deploy to huggingface spaces

main.py

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+from fastai.vision.all import *
+import gradio as gr
+learn = load_learner('petClassify.pkl')
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred, idx, probs = learn.predict(img)
+    return {labels[i] : float(probs[i]) for i in range(len(labels))}
+
+
+# print(predict('licensed-image.jpeg'))
+title = "Pet classifier"
+description = "Oxford pets classifier based on fine tuned resnet 50"
+article = "Plaintext"
+enable_queue = True
+interpretation= 'default'
+examples = ['licensed-image.jpeg']
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3),
+             description=description, title=title, article=article, enable_queue = enable_queue,
+             examples=examples, interpretation=interpretation
+             ).launch(share=True)

petClassify.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45e6dd07076c0a28afbb8c3c3bcf2af8b3b265398b3bd524a118c99c3ce50f5d
+size 103054845

requirements.txt

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+fastai
+scikit-image