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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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learn = load_learner('export.pkl') |
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labels = ('Lego (non Ninjago)', 'Lego Ninjago') |
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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 {labels[i]: float(probs[i]) for i in range(len(labels))} |
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title = "Lego Classifier" |
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description = "Classifies Lego into 'Ninjago' and 'Non Ninjago' with fastai. Created from the fastai demo for Gradio and HuggingFace Spaces." |
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examples = ['ninjago.jpeg', 'lego.jpeg'] |
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interpretation = 'default' |
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enable_queue = True |
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(192, 192)), outputs=gr.outputs.Label(), title=title, |
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description=description, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch() |
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