diff --git a/notebooks/EDA.ipynb b/notebooks/EDA.ipynb
index f3b62cd..21991f6 100644
--- a/notebooks/EDA.ipynb
+++ b/notebooks/EDA.ipynb
@@ -1,491 +1,1940 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-29T06:28:51.733973600Z",
-     "start_time": "2024-03-29T06:28:51.165303900Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "         id             name                                   link  \\\n0     17505   Стефен Ананикз  https://www.kinopoisk.ru/name/1114998   \n1     22644    Стикки Фингаз     https://www.kinopoisk.ru/name/6730   \n2     48531  Теодора Ричардс  https://www.kinopoisk.ru/name/1874602   \n3      4829    Коуди Роджерс  https://www.kinopoisk.ru/name/2859797   \n4     41086       Joe Ariola  https://www.kinopoisk.ru/name/6541368   \n...     ...              ...                                    ...   \n6044   7416  Кери Линн Прэтт    https://www.kinopoisk.ru/name/16713   \n6045   9894   Уильям Фихтнер    https://www.kinopoisk.ru/name/13096   \n6046   7419  Питер Сарсгаард    https://www.kinopoisk.ru/name/32953   \n6047   9900        Лили Лабё  https://www.kinopoisk.ru/name/2533068   \n6048   9908   Джош Карпентер  https://www.kinopoisk.ru/name/1500397   \n\n                                         specialization       films_years  \\\n0                     Продюсер,Режиссер,Актер,Сценарист    8, 2006 — 2019   \n1     Актер,Композитор,Сценарист,Режиссер,Продюсер,М...  104, 1992 — 2024   \n2                                               Актриса    3, 2013 — 2014   \n3                     Режиссер,Продюсер,Актер,Сценарист   12, 2012 — 2018   \n4                                                 Актер   10, 1999 — 2024   \n...                                                 ...               ...   \n6044                                            Актриса   47, 1994 — 2014   \n6045                  Актер,Продюсер,Режиссер,Сценарист  107, 1956 — 2024   \n6046                                     Актер,Продюсер  114, 1975 — 2024   \n6047                                  Актриса,Сценарист  121, 1998 — 2020   \n6048                                     Актер,Продюсер    9, 1992 — 2017   \n\n                                            description    path_to_folder  \n0                                                   NaN  actors_usa/17505  \n1               Там,Щит,Однажды ночью,Напарницы,Империя  actors_usa/22644  \n2                                                   NaN  actors_usa/48531  \n3                                                   NaN   actors_usa/4829  \n4                                         WWE SmackDown  actors_usa/41086  \n...                                                 ...               ...  \n6044  Одинокий мужчина,Доктор Хаус,Скорая помощь,Мыс...   actors_usa/7416  \n6045  Чёрный ястреб,Всё или ничего,Идеальный шторм,Э...   actors_usa/9894  \n6046  К-19,Воспитание чувств,Парни не плачут,Ключ от...   actors_usa/7419  \n6047                                                NaN   actors_usa/9900  \n6048                                                NaN   actors_usa/9908  \n\n[6049 rows x 7 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>id</th>\n      <th>name</th>\n      <th>link</th>\n      <th>specialization</th>\n      <th>films_years</th>\n      <th>description</th>\n      <th>path_to_folder</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17505</td>\n      <td>Стефен Ананикз</td>\n      <td>https://www.kinopoisk.ru/name/1114998</td>\n      <td>Продюсер,Режиссер,Актер,Сценарист</td>\n      <td>8, 2006 — 2019</td>\n      <td>NaN</td>\n      <td>actors_usa/17505</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>22644</td>\n      <td>Стикки Фингаз</td>\n      <td>https://www.kinopoisk.ru/name/6730</td>\n      <td>Актер,Композитор,Сценарист,Режиссер,Продюсер,М...</td>\n      <td>104, 1992 — 2024</td>\n      <td>Там,Щит,Однажды ночью,Напарницы,Империя</td>\n      <td>actors_usa/22644</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>48531</td>\n      <td>Теодора Ричардс</td>\n      <td>https://www.kinopoisk.ru/name/1874602</td>\n      <td>Актриса</td>\n      <td>3, 2013 — 2014</td>\n      <td>NaN</td>\n      <td>actors_usa/48531</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4829</td>\n      <td>Коуди Роджерс</td>\n      <td>https://www.kinopoisk.ru/name/2859797</td>\n      <td>Режиссер,Продюсер,Актер,Сценарист</td>\n      <td>12, 2012 — 2018</td>\n      <td>NaN</td>\n      <td>actors_usa/4829</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>41086</td>\n      <td>Joe Ariola</td>\n      <td>https://www.kinopoisk.ru/name/6541368</td>\n      <td>Актер</td>\n      <td>10, 1999 — 2024</td>\n      <td>WWE SmackDown</td>\n      <td>actors_usa/41086</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    </tr>\n    <tr>\n      <th>6044</th>\n      <td>7416</td>\n      <td>Кери Линн Прэтт</td>\n      <td>https://www.kinopoisk.ru/name/16713</td>\n      <td>Актриса</td>\n      <td>47, 1994 — 2014</td>\n      <td>Одинокий мужчина,Доктор Хаус,Скорая помощь,Мыс...</td>\n      <td>actors_usa/7416</td>\n    </tr>\n    <tr>\n      <th>6045</th>\n      <td>9894</td>\n      <td>Уильям Фихтнер</td>\n      <td>https://www.kinopoisk.ru/name/13096</td>\n      <td>Актер,Продюсер,Режиссер,Сценарист</td>\n      <td>107, 1956 — 2024</td>\n      <td>Чёрный ястреб,Всё или ничего,Идеальный шторм,Э...