| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('pets_model.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 = ['ragdoll.jpg', 'cat-1.jpeg', 'cat-2.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,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch() | |