freq_dict2anki_deck.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lets learn espanol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Scrape vocabularies from pdf (aka copy paste them into text file and read that)"
   ]
  },
  {
   "attachments": {
    "fffa54ee-118e-423e-936c-d8f7eab86aa9.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![grafik.png](attachment:fffa54ee-118e-423e-936c-d8f7eab86aa9.png)\n",
    "\n",
    "This is what our vocabularies look like in the pdf (starting at page 23)\n",
    "\n",
    "And this is what I want them to be like after scraping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>spanish</th>\n",
       "      <th>english</th>\n",
       "      <th>word_class</th>\n",
       "      <th>example_sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>el, la</td>\n",
       "      <td>the</td>\n",
       "      <td>art</td>\n",
       "      <td>esa mujer era la mujer que yo queria ser</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  spanish english word_class                          example_sentence\n",
       "0  el, la     the        art  esa mujer era la mujer que yo queria ser"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {\"spanish\": \"el, la\", \"english\": \"the\", \"word_class\": \"art\", \"example_sentence\": \"esa mujer era la mujer que yo queria ser\"}\n",
    "example_df = pd.DataFrame(d, index=[0])\n",
    "example_df.head()"
   ]
  },
  {
   "attachments": {
    "ff2630fa-2032-4e52-a6b1-253c5d1eec0d.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![grafik.png](attachment:ff2630fa-2032-4e52-a6b1-253c5d1eec0d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns = ['index', 'spanish', 'english', 'word_class', 'example_sentence'])\n",
    "i = 1\n",
    "check = 0\n",
    "with open('words.txt','r', encoding='utf8') as file: # have to specify encoding\n",
    "    while i <= 1000:       \n",
    "        \n",
    "        # Read in first line and append to dict\n",
    "        d = read_line1(file, i)\n",
    "        \n",
    "        # If read_line1 was not successfull we go to next line and try again\n",
    "        if d is None:\n",
    "            continue\n",
    "        else:\n",
    "            # Read second line aka example sentence\n",
    "            d = read_line2(file,d)\n",
    "        \n",
    "            # Skip third line\n",
    "            d = read_line3(file,d)\n",
    "            \n",
    "            # Clear paragraphs from example sentence\n",
    "            d['example_sentence'] = d['example_sentence'].replace(\"\\n\",\" \")\n",
    "            \n",
    "            if check:\n",
    "                print(\"VOCAB%03d: %s\\n\" % (i, d))\n",
    "            \n",
    "            # Append to df            \n",
    "            df = df.append(d, ignore_index=True)\n",
    "                           \n",
    "            # Increment counter\n",
    "            i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save df\n",
    "df.to_pickle('spanish_first1000.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⬆️ Step 1 check!!! \n",
    "\n",
    "## Step 2: write df to anki deck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created deck with 1000 flashcards\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import pandas as pd\n",
    "import random\n",
    "import genanki\n",
    "\n",
    "# Filename of the data file\n",
    "data_filename = \"spanish_first1000.pkl\"\n",
    "\n",
    "# Filename of the Anki deck to generate\n",
    "deck_filename = \"spanish1000.apkg\"\n",
    "\n",
    "# Title of the deck as shown in Anki\n",
    "anki_deck_title = \"spanish1000\"\n",
    "\n",
    "# Name of the card model\n",
    "anki_model_name = \"spanish\"\n",
    "\n",
    "# Create the deck model\n",
    "model_id = random.randrange(1 << 30, 1 << 31)\n",
    "\n",
    "style = \"\"\"\n",
    ".card {\n",
    " font-family: arial;\n",
    " font-size: 24px;\n",
    " text-align: center;\n",
    " color: black;\n",
    " background-color: white;\n",
    "}\n",
    ".hanzi {\n",
    " font-size: 64px;\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "anki_model = genanki.Model(\n",
    "    model_id,\n",
    "    anki_model_name,\n",
    "    fields=[{\"name\": \"spanish\"}, {\"name\": \"english\"},{\"name\": \"word_class\"}, {\"name\": \"example_sentence\"}],\n",
    "    templates=[\n",
    "        {\n",
    "            \"name\": \"Card 1\", # spanish in front, Rest back\n",
    "            \"qfmt\": '<p class=\"spanish\">{{spanish}}</p>', # front side\n",
    "            \"afmt\": '{{FrontSide}}<hr id=\"answer\"><p class=\"english\">{{english}}</p><p class=\"word_class\">{{word_class}}</p><p class=\"example_sentence\">{{example_sentence}}</p>' # back side\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"Card 2\", # english front, rest back\n",