</td>\n      <td>actors_usa/9894</td>\n    </tr>\n    <tr>\n      <th>6046</th>\n      <td>7419</td>\n      <td>Питер Сарсгаард</td>\n      <td>https://www.kinopoisk.ru/name/32953</td>\n      <td>Актер,Продюсер</td>\n      <td>114, 1975 — 2024</td>\n      <td>К-19,Воспитание чувств,Парни не плачут,Ключ от...</td>\n      <td>actors_usa/7419</td>\n    </tr>\n    <tr>\n      <th>6047</th>\n      <td>9900</td>\n      <td>Лили Лабё</td>\n      <td>https://www.kinopoisk.ru/name/2533068</td>\n      <td>Актриса,Сценарист</td>\n      <td>121, 1998 — 2020</td>\n      <td>NaN</td>\n      <td>actors_usa/9900</td>\n    </tr>\n    <tr>\n      <th>6048</th>\n      <td>9908</td>\n      <td>Джош Карпентер</td>\n      <td>https://www.kinopoisk.ru/name/1500397</td>\n      <td>Актер,Продюсер</td>\n      <td>9, 1992 — 2017</td>\n      <td>NaN</td>\n      <td>actors_usa/9908</td>\n    </tr>\n  </tbody>\n</table>\n<p>6049 rows × 7 columns</p>\n</div>"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv(\"../data/usa.csv\", delimiter=\";\")\n",
-    "data"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-29T06:36:41.472967600Z",
-     "start_time": "2024-03-29T06:36:41.378668700Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "outputs": [],
-   "source": [
-    "columns = data.columns"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-03-29T06:45:12.437750900Z",
-     "start_time": "2024-03-29T06:45:12.414952700Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\fatik\\AppData\\Local\\Temp\\ipykernel_12208\\3238385593.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "  dicter[x.id] = x[1]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "'Стефен Ананикз'"
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dicter = {}\n",
-    "def get_dict(x):\n",
-    "    dicter[x.id] = x[1]\n",
-    "data[\"json\"] = data.apply(lambda x: get_dict(x), axis=1)\n",
-    "import pickle\n",
-    "\n",
-    "with open('saved_dictionary.pkl', 'wb') as f:\n",
-    "    pickle.dump(dicter, f)\n",
-    "with open('saved_dictionary.pkl', 'rb') as f:\n",
-    "    loaded_dict = pickle.load(f)"
-   ],
-   "metadata": {
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-    "ExecuteTime": {
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-}
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:52:30.453063100Z",
+     "start_time": "2024-03-29T11:52:30.062816900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      id                                 name path_to_folder\n0      0                               5 Плюх     bloggers/0\n1      1                                10AGE     bloggers/1\n2      2                          163onmyneck     bloggers/2\n3      3                       Абрамов, Рахим     bloggers/3\n4      4           Ай, Айза-Лилуна Хамзатовна     bloggers/4\n..   ...                                  ...            ...\n414  464  Митрошина, Александра Александровна   bloggers/464\n415  465                          Сталингулаг   bloggers/465\n416  466           Тодоренко, Регина Петровна   bloggers/466\n417  468         Чекалина, Валерия Валерьевна   bloggers/468\n418  469            Шейдлин, Эллен Викторовна   bloggers/469\n\n[419 rows x 3 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>id</th>\n      <th>name</th>\n      <th>path_to_folder</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>5 Плюх</td>\n      <td>bloggers/0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>10AGE</td>\n      <td>bloggers/1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>163onmyneck</td>\n      <td>bloggers/2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Абрамов, Рахим</td>\n      <td>bloggers/3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Ай, Айза-Лилуна Хамзатовна</td>\n      <td>bloggers/4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>414</th>\n      <td>464</td>\n      <td>Митрошина, Александра Александровна</td>\n      <td>bloggers/464</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>465</td>\n      <td>Сталингулаг</td>\n      <td>bloggers/465</td>\n    </tr>\n    <tr>\n      <th>416</th>\n      <td>466</td>\n      <td>Тодоренко, Регина Петровна</td>\n      <td>bloggers/466</td>\n    </tr>\n    <tr>\n      <th>417</th>\n      <td>468</td>\n      <td>Чекалина, Валерия Валерьевна</td>\n      <td>bloggers/468</td>\n    </tr>\n    <tr>\n      <th>418</th>\n      <td>469</td>\n      <td>Шейдлин, Эллен Викторовна</td>\n      <td>bloggers/469</td>\n    </tr>\n  </tbody>\n</table>\n<p>419 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"../data/bloggers.csv\", delimiter=\";\")\n",
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:55:45.261783800Z",
