final_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import Isomap, TSNE\n",
    "from sklearn.cluster import HDBSCAN, KMeans\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score\n",
    "import seaborn as sns\n",
    "import colorcet as cc\n",
    "import plotly.graph_objects as px\n",
    "import umap\n",
    "import plotly.graph_objects as go\n",
    "from time import sleep\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import spotipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_dimensionality_reduction(df: pd.DataFrame, models: list, random_state: int = None):\n",
    "    '''\n",
    "    Visualize dimensionality reduction results for different models.\n",
    "\n",
    "    Inputs:\n",
    "    - df: pd.DataFrame\n",
    "        The input DataFrame (assumed to be already scaled).\n",
    "    - models: list\n",
    "        List of dimensionality reduction models to visualize.\n",
    "    - random_state: int or None, optional\n",
    "        Random state for reproducibility.\n",
    "\n",
    "    Returns:\n",
    "    - None (displays scatterplots)\n",
    "    '''\n",
    "\n",
    "    models_folder = 'models'\n",
    "    if not os.path.exists(models_folder):\n",
    "        os.makedirs(models_folder)\n",
    "\n",
    "    num_models = len(models)\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=num_models, figsize=(5 * num_models, 5))\n",
    "\n",
    "    for i, model in enumerate(models):\n",
    "        model_instance = None\n",
    "\n",
    "        if model == TSNE:\n",
    "            model_instance = model(n_components=3)  # TSNE does not support random_state during initialization\n",
    "        else:\n",
    "            try:\n",
    "                # Try to provide random_state if the model supports it\n",
    "                model_instance = model(n_components=3, random_state=random_state)\n",
    "            except TypeError:\n",
    "                # If the model does not support random_state, initialize without it\n",
    "                model_instance = model(n_components=3)\n",
    "\n",
    "        # Fit and transform the data\n",
    "        reduced_data = model_instance.fit_transform(df)\n",
    "\n",
    "        # Create a scatterplot for the first 2 components using Seaborn\n",
    "        sns.scatterplot(x=reduced_data[:, 0], y=reduced_data[:, 1], ax=axes[i])\n",
    "        axes[i].set_title(f'{model.__name__} Scatterplot (2D)')\n",
    "        axes[i].set_xlabel('Component 1')\n",
    "        axes[i].set_ylabel('Component 2')\n",
    "\n",
    "        # Save the model as a pickle file in the \"models\" folder\n",
    "        model_filename = os.path.join(models_folder, f'{model.__name__}.pkl')\n",
    "        with open(model_filename, 'wb') as model_file:\n",
    "            pickle.dump(model_instance, model_file)\n",
    "\n",
    "        print(f'Saved {model.__name__} model as {model_filename}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Display 3D scatterplots using Plotly for the first 3 components\n",
    "    for i, model in enumerate(models):\n",
    "        model_instance = None\n",
    "\n",
    "        if model == TSNE:\n",
    "            model_instance = model(n_components=3)  # TSNE does not support random_state during initialization\n",
    "        else:\n",
    "            model_instance = model(n_components=3)\n",
    "\n",
    "        reduced_data = model_instance.fit_transform(df)\n",
    "\n",
    "        # Plot 3D scatterplot using Plotly\n",
    "        fig_3d = px.scatter_3d(reduced_data, x=reduced_data[:, 0], y=reduced_data[:, 1], z=reduced_data[:, 2],\n",
    "                               title=f'{model.__name__} 3D Scatterplot')\n",
    "        fig_3d.show()\n",
    "\n",
    "def GetAudioFeatures(client: SpotifyClientCredentials, df: pd.DataFrame, columnID='id', chunk_size=100) -> pd.DataFrame:\n",
    "    \n",
    "    completed = 0\n",
    "    tracks = df[columnID].to_list()\n",
    "    result_df = pd.DataFrame()\n",
    "\n",
    "    for i in range(0, len(tracks), chunk_size):\n",
    "        try:\n",
    "            responses = client.audio_features(tracks=tracks[i:i+chunk_size])\n",
    "            \n",
    "            for response in responses:\n",
    "                dftemp = pd.DataFrame([response])\n",
    "                result_df = pd.concat([result_df, dftemp], ignore_index=True)\n",
    "\n",
    "        except BaseException as err:\n",
    "            print(f'Error processing tracks ({i} : {i+chunk_size}) -> {err}')\n",
    "        \n",
    "        completed += chunk_size\n",
    "        print(f'({completed}/{len(tracks)}) {round((completed/len(tracks)*100),1)}%')\n",
    "        sleep(1)\n",
    "\n",
    "    return pd.merge(df, result_df, on=columnID)\n",
    "\n",
    "def GetSong(client: SpotifyClientCredentials, limit=5) -> pd.DataFrame:\n",
    "    \n",
    "    Song = input(\"Please input your song Search:\")\n",
    "\n",
    "    try: \n",
    "        responses = client.search(Song, limit)\n",
