Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "7286e4c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spotify python api imports\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import pandas as pd\n",
    "\n",
    "# accessing spotify application through credentials\n",
    "cid = '5b85a9af539f47da8dcbbcf517e42650'\n",
    "secret = 'cb3ce73abd464866b285015d631ca1d5'\n",
    "clientCredentialsManager = SpotifyClientCredentials(client_id=cid, client_secret=secret)\n",
    "sp = spotipy.Spotify(client_credentials_manager = clientCredentialsManager)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "5c9a5d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "valenceForEmotion = {} # for lasya\n",
    "\n",
    "# method to access playlist and obtain song data\n",
    "def accessPlaylist(url, limit, offset):\n",
    "    # create an empty list and an empty df\n",
    "    listOfFeatures = ['artist', 'album', 'trackName', 'trackId', 'danceability', 'energy', 'loudness',\n",
    "                              'valence', 'tempo', 'acousticness']\n",
    "    playlistDf = pd.DataFrame(columns = listOfFeatures)\n",
    "    playlist = sp.user_playlist_tracks('', url, limit=limit, offset=offset)[\"items\"]\n",
    "\n",
    "    # loop through the given playlist and get song features\n",
    "    for track in playlist:\n",
    "        # create empty dict\n",
    "        playlistFeatures = {}\n",
    "        # get song data\n",
    "        playlistFeatures['artist'] = track['track']['album']['artists'][0]['name']\n",
    "        playlistFeatures['album'] = track['track']['album']['name']\n",
    "        playlistFeatures[\"trackName\"] = track[\"track\"][\"name\"]\n",
    "        playlistFeatures[\"trackId\"] = track[\"track\"][\"id\"]\n",
    "        playlistFeatures[\"popularity\"] = track[\"track\"][\"popularity\"]\n",
    "        playlistFeatures[\"albumReleaseDate\"] = track[\"track\"][\"album\"][\"release_date\"]\n",
    "        audioFeatures = sp.audio_features(playlistFeatures[\"trackId\"])[0]\n",
    "        \n",
    "        # get song features\n",
    "        for feature in listOfFeatures[4:]:\n",
    "            playlistFeatures[feature] = audioFeatures[feature]\n",
    "            # DO NOT DELETE - LASYA NEEDS FOR VALENCE ANALYSIS \n",
    "            if (feature == \"valence\"):\n",
    "                valenceForEmotion[playlistFeatures['trackName']] = audioFeatures[\"valence\"]\n",
    "        \n",
    "        trackDf = pd.DataFrame(playlistFeatures, index = [0])\n",
    "        playlistDf = pd.concat([playlistDf, trackDf], ignore_index = True)\n",
    "    # TEST --> print(playlistDf.valence[2])\n",
    "    return playlistDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "cdcbcd2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter your Spotify playlist URL: lknlk\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HTTP Error for GET to https://api.spotify.com/v1/playlists/lknlk/tracks with Params: {'limit': 100, 'offset': 0, 'fields': None, 'market': None, 'additional_types': 'track'} returned 404 due to Invalid playlist Id\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your entered playlist URL could not be found. We have provided our own playlist for you to explore instead.\n"
     ]
    }
   ],
   "source": [
    "# send any public playlist to method\n",
    "# 'https://open.spotify.com/playlist/2U9q9cml8EInaiYFDYwZ73?si=1f188bb952c041c1'\n",
    "result = str(input(\"Please enter your Spotify playlist URL: \"))\n",
    "result = result.strip()\n",
    "try:\n",
    "    songs = accessPlaylist(result, 100, 0)\n",
    "except:\n",
    "    songs = accessPlaylist('https://open.spotify.com/playlist/2U9q9cml8EInaiYFDYwZ73?si=1f188bb952c041c1', 100, 0)\n",
    "    print(\"Your entered playlist URL could not be found. We have provided our own playlist for you to explore instead.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "2906a88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put song data into table\n",
    "playlistTable = pd.concat([songs], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "fc18d8fe",
   "metadata": {
    "scrolled": true
   },
   "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>artist</th>\n",
       "      <th>album</th>\n",
       "      <th>trackName</th>\n",
       "      <th>trackId</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>popularity</th>\n",
