let's deploy

app.py

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+from fastai.vision.all import *
+import gradio as gr
+
+learn = load_learner('export.pkl')
+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))}
+
+title = "Bear Classifier"
+description = "A bear classifier trained on downloaded images of bear dataset with fastai."
+article="<p style='text-align: center'><a href='https://shuowangphd.github.io' target='_blank'>Back to my personal webpage.</a></p>"
+exa = ['grizzly.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=exa,
+             interpretation=interpretation,enable_queue=enable_queue).launch()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:835dab7fffe8aec0848119fc870b005911fdaddff8944bb90e16dd169a15515e
+size 46965909

requirements.txt

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+fastai
+scikit-image