Facial Emotion Detection Based Music Player.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPORTING LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saran\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import os\n",
    "from pandas import DataFrame\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "from tensorflow.keras.layers import LeakyReLU, Activation\n",
    "from tensorflow.keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "import cv2\n",
    "import argparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to load images from folder\n",
    "\n",
    "def load_images_from_folder(folder):\n",
    "    images = []\n",
    "    for filename in os.listdir(folder):\n",
    "        img = cv2.imread(os.path.join(folder,filename))\n",
    "        if img is not None:\n",
    "            images.append(img)\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "angry_fld=r\"C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\angry\"\n",
    "disgust_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\disgust'\n",
    "fear_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\fear'\n",
    "happy_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\happy'\n",
    "neutral_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\neutral'\n",
    "sad_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\sad'\n",
    "surprise_fld=r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "angry_list=load_images_from_folder(angry_fld)\n",
    "disgust_list=load_images_from_folder(disgust_fld)\n",
    "fear_list=load_images_from_folder(fear_fld)\n",
    "happy_list=load_images_from_folder(happy_fld)\n",
    "neutral_list=load_images_from_folder(neutral_fld)\n",
    "sad_list=load_images_from_folder(sad_fld)\n",
    "surprise_list=load_images_from_folder(surprise_fld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3995\n",
      "436\n"
     ]
    }
   ],
   "source": [
    "print(len(angry_list))\n",
    "print(len(disgust_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "angry_np=np.array(angry_list)\n",
    "disgust_np=np.array(disgust_list)\n",
    "fear_np=np.array(fear_list)\n",
    "happy_np=np.array(happy_list)\n",
    "neutral_np=np.array(neutral_list)\n",
    "sad_np=np.array(sad_list)\n",
    "surprise_np=np.array(surprise_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3171, 48, 48, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "entries1 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\angry')\n",
    "entries2 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\disgust')\n",
    "entries3 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\fear')\n",
    "entries4 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\happy')\n",
    "entries5 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\neutral')\n",
    "entries6 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\sad')\n",
    "entries7 = os.listdir(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\surprise')\n",
    "\n",
    "angry_df=[]\n",
    "disgust_df=[]\n",
    "fear_df=[]\n",
    "happy_df=[]\n",
    "neutral_df=[]\n",
    "sad_df=[]\n",
    "surprise_df=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for entry in entries1:\n",
    "    angry_df.append(entry)\n",
    "    \n",
    "for entry in entries2:\n",
    "    disgust_df.append(entry)\n",
    "    \n",
    "for entry in entries3:\n",
    "    fear_df.append(entry)\n",
    "    \n",
    "for entry in entries4:\n",
    "    happy_df.append(entry)\n",
    "    \n",
    "for entry in entries5:\n",
    "    neutral_df.append(entry)\n",
    "    \n",
    "for entry in entries6:\n",
    "    sad_df.append(entry)\n",
    "    \n",
    "for entry in entries7:\n",
    "    surprise_df.append(entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "angry_df=DataFrame(angry_df,columns=['Image Path'])\n",
    "disgust_df=DataFrame(disgust_df, columns=['Image Path'])\n",
    "fear_df=DataFrame(fear_df, columns=['Image Path'])\n",
    "happy_df=DataFrame(happy_df, columns=['Image Path'])\n",
    "neutral_df=DataFrame(neutral_df, columns=['Image Path'])\n",
    "sad_df=DataFrame(sad_df, columns=['Image Path'])\n",
