app.py

import gradio as gr
import torch
from torchvision.transforms import transforms
import numpy as np

from resnet18 import ResNet18

model = ResNet18(1, 5)

checkpoint = torch.load('C:\jason\semester 8\Magang\Hugging-face-bodypartxr\bodypartxr\acc=0.94.ckpt')

# The state dict will contains net.layer_name
# Our model doesn't contains `net.` so we have to rename it
state_dict = checkpoint['state_dict']
for key in list(state_dict.keys()):
    if 'net.' in key:
        state_dict[key.replace('net.', '')] = state_dict[key]
        del state_dict[key]

model.load_state_dict(state_dict)
model.eval()

class_names = ['abdominal', 'adult', 'others', 'pediatric', 'spine']
class_names.sort()

transformation_pipeline = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Grayscale(num_output_channels=1),
    transforms.CenterCrop((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.50807575], std=[0.20823])
])


def preprocess_image(image: np.ndarray):
    """Preprocess the input image.

    Note that the input image is in RGB mode.

    Parameters
    ----------
    image: np.ndarray
        Input image from callback.
    """

    image = transformation_pipeline(image)
    image = torch.unsqueeze(image, 0)

    return image


def image_classifier(inp):
    """Image Classifier Function.

    Parameters
    ----------
    inp: Optional[np.ndarray] = None
        Input image from callback

    Returns
    -------
    Dict
        A dictionary class names and its probability
    """

    # If input not valid, return dummy data or raise error
    if inp is None:
        return {'cat': 0.3, 'dog': 0.7}

    # preprocess
    image = preprocess_image(inp)
    image = image.to(dtype=torch.float32)

    # inference
    result = model(image)

    # postprocess
    result = torch.nn.functional.softmax(result, dim=1) # apply softmax
    result = result[0].detach().numpy().tolist() # take the first batch
    labeled_result = {name:score for name, score in zip(class_names, result)}

    return labeled_result

demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()