Song_Recommender.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yjJ1VIyu1i-"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "from ast import literal_eval\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiFron28uTxP"
      },
      "source": [
        "data = pd.read_csv('songdata.csv' ,encoding='latin-1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "AcSRwX7NuQbR",
        "outputId": "3fa4bb82-2400-46c1-9d3e-8269ce53b067"
      },
      "source": [
        "pip install spotipy"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "Collecting spotipy\n",
            "  Downloading https://files.pythonhosted.org/packages/02/9b/25b96cd2f1e2174ac787099b386e2afd427bf78dfb11e1c5857affc3409d/spotipy-2.18.0-py3-none-any.whl\n",
            "Collecting urllib3>=1.26.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/09/c6/d3e3abe5b4f4f16cf0dfc9240ab7ce10c2baa0e268989a4e3ec19e90c84e/urllib3-1.26.4-py2.py3-none-any.whl (153kB)\n",
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            "\u001b[?25hRequirement already satisfied: six>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spotipy) (1.15.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.25.0->spotipy) (2020.12.5)\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: urllib3, requests, spotipy\n",
            "  Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.25.1 spotipy-2.18.0 urllib3-1.26.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTjahmqnuPKe"
      },
      "source": [
        "import spotipy\n",
        "from spotipy.oauth2 import SpotifyClientCredentials\n",
        "import pandas as pd\n",
        "import time\n",
        "\n",
        "cid = '7f2ab65404b64bbc99e22b2efcb17983'\n",
        "secret = 'aa855c61f03f4077ac50a78fa35c71c6'\n",
        "client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)\n",
        "sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqyrxJI-z7od"
      },
      "source": [
        "def getTrackFeatures(id):\n",
        "  meta = sp.track(id)\n",
        "  features = sp.audio_features(id)\n",
        "\n",
        "  # meta\n",
        "  name = meta['name']\n",
        "  album = meta['album']['name']\n",
        "  artist = meta['album']['artists'][0]['name']\n",
        "  release_date = meta['album']['release_date']\n",
        "  duration_ms = meta['duration_ms']\n",
        "  popularity = meta['popularity']\n",
        "\n",
        "  #year\n",
        "  try:\n",
        "    year = int(release_date[:4])\n",
        "    if(year<1900 and year>2021):\n",
        "      year = 0\n",
        "  except:\n",
        "    year = 0\n",
        "\n",
        "  # features\n",
        "  if 'acousticness' in features[0]:\n",
        "    acousticness = features[0]['acousticness']\n",
        "  else:\n",
        "    acousticness = 0\n",
        "  danceability = features[0]['danceability'] if features[0]['danceability']!=None else 0\n",
        "  energy = features[0]['energy'] if features[0]['energy']!=None else 0\n",
        "  instrumentalness = features[0]['instrumentalness'] if features[0]['instrumentalness']!=None else 0\n",
        "  liveness = features[0]['liveness'] if features[0]['liveness']!=None else 0\n",
        "  loudness = features[0]['loudness'] if features[0]['loudness']!=None else 0\n",
        "  speechiness = features[0]['speechiness'] if features[0]['speechiness']!=None else 0\n",
        "  tempo = features[0]['tempo'] if features[0]['tempo']!=None else 0\n",
        "\n",
        "\n",
        "  track = [name, album, artist, release_date]\n",
        "  track2 = [duration_ms, popularity, danceability, acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, year]\n",
        "  return track, track2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "new_df = pd.DataFrame(columns=['id', 'name', 'artist', 'album', 'release_date', 'duration_ms', 'popularity', 'danceability', 'acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'year'])\n",
        "cat = [0, 1, 3, 7, 8, 10, 11, 12, 13, 14, 16, 18, 19]\n",
        "categories = sp.categories(country=\"IN\")['categories']['items']\n",
        "time.sleep(0.5)\n",
        "# for selected categories\n",
        "for i in cat:\n",
        "  id = categories[i]['id']\n",
        "  print(\"category: \", id)\n",
        "  # find all playlists\n",
        "  playlists = sp.category_playlists(id, country=\"IN\")['playlists']['items']\n",
        "  time.sleep(0.5)\n",
        "  for j in range(len(playlists)):\n",
        "    pid = playlists[j]['id']\n",
        "    # find top 50 tracks\n",
        "    tracks = sp.playlist_tracks(pid, limit=50)['items']\n",
        "    time.sleep(0.5)\n",
        "    try:\n",
        "      for k in range(len(tracks)):\n",
        "        songs = []\n",
        "        tid = tracks[k]['track']['id']\n",
