app.py

import gradio as gr
from PIL import Image
from typing import List, Dict

import torch

class GradioApp:
    def __init__(self) -> None:
        self.model = torch.load('pretrained_vit.pth', map_location='cpu')
    def predict(self, img_file: str, classes: List[str]) -> Dict[str, float]:
        classes = ['0', '1', '2']
        img = self.model.val_transform(Image.open(img_file)).unsqueeze(0)
        with torch.inference_mode():
            preds = torch.softmax(self.model(img), dim=1)[0].cpu().numpy()
        return {classes[i] : preds[i] for i in range(len(classes))}
    def launch(self):
        demo = gr.Interface(
            fn=self.predict,
            inputs=gr.Image(type='filepath'),
            outputs=gr.Label(num_top_classes=3),
        )
        demo.launch()

if __name__ == '__main__':
    app = GradioApp()
    app.launch()