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import gradio as gr |
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from fastai.vision.all import * |
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import skimage |
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def is_cat(x): |
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return x[0].isupper() |
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learn = load_learner('model.pkl') |
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categories = ('Dog','Cat') |
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labels = learn.dls.vocab |
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def predict(img): |
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img = PILImage.create(img) |
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pred,pred_idx,probs = learn.predict(img) |
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return dict(zip(categories, map(float,probs))) |
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title = "Pet Breed Classifier" |
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description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces." |
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article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>" |
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examples = ['dog.jpeg', 'cat.jpeg', 'dogcat.jpeg'] |
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interpretation='default' |
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enable_queue=True |
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image = gr.Image(height=192, width=192) |
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label = gr.Label(num_top_classes=3) |
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intf = gr.Interface( |
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fn=predict, |
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inputs=image, |
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outputs=label, |
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examples=examples, |
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title=title, |
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description=description |
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) |
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intf.launch() |
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