        "        track1, track2 = getTrackFeatures(tid)\n",
        "        track1.insert(0,tid)\n",
        "        track1.extend(track2)\n",
        "        songs.append(track1)\n",
        "        record = pd.DataFrame(songs, columns = ['id', 'name', 'album', 'artist', 'release_date', 'duration_ms', 'popularity', 'danceability', 'acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'year'])\n",
        "        new_df = pd.concat([new_df, record], ignore_index = True)\n",
        "    except:\n",
        "      pass\n",
        "\n",
        "new_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "category:  toplists\n",
            "category:  at_home\n",
            "category:  bollywood\n",
            "category:  indie_alt\n",
            "category:  pop\n",
            "category:  marathi\n",
            "category:  devotional\n",
            "category:  indian_classical\n",
            "category:  romance\n",
            "category:  kpop\n",
            "category:  party\n",
            "category:  edm_dance\n",
            "category:  decades\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>artist</th>\n",
              "      <th>album</th>\n",
              "      <th>release_date</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>popularity</th>\n",
              "      <th>danceability</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>748mdHapucXQri7IAO8yFK</td>\n",
              "      <td>Kiss Me More (feat. SZA)</td>\n",
              "      <td>Kiss Me More (feat. SZA)</td>\n",
              "      <td>Doja Cat</td>\n",
              "      <td>2021-04-09</td>\n",
              "      <td>208866</td>\n",
              "      <td>96</td>\n",
              "      <td>0.762</td>\n",
              "      <td>0.23500</td>\n",
              "      <td>0.762</td>\n",
              "      <td>0.701</td>\n",
              "      <td>0.000158</td>\n",
              "      <td>0.1230</td>\n",
              "      <td>-3.541</td>\n",
              "      <td>0.0286</td>\n",
              "      <td>110.968</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>463CkQjx2Zk1yXoBuierM9</td>\n",
              "      <td>Levitating (feat. DaBaby)</td>\n",
              "      <td>Levitating (feat. DaBaby)</td>\n",
              "      <td>Dua Lipa</td>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>203064</td>\n",
              "      <td>91</td>\n",
              "      <td>0.702</td>\n",
              "      <td>0.00883</td>\n",
              "      <td>0.702</td>\n",
              "      <td>0.825</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0674</td>\n",
              "      <td>-3.787</td>\n",
              "      <td>0.0601</td>\n",
              "      <td>102.977</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4iJyoBOLtHqaGxP12qzhQI</td>\n",
              "      <td>Peaches (feat. Daniel Caesar &amp; Giveon)</td>\n",
              "      <td>Justice</td>\n",
              "      <td>Justin Bieber</td>\n",
              "      <td>2021-03-19</td>\n",
              "      <td>198081</td>\n",
              "      <td>100</td>\n",
              "      <td>0.677</td>\n",
              "      <td>0.32100</td>\n",
              "      <td>0.677</td>\n",
              "      <td>0.696</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.4200</td>\n",
              "      <td>-6.181</td>\n",
              "      <td>0.1190</td>\n",
              "      <td>90.030</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37BZB0z9T8Xu7U3e65qxFy</td>\n",
              "      <td>Save Your Tears (with Ariana Grande) (Remix)</td>\n",
              "      <td>Save Your Tears (Remix)</td>\n",
              "      <td>The Weeknd</td>\n",
              "      <td>2021-04-23</td>\n",
              "      <td>191013</td>\n",
              "      <td>92</td>\n",
              "      <td>0.650</td>\n",
              "      <td>0.02150</td>\n",
              "      <td>0.650</td>\n",
              "      <td>0.825</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.0936</td>\n",
              "      <td>-4.645</td>\n",
              "      <td>0.0325</td>\n",
              "      <td>118.091</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3BZEcbdtXQSo7OrvKRJ6mb</td>\n",
              "      <td>MONTERO (Call Me By Your Name)</td>\n",
              "      <td>MONTERO (Call Me By Your Name)</td>\n",
              "      <td>Lil Nas X</td>\n",
              "      <td>2021-03-26</td>\n",
              "      <td>137875</td>\n",
              "      <td>88</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.29700</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.508</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.3840</td>\n",
              "      <td>-6.682</td>\n",
              "      <td>0.1520</td>\n",
