main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connessione a spotipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyOAuth\n",
    "import json\n",
    "import os \n",
    "from saved_tracks_extractor import get_audio_features_and_analysis\n",
    "\n",
    "# Configura le tue credenziali Spotify\n",
    "client_id = os.getenv('YOUR_CLIENT_ID')\n",
    "client_secret = os.getenv('YOUR_CLIENT_SECRET')\n",
    "redirect_uri = os.getenv('YOUR_REDIRECT_URI')\n",
    "\n",
    "scope = \"user-library-read playlist-read-private\"\n",
    "\n",
    "sp = spotipy.Spotify(auth_manager=SpotifyOAuth(client_id=client_id,\n",
    "                                               client_secret=client_secret,\n",
    "                                               redirect_uri=redirect_uri,\n",
    "                                               scope=scope))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total tracks: 9715\n",
      "Track IDs: ['5vmjHtzvHeX2HYmWNSFqP6', '51VWrOnuL89xwoazeOZyXd', '4lee8gdN6r99KkY77YVHye', '5t9i10vYdDzA7rsEPW3lKf', '44UT5iLVHmgy1IXmOqiQdn']\n",
      "Track IDs: ['0V31InZ8QrZ4bFHeZRXvXG', '09JdPiM1uQJz4iTrXLDmlo', '10whYsE8x94pqVdBguPJL5', '4qZWNigbOxdCnlgLSqEXj4', '4UriOPbcRKgEA3liG0lqE4']\n",
      "Track IDs: ['4rFm4cgZhCsGaPNbbEc0G4', '1Scdm7tCv8Vu0yUeFRs4Iv', '1fjitT9XjUeZMP646nsiTL', '6T5fW3JC57UIGeYzwx91hY', '0FMLxnU3BE0VAvzqK2yIGR']\n",
      "Track IDs: ['1hyOZrfiLof7tbqf5pJjta', '2n3FPgSY1SKMf3FdtyteRA', '2TtIKR20Mc66se6kGpmErP', '6F0bWpG7jhl0Hv92ZQnRHx', '4eNFa7cFWUv7koVWjjZYOi']\n",
      "Track IDs: ['57m0xwokoN3J49tso8tGDJ', '21SdDged8mWRNvDnwPzxH1', '2qNpmmWewO58DmBHH9i0ug', '2W10cBw9jdMsyQLHFiOPr4', '0T4njSTUzEEBWT33eyXEFQ']\n",
      "Track IDs: ['58l6rUrdGNOtLRsX93w40N', '6wVViUl2xSRoDK2T7dMZbR', '4jfsySxBIePLrNq5FYYjyv', '6FTUpZS85JMQzFEQ7wOqVK', '1SIXMGcsAeA6sNxaY1EG3O']\n",
      "Track IDs: ['3NYm3ZimuJNb0Z0px5OEwO', '27wbCVE2JA72qLZVt7pTuF', '40oMDrqiESAbKzfnK6avpB', '0O9havTTUkI5F5LitVgfYH', '2bIQ3AcOR8iJET9rjPenwZ']\n",
      "Track IDs: ['7HwJIBgn20ju7eNO6nruJr', '4oc6DZ88xXPRB73im61qQ3', '69VkZ3Cir9A2Ty8KK2iAPJ', '3CX5VhuqcEtpmw9geTjgFw', '04YmzxDpkbyZAqYAPxrIvL']\n",
      "Track IDs: ['4SDHCwOeB2wRqMU2apwjOm', '6gqc6STXIYeiS6hrIsffZm', '1TlkiBE60Atl0L5eWMJuhp', '5YI4ANY438BgIlVFf0RRY0', '2rgePSNSSgx9Vuh0nuQvGN']\n",
      "Track IDs: ['4w5BFGIlRDdPaadbCrl0o4', '1xBMyLQh9cxjwRdwUqdLxC', '5M4NtLXIQ23xuykkewzOcQ', '6RgEcaYUoCs8LUCz9ZHdAe', '1vP2JEXRsGrFbwOZ0foOQ5']\n",
      "---\n",
      "{\n",
      "    \"album\": [\n",
      "        \"The Lost Tapes\",\n",
      "        \"Owiny Sigoma Band\",\n",
      "        \"Owiny Sigoma Band\",\n",
      "        \"Shinjuku Mad\",\n",
      "        \"Ver\\u00e4nderet\"\n",
      "    ],\n",
      "    \"artist\": [\n",
      "        \"Owiny Sigoma Band\",\n",
      "        \"Owiny Sigoma Band\",\n",
      "        \"Owiny Sigoma Band\",\n",
      "        \"Shinjuku Mad\",\n",
      "        \"Fai Baba\"\n",
      "    ],\n",
      "    \"duration_s\": [\n",
      "        251.866,\n",
      "        273.853,\n",
      "        212.68,\n",
      "        133.448,\n",
      "        323.282\n",
      "    ],\n",
      "    \"name\": [\n",
      "        \"Changamooka\",\n",
      "        \"Wires\",\n",
      "        \"Nabed Nade Ei Piny Ka (Rework)\",\n",
      "        \"Kowloon\",\n",
      "        \"Ver\\u00e4nderet\"\n",
      "    ],\n",
      "    \"popularity\": [\n",
      "        9,\n",
      "        26,\n",
      "        33,\n",
      "        8,\n",
      "        35\n",
      "    ],\n",
      "    \"id\": [\n",
      "        \"5vmjHtzvHeX2HYmWNSFqP6\",\n",
      "        \"51VWrOnuL89xwoazeOZyXd\",\n",
      "        \"4lee8gdN6r99KkY77YVHye\",\n",
      "        \"5t9i10vYdDzA7rsEPW3lKf\",\n",
      "        \"44UT5iLVHmgy1IXmOqiQdn\"\n",
      "    ],\n",
      "    \"acousticness\": [\n",
      "        0.0709,\n",
      "        0.0424,\n",
      "        0.673,\n",
      "        0.492,\n",
