Create app.py

app.py

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