              "      <td>178.818</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                       id  ...  year\n",
              "0  748mdHapucXQri7IAO8yFK  ...  2021\n",
              "1  463CkQjx2Zk1yXoBuierM9  ...  2020\n",
              "2  4iJyoBOLtHqaGxP12qzhQI  ...  2021\n",
              "3  37BZB0z9T8Xu7U3e65qxFy  ...  2021\n",
              "4  3BZEcbdtXQSo7OrvKRJ6mb  ...  2021\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5C72gOPzTjRX"
      },
      "source": [
        "new_df = pd.read_csv('sdata.csv', encoding='latin-1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jT3v0lsmAjsd"
      },
      "source": [
        "### Content Based Recommender\n",
        "\n",
        "1. User enters the song name\n",
        "2. Extract the track ID from the name followed by the features\n",
        "3. Cosine similarity to find the TOP 5 similar songs.\n",
        "4. Display all the recommended songs with details (image, title, artist, genre)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yhz4JMPyAiYQ"
      },
      "source": [
        "def TFIDF(data, col):\n",
        "  #Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n",
        "  tfidf = TfidfVectorizer(stop_words='english')\n",
        "  #Construct the required TF-IDF matrix by fitting and transforming the data\n",
        "  tfidf1 = tfidf.fit_transform(data[col])\n",
        "  return tfidf1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3uG4c1NGwTQ"
      },
      "source": [
        "def get_recommendation_diff(new_df, record):\n",
        "  temp_df = new_df[['id','name', 'album', 'artist', 'release_date']]\n",
        "  temp_df = pd.concat([temp_df, record], ignore_index = True)\n",
        "\n",
        "  col = ['name', 'album', 'artist', 'release_date']\n",
        "  data = pd.DataFrame(columns=col)\n",
        "  id = []\n",
        "  for i in col:\n",
        "    tf = TFIDF(temp_df, i)\n",
        "    cosine_sim = linear_kernel(tf, tf)\n",
        "    data[i] = cosine_sim[-1]\n",
        "    d1 = data.sort_values(by=[i], ascending=False)\n",
        "    id.append(list(d1.head(7).index))\n",
        "\n",
        "  tid = []\n",
        "  for i in range(4):\n",
        "    track_id = []\n",
        "    for j in id[i]:\n",
        "      track_id.append(temp_df.iloc[j, 0])\n",
        "    tid.append(track_id)\n",
        "  return tid"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7jjhSneINCD",
        "outputId": "9c460e99-144b-4ae1-be8f-842e652f253d"
      },
      "source": [
        "song = input('Enter a song name:')\n",
        "track_id = sp.search(q='track:' + song, type='track')\n",
        "id = track_id['tracks']['items'][0]['id']\n",
        "song_name = sp.track(id)['name']\n",
        "\n",
        "tracks = []\n",
        "track, _ = getTrackFeatures(id)\n",
        "track.insert(0, id)\n",
        "tracks.append(track)\n",
        "\n",
        "# create record\n",
        "record = pd.DataFrame(tracks, columns = ['id', 'name', 'album', 'artist', 'release_date'])\n",
        "record.head()\n",
        "\n",
        "tid = get_recommendation_diff(new_df, record)\n",
        "\n",
        "prev = song_name\n",
        "col = ['name', 'album', 'artist', 'release_date']\n",
        "for i in range(4):\n",
        "  print(\"Recommendations based on\", col[i])\n",
        "  for j in tid[i]:\n",
        "      track = sp.track(j)\n",
        "      time.sleep(0.5)\n",
        "      if(track['name']==prev):\n",
        "       continue\n",
        "      prev = track['name']\n",
        "      print('track    : ' + track['name'])\n",
        "      print('audio    : ' + str(track['preview_url']))\n",
        "      print('cover art: ' + str(track['album']['images'][0]['url']))\n",
        "      print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter a song name:Kal Ho Naa Ho\n",
            "Recommendations based on name\n",
            "track    : Tumhen Ho Na Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/2f7e74bd4c218d1a8485a7b82cbbb7c177fd6138?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273ce40ee9cc7c0e64f472297a3\n",
            "\n",
            "track    : Tum Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/ae106c3d2357b9fbf009be8240cca3321a96e122?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27354e544672baa16145d67612b\n",
            "\n",
            "track    : Ho Hey\n",
            "audio    : https://p.scdn.co/mp3-preview/57e57e6eb594e55de01b85cea4d470243f6121f5?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2734241433fb84b10663492c0e3\n",
            "\n",
            "track    : Aur Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/308048ebc3636fdb34d8ce105378f0dea936379e?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27354e544672baa16145d67612b\n",
            "\n",
