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Update app.py
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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)