      "        0.307\n",
      "    ],\n",
      "    \"danceability\": [\n",
      "        0.731,\n",
      "        0.761,\n",
      "        0.83,\n",
      "        0.861,\n",
      "        0.553\n",
      "    ],\n",
      "    \"energy\": [\n",
      "        0.688,\n",
      "        0.624,\n",
      "        0.294,\n",
      "        0.636,\n",
      "        0.353\n",
      "    ],\n",
      "    \"instrumentalness\": [\n",
      "        0.142,\n",
      "        0.351,\n",
      "        0.875,\n",
      "        0.00302,\n",
      "        0.0596\n",
      "    ],\n",
      "    \"key\": [\n",
      "        2,\n",
      "        7,\n",
      "        8,\n",
      "        6,\n",
      "        2\n",
      "    ],\n",
      "    \"liveness\": [\n",
      "        0.119,\n",
      "        0.223,\n",
      "        0.109,\n",
      "        0.161,\n",
      "        0.127\n",
      "    ],\n",
      "    \"loudness\": [\n",
      "        -9.884,\n",
      "        -11.263,\n",
      "        -9.664,\n",
      "        -4.863,\n",
      "        -10.315\n",
      "    ],\n",
      "    \"mode\": [\n",
      "        0,\n",
      "        0,\n",
      "        1,\n",
      "        1,\n",
      "        1\n",
      "    ],\n",
      "    \"speechiness\": [\n",
      "        0.0377,\n",
      "        0.0426,\n",
      "        0.0615,\n",
      "        0.047,\n",
      "        0.0872\n",
      "    ],\n",
      "    \"valence\": [\n",
      "        0.639,\n",
      "        0.761,\n",
      "        0.846,\n",
      "        0.781,\n",
      "        0.15\n",
      "    ],\n",
      "    \"tempo\": [\n",
      "        124.011,\n",
      "        129.217,\n",
      "        121.356,\n",
      "        116.049,\n",
      "        87.761\n",
      "    ],\n",
      "    \"time_signature\": [\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4\n",
      "    ]\n",
      "}\n",
      "---\n",
      "{\n",
      "    \"album\": [\n",
      "        \"Jazz engag\\u00e9\",\n",
      "        \"Ceremonial\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\"\n",
      "    ],\n",
      "    \"artist\": [\n",
      "        \"Chocolat\",\n",
      "        \"Anchorsong\",\n",
      "        \"Arp\",\n",
      "        \"Ariel Pink\",\n",
      "        \"Drugdealer\"\n",
      "    ],\n",
      "    \"duration_s\": [\n",
      "        197.373,\n",
      "        155.96,\n",
      "        320.508,\n",
      "        196.472,\n",
      "        249.126\n",
      "    ],\n",
      "    \"name\": [\n",
      "        \"Fou fou fou mon minou\",\n",
      "        \"Expo\",\n",
      "        \"Il Sogno Di Monica\",\n",
      "        \"Nonsequitur Segues\",\n",
      "        \"Be Thou by My Side\"\n",
      "    ],\n",
      "    \"popularity\": [\n",
      "        32,\n",
      "        26,\n",
      "        25,\n",
      "        35,\n",
      "        15\n",
      "    ],\n",
      "    \"id\": [\n",
      "        \"0V31InZ8QrZ4bFHeZRXvXG\",\n",
      "        \"09JdPiM1uQJz4iTrXLDmlo\",\n",
      "        \"10whYsE8x94pqVdBguPJL5\",\n",
      "        \"4qZWNigbOxdCnlgLSqEXj4\",\n",
      "        \"4UriOPbcRKgEA3liG0lqE4\"\n",
      "    ],\n",
      "    \"acousticness\": [\n",
      "        0.114,\n",
      "        0.00245,\n",
      "        0.806,\n",
      "        0.814,\n",
      "        0.858\n",
      "    ],\n",
      "    \"danceability\": [\n",
      "        0.633,\n",
      "        0.833,\n",
      "        0.582,\n",
      "        0.871,\n",
      "        0.479\n",
      "    ],\n",
      "    \"energy\": [\n",
      "        0.835,\n",
      "        0.704,\n",
      "        0.503,\n",
      "        0.722,\n",
      "        0.25\n",
      "    ],\n",
      "    \"instrumentalness\": [\n",
      "        0.806,\n",
      "        0.889,\n",
      "        0.908,\n",
      "        0.93,\n",
      "        9.74e-06\n",
      "    ],\n",
      "    \"key\": [\n",
