Upload 3 files

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+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 = "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
+
+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()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3930ab835c8564db7f5a8b1db76a8f5a082a0f8c0cb2d439d4f629a1563ee502
+size 103045373

requirements.txt

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+fastai
+scikit-image