{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73f81039",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "from termcolor import colored\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8a8891e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install termcolor==1.1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44668ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Ner_Extractor:\n",
    "    \n",
    "    def __init__(self, model_checkpoint):\n",
    "        \n",
    "        self.token_pred_pipeline = pipeline(\"token-classification\", \n",
    "                                            model=model_checkpoint, \n",
    "                                            aggregation_strategy=\"average\")\n",
    "    \n",
    "    @staticmethod\n",
    "    def text_color(txt, txt_c=\"blue\", txt_hglt=\"on_yellow\"):\n",
    "        return colored(txt, txt_c, txt_hglt)\n",
    "    \n",
    "    @staticmethod\n",
    "    def concat_entities(ner_result):\n",
    "        \n",
    "        entities = []\n",
    "        prev_entity = None\n",
    "        prev_end = 0\n",
    "        for i in range(len(ner_result)):\n",
    "            if (ner_result[i][\"entity_group\"] == prev_entity) &\\\n",
    "               (ner_result[i][\"start\"] == prev_end):\n",
    "                entities[i-1][2] = ner_result[i][\"end\"]\n",
    "                prev_entity = ner_result[i][\"entity_group\"]\n",
    "                prev_end = ner_result[i][\"end\"]\n",
    "            else:\n",
    "                entities.append([ner_result[i][\"entity_group\"], \n",
    "                                 ner_result[i][\"start\"], \n",
    "                                 ner_result[i][\"end\"]])\n",
    "                prev_entity = ner_result[i][\"entity_group\"]\n",
    "                prev_end = ner_result[i][\"end\"]\n",
    "        \n",
    "        return entities\n",
    "    \n",
    "    \n",
    "    def colored_text(self, text, entities):\n",
    "        \n",
    "        colored_text = \"\"\n",
    "        init_pos = 0\n",
    "        for ent in entities:\n",
    "            if ent[1] > init_pos:\n",
    "                colored_text += text[init_pos: ent[1]]\n",
    "                colored_text += self.text_color(text[ent[1]: ent[2]]) + f\"({ent[0]})\"\n",
    "                init_pos = ent[2]\n",
    "            else:\n",
    "                colored_text += self.text_color(text[ent[1]: ent[2]]) + f\"({ent[0]})\"\n",
    "                init_pos = ent[2]\n",
    "        \n",
    "        return colored_text\n",
    "    \n",
    "    \n",
    "    def get_entities(self, text: str):\n",
    "        \n",
    "        entities = self.token_pred_pipeline(text)\n",
    "        concat_ent = self.concat_entities(entities)\n",
    "        \n",
    "        return concat_ent\n",
    "    \n",
    "    \n",
    "    def show_ents_on_text(self, text: str):\n",
    "        \n",
    "        entities = self.get_entities(text)\n",
    "        \n",
    "        return self.colored_text(text, entities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaa0a5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "seqs_example = [\"Из Дзюбы вышел бы отличный бразилец». Интервью Клаудиньо\",\n",
    "\"Самый яркий бразилец «Зенита» рассказал о встрече с Пеле, страшном морозе в Самаре и любимых финтах Роналдиньо\",\n",
    "\"Стали известны подробности нового иска РФС к УЕФА и ФИФА\",\n",
    "\"Реванш «Баварии», голы от «Реала» с «Челси»: ставим на ЛЧ\",\n",
    "\"Кварацхелия не вернется в «Рубин» и станет игроком «Наполи»\",\n",
    "\"«Манчестер Сити» сделал грандиозное предложение по Холанду\",\n",
    "\"В России хотят возродить Кубок лиги. Он проводился в 2003 году\",\n",
    "\"Экс-футболиста сборной Украины уволили с ТВ за слова о россиянах\",\n",
    "\"Экс-игрок «Реала» находится в критическом состоянии после ДТП\",\n",
    "\"Аршавин посмеялся над показателями Глушакова в игре с ЦСКА\",\n",
    "\"Арьен Роббен пробежал 42-километровый марафон\",\n",
    "\"Бывший игрок «Спартака» предложил бить футболистов палками\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "380d9824",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "extractor = Ner_Extractor(model_checkpoint = \"surdan/LaBSE_ner_nerel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ebcf51",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "show_entities_in_text = (extractor.show_ents_on_text(i) for i in seqs_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e03b28c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "l_entities = [extractor.get_entities(i) for i in seqs_example]\n",
    "len(l_entities), len(seqs_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d4ae84",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(seqs_example)):\n",
    "    print(next(show_entities_in_text, \"End of generator\"))\n",
    "    print(\"-*-\"*25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47fbcff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41c32b90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07bb735e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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