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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()
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