generate-pages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "from io import BytesIO\n",
    "from pathlib import Path\n",
    "\n",
    "from urllib.parse import urlparse\n",
    "from urllib.parse import parse_qs\n",
    "\n",
    "import requests\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from PIL import Image\n",
    "from jinja2 import Template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = Path('08-deep-learning')\n",
    "\n",
    "os.makedirs(folder / 'images', exist_ok=True)\n",
    "\n",
    "with open(folder / 'meta.json') as f_in:\n",
    "    data = json.load(f_in)\n",
    "\n",
    "\n",
    "session = data['session']\n",
    "session_name = data['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lesson</th>\n",
       "      <th>name</th>\n",
       "      <th>page_name</th>\n",
       "      <th>video</th>\n",
       "      <th>slides</th>\n",
       "      <th>notebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fashion classification</td>\n",
       "      <td>01-fashion-classification.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=it1Lu7NmMpw</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>TensorFlow and Keras</td>\n",
       "      <td>02-tensorflow-keras.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=R6o_CUmoN9Q</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pre-trained convolutional neural networks</td>\n",
       "      <td>03-pretrained-models.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=qGDXEz-cr6M</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Convolutional neural networks</td>\n",
       "      <td>04-conv-neural-nets.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=BN-fnYzbdc8</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Transfer learning</td>\n",
       "      <td>05-transfer-learning.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=WKHylqfNmq4</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Adjusting the learning rate</td>\n",
       "      <td>06-learning-rate.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=2gPmRRGz0Hc</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Checkpointing</td>\n",
       "      <td>07-checkpointing.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=NRpGUx0o3Ps</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Adding more layers</td>\n",
       "      <td>08-more-layers.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=bSRRrorvAZs</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Regularization and dropout</td>\n",
       "      <td>09-dropout.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=74YmhVM6FTM</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Data augmentation</td>\n",
       "      <td>10-augmentation.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=aoPfVsS3BDE</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Training a larger model</td>\n",
       "      <td>11-large-model.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=_QpDGJwFjYA</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Using the model</td>\n",
       "      <td>12-using-model.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=cM1WHKae1wo</td>\n",
       "      <td>https://www.slideshare.net/AlexeyGrigorev/ml-z...</td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Summary</td>\n",
       "      <td>13-summary.md</td>\n",
       "      <td>https://www.youtube.com/watch?v=mn0BcXJlRFM</td>\n",
       "      <td></td>\n",
       "      <td>https://github.com/alexeygrigorev/mlbookcamp-c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Explore more</td>\n",
       "      <td>14-explore-more.md</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Homework</td>\n",
       "      <td>homework.md</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    lesson                                       name  \\\n",
       "0        1                     Fashion classification   \n",
       "1        2                       TensorFlow and Keras   \n",
       "2        3  Pre-trained convolutional neural networks   \n",
       "3        4              Convolutional neural networks   \n",
       "4        5                          Transfer learning   \n",
       "5        6                Adjusting the learning rate   \n",
       "6        7                              Checkpointing   \n",
       "7        8                         Adding more layers   \n",
       "8        9                 Regularization and dropout   \n",
       "9       10                          Data augmentation   \n",
       "10      11                    Training a larger model   \n",
       "11      12                            Using the model   \n",