    "surprise_df=DataFrame(surprise_df, columns=['Image Path'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for a in range(len(angry_df)):\n",
    "    angry_df['Emotion']='angry'\n",
    "for b in range(len(disgust_df)):\n",
    "    disgust_df['Emotion']='disgust'\n",
    "for c in range(len(fear_df)):\n",
    "    fear_df['Emotion']='fear'\n",
    "for d in range(len(happy_df)):\n",
    "    happy_df['Emotion']='happy'\n",
    "for e in range(len(neutral_df)):\n",
    "    neutral_df['Emotion']='neutral'\n",
    "for f in range(len(sad_df)):\n",
    "    sad_df['Emotion']='sad'\n",
    "for g in range(len(surprise_df)):\n",
    "    surprise_df['Emotion']='surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Image Path   Emotion\n",
      "0  Training_10013223.jpg  surprise\n",
      "1   Training_1002457.jpg  surprise\n",
      "2  Training_10028230.jpg  surprise\n",
      "3  Training_10060820.jpg  surprise\n",
      "4  Training_10073433.jpg  surprise\n"
     ]
    }
   ],
   "source": [
    "print(surprise_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [angry_df, disgust_df, fear_df, happy_df, neutral_df, sad_df, surprise_df]\n",
    "\n",
    "emotion_df = pd.concat(dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Image Path   Emotion\n",
      "0      Training_10118481.jpg     angry\n",
      "1      Training_10120469.jpg     angry\n",
      "2      Training_10131352.jpg     angry\n",
      "3      Training_10161559.jpg     angry\n",
      "4       Training_1021836.jpg     angry\n",
      "...                      ...       ...\n",
      "28704  Training_99916297.jpg  surprise\n",
      "28705  Training_99924420.jpg  surprise\n",
      "28706  Training_99937001.jpg  surprise\n",
      "28707  Training_99951755.jpg  surprise\n",
      "28708  Training_99984132.jpg  surprise\n",
      "\n",
      "[28709 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(emotion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Image Path  Emotion\n",
      "20000  Training_87103651.jpg  neutral\n"
     ]
    }
   ],
   "source": [
    "print(emotion_df.loc[[20000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_folder = r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train'\n",
    "folders = [os.path.join(root_folder, x) for x in ('angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise')]\n",
    "all_images = [img for folder in folders for img in load_images_from_folder(folder)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    [[[50, 50, 50], [32, 32, 32], [15, 15, 15], [1...\n",
      "1    [[[29, 29, 29], [24, 24, 24], [29, 29, 29], [1...\n",
      "2    [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...\n",
      "3    [[[155, 155, 155], [138, 138, 138], [98, 98, 9...\n",
      "4    [[[211, 211, 211], [215, 215, 215], [220, 220,...\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "all_images = pd.Series(all_images)\n",
    "print(all_images.head())\n",
    "#x = np.array(all_images)\n",
    "#print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [[[50, 50, 50], [32, 32, 32], [15, 15, 15], [1...\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_images.loc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2e27f414a88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(all_images[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28709,)\n"
     ]
    }
   ],
   "source": [
    "images=[]\n",
    "images = all_images.to_numpy()\n",
    "print(images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'angry'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_df['Emotion'][1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_df.to_csv(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\train\\emotion data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28709, 1)\n",
      "  Emotion\n",
      "0   angry\n",
      "1   angry\n",
      "2   angry\n",
      "3   angry\n",
      "4   angry\n",
      "        Emotion\n",
      "0         angry\n",
      "1         angry\n",
      "2         angry\n",
      "3         angry\n",
      "4         angry\n",
      "...         ...\n",
      "28704  surprise\n",
      "28705  surprise\n",
      "28706  surprise\n",
      "28707  surprise\n",
      "28708  surprise\n",
      "\n",
