app.py

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")
# Create save directory in current working directory for cross-platform compatibility
save_dir = os.path.join(os.getcwd(), "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 OCT Image"),
    outputs=[
        gr.Image(type="pil", label="Grad-CAM Heatmap"),
        gr.Text(label="Diabetic Retinopathy Prediction")
    ],
    title="AI Diabetic Retinopathy Detection",
    description="Upload an OCT image to analyze for diabetic retinopathy. The AI will show a Grad-CAM heatmap highlighting areas of interest.",
    examples=[
        ["example_oct.jpg"] if os.path.exists("example_oct.jpg") else None
    ]
).launch(server_name="0.0.0.0", server_port=7860)