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| from fastai.vision.all import * | |
| import gradio as gr | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('export.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # Create and launch the Gradio interface | |
| title = "Pet Breed Classifier" | |
| examples = ['beagle.jpeg'] | |
| description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces." | |
| gr.Interface(fn=predict, inputs="image", outputs="label", title=title, description=description, examples=examples).launch(share=True) | |