Update app.py

app.py

@@ -1,20 +1,23 @@
 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)
+    # buffer = io.BytesIO(image)
+    # YourImage.save(buffer, format='JPEG', quality=75)
 
-    # You probably want
-    desiredObject = buffer.getbuffer()
+    # # You probably want
+    # desiredObject = buffer.getbuffer()
     
     pipline = pipeline(task="image-classification", model=model)
-    output=pipline(model(Image.open(desiredObject), return_tensors='pt'))
+    #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")