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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
import os
import datetime
# Setup
device = torch.device("cpu")
save_dir = "/home/user/app/saved_predictions"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print("📁 Folder created:", save_dir)
os.makedirs(save_dir, exist_ok=True)
# 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
target_layer = model.layer4[-1]
cam = GradCAM(model=model, target_layers=[target_layer])
# Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
# Predict and save
def predict_retinopathy(image):
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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 = "DR" if pred == 0 else "NoDR"
# 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)
# Save image with label and confidence
filename = f"{timestamp}_{label}_{confidence:.2f}.png"
cam_pil.save(os.path.join(save_dir, filename))
return cam_pil, f"{label} (Confidence: {confidence:.2f})"
# Gradio app
gr.Interface(
fn=predict_retinopathy,
inputs=gr.Image(type="pil", label="Upload Retinal Image"),
outputs=[
gr.Image(type="pil", label="Grad-CAM Heatmap"),
gr.Text(label="Prediction")
],
title="OpthaDetect – AI Retinal Screening",
description=(
"A lightweight AI system for detecting Diabetic Retinopathy from retinal images. "
"Upload an image to classify DR and visualise the Grad-CAM heatmap showing important regions."
),
article=(
"⚕️ **OpthaDetect** is an AI-powered ophthalmic decision-support tool. "
"It highlights retinal risk regions using Grad-CAM for better clinical interpretability."
)
).launch()
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