       "12      13                                    Summary   \n",
       "13      14                               Explore more   \n",
       "14      15                                   Homework   \n",
       "\n",
       "                       page_name                                        video  \\\n",
       "0   01-fashion-classification.md  https://www.youtube.com/watch?v=it1Lu7NmMpw   \n",
       "1         02-tensorflow-keras.md  https://www.youtube.com/watch?v=R6o_CUmoN9Q   \n",
       "2        03-pretrained-models.md  https://www.youtube.com/watch?v=qGDXEz-cr6M   \n",
       "3         04-conv-neural-nets.md  https://www.youtube.com/watch?v=BN-fnYzbdc8   \n",
       "4        05-transfer-learning.md  https://www.youtube.com/watch?v=WKHylqfNmq4   \n",
       "5            06-learning-rate.md  https://www.youtube.com/watch?v=2gPmRRGz0Hc   \n",
       "6            07-checkpointing.md  https://www.youtube.com/watch?v=NRpGUx0o3Ps   \n",
       "7              08-more-layers.md  https://www.youtube.com/watch?v=bSRRrorvAZs   \n",
       "8                  09-dropout.md  https://www.youtube.com/watch?v=74YmhVM6FTM   \n",
       "9             10-augmentation.md  https://www.youtube.com/watch?v=aoPfVsS3BDE   \n",
       "10             11-large-model.md  https://www.youtube.com/watch?v=_QpDGJwFjYA   \n",
       "11             12-using-model.md  https://www.youtube.com/watch?v=cM1WHKae1wo   \n",
       "12                 13-summary.md  https://www.youtube.com/watch?v=mn0BcXJlRFM   \n",
       "13            14-explore-more.md                                                \n",
       "14                   homework.md                                                \n",
       "\n",
       "                                               slides  \\\n",
       "0   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "1   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "2   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "3   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "4   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "5   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "6   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "7   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "8   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "9   https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "10  https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "11  https://www.slideshare.net/AlexeyGrigorev/ml-z...   \n",
       "12                                                      \n",
       "13                                                      \n",
       "14                                                      \n",
       "\n",
       "                                             notebook  \n",
       "0   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "1   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "2   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "3   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "4   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "5   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "6   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "7   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "8   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "9   https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "10  https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "11  https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "12  https://github.com/alexeygrigorev/mlbookcamp-c...  \n",
       "13                                                     \n",
       "14                                                     "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(folder / data['data'])\n",
    "df = df.fillna('')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_string = \"\"\"\n",
    "## {{ session }}.{{ lesson }} {{ name }}\n",
    "\n",
    "{% if video %}<a href=\"{{ video }}\"><img src=\"{{ thumbnail }}\"></a>{% endif %}\n",
    "{% if coming_soon %}COMING SOON{% endif %} \n",
    "\n",
    "{% if slides %}[Slides]({{ slides }}){% endif %}\n",
    "\n",
    "\n",
    "## Notes\n",
    "\n",
    "Add notes from the video (PRs are welcome)\n",
    "\n",
    "\n",
    "<table>\n",
    "   <tr>\n",
    "      <td>⚠️</td>\n",
    "      <td>\n",
    "         The notes are written by the community. <br>\n",
    "         If you see an error here, please create a PR with a fix.\n",
    "      </td>\n",
    "   </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "## Navigation\n",
    "\n",
    "{{ navigation }}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_thumbnail(video, session, lesson):\n",
