Update app.py

app.py

@@ -2,29 +2,18 @@ import gradio as gr
 from transformers import pipeline
 
 model = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-def preprocess(image):
-    # Define your preprocessing function here to break the uploaded image into multiple images
+def predict(image):
+    # Define your prediction function here
     # For example:
     images = [image.crop((0, 0, 100, 100)), image.crop((100, 100, 200, 200))]
-    return images
-
-def predict(image):
-    # Apply preprocessing function to uploaded image
-    images = preprocess(image)
-    
-    # Apply model to all preprocessed images
-    predictions = []
-    for img in images:
-        pred = model(img)
-        predictions.append(pred[0])
-    
-    # Return predictions alongside images
+    predictions = ['cat', 'dog']
     return images, predictions
 
+# Create Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.inputs.Image(type='pil'),
-    outputs=[gr.outputs.Image(type='pil', label='Image'), 'label']
+    outputs=[gr.outputs.Image(type='pil', label='Images'), gr.outputs.Label(label='Predictions')]
 )
 
 # Launch Gradio app