let's deploy to huggingface spaces

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 = "Bears Classifier"
+description = "A simple bears classifier trained on the dataset created with fastai & bing search."
+examples = ['grizzly.jpg']
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(shape=(512, 512)),
+             outputs=gr.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             examples=examples,
+             ).launch(share=True, enable_queue=True)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c854dbacbfaa6d33080db336a6dbacaf0c7af3487212b023f72b267caf6711e0
+size 46967009

requirements.txt

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+fastai