Update app.py

app.py

@@ -1,22 +1,20 @@
-__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
-
-#Cell
-from fastai.vision.all import *
 import gradio as gr
+from fastai.vision.all import *
+import skimage
 
-def is_cat(x): return x[0].isupper()
-
-learn = load_learner('model.pkl')
-
-categories = ('Dog', 'Cat')
+learn = load_learner('export.pkl')
 
-def classify_image(img):
-    pred, idx, probs =learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
+labels = learn.dls.vocab
+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))}
 
-image = gr.input.Image(shape=(192, 192))
-label = gr.outputs.label()
-examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
+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
 
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
+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()