    "    except BaseException as err:\n",
    "        print(f'Error processing tracks ({Song}) -> {err}')\n",
    "\n",
    "    song_names = []\n",
    "    artists = []\n",
    "    song_id = []\n",
    "\n",
    "    for i in range(0, limit):\n",
    "        song_names.append(responses['tracks']['items'][i]['artists'][0]['name'])\n",
    "        artists.append(responses['tracks']['items'][i]['name'])\n",
    "        song_id.append(responses['tracks']['items'][i]['id'])\n",
    "\n",
    "    print('The following Songs were found from Spotify, please indicate the track you would like to select')\n",
    "    Selected_songs = pd.DataFrame({\"Songs\": song_names, \"Artists\": artists, \"Spotify Song ID\": song_id})\n",
    "    display(Selected_songs)\n",
    "    Selected_Song = input()\n",
    "\n",
    "    print(f\"Song {Selected_Song} Selected, downloading Spotify Data ...\")\n",
    "\n",
    "    Selected_Song_df = Selected_songs.loc[[int(Selected_Song)]]\n",
    "    Selected_Song_df.columns = ['song', 'artist', 'id']\n",
    "    return GetAudioFeatures(client=client, df=Selected_Song_df, chunk_size=1)\n",
    "\n",
    "def drop_columns(df:pd.DataFrame, drop):\n",
    "    df2 = df.copy()\n",
    "    for col in drop:\n",
    "        if col in df2.columns:\n",
    "            df2 = df2.drop(col, axis=1)\n",
    "    return df2\n",
    "\n",
    "def SaveModel(model, name):\n",
    "\n",
    "    # Check if folder exists\n",
    "    models_folder = './models'\n",
    "    if not os.path.exists(models_folder):\n",
    "        os.makedirs(models_folder)\n",
    "\n",
    "    # Save the model as a pickle file in the \"models\" folder\n",
    "    model_filename = os.path.join(models_folder, f'{name}.pkl')\n",
    "    with open(model_filename, 'wb') as model_file:\n",
    "        pickle.dump(model, model_file)\n",
    "    \n",
    "\n",
    "def StandardScaler_(df:pd.DataFrame, scaler = None):\n",
    "    # Train Scaler if not provided\n",
    "    if scaler is None:\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(df)\n",
    "        SaveModel(scaler, 'scaler')\n",
    "    \n",
    "    # Transform Data\n",
    "    X_scaled = scaler.transform(df)\n",
    "    return pd.DataFrame(X_scaled, columns = df.columns)\n",
    "\n",
    "def UMAP_(df:pd.DataFrame, reducer = None):\n",
    "    # Train UMAP if not provided\n",
    "    if reducer is None:\n",
    "        reducer = umap.UMAP(n_components=3, random_state=69)\n",
    "        reducer.fit(df)\n",
    "        SaveModel(reducer, 'reducer')\n",
    "\n",
    "    X_umap_transformed = reducer.transform(df)\n",
    "    return pd.DataFrame(X_umap_transformed, columns=[\"UMAP_1\", \"UMAP_2\", \"UMAP_3\"])\n",
    "\n",
    "\n",
    "def HDBSCAN_(df:pd.DataFrame):\n",
    "    df2 = df.copy()\n",
    "\n",
    "    model = HDBSCAN(min_samples=24)\n",
    "    SaveModel(model, 'hdbscan')\n",
    "\n",
    "    yhat = model.fit_predict(df2)\n",
    "    df2[\"cluster\"] = yhat\n",
    "    return df2\n",
    "\n",
    "\n",
    "def load_pickle_model(model_path):\n",
    "    try:\n",
    "        with open(model_path, 'rb') as file:\n",
    "            loaded_model = pickle.load(file)\n",
    "        return loaded_model\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: The file {model_path} was not found.\")\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error loading the pickle model: {e}\")\n",
    "        return None\n",
    "    \n",
    "def SearchBulk(client: SpotifyClientCredentials, tracks:list, chunk_size=5) -> pd.DataFrame:\n",
    "    song_names = []\n",
    "    artists = []\n",
    "\n",
    "    for i in range(0, len(tracks)):\n",
    "        try:\n",
    "            response = client.search(tracks[i], limit=1)\n",
    "            song_names.append(response['tracks']['items'][0]['name'])\n",
    "            artists.append(response['tracks']['items'][0]['artists'][0]['name'])\n",
    "\n",
    "        except BaseException as err:\n",
    "            print(f'Error processing tracks ({i}) -> {err}')\n",
    "        \n",
    "        if i % chunk_size == 0:\n",
    "            sleep(1)\n",
    "            print(f'({i}/{len(tracks)}) {round((i/len(tracks)*100),1)}%')\n",
    "    \n",
    "    return pd.DataFrame({\"Song Name\":song_names, \"Artists\": artists})\n",
    "\n",
    "def determine_hotness(df1: pd.DataFrame, df2: pd.DataFrame):\n",
    "    '''\n",
    "    Takes a dataframe with 1 song (df1), checks if the id exists in another dataframe (df2):\n",
    "    - If it exists:\n",
    "        - If the existing id hotness is 1 -> set hot_or_not to 1 for the song in df1\n",
    "        - If the existing id hotness is 0 -> set hot_or_not to 0 for the song in df1\n",
    "    - If it does not exist:\n",
    "        - Set hot_or_not to 0 for the song in df1\n",
    "    - Returns df1\n",
    "    '''\n",
    "    if df1['id'].iloc[0] in df2['id'].values:\n",
    "        if df2.loc[df2['id'] == df1['id'].iloc[0], 'hot_or_not'].any() == 1:\n",