       "      <th>albumReleaseDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Various Artists</td>\n",
       "      <td>Happy New Year</td>\n",
       "      <td>India Waale</td>\n",
       "      <td>69EoR3Z8jgRODL3AmQs5ZP</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.75300</td>\n",
       "      <td>-5.453</td>\n",
       "      <td>0.6190</td>\n",
       "      <td>128.016</td>\n",
       "      <td>0.037800</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2014-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Kid LAROI</td>\n",
       "      <td>F*CK LOVE 3: OVER YOU</td>\n",
       "      <td>STAY (with Justin Bieber)</td>\n",
       "      <td>5PjdY0CKGZdEuoNab3yDmX</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.76400</td>\n",
       "      <td>-5.484</td>\n",
       "      <td>0.4780</td>\n",
       "      <td>169.928</td>\n",
       "      <td>0.038300</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2021-07-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE ANXIETY</td>\n",
       "      <td>THE ANXIETY</td>\n",
       "      <td>Meet Me At Our Spot</td>\n",
       "      <td>07MDkzWARZaLEdKxo6yArG</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.47000</td>\n",
       "      <td>-7.930</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>94.995</td>\n",
       "      <td>0.015300</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2020-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Weeknd</td>\n",
       "      <td>After Hours</td>\n",
       "      <td>Blinding Lights</td>\n",
       "      <td>0VjIjW4GlUZAMYd2vXMi3b</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.73000</td>\n",
       "      <td>-5.934</td>\n",
       "      <td>0.3340</td>\n",
       "      <td>171.005</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2020-03-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dua Lipa</td>\n",
       "      <td>Future Nostalgia</td>\n",
       "      <td>Levitating (feat. DaBaby)</td>\n",
       "      <td>5nujrmhLynf4yMoMtj8AQF</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.82500</td>\n",
       "      <td>-3.787</td>\n",
       "      <td>0.9150</td>\n",
       "      <td>102.977</td>\n",
       "      <td>0.008830</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2020-03-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Drake</td>\n",
       "      <td>Certified Lover Boy</td>\n",
       "      <td>Way 2 Sexy (with Future &amp; Young Thug)</td>\n",
       "      <td>0k1WUmIRnG3xU6fvvDVfRG</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.59700</td>\n",
       "      <td>-6.035</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>136.008</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2021-09-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Playboi Carti</td>\n",
       "      <td>Whole Lotta Red</td>\n",
       "      <td>Sky</td>\n",
       "      <td>29TPjc8wxfz4XMn21O7VsZ</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.90300</td>\n",
       "      <td>-4.184</td>\n",
       "      <td>0.5650</td>\n",
       "      <td>139.980</td>\n",
       "      <td>0.258000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2020-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ludwig van Beethoven</td>\n",
       "      <td>Beethoven: Piano Sonatas, Vol.3</td>\n",
       "      <td>Sonata No. 14 \"Moonlight\" in C-Sharp Minor\", O...</td>\n",
       "      <td>3DNRdudZ2SstnDCVKFdXxG</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.00527</td>\n",
       "      <td>-37.264</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>170.612</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2007-09-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mariah Carey</td>\n",
       "      <td>Merry Christmas</td>\n",
       "      <td>All I Want for Christmas Is You</td>\n",
       "      <td>0bYg9bo50gSsH3LtXe2SQn</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>-7.463</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>150.273</td>\n",
       "      <td>0.164000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1994-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Céline Dion</td>\n",
       "      <td>Let's Talk About Love</td>\n",
       "      <td>My Heart Will Go On - Love Theme from \"Titanic\"</td>\n",
       "      <td>33LC84JgLvK2KuW43MfaNq</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.27600</td>\n",
       "      <td>-11.729</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>99.195</td>\n",