      "[28709 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "y = emotion_df.drop(['Image Path'], axis=1)\n",
    "\n",
    "print(y.shape)\n",
    "print(y.head())\n",
    "#y = pd.get_dummies(y)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Emotion  Label\n",
      "0         angry      0\n",
      "1         angry      0\n",
      "2         angry      0\n",
      "3         angry      0\n",
      "4         angry      0\n",
      "...         ...    ...\n",
      "28704  surprise      6\n",
      "28705  surprise      6\n",
      "28706  surprise      6\n",
      "28707  surprise      6\n",
      "28708  surprise      6\n",
      "\n",
      "[28709 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_text_to_label = {'angry':0, 'disgust':1, 'fear':2, 'happy':3, 'neutral':4, 'sad':5, 'surprise':6}\n",
    "y['Label'] = y['Emotion'].map(emotion_text_to_label)\n",
    "print(y)\n",
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7215\n",
       "4    4965\n",
       "5    4830\n",
       "2    4097\n",
       "0    3995\n",
       "6    3171\n",
       "1     436\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y.Label)\n",
    "plt.show()\n",
    "#0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4:'neutral', 5:'sad', 6:'surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Emotion</th>\n",
       "      <th>Label</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>angry</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[50, 50, 50], [32, 32, 32], [15, 15, 15], [1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>angry</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[29, 29, 29], [24, 24, 24], [29, 29, 29], [1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>angry</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>angry</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[155, 155, 155], [138, 138, 138], [98, 98, 9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>angry</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[211, 211, 211], [215, 215, 215], [220, 220,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28704</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "      <td>[[[27, 27, 27], [26, 26, 26], [26, 26, 26], [2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28705</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "      <td>[[[7, 7, 7], [6, 6, 6], [13, 13, 13], [39, 39,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28706</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "      <td>[[[124, 124, 124], [129, 129, 129], [125, 125,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28707</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "      <td>[[[242, 242, 242], [242, 242, 242], [242, 242,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28708</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "      <td>[[[194, 194, 194], [197, 197, 197], [191, 191,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28709 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Emotion  Label                                             pixels\n",
       "0         angry      0  [[[50, 50, 50], [32, 32, 32], [15, 15, 15], [1...\n",
       "1         angry      0  [[[29, 29, 29], [24, 24, 24], [29, 29, 29], [1...\n",
       "2         angry      0  [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...\n",
       "3         angry      0  [[[155, 155, 155], [138, 138, 138], [98, 98, 9...\n",
       "4         angry      0  [[[211, 211, 211], [215, 215, 215], [220, 220,...\n",
       "...         ...    ...                                                ...\n",
       "28704  surprise      6  [[[27, 27, 27], [26, 26, 26], [26, 26, 26], [2...\n",
       "28705  surprise      6  [[[7, 7, 7], [6, 6, 6], [13, 13, 13], [39, 39,...\n",
       "28706  surprise      6  [[[124, 124, 124], [129, 129, 129], [125, 125,...\n",
       "28707  surprise      6  [[[242, 242, 242], [242, 242, 242], [242, 242,...\n",
       "28708  surprise      6  [[[194, 194, 194], [197, 197, 197], [191, 191,...\n",
       "\n",
       "[28709 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y['pixels'] = images\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#all_images has the image matrices, and y contains the corresponding serial numbers, so we have to access all_images via y\n",
