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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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training_set = "mane6-8000" |
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def get_x(r): return os.path.join(training_set, r['fname'].replace('png', 'jpg').replace('gif', 'jpg').replace('svg', 'jpg')) |
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def get_y(r): return r['tag'] |
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learn = load_learner('export.pkl') |
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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 {labels[i]: float(probs[i]) for i in range(len(labels))} |
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title = "Ember" |
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description = "" |
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examples = ["examples/1.jpg", "examples/2.jpg", "examples/3.jpg", "examples/4.jpg"] |
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interpretation='default' |
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enable_queue=True |
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gr.Interface( |
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fn=predict, |
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inputs=gr.inputs.Image(shape=(400, 400)), |
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outputs=gr.outputs.Label(num_top_classes=3), |
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title=title, |
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description=description, |
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examples=examples, |
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interpretation=interpretation, |
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enable_queue=enable_queue).launch() |
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""" |
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LINKS |
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https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial |
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""" |
