diff --git a/clustering.ipynb b/clustering.ipynb
index 18c49a3..c78dfc2 100644
--- a/clustering.ipynb
+++ b/clustering.ipynb
@@ -14,7 +14,7 @@
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python_defaultSpec_1597369656347",
+   "name": "python_defaultSpec_1597905222045",
    "display_name": "Python 3.7.7 64-bit ('base': conda)"
   }
  },
@@ -23,7 +23,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -60,7 +60,7 @@
       "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>Lyrical/Misc</th>\n      <th>Bragging/Combative</th>\n      <th>Money/Party</th>\n      <th>Romance/Sex</th>\n      <th>words_per_line</th>\n      <th>unique_words</th>\n      <th>unique_word_rate</th>\n      <th>syllables_per_line</th>\n      <th>syllables_per_word</th>\n    </tr>\n    <tr>\n      <th>artist</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>03 Greedo</th>\n      <td>0.012388</td>\n      <td>0.040464</td>\n      <td>0.021360</td>\n      <td>0.011842</td>\n      <td>35.125816</td>\n      <td>185.375000</td>\n      <td>0.070810</td>\n      <td>9.211347</td>\n      <td>0.261876</td>\n    </tr>\n    <tr>\n      <th>070 Shake</th>\n      <td>0.015179</td>\n      <td>0.001804</td>\n      <td>0.021861</td>\n      <td>0.036665</td>\n      <td>30.507007</td>\n      <td>137.470588</td>\n      <td>0.071274</td>\n      <td>8.091570</td>\n      <td>0.264949</td>\n    </tr>\n    <tr>\n      <th>2 Chainz</th>\n      <td>0.018329</td>\n      <td>0.023071</td>\n      <td>0.021967</td>\n      <td>0.006407</td>\n      <td>32.916186</td>\n      <td>194.111111</td>\n      <td>0.090778</td>\n      <td>8.641371</td>\n      <td>0.262462</td>\n    </tr>\n    <tr>\n      <th>2 Pistols</th>\n      <td>0.013755</td>\n      <td>0.030431</td>\n      <td>0.018532</td>\n      <td>0.021855</td>\n      <td>28.376804</td>\n      <td>178.272727</td>\n      <td>0.085574</td>\n      <td>7.243027</td>\n      <td>0.255224</td>\n    </tr>\n    <tr>\n      <th>21 Savage</th>\n      <td>0.007464</td>\n      <td>0.046448</td>\n      <td>0.046098</td>\n      <td>0.008801</td>\n      <td>33.424155</td>\n      <td>189.095238</td>\n      <td>0.070440</td>\n      <td>8.484020</td>\n      <td>0.253769</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Zebra Katz</th>\n      <td>0.025077</td>\n      <td>0.012421</td>\n      <td>0.018921</td>\n      <td>0.016527</td>\n      <td>25.122420</td>\n      <td>132.944444</td>\n      <td>0.087163</td>\n      <td>6.521429</td>\n      <td>0.259716</td>\n    </tr>\n    <tr>\n      <th>Zelooperz</th>\n      <td>0.018682</td>\n      <td>0.037817</td>\n      <td>0.003213</td>\n      <td>0.006282</td>\n      <td>29.884914</td>\n      <td>151.060606</td>\n      <td>0.095839</td>\n      <td>7.746176</td>\n      <td>0.258715</td>\n    </tr>\n    <tr>\n      <th>Zion I</th>\n      <td>0.031290</td>\n      <td>0.000425</td>\n      <td>0.003131</td>\n      <td>0.006091</td>\n      <td>34.300381</td>\n      <td>210.200000</td>\n      <td>0.104322</td>\n      <td>8.986945</td>\n      <td>0.262015</td>\n    </tr>\n    <tr>\n      <th>bbno$</th>\n      <td>0.009183</td>\n      <td>0.010923</td>\n      <td>0.004047</td>\n      <td>0.009563</td>\n      <td>31.152735</td>\n      <td>92.333333</td>\n      <td>0.055000</td>\n      <td>8.575163</td>\n      <td>0.275106</td>\n    </tr>\n    <tr>\n      <th>will.i.am</th>\n      <td>0.004096</td>\n      <td>0.019755</td>\n      <td>0.047252</td>\n      <td>0.040928</td>\n      <td>33.323155</td>\n      <td>163.733333</td>\n      <td>0.067369</td>\n      <td>8.664136</td>\n      <td>0.259974</td>\n    </tr>\n  </tbody>\n</table>\n<p>1260 rows × 9 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 3
+     "execution_count": 5
     }
    ],
    "source": [
@@ -71,7 +71,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "web_X = X.copy()\n",
+    "web_X['cluster'] = X_labeled['cluster']\n",
+    "web_X['cluster'] = web_X['cluster'].map({0:'Club/Trap', 1:'Lyrical: Dense', 2: 'Archetypal', 3: 'Lyrical: Diverse', 4:'R&B'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('web_features.pkl', 'wb') as f:\n",
+    "    pkl.dump(web_X, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -91,7 +112,7 @@
       "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>Lyrical/Misc</th>\n      <th>Bragging/Combative</th>\n      <th>Money/Party</th>\n      <th>Romance/Sex</th>\n      <th>words_per_line</th>\n      <th>unique_words</th>\n      <th>unique_word_rate</th>\n      <th>syllables_per_line</th>\n      <th>syllables_per_word</th>\n    </tr>\n    <tr>\n      <th>artist</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>03 Greedo</th>\n      <td>-1.108094</td>\n      <td>1.115106</td>\n      <td>0.780540</td>\n      <td>-0.227123</td>\n      <td>0.514503</td>\n      <td>-0.344285</td>\n      <td>-1.123740</td>\n      <td>0.482583</td>\n      <td>-0.244783</td>\n    </tr>\n    <tr>\n      <th>070 Shake</th>\n      <td>-0.834664</td>\n      <td>-1.035582</td>\n      <td>0.819309</td>\n      <td>1.803736</td>\n      <td>-0.442401</td>\n      <td>-1.017869</td>\n      <td>-1.090486</td>\n      <td>-0.394098</td>\n      <td>0.285492</td>\n    </tr>\n    <tr>\n      <th>2 Chainz</th>\n      <td>-0.526146</td>\n      <td>0.147511</td>\n      <td>0.827469</td>\n      <td>-0.671790</td>\n      <td>0.056722</td>\n      <td>-0.221446</td>\n      <td>0.307881</td>\n      <td>0.036345</td>\n      <td>-0.143757</td>\n    </tr>\n    <tr>\n      <th>2 Pistols</th>\n      <td>-0.974242</td>\n      <td>0.556940</td>\n      <td>0.561764</td>\n      <td>0.592036</td>\n      <td>-0.883727</td>\n      <td>-0.444150</td>\n      <td>-0.065240</td>\n      <td>-1.058428</td>\n      <td>-1.392879</td>\n    </tr>\n    <tr>\n      <th>21 Savage</th>\n      <td>-1.590426</td>\n      <td>1.447984</td>\n      <td>2.694137</td>\n      <td>-0.475869</td>\n      <td>0.161960</td>\n      <td>-0.291975</td>\n      <td>-1.150268</td>\n      <td>-0.086846</td>\n      <td>-1.644092</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Zebra