app.py

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

import torch

from model import ClassifierModel

class GradioApp:

    def __init__(self) -> None:

        self.models: Dict[str, Union[str, ClassifierModel]] = {
            'Custom': 'models/my_vit.pth',
            'Pretrained': 'models/pretrained_vit.pth'
        }
        with open('classname.txt') as f:
            self.classes: List[str] = [line.strip() for line in f.readlines()]
    
    def predict(self, img_file: str, model_name: str) -> Dict[str, float]:
        
        if isinstance(self.models[model_name], str):
            self.models[model_name] = torch.load(self.models[model_name], map_location='cpu')

        img = torch.unsqueeze(self.models[model_name].val_transform(Image.open(img_file)), 0)
        with torch.inference_mode():
            preds = torch.softmax(self.models[model_name](img), dim=1)[0].numpy()
        return dict(zip(self.classes, preds))
    
    def launch(self):
    
        demo = gr.Interface(
            fn=self.predict,
            inputs=[gr.Image(type='filepath'), gr.Radio(('Custom', 'Pretrained'))],
            outputs=gr.Label(num_top_classes=3),
        )
        demo.launch()

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