Update 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 pytorch_grad_cam.utils.image import show_cam_on_image
+from custom_resnet import Net
+import gradio as gr
+from io import BytesIO
+import os, re
+import matplotlib.pyplot as plt
+
+
+model = Net()
+model.load_state_dict(torch.load("custom_resnet_model.pt", map_location=torch.device('cpu')), strict=False)
+
+inv_normalize = transforms.Normalize(
+    mean=[0.4914, 0.4822, 0.4471],
+    std=[0.2469, 0.2433, 0.2615]
+)
+classes = ('plane', 'car', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck')
+
+def inference(input_img, transparency = 0.5, target_layer_number = -1, top_predictions=3, miss_classified_images_count=3):
+    transform = transforms.ToTensor()
+    org_img = input_img
+    input_img = transform(input_img)
+    input_img = input_img
+    input_img = input_img.unsqueeze(0)
+    outputs = model(input_img)
+    softmax = torch.nn.Softmax(dim=0)
+    o = softmax(outputs.flatten())
+    confidences = {classes[i]: float(o[i]) for i in range(10)}
+    _, prediction = torch.max(outputs, 1)
+    target_layers = [[model.X3],[model.R3]]
+    cam = GradCAM(model=model, target_layers=target_layers[target_layer_number], use_cuda=False)
+    grayscale_cam = cam(input_tensor=input_img, targets=None)
+    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(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
+
+    # Sort the confidences dictionary based on confidence values
+    sorted_confidences = dict(sorted(confidences.items(), key=lambda item: item[1], reverse=True))
+    
+    # Pick the top n predictions
+    top_n_confidences = dict(list(sorted_confidences.items())[:top_predictions])
+
+
+    files = os.listdir('./misclassfied_images/')
+
+    # Plot the misclassified images
+    fig = plt.figure(figsize=(12, 5))
+    for i in range(miss_classified_images_count):
+        sub = fig.add_subplot(2, 5, i+1)
+        npimg = Image.open('./misclassfied_images/' + files[i])
+
+        # Use regex to extract target and predicted classes
+        match = re.search(r'Target_(\w+)_Pred_(\w+)_\d+.jpeg', files[i])
+        target_class = match.group(1)
+        predicted_class = match.group(2)
+        
+        plt.imshow(npimg, cmap='gray', interpolation='none')
+        sub.set_title("Actual: {}, Pred: {}".format(target_class, predicted_class),color='red')
+    plt.tight_layout()
+    buffer = BytesIO()
+    plt.savefig(buffer,format='png')
+    visualization_missclassified = Image.open(buffer)
+    
+    return top_n_confidences, visualization, visualization_missclassified
+
+title = "Jaiyesh's ResNet18 Model with GradCAM"
+description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
+examples = [["cat.jpg", 0.8, -1,3,3], 
+            ["dog.jpeg", 0.8, -1,3,3], 
+            ["plane.jpeg", 0.8, -1,3,3], 
+            ["deer.jpeg", 0.8, -1,3,3], 
+            ["horse.jpeg", 0.8, -1,3,3], 
+            ["bird.jpeg", 0.8, -1,3,3],
+            ["frog.jpeg", 0.8, -1,3,3], 
+            ["ship.jpeg", 0.8, -1,3,3], 
+            ["truck.jpeg", 0.8, -1,3,3], 
+            ["car.jpeg", 0.8, -1,3,3]]
+demo = gr.Interface(
+    inference, 
+    inputs = [gr.Image(shape=(32, 32), label="Input Image"), 
+              gr.Slider(0, 1, value = 0.8, label="Opacity of GradCAM"), 
+              gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"),
+              gr.Slider(2, 10, value = 3, step=1, label="Top Predictions"),
+              gr.Slider(1, 10, value = 3, step=1, label="Misclassified Images"),], 
+    outputs = [gr.Label(label="Top Predictions"), 
+               gr.Image(shape=(32, 32), label="Output").style(width=128, height=128),
+               gr.Image(shape=(640, 360), label="Misclassified Images").style(width=640, height=360)],
+    title = title,
+    description = description,
+    examples = examples,
+) 
+demo.launch()