feat: add basic pet classifier with gradio interface

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()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b7d9eac1c2023f76f762b3a19fe74d914ee83b8982be318da94dd054e19198d
+size 47060011

requirements.txt

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+fastai
+scikit-image