       "      <td>0.732000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1997-10-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Luis Fonsi</td>\n",
       "      <td>Despacito Feat. Justin Bieber (Remix)</td>\n",
       "      <td>Despacito - Remix</td>\n",
       "      <td>6rPO02ozF3bM7NnOV4h6s2</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.81600</td>\n",
       "      <td>-4.353</td>\n",
       "      <td>0.8160</td>\n",
       "      <td>178.085</td>\n",
       "      <td>0.228000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2017-04-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Far East Movement</td>\n",
       "      <td>Free Wired</td>\n",
       "      <td>Like A G6</td>\n",
       "      <td>4DvhkX2ic4zWkQeWMwQ2qf</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.83700</td>\n",
       "      <td>-8.126</td>\n",
       "      <td>0.7780</td>\n",
       "      <td>124.913</td>\n",
       "      <td>0.006760</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Various Artists</td>\n",
       "      <td>Mulan</td>\n",
       "      <td>I'll Make a Man Out of You - From \"Mulan\"/Soun...</td>\n",
       "      <td>28UMEtwyUUy5u0UWOVHwiI</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.32200</td>\n",
       "      <td>-17.669</td>\n",
       "      <td>0.4960</td>\n",
       "      <td>113.852</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1998-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  artist                                  album  \\\n",
       "0        Various Artists                         Happy New Year   \n",
       "1          The Kid LAROI                  F*CK LOVE 3: OVER YOU   \n",
       "2            THE ANXIETY                            THE ANXIETY   \n",
       "3             The Weeknd                            After Hours   \n",
       "4               Dua Lipa                       Future Nostalgia   \n",
       "5                  Drake                    Certified Lover Boy   \n",
       "6          Playboi Carti                        Whole Lotta Red   \n",
       "7   Ludwig van Beethoven        Beethoven: Piano Sonatas, Vol.3   \n",
       "8           Mariah Carey                        Merry Christmas   \n",
       "9            Céline Dion                  Let's Talk About Love   \n",
       "10            Luis Fonsi  Despacito Feat. Justin Bieber (Remix)   \n",
       "11     Far East Movement                             Free Wired   \n",
       "12       Various Artists                                  Mulan   \n",
       "\n",
       "                                            trackName                 trackId  \\\n",
       "0                                         India Waale  69EoR3Z8jgRODL3AmQs5ZP   \n",
       "1                           STAY (with Justin Bieber)  5PjdY0CKGZdEuoNab3yDmX   \n",
       "2                                 Meet Me At Our Spot  07MDkzWARZaLEdKxo6yArG   \n",
       "3                                     Blinding Lights  0VjIjW4GlUZAMYd2vXMi3b   \n",
       "4                           Levitating (feat. DaBaby)  5nujrmhLynf4yMoMtj8AQF   \n",
       "5               Way 2 Sexy (with Future & Young Thug)  0k1WUmIRnG3xU6fvvDVfRG   \n",
       "6                                                 Sky  29TPjc8wxfz4XMn21O7VsZ   \n",
       "7   Sonata No. 14 \"Moonlight\" in C-Sharp Minor\", O...  3DNRdudZ2SstnDCVKFdXxG   \n",
       "8                     All I Want for Christmas Is You  0bYg9bo50gSsH3LtXe2SQn   \n",
       "9     My Heart Will Go On - Love Theme from \"Titanic\"  33LC84JgLvK2KuW43MfaNq   \n",
       "10                                  Despacito - Remix  6rPO02ozF3bM7NnOV4h6s2   \n",
       "11                                          Like A G6  4DvhkX2ic4zWkQeWMwQ2qf   \n",
       "12  I'll Make a Man Out of You - From \"Mulan\"/Soun...  28UMEtwyUUy5u0UWOVHwiI   \n",
       "\n",
       "    danceability   energy  loudness  valence    tempo  acousticness  \\\n",
       "0          0.720  0.75300    -5.453   0.6190  128.016      0.037800   \n",
       "1          0.591  0.76400    -5.484   0.4780  169.928      0.038300   \n",
       "2          0.773  0.47000    -7.930   0.3990   94.995      0.015300   \n",
       "3          0.514  0.73000    -5.934   0.3340  171.005      0.001460   \n",
       "4          0.702  0.82500    -3.787   0.9150  102.977      0.008830   \n",
       "5          0.803  0.59700    -6.035   0.3310  136.008      0.000619   \n",
