Update app.py

app.py

@@ -6,8 +6,15 @@ from transformers import ViTImageProcessor,pipeline
 model = ViTImageProcessor.from_pretrained('SeyedAli/Food-Image-Classification-VIT')
 
 def FoodClassification(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(image), return_tensors='pt'))
+    output=pipline(model(Image.open(desiredObject), return_tensors='pt'))
     return output
 
 iface = gr.Interface(fn=FoodClassification, inputs="image", outputs="text")