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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 os |
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learn = load_learner('learner.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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for root, dirs, files in os.walk(r'sample_images/'): |
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for filename in files: |
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print(filename) |
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title = "Paddy Disease Classifier with EdgeNeXt" |
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description = "9 Diseases + 1 Normal class." |
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interpretation='default' |
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examples = ["sample_images/"+file for file in files] |
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article="<p style='text-align: center'><a href='https://dicksonneoh.com/portfolio/bringing_high_quality_image_models_to_mobile/' target='_blank'>Blog post</a></p>" |
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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=(224, 224)), |
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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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article=article, |
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examples=examples, |
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interpretation=interpretation, |
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enable_queue=enable_queue, |
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theme="grass", |
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).launch() |