emotify_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yriHEJ1PEj4P"
   },
   "source": [
    "## 라이브러리 설치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H1eOqrH2Ees-",
    "outputId": "1e63f7d3-c2e4-4b63-d377-9f12ace8fcfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install cmake\n",
    "!pip install transformers\n",
    "!pip install --upgrade pip\n",
    "!pip install kiwipiepy\n",
    "!pip install kiwipiepy transformers torch sentencepiece\n",
    "!pip install scikit-learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jRYaN6lkNPE_",
    "outputId": "e9552e8d-a860-4c1b-b203-4d52d5fdeaa4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: pydub, websockets, uvicorn, tomlkit, semantic-version, ruff, python-multipart, markupsafe, ffmpy, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio\n",
      "  Attempting uninstall: markupsafe\n",
      "    Found existing installation: MarkupSafe 3.0.2\n",
      "    Uninstalling MarkupSafe-3.0.2:\n",
      "      Successfully uninstalled MarkupSafe-3.0.2\n",
      "Successfully installed aiofiles-23.2.1 fastapi-0.115.5 ffmpy-0.4.0 gradio-5.7.1 gradio-client-1.5.0 markupsafe-2.1.5 pydub-0.25.1 python-multipart-0.0.12 ruff-0.8.1 safehttpx-0.1.1 semantic-version-2.10.0 starlette-0.41.3 tomlkit-0.12.0 uvicorn-0.32.1 websockets-12.0\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNBiQXNWEo-k"
   },
   "source": [
    "## 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WTTihoNGEs0P",
    "outputId": "37446355-12c7-4d40-c400-2b6b2dba977a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in df_merged:\n",
      "['우울' '억울' '추스리지못함' '후회함' '종잡을수없음' '섭섭함' '죽고싶음' '울음' '허무함' '후회' '그리움' '눈물'\n",
      " '비참' '서러움' '속상함' '슬픔' '안타까움' '불만' '연민' '서운함' '외로움' '쓸쓸함' '무의미함' '적적함'\n",
      " '울적함' '고단함' '외톨이' '고독감' '겉돌다' '화' '원망' '죽이고싶음' '미움' '분노' '증오' '허망' '조울'\n",
      " '흥미감소' '즐거움감소' '의욕없음' '무기력' '괴로움' '귀찮음' '허무  ' '기분우울' '허무' '감정적마비' '공허'\n",
      " '허탈' '무의미' '자괴감' '패배감' '의욕감소' '식욕감소' '체중감소' '입맛감소' '살빠짐' '식사량감소' '예민'\n",
      " '메슥거림' '소화불량' '폭식' '체중증가' '살찌다' '과체중' '과식증' '불면' '잠드는데 어려움' '잠이안옴' '자다깸'\n",
      " '자도피로안풀림' '수면시간감소' '수면장애' '잠설침' '얕게잠' '몽롱' '늘어짐' '상쾌하지않음' '뒤척임' '성욕감소'\n",
      " '쇠약' '조각잠' '비몽사몽' '안절부절못함' '조마조마함' '가만히못있음' '신경날카로움' '짜증' '혼란' '느릿느릿'\n",
      " '무거움' '떨림' '답답' '기분처짐' '숨참' '신경질' '감정기복' '걱정' '욱함 ' '불안' '나른함' '힘없음'\n",
      " '탈력감' '슬럼프' '처짐' '누움' '멍함' '졸림' '힘빠짐' '많이잠' '찌뿌둥함' '죄책감' '죄인' '잘못' '질책'\n",
      " '자책' '배신감' '자존감감소' '절망감' '미안함' '사고력' '집중력저하' '책이안읽힘' 'tv집중안됨' '둔화' '피곤'\n",
      " '정서불안(불안감)' '불안정' '우유부단' '느긋' '위축' '자신감감소' '소심' '결단력없음' '하찮음' '못남' '비웃음'\n",
      " '업신여김' '패배자' '눈초리' '냉소' '좌절' '감시' '해코지' '손가락질' '째려보다' '괴롭다' '피해의식' '한심'\n",
      " '앞이 깜깜함' '희망없음' '가치 없음' '포기' '고통' '나약' '막막함' '좌절감' '긴장' '낭떠러지 끝에 있는 느낌'\n",
