Amar Gill
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import gradio as gr
from skimage import PILImage
from fastai.vision.all import load_learner
learn = load_learner("model.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.PNG"]
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()