app.py

import gradio as gr
from  PIL import Image
import torch
from torchvision.io import read_image
from transformers import ViTImageProcessor,pipeline

model = ViTImageProcessor.from_pretrained('SeyedAli/Food-Image-Classification-VIT')

def FoodClassification(image):
    image = read_image(image)
    # Encode your PIL Image as a JPEG without writing to disk
    # buffer = io.BytesIO(image)
    # YourImage.save(buffer, format='JPEG', quality=75)

    # # You probably want
    # desiredObject = buffer.getbuffer()
    
    pipline = pipeline(task="image-classification", model=model)
    #output=pipline(model(Image.open(desiredObject), return_tensors='pt'))
    output=pipline(image, return_tensors='pt'))
    return output

iface = gr.Interface(fn=FoodClassification, inputs="image", outputs="text")
iface.launch(share=False)