      " '공포' '압박감' '중립' '행복']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 1. 엑셀 파일 읽어오기\n",
    "excel_path1 = '/content/wellness_final.xlsx'  #동진\n",
    "excel_path2 = '/content/7label.xlsx'\n",
    "\n",
    "df1 = pd.read_excel(excel_path1)\n",
    "df2 = pd.read_excel(excel_path2)\n",
    "\n",
    "df1_selected = df1.iloc[1:19770, [0, 1]] #동진\n",
    "df1_selected.columns = ['keyword', 'text']\n",
    "\n",
    "df2_selected = df2.iloc[1:, [1, 0]] #혜란\n",
    "df2_selected.columns = ['keyword', 'text']\n",
    "df2_selected = df2_selected[df2_selected['keyword'].isin(['행복', '중립'])]\n",
    "\n",
    "# 행 병합\n",
    "df_merged = pd.concat([df1_selected, df2_selected], axis=0)\n",
    "\n",
    "# 제외할 키워드 목록\n",
    "exclude_keywords = ['충동', '낙 없음', '실망', '모욕감', '절박', '곤란']\n",
    "# 제외할 키워드에 해당하는 행 제거\n",
    "df_merged = df_merged[~df_merged['keyword'].isin(exclude_keywords)]\n",
    "\n",
    "# NaN 값이 있는 행 제거\n",
    "df_merged = df_merged.dropna()\n",
    "# 키워드 값 출력\n",
    "print(\"Keywords in df_merged:\")\n",
    "print(df_merged['keyword'].unique())\n",
    "\n",
    "# 텍스트 열 지정\n",
    "texts = df_merged['text'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HEIJcsNkKEAK"
   },
   "outputs": [],
   "source": [
    "# 신조어 데이터셋 불러오기\n",
    "df4 = pd.read_csv('./korean_slang.csv')\n",
    "new_words_dict = pd.Series(df4.뜻풀이.values, index=df4.신조어).to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MXectJhLHATQ"
   },
   "source": [
    "### 감정분석용 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OHJtwVYHGGke"
   },
   "outputs": [],
   "source": [
    "df3 = pd.read_excel(excel_path2, usecols=[1, 0], names=['text', 'label'])\n",
    "\n",
    "# 레이블을 숫자로 매핑!pip install scikit-learn\n",
    "label_mapping = {\n",
    "    '행복': 0,\n",
    "    '중립': 1,\n",
    "    '슬픔': 2,\n",
    "    '공포': 3,\n",
    "    '혐오': 4,\n",
    "    '분노': 5,\n",
    "    '놀람': 6\n",
    "}\n",
    "\n",
    "# 'label' 열을 숫자로 매핑\n",
    "df3['label'] = df3['label'].map(label_mapping)\n",
    "\n",
    "# NaN 값이 있는 행 제거\n",
    "df3 = df3.dropna(subset=['text'])\n",
    "df3 = df3.dropna(subset=['label'])\n",
    "\n",
    "df3['label'] = df3['label'].astype(int)\n",
    "\n",
    "# 텍스트와 레이블을 리스트로 변환\n",
    "train_texts = df3['text'][1:].tolist()\n",
    "train_texts = [str(text) for text in train_texts]\n",
    "train_labels = df3['label'][1:].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kTfXygzRCLa_"
   },
   "outputs": [],
   "source": [
    "# 키워드 병합 규칙 적용\n",
    "df_merged['keyword'] = df_merged['keyword'].replace({\n",
    "    '수면장애': '불면', '잠이안옴': '불면', '잠드는데 어려움': '불면',\t'자다깸': '불면',\t'자도피로안풀림': '불면',\t'수면시간감소': '불면',\t'잠설침': '불면',\t'얕게잠': '불면',\t'몽롱': '불면',\t'조각잠': '불면',\t'비몽사몽': '불면',\t'피곤': '불면',\n",
    "    '눈물': '슬픔', '울음': '슬픔',\n",