    "            df1['hot_or_not'] = 1\n",
    "        else:\n",
    "            df1['hot_or_not'] = 0\n",
    "    else:\n",
    "        df1['hot_or_not'] = 0\n",
    "\n",
    "    return df1\n",
    "\n",
    "def GetRecommened(pick, main, main_include_name, client: SpotifyClientCredentials):\n",
    "    \n",
    "    # Clean Pick columns\n",
    "    pick = drop_columns(pick, ['Unnamed: 0', \n",
    "                               'analysis_url', \n",
    "                               'track_href', \n",
    "                               'uri', \n",
    "                               'type', \n",
    "                               'artists', \n",
    "                               'name', \n",
    "                               'Unnamed: 0.1', \n",
    "                               'genre',\t\n",
    "                               'song_name', \n",
    "                               'title', \n",
    "                               'Artists', \n",
    "                               'duration_ms', \n",
    "                               'time_signature', \n",
    "                               'song', \n",
    "                               'artist'])\n",
    "\n",
    "    pick = determine_hotness(pick, main2)\n",
    "    pick.set_index('id', inplace=True)\n",
    "    \n",
    "    pick_hotness = pick.iloc[0, -1]\n",
    "    \n",
    "    # Standard Scaler\n",
    "    scaler = load_pickle_model(\"./models/scaler.pkl\")\n",
    "    pick_scaled = StandardScaler_(df=pick, scaler=scaler)\n",
    "\n",
    "    # UMAP\n",
    "    reducer = load_pickle_model(\"./models/reducer.pkl\")\n",
    "    pick_UMAP = UMAP_(df=pick_scaled, reducer=reducer)\n",
    "\n",
    "    # concat song list and picked song\n",
    "    pick_merged = pd.concat([pick_UMAP, main])\n",
    "\n",
    "    # HDBSCAN\n",
    "    pick_scanned = HDBSCAN_(df=pick_merged)\n",
    "\n",
    "    # Split dataframs into two\n",
    "    pick_cluster = pick_scanned.iloc[[0]]['cluster'].iloc[0]\n",
    "\n",
    "    clusters = pick_scanned.reset_index(drop=True)\n",
    "    clusters = clusters.drop(clusters.index[0])\n",
    "\n",
    "    # Add hot_or_not to clusters dataframe\n",
    "    clusters = clusters.reset_index(drop=True)\n",
    "    \n",
    "    clusters['hot_or_not'] = main2['hot_or_not']\n",
    "    clusters['hot_or_not'] = clusters['hot_or_not'].astype(int)\n",
    "\n",
    "    # Get 5 random songs from same cluster\n",
    "    random_picks = clusters[(clusters['cluster'] == pick_cluster) & (clusters['hot_or_not'] == pick_hotness)].sample(5)\n",
    "    list_recommended = list(random_picks.index)\n",
    "\n",
    "    # Getting the songs from main_include_name_artist\n",
    "    print(\"Here are 5 recommended Songs for you to listen to:\")\n",
    "    # display(SearchBulk(client, list(main_include_name.iloc[list_recommended]['song_name'])))\n",
    "    display(main_include_name_artist.reset_index().iloc[list_recommended][['name', 'artists']])   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input CSVs from ./Songs And combine to create Main DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>...</th>\n",
       "      <th>uri</th>\n",
       "      <th>track_href</th>\n",
       "      <th>analysis_url</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>genre</th>\n",
       "      <th>song_name</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>Artists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.814</td>\n",
       "      <td>2</td>\n",
       "      <td>-7.364</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.013400</td>\n",
       "      <td>0.0556</td>\n",
       "      <td>...</td>\n",
       "      <td>spotify:track:2Vc6NJ9PW9gD9q343XFRKx</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/2Vc6NJ9PW9gD...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/2Vc6...</td>\n",
       "      <td>124539</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark Trap</td>\n",
       "      <td>Mercury: Retrograde</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ghostemane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.493</td>\n",
       "      <td>8</td>\n",
       "      <td>-7.230</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.4010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>...</td>\n",
       "      <td>spotify:track:7pgJBLVz5VmnL7uGHmRj6p</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/7pgJBLVz5Vmn...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/7pgJ...</td>\n",
       "      <td>224427</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark Trap</td>\n",
       "      <td>Pathology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Don Kenobi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.893</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.783</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3720</td>\n",
       "      <td>...</td>\n",