      "        11,\n",
      "        7,\n",
      "        6,\n",
      "        0,\n",
      "        1\n",
      "    ],\n",
      "    \"liveness\": [\n",
      "        0.386,\n",
      "        0.0916,\n",
      "        0.16,\n",
      "        0.148,\n",
      "        0.107\n",
      "    ],\n",
      "    \"loudness\": [\n",
      "        -4.437,\n",
      "        -8.738,\n",
      "        -11.931,\n",
      "        -8.813,\n",
      "        -10.224\n",
      "    ],\n",
      "    \"mode\": [\n",
      "        0,\n",
      "        1,\n",
      "        0,\n",
      "        0,\n",
      "        1\n",
      "    ],\n",
      "    \"speechiness\": [\n",
      "        0.0252,\n",
      "        0.119,\n",
      "        0.0415,\n",
      "        0.131,\n",
      "        0.0262\n",
      "    ],\n",
      "    \"valence\": [\n",
      "        0.773,\n",
      "        0.133,\n",
      "        0.0894,\n",
      "        0.967,\n",
      "        0.239\n",
      "    ],\n",
      "    \"tempo\": [\n",
      "        103.961,\n",
      "        100.003,\n",
      "        129.983,\n",
      "        117.046,\n",
      "        134.323\n",
      "    ],\n",
      "    \"time_signature\": [\n",
      "        4,\n",
      "        4,\n",
      "        5,\n",
      "        4,\n",
      "        4\n",
      "    ]\n",
      "}\n",
      "---\n",
      "{\n",
      "    \"album\": [\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\"\n",
      "    ],\n",
      "    \"artist\": [\n",
      "        \"Robert Lester Folsom\",\n",
      "        \"Jefre Cantu-Ledesma\",\n",
      "        \"Dungen\",\n",
      "        \"Pill\",\n",
      "        \"Allah-Las\"\n",
      "    ],\n",
      "    \"duration_s\": [\n",
      "        81.717,\n",
      "        104.912,\n",
      "        187.071,\n",
      "        181.701,\n",
      "        146.436\n",
      "    ],\n",
      "    \"name\": [\n",
      "        \"Strolling Along\",\n",
      "        \"Crescent\",\n",
      "        \"Puerto Vant\\u00f6r\",\n",
      "        \"Naked Muse\",\n",
      "        \"Marionberry Jam\"\n",
      "    ],\n",
      "    \"popularity\": [\n",
      "        21,\n",
      "        22,\n",
      "        10,\n",
      "        19,\n",
      "        27\n",
      "    ],\n",
      "    \"id\": [\n",
      "        \"4rFm4cgZhCsGaPNbbEc0G4\",\n",
      "        \"1Scdm7tCv8Vu0yUeFRs4Iv\",\n",
      "        \"1fjitT9XjUeZMP646nsiTL\",\n",
      "        \"6T5fW3JC57UIGeYzwx91hY\",\n",
      "        \"0FMLxnU3BE0VAvzqK2yIGR\"\n",
      "    ],\n",
      "    \"acousticness\": [\n",
      "        0.411,\n",
      "        0.451,\n",
      "        0.144,\n",
      "        0.724,\n",
      "        0.481\n",
      "    ],\n",
      "    \"danceability\": [\n",
      "        0.452,\n",
      "        0.295,\n",
      "        0.707,\n",
      "        0.426,\n",
      "        0.717\n",
      "    ],\n",
      "    \"energy\": [\n",
      "        0.728,\n",
      "        0.554,\n",
      "        0.43,\n",
      "        0.677,\n",
      "        0.588\n",
      "    ],\n",
      "    \"instrumentalness\": [\n",
      "        0.628,\n",
      "        0.915,\n",
      "        0.96,\n",
      "        0.00468,\n",
      "        0.874\n",
      "    ],\n",
      "    \"key\": [\n",
      "        0,\n",
      "        5,\n",
      "        2,\n",
      "        2,\n",
      "        5\n",
      "    ],\n",
      "    \"liveness\": [\n",
      "        0.178,\n",
      "        0.157,\n",
      "        0.0999,\n",
      "        0.267,\n",
      "        0.0901\n",
      "    ],\n",
      "    \"loudness\": [\n",
      "        -12.227,\n",
      "        -12.413,\n",
      "        -12.292,\n",
      "        -9.701,\n",
      "        -5.264\n",
      "    ],\n",
      "    \"mode\": [\n",
      "        1,\n",
      "        0,\n",
      "        1,\n",
      "        1,\n",
      "        1\n",
