"{confidence:.4f}"]
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if not os.path.exists(log_path):
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with open(log_path, mode='w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow(["timestamp", "image_name", "prediction", "confidence"])
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with open(log_path, mode='a', newline='') as file:
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writer = csv.writer(file)
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writer.writerow(row)
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# Prediction function
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def predict_retinopathy(image):
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img = image.convert("RGB").resize((224, 224))
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img_tensor = transform(img).unsqueeze(0).to(device)
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grayscale_cam = cam(input_tensor=img_tensor, targets=[ClassifierOutputTarget(pred)])[0]
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cam_image = show_cam_on_image(rgb_img_np, grayscale_cam, use_rgb=True)
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# Logging
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filename = getattr(image, "filename", "uploaded_image")
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log_prediction(filename, label, confidence)
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cam_pil = Image.fromarray(cam_image)
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return cam_pil, f"{label} (Confidence: {confidence:.2f})"
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# Gradio interface
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gr.Interface(
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fn=predict_retinopathy,
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inputs=gr.Image(type="pil"),
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gr.Text(label="Prediction")
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],
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title="Diabetic Retinopathy Detection",
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description="Upload a retinal image to classify DR and view Grad-CAM heatmap. All predictions are logged for analysis."
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).launch()
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