       "      <td>spotify:track:0vSWgAlfpye0WCGeNmuNhy</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0vSWgAlfpye0...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0vSW...</td>\n",
       "      <td>98821</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark Trap</td>\n",
       "      <td>Symbiote</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gizmo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>...</td>\n",
       "      <td>spotify:track:0VSXnJqQkwuH2ei1nOQ1nu</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0VSXnJqQkwuH...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0VSX...</td>\n",
       "      <td>123661</td>\n",
       "      <td>3</td>\n",
       "      <td>Dark Trap</td>\n",
       "      <td>ProductOfDrugs (Prod. The Virus and Antidote)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kamiyada+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.624</td>\n",
       "      <td>2</td>\n",
       "      <td>-7.668</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1660</td>\n",
       "      <td>...</td>\n",
       "      <td>spotify:track:4jCeguq9rMTlbMmPHuO7S3</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/4jCeguq9rMTl...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/4jCe...</td>\n",
       "      <td>123298</td>\n",
       "      <td>4</td>\n",
       "      <td>Dark Trap</td>\n",
       "      <td>Venom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$uicideboy$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  danceability  energy  key  loudness  mode  speechiness  \\\n",
       "0             0         0.831   0.814    2    -7.364     1       0.4200   \n",
       "1             1         0.719   0.493    8    -7.230     1       0.0794   \n",
       "2             2         0.850   0.893    5    -4.783     1       0.0623   \n",
       "3             3         0.476   0.781    0    -4.710     1       0.1030   \n",
       "4             4         0.798   0.624    2    -7.668     1       0.2930   \n",
       "\n",
       "   acousticness  instrumentalness  liveness  ...  \\\n",
       "0        0.0598          0.013400    0.0556  ...   \n",
       "1        0.4010          0.000000    0.1180  ...   \n",
       "2        0.0138          0.000004    0.3720  ...   \n",
       "3        0.0237          0.000000    0.1140  ...   \n",
       "4        0.2170          0.000000    0.1660  ...   \n",
       "\n",
       "                                    uri  \\\n",
       "0  spotify:track:2Vc6NJ9PW9gD9q343XFRKx   \n",
       "1  spotify:track:7pgJBLVz5VmnL7uGHmRj6p   \n",
       "2  spotify:track:0vSWgAlfpye0WCGeNmuNhy   \n",
       "3  spotify:track:0VSXnJqQkwuH2ei1nOQ1nu   \n",
       "4  spotify:track:4jCeguq9rMTlbMmPHuO7S3   \n",
       "\n",
       "                                          track_href  \\\n",
       "0  https://api.spotify.com/v1/tracks/2Vc6NJ9PW9gD...   \n",
       "1  https://api.spotify.com/v1/tracks/7pgJBLVz5Vmn...   \n",
       "2  https://api.spotify.com/v1/tracks/0vSWgAlfpye0...   \n",
       "3  https://api.spotify.com/v1/tracks/0VSXnJqQkwuH...   \n",
       "4  https://api.spotify.com/v1/tracks/4jCeguq9rMTl...   \n",
       "\n",
       "                                        analysis_url duration_ms  \\\n",
       "0  https://api.spotify.com/v1/audio-analysis/2Vc6...      124539   \n",
       "1  https://api.spotify.com/v1/audio-analysis/7pgJ...      224427   \n",
       "2  https://api.spotify.com/v1/audio-analysis/0vSW...       98821   \n",
       "3  https://api.spotify.com/v1/audio-analysis/0VSX...      123661   \n",
       "4  https://api.spotify.com/v1/audio-analysis/4jCe...      123298   \n",
       "\n",
       "  time_signature      genre                                      song_name  \\\n",
       "0              4  Dark Trap                            Mercury: Retrograde   \n",
       "1              4  Dark Trap                                      Pathology   \n",
       "2              4  Dark Trap                                       Symbiote   \n",
       "3              3  Dark Trap  ProductOfDrugs (Prod. The Virus and Antidote)   \n",
       "4              4  Dark Trap                                          Venom   \n",
       "\n",
       "   Unnamed: 0  title      Artists  \n",
       "0         NaN    NaN   Ghostemane  \n",
       "1         NaN    NaN   Don Kenobi  \n",
       "2         NaN    NaN        gizmo  \n",
       "3         NaN    NaN    Kamiyada+  \n",
       "4         NaN    NaN  $uicideboy$  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <td>0.733</td>\n",
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       "   Unnamed: 0                      id  \\\n",
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       "\n",
       "                                            name       artists  danceability  \\\n",
       "0                                    Lovin On Me   Jack Harlow         0.943   \n",
       "1                                   Cruel Summer  Taylor Swift         0.552   \n",
       "2                                         greedy    Tate McRae         0.750   \n",