    "emotion_label_to_text = {'angry':'angry','disgust':'disgust','fear':'fear','happy':'happy','neutral':'neutral','sad':'sad','surprise':'surprise'}\n",
    "fig = plt.figure(1, (14, 14))\n",
    "\n",
    "k = 0\n",
    "j = 0\n",
    "for label in sorted(y.Emotion.unique()):\n",
    "    for j in range(7):\n",
    "        px = y[y.Emotion==label].pixels.iloc[k]\n",
    "        px = px.astype('int64')\n",
    "\n",
    "        k += 1\n",
    "        ax = plt.subplot(7, 7, k)\n",
    "        ax.imshow(px)\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        ax.set_title(emotion_label_to_text[label])\n",
    "        plt.tight_layout()\n",
    "\n",
    "        \n",
    "#0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4:'neutral', 5:'sad', 6:'surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 48, 3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.pixels[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_array = y.pixels.apply(lambda x: y.pixels[0].astype('float32'))\n",
    "img_array = np.stack(img_array, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 48, 48, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 7)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "img_labels = le.fit_transform(y.Label)\n",
    "img_labels = np_utils.to_categorical(img_labels)\n",
    "img_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}\n"
     ]
    }
   ],
   "source": [
    "le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
    "print(le_name_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25838, 48, 48, 3), (2871, 48, 48, 3), (25838, 7), (2871, 7))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(img_array, img_labels,\n",
    "                                                    shuffle=True, stratify=img_labels,\n",
    "                                                    test_size=0.1, random_state=42)\n",
    "X_train.shape, X_valid.shape, y_train.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25838"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_width = X_train.shape[1]\n",
    "img_height = X_train.shape[2]\n",
    "img_depth = X_train.shape[3]\n",
    "num_classes = y_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255.\n",
    "X_valid = X_valid / 255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAINING CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(16,(3,3), activation='relu', input_shape=(img_width, img_height, img_depth)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(16, (3,3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(7, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "808/808 [==============================] - 57s 71ms/step - loss: 2.1880 - accuracy: 0.1857 - val_loss: 1.8256 - val_accuracy: 0.2515\n",
      "Epoch 2/20\n",
      "808/808 [==============================] - 62s 76ms/step - loss: 1.8500 - accuracy: 0.2249 - val_loss: 1.8178 - val_accuracy: 0.2515\n",
      "Epoch 3/20\n",
      "808/808 [==============================] - 65s 80ms/step - loss: 1.8307 - accuracy: 0.2399 - val_loss: 1.8159 - val_accuracy: 0.2515\n",
      "Epoch 4/20\n",
      "808/808 [==============================] - 70s 86ms/step - loss: 1.8252 - accuracy: 0.2414 - val_loss: 1.8157 - val_accuracy: 0.2515\n",
      "Epoch 5/20\n",
      "808/808 [==============================] - 69s 86ms/step - loss: 1.8253 - accuracy: 0.2425 - val_loss: 1.8138 - val_accuracy: 0.2515\n",
      "Epoch 6/20\n",
      "808/808 [==============================] - 72s 89ms/step - loss: 1.8245 - accuracy: 0.2448 - val_loss: 1.8127 - val_accuracy: 0.2515\n",
      "Epoch 7/20\n",
      "808/808 [==============================] - 73s 90ms/step - loss: 1.8212 - accuracy: 0.2458 - val_loss: 1.8128 - val_accuracy: 0.2515\n",
      "Epoch 8/20\n",
      "808/808 [==============================] - 69s 85ms/step - loss: 1.8198 - accuracy: 0.2479 - val_loss: 1.8669 - val_accuracy: 0.1390\n",
      "Epoch 9/20\n",
      "808/808 [==============================] - 71s 88ms/step - loss: 1.8186 - accuracy: 0.2482 - val_loss: 1.8108 - val_accuracy: 0.2515\n",
      "Epoch 10/20\n",
      "808/808 [==============================] - 70s 87ms/step - loss: 1.8194 - accuracy: 0.2477 - val_loss: 1.8155 - val_accuracy: 0.2515\n",
