app.py

import torch, torchvision
from torchvision import transforms
import numpy as np
import gradio as gr
from PIL import Image
from pytorch_grad_cam import GradCAM
from resnet import ResNet18
import gradio as gr

model = ResNet18()
model.load_state_dict(torch.load("model.pth"), strict=False, map_location=torch.device('cpu'))

def inference(input_img, transparency):
    transform = transforms.ToTensor()
    input_img = transform(input_img)
    input_img = input_img
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    _, prediction = torch.max(outputs, 1)
    target_layers = [model.layer2[-2]]
    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
    grayscale_cam = cam(input_tensor=input_img, targets=targets)
    grayscale_cam = grayscale_cam[0, :]
    img = input_img.squeeze(0)
    img = inv_normalize(img)
    rgb_img = np.transpose(img, (1, 2, 0))
    rgb_img = rgb_img.numpy()
    visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
    return classes[prediction[0].item()], visualization

demo = gr.Interface(inference, [gr.Image(shape=(32, 32)), gr.Slider(0, 1)], ["text", gr.Image(shape=(32, 32)).style(width=128, height=128)])
demo.launch()