      "    ],\n",
      "    \"speechiness\": [\n",
      "        0.0974,\n",
      "        0.0427,\n",
      "        0.0296,\n",
      "        0.0892,\n",
      "        0.0414\n",
      "    ],\n",
      "    \"valence\": [\n",
      "        0.876,\n",
      "        0.206,\n",
      "        0.589,\n",
      "        0.497,\n",
      "        0.709\n",
      "    ],\n",
      "    \"tempo\": [\n",
      "        143.248,\n",
      "        79.186,\n",
      "        115.063,\n",
      "        81.37,\n",
      "        109.805\n",
      "    ],\n",
      "    \"time_signature\": [\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4\n",
      "    ]\n",
      "}\n",
      "---\n",
      "{\n",
      "    \"album\": [\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"A Decade Deeper\",\n",
      "        \"Pop Impressions (Remastered)\"\n",
      "    ],\n",
      "    \"artist\": [\n",
      "        \"PAINT\",\n",
      "        \"Jassbusters\",\n",
      "        \"Gregg Kowalsky\",\n",
      "        \"Tonstartssbandht\",\n",
      "        \"Janko Nilovic\"\n",
      "    ],\n",
      "    \"duration_s\": [\n",
      "        208.579,\n",
      "        216.525,\n",
      "        179.744,\n",
      "        198.82,\n",
      "        102.851\n",
      "    ],\n",
      "    \"name\": [\n",
      "        \"Land Man\",\n",
      "        \"B'nD (NY)\",\n",
      "        \"Limon, Limon\",\n",
      "        \"Livin' in Hope\",\n",
      "        \"Trumpet Sketches - Remastered\"\n",
      "    ],\n",
      "    \"popularity\": [\n",
      "        8,\n",
      "        24,\n",
      "        5,\n",
      "        12,\n",
      "        31\n",
      "    ],\n",
      "    \"id\": [\n",
      "        \"1hyOZrfiLof7tbqf5pJjta\",\n",
      "        \"2n3FPgSY1SKMf3FdtyteRA\",\n",
      "        \"2TtIKR20Mc66se6kGpmErP\",\n",
      "        \"6F0bWpG7jhl0Hv92ZQnRHx\",\n",
      "        \"4eNFa7cFWUv7koVWjjZYOi\"\n",
      "    ],\n",
      "    \"acousticness\": [\n",
      "        0.975,\n",
      "        0.638,\n",
      "        0.339,\n",
      "        0.4,\n",
      "        0.055\n",
      "    ],\n",
      "    \"danceability\": [\n",
      "        0.648,\n",
      "        0.743,\n",
      "        0.167,\n",
      "        0.212,\n",
      "        0.583\n",
      "    ],\n",
      "    \"energy\": [\n",
      "        0.66,\n",
      "        0.251,\n",
      "        0.334,\n",
      "        0.567,\n",
      "        0.641\n",
      "    ],\n",
      "    \"instrumentalness\": [\n",
      "        0.898,\n",
      "        0.73,\n",
      "        0.731,\n",
      "        0.000301,\n",
      "        0.888\n",
      "    ],\n",
      "    \"key\": [\n",
      "        0,\n",
      "        1,\n",
      "        2,\n",
      "        9,\n",
      "        8\n",
      "    ],\n",
      "    \"liveness\": [\n",
      "        0.188,\n",
      "        0.0782,\n",
      "        0.107,\n",
      "        0.324,\n",
      "        0.419\n",
      "    ],\n",
      "    \"loudness\": [\n",
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      "        -10.221,\n",
      "        -15.228,\n",
      "        -7.503,\n",
      "        -6.35\n",
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      "    \"mode\": [\n",
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      "        1,\n",
      "        1,\n",
      "        1\n",
      "    ],\n",
      "    \"speechiness\": [\n",
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      "        0.0417,\n",
      "        0.0458,\n",
      "        0.0847,\n",
      "        0.0382\n",
      "    ],\n",
      "    \"valence\": [\n",
      "        0.415,\n",
      "        0.0963,\n",
      "        0.039,\n",
      "        0.35,\n",
      "        0.677\n",
      "    ],\n",
      "    \"tempo\": [\n",
      "        113.017,\n",
      "        90.006,\n",
      "        126.385,\n",
      "        71.457,\n",
      "        100.263\n",
