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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 = "애완동물 품종 분류기"
+description = "fastai를 사용하여 Oxford Pets 데이터 세트에서 훈련된 애완 ​​동물 품종 분류기입니다. Gradio와 HuggingFace Spaces 데모로 제작되었습니다."
+#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 sha256:55f77d239cd8e1b9088d9413dd271f75d37855443dcf2f6953605fde43241bb0
+size 103045373

requirements.txt

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+fastai
+scikit-image