let's deploy to huggingface spaces

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('./model.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 Type Classifier'
+description = 'FasiAI Example on how to classify types of bears.'
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Tutorial</a></p>"
+interpretation='default'
+enable_queue = True
+examples = ['./black_bear.jpeg']
+
+iface = gr.Interface(fn=predict, inputs=gr.Image(shape=(512, 512)), outputs="label", title=title, description=description, article=article, examples=examples, interpretation=interpretation, enable_queue=enable_queue)
+iface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a7ce3e6b1d8b8b11c207a91d65d23dd5b9a09ccef1ea8571668137e246fa7dc
+size 46971287

requirements.txt

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+fastai
+scikit-image