Permeable-Glossary/Database-making.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrap System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " This is how I scrapped my website to make a database and then perform more relationships and make a better shearch box."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "glossary = {\n",
    "    'title': f'{title}',\n",
    "    'properties': {\n",
    "    }\n",
    "    'words':{\n",
    "        'reflection': {\n",
    "            'voices': ['','',''],\n",
    "            'properties': ['','',''],\n",
    "    }\n",
    "    'version': '0.1',\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"2.0/index.html\") as file_in:\n",
    "    soup = BeautifulSoup(file_in, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
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   "source": [
    "TITLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = soup.find(id=\"title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "glossary_bag = [] \n",
    "word_no = 1\n",
    "words = soup.find_all('div',attrs={'class':'word'})\n",
    "\n",
    "for word in words:\n",
    "    \n",
    "    title = word.find('h1').text\n",
    "    \n",
    "    voices = word.find_all('p')\n",
    "    \n",
    "    links = word.find_all('a')\n",
    "    \n",
    "    properties = word.get('class')\n",
    "    \n",
    "    li_voices = []\n",
    "    \n",
    "    li_links = []\n",
    "    \n",
    "    for voice in voices:\n",
    "        li_voices.append(voice.text)\n",
    "    \n",
    "    for link in links:\n",
    "        url = link.get('href')\n",
    "        li_links.append(url)\n",
    "        \n",
    "    word = {\n",
    "        'Word#': str(word_no), \n",
    "        'title': title, \n",
    "        'properties': properties,\n",
    "        'voices': li_voices,\n",
    "        'links': li_links\n",
    "    }\n",
    "    \n",
    "    glossary_bag.append(word)\n",
    "    \n",
    "    word_no += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    " with open('glossary.json', 'w+', encoding='utf-8') as f:\n",
    "        json.dump(glossary_bag, f, indent=5, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='glossary.json' mode='w' encoding='utf-8'>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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