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import gradio as gr | |
from fastai.vision.all import load_learner, PILImage | |
def is_cat(x): | |
return x[0].isupper() | |
learn = load_learner("model.pkl") | |
labels = learn.dls.vocab | |
categories = ("Dog", "Cat") | |
def predict(img): | |
img = PILImage.create(img) | |
_, _, probs = learn.predict(img) | |
return dict(zip(categories, map(float, probs))) | |
# return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
title = "Cat or Dog Classifier" | |
description = "Classifier to determine if a photo is a cat or not." | |
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", "poodle.jpg", "panda.jpg"] | |
interpretation = "default" | |
enable_queue = True | |
gr.Interface( | |
fn=predict, | |
inputs=gr.components.Image(shape=(512, 512)), | |
outputs=gr.components.Label(num_top_classes=3), | |
title=title, | |
description=description, | |
article=article, | |
examples=examples, | |
interpretation=interpretation, | |
).launch(enable_queue=enable_queue) | |