Update app.py

app.py

@@ -10,7 +10,10 @@ feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
 
 # Define the prediction function
 def classify_image(img):
+    # Preprocess the image and extract features
     inputs = feature_extractor(images=img, return_tensors="pt")
+    
+    # Run the model in evaluation mode
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.softmax(outputs.logits, dim=-1)
@@ -22,8 +25,8 @@ def classify_image(img):
 # Create the Gradio interface
 iface = gr.Interface(
     fn=classify_image,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
+    inputs=gr.Image(type="pil"),  # Image input type as PIL (Pillow Image)
+    outputs="text",  # Text output will display the predicted label
     title="Food Image Classification",
     description="Upload an image to classify if it’s an apple pie, etc."
 )