voila_sample_bear_classifier.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from fastai.vision.all import *\n",
    "from ipywidgets import widgets \n",
    "from fastai.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "path = Path()\n",
    "# path.ls(file_exts='.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_inf = load_learner(path/'export.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Notebook app from the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "btn_upload = SimpleNamespace(data = ['images/grizzly.jpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = PILImage.create(btn_upload.data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_pl = widgets.Output()\n",
    "out_pl.clear_output()\n",
    "with out_pl: display(img.to_thumb(128,128))\n",
    "# out_pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "pred,pred_idx,probs = learn_inf.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl_pred = widgets.Label()\n",
    "lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "# lbl_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "btn_run = widgets.Button(description='Classify')\n",
    "# btn_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_click_classify(change):\n",
    "    img = PILImage.create(btn_upload.data[-1])\n",
    "    out_pl.clear_output()\n",
    "    with out_pl: display(img.to_thumb(128,128))\n",
    "    pred,pred_idx,probs = learn_inf.predict(img)\n",
    "    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "\n",
    "btn_run.on_click(on_click_classify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "#Putting back btn_upload to a widget for next cell\n",
    "btn_upload = widgets.FileUpload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "data": {
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       "model_id": "8cf0770ed9d04b13921e0940f7db2cb4",
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      "text/plain": [
       "VBox(children=(Label(value='Select your bear!'), FileUpload(value={}, description='Upload'), Button(descriptio…"
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     "output_type": "display_data"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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   "source": [
    "VBox([widgets.Label('Select your bear!'), \n",
    "      btn_upload, btn_run, out_pl, lbl_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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