Katz</th>\n      <td>0.134856</td>\n      <td>-0.444954</td>\n      <td>0.591835</td>\n      <td>0.156195</td>\n      <td>-1.557956</td>\n      <td>-1.081511</td>\n      <td>0.048671</td>\n      <td>-1.623372</td>\n      <td>-0.617658</td>\n    </tr>\n    <tr>\n      <th>Zelooperz</th>\n      <td>-0.491603</td>\n      <td>0.967847</td>\n      <td>-0.623256</td>\n      <td>-0.681979</td>\n      <td>-0.571284</td>\n      <td>-0.826780</td>\n      <td>0.670745</td>\n      <td>-0.664509</td>\n      <td>-0.790359</td>\n    </tr>\n    <tr>\n      <th>Zion I</th>\n      <td>0.743504</td>\n      <td>-1.112303</td>\n      <td>-0.629578</td>\n      <td>-0.697615</td>\n      <td>0.343493</td>\n      <td>0.004780</td>\n      <td>1.278952</td>\n      <td>0.306897</td>\n      <td>-0.220902</td>\n    </tr>\n    <tr>\n      <th>bbno$</th>\n      <td>-1.422031</td>\n      <td>-0.528276</td>\n      <td>-0.558746</td>\n      <td>-0.413536</td>\n      <td>-0.308622</td>\n      <td>-1.652544</td>\n      <td>-2.257284</td>\n      <td>-0.015490</td>\n      <td>2.038551</td>\n    </tr>\n    <tr>\n      <th>will.i.am</th>\n      <td>-1.920378</td>\n      <td>-0.036947</td>\n      <td>2.783435</td>\n      <td>2.152477</td>\n      <td>0.141036</td>\n      <td>-0.648589</td>\n      <td>-1.370463</td>\n      <td>0.054167</td>\n      <td>-0.573045</td>\n    </tr>\n  </tbody>\n</table>\n<p>1260 rows × 9 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 17
     }
    ],
    "source": [
@@ -100,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +130,7 @@
       "text/plain": "KMeans(n_clusters=5, n_init=3, random_state=77)"
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 18
     }
    ],
    "source": [
@@ -120,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +151,7 @@
       "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>Lyrical/Misc</th>\n      <th>Bragging/Combative</th>\n      <th>Money/Party</th>\n      <th>Romance/Sex</th>\n      <th>words_per_line</th>\n      <th>unique_words</th>\n      <th>unique_word_rate</th>\n      <th>syllables_per_line</th>\n      <th>syllables_per_word</th>\n      <th>cluster</th>\n    </tr>\n    <tr>\n      <th>artist</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>03 Greedo</th>\n      <td>0.012388</td>\n      <td>0.040464</td>\n      <td>0.021360</td>\n      <td>0.011842</td>\n      <td>35.125816</td>\n      <td>185.375000</td>\n      <td>0.070810</td>\n      <td>9.211347</td>\n      <td>0.261876</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>070 Shake</th>\n      <td>0.015179</td>\n      <td>0.001804</td>\n      <td>0.021861</td>\n      <td>0.036665</td>\n      <td>30.507007</td>\n      <td>137.470588</td>\n      <td>0.071274</td>\n      <td>8.091570</td>\n      <td>0.264949</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2 Chainz</th>\n      <td>0.018329</td>\n      <td>0.023071</td>\n      <td>0.021967</td>\n      <td>0.006407</td>\n      <td>32.916186</td>\n      <td>194.111111</td>\n      <td>0.090778</td>\n      <td>8.641371</td>\n      <td>0.262462</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2 Pistols</th>\n      <td>0.013755</td>\n      <td>0.030431</td>\n      <td>0.018532</td>\n      <td>0.021855</td>\n      <td>28.376804</td>\n      <td>178.272727</td>\n      <td>0.085574</td>\n      <td>7.243027</td>\n      <td>0.255224</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21 Savage</th>\n      <td>0.007464</td>\n      <td>0.046448</td>\n      <td>0.046098</td>\n      <td>0.008801</td>\n      <td>33.424155</td>\n      <td>189.095238</td>\n      <td>0.070440</td>\n      <td>8.484020</td>\n      <td>0.253769</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Zebra Katz</th>\n      <td>0.025077</td>\n      <td>0.012421</td>\n      <td>0.018921</td>\n      <td>0.016527</td>\n      <td>25.122420</td>\n      <td>132.944444</td>\n      <td>0.087163</td>\n      <td>6.521429</td>\n      <td>0.259716</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Zelooperz</th>\n      <td>0.018682</td>\n      <td>0.037817</td>\n      <td>0.003213</td>\n      <td>0.006282</td>\n      <td>29.884914</td>\n      <td>151.060606</td>\n      <td>0.095839</td>\n      <td>7.746176</td>\n      <td>0.258715</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Zion I</th>\n      <td>0.031290</td>\n      <td>0.000425</td>\n      <td>0.003131</td>\n      <td>0.006091</td>\n      <td>34.300381</td>\n      <td>210.200000</td>\n      <td>0.104322</td>\n      <td>8.986945</td>\n      <td>0.262015</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>bbno$</th>\n      <td>0.009183</td>\n      <td>0.010923</td>\n      <td>0.004047</td>\n      <td>0.009563</td>\n      <td>31.152735</td>\n      <td>92.333333</td>\n      <td>0.055000</td>\n      <td>8.575163</td>\n      <td>0.275106</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>will.i.am</th>\n      <td>0.004096</td>\n      <td>0.019755</td>\n      <td>0.047252</td>\n      <td>0.040928</td>\n      <td>33.323155</td>\n      <td>163.733333</td>\n      <td>0.067369</td>\n      <td>8.664136</td>\n      <td>0.259974</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1260 rows × 10 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 16
+     "execution_count": 19
     }
    ],
    "source": [
@@ -203,13 +224,20 @@
     "* 0: Hard to interpret. Chiefly generic money/party semantic content, most simplistic word usage. Seems to house most of the trap/'vibe'/mumble rappers. Some artists don't seem very accurately placed (e.g. Cardi/Nicki/Megan). \n",
     "* 1: Clearly home to most 'lyrical' rappers. Dominant cluster in terms of vocabulary and syllable density.\n",
     "* 2: Very non-descript, middle of the pack by every metric. Seems to house a lot of older, 'standard' flow rappers.\n",
-    "* 3: Seems to capture most 'musically' oriented rappers -- lyrical diversity but not massive density, widest variety of topics.\n",
-    "* 4: Effectively captures most R&B artists. Romance/Sex oriented lyrics with minimal lyrical diversity."
