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{
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     "text": [
      "* 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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://b83dd3f618e0e3e8c5.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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     "metadata": {},
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       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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      ]
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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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