Update app.py

app.py

@@ -1,24 +1,32 @@
+# %% app.ipynb 1
 import gradio as gr
 from fastai.vision.all import *
-import skimage
 
+# %% app.ipynb 2
 learn = load_learner('pets-model.pkl')
-
 labels = learn.dls.vocab
 
-
+# %% app.ipynb 3
 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))}
 
-
+# %% app.ipynb 4
 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
 
-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()
+
+# %% app.ipynb 5
+image = gr.inputs.Image(shape=(224,224))
+label = gr.outputs.Label(num_top_classes=3)
+examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
+
+# %% app.ipynb 6
+intf = gr.Interface(fn=predict, inputs=image, outputs=label, title=title,
+             description=description, article=article, examples=examples, interpretation=interpretation, enable_queue=enable_queue)
+intf.launch(inline=False)
+