+     "start_time": "2024-03-29T11:55:45.199207500Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fatik\\AppData\\Local\\Temp\\ipykernel_18100\\497292032.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  dicter[x.id] = x[1]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "{0: '5 Плюх',\n 1: '10AGE',\n 2: '163onmyneck',\n 3: 'Абрамов, Рахим',\n 4: 'Ай, Айза-Лилуна Хамзатовна',\n 5: 'Алёхин, Евгений Игоревич',\n 6: 'Ант (музыкант)',\n 7: 'Антоха MC',\n 8: 'Ассаи',\n 10: 'Бакей',\n 11: 'Баста (музыкант)',\n 12: 'Бледный (музыкант)',\n 13: 'Брутто (рэпер)',\n 14: 'Бурнышев, Игорь Юрьевич',\n 15: 'Варчун (рэпер)',\n 16: 'ВесЪ (рэпер)',\n 17: 'Винт (рэпер)',\n 18: 'Вис Виталис',\n 19: 'Влади (рэпер)',\n 20: 'Галат, Владимир Александрович',\n 21: 'Ганвест',\n 23: 'Горозия, Леван Емзарович',\n 24: 'Грязный Луи',\n 25: 'Грязный Рамирес',\n 26: 'Гуф',\n 27: 'Дельфин (музыкант)',\n 28: 'Децл',\n 29: 'Джарахов, Эльдар Казанфарович',\n 31: 'Джиган',\n 32: 'Джизус',\n 33: 'Жак-Энтони',\n 34: 'Жиган, Рома',\n 35: 'Замай (рэпер)',\n 36: 'Звонкий',\n 37: 'Змей (рэпер)',\n 38: 'Инстасамка',\n 39: 'Кажэ Обойма',\n 40: 'Калюжный, Глеб Дмитриевич',\n 41: 'Карандаш (рэпер)',\n 42: 'Энди Картрайт',\n 43: 'Качер, Артём',\n 44: 'Кит, Андрей',\n 45: 'Кравц',\n 46: 'Крид, Егор',\n 47: 'Крип-а-Крип',\n 49: 'Крэк (рэпер)',\n 50: 'Купер (рэпер)',\n 51: 'Лана (певица)',\n 52: 'Лигалайз',\n 54: 'Лиззка',\n 55: 'Лика Стар',\n 56: 'Лок Дог',\n 57: 'ЛСП',\n 58: 'Маваши, Миша',\n 59: 'Мартич, Петар',\n 61: 'Михаил Краснодеревщик',\n 62: 'Михей (певец)',\n 63: 'Молодой Платон',\n 64: 'Моргенштерн',\n 65: 'Мот (рэпер)',\n 66: 'Мэйти',\n 67: 'Наум Блик',\n 68: 'Нигатив',\n 69: 'Нурминский (музыкант)',\n 70: 'Паша Техник',\n 71: 'Перминов, Алексей Александрович',\n 72: 'Петрова, Милана Вадимовна',\n 74: 'ПИЭМ',\n 75: 'Птаха (рэпер)',\n 76: 'Рем Дигга',\n 78: 'Рич (рэпер)',\n 80: 'СД (музыкант)',\n 81: 'Сидоров, Антон Валерьевич',\n 82: 'Симановская, Марина Игоревна',\n 83: 'Скриптонит',\n 84: 'Скул, Саша',\n 85: 'Слава КПСС',\n 86: 'Словетский',\n 87: 'Смоки Мо',\n 89: 'Сява (рэпер)',\n 90: 'Тати (певица)',\n 91: 'Тимати',\n 92: 'Типси Тип',\n 93: 'Титомир, Богдан Петрович',\n 94: 'Трутень (рэпер)',\n 95: 'Федук',\n 97: 'Фогель (певец)',\n 98: 'Хамиль',\n 99: 'Ханза',\n 100: 'Хаски (рэпер)',\n 101: 'Хип-Хоп Одинокой Старухи',\n 102: 'Хованский, Юрий Михайлович',\n 103: 'Чаян Фамали',\n 104: 'Шеff',\n 105: 'Шип, Егор',\n 106: 'Шым',\n 107: 'Элджей',\n 108: 'Abbalbisk',\n 109: 'Al Solo',\n 110: 'Alblak 52',\n 111: 'Alizade',\n 112: 'Andro',\n 113: 'ATL (рэпер)',\n 114: 'Babangida',\n 115: 'Bahh Tee',\n 116: 'Basic Boy',\n 117: 'Big Baby Tape',\n 118: 'Big Russian Boss',\n 119: 'Blago White',\n 120: 'Bodiev',\n 121: 'Booker',\n 122: 'Boulevard Depo',\n 123: 'Bushido Zho',\n 124: 'Capella (музыкант)',\n 126: 'Coldcloud',\n 127: 'CYGO',\n 128: 'Deep-Ex-Sense',\n 130: 'Dime',\n 131: 'Dino MC 47',\n 132: 'DJ 108',\n 134: 'DJ Кефир',\n 138: 'Doni (рэпер)',\n 139: 'Drago',\n 140: 'Dzham',\n 141: 'Face',\n 142: 'Hensy',\n 143: 'Horus',\n 145: 'Iroh (музыкант)',\n 146: 'Ivan Valeev',\n 147: 'Jahmal TGK',\n 149: 'Jeembo',\n 150: 'Johnyboy',\n 151: 'Kizaru',\n 153: 'Kristina Si',\n 154: 'Lizer',\n 155: 'Loqiemean',\n 156: 'Lovv66',\n 157: 'Luxor (певец)',\n 158: 'Macan',\n 159: 'Markul',\n 160: 'Matrang',\n 161: 'Mayot',\n 162: 'MC 1.8',\n 163: 'MC Молодой',\n 165: 'Metox',\n 166: 'Mnogoznaal',\n 167: 'Moneyken',\n 168: 'Mozee Montana',\n 169: 'Mr. Bruce',\n 170: 'Murda Killa',\n 171: 'Mutabor',\n 173: 'Natan',\n 174: 'Nel (рэпер)',\n 175: 'Noa (певец)',\n 176: 'Noize MC',\n 177: 'Obladaet',\n 178: 'OFFMi',\n 179: 'OG Buda',\n 180: 'Oxxxymiron',\n 181: 'Pharaoh',\n 182: 'PLC (рэпер)',\n 183: \"Pra(Killa'Gramm)\",\n 184: 'Pyrokinesis',\n 185: 'Ramil’',\n 187: 'RE-pac',\n 188: 'Redo',\n 189: 'Rickey F',\n 190: 'Saluki',\n 191: 'Say Mo',\n 192: 'Scally Milano',\n 193: 'Schokk',\n 194: 'Seemee',\n 196: 'Sir-J',\n 197: 'Slame',\n 198: 'Slava Marlow',\n 199: 'Slim',\n 200: 'Абдурахманов, Тумсо Умалтович',\n 201: 'Айс, Саша',\n 202: 'Албуров, Георгий Валентинович',\n 203: 'Александр (Митрофанов)',\n 204: 'Антоний (Попович)',\n 205: 'Бабешкин, Вадим',\n 206: 'Бегичев, Павел Александрович',\n 207: 'Белова, Мария',\n 208: 'Беседина, Дарья Станиславовна',\n 209: 'Mia Boyka',\n 210: 'Бойко, Сергей Андреевич',\n 211: 'Бондаренко, Николай Николаевич',\n 212: 'Брюханова, Анастасия Андреевна',\n 213: 'Булычёв, Богдан Юрьевич',\n 214: 'Василенко, Елизавета Вадимовна',\n 215: 'Гайд, Павел',\n 217: 'Горбачёва, Ирина Анатольевна',\n 218: 'Гордей, Дмитрий Михайлович',\n 219: 'Mary Gu',\n 220: 'Гудков, Александр Владимирович',\n 222: 'Делягин, Михаил Геннадьевич',\n 223: 'Доренко, Сергей Леонидович',\n 224: 'Дробышевский, Станислав Владимирович',\n 225: 'Дружко, Сергей Евгеньевич',\n 226: 'Есьман, Соня',\n 227: 'Жданов, Иван Юрьевич',\n 228: 'Жуков, Клим Александрович',\n 229: 'Заруцкий, Константин Сергеевич',\n 230: 'Клэп, Катя',\n 231: 'Коваленко, Святослав Владимирович',\n 232: 'Коваль, Юля',\n 233: 'Ковальчук, Никита Васильевич',\n 235: 'Кросс, Карина',\n 236: 'Крыгина, Елена Александровна',\n 237: 'Купер, Софа',\n 238: 'Кшиштовский, Михаил Александрович',\n 239: 'Ляскин, Николай Николаевич',\n 240: 'Мальцев, Вячеслав Вячеславович',\n 241: 'Марцинкевич, Максим Сергеевич',\n 242: 'Матвиенко, Сергей Борисович',\n 243: 'Милонов, Виталий Валентинович',\n 245: 'Мэй, Энни',\n 246: 'Наки, Майкл',\n 247: 'Никифоров, Сергей Игоревич',\n 248: 'Осташко, Руслан Станиславович',\n 249: 'Павленко, Андрей Николаевич (онколог)',\n 250: 'Певчих, Мария Константиновна',\n 251: 'Пивоваров, Алексей Владимирович',\n 252: 'Плющев, Александр Владимирович',\n 253: 'Пузо, Виктор',\n 254: 'Пучков, Дмитрий Юрьевич',\n 255: 'Пьяных, Глеб Валентинович',\n 256: 'Саева, Дина',\n 257: 'Соболь, Любовь Эдуардовна',\n 258: 'Соколов, Александр Борисович (научный журналист)',\n 259: 'Стас «Ай, Как Просто!»',\n 260: 'Сурдин, Владимир Георгиевич',\n 261: 'Сыендук',\n 262: 'Тимонова, Евгения Валентиновна',\n 263: 'Тюняев, Андрей Александрович',\n 264: 'Усманов, Алишер Бурханович',\n 265: 'Усольцев, Александр Павлович',\n 266: 'Федункив, Марина Гавриловна',\n 267: 'Ханкишиев, Сталик',\n 268: 'Ходорковский, Михаил Борисович',\n 269: 'Черных, Герман Александрович',\n 270: 'Шаведдинов, Руслан Табризович',\n 271: 'Шпагина, Анастасия',\n 272: 'Щербаков, Алексей Сергеевич (комик)',\n 273: 'Anivar',\n 276: 'Tenderlybae',\n 277: 'Wylsacom',\n 278: 'Агутин, Леонид Николаевич',\n 279: 'Адушкина, Катя',\n 280: 'Алибутаев, Омар Насрудинович',\n 281: 'Альбац, Евгения Марковна',\n 282: 'Андронов, Алексей Александрович',\n 283: 'Анин, Роман Александрович',\n 284: 'Анкудинова, Диана Дмитриевна',\n 286: 'Астер, Диана',\n 287: 'Аурум, Алёна',\n 288: 'Барецкий, Станислав Валерьевич',\n 289: 'Басков, Николай Викторович',\n 290: 'Батрутдинов, Тимур Тахирович',\n 291: 'Безруков, Сергей Витальевич',\n 292: 'Бондарчук, Светлана Витальевна',\n 293: 'Борзенков, Михаил Сергеевич',\n 294: 'Борзунова, Мария Михайловна',\n 295: 'Борисов, Максим Олегович',\n 296: 'Брайн Мапс',\n 297: 'Бустер (видеоблогер)',\n 298: 'Буфеев, Константин Валентинович',\n 299: 'Быков, Дмитрий Львович',\n 300: 'Варламов, Илья Александрович',\n 301: 'Вассерман, Анатолий Александрович',\n 302: 'Вики Шоу',\n 303: 'Винокур, Владимир Натанович',\n 305: 'Волков, Леонид Михайлович (политик)',\n 306: 'Воля, Павел Алексеевич',\n 307: 'Вэй, Маша',\n 308: 'Галкин, Максим Александрович',\n 309: 'Гасанов, Гусейн (блогер)',\n 310: 'Генич, Константин Михайлович',\n 311: 'Гогунский, Виталий Евгеньевич',\n 312: 'Голополосов, Максим Сергеевич',\n 313: 'Гордеева, Катерина Владимировна',\n 314: 'Горин, Геннадий Александрович',\n 316: 'Гудков, Дмитрий Геннадьевич',\n 317: 'Гуриев, Сергей Маратович',\n 318: 'Дава',\n 319: 'Данилова, Арина Антоновна',\n 320: 'Дед Архимед',\n 321: 'Дёмочка, Виталий Евгеньевич',\n 322: 'Дёмушкин, Дмитрий Николаевич',\n 323: 'Дергилёва, Валерия Олеговна',\n 324: 'Джигурда, Никита Борисович',\n 325: 'Дзядко, Тихон Викторович',\n 326: 'Долин, Антон Владимирович',\n 327: 'Дудь, Юрий Александрович',\n 329: 'Елагин, Александр Викторович',\n 330: 'Жёлудь, Рома',\n 331: 'Жигалова, Алёна Евгеньевна',\n 332: 'Жосул, Елена Викторовна',\n 333: 'Жуков, Сергей Евгеньевич',\n 335: 'Зубов, Андрей Борисович',\n 336: 'Зыгарь, Михаил Викторович',\n 337: 'Ивакова, Мария Игоревна',\n 338: 'Ивангай',\n 341: 'Ивлев, Константин Витальевич',\n 343: 'Ильюхина, Мария Сергеевна',\n 344: 'Иосилевич, Михаил Александрович',\n 345: 'Истомина, Карина Игоревна',\n 346: 'Казанский, Денис Олегович',\n 347: 'Казарновская, Любовь Юрьевна',\n 348: 'Калашников, Максим Александрович',\n 349: 'Камикадзе Ди',\n 350: 'Каныгин, Павел Юрьевич',\n 351: 'Караулов, Андрей Викторович',\n 352: 'Карнавал, Валя',\n 353: 'Кац, Максим Евгеньевич',\n 354: 'Кашпировский, Анатолий Михайлович',\n 355: 'Клинаев, Егор Дмитриевич',\n 356: 'Кока, Клава',\n 357: 'Колезев, Дмитрий Евгеньевич',\n 358: 'Котрикадзе, Екатерина Бесикиевна',\n 359: 'Ксения Ларина',\n 360: 'Куваев, Олег Игоревич',\n 361: 'Куплинов, Дмитрий Алексеевич',\n 362: 'Кучер, Станислав Александрович',\n 363: 'Кушанашвили, Отар Шалвович',\n 364: 'Лабковский, Михаил Александрович',\n 365: 'Лазарева, Татьяна Юрьевна',\n 366: 'Лайк Настя',\n 367: 'Лапенко, Антон Вячеславович',\n 369: 'Ларин, Дмитрий (видеоблогер)',\n 370: 'Латынина, Юлия Леонидовна',\n 371: 'Лебедев, Артемий Андреевич',\n 372: 'Лисевский, Богдан Владимирович',\n 373: 'Литвин, Михаил Андреевич',\n 375: 'Люба и Аркаша',\n 376: 'Лядов, Антон Вячеславович',\n 377: 'Мавроматти, Олег Юрьевич',\n 378: 'Мазеин, Максим Александрович',\n 379: 'Макарычев, Сергей Юрьевич',\n 380: 'Максимов, Георгий Валерьевич',\n 381: 'Мама отличника',\n 382: 'Манучаров, Вячеслав Рафаэлевич',\n 383: 'Масленников, Дима',\n 384: 'Мезенцев, Сергей Сергеевич',\n 385: 'Меньшова, Юлия Владимировна',\n 386: 'Месси (пума)',\n 387: 'Милов, Владимир Станиславович',\n 388: 'Милохин, Даня',\n 389: 'Минаев, Сергей Сергеевич',\n 390: 'Михалков, Никита Сергеевич',\n 392: 'Миша Марвин',\n 393: 'Могучев, Всеволод Вячеславович',\n 395: 'Музыченко, Юрий Юрьевич',\n 396: 'Мусагалиев, Азамат Тахирович',\n 397: 'Мэддисон, Илья',\n 398: 'Мясников, Вячеслав Владимирович',\n 399: 'Набутов, Виктор Кириллович',\n 400: 'Набутов, Кирилл Викторович',\n 401: 'Навальный, Алексей Анатольевич',\n 402: 'Наумова, Марьяна Александровна',\n 403: 'Невзоров, Александр Глебович',\n 405: 'Нечаев, Андрей Алексеевич',\n 406: 'Нифёдов, Андрей',\n 407: 'Орлов, Геннадий Сергеевич',\n 409: 'Панкратова, Юлия Викторовна',\n 410: 'Парфёнов, Леонид Геннадьевич',\n 411: 'Платошкин, Николай Николаевич',\n 413: 'Поддубный, Евгений Евгеньевич',\n 414: 'Покров, Аня',\n 415: 'Понасенков, Евгений Николаевич',\n 416: 'Понкрашов, Антон Александрович',\n 417: 'Поперечный, Данила Алексеевич',\n 418: 'Попов, Алексей Львович',\n 419: 'Потапенко, Дмитрий Валерьевич',\n 420: 'Прилепин, Захар',\n 421: 'Прусикин, Илья Владимирович',\n 422: 'Пушман, Юлия',\n 423: 'Пушной, Александр Борисович',\n 424: 'Радзинский, Эдвард Станиславович',\n 425: 'Раевский, Александр Евгеньевич (японист)',\n 426: 'Рашкин, Валерий Фёдорович',\n 427: 