smili_predicter.ipynb

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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastbook\n",
    "fastbook.setup_book()\n",
    "from fastbook import *\n",
    "from fastai.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['happy', 'sad']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf = load_learner('model.pkl')\n",
    "inf.dls.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "model_id": "e5fd52b9773d48ccbfadee5cb04be04d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(FileUpload(value={}, description='Upload'), Button(description='Classify', style=ButtonStyle())…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# inference test\n",
    "lab = widgets.Label()\n",
    "layout = widgets.Output()\n",
    "\n",
    "def infer(_):\n",
    "    if not up.data:\n",
    "        return\n",
    "    \n",
    "    # display image\n",
    "    img = PILImage.create(up.data[-1])\n",
    "    layout.clear_output()\n",
    "    with layout:\n",
    "        display(img.to_thumb(127,127))    \n",
    "\n",
    "    # predict\n",
    "    pred, idx, probs = inf.predict(img)    \n",
    "    lab.value = f'Prediction: {pred}; Probability: {probs[idx]:.04f}'\n",
    "\n",
    "up= widgets.FileUpload()\n",
    "btn_run = widgets.Button(description='Classify')\n",
    "btn_run.on_click(infer)\n",
    "VBox([up, btn_run, lab, layout])"
   ]
  }
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