      "    ],\n",
      "    \"time_signature\": [\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4\n",
      "    ]\n",
      "}\n",
      "---\n",
      "{\n",
      "    \"album\": [\n",
      "        \"Chant Amazigh (Habibi Funk 017)\",\n",
      "        \"Chant Amazigh (Habibi Funk 017)\",\n",
      "        \"Oh No It's Devo / Freedom Of Choice\",\n",
      "        \"Oh No It's Devo / Freedom Of Choice\",\n",
      "        \"Oh No It's Devo / Freedom Of Choice\"\n",
      "    ],\n",
      "    \"artist\": [\n",
      "        \"Majid Soula\",\n",
      "        \"Majid Soula\",\n",
      "        \"DEVO\",\n",
      "        \"DEVO\",\n",
      "        \"DEVO\"\n",
      "    ],\n",
      "    \"duration_s\": [\n",
      "        267.874,\n",
      "        247.25,\n",
      "        154.533,\n",
      "        163.693,\n",
      "        213.666\n",
      "    ],\n",
      "    \"name\": [\n",
      "        \"Netseweth Sifassan Nagh\",\n",
      "        \"Win Terram\",\n",
      "        \"What I Must\",\n",
      "        \"Big Mess\",\n",
      "        \"Out Of Sync\"\n",
      "    ],\n",
      "    \"popularity\": [\n",
      "        29,\n",
      "        22,\n",
      "        8,\n",
      "        25,\n",
      "        13\n",
      "    ],\n",
      "    \"id\": [\n",
      "        \"57m0xwokoN3J49tso8tGDJ\",\n",
      "        \"21SdDged8mWRNvDnwPzxH1\",\n",
      "        \"2qNpmmWewO58DmBHH9i0ug\",\n",
      "        \"2W10cBw9jdMsyQLHFiOPr4\",\n",
      "        \"0T4njSTUzEEBWT33eyXEFQ\"\n",
      "    ],\n",
      "    \"acousticness\": [\n",
      "        0.227,\n",
      "        0.188,\n",
      "        0.119,\n",
      "        0.197,\n",
      "        0.158\n",
      "    ],\n",
      "    \"danceability\": [\n",
      "        0.575,\n",
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      "    \"time_signature\": [\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4,\n",
      "        4\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty list to store the results\n",
    "results = []\n",
    "\n",
    "# Set the batch size (max 50 for Spotify API)\n",
    "batch_size = 5\n",
    "\n",
    "# Get the total number of saved tracks\n",
    "total_tracks = sp.current_user_saved_tracks(limit=1)['total']\n",
    "print(f\"Total tracks: {total_tracks}\")\n",
    "\n",
    "total_tracks = 50\n",
    "# Loop through batches of saved tracks\n",
    "for offset in range(0, total_tracks, batch_size):\n",
    "    tracks = sp.current_user_saved_tracks(limit=batch_size, offset=offset)\n",
    "    track_ids = [item['track']['id'] for item in tracks['items']]\n",
    "    print(f\"Track IDs: {track_ids}\")    \n",
    "    # Apply the get_audio_features_and_analysis function\n",
    "    track_data = get_audio_features_and_analysis(track_ids, sp)\n",
    "    # Append the result to the list\n",
    "    results.append(track_data)\n",
    "\n",
    "# Print or process the results as needed\n",
    "for result in results:\n",
    "    print(\"---\")\n",
    "    print(json.dumps(result, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import json\n",
    "import os\n",
    "import time\n",
    "\n",
    "def update_pickle(new_data, pickle_file='spotify_data.pickle'):\n",
    "    if os.path.exists(pickle_file):\n",
    "        with open(pickle_file, 'rb') as f:\n",
    "            existing_data = pickle.load(f)\n",
    "        for key in new_data:\n",
    "            if key in existing_data:\n",
    "                existing_data[key].extend(new_data[key])\n",
    "            else:\n",
    "                existing_data[key] = new_data[key]\n",
    "    else:\n",
    "        existing_data = new_data\n",
    "    \n",
    "    with open(pickle_file, 'wb') as f:\n",
    "        pickle.dump(existing_data, f)\n",
    "\n",
    "# Set the batch size (max 50 for Spotify API)\n",
    "batch_size = 50\n",
    "\n",
    "# Get the total number of saved tracks\n",
