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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()
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