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"* Running on local URL: http://127.0.0.1:7865\n",
"* Running on public URL: https://b83dd3f618e0e3e8c5.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co./spaces)\n"
]
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"source": [
"import gradio as gr\n",
"from fastai.learner import load_learner\n",
"from fastai.vision.all import PILImage\n",
"\n",
"def label_func(f): return f[0].isupper()\n",
"learn = load_learner('export.pkl')\n",
"\n",
"labels = learn.dls.vocab\n",
"\n",
"\n",
"def predict(img):\n",
" img = PILImage.create(img)\n",
" pred, pred_idx, probs = learn.predict(img)\n",
" return {labels[i]: float(probs[i]) for i in range(len(labels))}\n",
"\n",
"\n",
"title = \"Pet Breed Classifier\"\n",
"description = \"A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces.\"\n",
"article = \"<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>\"\n",
"examples = ['siamese.jpg']\n",
"interpretation = 'default'\n",
"enable_queue = True\n",
"\n",
"gr.Interface(\n",
" fn=predict,\n",
" inputs=gr.Image(type=\"filepath\"),\n",
" outputs=gr.Label(num_top_classes=3)\n",
").launch(share=True)\n"
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