    "total_tracks = sp.current_user_saved_tracks(limit=1)['total']\n",
    "print(f\"Total tracks: {total_tracks}\")\n",
    "total_tracks = 200\n",
    "\n",
    "# Uncomment the next line if you want to limit the number of tracks for testing\n",
    "# total_tracks = 5\n",
    "\n",
    "# Start timer for the entire process\n",
    "start_time_total = time.time()\n",
    "\n",
    "# Loop through batches of saved tracks\n",
    "for offset in range(0, total_tracks, batch_size):\n",
    "    # Start timer for this batch\n",
    "    start_time_batch = time.time()\n",
    "\n",
    "    tracks = sp.current_user_saved_tracks(limit=batch_size, offset=offset)\n",
    "    track_ids = [item['track']['id'] for item in tracks['items']]\n",
    "    print(f\"Processing track IDs: {track_ids}\")    \n",
    "    \n",
    "    # Apply the get_audio_features_and_analysis function\n",
    "    track_data = get_audio_features_and_analysis(track_ids, audio_analysis=True)\n",
    "    \n",
    "    # Update the pickle file with this batch of data\n",
    "    update_pickle(track_data)\n",
    "    \n",
    "    print(f\"Processed and saved tracks {offset} to {min(offset+batch_size, total_tracks)}\")\n",
    "    \n",
    "    # Optional: print the data for this batch\n",
    "    print(\"---\")\n",
    "    print(json.dumps(track_data, indent=4))\n",
    "\n",
    "    # End timer for this batch\n",
    "    end_time_batch = time.time()\n",
    "    print(f\"Time taken for this batch: {end_time_batch - start_time_batch:.2f} seconds\")\n",
    "\n",
    "# End timer for the entire process\n",
    "end_time_total = time.time()\n",
    "print(f\"Total time taken: {end_time_total - start_time_total:.2f} seconds\")\n",
    "\n",
    "print(\"All data processed and saved.\")\n",
    "\n",
    "# If you want to read and display all the data at the end:\n",
    "with open('spotify_data.pickle', 'rb') as f:\n",
    "    all_data = pickle.load(f)\n",
    "\n",
    "print(\"\\nAll data from pickle file:\")\n",
    "print(json.dumps(all_data, indent=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('spotify_data.pickle', 'rb') as f:\n",
    "    pickle_data = pickle.load(f)\n",
    "\n",
    "print(pickle_data.keys())\n",
    "for key in pickle_data:\n",
    "    print(f\"{key}: {len(pickle_data[key])}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data from pickle file\n",
    "import pickle\n",
    "with open('spotify_data.pickle', 'rb') as f:\n",
    "    pickle_data = pickle.load(f)\n",
    "\n",
    "# Convert the data to a pandas DataFrame\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(pickle_data)\n",
    "df.head()\n",
    "len(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def perform_kmeans_clustering(df, features, max_clusters=10, optimal_k=None):\n",
    "    \"\"\"\n",
    "    Perform K-means clustering on the given DataFrame.\n",
    "    \n",
    "    Parameters:\n",
    "    df (pandas.DataFrame): Input DataFrame\n",
    "    features (list): List of feature column names to use for clustering\n",
    "    max_clusters (int): Maximum number of clusters to consider for elbow method\n",
    "    optimal_k (int): Optimal number of clusters. If None, user should determine from elbow curve.\n",
    "    \n",
    "    Returns:\n",
    "    pandas.DataFrame: Original DataFrame with added 'Cluster' column\n",
    "    \"\"\"\n",
    "    \n",
    "    # Normalize the features\n",
    "    scaler = StandardScaler()\n",
    "    normalized_features = scaler.fit_transform(df[features])\n",
    "\n",
    "    # Determine optimal number of clusters (elbow method)\n",
    "    inertias = []\n",
    "    for k in range(1, max_clusters + 1):\n",
    "        kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "        kmeans.fit(normalized_features)\n",
    "        inertias.append(kmeans.inertia_)\n",
    "\n",
    "    # Plot elbow curve\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(range(1, max_clusters + 1), inertias, marker='o')\n",