       "3                             Paint The Town Red      Doja Cat         0.868   \n",
       "4  I Remember Everything (feat. Kacey Musgraves)    Zach Bryan         0.429   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  ...  instrumentalness  liveness  \\\n",
       "0   0.558    2    -4.911     1       0.0568  ...          0.000002    0.0937   \n",
       "1   0.702    9    -5.707     1       0.1570  ...          0.000021    0.1050   \n",
       "2   0.733    6    -3.180     0       0.0319  ...          0.000000    0.1140   \n",
       "3   0.538    5    -8.603     1       0.1740  ...          0.000003    0.0901   \n",
       "4   0.453    0    -7.746     1       0.0459  ...          0.000002    0.1020   \n",
       "\n",
       "   valence    tempo            type                                   uri  \\\n",
       "0    0.606  104.983  audio_features  spotify:track:4xhsWYTOGcal8zt0J161CU   \n",
       "1    0.564  169.994  audio_features  spotify:track:1BxfuPKGuaTgP7aM0Bbdwr   \n",
       "2    0.844  111.018  audio_features  spotify:track:3rUGC1vUpkDG9CZFHMur1t   \n",
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       "\n",
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       "3  https://api.spotify.com/v1/tracks/2IGMVunIBsBL...   \n",
       "4  https://api.spotify.com/v1/tracks/4KULAymBBJcP...   \n",
       "\n",
       "                                        analysis_url duration_ms  \\\n",
       "0  https://api.spotify.com/v1/audio-analysis/4xhs...      138411   \n",
       "1  https://api.spotify.com/v1/audio-analysis/1Bxf...      178427   \n",
       "2  https://api.spotify.com/v1/audio-analysis/3rUG...      131872   \n",
       "3  https://api.spotify.com/v1/audio-analysis/2IGM...      231750   \n",
       "4  https://api.spotify.com/v1/audio-analysis/4KUL...      227196   \n",
       "\n",
       "   time_signature  \n",
       "0               4  \n",
       "1               4  \n",
       "2               1  \n",
       "3               4  \n",
       "4               4  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# input Pandas code to load CSVs correctly to TOP100 & NOT_HOT pd dataFrames\n",
    "try: \n",
    "    NOT_HOT = pd.read_csv(\"./songs/NOT_HOT.csv\")\n",
    "    TOP100 = pd.read_csv(\"./songs/TOP100.csv\")\n",
    "    display(NOT_HOT.head(), TOP100.head())\n",
    "except:\n",
    "    raise ValueError (\"No Database for Hot or NotHot Songs found\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean Up Column Names of CSVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOT_HOT = NOT_HOT.rename(columns={'song_name': 'name', 'Artists': 'artists'})\n",
    "\n",
    "TOP100 = drop_columns(TOP100, ['Unnamed: 0', 'analysis_url', 'track_href', 'uri', 'type', 'duration_ms', 'time_signature'])\n",
    "NOT_HOT = drop_columns(NOT_HOT, ['Unnamed: 0', 'analysis_url', 'track_href', 'uri', 'type', 'Unnamed: 0.1', 'genre', 'title', 'duration_ms', 'time_signature'])\n",
    "\n",
    "TOP100['hot_or_not'] = 1\n",
    "NOT_HOT['hot_or_not'] = 0\n",
    "\n",
    "TOP100.set_index('id', inplace=True)\n",
    "NOT_HOT.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_include_name_artist = pd.concat([TOP100, NOT_HOT])\n",
    "main = main_include_name_artist.drop(['name', 'artists'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "main2 = main.copy().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Tomkillsjerry</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.301</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.927</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0308</td>\n",
       "      <td>0.03160</td>\n",
       "      <td>0.856000</td>\n",
       "      <td>0.0919</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>119.989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7o5E34q2K5cpQfy2P1WEGW</th>\n",
       "      <td>The Cure</td>\n",
       "      <td>Tomkillsjerry</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.555</td>\n",
       "      <td>8</td>\n",
       "      <td>-10.284</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.01680</td>\n",
       "      <td>0.907000</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>220.036</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0Wdp9TyN3PqCoxSzH8vRwO</th>\n",
       "      <td>Blocks</td>\n",
       "      <td>Tomkillsjerry</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.367</td>\n",
       "      <td>8</td>\n",
       "      <td>-11.263</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>0.00349</td>\n",
       "      <td>0.956000</td>\n",
       "      <td>0.3730</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>130.002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2K7NWYBbqGzNLggH7LIhxy</th>\n",
       "      <td>Liu Kang</td>\n",
       "      <td>Tomkillsjerry</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.572</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.371</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0755</td>\n",