+    "* 3: Seems to capture most 'melodically' oriented rappers -- lyrical diversity but not massive density, widest variety of topics.\n",
+    "* 4: Effectively captures most R&B artists. Romance/Sex oriented lyrics with minimal lyrical diversity.\n",
+    "\n",
+    "Names: \n",
+    "* 0: Club/Trap\n",
+    "* 1: Lyrical: Dense\n",
+    "* 2: Archetypal\n",
+    "* 3: Lyrical: Diverse\n",
+    "* 4: R&B"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -268,7 +296,7 @@
       "text/plain": "cluster\n0    122\n1    217\n2    277\n3    401\n4    243\nName: Lyrical/Misc, dtype: int64"
      },
      "metadata": {},
-     "execution_count": 115
+     "execution_count": 7
     }
    ],
    "source": [
@@ -277,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -288,7 +316,7 @@
       "text/plain": "6217.407430933533"
      },
      "metadata": {},
-     "execution_count": 84
+     "execution_count": 8
     }
    ],
    "source": [
@@ -297,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +346,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'kmeans_scree_plot' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-1620267753bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkmeans_scree_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'kmeans_scree_plot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "kmeans_scree_plot(X, StandardScaler, 1, 30, 30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import silhouette_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "0.15726646311836764"
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "source": [
+    "silhouette_score(test.data.drop(columns=['cluster']), test.data['cluster'], metric='euclidean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "            Lyrical/Misc  Bragging/Combative  Money/Party  Romance/Sex  \\\nartist                                                                   \n03 Greedo      -1.108094            1.115106     0.780540    -0.227123   \n070 Shake      -0.834664           -1.035582     0.819309     1.803736   \n2 Chainz       -0.526146            0.147511     0.827469    -0.671790   \n2 Pistols      -0.974242            0.556940     0.561764     0.592036   \n21 Savage      -1.590426            1.447984     2.694137    -0.475869   \n...                  ...                 ...          ...          ...   \nZebra Katz      0.134856           -0.444954     0.591835     0.156195   \nZelooperz      -0.491603            0.967847    -0.623256    -0.681979   \nZion I          0.743504           -1.112303    -0.629578    -0.697615   \nbbno$          -1.422031           -0.528276    -0.558746    -0.413536   \nwill.i.am      -1.920378           -0.036947     2.783435     2.152477   \n\n            words_per_line  unique_words  unique_word_rate  \\\nartist                                                       \n03 Greedo         0.514503     -0.344285         -1.123740   \n070 Shake        -0.442401     -1.017869         -1.090486   \n2 Chainz          0.056722     -0.221446          0.307881   \n2 Pistols        -0.883727     -0.444150         -0.065240   \n21 Savage         0.161960     -0.291975         -1.150268   \n...                    ...           ...               ...   \nZebra Katz       -1.557956     -1.081511          0.048671   \nZelooperz        -0.571284     -0.826780          0.670745   \nZion I            0.343493      0.004780          1.278952   \nbbno$            -0.308622     -1.652544         -2.257284   \nwill.i.am         0.141036     -0.648589         -1.370463   \n\n            syllables_per_line  syllables_per_word  cluster  \nartist                                                       \n03 Greedo             0.482583           -0.244783        2  \n070 Shake            -0.394098            0.285492        4  \n2 Chainz              0.036345           -0.143757        2  \n2 Pistols            -1.058428           -1.392879        2  \n21 Savage            -0.086846           -1.644092        0  \n...                        ...                 ...      ...  \nZebra Katz           -1.623372           -0.617658        4  \nZelooperz            -0.664509           -0.790359        2  \nZion I                0.306897           -0.220902        3  \nbbno$                -0.015490            2.038551        4  \nwill.i.am             0.054167           -0.573045        0  \n\n[1260 rows x 10 columns]",
+      "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>Lyrical/Misc</th>\n      <th>Bragging/Combative</th>\n      <th>Money/Party</th>\n      <th>Romance/Sex</th>\n      <th>words_per_line</th>\n      <th>unique_words</th>\n      <th>unique_word_rate</th>\n      <th>syllables_per_line</th>\n      <th>syllables_per_word</th>\n      <th>cluster</th>\n    </tr>\n    <tr>\n      <th>artist</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>03 Greedo</th>\n      <td>-1.108094</td>\n      <td>1.115106</td>\n      <td>0.780540</td>\n      <td>-0.227123</td>\n      <td>0.514503</td>\n      <td>-0.344285</td>\n      <td>-1.123740</td>\n      <td>0.482583</td>\n      <td>-0.244783</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>070 Shake</th>\n      <td>-0.834664</td>\n      <td>-1.035582</td>\n      <td>0.819309</td>\n      <td>1.803736</td>\n      <td>-0.442401</td>\n      <td>-1.017869</td>\n      <td>-1.090486</td>\n      <td>-0.394098</td>\n      <td>0.285492</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2 Chainz</th>\n      <td>-0.526146</td>\n      <td>0.147511</td>\n      <td>0.827469</td>\n      <td>-0.671790</td>\n      <td>0.056722</td>\n      <td>-0.221446</td>\n      <td>0.307881</td>\n      <td>0.036345</td>\n      <td>-0.143757</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2 Pistols</th>\n      <td>-0.974242</td>\n      <td>0.556940</td>\n      <td>0.561764</td>\n      <td>0.592036</td>\n      <td>-0.883727</td>\n      <td>-0.444150</td>\n      <td>-0.065240</td>\n      <td>-1.058428</td>\n      <td>-1.392879</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21 Savage</th>\n      <td>-1.590426</td>\n      <td>1.447984</td>\n      <td>2.694137</td>\n      <td>-0.475869</td>\n      <td>0.161960</td>\n      <td>-0.291975</td>\n      <td>-1.150268</td>\n      <td>-0.086846</td>\n      <td>-1.644092</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Zebra Katz</th>\n      <td>0.134856</td>\n      <td>-0.444954</td>\n      <td>0.591835</td>\n      <td>0.156195</td>\n      <td>-1.557956</td>\n      <td>-1.081511</td>\n      <td>0.048671</td>\n      <td>-1.623372</td>\n      <td>-0.617658</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Zelooperz</th>\n      <td>-0.491603</td>\n      <td>0.967847</td>\n      <td>-0.623256</td>\n      <td>-0.681979</td>\n      <td>-0.571284</td>\n      <td>-0.826780</td>\n      <td>0.670745</td>\n      <td>-0.664509</td>\n      <td>-0.790359</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Zion I</th>\n      <td>0.743504</td>\n      <td>-1.112303</td>\n      <td>-0.629578</td>\n      <td>-0.697615</td>\n      <td>0.343493</td>\n      <td>0.004780</td>\n      <td>1.278952</td>\n      <td>0.306897</td>\n      <td>-0.220902</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>bbno$</th>\n      <td>-1.422031</td>\n      <td>-0.528276</td>\n      <td>-0.558746</td>\n      <td>-0.413536</td>\n      <td>-0.308622</td>\n      <td>-1.652544</td>\n      <td>-2.257284</td>\n      <td>-0.015490</td>\n      <td>2.038551</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>will.i.am</th>\n      <td>-1.920378</td>\n      <td>-0.036947</td>\n      <td>2.783435</td>\n      <td>2.152477</td>\n      <td>0.141036</td>\n      <td>-0.648589</td>\n      <td>-1.370463</td>\n      <td>0.054167</td>\n      <td>-0.573045</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1260 rows × 10 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "source": [