      "Epoch 11/20\n",
      "808/808 [==============================] - 75s 93ms/step - loss: 1.8164 - accuracy: 0.2483 - val_loss: 1.8143 - val_accuracy: 0.2515\n",
      "Epoch 12/20\n",
      "808/808 [==============================] - 71s 88ms/step - loss: 1.8163 - accuracy: 0.2501 - val_loss: 1.8128 - val_accuracy: 0.2515\n",
      "Epoch 13/20\n",
      "808/808 [==============================] - 76s 94ms/step - loss: 1.8149 - accuracy: 0.2502 - val_loss: 1.8119 - val_accuracy: 0.2515\n",
      "Epoch 14/20\n",
      "808/808 [==============================] - 71s 87ms/step - loss: 1.8145 - accuracy: 0.2505 - val_loss: 1.8108 - val_accuracy: 0.2515\n",
      "Epoch 15/20\n",
      "808/808 [==============================] - 72s 89ms/step - loss: 1.8147 - accuracy: 0.2517 - val_loss: 1.8109 - val_accuracy: 0.2515\n",
      "Epoch 16/20\n",
      "808/808 [==============================] - 66s 82ms/step - loss: 1.8137 - accuracy: 0.2510 - val_loss: 1.8102 - val_accuracy: 0.2515\n",
      "Epoch 17/20\n",
      "808/808 [==============================] - 72s 89ms/step - loss: 1.8131 - accuracy: 0.2512 - val_loss: 1.8103 - val_accuracy: 0.2515\n",
      "Epoch 18/20\n",
      "808/808 [==============================] - 63s 78ms/step - loss: 1.8118 - accuracy: 0.2513 - val_loss: 1.8106 - val_accuracy: 0.2515\n",
      "Epoch 19/20\n",
      "808/808 [==============================] - 63s 78ms/step - loss: 1.8121 - accuracy: 0.2513 - val_loss: 1.8108 - val_accuracy: 0.2515\n",
      "Epoch 20/20\n",
      "808/808 [==============================] - 61s 76ms/step - loss: 1.8119 - accuracy: 0.2512 - val_loss: 1.8118 - val_accuracy: 0.2515\n",
      "WARNING:tensorflow:From C:\\Users\\saran\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: emotion_detector.model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))\n",
    "\n",
    "model.save_weights('model.h5')\n",
    "model.save('emotion_detector.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: emotion_detector.model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save_weights('model.h5')\n",
    "model.save('emotion_detector.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "v=np.argmax(model.predict(X_valid[[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'happy'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_dict = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4:'neutral', 5:'sad', 6:'surprise'}\n",
    "ans = label_dict[v]\n",
    "ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING OPENCV TO GET LIVE FEED FROM WEBCAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "angry\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "neutral\n",
      "angry\n",
      "neutral\n",
      "angry\n",
      "neutral\n",
      "neutral\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
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      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "neutral\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "neutral\n",
      "angry\n",
      "angry\n",
      "neutral\n",
      "neutral\n",
      "angry\n",
      "angry\n",
      "neutral\n",
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      "angry\n",
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      "angry\n",
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      "angry\n",
      "angry\n",
      "neutral\n",
      "neutral\n",
      "angry\n",
      "angry\n",
      "angry\n",
      "sad\n",
      "angry\n",
      "disgust\n",
      "sad\n",
      "sad\n",
      "sad\n",
      "sad\n",
      "sad\n",
      "happy\n",
      "happy\n",
      "angry\n",
      "angry\n",
      "sad\n",
      "angry\n",
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      "disgust\n",
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      "happy\n",
      "disgust\n",
      "happy\n",
      "angry\n",
      "disgust\n",
      "sad\n",
      "happy\n",
      "neutral\n",
      "sad\n",
      "happy\n",
      "angry\n",
      "disgust\n",
      "sad\n",
      "disgust\n",
      "disgust\n",
      "disgust\n",
      "happy\n",
      "happy\n",
      "happy\n",
      "sad\n",
      "sad\n",