       "      <td>0.01100</td>\n",
       "      <td>0.921000</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>113.338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7bHO5sfLqKChU9FtQPlInR</th>\n",
       "      <td>Malvada</td>\n",
       "      <td>Tomkillsjerry</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9</td>\n",
       "      <td>-11.677</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.836000</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>0.0688</td>\n",
       "      <td>200.094</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 name  \\\n",
       "id                                                                      \n",
       "4xhsWYTOGcal8zt0J161CU                                    Lovin On Me   \n",
       "1BxfuPKGuaTgP7aM0Bbdwr                                   Cruel Summer   \n",
       "3rUGC1vUpkDG9CZFHMur1t                                         greedy   \n",
       "2IGMVunIBsBLtEQyoI1Mu7                             Paint The Town Red   \n",
       "4KULAymBBJcPRpk1yO4dOG  I Remember Everything (feat. Kacey Musgraves)   \n",
       "...                                                               ...   \n",
       "1YiFZjmohJD78nlsSjjHGT                                          Focus   \n",
       "7o5E34q2K5cpQfy2P1WEGW                                       The Cure   \n",
       "0Wdp9TyN3PqCoxSzH8vRwO                                         Blocks   \n",
       "2K7NWYBbqGzNLggH7LIhxy                                       Liu Kang   \n",
       "7bHO5sfLqKChU9FtQPlInR                                        Malvada   \n",
       "\n",
       "                              artists  danceability  energy  key  loudness  \\\n",
       "id                                                                           \n",
       "4xhsWYTOGcal8zt0J161CU    Jack Harlow         0.943   0.558    2    -4.911   \n",
       "1BxfuPKGuaTgP7aM0Bbdwr   Taylor Swift         0.552   0.702    9    -5.707   \n",
       "3rUGC1vUpkDG9CZFHMur1t     Tate McRae         0.750   0.733    6    -3.180   \n",
       "2IGMVunIBsBLtEQyoI1Mu7       Doja Cat         0.868   0.538    5    -8.603   \n",
       "4KULAymBBJcPRpk1yO4dOG     Zach Bryan         0.429   0.453    0    -7.746   \n",
       "...                               ...           ...     ...  ...       ...   \n",
       "1YiFZjmohJD78nlsSjjHGT  Tomkillsjerry         0.644   0.301   10    -8.927   \n",
       "7o5E34q2K5cpQfy2P1WEGW  Tomkillsjerry         0.603   0.555    8   -10.284   \n",
       "0Wdp9TyN3PqCoxSzH8vRwO  Tomkillsjerry         0.626   0.367    8   -11.263   \n",
       "2K7NWYBbqGzNLggH7LIhxy  Tomkillsjerry         0.616   0.572   11    -9.371   \n",
       "7bHO5sfLqKChU9FtQPlInR  Tomkillsjerry         0.520   0.407    9   -11.677   \n",
       "\n",
       "                        mode  speechiness  acousticness  instrumentalness  \\\n",
       "id                                                                          \n",
       "4xhsWYTOGcal8zt0J161CU     1       0.0568       0.00260          0.000002   \n",
       "1BxfuPKGuaTgP7aM0Bbdwr     1       0.1570       0.11700          0.000021   \n",
       "3rUGC1vUpkDG9CZFHMur1t     0       0.0319       0.25600          0.000000   \n",
       "2IGMVunIBsBLtEQyoI1Mu7     1       0.1740       0.26900          0.000003   \n",
       "4KULAymBBJcPRpk1yO4dOG     1       0.0459       0.55400          0.000002   \n",
       "...                      ...          ...           ...               ...   \n",
       "1YiFZjmohJD78nlsSjjHGT     0       0.0308       0.03160          0.856000   \n",
       "7o5E34q2K5cpQfy2P1WEGW     0       0.0277       0.01680          0.907000   \n",
       "0Wdp9TyN3PqCoxSzH8vRwO     1       0.0343       0.00349          0.956000   \n",
       "2K7NWYBbqGzNLggH7LIhxy     0       0.0755       0.01100          0.921000   \n",
       "7bHO5sfLqKChU9FtQPlInR     0       0.0394       0.27700          0.836000   \n",
       "\n",
       "                        liveness  valence    tempo  hot_or_not  \n",
       "id                                                              \n",
       "4xhsWYTOGcal8zt0J161CU    0.0937   0.6060  104.983           1  \n",
       "1BxfuPKGuaTgP7aM0Bbdwr    0.1050   0.5640  169.994           1  \n",
       "3rUGC1vUpkDG9CZFHMur1t    0.1140   0.8440  111.018           1  \n",
       "2IGMVunIBsBLtEQyoI1Mu7    0.0901   0.7320   99.968           1  \n",
       "4KULAymBBJcPRpk1yO4dOG    0.1020   0.1550   77.639           1  \n",
       "...                          ...      ...      ...         ...  \n",
       "1YiFZjmohJD78nlsSjjHGT    0.0919   0.0768  119.989           0  \n",
       "7o5E34q2K5cpQfy2P1WEGW    0.1070   0.1290  220.036           0  \n",
       "0Wdp9TyN3PqCoxSzH8vRwO    0.3730   0.1290  130.002           0  \n",
       "2K7NWYBbqGzNLggH7LIhxy    0.1440   0.0389  113.338           0  \n",
       "7bHO5sfLqKChU9FtQPlInR    0.0901   0.0688  200.094           0  \n",
       "\n",