+    "test.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
    "metadata": {
     "tags": []
    },
@@ -326,14 +432,53 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "[(1, 11339.999999999995), (2, 8614.935817990692), (3, 7331.596649107567), (4, 6706.899426784931), (5, 6214.874937951342), (6, 5840.953491987842), (7, 5559.502866760166), (8, 5320.678783523608), (9, 5101.243093356314), (10, 4933.883314182881), (11, 4774.095989141835), (12, 4631.5356525948055), (13, 4483.546130619044), (14, 4386.907144179993), (15, 4274.258909785878), (16, 4172.932674590118), (17, 4079.41095828764), (18, 3993.685470226637), (19, 3928.460942792269), (20, 3853.8055212686886), (21, 3794.5422758937784), (22, 3743.1758529489825), (23, 3682.636851107108), (24, 3634.5868625389016), (25, 3569.9979365253616), (26, 3525.3481391385885), (27, 3488.460420697032), (28, 3441.828583305377), (29, 3411.523404539594), (30, 3356.094963840744)]\n"
+     "text": "[t-SNE] Computing 121 nearest neighbors...\n[t-SNE] Indexed 1260 samples in 0.001s...\n[t-SNE] Computed neighbors for 1260 samples in 0.059s...\n[t-SNE] Computed conditional probabilities for sample 1000 / 1260\n[t-SNE] Computed conditional probabilities for sample 1260 / 1260\n[t-SNE] Mean sigma: 0.811085\n[t-SNE] KL divergence after 250 iterations with early exaggeration: 66.902618\n[t-SNE] KL divergence after 300 iterations: 1.349206\n"
+    }
+   ],
+   "source": [
+    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
+    "tsne_results = tsne.fit_transform(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = X_scaled.copy()\n",
+    "xs['tsne-2d-one'] = tsne_results[:,0]\n",
+    "xs['tsne-2d-two'] = tsne_results[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4c9d0b850>"
+     },
+     "metadata": {},
+     "execution_count": 153
     },
     {
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -341,8 +486,200 @@
     }
    ],
    "source": [
-    "kmeans_scree_plot(X, StandardScaler, 1, 30, 30)"
+    "\n",
+    "plt.figure(figsize=(16,10))\n",
+    "sns.scatterplot(\n",
+    "    x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n",
+    "    data=xs,\n",
+    "    hue=y,\n",
+    "    palette=sns.color_palette(\"hls\", 5),\n",
+    "    legend=\"full\",\n",
+    "    alpha=0.8\n",
+    ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = list(test.data['cluster'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs['cluster'] = test.data['cluster']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = xs.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs.to_csv('tsne_df_v1.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import pairwise_distances\n",
+    "from scipy.spatial.distance import pdist\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommend(artist, data, n_recs):\n",
+    "    \"\"\"\n",
+    "    calculate n_recs closest artists in data by euclidean distance\n",
+    "    \"\"\"\n",
+    "    distances = []\n",
+    "    target = data.loc[artist].to_numpy()\n",
+    "    artist_list = list(data.index)\n",
+    "    artist_list.remove(artist)\n",
+    "    \n",
+    "    for name in artist_list:\n",
+    "        comparison = data.loc[name].to_numpy()\n",
+    "        stack = np.vstack((target, comparison))\n",
+    "        dist = pdist(stack)\n",
+    "        # dist = pairwise_distances(target, comparison)\n",
+    "        distances.append(dist[0])\n",
+    "\n",
+    "    top_n_ind = np.argsort(distances)[0:n_recs]\n",
+    "    \n",
+    "    return [artist_list[i] for i in top_n_ind]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rec_X = X_scaled.reset_index() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "['R. Kelly', 'Benny Blanco', 'Murs', 'Fergie', 'Verse Simmonds']"
+     },
+     "metadata": {},
+     "execution_count": 137
+    }
+   ],
+   "source": [
+    "recommend('Usher', X_scaled, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "array(['03 Greedo', '070 Shake', '2 Chainz', ..., 'Zion I', 'bbno$',\n       'will.i.am'], dtype=object)"
+     },
+     "metadata": {},
+     "execution_count": 71
+    }
+   ],
+   "source": [
+    "X_scaled.index.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = X_scaled.loc['Drake'].to_numpy()\n",
+    "comparison = X_scaled.loc['03 Greedo'].to_numpy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "array([[ 1.21449767,  0.11735563,  0.51996137,  0.46902333,  0.95122359,\n         0.47401541, -0.07547929,  0.97886132,  0.04968017],\n       [-1.10809419,  1.11510646,  0.78053979, -0.22712324,  0.51450271,\n        -0.34428497, -1.12373974,  0.48258289, -0.24478329]])"
+     },
+     "metadata": {},
+     "execution_count": 118
+    }
+   ],
+   "source": [
+    "stack = np.vstack((target, comparison))\n",
+    "stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "3.0388571023952555"
+     },
+     "metadata": {},
+     "execution_count": 122
+    }
+   ],
+   "source": [
+    "pdist(stack)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('scaled_df_v1.pkl', 'wb') as f:\n",
+    "    pkl.dump(X_scaled, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ]
 }
\ No newline at end of file
diff --git a/data_collection.ipynb b/data_collection.ipynb
index ac3d7df..560fb59 100644
--- a/data_collection.ipynb
+++ b/data_collection.ipynb
@@ -10,11 +10,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.7.7-final"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python_defaultSpec_1597104935903",
+   "name": "python_defaultSpec_1597907847001",
    "display_name": "Python 3.7.7 64-bit ('anaconda3': virtualenv)"
   }
  },
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,6 +127,48 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_artist_profile(artist_name):\n",
+    "    url_search = \"/search?q=\" \n",
+    "    querystring = url_api + url_search + quote(artist_name)\n",
+    "    # get API response\n",
+    "    response = requests.get(querystring, headers=headers)\n",
+    "    response_artist = response.json()\n",
+    "    return response_artist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prof = get_artist_profile('Drake')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "'https://genius.com/artists/Drake'"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "prof['response']['hits'][0]['result']['primary_artist']['url']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -146,6 +188,25 @@
     "    return url_artist"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_artist_url(artist_name):\n",
+    "    # generate and store url, modify artist name to remove spaces\n",
+    "    url_search = \"/search?q=\" \n",
+    "    querystring = url_api + url_search + quote(artist_name)\n",
+    "    # get API response\n",
+    "    response = requests.get(querystring, headers=headers)\n",
+    "    response_artist = response.json()\n",
+    "    # pull artist url -- assumes they are the primary artist in the first search result since all searches return song objects\n",
+    "    url_artist = response_artist['response']['hits'][0]['result']['primary_artist']['url']\n",
+    "\n",
+    "    return url_artist"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -257,6 +318,47 @@
     "#     pkl.dump(artists, f)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Pull and store artist URL's\n",
+    "\n",
+    "**note: this didn't check for primary_artist so it didn't pull properly for anyone who's not the primary artist on their first searched song\n",