      "happy\n",
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      "sad\n",
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      "disgust\n",
      "happy\n",
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      "disgust\n",
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      "neutral\n",
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      "happy\n",
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      "neutral\n",
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      "happy\n",
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      "happy\n",
      "neutral\n",
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      "happy\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-85-f6d44153f12a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'All'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m13\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#13 is the Enter Key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from time import sleep\n",
    "from keras.preprocessing.image import img_to_array\n",
    "\n",
    "#face_classifier = cv2.CascadeClassifier(r'C:\\Users\\saran\\Documents\\datasets\\emotions\\haarcascade_frontalface_default.xml')\n",
    "face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n",
    "\n",
    "def face_detector(img):\n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(img, 1.3, 5)\n",
    "    if faces is ():\n",
    "        return (0,0,0,0), np.zeros((48,48)), img\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "        roi_gray = img[y:y+h, x:x+w]\n",
    "\n",
    "    try:\n",
    "        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n",
    "    except:\n",
    "        return (x,w,y,h), np.zeros((48,48,3), np.uint8), img\n",
    "    return (x,w,y,h), roi_gray, img\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    rect, face, image = face_detector(frame)\n",
    "    if np.sum([face]) != 0.0:\n",
    "        roi = face.astype(\"float\") / 255.0\n",
    "        roi = img_to_array(roi)\n",
    "        roi = np.expand_dims(roi, axis=0)\n",
    "\n",
    "        # make a prediction on the ROI, then lookup the class\n",
    "        #preds = model.predict(roi)[0]\n",
    "        preds = model.predict(roi[[0]])\n",
    "        pred_label = np.argmax(preds)\n",
    "        label_dict = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4:'neutral', 5:'sad', 6:'surprise'}\n",
    "        ans = label_dict[pred_label]\n",
    "        #label = class_labels[preds.argmax()]  \n",
    "        label_position = (rect[0] + int((rect[1]/2)), rect[2] + 25)\n",
    "        cv2.putText(image, ans, label_position , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
    "        print(ans)\n",
    "    else:\n",
    "        cv2.putText(image, \"No Face Found\", (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
    "        \n",
    "    cv2.imshow('All', image)\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'happy'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#enter your own ids from spotify developer \n",
    "client_id = 'f9ccbfc6eaca44ffa4f611d4f88b0157'\n",
    "client_secret = '1b3355c4b26c448f901d5f65f8696c80'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import json\n",
    "import spotipy\n",
    "import webbrowser\n",
    "import spotipy.util as util\n",
    "from json.decoder import JSONDecodeError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beautiful Lies\n",
      "Beautiful Lies\n",
      "Beautiful Lies (Deluxe)\n",
      "Beautiful Lies (Deluxe)\n",
      "Fire Within\n",
      "Fire Within\n",
      "Fire Within (Deluxe)\n",
      "Fire Within (Deluxe)\n",
      "Fire Within (Deluxe)\n",
      "Live in London\n",
      "Birdy\n",
      "Birdy\n",
      "Birdy\n",
      "Birdy\n",
      "Birdy (Deluxe Version)\n"
     ]
    }
   ],
   "source": [
    "#using spotipy for playing music from respective music playlists\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "\n",
    "birdy_uri = 'spotify:artist:2WX2uTcsvV5OnS0inACecP'\n",
    "medici_uri = 'spotify:playlist:3Ja9FfRpKcBJa08YhCBnXZ'\n",
    "spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id=client_id ,client_secret=client_secret))\n",
