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Download app.py from Hamzazia761/Pet_Classifier: direct link, hf CLI and curl.
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https://huggingface.co/spaces/Hamzazia761/Pet_Classifier/resolve/main/app.py
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hf download hf://spaces/Hamzazia761/Pet_Classifier/app.py
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curl -L -o app.py https://huggingface.co/spaces/Hamzazia761/Pet_Classifier/resolve/main/app.py
955 Bytes
| 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 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 | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(shape=(512, 512)), | |
| outputs=gr.Label(num_top_classes=3), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue | |
| ) |