+    "e.g. a bunch of people's names will link to Kanye\n",
+    "\"\"\"\n",
+    "all_artist_urls = {}\n",
+    "for artist in artists:\n",
+    "    try:\n",
+    "        all_artist_urls[artist] = get_artist_url(artist)\n",
+    "    except:\n",
+    "        all_artist_urls[artist] = None    \n",
+    "\n",
+    "with open('all_artist_urls.pkl', 'wb') as f:\n",
+    "    pkl.dump(all_artist_urls, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "'https://genius.com/artists/Earl-sweatshirt'"
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "all_artist_urls['Earl Sweatshirt']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
diff --git a/preprocessing.ipynb b/preprocessing.ipynb
index 8639af0..4ef3fd9 100644
--- a/preprocessing.ipynb
+++ b/preprocessing.ipynb
@@ -10,11 +10,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.7.7-final"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python_defaultSpec_1597266629003",
+   "name": "python_defaultSpec_1597907492795",
    "display_name": "Python 3.7.7 64-bit ('base': conda)"
   }
  },
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/webapp/__pycache__/predictor_api.cpython-37.pyc b/webapp/__pycache__/predictor_api.cpython-37.pyc
new file mode 100644
index 0000000..fd474a2
Binary files /dev/null and b/webapp/__pycache__/predictor_api.cpython-37.pyc differ
diff --git a/webapp/all_artist_urls.pkl b/webapp/all_artist_urls.pkl
new file mode 100644
index 0000000..48611e7
Binary files /dev/null and b/webapp/all_artist_urls.pkl differ
diff --git a/webapp/artist_features.pkl b/webapp/artist_features.pkl
new file mode 100644
index 0000000..e9debd2
Binary files /dev/null and b/webapp/artist_features.pkl differ
diff --git a/webapp/predictor_api.py b/webapp/predictor_api.py
new file mode 100644
index 0000000..3ecc8cf
--- /dev/null
+++ b/webapp/predictor_api.py
@@ -0,0 +1,39 @@
+import pandas as pd
+import pickle as pkl
+import numpy as np
+from scipy.spatial.distance import pdist
+
+tsne = pd.read_csv('tsne_df_v1.csv')
+
+with open('web_features.pkl', 'rb') as f:
+    artist_features = pkl.load(f)
+
+with open('scaled_df_v1.pkl', 'rb') as f:
+    data = pkl.load(f)
+
+def show_stats(artist):
+    return artist_features.loc[artist]
+
+def recommend(artist, n_recs=5):
+    """
+    calculate n_recs closest artists in data by euclidean distance
+    """
+    distances = []
+    target = data.loc[artist].to_numpy()
+    artist_list = list(data.index)
+    artist_list.remove(artist)
+    
+    for name in artist_list:
+        comparison = data.loc[name].to_numpy()
+        stack = np.vstack((target, comparison))
+        dist = pdist(stack)
+        # dist = pairwise_distances(target, comparison)
+        distances.append(dist[0])
+
+    top_n_ind = np.argsort(distances)[0:n_recs]
+    
+    return [artist_list[i] for i in top_n_ind]
+
+# def show_pic(artist):
+
+# def show_songs(artist):
\ No newline at end of file
diff --git a/webapp/predictor_app.py b/webapp/predictor_app.py
new file mode 100644
index 0000000..aad8a42
--- /dev/null
+++ b/webapp/predictor_app.py
@@ -0,0 +1,63 @@
+import flask
+from flask import request
+from predictor_api import show_stats, recommend
+
+import pickle as pkl
+
+with open('all_artist_urls.pkl', 'rb') as f:
+    links = pkl.load(f)
+
+app = flask.Flask(__name__)
+
+
+
+@app.route("/", methods=["GET", "POST"])
+def landing():
+    return flask.render_template('landing.html')
+
+@app.route("/profile", methods=["GET", "POST"])
+def get_artist_profile():
+    """
+    load rapper picture, stats, songs 
+    """
+
+    # look for the name searched from the landing page
+    artist = request.form['artist']
+    query = request.args.to_dict()
+    
+    if query == {}:
+        query = artist
+    else:
+        query = query['artist']
+
+    stats = show_stats(query)
+
+    return flask.render_template('profile.html',
+                                stats = stats,
+                                artist = artist)
+
+@app.route("/recommend", methods=["GET", "POST"])
+def get_recs():
+    """
+    generate recommendations
+    """
+    artist = request.form['artist']
+    query = request.args.to_dict()
+    
+    if query == {}:
+        query = artist
+    else:
+        query = query['artist']
+
+    recs = recommend(artist)
+    stats = [show_stats(rec) for rec in recs]
+
+    return flask.render_template('recommender.html',
+                                 recs = recs,
+                                 stats = stats,
+                                 links = links,
+                                 artist = artist)
+
+if __name__=='__main__':
+    app.run(debug=True)
+
diff --git a/webapp/scaled_df_v1.pkl b/webapp/scaled_df_v1.pkl
new file mode 100644
index 0000000..002edc4
Binary files /dev/null and b/webapp/scaled_df_v1.pkl differ
diff --git a/webapp/templates/landing.html b/webapp/templates/landing.html
new file mode 100644
index 0000000..bd1077b
--- /dev/null
+++ b/webapp/templates/landing.html
@@ -0,0 +1,40 @@
+<!DOCTYPE html>
+<html lang="en">
+
+
+<head>
+</head>
+
+<body style="background-color:#b997e0;">
+    <div class='tableauPlaceholder' id='viz1597813392758' style='position: relative'><noscript><a href='#'><img alt=' ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;ta&#47;tab_viz_v1&#47;Dashboard1&#47;1_rss.png' style='border: none' /></a>
+    </noscript><object class='tableauViz'  style='display:none;'>
+        <param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> 
+        <param name='embed_code_version' value='3' /> 
+        <param name='site_root' value='' />
+        <param name='name' value='tab_viz_v1&#47;Dashboard1' />
+        <param name='tabs' value='no' />
+        <param name='toolbar' value='yes' />
+        <param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;ta&#47;tab_viz_v1&#47;Dashboard1&#47;1.png' /> 
+        <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' />
+        <param name='display_spinner' value='yes' />
+        <param name='display_overlay' value='yes' />
+        <param name='display_count' value='yes' />
+        <param name='language' value='en' />
+        <param name='filter' value='publish=yes' /></object>
+    </div>                
+    <script type='text/javascript'>                    var divElement = document.getElementById('viz1597813392758');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.width='1366px';vizElement.style.height='795px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.width='1366px';vizElement.style.height='795px';} else { vizElement.style.width='100%';vizElement.style.height='977px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>
+    <p>
+        RAP GEO v1
+        <form action='/profile' method="POST">
+            <input type="text" name="artist">
+            <input type="submit" value="Check Profile">
+        </form>
+        <form action='/recommend' method="POST">
+            <input type="text" name="artist">
+            <input type="submit" value="Get Recommendations">
+        </form>
+    </p>
+
+</body>
+</html>
+
diff --git a/webapp/templates/profile.html b/webapp/templates/profile.html
new file mode 100644
index 0000000..773e98a
--- /dev/null
+++ b/webapp/templates/profile.html
@@ -0,0 +1,48 @@
+<!DOCTYPE html>
+<html lang="en">
+
+
+<head>
+</head>
+
+<body style="background-color:#b997e0;">
+
+  <p>
+    <b>Artist</b>:<br>
+    <b>{{ artist }}</b> <br>
+    <br>
+
+    <b>Lyrical Content</b>:<br>
+    {{ stats['Lyrical/Misc'].round(3) }} Misc. Lyricism <br>
+    {{ stats['Bragging/Combative'].round(3) }} Flexing & Threats<br>