       "6          0.785  0.90300    -4.184   0.5650  139.980      0.258000   \n",
       "7          0.184  0.00527   -37.264   0.1510  170.612      0.995000   \n",
       "8          0.336  0.62700    -7.463   0.3500  150.273      0.164000   \n",
       "9          0.428  0.27600   -11.729   0.0382   99.195      0.732000   \n",
       "10         0.653  0.81600    -4.353   0.8160  178.085      0.228000   \n",
       "11         0.435  0.83700    -8.126   0.7780  124.913      0.006760   \n",
       "12         0.672  0.32200   -17.669   0.4960  113.852      0.144000   \n",
       "\n",
       "    popularity albumReleaseDate  \n",
       "0         48.0       2014-09-26  \n",
       "1         92.0       2021-07-23  \n",
       "2         92.0       2020-03-13  \n",
       "3         93.0       2020-03-20  \n",
       "4         88.0       2020-03-27  \n",
       "5         91.0       2021-09-03  \n",
       "6         88.0       2020-12-25  \n",
       "7         70.0       2007-09-25  \n",
       "8         94.0       1994-11-01  \n",
       "9         76.0       1997-10-24  \n",
       "10        73.0       2017-04-17  \n",
       "11        71.0       2010-01-01  \n",
       "12        69.0       1998-01-01  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine number of songs in the playlist to display the correct number in the table\n",
    "numberOfSongs = 1\n",
    "#for trackName in songs:\n",
    "for index, row in songs.iterrows():\n",
    "    numberOfSongs += 1\n",
    "playlistTable.head(numberOfSongs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "d89a7601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mEmotions associated with your music are average, slightly on the sadder side.\u001b[0m\n",
      "Your average valance is 0.48232307692307685\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imports for graphing and spotify api\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import numpy as np\n",
    "import spotipy as sp\n",
    "from termcolor import colored\n",
    "\n",
    "totalValence = 0.0\n",
    "count = 0\n",
    "songsForEmotion = []\n",
    "graphHeight = []\n",
    "colorsForEmotion = []\n",
    "\n",
    "# looping through valence values\n",
    "for key, value in valenceForEmotion.items():\n",
    "    totalValence += value\n",
    "    count += 1\n",
    "    songsForEmotion.append(count) \n",
    "    graphHeight.append(1) # every bar will have same height\n",
    "    # print(\"The valence of \" + key + \" is \" + str(value))\n",
    "    \n",
    "    # adjusting the color of the bar based on the song's happiness\n",
    "    if (value < 0.3):\n",
    "        colorsForEmotion.append(\"black\")\n",
    "    elif (value < 0.5):\n",
    "        colorsForEmotion.append(\"blue\")\n",
    "    elif (value < 0.7):\n",
    "        colorsForEmotion.append(\"green\")\n",
    "    else:\n",
    "        colorsForEmotion.append(\"orange\")\n",
    "\n",
    "avgValence = totalValence / count\n",
    "\n",
    "# printing output to the user\n",
    "if (avgValence < 0.3):\n",
    "    print(colored(\"Emotions associated with your music are very sad and gloomy.\", 'black'))\n",
    "    print(\"Your average valence is \" + str(avgValence))\n",
    "elif (avgValence < 0.5):\n",
    "    print(colored(\"Emotions associated with your music are average, slightly on the sadder side.\", 'blue'))\n",
    "    print(\"Your average valance is \" + str(avgValence))\n",
    "elif (avgValence < 0.7):\n",
    "    print(colored(\"Emotions associated with your music are average, slightly on the happier side.\", 'green'))\n",
    "    print(\"Your average valance is \" + str(avgValence))\n",
    "else:\n",
    "    print(colored(\"Emotions associated with your music are very happy and hype.\", 'orange'))\n",
    "    print(\"Your average valance is \" + str(avgValence))\n",
    "    \n",
    "# putting data into the graph and displaying it\n",
    "plt.bar(songsForEmotion, graphHeight, color=colorsForEmotion)\n",
    "plt.title('Emotions of Your Music Depicted Through Colors')\n",
    "plt.xlabel('Track # in Playlist')\n",
    "plt.yticks([])\n",
    "plt.xticks(np.arange(1, count + 1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "c7a86f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports for graphs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# declaring a method to graph a comparison between two songs\n",
    "def audioAnalysisGrapher(name1, name2, decision):\n",
    "    index1 = int(0)\n",
