| import gradio as gr | |
| from fastai.vision.all import * | |
| 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 = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>" | |
| examples = ['siamese.jpg'] | |
| interpretation = 'default' | |
| enable_queue = True | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="filepath"), | |
| outputs=gr.Label(num_top_classes=3) | |
| ).launch(share=True) | |