+    {{ stats['Money/Party'].round(3) }} Money & Partying <br>
+    {{ stats['Romance/Sex'].round(3) }} Sex & Romance<br>
+    <br>
+
+    <b>Flow Statistics</b>:<br>
+    {{ (stats['unique_word_rate'] * 100).round(1) }}% Word Uniqueness <br>
+    {{ stats['syllables_per_line'].round(1) }} Syllables/Line<br>
+    <br>
+
+    <b>Style</b><br> 
+    {{ stats['cluster'] }} <br>
+
+    <form action="/recommend" method="POST">
+      <input type="hidden" name="artist" value="{{ artist }}">
+      <input type="submit" value="Get Recommendations">
+    </form>
+    <br>
+    
+    <form action='/profile' method="POST">
+      <input type="text" name="artist">
+      <input type="submit" value="Find Another Artist">
+    </form>
+
+    <form action='/' method="POST">
+      <input type="submit" value="Return to Dashboard">
+    </form>
+
+  </p>
+</body>
+</html>
+
diff --git a/webapp/templates/recommender.html b/webapp/templates/recommender.html
new file mode 100644
index 0000000..1940b50
--- /dev/null
+++ b/webapp/templates/recommender.html
@@ -0,0 +1,46 @@
+<!DOCTYPE html>
+<html lang="en">
+
+
+<head>
+</head>
+<body style="background-color:#b997e0;">
+
+
+    Recommended Artists for <b>{{ artist }}</b>: <br>
+    <br>
+
+    <form action='/profile' method="POST">
+        <input type="text" name="artist">
+        <input type="submit" value="Find Another Artist">
+    </form>
+    <br>
+
+    <form action='/' method="POST">
+        <input type="submit" value="Return to Dashboard">
+    </form>
+    <br>
+    
+    {% for i in range(recs|length) %}
+        <a href="{{ links[recs[i]] }}">{{recs[i]}}</a><br>
+   
+        Lyrical Content:<br>
+        {{ stats[i]['Lyrical/Misc'].round(3) }} Misc. Lyricism <br>
+        {{ stats[i]['Bragging/Combative'].round(3) }} Flexing & Threats<br>
+        {{ stats[i]['Money/Party'].round(3) }} Money & Partying <br>
+        {{ stats[i]['Romance/Sex'].round(3) }} Sex & Romance<br>
+        <br>
+
+        Flow Statistics:<br>
+        {{ (stats[i]['unique_word_rate'] * 100).round(1) }}% Word Uniqueness <br>
+        {{ stats[i]['syllables_per_line'].round(1) }} Syllables/Line<br>
+        <br>
+
+        Style<br> 
+        {{ stats[i]['cluster'] }} <br>
+        <br>
+    {% endfor %}
+   
+
+</body>
+</html>
\ No newline at end of file
diff --git a/webapp/templates/salesman.ipynb b/webapp/templates/salesman.ipynb
new file mode 100644
index 0000000..7f981f6
--- /dev/null
+++ b/webapp/templates/salesman.ipynb
@@ -0,0 +1,161 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python_defaultSpec_1597853401758",
+   "display_name": "Python 3.7.7 64-bit ('base': conda)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def one_stop(mat, origin, target):\n",
+    "    \n",
+    "    min_dist = mat[origin][target]\n",
+    "    \n",
+    "    # check every row's distance from origin city and add its distance to target city\n",
+    "    for stop, row in enumerate(mat):\n",
+    "        one_stop = mat[origin][stop] + mat[stop][target]\n",
+    "        if one_stop < min_dist: \n",
+    "            min_dist = one_stop\n",
+    "    \n",
+    "    return min_dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "[[0, 4, 9, 3, 1, 2],\n [4, 0, 2, 1, 4, 2],\n [9, 2, 0, 7, 2, 1],\n [3, 1, 7, 0, 6, 6],\n [1, 4, 2, 6, 0, 2],\n [2, 2, 1, 6, 2, 0]]"
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "test = [[0, 4, 9, 3, 1, 2], [4, 0, 2, 1, 4, 2], [9, 2, 0, 7, 2, 1], [3, 1, 7, 0, 6, 6], [1, 4, 2, 6, 0, 2], [2, 2, 1, 6, 2, 0]]\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "0"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "one_stop(test, 3, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def two_stop(mat, origin, target):\n",
+    "    \n",
+    "    min_dist = mat[origin][target]\n",
+    "    \n",
+    "    # check every row's distance from origin city and add its distance to target city\n",
+    "    for stop_one, row in enumerate(mat):\n",
+    "        for stop_two, row in enumerate(mat):\n",
+    "            two_stop = mat[origin][stop_one] + mat[stop_one][stop_two] + mat[stop_two][target]\n",
+    "            if two_stop < min_dist: \n",
+    "                min_dist = two_stop\n",
+    "        \n",
+    "    return min_dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "3"
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "source": [
+    "two_stop(test, 3, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max n-1 stops\n",
+    "def any_stop(mat, origin, target):\n",
+    "    min_dist = mat[origin][target]\n",
+    "    for stop in range(len(mat)):\n",
+    "        dist = one_stop(mat, origin, stop) + one_stop(mat, stop, target)\n",
+    "        if dist < min_dist:\n",
+    "            min_dist = dist\n",
+    "    any_stop()\n",
+    "    return min_dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "3"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "any_stop(test, 3, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/webapp/tsne_df_v1.csv b/webapp/tsne_df_v1.csv
new file mode 100644
index 0000000..642334e
--- /dev/null
+++ b/webapp/tsne_df_v1.csv
@@ -0,0 +1,1261 @@
+,artist,Lyrical/Misc,Bragging/Combative,Money/Party,Romance/Sex,words_per_line,unique_words,unique_word_rate,syllables_per_line,syllables_per_word,tsne-2d-one,tsne-2d-two,cluster
+0,03 Greedo,-1.1080941931457096,1.1151064599355913,0.7805397945180766,-0.22712323899451012,0.5145027064394344,-0.3442849685874302,-1.123739744281131,0.4825828911281931,-0.24478329254125955,-0.20321342,-3.2974455,2
+1,070 Shake,-0.8346640762067865,-1.0355821334255704,0.8193086845809541,1.8037359495397118,-0.4424011749602101,-1.0178692303633279,-1.090486109866795,-0.3940983834046242,0.28549218795758924,8.416159,-11.573661,4
+2,2 Chainz,-0.5261461878799091,0.1475108770699726,0.8274689595628898,-0.6717897344935416,0.05672154996784091,-0.22144645044690528,0.30788140059448044,0.03634488135925515,-0.1437566108260485,-1.2666713,0.681996,2
+3,2 Pistols,-0.9742417673045877,0.5569399561392002,0.5617636220089659,0.5920356245773102,-0.8837270546937069,-0.44415007402758794,-0.06524017431433107,-1.058427976445754,-1.3928787949580503,-3.2702534,-7.262373,2
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+754,Mike Will Made It,-0.3805690511867677,-0.6320730065373881,-0.289483846386939,-1.0269911514202086,-0.2235711343733509,-0.3530730978661641,-0.8190728617093402,-0.15135927715622927,0.3667199912468878,0.41171464,-8.462967,3
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+944,"Royce da 5'9""",0.8810021680261766,0.2070242499724756,-0.38401610929126956,-0.7432142874278098,0.638302354774927,0.6500519132591333,0.31571176753351515,0.8540142740775066,1.294290749932086,0.8215852,14.80113,1
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+1065,Stunna 4 Vegas,0.8159933803915842,3.4207463867076484,3.5715358519084957,-1.195933399240608,2.355482508369992,1.787715194433412,0.3349090091285411,2.097298139934886,-1.2324743488071375,-15.423986,-1.9502785,0
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+1069,Suga Free,-0.053387837803902834,0.515514215018544,-0.5006167417344992,0.36356103667778183,0.24832020375307814,0.8746285623729606,-0.3570300676968117,0.19216272682008403,-0.3156168898650182,-6.1152997,0.27942008,2
+1070,Swae Lee,-1.481132204586914,-0.9148149106061283,3.036233947331398,1.441079192948749,-0.9917913371363142,-1.2459470325855275,-0.07012612989516422,-0.8159733374682621,1.313680995663338,7.0295434,-5.4080677,0
+1071,Swagg Man,-0.5730573997787827,-0.9579285310002794,-0.7294447686820612,-0.8973187723425654,-1.8135131202811752,-1.136974229529227,-1.4208307456058198,-1.9929399063861866,-1.7370781642325923,-2.2040594,-14.779575,4
