Update

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+import torch.nn.functional as F
+import numpy as np
+from torchvision import models, transforms
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+
+# Set device (CPU for Hugging Face Spaces)
+device = torch.device("cpu")
+
+# Load model
+model = models.resnet50(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, 2)
+model.load_state_dict(torch.load("resnet50_dr_classifier.pth", map_location=device))
+model.to(device)
+model.eval()
+
+# Grad-CAM setup
+target_layer = model.layer4[-1]
+cam = GradCAM(model=model, target_layers=[target_layer])
+
+# Image transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+def predict_retinopathy(image):
+    img = image.convert("RGB").resize((224, 224))
+    img_tensor = transform(img).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(img_tensor)
+        probs = F.softmax(output, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][pred].item()
+
+    label = "Diabetic Retinopathy (DR)" if pred == 0 else "No DR"
+
+    # Grad-CAM
+    rgb_img_np = np.array(img).astype(np.float32) / 255.0
+    rgb_img_np = np.ascontiguousarray(rgb_img_np)
+    grayscale_cam = cam(input_tensor=img_tensor, targets=[ClassifierOutputTarget(pred)])[0]
+    cam_image = show_cam_on_image(rgb_img_np, grayscale_cam, use_rgb=True)
+
+    cam_pil = Image.fromarray(cam_image)
+    return cam_pil, f"{label} (Confidence: {confidence:.2f})"
+
+# Gradio UI
+gr.Interface(
+    fn=predict_retinopathy,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Grad-CAM"),
+        gr.Text(label="Prediction")
+    ],
+    title="Diabetic Retinopathy Detection",
+    description="Upload a retinal image to classify DR and view Grad-CAM heatmap."
+).launch()