    "    if type(lesson) in [np.int64]:\n",
    "        thumbnail_file = 'thumbnail-%d-%02d.jpg' % (session, lesson)\n",
    "    else:\n",
    "        thumbnail_file = 'thumbnail-%d-%s.jpg' % (session, lesson)\n",
    "    thumbnail_file_full = '%s/images/%s' % (folder, thumbnail_file)\n",
    "\n",
    "    if os.path.exists(thumbnail_file_full):\n",
    "        print(thumbnail_file_full, 'exists')\n",
    "        return 'images/%s' % thumbnail_file\n",
    "\n",
    "    video_id = parse_qs(urlparse(video).query)['v'][0]\n",
    "    print('processing video', video_id)\n",
    "    thumbnail_url = 'https://img.youtube.com/vi/%s/0.jpg' % video_id\n",
    "\n",
    "    response = requests.get(thumbnail_url)\n",
    "    thumbnail = Image.open(BytesIO(response.content))\n",
    "    w_img, h_img = thumbnail.size\n",
    "\n",
    "    play = Image.open('../images/play.png')\n",
    "    w_play, h_play = play.size\n",
    "    \n",
    "    x0 = w_img // 2 - w_play // 2\n",
    "    y0 = h_img // 2 - h_play // 2\n",
    "\n",
    "    thumbnail.paste(play, (x0, y0), play)\n",
    "    thumbnail.save(thumbnail_file_full, quality=90)\n",
    "\n",
    "    print('saved to', thumbnail_file_full)\n",
    "\n",
    "    return 'images/%s' % thumbnail_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing video 2viqmJ_NpgE\n",
      "saved to 07-bentoml-production/images/thumbnail-7-01.jpg\n",
      "\n",
      "## 7.1 Intro/Session Overview\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=2viqmJ_NpgE&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-01.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Next: [Building Your Prediction Service with BentoML](02-build-bento-service.md)\n",
      "\n",
      "created 07-bentoml-production/01-intro.md\n",
      "\n",
      "\n",
      "processing video bWdEVlUw1CA\n",
      "saved to 07-bentoml-production/images/thumbnail-7-02.jpg\n",
      "\n",
      "## 7.2 Building Your Prediction Service with BentoML\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=bWdEVlUw1CA&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-02.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Intro/Session Overview](01-intro.md)\n",
      "* Next: [Deploying Your Prediction Service](03-deploy-bento-service.md)\n",
      "\n",
      "created 07-bentoml-production/02-build-bento-service.md\n",
      "\n",
      "\n",
      "processing video qpjLm_Lm4FA\n",
      "saved to 07-bentoml-production/images/thumbnail-7-03.jpg\n",
      "\n",
      "## 7.3 Deploying Your Prediction Service\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=qpjLm_Lm4FA&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-03.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Building Your Prediction Service with BentoML](02-build-bento-service.md)\n",
      "* Next: [Sending, Receiving and Validating Data](04-validation.md)\n",
      "\n",
      "created 07-bentoml-production/03-deploy-bento-service.md\n",
      "\n",
      "\n",
      "processing video zNYtXde0BCA\n",
      "saved to 07-bentoml-production/images/thumbnail-7-04.jpg\n",
      "\n",
      "## 7.4 Sending, Receiving and Validating Data\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=zNYtXde0BCA&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-04.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Deploying Your Prediction Service](03-deploy-bento-service.md)\n",
      "* Next: [High-Performance Serving](05-high-performance.md)\n",
      "\n",
      "created 07-bentoml-production/04-validation.md\n",
      "\n",
      "\n",
      "processing video LjDYPWhJBrY\n",
      "saved to 07-bentoml-production/images/thumbnail-7-05.jpg\n",
      "\n",
      "## 7.5 High-Performance Serving\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=LjDYPWhJBrY&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-05.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Sending, Receiving and Validating Data](04-validation.md)\n",
      "* Next: [Bento Production Deployment](06-production-deployment.md)\n",
      "\n",
      "created 07-bentoml-production/05-high-performance.md\n",
      "\n",
      "\n",
      "processing video aF-TfJXQX-w\n",
      "saved to 07-bentoml-production/images/thumbnail-7-06.jpg\n",
      "\n",
      "## 7.6 Bento Production Deployment\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=aF-TfJXQX-w&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-06.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [High-Performance Serving](05-high-performance.md)\n",
      "* Next: [(Optional) Advanced Example: Deploying Stable Diffusion Model](07-stable-diffusion.md)\n",
      "\n",
      "created 07-bentoml-production/06-production-deployment.md\n",