+1072,Swarmz,-0.11118332160506762,-0.3971733134866737,-0.40493732797383475,-0.22973100371570884,-0.8099074617105305,0.1285163866084532,-0.782158898630288,-1.0025189764528366,-1.4923699408578324,-1.9594113,-10.191657,3
+1073,Sweet Tee,2.480895147653612,-0.9614888815876674,-0.47819323890367754,-0.3390321383830473,0.4961563465300205,0.49691476597297773,0.7689642643254444,0.5080171801692814,0.04705372056483145,-0.818302,17.009428,1
+1074,Swings,-1.1406939480270593,-0.18329266934639785,2.4029480717816116,0.10188122122312152,-0.3836399615455255,-0.7085822209552124,-0.6745428303239598,-0.445224726716167,-0.36772694502737896,4.340774,-5.687713,0
+1075,SwizZz,1.762761771812359,0.36371254837268574,-0.5642902070820461,-0.03992663812572028,1.0751026931337913,1.081149600423207,-0.5673099188631402,1.1705717060452159,0.5441757191251173,-8.650406,12.813306,1
+1076,Swizz Beatz,-0.854834872921242,0.70878840295968,0.6674843416944597,-0.6010767000143977,-0.019691154897718763,-0.37135240676593073,-0.760724715784977,-0.08542767809829135,-0.45495703188100595,-1.5280612,-4.551255,2
+1077,Syd Tha Kyd,-0.7785742218378401,-0.9522542222516299,2.156389458507711,2.2876312071480633,-1.1077529703445435,-1.249462284297021,-0.3683425175842083,-1.128400293300617,0.14181945109181762,9.629805,-11.880435,4
+1078,T La Rock,-0.24195784851109325,-1.1359460603696783,0.5113417268983801,-0.5594342767017668,-1.1485892415392,-0.47610690776843856,1.4331702651420979,-0.8251557768009042,2.586709924823833,-1.2903014,6.016344,3
+1079,T-Bone,1.1151228566444538,-0.8628004137435071,-0.6831719555959168,-0.9745941390541635,1.5492260298590523,0.6986263005452266,1.1221949575103911,1.5968889274298392,0.2789177609860279,-3.5084558,14.8178005,1
+1080,T-Nutty,0.731994011985654,1.188295307459533,-0.8522470480919085,-1.1073716640030378,-0.023238909767323696,2.2833656857540032,0.3993012898613264,0.1085552694778167,0.8722428766773639,-10.257723,4.674453,1
+1081,T-Pain,-0.9203200948213746,0.5142625292651654,2.7704201944985876,2.0324793256033313,-0.39220555264256995,-0.4233781320960352,-1.287419967496108,-0.49782766916388177,-0.6210883120302954,6.491043,-6.3334856,0
+1082,T-Wayne,-0.7297909151717723,-0.09887301597134837,-0.2562292750027592,0.3232129361449309,0.6068706929905796,-1.0250975228851713,-0.7867074892814955,0.5220623131135836,-0.5771729454965508,2.229838,0.4375696,2
+1083,T. Mills,-0.011204481512586894,-0.6681285100444322,-0.12167018031151378,0.7251710780173295,-0.39223612161015964,-0.9005735519312855,-0.38660417573997663,-0.3679833683353401,0.0474685150238916,5.448187,-9.6184,4
+1084,T.I.,-0.030892380125847767,0.8384392053763031,1.069076803777119,2.47284824432093,-0.306967712469946,-0.18383883689854583,-0.15923899405136402,-0.3088153670756527,-0.054126491165936534,11.854093,-5.3480673,0
+1085,TD Cruze,-1.1637468359161107,0.28652526024767305,-0.8006120739148546,-0.4391703036770757,-2.2245755959912095,-0.09645972292713444,0.1274091710149895,-2.300027066991067,-0.868781026895993,-1.4479128,-12.631015,4
+1086,Tabi Bonney,0.24365826382001396,-0.9069241665000339,-0.18001525831790394,1.1361456480265848,0.12185983568033193,-0.6167169762281808,0.864383838652388,0.1410906789901955,0.09048594107671426,8.365683,4.353222,3
+1087,Tablo,-1.395908355591976,-1.1187006122120176,0.4896821122623127,3.730798883444356,-1.6760019047225971,-0.9260591268396138,-0.6677431148707358,-1.516531850407085,1.358946197004947,8.637801,-15.668863,4
+1088,Taio Cruz,-0.8076678806326635,-1.1359460603696783,-0.7975178432525591,4.816038068595735,-1.1637582869963208,-1.7205060136371575,-2.662852378312645,-1.1584457282456728,-0.4951539762316544,8.952366,-14.89466,4
+1089,Tairrie B,0.7669625997538433,-0.8557148476074226,-0.5965149908873985,-0.5028116041176389,-0.17785462195395765,1.2770786300269794,0.5639368341183406,-0.09389471110029085,0.5874717074141684,0.5401754,9.399594,3
+1090,Takeoff,-2.3216156808818185,2.5217578633921853,-0.871779379147648,-1.195933399240608,-1.592325763786072,-1.9525126265957322,2.6678144332634823,-1.4188924733036359,1.4640085552778976,-4.476939,-10.471117,2
+1091,Talib Kweli,1.36776066020632,-0.9071935351300008,-0.29743859771459,-0.6841161099600179,0.9986603684482924,2.087683340480862,0.4460874901054481,1.0883602560404313,0.5370839932000316,-4.982242,17.269993,1
+1092,Target,0.842813727637711,0.054296789283670994,-0.02588073464152185,-0.18019228998562276,0.286128962455088,0.10281868444167286,0.5740623268178123,0.27242330055369546,-0.08744377907804503,-0.43903494,4.5423074,3
+1093,Tay Dizm,0.4853836213594973,-0.3515563215857653,0.028254964747516247,-0.8445498862194867,-1.9758518277261397,-0.6659305001890906,-0.2916109371496051,-2.1171421322803115,-1.3510806756863785,-1.4163802,-13.13032,4
+1094,Tay-K,-1.7866319483404156,1.5491702177780617,-0.04910080180986781,-0.5342361109251647,1.2471532282549318,-1.1608779411673835,0.3535666671081192,1.0643678927115665,-1.0518443419927799,-10.36339,-4.290095,2
+1095,Teairra Marí,-0.1309085907298105,-0.8068412102641775,0.5157017791952508,1.776416353353867,-1.3825776025928227,-0.994446751045034,-0.7184580170086374,-1.300185996221338,0.5340918707448027,6.3597465,-14.256527,4
+1096,Tech N9ne,0.7636720451059834,-0.5386383464763631,-0.5614007416841673,0.15546020880515,0.7786215393367892,1.0267665886512771,0.16191196170411268,0.9339409715373092,0.9664686665761094,2.2437446,13.581652,1
+1097,Tedashii,0.4727714712927177,-1.003475984803778,0.15253989634971374,0.6100374133657152,-0.4908523836220224,-0.4151758781025501,-0.3294015718161864,-0.4169699837677066,0.43375105651931783,9.129055,-5.794377,3
+1098,Tee Grizzley,-0.5366952242076097,2.4073593485811746,0.19631810317008913,-0.31380124771582624,1.7546456352926012,0.515475295009664,-0.6684689212292064,1.5897181722200227,-0.8052231779263193,-12.899782,0.9529606,2
+1099,TeeFlii,-1.558689740328934,-0.28533186568720353,1.2616253966210436,2.036325389514391,-1.2056832953835646,-1.3038722673097038,-0.8882795100914358,-1.2046607746852767,-0.05185277807776377,9.312942,-12.398955,4
+1100,Tekitha,-1.0701965189159204,-1.1359460603696783,-0.7093322693771413,0.8043755398435395,-1.4511544606587163,-1.0315166781844205,0.4092716170256888,-1.4511712297329635,0.30449399059478666,2.2884145,-14.179485,4
+1101,Termanology,1.5066004325919422,-0.6469541593831112,-0.871779379147648,-1.195933399240608,-0.8147639051044078,0.1425773934544274,2.6607971470918264,-0.705711808524047,0.8082458445125048,-0.47845355,7.050351,3
+1102,Terrace Martin,0.38301704411845083,-0.7922887829698467,-0.7731507767869832,0.5560754322721537,-0.2324238373784495,-0.7256897792844811,2.7048543578346527,-0.31371517621007583,-0.6843416126115137,-3.1873925,6.144389,3
+1103,Teyana Taylor,0.4848938291238941,-0.862800413743507,-0.5290933332984363,0.12860654856194198,0.6555137499513531,-0.6588999967661034,-0.9363914464636294,0.8979838972659295,1.514935316706375,11.898401,3.8798308,3
+1104,Tha Chill,-0.5608125938887057,1.2358593660879194,-0.871779379147648,-1.1951152769751339,1.0113807532350707,-1.5798959451774153,2.5776808890975804,0.9248571810276778,-0.06289186562363756,-5.8173065,7.6248193,2
+1105,Tha City Paper,-0.8424431293604829,0.7254497310990965,-0.8539875528394497,0.07870109036802261,0.4696026084443021,1.365884989054185,0.9644020223320431,0.6018110324886822,0.8516768917259097,-8.762589,1.7370558,2
+1106,Tha Trademarc,1.64194635369693,-0.47769843028736253,-0.019770073656859673,0.05613455121202889,0.49597621461582325,0.6968154132999118,0.7613557442236597,0.31440129589087923,-1.1197733073170149,2.713748,9.657464,3