       "[3100 rows x 14 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_include_name_artist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>danceability</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        danceability  energy  key  loudness  mode  \\\n",
       "id                                                                  \n",
       "4xhsWYTOGcal8zt0J161CU         0.943   0.558    2    -4.911     1   \n",
       "1BxfuPKGuaTgP7aM0Bbdwr         0.552   0.702    9    -5.707     1   \n",
       "3rUGC1vUpkDG9CZFHMur1t         0.750   0.733    6    -3.180     0   \n",
       "2IGMVunIBsBLtEQyoI1Mu7         0.868   0.538    5    -8.603     1   \n",
       "4KULAymBBJcPRpk1yO4dOG         0.429   0.453    0    -7.746     1   \n",
       "...                              ...     ...  ...       ...   ...   \n",
       "1YiFZjmohJD78nlsSjjHGT         0.644   0.301   10    -8.927     0   \n",
       "7o5E34q2K5cpQfy2P1WEGW         0.603   0.555    8   -10.284     0   \n",
       "0Wdp9TyN3PqCoxSzH8vRwO         0.626   0.367    8   -11.263     1   \n",
       "2K7NWYBbqGzNLggH7LIhxy         0.616   0.572   11    -9.371     0   \n",
       "7bHO5sfLqKChU9FtQPlInR         0.520   0.407    9   -11.677     0   \n",
       "\n",
       "                        speechiness  acousticness  instrumentalness  liveness  \\\n",
       "id                                                                              \n",
       "4xhsWYTOGcal8zt0J161CU       0.0568       0.00260          0.000002    0.0937   \n",
       "1BxfuPKGuaTgP7aM0Bbdwr       0.1570       0.11700          0.000021    0.1050   \n",
       "3rUGC1vUpkDG9CZFHMur1t       0.0319       0.25600          0.000000    0.1140   \n",
       "2IGMVunIBsBLtEQyoI1Mu7       0.1740       0.26900          0.000003    0.0901   \n",
       "4KULAymBBJcPRpk1yO4dOG       0.0459       0.55400          0.000002    0.1020   \n",
       "...                             ...           ...               ...       ...   \n",
       "1YiFZjmohJD78nlsSjjHGT       0.0308       0.03160          0.856000    0.0919   \n",
       "7o5E34q2K5cpQfy2P1WEGW       0.0277       0.01680          0.907000    0.1070   \n",
       "0Wdp9TyN3PqCoxSzH8vRwO       0.0343       0.00349          0.956000    0.3730   \n",
       "2K7NWYBbqGzNLggH7LIhxy       0.0755       0.01100          0.921000    0.1440   \n",
       "7bHO5sfLqKChU9FtQPlInR       0.0394       0.27700          0.836000    0.0901   \n",
       "\n",
       "                        valence    tempo  hot_or_not  \n",
       "id                                                    \n",
       "4xhsWYTOGcal8zt0J161CU   0.6060  104.983           1  \n",
       "1BxfuPKGuaTgP7aM0Bbdwr   0.5640  169.994           1  \n",
       "3rUGC1vUpkDG9CZFHMur1t   0.8440  111.018           1  \n",
       "2IGMVunIBsBLtEQyoI1Mu7   0.7320   99.968           1  \n",
       "4KULAymBBJcPRpk1yO4dOG   0.1550   77.639           1  \n",
       "...                         ...      ...         ...  \n",
       "1YiFZjmohJD78nlsSjjHGT   0.0768  119.989           0  \n",
       "7o5E34q2K5cpQfy2P1WEGW   0.1290  220.036           0  \n",
       "0Wdp9TyN3PqCoxSzH8vRwO   0.1290  130.002           0  \n",
       "2K7NWYBbqGzNLggH7LIhxy   0.0389  113.338           0  \n",
       "7bHO5sfLqKChU9FtQPlInR   0.0688  200.094           0  \n",
       "\n",
       "[3100 rows x 12 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale Data with Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Standard Scaler Function to the Dataset\n",
    "X_scaled_df = StandardScaler_(main)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform UMAP based on function above / 3 axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Juan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\umap\\umap_.py:1943: UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
     ]
    }
   ],
   "source": [
    "# Apply UMAP transformation to scaled data\n",
    "X_umap_transformed_df = UMAP_(X_scaled_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HDBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UMAP_1</th>\n",
       "      <th>UMAP_2</th>\n",
       "      <th>UMAP_3</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.956457</td>\n",
       "      <td>4.598516</td>\n",
       "      <td>8.466094</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.836294</td>\n",
       "      <td>4.631825</td>\n",
       "      <td>8.540279</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.174124</td>\n",
       "      <td>4.858671</td>\n",
       "      <td>8.519135</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.980301</td>\n",
       "      <td>4.532536</td>\n",
       "      <td>8.507689</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.721245</td>\n",
       "      <td>4.761799</td>\n",
       "      <td>9.171669</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3095</th>\n",
       "      <td>-7.807477</td>\n",
       "      <td>2.936217</td>\n",
       "      <td>7.947117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3096</th>\n",