'Рогов, Кирилл Юрьевич',\n 428: 'Рожков, Андрей Борисович',\n 429: 'Ройзман, Евгений Вадимович',\n 430: 'Романов, Дмитрий Анатольевич (актёр)',\n 431: 'Рубальская, Лариса Алексеевна',\n 432: 'Савин, Евгений Леонидович',\n 433: 'Садальский, Станислав Юрьевич',\n 435: 'Светов, Михаил Владимирович',\n 436: 'Сёмин, Константин Викторович',\n 437: 'Синяк, Игорь Витальевич',\n 438: 'Киркоров, Филипп Бедросович',\n 439: 'Walkie',\n 440: 'Бузова, Ольга Игоревна',\n 441: 'Воропанов, Сергей Александрович',\n 442: 'Гаврилина, Юля',\n 443: 'Горячёва, Маха',\n 444: 'Дегтярёв, Михаил Владимирович',\n 446: 'Корейка, Даша',\n 447: 'Малозёмов, Сергей Александрович',\n 448: 'Михеева, Алла Андреевна',\n 449: 'Монеточка',\n 450: 'Незлобин, Александр Васильевич',\n 451: 'Сажина, Елена Дмитриевна',\n 452: 'Утяшева, Ляйсан Альбертовна',\n 453: 'Хабиб (певец)',\n 454: 'Хасбик',\n 455: 'Чеботина, Люся',\n 456: 'Dead Blonde',\n 459: 'Xolidayboy',\n 460: 'Блиновская, Елена Олеговна',\n 461: 'Боня, Виктория Анатольевна',\n 462: 'Жуковская, Евгения Евгеньевна',\n 464: 'Митрошина, Александра Александровна',\n 465: 'Сталингулаг',\n 466: 'Тодоренко, Регина Петровна',\n 468: 'Чекалина, Валерия Валерьевна',\n 469: 'Шейдлин, Эллен Викторовна'}"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dicter = {}\n",
+    "def get_dict(x):\n",
+    "    dicter[x.id] = x[1]\n",
+    "data[\"json\"] = data.apply(lambda x: get_dict(x), axis=1)\n",
+    "import pickle\n",
+    "\n",
+    "with open('saved_dictionary_bloggers.pkl', 'wb') as f:\n",
+    "    pickle.dump(dicter, f)\n",
+    "with open('saved_dictionary_bloggers.pkl', 'rb') as f:\n",
+    "    loaded_dict = pickle.load(f)\n",
+    "loaded_dict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:55:53.571632300Z",
+     "start_time": "2024-03-29T11:55:53.530577700Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "2.jpg\n",
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+      "5.jpg\n",
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+      "3.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
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+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
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+      "2.jpg\n",
+      "4.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
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+      "3.jpg\n",
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+      "3.jpg\n",
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+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
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+      "2.jpg\n",
+      "1.jpg\n",
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+      "3.jpg\n",
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+      "2.jpg\n",
+      "3.jpg\n",
+      "1.jpg\n",
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+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "7.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "7.jpg\n",
+      "1.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "7.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "7.jpg\n",
+      "8.jpg\n",
+      "9.jpg\n",
+      "1.jpg\n",
+      "1.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "7.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "6.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "6.jpg\n",
+      "7.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "1.jpg\n",
+      "2.jpg\n",
+      "3.jpg\n",
+      "4.jpg\n",
+      "5.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_clear = True\n",
+    "dirr = '../data/bloggers'\n",
+    "if is_clear:\n",
+    "    for filename in os.listdir(dirr):\n",
+    "        for image in os.listdir(os.path.join(dirr, filename)):\n",
+    "            if image.endswith(\".txt\"):\n",
+    "                os.remove(os.path.join(dirr, filename, image))\n",
+    "                continue\n",
+    "            print(image)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:56:30.247662400Z",
+     "start_time": "2024-03-29T11:56:29.874029600Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/FastMTCNN.py b/notebooks/FastMTCNN.py
new file mode 100644
index 0000000..227aab1
--- /dev/null
+++ b/notebooks/FastMTCNN.py
@@ -0,0 +1,117 @@
+from facenet_pytorch import MTCNN
+import torch
+from imutils.video import FileVideoStream
+import cv2
+import time
+from tqdm.notebook import tqdm
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+class FastMTCNN(object):
+    """Fast MTCNN implementation."""
+
+    def __init__(self, stride, resize=1, *args, **kwargs):
+        """Constructor for FastMTCNN class.
+
+        Arguments:
+            stride (int): The detection stride. Faces will be detected every `stride` frames
+                and remembered for `stride-1` frames.
+
+        Keyword arguments:
+            resize (float): Fractional frame scaling. [default: {1}]
+            *args: Arguments to pass to the MTCNN constructor. See help(MTCNN).
+            **kwargs: Keyword arguments to pass to the MTCNN constructor. See help(MTCNN).
+        """
+        self.stride = stride
+        self.resize = resize
+        self.mtcnn = MTCNN(*args, **kwargs)
+
+    def __call__(self, frames):
+        """Detect faces in frames using strided MTCNN."""