+1107,The 6th Letter,-1.3668473496128593,0.8548833097447638,-0.819435310443818,-1.195933399240608,-0.21570704897694346,-0.23238278910488516,0.42353410336909025,-0.3549429667642472,-1.0167754479109485,-6.464848,-5.1189938,2
+1108,The Alchemist,1.0939994533198996,0.9483610561563406,-0.7475460180564891,-0.6195662632141123,1.7650621563391764,0.4828537591270035,1.7431588327379532,1.912871382119911,0.8332609025948855,-5.6734056,13.329814,1
+1109,The Coup,0.2023727623146568,-0.5419389890036918,-0.4782143359309205,-0.5582070933035557,0.6759236651127933,0.7638182413765615,0.3502990454194197,0.8486217760207141,1.0937099358828357,0.86285895,13.423665,1
+1110,The D.O.C.,0.7783638251873094,0.7011833329684611,-0.3172452305402491,-0.831079079951767,0.08807979725657814,1.3304900407867326,-0.3852156365675868,0.22090226974403654,0.8625649007133218,-10.121453,5.1912036,3
+1111,The Dirtball,0.15945883153752707,-0.4172728470336806,-0.5749014002772003,-0.6308954780642643,-0.11755941444735851,0.9568930942734566,-0.07696558840679883,-0.09036772666107513,0.15649107684601343,1.6898414,7.885946,3
+1112,The Game,0.001292567378826106,1.2770592980413484,-0.5190061413393535,0.22710846932502232,0.5843928265571671,0.8934351590294508,0.2338888232861064,0.5897484986381711,0.11486924014533101,-9.202642,1.5012767,2
+1113,The Jacka,-0.294855409956227,-1.1359460603696783,-0.7379539030033734,-0.6477914813729683,-0.07250066183804948,0.2550654482222212,-1.4250198788966784,0.16939951629642264,1.6509372240376863,12.53212,1.6333355,3
+1114,The Lady of Rage,0.9652464325499083,-0.7803155869249022,-0.3971665896060248,-0.9058718687543401,0.22172463276336363,1.4873209572694168,0.4167000778119478,0.2315399481342627,0.0952502240274243,0.08667101,9.556423,1
+1115,The Notorious B.I.G.,-0.3608140976841101,0.5433526333483161,-0.02769903972899156,0.48701227769504885,-0.16982563000238202,0.5761251045386329,0.38075105631573547,-0.2770418021111517,-0.6012118181748907,-0.5390664,0.2507699,2
+1116,The Real Roxanne,1.3939482184032315,-1.1359460603696783,-0.7464630373246857,0.3638166998857425,-0.15317834276650438,1.1971529069024944,0.0013817775866229933,-0.06986179460183031,0.5594254328452571,6.0555396,11.151535,3
+1117,The Weeknd,-1.43821886948292,-0.7987611751133856,0.9822895841433159,3.926865030989319,-1.439308142102852,-1.3132776230595193,-1.6916034919020817,-1.2425103421205241,1.6581184100389614,8.989688,-15.712748,4
+1118,The-Dream,-1.3224395202515125,-0.02945585438300945,0.960005172931208,5.455400619063735,-0.22074088747611004,0.8456277357531388,-0.3059319541898258,-0.029167859957893474,1.3317952284320456,9.9554,-14.579354,4
+1119,Theophilus London,-0.03134718720176445,-0.887728165995978,-0.4039243357927263,1.3309735713870274,-1.1238787687380523,-0.9454767077221496,0.3465851321501726,-1.1466489392941395,-0.2400318404522932,4.734792,-13.417056,4
+1120,Tierra Whack,1.7456190435662513,2.7420545559868157,2.9650666420986043,-1.195933399240608,0.572293488739864,2.659497618883814,0.21208045218420238,0.4973105169541973,-0.44713495661563735,-15.437432,-1.9257648,1
+1121,Tiffany Foxx,-1.0848614746760366,1.1864300502584197,-0.31945920592792537,-0.22082790847378503,-0.9380353466261354,-0.5472390600480729,-0.31933397197474506,-1.024484714941139,-0.758926833887376,-5.4122176,-7.207472,2
+1122,Tim Dog,0.738229443908139,1.4128712675870827,-0.5258205893287009,-0.5747898022998958,-0.8671175570808078,0.3102278596948895,0.6801442183746544,-0.9480099823669156,-0.7339083366486965,-4.0329885,3.2253084,2
+1123,Timaya,-1.845537627875614,-0.968312886880161,-0.6770147491265008,1.3067935133185715,-0.7635708266993303,-1.0682315293933533,-2.0694095459024524,-0.34160586639757384,3.0772033524438593,2.697982,-19.29401,4
+1124,Timbaland,-0.4414665524800853,-0.596144989542262,0.8434558179847493,1.1365331796260199,-0.5777226012871525,-1.4510033824226516,-1.3415070123647381,-0.598277847722863,-0.2384388452949007,7.8990154,-11.23781,4
+1125,Tinie Tempah,0.29057520428304745,-0.72395845251494,0.9258872085232671,0.49262789763130804,-0.3166588684242729,0.26912645506819544,-0.032813725219638684,-0.19767297665103586,0.8202302521916799,8.98105,-0.38378248,3
+1126,Tink,-0.010188162623709463,-0.18499867066952172,0.0998311504606846,1.789979120521111,-0.4139003115473136,-0.6813976077196622,0.04598156106248741,-0.39708614287436256,-0.047458019301681344,8.198029,-9.792241,4
+1127,Tion Wayne,-0.2822659354559106,-0.2548417055140111,0.24188580672349214,0.34749970581323186,-0.735975640367801,-0.4219459925098712,-0.44525426565105697,-0.7883012658643661,-0.3236803251047385,3.8725379,-9.722225,4
+1128,TobyMac,-0.6513308637161148,-1.1316545663580946,-0.36782497806594694,0.5396935078611129,-1.3935397623274366,-1.1255915097015339,-1.9192458433884014,-1.2611082180297613,1.0543404176929045,3.4231157,-16.881592,4
+1129,Tommy Wright III,-1.0249886955697554,1.6654101014084859,-0.787384237833542,-0.5636476063689583,-0.014574315377904943,1.072009945973324,0.052203800878043306,-0.22144428087767434,-1.5511891144830576,-9.986107,-2.9479458,2
+1130,Tone Lōc,0.8379963546679078,-1.0472796370876123,-0.3302295088101457,1.1648527965078148,-0.05523805462471613,0.5957744602592889,0.6424876807160597,0.06637394227816412,0.804019970541444,8.533782,5.2200565,3
+1131,Tone Trump,-1.6721511764721195,-0.15518073450014735,-0.5290933332984363,1.71085500998883,-0.9582802216649355,-0.3636188530006447,1.7801612694883484,-0.9169553240204713,0.2896469153251192,-4.3988547,-9.122536,4
+1132,Tonedeff,-0.11053026529093031,-0.7161677456717863,-0.6116706140984439,0.9892711718406837,2.387669577007546,1.1772331472040307,0.8254615188874264,2.447323200283617,0.34644112547992734,-5.5568724,14.423949,1
+1133,Tony Yayo,-0.9547035097607116,0.4170899919445417,-0.6296558298230356,0.4801536860361579,0.08947665912694357,-0.5970315666438168,0.265100190096027,-0.06929613603215365,-1.0854291773456946,-5.0560675,-4.6846714,2
+1134,Too Short,-0.8568103682715069,2.016150992997808,-0.07130190681183725,0.5268684673947286,0.1294127758961563,0.34505589203645637,-0.6263010674777482,-0.0790564527526053,-1.3821317208721102,-9.383563,-5.0432663,2
+1135,Torch,-0.3689410207044872,-1.1359460603696783,-0.5454526454111275,-0.02007959798181152,-1.576952811112952,-1.3072686457749152,-1.0936221130744486,-1.458571996644719,0.8570628604281788,2.908648,-16.276945,4
+1136,Torch (Triple C),-0.08326516417569137,0.6283745471148465,-0.7333125570099304,0.23864399326820687,-0.28562551825741717,0.613621122794564,0.9787150888330651,-0.24210123890872273,0.2893951880202309,-4.3986425,3.0768034,3
+1137,Tory Lanez,-0.09768682444622614,0.5047512628305421,0.7355445236371981,1.9021592663988542,0.19575606594796285,-0.3026878233347563,-0.6803042595186716,0.1949687062015417,-0.04119235875418688,11.664142,-5.113586,0
+1138,Tracey Lee,0.2914958663800447,-0.20103614273503248,0.25463109016580404,-0.7622117239299874,-0.5656382452067247,0.3956755166819635,0.8577376887476057,-0.5863187563680672,-0.10024541074115616,-1.3709525,4.4998345,3
+1139,Trae tha Truth,0.05790384239702154,2.704473077564388,-0.550581046231043,-0.33008733494719716,1.5760895948764067,0.9346807791109752,-0.11779599624452829,1.6104720584571937,0.1833097100107448,-12.998763,0.6032001,2
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+1240,YoungBoy Never Broke Again,-0.9384424075386888,0.3121152800945242,2.732225784760878,-0.5643430102946113,1.2863423384381167,-0.19488677084895403,-1.3127656776000718,1.8088721370517233,3.003304545882491,3.4621153,-2.082687,0
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diff --git a/webapp/web_features.pkl b/webapp/web_features.pkl
new file mode 100644
index 0000000..6df2866
Binary files /dev/null and b/webapp/web_features.pkl differ