Deployment_News_headlines_1.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for web app, first we load ll the necessary pakages\n",
    "from fastai.text.all import * \n",
    "import ipywidgets as widgets\n",
    "from fastai.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!jupyter serverextension enable voila --user\n",
    "#!jupyter serverextension enable voila --sys-prefix\n",
    "#!jupyter serverextension enable voila --debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install voila"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we change the directory to the path where our model is located\n",
    "path=Path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install spacy==2.3.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application for News Headline Classification\n",
    " ### This is a project created to Classify News Headlines, It can classify news into total 4 Categories:\n",
    " \n",
    " 1. Business\n",
    " 2. Entertainment\n",
    " 3. Health\n",
    " 4. Science And Technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we load our model\n",
    "learn = load_learner(path/'export.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    news=widgets.Text(\n",
    "    value='',\n",
    "    placeholder='Type the Headline',\n",
    "    description='News:',\n",
    "    disabled=False\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide_output\n",
    "out_pl = widgets.Output()\n",
    "out_pl.clear_output()\n",
    "with out_pl: display(news.value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred,pred_idx,probs = learn.predict(news.value)\n",
    "if news.value!='':\n",
    "    if pred=='0':\n",
    "        pred='Business'\n",
    "    elif pred=='1':\n",
    "        pred='Entertainment'\n",
    "    elif pred=='2':\n",
    "        pred='Health'\n",
    "    elif pred=='3':\n",
    "        pred='Science & Technology'\n",
    "    lbl_pred = widgets.Label()\n",
    "    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "else:\n",
    "    pred='None'\n",
    "    lbl_pred= widgets.Label()\n",
    "    lbl_pred.value = f'Prediction: {pred}'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide_output\n",
    "btn_run = widgets.Button(description='Classify')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_click_classify(change):\n",
    "    widgets.Text(\n",
    "    value='',\n",
    "    placeholder='Type something',\n",
    "    description='News:',\n",
    "    disabled=False\n",
    "    )\n",
    "    out_pl.clear_output()\n",
    "    with out_pl: display(news.value)\n",
    "    pred,pred_idx,probs = learn.predict(news.value)\n",
    "    \n",
    "    \n",
    "    if news.value!='':\n",
    "        if pred=='0':\n",
    "            pred='Business'\n",
    "        elif pred=='1':\n",
    "            pred='Entertainment'\n",
    "        elif pred=='2':\n",
    "            pred='Health'\n",
    "        elif pred=='3':\n",
    "            pred='Science & Technology'\n",
    "        lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "    else:\n",
    "        pred='None'\n",
    "        lbl_pred.value = f'Prediction: {pred}'\n",
    "    \n",
    "btn_run.on_click(on_click_classify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8757c51d0b946d0be5c6f81b4febd9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Type the News Headline in the Box!'), Text(value='', description='News:', placehol…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_output\n",
    "VBox([widgets.Label('Type the News Headline in the Box!'), \n",
    "      news, btn_run, out_pl, lbl_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install voila--debug\n",
    "#!jupyter serverextension enable --sys-prefix voila "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#voila--debug"
   ]
  }
 ],
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