| import gradio as gr |
| from fastai.learner import load_learner |
| from fastai.vision.all import PILImage |
|
|
| def label_func(f): return f[0].isupper() |
| 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) |
|
|