    "\n",
    "results = spotify.artist_albums(birdy_uri, album_type='album')\n",
    "albums = results['items']\n",
    "while results['next']:\n",
    "    results = spotify.next(results)\n",
    "    albums.extend(results['items'])\n",
    "\n",
    "for album in albums:\n",
    "    print(album['name'])\n",
    "#prints the searches based on a given uri from spotify "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4:'neutral', 5:'sad', 6:'surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "angry_fear_disgust_uri = ['spotify:playlist:71Xpaq3Hbpxz6w9yDmIsaH', 'spotify:playlist:7L08IETH8EQmm7k4r8rivb', 'spotify:playlist:0oefY0sTSMPlST1qoS5ZOR', 'spotify:playlist:0hlSIvZUJQK4MIQezzCU92', 'spotify:playlist:15Glcc8LdAloMFFfbODCl7', 'spotify:playlist:0E7gBoOpSrbekg9ZfGLMTt', 'spotify:playlist:0kbIVraS3Xa7FhvAb9KSGe', 'spotify:playlist:6ft4ijUITtTeVC0dUCDdvH', 'spotify:playlist:1aUGVrGipiT9vOb11jO4n4', 'spotify:playlist:4Xy9Lan6JV5OUZkjEfrUBo', 'spotify:playlist:37i9dQZF1DX9qNs32fujYe', 'spotify:playlist:37i9dQZF1DWTcqUzwhNmKv', 'spotify:playlist:37i9dQZF1DX2LTcinqsO68', 'spotify:playlist:37i9dQZF1DWWOaP4H0w5b0', 'spotify:playlist:37i9dQZF1DXbj9Ksq4BAdj', 'spotify:playlist:37i9dQZF1DX1kydukZhLms', 'spotify:playlist:37i9dQZF1DWXRqgorJj26U', 'spotify:playlist:5BygwTQ3OrbiwVsQhXFHMz', 'spotify:playlist:37i9dQZF1DX2SThom9u2re', 'spotify:playlist:37i9dQZF1DX1X7WV84927n']\n",
    "\n",
    "happy_surprise_uri = ['spotify:playlist:37i9dQZF1DX5IDTimEWoTd', 'spotify:playlist:37i9dQZF1DXdPec7aLTmlC', 'spotify:playlist:1h90L3LP8kAJ7KGjCV2Xfd', 'spotify:playlist:37i9dQZF1DX1H4LbvY4OJi', 'spotify:playlist:37i9dQZF1DWVlYsZJXqdym', 'spotify:playlist:37i9dQZF1DX0UrRvztWcAU', 'spotify:playlist:37i9dQZF1DWSf2RDTDayIx', 'spotify:playlist:10r5uRJGNzE4XZ4H2X15Nf', 'spotify:playlist:37i9dQZF1DWZKuerrwoAGz', 'spotify:playlist:37i9dQZF1DX9u7XXOp0l5L', 'spotify:playlist:37i9dQZF1DWZ72qOlbizxi', 'spotify:playlist:37i9dQZF1DWTwbZHrJRIgD', 'spotify:playlist:37i9dQZF1DX84kJlLdo9vT', 'spotify:playlist:37i9dQZF1DWSkMjlBZAZ07', 'spotify:playlist:37i9dQZF1DXeby79pVadGa', 'spotify:playlist:37i9dQZF1DX4uPi2roRUwU', 'spotify:playlist:1ufLsZeJmsS3EnrASztPbm', 'spotify:playlist:4AnAUkQNrLKlJCInZGSXRO', 'spotify:playlist:37i9dQZF1DXaK0O81Xtkis', 'spotify:playlist:7cWNW1fBpQRcJNpCP0eqpX']\n",
    "\n",
    "sad_uri = ['spotify:playlist:0IQo4hoPuOVMi6QuOBWEYU', 'spotify:playlist:7ABD15iASBIpPP5uJ5awvq', 'spotify:playlist:54ozEbxQMa0OeozoSoRvcL', 'spotify:playlist:2IbZGv306zaldI0lap578G', 'spotify:playlist:37i9dQZF1DX7qK8ma5wgG1', 'spotify:playlist:37i9dQZF1DWZUAeYvs88zc', 'spotify:playlist:7xySbQAypJdiEBRRlycJIY', 'spotify:playlist:7pKEWS1ddsDJgRrqNRyjMA', 'spotify:playlist:37i9dQZF1DWVrtsSlLKzro', 'spotify:playlist:37i9dQZF1DWVV27DiNWxkR', 'spotify:playlist:7kzKtw5vug3IIxKfLadi8A', 'spotify:playlist:0l8C7zqEvBkEFZPNA5gttr', 'spotify:playlist:1nVWPImtwYUYdvCTPHTtpJ', 'spotify:playlist:53NEfJTEt86ytgk5SaqMjZ']\n",
    "\n",
    "neutral_uri = ['spotify:playlist:0vvXsWCC9xrXsKd4FyS8kM', 'spotify:playlist:37i9dQZF1DWWQRwui0ExPn', 'spotify:playlist:32hJXySZtt9YvnwcYINGZ0', 'spotify:playlist:37i9dQZF1DX8Uebhn9wzrS', 'spotify:playlist:4gHPH0i2mD00zgEmWxONn4', 'spotify:playlist:5FmmxErJczcrEwIFGIviYo', 'spotify:playlist:5nnhh7IDFWViDWINeh3EhX', 'spotify:playlist:37i9dQZF1DXc8kgYqQLMfH', 'spotify:playlist:6Q3Qo3XxSj5Cu9pQJlBfzG', 'spotify:playlist:3LFIBdP7eZXJKqf3guepZ1', 'spotify:playlist:74sUjcvpGfdOvCHvgzNEDO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(angry_fear_disgust_uri)+len(happy_surprise_uri)+len(sad_uri)+len(neutral_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "if ans == 'angry' or ans == 'fear' or ans == 'disgust':\n",
    "    link = angry_fear_disgust_uri[random.randrange(0,len(angry_fear_disgust_uri))]\n",
    "elif ans == 'happy' or ans == 'surprise':\n",
    "    link = happy_surprise_uri[random.randrange(0,len(happy_surprise_uri))]\n",
    "elif ans == 'sad':\n",
    "    link = sad_uri[random.randrange(0, len(sad_uri))]\n",
    "elif ans == 'neutral':\n",
    "    link = neutral_uri[random.randrange(0, len(neutral_uri))]\n",
    "    \n",
    "webbrowser.open(link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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