    "    plt.xlabel('Number of clusters')\n",
    "    plt.ylabel('Inertia')\n",
    "    plt.title('Elbow Method')\n",
    "    plt.show()\n",
    "\n",
    "    if optimal_k is None:\n",
    "        print(\"Please determine the optimal number of clusters from the elbow curve.\")\n",
    "        return df\n",
    "\n",
    "    # Apply K-means with optimal number of clusters\n",
    "    kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(normalized_features)\n",
    "\n",
    "    # Add cluster labels to the original dataset\n",
    "    df_with_clusters = df.copy()\n",
    "    df_with_clusters['Cluster'] = cluster_labels\n",
    "\n",
    "    # Visualize results (using first two features)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.scatter(df[features[0]], df[features[1]], c=cluster_labels, cmap='viridis')\n",
    "    plt.xlabel(features[0])\n",
    "    plt.ylabel(features[1])\n",
    "    plt.title('K-means Clustering Results')\n",
    "    plt.show()\n",
    "\n",
    "    return df_with_clusters\n",
    "\n",
    "# Example usage:\n",
    "# features = ['energy', 'danceability', 'tempo']\n",
    "# result_df = perform_kmeans_clustering(df, features, optimal_k=8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_kmeans_clustering(df, ['energy', 'danceability', 'tempo'], optimal_k=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le altre cose da importare oltre ai saved tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postgres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2\n",
    "import json\n",
    "from psycopg2.extras import Json\n",
    "\n",
    "# Database connection parameters\n",
    "db_params = {\n",
    "    \"host\": \"localhost\",\n",
    "    \"database\": \"your_database_name\",\n",
    "    \"user\": \"your_username\",\n",
    "    \"password\": \"your_password\"\n",
    "}\n",
    "\n",
    "# Assume 'results' is your list of JSON objects, one for each song\n",
    "\n",
    "# Create a table in your PostgreSQL database (run this once)\n",
    "def create_table():\n",
    "    conn = psycopg2.connect(**db_params)\n",
    "    cur = conn.cursor()\n",
    "    cur.execute(\"\"\"\n",
    "    CREATE TABLE IF NOT EXISTS songs (\n",
    "        id SERIAL PRIMARY KEY,\n",
    "        name VARCHAR(255),\n",
    "        artist VARCHAR(255),\n",
    "        danceability FLOAT,\n",
    "        energy FLOAT,\n",
    "        key INTEGER,\n",
    "        loudness FLOAT,\n",
    "        mode INTEGER,\n",
    "        speechiness FLOAT,\n",
    "        acousticness FLOAT,\n",
    "        instrumentalness FLOAT,\n",
    "        liveness FLOAT,\n",
    "        valence FLOAT,\n",
    "        tempo FLOAT,\n",
    "        duration_ms INTEGER,\n",
    "        time_signature INTEGER,\n",
    "        segments INTEGER,\n",
    "        bars INTEGER,\n",
    "        beats INTEGER,\n",
    "        sections INTEGER,\n",
    "        tatums INTEGER,\n",
    "        full_data JSONB\n",
    "    )\n",
    "    \"\"\")\n",
    "    conn.commit()\n",
    "    cur.close()\n",
    "    conn.close()\n",
    "\n",
    "# Insert data into the database\n",
    "def insert_songs(songs):\n",
    "    conn = psycopg2.connect(**db_params)\n",
    "    cur = conn.cursor()\n",
    "\n",
    "    for song in songs:\n",
    "        cur.execute(\"\"\"\n",
    "        INSERT INTO songs (\n",
    "            name, artist, danceability, energy, key, loudness, mode,\n",
    "            speechiness, acousticness, instrumentalness, liveness,\n",
    "            valence, tempo, duration_ms, time_signature, segments,\n",
    "            bars, beats, sections, tatums, full_data\n",
    "        ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n",
    "        \"\"\", (\n",
    "            song['name'], song['artist'], song['danceability'], song['energy'],\n",
    "            song['key'], song['loudness'], song['mode'], song['speechiness'],\n",