+        if self.resize != 1:
+            frames = [
+                cv2.resize(f, (int(f.shape[1] * self.resize), int(f.shape[0] * self.resize)))
+                for f in frames
+            ]
+
+        boxes, probs = self.mtcnn.detect(frames[::self.stride])
+
+        faces = []
+        for i, frame in enumerate(frames):
+            box_ind = int(i / self.stride)
+            if boxes[box_ind] is None:
+                continue
+            for box in boxes[box_ind]:
+                box = [int(b) for b in box]
+                faces.append(frame[box[1]:box[3], box[0]:box[2]])
+                image = frame[box[1]:box[3], box[0]:box[2]]
+                cv2.imshow("test", cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+                cv2.waitKey(10)
+
+        return faces
+
+
+fast_mtcnn = FastMTCNN(
+    stride=4,
+    resize=1,
+    margin=14,
+    factor=0.6,
+    keep_all=True,
+    device=device
+)
+def run_detection(fast_mtcnn, filenames):
+    frames = []
+    frames_processed = 0
+    faces_detected = 0
+    batch_size = 60
+    start = time.time()
+    if filenames == 0:
+        cap = cv2.VideoCapture(0)
+        while True:
+            _, frame = cap.read()
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+            if len(frames) >= batch_size:
+                faces = fast_mtcnn(frames)
+                frames_processed += len(frames)
+                faces_detected += len(faces)
+                frames = []
+                print(
+                    f'Frames per second: {frames_processed / (time.time() - start):.3f},',
+                    f'faces detected: {faces_detected}\r',
+                    end=''
+                )
+
+    for filename in tqdm(filenames):
+
+        v_cap = FileVideoStream(filename).start()
+        v_len = int(v_cap.stream.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        for j in range(v_len):
+
+            frame = v_cap.read()
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+
+            if len(frames) >= batch_size or j == v_len - 1:
+
+                faces = fast_mtcnn(frames)
+
+                frames_processed += len(frames)
+                faces_detected += len(faces)
+                frames = []
+
+                print(
+                    f'Frames per second: {frames_processed / (time.time() - start):.3f},',
+                    f'faces detected: {faces_detected}\r',
+                    end=''
+                )
+
+        v_cap.stop()
+
+
+run_detection(fast_mtcnn, 0)
+
+
diff --git a/notebooks/blur_detection.ipynb b/notebooks/blur_detection.ipynb
new file mode 100644
index 0000000..019c28c
--- /dev/null
+++ b/notebooks/blur_detection.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from imutils import paths\n",
+    "import cv2"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T16:23:19.528528400Z",
+     "start_time": "2024-03-29T16:23:18.500152Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "def variance_of_laplacian(image):\n",
+    "\treturn cv2.Laplacian(image, cv2.CV_64F).var()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T16:23:23.429844400Z",
+     "start_time": "2024-03-29T16:23:23.303709900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Blurry 1\n",
+      "Blurry 2\n",
+      "Blurry 3\n",
+      "Blurry 4\n",
+      "Blurry 5\n",
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+      "683\n"
+     ]
+    }
+   ],
+   "source": [
+    "dir_ = 'C://Users//fatik//PycharmProjects//UfaHack2024//data//bloggers'\n",
+    "threshold = 100\n",
+    "i = 0\n",
+    "for imagePath in paths.os.listdir(dir_):\n",
+    "    for image_name in os.listdir(os.path.join(dir_, imagePath )):\n",
+    "        image = cv2.imread(os.path.join(dir_, imagePath, image_name))\n",
+    "        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
+    "        fm = variance_of_laplacian(gray)\n",
+    "        text = \"Not Blurry\"\n",
+    "        if fm < threshold:\n",
+    "            i+=1\n",
+    "            text = \"Blurry\"\n",
+    "            os.remove(os.path.join(dir_, imagePath, image_name))\n",
+    "            print(text, i)\n",
+    "        cv2.putText(image, \"{}: {:.2f}\".format(text, fm), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)\n",
+    "print(i)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T16:26:06.297104300Z",
+     "start_time": "2024-03-29T16:23:54.923351100Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/catboost.ipynb b/notebooks/catboost.ipynb
new file mode 100644
index 0000000..3c38961
--- /dev/null
+++ b/notebooks/catboost.ipynb
@@ -0,0 +1,614 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "from catboost import CatBoostClassifier\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "from sklearn.model_selection import train_test_split"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T15:52:09.761101700Z",
+     "start_time": "2024-03-29T15:51:56.199239Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [],
+   "source": [
+    "with open('actors_usa_embeddings.pkl', 'rb') as f:\n",
+    "    dict_usa = pickle.load(f)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:36.800593700Z",
+     "start_time": "2024-03-29T18:30:36.753716200Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "            0                                                  1\n0         1_1  [-1.5706723928451538, 0.6335924863815308, 1.53...\n1     10009_1  [0.5788402557373047, -0.5220502614974976, -1.7...\n2     10013_1  [-0.5845983028411865, 1.5863220691680908, -1.1...\n3     10013_2  [-0.41548410058021545, 1.5801037549972534, -0....\n4      1003_1  [-0.052914053201675415, -0.35606303811073303, ...\n...       ...                                                ...\n1051  13616_1  [0.6910120248794556, 1.2189278602600098, -1.64...\n1052  13616_2  [0.2329411804676056, 1.3537031412124634, -1.17...\n1053  13616_3  [0.8600691556930542, 1.441170573234558, -1.856...\n1054  13616_4  [1.2998018264770508, 2.4858486652374268, -1.51...\n1055  13631_2  [0.08967465162277222, 1.597952127456665, -1.96...\n\n[1056 rows x 2 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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1_1</td>\n      <td>[-1.5706723928451538, 0.6335924863815308, 1.53...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10009_1</td>\n      <td>[0.5788402557373047, -0.5220502614974976, -1.7...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10013_1</td>\n      <td>[-0.5845983028411865, 1.5863220691680908, -1.1...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10013_2</td>\n      <td>[-0.41548410058021545, 1.5801037549972534, -0....</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1003_1</td>\n      <td>[-0.052914053201675415, -0.35606303811073303, ...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1051</th>\n      <td>13616_1</td>\n      <td>[0.6910120248794556, 1.2189278602600098, -1.64...</td>\n    </tr>\n    <tr>\n      <th>1052</th>\n      <td>13616_2</td>\n      <td>[0.2329411804676056, 1.3537031412124634, -1.17...</td>\n    </tr>\n    <tr>\n      <th>1053</th>\n      <td>13616_3</td>\n      <td>[0.8600691556930542, 1.441170573234558, -1.856...</td>\n    </tr>\n    <tr>\n      <th>1054</th>\n      <td>13616_4</td>\n      <td>[1.2998018264770508, 2.4858486652374268, -1.51...</td>\n    </tr>\n    <tr>\n      <th>1055</th>\n      <td>13631_2</td>\n      <td>[0.08967465162277222, 1.597952127456665, -1.96...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1056 rows × 2 columns</p>\n</div>"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_usa = pd.DataFrame.from_dict(dict_usa.items())\n",