    "    index2 = int(1)\n",
    "    count = 0\n",
    "    one = False\n",
    "    two = False\n",
    "    # determining what index the song name correlates to\n",
    "    if decision == \"name\":\n",
    "        for index, row in songs.iterrows():\n",
    "            nameTest = str(row['trackName'])\n",
    "            nameTester = nameTest.upper()\n",
    "            if name1 == nameTester:\n",
    "                index1 = count\n",
    "                one = True\n",
    "            if name2 == nameTester:\n",
    "                index2 = count\n",
    "                two = True\n",
    "            if name1 in nameTester:\n",
    "                index1 = count\n",
    "                name1 = nameTest\n",
    "                one = True\n",
    "            if name2 in nameTester:\n",
    "                index2 = count\n",
    "                name2 = nameTest\n",
    "                two = True\n",
    "            count += 1\n",
    "    else:\n",
    "        numberOne = int(name1)\n",
    "        numberTwo = int(name2)\n",
    "        for index, row in songs.iterrows():\n",
    "            nameTest = str(row['trackName'])\n",
    "            if numberOne == count + 1:\n",
    "                index1 = count\n",
    "                name1 = nameTest\n",
    "                one = True\n",
    "            elif numberTwo == count + 1:\n",
    "                index2 = count\n",
    "                name2 = nameTest\n",
    "                two = True\n",
    "            count += 1\n",
    "    if (one == False):\n",
    "        print(\"\")\n",
    "        print(\"The first song \" + decision + \" entered does not exist within the playlist\")\n",
    "    elif (two == False):\n",
    "        print(\"\")\n",
    "        print(\"The second song \" + decision + \" entered does not exist within the playlist\")\n",
    "    \n",
    "    if (one == True and two == True):\n",
    "        # obtaining song features\n",
    "        firstAverages = (songs.valence[index1]*100, songs.danceability[index1]*100, songs.energy[index1]*100, songs.tempo[index1])\n",
    "        secondAverages = (songs.valence[index2]*100, songs.danceability[index2]*100, songs.energy[index2]*100, songs.tempo[index2])\n",
    "\n",
    "        # creating plot\n",
    "        fig, ax = plt.subplots()\n",
    "        index = np.arange(4)\n",
    "        barWidth = 0.2\n",
    "        opacity1 = 0.8\n",
    "        opacity2 = 0.8\n",
    "    \n",
    "        # changing the color of the bar based on the song's valence\n",
    "        color1 = 'gray'\n",
    "        if (songs.valence[index1] < 0.3):\n",
    "            color1 = 'black'\n",
    "        elif (songs.valence[index1] < 0.5):\n",
    "            color1 = 'blue'\n",
    "        elif (songs.valence[index1] < 0.7):\n",
    "            color1 = 'green'\n",
    "        else:\n",
    "            color1 = 'orange'\n",
    "       \n",
    "        # changing the color of the bar based on the song's valence\n",
    "        color2 = 'white'\n",
    "        if (songs.valence[index2] < 0.3):\n",
    "            color2 = 'black'\n",
    "        elif (songs.valence[index2] < 0.5):\n",
    "            color2 = 'blue'\n",
    "        elif (songs.valence[index2] < 0.7):\n",
    "            color2 = 'green'\n",
    "        else:\n",
    "            color2 = 'orange'\n",
    "    \n",
    "        # adjusting opacity if both songs represent the same color correlating to valence\n",
    "        if (color1 == color2):\n",
    "            opacity1 = 0.9\n",
    "            opacity2 = 0.5\n",
    "    \n",
    "        bars1 = plt.bar(index, firstAverages, barWidth,\n",
    "        alpha=opacity1,\n",
    "        color=color1,\n",
    "        label=name1)\n",
    "    \n",
    "        bars2 = plt.bar(index + barWidth, secondAverages, barWidth,\n",
    "        alpha=opacity2,\n",
    "        color=color2,\n",
    "        label=name2)\n",
    "\n",
    "        plt.xlabel('')\n",
    "        plt.ylabel('Mean Values')\n",
    "        plt.title('Audio Analysis Comparison')\n",
    "        plt.xticks(index + barWidth, ('Valence (x100)', 'Danceability (x100)', 'Energy (x100)', 'Tempo'))\n",
    "        plt.legend()\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "c98e692b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Would you like to enter track names or track numbers? num\n",
      "What is the number of the first track you would like to analyze? 1\n",