            "track    : Saath Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/54bc6f4e349506c0d58d5a182176ac0461bd6184?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273f20bc5b7fc42bce8d2f3aa7c\n",
            "\n",
            "Recommendations based on album\n",
            "track    : Kal Ho Naa Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/f8ab488c2309bf7e2f7fac927c121ad0c80962b3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Pretty Woman\n",
            "audio    : https://p.scdn.co/mp3-preview/5723f3dd34a284f79483f1635d427e482393927e?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Maahi Ve\n",
            "audio    : https://p.scdn.co/mp3-preview/d435eadc05902723f3c3a6ccb16c3c1175a3ff51?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Kal Ho Naa Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/f8ab488c2309bf7e2f7fac927c121ad0c80962b3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : May I\n",
            "audio    : https://p.scdn.co/mp3-preview/0a7dca7781854e331f60fa1a8ce6f29e320225a7?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2733ae4c6b039dd0433a53631bb\n",
            "\n",
            "track    : Saath Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/54bc6f4e349506c0d58d5a182176ac0461bd6184?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273f20bc5b7fc42bce8d2f3aa7c\n",
            "\n",
            "track    : Morni Banke\n",
            "audio    : https://p.scdn.co/mp3-preview/c888933df7f87d3b708bd9db8598b6700cae3446?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273de3dc4cf172db896b0d6b379\n",
            "\n",
            "Recommendations based on artist\n",
            "track    : Kal Ho Naa Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/f8ab488c2309bf7e2f7fac927c121ad0c80962b3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Soorma Anthem\n",
            "audio    : https://p.scdn.co/mp3-preview/e021e20371b1e281b142cd61a2030feda051fcc3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273c34d715d5ebccd6b155abbaa\n",
            "\n",
            "track    : Tainu Leke\n",
            "audio    : https://p.scdn.co/mp3-preview/c00a102a4226ee183d9b4c7c90dbdfec2b894bc9?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27380cb44e037bdc5dd5bcace15\n",
            "\n",
            "track    : Tanhayee\n",
            "audio    : https://p.scdn.co/mp3-preview/b3d1d9880daab4d32707480000b33830a29a6a93?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2735da5e18b4f9a33d42e737c21\n",
            "\n",
            "track    : Unnai Kaanadhu Naan\n",
            "audio    : https://p.scdn.co/mp3-preview/fbb37c34fa3a8665cdcb24256dbe22b4a7531cba?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27356a53edf50148978d3ecd95d\n",
            "\n",
            "track    : Garaj Garaj Jugalbandi\n",
            "audio    : https://p.scdn.co/mp3-preview/4cdb1511f07c8f407bf409aec58fabccbbb1445e?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2733d82f8ee55e79185ccf2427f\n",
            "\n",
            "track    : Mitwa\n",
            "audio    : https://p.scdn.co/mp3-preview/1212f666309e8cca20f0688f86eba8ceea699c8a?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273afbf50bfb7b032b69deabade\n",
            "\n",
            "Recommendations based on release_date\n",
            "track    : Kal Ho Naa Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/f8ab488c2309bf7e2f7fac927c121ad0c80962b3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Pretty Woman\n",
            "audio    : https://p.scdn.co/mp3-preview/5723f3dd34a284f79483f1635d427e482393927e?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Kal Ho Naa Ho\n",
            "audio    : https://p.scdn.co/mp3-preview/f8ab488c2309bf7e2f7fac927c121ad0c80962b3?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Maahi Ve\n",
            "audio    : https://p.scdn.co/mp3-preview/d435eadc05902723f3c3a6ccb16c3c1175a3ff51?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273a2055e0b847ff66fb5206099\n",
            "\n",
            "track    : Sivan Avan\n",
            "audio    : https://p.scdn.co/mp3-preview/22825577b35f8b4e227810bf3119bd7e555d4ec7?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273405c3abce24a2692244e5dd4\n",
            "\n",
            "track    : Kolaru Thirupathigam\n",
            "audio    : https://p.scdn.co/mp3-preview/29791298c41154e211a683b67caf7abb08e4d982?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273405c3abce24a2692244e5dd4\n",
            "\n",
            "track    : Kadhalagi\n",
            "audio    : https://p.scdn.co/mp3-preview/1831078b9b24e203605386512adac9a05d7ce65e?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273405c3abce24a2692244e5dd4\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppWlDR9njTb5"
      },
      "source": [
        "### Collaborative Filtering Based Recommender (KNN)\n",
