initial commit

app.py

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+import gradio as gr
+from fastai.vision.all import *
+
+learn = load_learner(fname='pet_breeds.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 = ["chihuahua.jpg", "siamese.jpg"]
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(height=512, width=512),
+             outputs=gr.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             article=article,
+             examples=examples).launch()