       "      <td>-7.006412</td>\n",
       "      <td>2.812489</td>\n",
       "      <td>8.343808</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>4.843967</td>\n",
       "      <td>1.364753</td>\n",
       "      <td>6.161686</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>-7.741531</td>\n",
       "      <td>3.079112</td>\n",
       "      <td>7.689047</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3099</th>\n",
       "      <td>-7.111504</td>\n",
       "      <td>2.882247</td>\n",
       "      <td>8.365265</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         UMAP_1    UMAP_2    UMAP_3  cluster\n",
       "0     17.956457  4.598516  8.466094        0\n",
       "1     17.836294  4.631825  8.540279        0\n",
       "2     18.174124  4.858671  8.519135        0\n",
       "3     17.980301  4.532536  8.507689        0\n",
       "4     17.721245  4.761799  9.171669        0\n",
       "...         ...       ...       ...      ...\n",
       "3095  -7.807477  2.936217  7.947117        1\n",
       "3096  -7.006412  2.812489  8.343808        1\n",
       "3097   4.843967  1.364753  6.161686        3\n",
       "3098  -7.741531  3.079112  7.689047        1\n",
       "3099  -7.111504  2.882247  8.365265        1\n",
       "\n",
       "[3100 rows x 4 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HDBSCAN_df = HDBSCAN_(X_umap_transformed_df)\n",
    "HDBSCAN_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Credentials from credentials file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import credentials as c\n",
    "    a, b = c.a, c.b\n",
    "    #Initialize SpotiPy with user credentias #\n",
    "    sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(client_id=a,\n",
    "                                                            client_secret=b))\n",
    "except:\n",
    "    raise ValueError (\"No Credentials or Spotify Connection was made, please make sure all files are in order\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get Song Audio Features and Song ID from Spotipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following Songs were found from Spotify, please indicate the track you would like to select\n"
     ]
    },
    {
     "data": {
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       "      <th>Songs</th>\n",
       "      <th>Artists</th>\n",
       "      <th>Spotify Song ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tate McRae</td>\n",
       "      <td>greedy</td>\n",
       "      <td>3rUGC1vUpkDG9CZFHMur1t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tate McRae</td>\n",
       "      <td>greedy</td>\n",
       "      <td>2dHH3pG8xx0YOZDKyDD9eG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ariana Grande</td>\n",
       "      <td>Greedy</td>\n",
       "      <td>1FFUXkoWIc9of8yOscGiaC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tate McRae</td>\n",
       "      <td>greedy</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>greedy sped up</td>\n",
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       "           Songs         Artists         Spotify Song ID\n",
       "0     Tate McRae          greedy  3rUGC1vUpkDG9CZFHMur1t\n",
       "1     Tate McRae          greedy  2dHH3pG8xx0YOZDKyDD9eG\n",
       "2  Ariana Grande          Greedy  1FFUXkoWIc9of8yOscGiaC\n",
       "3     Tate McRae          greedy  4kfSXPK13aXkLzuz02hCSC\n",
       "4     Tate McRae  greedy sped up  2yZKBFIqeOEpnBsIhAg9ZO"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Song 0 Selected, downloading Spotify Data ...\n",
      "(1/1) 100.0%\n",
      "Here are 5 recommended Songs for you to listen to:\n"
     ]
    },
    {
     "data": {
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       "      <th>name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thinkin’ Bout Me</td>\n",
       "      <td>Morgan Wallen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Que Onda</td>\n",
       "      <td>Calle 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dance The Night</td>\n",
       "      <td>Dua Lipa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lose Control</td>\n",
       "      <td>Teddy Swims</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>bad idea right?</td>\n",
       "      <td>Olivia Rodrigo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name         artists\n",
       "10  Thinkin’ Bout Me   Morgan Wallen\n",
       "73          Que Onda        Calle 24\n",
       "22   Dance The Night        Dua Lipa\n",
       "11      Lose Control     Teddy Swims\n",
       "79   bad idea right?  Olivia Rodrigo"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "running = True\n",
    "\n",
    "while running:\n",
    "    pick = GetSong(sp)\n",
    "    GetRecommened(pick, X_umap_transformed_df, main_include_name_artist, sp)\n",
    "    if input(\"Want another Song? (Y/n)\") == \"n\":\n",
    "        running = False\n",
    "    os.system\n",
    "    "
   ]
  }
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