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))} title = "Pet Breed Classifier" description = "A pet breed classifier (both cats and dogs) trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces by Dan Waldie." examples = ['wheaten.jpeg', 'havanese.jpeg', 'cockerspaniel.jpg', 'pyrenees.jpeg'] interpretation='default' enable_queue=True 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()