    "            \"qfmt\": '<p class=\"english\">{{english}}</p>', # front side\n",
    "            \"afmt\": '{{FrontSide}}<hr id=\"answer\"><p class=\"spanish\">{{spanish}}</p><p class=\"word_class\">{{word_class}}</p><p class=\"example_sentence\">{{example_sentence}}</p>', # back side\n",
    "        },\n",
    "    ],\n",
    "    css=style,\n",
    ")\n",
    "\n",
    "# Retrieve vocabulary\n",
    "df = pd.read_pickle(data_filename)\n",
    "anki_notes = []\n",
    "\n",
    "# Loop through\n",
    "for index, row in df.iterrows():\n",
    "    anki_note = genanki.Note(\n",
    "    model=anki_model,\n",
    "    # spanish english word_class example sentence\n",
    "    fields=[row['spanish'], row['english'], row['word_class'], row['example_sentence']],\n",
    "    )\n",
    "    anki_notes.append(anki_note)\n",
    "\n",
    "# Shuffle flashcards\n",
    "random.shuffle(anki_notes)\n",
    "\n",
    "anki_deck = genanki.Deck(model_id, anki_deck_title)\n",
    "anki_package = genanki.Package(anki_deck)\n",
    "\n",
    "# Add flashcards to the deck\n",
    "for anki_note in anki_notes:\n",
    "    anki_deck.add_note(anki_note)\n",
    "\n",
    "# Save the deck to a file\n",
    "anki_package.write_to_file(deck_filename)\n",
    "\n",
    "print(\"Created deck with {} flashcards\".format(len(anki_deck.notes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subfunctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "def skip_line(file):\n",
    "    \"\"\"\n",
    "    reads in line of file but does not return it and thereby (hopefully) effectively skips it\n",
    "    \"\"\"\n",
    "    file.readline()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readnsplit(file):\n",
    "    \"\"\"\n",
    "    Read line, split it, return it\n",
    "    \"\"\"\n",
    "    return file.readline().split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_line1(file, i, d=init_vocabulary()):\n",
    "    \"\"\"\n",
    "    Write line into dictionary if beginning of string is a number (check0) and if that number matches i (check1)\n",
    "    \"\"\"        \n",
    "    line = readnsplit(file)\n",
    "    \n",
    "    # Debug\n",
    "    di = 0 \n",
    "    if i == di:\n",
    "        print(\"\\nDEBUG LINE: %s\\n\" % line)    \n",
    "    \n",
    "    # check0\n",
    "    if not line[0][0].isnumeric(): # necessary to use two different if statements as letter at start of line will cast error at check1\n",
    "        return None    \n",
    "    \n",
    "    # check1\n",
    "    if not int(line[0]) == i:        \n",
    "        return None\n",
    "    \n",
    "    # write to dict\n",
    "    else:\n",
    "        d['index'] = line[0]             \n",
    "    \n",
    "    # Join elements that are seperated by comma\n",
    "    j = 1    \n",
    "    while j < len(line):        \n",
    "        word = line[j]        \n",
    "        if word[-1] == ',': # this should take care of spanish multiple spanish values like \"el, la\" and some english stuff like \"very, really\"\n",
    "            line[j:(j+2)] = [\" \".join(line[j:j+2])] # join me and the next one\n",
    "        j += 1       \n",
    "    \n",
    "    line[3:] = [\" \". join(line[3:])] # this takes care of the rest of the english values\n",
    "    \n",
    "    # Append to dictionary       \n",
    "    d['spanish'] = line[1]\n",
    "    d['english'] = line[3]\n",
    "    d['word_class'] = line[2] \n",
    "    return d        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_line2(file, d):\n",
    "    \"\"\"\n",
    "    Read example sentence. In case line 1 is not finished yet clear that up first.\n",
    "    \"\"\"\n",
    "    line = file.readline()\n",
    "    if line[0] == '•':\n",
    "        d['example_sentence'] = line[2:]\n",
    "    else:\n",
    "        d['english'] = d['english'] + line\n",
    "        d = read_line2(file,d)\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_line3(file,d):\n",
    "    \"\"\"\n",
    "    Moves filepointer to next line. Read in remains of example senetence if necessary and then skip line.\n",
    "    \"\"\"\n",
    "    line = file.readline()\n",
    "    if len(line.split()) < 2 or not line.split()[1] == \"|\":\n",
    "        d['example_sentence'] = d['example_sentence'] + line\n",
    "        skip_line(file)\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_vocabulary():\n",
    "    return {\"index\": 0, \"spanish\": \"\", \"english\": \"\", \"word_class\": \"\", \"example_sentence\": \"\"}"
   ]
  }
 ],
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