        "\n",
        "1.   Input song name and extracr ID\n",
        "2.   Extract Features and make a dataframe (numeric columns)\n",
        "3.   K nearest neighbors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "1F10-txMfOgi"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP9h8hf9jXaw"
      },
      "source": [
        "def euclid(a, b):\n",
        "  distance = np.linalg.norm(a - b)\n",
        "  return distance\n",
        "\n",
        "def getNeighbors(record, data, K):\n",
        "    distances = []\n",
        "\n",
        "    for i in range(data.shape[0]):\n",
        "      if data.iloc[i,0] != record.iloc[0,0]:\n",
        "        dist = euclid(record.iloc[0, 1:].values, data.iloc[i, 1:].values)\n",
        "        distances.append((data.iloc[i,0], dist))\n",
        "\n",
        "    distances.sort(key=lambda x : x[1])\n",
        "    neighbors = []\n",
        "    for x in range(K):\n",
        "        neighbors.append(distances[x][0])\n",
        "    return neighbors"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JX_G4pb9jgxL",
        "outputId": "fc4c8c21-b302-4af3-a568-028da05c8839"
      },
      "source": [
        "track= input('Enter a Song Name: ')\n",
        "track_id = sp.search(q='track:' + track, type='track')\n",
        "id = track_id['tracks']['items'][0]['id']\n",
        "track_name = sp.track(id)['name']\n",
        "\n",
        "tracks = []\n",
        "_, track = getTrackFeatures(id)\n",
        "track.insert(0,id)\n",
        "tracks.append(track)\n",
        "\n",
        "# create dataset\n",
        "record = pd.DataFrame(tracks, columns = ['id', 'duration_ms', 'popularity', 'danceability', 'acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'year'])\n",
        "data = new_df[['id', 'duration_ms', 'popularity', 'danceability', 'acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'year']]\n",
        "\n",
        "K = 10\n",
        "\n",
        "neighbors = getNeighbors(record, data, K)\n",
        "\n",
        "prev = [track_name]\n",
        "print('\\nRecommended songs: \\n')\n",
        "for neighbor in neighbors:\n",
        "  # Getting images,audio from Track ID\n",
        "    track = sp.track(neighbor)\n",
        "    if(track['name'] in prev):\n",
        "      continue\n",
        "    prev.append(track['name'])\n",
        "    print('track    : ' + track['name'])\n",
        "    print('audio    : ' + str(track['preview_url']))\n",
        "    print('cover art: ' + str(track['album']['images'][0]['url']))\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter a Song Name: New Rules\n",
            "\n",
            "Recommended songs: \n",
            "\n",
            "track    : All Your Exes\n",
            "audio    : None\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273e17a1f8123db65688773cb27\n",
            "\n",
            "track    : SLOW DANCING IN THE DARK\n",
            "audio    : https://p.scdn.co/mp3-preview/483355f39bb264b9828633561ab14a7a48e75270?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27360ba1d6104d0475c7555a6b2\n",
            "\n",
            "track    : burn\n",
            "audio    : https://p.scdn.co/mp3-preview/00a98b949f1de65e34384226a9e4091c025bdfa7?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273d90cef642edf69504f7155c8\n",
            "\n",
            "track    : DDU-DU DDU-DU\n",
            "audio    : None\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b27398135e0d2d1675f2b502f01c\n",
            "\n",
            "track    : Oh Sanam\n",
            "audio    : https://p.scdn.co/mp3-preview/3056dc54b18568a8a85dd0ea01443dabb233403a?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273e1306e7b70b5074f52abc232\n",
            "\n",
            "track    : 1 Billion Views\n",
            "audio    : https://p.scdn.co/mp3-preview/65b5b3e159887a96d6cb890872fe838e73667cbe?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273c158fb5a0bf5de6459e7e59d\n",
            "\n",
            "track    : Wonderful\n",
            "audio    : https://p.scdn.co/mp3-preview/085c35e092dcab6b9c25ce37489084637050f8af?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2731228b64fde4e6a3d055c09a6\n",
            "\n",
            "track    : Hauli Hauli (From \"De De Pyaar De\")\n",
            "audio    : https://p.scdn.co/mp3-preview/5b9ebff38bb01d33b8bb2ec0716500e14ff164e7?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b2738af4338d27f144a6296043a4\n",
            "\n",
            "track    : Hauli Hauli\n",
            "audio    : https://p.scdn.co/mp3-preview/5b9ebff38bb01d33b8bb2ec0716500e14ff164e7?cid=7f2ab65404b64bbc99e22b2efcb17983\n",
            "cover art: https://i.scdn.co/image/ab67616d0000b273f7462fb0201cc12b3549ffe5\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}