Create app.py

app.py

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+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+import gradio as gr
+from PIL import Image
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from resnet import ResNet18
+
+model = ResNet18()
+model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
+
+inv_normalize = transforms.Normalize(
+    mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
+    std=[1/0.23, 1/0.23, 1/0.23]
+)
+
+classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+def resize_image_pil(image, new_width, new_height):
+    img = Image.fromarray(np.array(image))
+    width, height = img.size
+    width_scale = new_width / width
+    height_scale = new_height / height
+    scale = min(width_scale, height_scale)
+    resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
+    resized = resized.crop((0, 0, new_width, new_height))
+    return resized
+
+def inference(input_img, show_gradcam, num_gradcam, target_layer_number, opacity, show_misclassified, num_misclassified, num_top_classes):
+    input_img = resize_image_pil(input_img, 32, 32)
+    input_img = np.array(input_img)
+    org_img = input_img
+    input_img = input_img.reshape((32, 32, 3))
+    transform = transforms.ToTensor()
+    input_img = transform(input_img)
+    input_img = input_img.unsqueeze(0)
+    
+    outputs = model(input_img)
+    softmax = torch.nn.Softmax(dim=1)
+    probs = softmax(outputs)
+    
+    top_probs, top_labels = torch.topk(probs, k=min(num_top_classes, 10))
+    top_classes = [classes[idx] for idx in top_labels[0]]
+    confidences = {cls: float(prob) for cls, prob in zip(top_classes, top_probs[0])}
+    
+    _, prediction = torch.max(outputs, 1)
+    predicted_class = classes[prediction[0].item()]
+    
+    results = [predicted_class, confidences]
+    
+    if show_gradcam:
+        target_layers = [model.layer2[target_layer_number]]
+        cam = GradCAM(model=model, target_layers=target_layers)
+        grayscale_cam = cam(input_tensor=input_img, targets=None)
+        grayscale_cam = grayscale_cam[0, :]
+        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=opacity)
+        results.append(visualization)
+    
+    if show_misclassified:
+        # This part would require a dataset of images to check for misclassifications
+        # For demonstration, we'll just return a placeholder message
+        results.append("Misclassified images feature would be implemented here")
+    
+    return results
+
+def launch():
+    with gr.Blocks() as demo:
+        gr.Markdown("# CIFAR10 ResNet18 Model with GradCAM")
+        
+        with gr.Row():
+            input_image = gr.Image(width=256, height=256, label="Input Image")
+            output_image = gr.Image(width=256, height=256, label="GradCAM Output")
+        
+        with gr.Row():
+            prediction = gr.Textbox(label="Predicted Class")
+            confidences = gr.Label(label="Top Class Confidences")
+        
+        with gr.Row():
+            show_gradcam = gr.Checkbox(label="Show GradCAM")
+            num_gradcam = gr.Slider(1, 5, value=1, step=1, label="Number of GradCAM images")
+            target_layer = gr.Slider(-2, -1, value=-2, step=1, label="Target Layer")
+            opacity = gr.Slider(0, 1, value=0.5, label="GradCAM Opacity")
+        
+        with gr.Row():
+            show_misclassified = gr.Checkbox(label="Show Misclassified Images")
+            num_misclassified = gr.Slider(1, 10, value=5, step=1, label="Number of Misclassified Images")
+        
+        num_top_classes = gr.Slider(1, 10, value=3, step=1, label="Number of Top Classes to Show")
+        
+        submit_btn = gr.Button("Submit")
+        
+        example_images = gr.Dataset(
+            components=[input_image],
+            samples=[["cat.jpg"], ["dog.jpg"], ["bird.jpg"], ["plane.jpg"], ["car.jpg"],
+                     ["deer.jpg"], ["frog.jpg"], ["horse.jpg"], ["ship.jpg"], ["truck.jpg"]]
+        )
+        
+        submit_btn.click(
+            inference,
+            inputs=[input_image, show_gradcam, num_gradcam, target_layer, opacity, 
+                    show_misclassified, num_misclassified, num_top_classes],
+            outputs=[prediction, confidences, output_image]
+        )
+    
+    demo.launch()
+
+if __name__ == "__main__":
+    launch()