+    "data_usa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:39.592265600Z",
+     "start_time": "2024-03-29T18:30:39.569341700Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "data_usa.rename(columns={0: \"id\", 1: \"embd\"}, inplace=True, errors=\"ignore\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:43.890527400Z",
+     "start_time": "2024-03-29T18:30:43.859276Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "           id                                               embd\n0         1_1  [-1.5706723928451538, 0.6335924863815308, 1.53...\n1     10009_1  [0.5788402557373047, -0.5220502614974976, -1.7...\n2     10013_1  [-0.5845983028411865, 1.5863220691680908, -1.1...\n3     10013_2  [-0.41548410058021545, 1.5801037549972534, -0....\n4      1003_1  [-0.052914053201675415, -0.35606303811073303, ...\n...       ...                                                ...\n1051  13616_1  [0.6910120248794556, 1.2189278602600098, -1.64...\n1052  13616_2  [0.2329411804676056, 1.3537031412124634, -1.17...\n1053  13616_3  [0.8600691556930542, 1.441170573234558, -1.856...\n1054  13616_4  [1.2998018264770508, 2.4858486652374268, -1.51...\n1055  13631_2  [0.08967465162277222, 1.597952127456665, -1.96...\n\n[1056 rows x 2 columns]",
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+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_usa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:45.270827700Z",
+     "start_time": "2024-03-29T18:30:45.243534900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [
+    {
+     "data": {
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<td>1.240614</td>\n      <td>-0.750917</td>\n      <td>0.343088</td>\n      <td>0.759789</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1003_1</td>\n      <td>-0.052914</td>\n      <td>-0.356063</td>\n      <td>-0.832296</td>\n      <td>-0.387270</td>\n      <td>0.796773</td>\n      <td>-0.258821</td>\n      <td>-0.461007</td>\n      <td>0.343941</td>\n      <td>0.571979</td>\n      <td>...</td>\n      <td>0.492704</td>\n      <td>1.220060</td>\n      <td>0.774966</td>\n      <td>-0.848771</td>\n      <td>0.852844</td>\n      <td>-0.558431</td>\n      <td>0.560696</td>\n      <td>-1.167510</td>\n      <td>1.353986</td>\n      <td>-0.561515</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1051</th>\n      <td>13616_1</td>\n      <td>0.691012</td>\n      <td>1.218928</td>\n      <td>-1.645177</td>\n      <td>-0.200526</td>\n      <td>1.032170</td>\n      <td>-0.010177</td>\n      <td>-0.753952</td>\n      <td>0.626138</td>\n      <td>1.611161</td>\n      <td>...</td>\n      <td>-0.331170</td>\n      <td>-0.605570</td>\n      <td>1.232265</td>\n      <td>-0.853597</td>\n      <td>-0.000137</td>\n      <td>1.154459</td>\n      <td>-1.125317</td>\n      <td>-1.453662</td>\n      <td>-0.933203</td>\n      <td>2.152028</td>\n    </tr>\n    <tr>\n      <th>1052</th>\n      <td>13616_2</td>\n      <td>0.232941</td>\n      <td>1.353703</td>\n      <td>-1.175303</td>\n      <td>0.109807</td>\n      <td>1.326917</td>\n      <td>-0.320142</td>\n      <td>-0.360853</td>\n      <td>0.236935</td>\n      <td>1.272749</td>\n      <td>...</td>\n      <td>-0.093058</td>\n      <td>-0.735331</td>\n      <td>1.791282</td>\n      <td>-0.932450</td>\n      <td>-0.078320</td>\n      <td>0.398722</td>\n      <td>-2.315437</td>\n      <td>-1.291452</td>\n      <td>-0.972061</td>\n      <td>1.980307</td>\n    </tr>\n    <tr>\n      <th>1053</th>\n      <td>13616_3</td>\n      <td>0.860069</td>\n      <td>1.441171</td>\n      <td>-1.856320</td>\n      <td>-0.822068</td>\n      <td>2.987537</td>\n      <td>-0.414389</td>\n      <td>-0.009051</td>\n      <td>1.364074</td>\n      <td>0.779438</td>\n      <td>...</td>\n      <td>0.194024</td>\n      <td>-1.225692</td>\n      <td>0.427923</td>\n      <td>0.216618</td>\n      <td>0.209311</td>\n      <td>0.205166</td>\n      <td>-2.612580</td>\n      <td>-0.337485</td>\n      <td>-1.237361</td>\n      <td>0.451241</td>\n    </tr>\n    <tr>\n      <th>1054</th>\n      <td>13616_4</td>\n      <td>1.299802</td>\n      <td>2.485849</td>\n      <td>-1.519942</td>\n      <td>-1.161255</td>\n      <td>1.662391</td>\n      <td>0.192611</td>\n      <td>-0.043378</td>\n      <td>1.498528</td>\n      <td>0.937637</td>\n      <td>...</td>\n      <td>1.762125</td>\n      <td>-0.648232</td>\n      <td>0.858571</td>\n      <td>-0.705204</td>\n      <td>-0.280197</td>\n      <td>0.615498</td>\n      <td>-1.899799</td>\n      <td>-1.392390</td>\n      <td>-1.029162</td>\n      <td>1.198919</td>\n    </tr>\n    <tr>\n      <th>1055</th>\n      <td>13631_2</td>\n      <td>0.089675</td>\n      <td>1.597952</td>\n      <td>-1.962265</td>\n      <td>-2.278883</td>\n      <td>0.774878</td>\n      <td>-1.617782</td>\n      <td>-0.833502</td>\n      <td>1.292249</td>\n      <td>-0.641687</td>\n      <td>...</td>\n      <td>0.419192</td>\n      <td>1.590851</td>\n      <td>-1.200108</td>\n      <td>1.717416</td>\n      <td>-1.234612</td>\n      <td>0.177451</td>\n      <td>-0.677651</td>\n      <td>0.334710</td>\n      <td>0.589466</td>\n      <td>0.305988</td>\n    </tr>\n  </tbody>\n</table>\n<p>1056 rows × 129 columns</p>\n</div>"
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_cols = pd.DataFrame(data_usa['embd'].apply(pd.Series))\n",
+    "df_usa = pd.concat([data_usa, new_cols], axis=1)\n",
+    "df_usa.drop([\"embd\"], axis=1, inplace=True, errors=\"ignore\")\n",
+    "df_usa"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:49.465033800Z",
+     "start_time": "2024-03-29T18:30:49.271020400Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [],
+   "source": [
+    "df_usa[\"id\"] = df_usa[\"id\"].apply(lambda x: str(x)[:str(x).find(\"_\")])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:52.716203400Z",
+     "start_time": "2024-03-29T18:30:52.692504100Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "outputs": [],
+   "source": [
+    "df_usa[\"id\"] = df_usa[\"id\"].astype(int)\n",
+    "y, X = df_usa[\"id\"], df_usa.drop([\"id\"], axis=1)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=1)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:30:57.870505500Z",
+     "start_time": "2024-03-29T18:30:57.845736100Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "id\n13078    4\n10593    4\n10766    4\n13209    4\n1320     4\n        ..\n12114    1\n12143    1\n12151    1\n12163    1\n13631    1\nName: count, Length: 457, dtype: int64"
