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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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import timm |
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learn = load_learner('model.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 = "Food Classifier" |
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description = "A food classifier trained on the food 101 dataset with fastai." |
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examples = ['hamburger.jpg'] |
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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=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch() |