      "What is the number of the track you would like to compare it to? 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input1 = \"\"\n",
    "input2 = \"\"\n",
    "decision = str(input(\"Would you like to enter track names or track numbers? \"))\n",
    "decision = decision.lower()\n",
    "decision = decision.strip()\n",
    "tester = False\n",
    "while tester == False:\n",
    "    if \"name\" in decision:\n",
    "        decision = \"name\"\n",
    "        tester = True\n",
    "    elif \"num\" in decision:\n",
    "        decision = \"number\"\n",
    "        tester = True\n",
    "    else:\n",
    "        print(\"\")\n",
    "        decision = str(input(\"We could not recognize your input. Please retry. Would you like to enter track names or track numbers? \"))\n",
    "        decision = decision.lower()\n",
    "        decision = decision.strip()\n",
    "while input1 == input2:\n",
    "    input1 = str(input('What is the ' + decision + ' of the first track you would like to analyze? '))\n",
    "    input1 = input1.strip()\n",
    "    input2 = str(input('What is the ' + decision + ' of the track you would like to compare it to? '))\n",
    "    input2 = input2.strip()\n",
    "    if (input1 == input2):\n",
    "        print(\"\")\n",
    "        print(\"Please enter two different song \" + decision + \"s\")\n",
    "        print(\"\")\n",
    "input1 = input1.upper()\n",
    "input1 = input1.strip()\n",
    "input2 = input2.upper()\n",
    "input2 = input2.strip()\n",
    "audioAnalysisGrapher(input1, input2, decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "21ef2c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, you can observe the correlation between two features in your playlist!\n",
      "The features you can compare are: energy, danceability, loudness, valence,\n",
      "                                  tempo, acousticness, and popularity\n",
      "\n",
      "Which feature would you like to represent the x-axis? popularity\n",
      "Which feature would you like to represent the y-axis? valence\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import chartify\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "featuresArray = ['energy', 'danceability', 'loudness',\n",
    "                              'valence', 'tempo', 'acousticness', 'popularity']\n",
    "\n",
    "print('Here, you can observe the correlation between two features in your playlist!')\n",
    "print('The features you can compare are: energy, danceability, loudness, valence,')\n",
    "print('                                  tempo, acousticness, and popularity')\n",
    "\n",
    "print('')\n",
    "xFeature = \"\"\n",
    "yFeature = \"\"\n",
    "done = False\n",
    "\n",
    "while xFeature == yFeature or done == False:\n",
    "    xFeature = str(input('Which feature would you like to represent the x-axis? '))\n",
    "    xFeature = xFeature.lower()\n",
    "    xFeature = xFeature.strip()\n",
    "    yFeature = str(input('Which feature would you like to represent the y-axis? '))\n",
    "    yFeature = yFeature.lower()\n",
    "    yFeature = yFeature.strip()\n",
    "    if (xFeature == yFeature):\n",
    "        print(\"\")\n",
    "        print(\"Please enter two different song features\")\n",
    "    else:\n",
    "        xFrequency = featuresArray.count(xFeature)\n",
    "        yFrequency = featuresArray.count(yFeature)\n",
    "        if (xFrequency == 0):\n",
    "            print(\"\")\n",
    "            print(\"Your inputted feature for the x-axis was not recognized. Please try again.\")\n",
    "        elif (yFrequency == 0):\n",
    "            print(\"\")\n",
    "            print(\"Your inputted feature for the y-axis was not recognized. Please try again.\")\n",
    "        else:\n",
    "            done = True\n",
    "\n",
    "xArray = []\n",
    "yArray = []\n",
    "\n",
    "for index, row in songs.iterrows():\n",
    "    xArray.append(row[xFeature])\n",
    "    yArray.append(row[yFeature])\n",
    "\n",
    "    \n",
    "xFeature = xFeature.capitalize()\n",
    "yFeature = yFeature.capitalize()\n",
    "\n",
    "plt.scatter(xArray, yArray)\n",
    "plt.title(xFeature + ' & ' + yFeature + ' Correlation')\n",
    "plt.xlabel(xFeature)\n",
    "plt.ylabel(yFeature)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08aef71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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