Spaces:
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| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| def is_cat(x): return x[0].isupper() # fix hopefully | |
| learn = load_learner('export.pkl') | |
| # fixing labels = learn.dls.vocab | |
| labels = ['dog', 'cat'] # manually set the labels | |
| 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'] | |
| # fix: interpretation='default' | |
| enable_queue=True | |
| # fix by removing "outputs." and "inputs." | |
| # fix by shape=(512, 512) changed to height=512, width=512 then changed to type="pil" | |
| # fix: removed interpretation=interpretation | |
| # fix: enable_queue=enable_queue | |
| gr.Interface(fn=predict,inputs=gr.Image(type="pil"),outputs=gr.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples).launch() |