app.py

import torch
import torch.nn.functional as F
from torchvision import models, transforms
from PIL import Image
import gradio as gr
import numpy as np
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image

# Define labels
labels = ['Healthy', 'Autistic', 'NDD']

# Load model
model = models.convnext_tiny(weights=None)
model.classifier[2] = torch.nn.Sequential(
    torch.nn.Linear(model.classifier[2].in_features, 512),
    torch.nn.GELU(),
    torch.nn.Dropout(p=0.5),
    torch.nn.Linear(512, 3)
)
model.load_state_dict(torch.load("autism_model_weights.pth", map_location=torch.device("cpu")))
model.eval()

# Grad-CAM setup
target_layers = [model.features[-1]]
cam = GradCAM(model=model, target_layers=target_layers)

# Image preprocessing
def preprocess(img):
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406],
                             [0.229, 0.224, 0.225])
    ])
    return transform(img).unsqueeze(0)

# Prediction function
def classify(image):
    input_tensor = preprocess(image)
    with torch.no_grad():
        outputs = model(input_tensor)
        probs = F.softmax(outputs[0], dim=0)
        top_probs, top_idxs = torch.topk(probs, 3)
    preds = {labels[i]: float(top_probs[j]) for j, i in enumerate(top_idxs)}


    # Grad-CAM
    grayscale_cam = cam(input_tensor=input_tensor)[0]
    rgb_img = np.array(image.resize((224, 224))) / 255.0
    cam_img = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
    return preds, Image.fromarray(cam_img)

# Gradio interface
demo = gr.Interface(
    fn=classify,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(num_top_classes=3), gr.Image(type="pil")],
    title="AutismLens",
    description="Generate new test"
)

demo.launch()