    "            song['acousticness'], song['instrumentalness'], song['liveness'],\n",
    "            song['valence'], song['tempo'], song['duration_ms'], song['time_signature'],\n",
    "            song['segments'], song['bars'], song['beats'], song['sections'],\n",
    "            song['tatums'], Json(song)\n",
    "        ))\n",
    "\n",
    "    conn.commit()\n",
    "    cur.close()\n",
    "    conn.close()\n",
    "\n",
    "# Main execution\n",
    "if __name__ == \"__main__\":\n",
    "    # Create the table (run this once)\n",
    "    create_table()\n",
    "\n",
    "    # Insert the songs\n",
    "    insert_songs(results)  # 'results' is your list of song JSONs\n",
    "\n",
    "    print(\"Data inserted successfully!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQlite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import json\n",
    "\n",
    "# Database connection parameters\n",
    "db_params = {\n",
    "    \"database\": \"your_database_name.db\"  # SQLite uses a file-based database\n",
    "}\n",
    "\n",
    "# Assume 'results' is your list of JSON objects, one for each song\n",
    "\n",
    "# Create a table in your SQLite database (run this once)\n",
    "def create_table():\n",
    "    conn = sqlite3.connect(**db_params)\n",
    "    cur = conn.cursor()\n",
    "    cur.execute(\"\"\"\n",
    "    CREATE TABLE IF NOT EXISTS songs (\n",
    "        id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
    "        name TEXT,\n",
    "        artist TEXT,\n",
    "        danceability REAL,\n",
    "        energy REAL,\n",
    "        key INTEGER,\n",
    "        loudness REAL,\n",
    "        mode INTEGER,\n",
    "        speechiness REAL,\n",
    "        acousticness REAL,\n",
    "        instrumentalness REAL,\n",
    "        liveness REAL,\n",
    "        valence REAL,\n",
    "        tempo REAL,\n",
    "        duration_ms INTEGER,\n",
    "        time_signature INTEGER,\n",
    "        segments INTEGER,\n",
    "        bars INTEGER,\n",
    "        beats INTEGER,\n",
    "        sections INTEGER,\n",
    "        tatums INTEGER,\n",
    "        full_data TEXT\n",
    "    )\n",
    "    \"\"\")\n",
    "    conn.commit()\n",
    "    cur.close()\n",
    "    conn.close()\n",
    "\n",
    "# Insert data into the database\n",
    "def insert_songs(songs):\n",
    "    conn = sqlite3.connect(**db_params)\n",
    "    cur = conn.cursor()\n",
    "\n",
    "    for song in songs:\n",
    "        cur.execute(\"\"\"\n",
    "        INSERT INTO songs (\n",
    "            name, artist, danceability, energy, key, loudness, mode,\n",
    "            speechiness, acousticness, instrumentalness, liveness,\n",
    "            valence, tempo, duration_ms, time_signature, segments,\n",
    "            bars, beats, sections, tatums, full_data\n",
    "        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
    "        \"\"\", (\n",
    "            song['name'], song['artist'], song['danceability'], song['energy'],\n",
    "            song['key'], song['loudness'], song['mode'], song['speechiness'],\n",
    "            song['acousticness'], song['instrumentalness'], song['liveness'],\n",
    "            song['valence'], song['tempo'], song['duration_ms'], song['time_signature'],\n",
    "            song['segments'], song['bars'], song['beats'], song['sections'],\n",
    "            song['tatums'], json.dumps(song)  # Store the JSON as a string\n",
    "        ))\n",
    "\n",
    "    conn.commit()\n",
    "    cur.close()\n",
    "    conn.close()\n",
    "\n",
    "# Main execution\n",
    "if __name__ == \"__main__\":\n",
    "    # Create the table (run this once)\n",
    "    create_table()\n",
    "\n",
    "    # Insert the songs\n",
    "    insert_songs(results)  # 'results' is your list of song JSONs\n",
    "\n",
    "    print(\"Data inserted successfully!\")\n"
   ]
  }
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