      "\n",
      "\n",
      "processing video NMIi_DDVxAs\n",
      "saved to 07-bentoml-production/images/thumbnail-7-07.jpg\n",
      "\n",
      "## 7.7 (Optional) Advanced Example: Deploying Stable Diffusion Model\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=NMIi_DDVxAs&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-07.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Bento Production Deployment](06-production-deployment.md)\n",
      "* Next: [Summary](08-summary.md)\n",
      "\n",
      "created 07-bentoml-production/07-stable-diffusion.md\n",
      "\n",
      "\n",
      "processing video eeLuhv8FpVs\n",
      "saved to 07-bentoml-production/images/thumbnail-7-08.jpg\n",
      "\n",
      "## 7.8 Summary\n",
      "\n",
      "<a href=\"https://www.youtube.com/watch?v=eeLuhv8FpVs&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR\"><img src=\"images/thumbnail-7-08.jpg\"></a>\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [(Optional) Advanced Example: Deploying Stable Diffusion Model](07-stable-diffusion.md)\n",
      "* Next: [Homework](homework.md)\n",
      "\n",
      "created 07-bentoml-production/08-summary.md\n",
      "\n",
      "\n",
      "\n",
      "## 7.9 Homework\n",
      "\n",
      "\n",
      " \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "## Notes\n",
      "\n",
      "Add notes from the video (PRs are welcome)\n",
      "\n",
      "\n",
      "<table>\n",
      "   <tr>\n",
      "      <td>⚠️</td>\n",
      "      <td>\n",
      "         The notes are written by the community. <br>\n",
      "         If you see an error here, please create a PR with a fix.\n",
      "      </td>\n",
      "   </tr>\n",
      "</table>\n",
      "\n",
      "\n",
      "## Navigation\n",
      "\n",
      "* [Machine Learning Zoomcamp course](../)\n",
      "* [Session 7: Production-Ready Machine Learning (Bento ML)](./)\n",
      "* Previous: [Summary](08-summary.md)\n",
      "\n",
      "APPENDED TO 07-bentoml-production/homework.md\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "create_files = True\n",
    "\n",
    "for i in range(len(df)):\n",
    "    row = df.loc[i]\n",
    "    params = row.to_dict()\n",
    "    params['session'] = session\n",
    "\n",
    "    navigation = [\n",
    "        '* [Machine Learning Zoomcamp course](../)',\n",
    "        '* [Session %s: %s](./)' % (session, session_name) \n",
    "    ]\n",
    "\n",
    "    if i > 0:\n",
    "        prev_row = df.loc[i - 1]\n",
    "        line = '* Previous: [%s](%s)' % (prev_row['name'], prev_row.page_name)\n",
    "        navigation.append(line)\n",
    "\n",
    "    if i + 1 < len(df):\n",
    "        next_row = df.loc[i + 1]\n",
    "        line = '* Next: [%s](%s)' % (next_row['name'], next_row.page_name)\n",
    "        navigation.append(line)\n",
    "        \n",
    "    params['navigation'] = '\\n'.join(navigation)\n",
    "    \n",
    "    if row.video and row.video != 'NOT_READY':\n",
    "        thumbnail = download_thumbnail(row.video, session, row.lesson)\n",
    "        params['thumbnail'] = thumbnail\n",
    "\n",
    "    if row.video == 'NOT_READY':\n",
    "        del params['video']\n",
    "        params['coming_soon'] = True\n",
    "    \n",
    "    if 'video' in params and len(params['video']) > 0:\n",
    "        params['video'] = params['video'] + '&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR'\n",
    "\n",
    "    template = Template(template_string)\n",
    "    rendered = template.render(**params)\n",
    "    print(rendered)\n",
    "    \n",
    "    page_full = '%s/%s' % (folder, row.page_name)\n",
    "    \n",
    "    print()\n",
    "\n",
    "    if create_files:\n",
    "        if (not os.path.exists(page_full)) or os.path.getsize(page_full) <= 1:\n",
    "            with open(page_full, 'w', encoding='utf-8') as f_out:\n",
    "                f_out.write(rendered)\n",
    "            print(\"created\", page_full)\n",
    "        else:\n",
    "            with open(page_full, 'a', encoding='utf-8') as f_out:\n",
    "                f_out.write('\\n\\n========\\n\\n')\n",
    "                f_out.write(rendered)\n",
    "            print(\"APPENDED TO\", page_full)\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing video WZCjsyV8hZE\n",
      "saved to 08-deep-learning/images/thumbnail-8-01b.jpg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'images/thumbnail-8-01b.jpg'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_thumbnail('https://www.youtube.com/watch?v=WZCjsyV8hZE', 8, '01b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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