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T18:40:33.247010800Z",
+     "start_time": "2024-03-29T18:40:33.123293900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[]"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_features = [col for col in df_usa.columns if df_usa[col].dtype.name == 'category' or df_usa[col].dtype.name == 'object']\n",
+    "cat_features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T17:08:02.745660400Z",
+     "start_time": "2024-03-29T17:08:02.735557900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2f8ef01d3ad3469ba13c90ed7dcc9b43"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate set to 0.185024\n",
+      "0:\tlearn: 6.0784997\ttest: 6.1433807\tbest: 6.1433807 (0)\ttotal: 2.35s\tremaining: 11m 43s\n",
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+      "3:\tlearn: 5.8057325\ttest: 6.0476334\tbest: 6.0476334 (3)\ttotal: 9.36s\tremaining: 11m 32s\n",
+      "4:\tlearn: 5.7158628\ttest: 6.0047780\tbest: 6.0047780 (4)\ttotal: 11.5s\tremaining: 11m 21s\n",
+      "5:\tlearn: 5.6240899\ttest: 5.9781342\tbest: 5.9781342 (5)\ttotal: 13.9s\tremaining: 11m 19s\n",
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+      "299:\tlearn: 0.0889600\ttest: 2.4474767\tbest: 2.4474767 (299)\ttotal: 6m 19s\tremaining: 0us\n",
+      "bestTest = 2.447476705\n",
+      "bestIteration = 299\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<catboost.core.CatBoostClassifier at 0x1c3b6fb2ce0>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params_cat = {\n",
+    "    \"iterations\":300,\n",
+    "    \"depth\":7,\n",
+    "    #\"learning_rate\":0.1,\n",
+    "    #\"eval_metric\":'F1',\n",
+    "    \"loss_function\": \"MultiClass\",\n",
+    "    \"random_seed\":1,\n",
+    "    \"bootstrap_type\":'Bayesian',\n",
+    "    \"bagging_temperature\":1,\n",
+    "    \"od_type\":'Iter',\n",
+    "    \"od_wait\":20,\n",
+    "    \"task_type\":'GPU',\n",
+    "}\n",
+    "cat_model = CatBoostClassifier(**params_cat)\n",
+    "cat_model.fit(\n",
+    "    X_train, y_train,\n",
+    "    eval_set=(X_test, y_test),\n",
+    "    use_best_model=True,\n",
+    "    verbose=True,\n",
+    "    plot=True\n",
+    " )"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T17:14:29.140678900Z",
+     "start_time": "2024-03-29T17:08:08.540374300Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "cat_model.save_model(\"catboost_blogger.cbm\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T17:26:38.910177200Z",
+     "start_time": "2024-03-29T17:26:38.604629500Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/catboost_info/catboost_training.json b/notebooks/catboost_info/catboost_training.json
new file mode 100644
index 0000000..f4f705c
--- /dev/null
+++ b/notebooks/catboost_info/catboost_training.json
@@ -0,0 +1,304 @@
+{
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new file mode 100644
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diff --git a/notebooks/catboost_info/tmp/cat_feature_index.587ae0ef-a03adf04-8586cae7-acc6ce77.tmp b/notebooks/catboost_info/tmp/cat_feature_index.587ae0ef-a03adf04-8586cae7-acc6ce77.tmp
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diff --git a/notebooks/deepface_notebooks.ipynb b/notebooks/deepface_notebooks.ipynb
new file mode 100644
index 0000000..603860c
--- /dev/null
+++ b/notebooks/deepface_notebooks.ipynb
@@ -0,0 +1,8544 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\fatik\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cv2\n",
+    "from deepface import DeepFace\n",
+    "import os\n",
+    "from mtcnn import MTCNN"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:51:18.815961100Z",
+     "start_time": "2024-03-29T11:51:14.103338900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "models = [\n",
+    "  \"VGG-Face\",\n",
+    "  \"Facenet\",\n",
+    "  \"Facenet512\",\n",
+    "  \"OpenFace\",\n",
+    "  \"DeepFace\",\n",
+    "  \"DeepID\",\n",
+    "  \"ArcFace\",\n",
+    "  \"Dlib\",\n",
+    "  \"SFace\",\n",
+    "  \"GhostFaceNet\",\n",
+    "]\n",
+    "backends = [\n",
+    "  'opencv',\n",
+    "  'ssd',\n",
+    "  'dlib',\n",
+    "  'mtcnn',\n",
+    "  'retinaface',\n",
+    "  'mediapipe',\n",
+    "  'yolov8',\n",
+    "  'yunet',\n",
+    "  'fastmtcnn',\n",
+    "]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T11:51:19.274075400Z",
+     "start_time": "2024-03-29T11:51:19.216042200Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  0%|          | 0/419 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "47ae2f30569d4637a9b7d6774e789a59"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "216\n",
+      "1/1 [==============================] - 0s 58ms/step\n",
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+      "1/1 [==============================] - 0s 27ms/step\n",
+      "1/1 [==============================] - 0s 24ms/step\n",
+      "1/1 [==============================] - 0s 22ms/step\n",
+      "1/1 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 25ms/step\n",
+      "1/1 [==============================] - 0s 21ms/step\n",
+      "1/1 [==============================] - 0s 21ms/step\n",
+      "14/14 [==============================] - 0s 5ms/step\n",
+      "1/1 [==============================] - 0s 29ms/step\n",
+      "1/1 [==============================] - 0s 48ms/step\n",
+      "1/1 [==============================] - 0s 39ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "detector = MTCNN()\n",
+    "dicter3 = {}\n",
+    "embeddings1 = []\n",
+    "z = 0\n",
+    "dir_ = r'C:\\Users\\fatik\\PycharmProjects\\UfaHack2024\\data\\bloggers'\n",
+    "for filename in tqdm(os.listdir(dir_)):\n",
+    "  z+=1\n",
+    "  print(z)\n",
+    "  if z==500:\n",
+    "    break\n",
+    "  i = 0\n",
+    "  for image in os.listdir(os.path.join(dir_, filename)):\n",
+    "    i+=1\n",
+    "    img = cv2.cvtColor(cv2.imread(os.path.join(dir_, filename, image)), cv2.COLOR_BGR2RGB)\n",
+    "    detections = detector.detect_faces(img)\n",
+    "    if len(detections) > 1:\n",
+    "      continue\n",
+    "    for detection in detections:\n",
+    "      confidence = detection[\"confidence\"]\n",
+    "      if confidence > 0.9:\n",
+    "        x, y, w, h = detection[\"box\"]\n",
+    "        detected_face = img[int(y):int(y + h), int(x):int(x + w)]\n",
+    "        embedding = DeepFace.represent(detected_face, model_name='Facenet', enforce_detection=False)\n",
+    "        dicter3[f'{filename}_{i}'] = embedding[0][\"embedding\"]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "ExecuteTime": {
+     "start_time": "2024-03-29T17:06:57.675570400Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open('actors_ussr_embeddings.pkl', 'wb') as f:\n",
+    "    pickle.dump(dicter3, f)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-03-29T17:05:49.924417200Z",
+     "start_time": "2024-03-29T17:05:49.760230900Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}