import gradio as gr import fastai 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))} title = "Pet Breed Classifier" description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces." article="

Blog post

" examples = ['siamese.jpg'] interpretation='default' enable_queue=True gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=len(labels)),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()