let's deploy to huggingface spaces

app.py

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+import gradio as gr
+from fastai.vision.all import *
+from fastai.vision.widgets import *
+from fastai.callback.preds import load_learner
+import skimage
+
+learner = load_learner('export (2).pkl')
+
+labels = learner.dls.vocab
+def classify_image(img):
+    pred, idx, probs = learner.predict(img)
+    return dict(zip(learner.dls.vocab, map(float, probs)))
+title = "Oklahoma Snake Classifier"
+description = "A snake classifier trained on pictures of Oklahoma Snakes 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 = ['cottonmouth.jpg', 'rattlesnake.jpeg']
+interpretation='default'
+enable_queue=True
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label(num_top_classes=3)
+out_pl = widgets.Output()
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()

export (2).pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8aa22189cf5eccc9e0b808b706ced2264f5792553d2884346cee080d56039f5
+size 47005711

requirements.txt

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+fastai
+scikit-image