app.py

import torch
import torchvision
from torchvision import transforms
import gradio as gr
import numpy as np
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):

    # convert to PIL IMage
    img = Image.fromarray(np.array(image))
    # get original size
    width, height = img.size

    # calculate scale
    width_scale = new_width/width
    height_scale = new_height/height

    scale = min(width_scale, height_scale)

    # resize
    resized = img.resize(size=(int(width*scale), int(height*scale)), resample=Image.NEAREST)

    # crop resized image
    resized = resized.crop((0, 0, new_width, new_height))

    return resized

# def inference(input_img, transparency):
#     transform = transforms.ToTensor()
#     input_img = transform(input_img)
#     input_img = input_img.to(device)
#     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=True)
#     grayscale_cam = cam(input_tensor=input_img, targets=targets)
#     grayscale_cam = grayscale_cam[0, :]
#     img = input_img.squeeze(0).to('cpu')
#     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

def inference(input_img, transparency=0.5, target_layer_number=-1):
    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=0)
    o = softmax(outputs.flatten())
    confidences = {classes[i] : float(o[i]) for i in range(10)}
    _, prediction = torch.max(outputs, 1)
    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=transparency
    )

    return classes[prediction[0].item()], visualization, confidences
    



demo = gr.Interface(
    fn=inference,
    inputs=[
        gr.Image(width=256, height=256, label="Input Image"),
        gr.Slider(0,1, value=0.5, label="Overall opacity value"),
        gr.Slider(-2, -1, value=-2, label="Which model layer to use for GradCAM?")
    ],
    outputs = [
        "text",
        gr.Image(width=256, height=256, label="Output"),
        gr.Label(num_top_classes=3)
    ],

    title="CIFAR10 trained on ResNet18 with GradCAM",
    
    description = "A simple Gradio interface to infer on ResNet model with GradCAM results shown on top.",
    
    examples = [
    ["cat.jpg", 0.5, -1],
    ["dog.jpg", 0.7, -2]
]
)

demo.launch()