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| import gradio as gr | |
| from torchvision import transforms | |
| import torch | |
| from utils import CustomResnet, main_inference, get_misclassified_images, get_gradcam | |
| 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] | |
| ) | |
| model = CustomResnet() | |
| classes = ('plane', 'car', 'bird', 'cat', 'deer', | |
| 'dog', 'frog', 'horse', 'ship', 'truck') | |
| targets = None | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model.load_state_dict(torch.load("best_model.pth",map_location=torch.device("cpu")),strict=False) | |
| model.to(device) | |
| # Define the input and output components of the Gradio app | |
| input_component = gr.inputs.Image(shape=(32, 32)) | |
| num_of_output_classes = gr.inputs.Slider(minimum=0, maximum=10, default=5, step=1,label="Top class count") | |
| # Adding a checkbox to the interface to show/hide misclassified images | |
| show_misclassified_checkbox = gr.inputs.Checkbox(default=False, label="Show Misclassified Images") | |
| # Input field to specify the number of misclassified images to display | |
| num_images_input = gr.inputs.Slider(minimum=0, maximum=20, default=15, step=5,label="Missclassified Images Count") | |
| # Adding a checkbox to the interface to show/hide GradCAM output | |
| show_gradcam_checkbox = gr.inputs.Checkbox(default=False, label="Show GradCAM Output") | |
| # Slider for adjusting the opacity of the GradCAM overlay | |
| opacity_slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.7,step=0.1, label="GradCAM Opacity") | |
| #layer_options = ['layer1', 'layer2', 'layer3'] | |
| layer_options = ['convblock1', 'resblock1','convblock2',"convblock3","resblock2"] | |
| layer_input = gr.inputs.Dropdown(layer_options,label="Select a Layer",default="convblock3") | |
| gr.Interface( | |
| fn=lambda image, num_of_output_classes,show_misclassified, num_images, show_gradcam, opacity,layer: [main_inference(num_of_output_classes,classes,model,image), | |
| get_misclassified_images(show_misclassified, num_images) if show_misclassified else None, | |
| get_gradcam(model,image, opacity,layer) if show_gradcam else None], | |
| inputs=[input_component, num_of_output_classes,show_misclassified_checkbox, num_images_input, show_gradcam_checkbox, opacity_slider,layer_input], | |
| outputs=[gr.outputs.Label(), gr.Image(shape=(500, 500)), gr.Image(shape=(250, 250))], | |
| title="CIFAR10 Trained on Custom Residual CNN Architecture", | |
| examples=[ | |
| ["example_images/example_1.png",5,True,5,True,0.2,'convblock3'], # You can provide your own example input values here | |
| ["example_images/example_2.png",5,False,5,True,0.3,'convblock3'], | |
| ["example_images/example_3.png",5,True,15,False,0.2,'convblock3'] , | |
| ["example_images/example_4.png",5,True,20,True,0.5,'convblock3'] , | |
| ["example_images/example_5.png",5,False,5,False,0.2,'convblock3'] , | |
| ["example_images/example_6.png",5,True,10,True,0.3,'convblock3'] , | |
| ["example_images/example_7.png",5,True,5,True,0.4,'convblock3'] , | |
| ["example_images/example_8.png",5,False,5,False,0.6,'convblock3'] , | |
| ["example_images/example_9.png",5,True,20,False,0.2,'convblock3'] , | |
| ["example_images/example_10.png",5,False,5,True,0.7,'convblock3'] | |
| ], | |
| layout="horizontal" | |
| ).launch() | |