try it with siamese image

app.py

@@ -9,23 +9,13 @@ def predict(img):
     img = PILImage.create(img)
     pred,pred_idx,probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
+ 
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['siamese.jpg']
+interpretation='default'
+enable_queue=True
 
-title = "Welcome to Samed's Paradise"
-description = "First trial."
-article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
-examples = ['samed_photo_2.jpeg']
-interpretation = 'default'
-enable_queue = True
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
 
-# Updated Gradio interface creation
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(shape=(512, 512)),
-    outputs=gr.Label(num_top_classes=3),
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    interpretation=interpretation,
-    enable_queue=enable_queue
-).launch()