import gradio as gr from fastai.vision.all import * import skimage def is_cat(x): return x[0].isupper() learn = load_learner('model.pkl') categories = ('Dog','Cat') labels = learn.dls.vocab def predict(img): img = PILImage.create(img) pred,pred_idx,probs = learn.predict(img) return dict(zip(categories, map(float,probs))) 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="

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" examples = ['dog.jpeg', 'cat.jpeg', 'dogcat.jpeg'] interpretation='default' enable_queue=True image = gr.Image(height=192, width=192) label = gr.Label(num_top_classes=3) intf = gr.Interface( fn=predict, inputs=image, outputs=label, examples=examples, title=title, description=description ) intf.launch() # gr.Interface(fn=predict,inputs=gr.components.Image(height=512, width=512),outputs=gr.components.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()