    "    '식욕부진': '식욕감소',\t'체중감소': '식욕감소',\t'입맛감소': '식욕감소',\t'살빠짐': '식욕감소',\t'식사량감소': '식욕감소',\n",
    "    '위축':'자존감감소',\t'자신감감소':'자존감감소',\t'소심':'자존감감소',\t'하찮음':'자존감감소',\t'못남':'자존감감소',\t'한심':'자존감감소',\t'가치 없음':'자존감감소',\t'나약':'자존감감소',\n",
    "    '패배자':'패배감',\t'포기':'패배감',\n",
    "    '비웃음':'피해의식',\t'업신여김':'피해의식',\t'눈초리':'피해의식',\t'냉소':'피해의식',\t'감시':'피해의식',\t'해코지':'피해의식',\t'손가락질':'피해의식',\t'째려보다':'피해의식',\n",
    "    '죄인':'죄책감',\t'잘못':'죄책감',\t'질책':'죄책감',\t'자책':'죄책감',\n",
    "    '안타까움':'연민',\n",
    "    '허무함': '허무', '허망': '허무',\t'공허': '허무',\t'허탈': '허무', '허망함':'허무','허무  ':'허무',\n",
    "    '희망없음':'절망감',\t'낭떠러지 끝에 있는 느낌':'절망감',\n",
    "    '후회함': '후회',\n",
    "    '종잡을수없음':'혼란',\t'우유부단':'혼란',\t'결단력없음':'혼란',\n",
    "    '쓸쓸함': '외로움', '적적함': '외로움',\t'겉돌다': '외로움',\n",
    "    '무의미':'무의미함',\n",
    "    '고독감':'외톨이',\n",
    "    '욱함':'화',\n",
    "    '감정기복':'조울',\n",
    "    '증오':'분노',\n",
    "    '자괴감':'괴로움',\t'괴롭다':'괴로움',\t'고통':'괴로움',\n",
    "    '신경날카로움':'예민',\t'짜증':'예민',\t'신경질':'예민',\n",
    "    '메슥거림':'소화불량',\n",
    "    '폭식':'체중증가',\t'살찌다':'체중증가',\t'과체중':'체중증가',\t'과식증':'체중증가',\n",
    "    '섭섭함': '서운함',\n",
    "    '기분우울': '침울',\t'울적함':'침울', '상쾌하지않음':'침울',\t'기분처짐':'침울',\n",
    "    '흥미상실':'의욕감소', '무의욕':'의욕감소',\t'의욕없음':'의욕감소',\t'흥미감소':'의욕감소',\t'즐거움감소':'의욕감소',\t'나른한':'의욕감소',\t'성욕감소':'의욕감소',\n",
    "    '쇠약':'무기력',\t'힘없음':'무기력',\t'탈력감':'무기력',\t'졸림':'무기력',\t'힘빠짐':'무기력',\t'많이잠':'무기력',\t'찌뿌둥함':'무기력',\n",
    "    '안절부절못함':'불안',\t'조마조마함':'불안',\t'가만히못있음':'불안',\t'떨림':'불안',\t'걱정':'불안',\t'정서불안(불안감)':'불안',\t'불안정':'불안',\t'긴장':'불안',\t'압박감':'불안',\n",
    "    '귀찮음':'늘어짐',\t'무거움':'늘어짐',\t'느릿느릿':'늘어짐',\t'처짐':'늘어짐',\t'누움':'늘어짐',\t'둔화':'늘어짐',\t'느긋':'늘어짐',\n",
    "    '숨참':'답답',\t'앞이 깜깜함':'답답',\t'막막함':'답답',\n",
    "    '집중력저하':'사고력',\t'책이안읽힘':'사고력',\t'tv집중안됨':'사고력',\n",
    "    '좌절감':'좌절'\n",
    "})"
   ]
  },
  {
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      "4f173e43dfc64b569b4069ea44994e45",
      "c912ea285e7c490f820ce28f0c3ad124"
     ]
    },
    "id": "16VbSPotEHfx",
    "outputId": "c0fdf2cf-f668-4728-b6c6-9cda74b40fab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at beomi/KcELECTRA-base-v2022 and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
     ]
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     },
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    {
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     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
      "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
     ]
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models loaded.\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import torch\n",
    "from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, T5ForConditionalGeneration, T5Tokenizer, MarianMTModel, MarianTokenizer, BartTokenizer, BartForConditionalGeneration\n",
    "from sentence_transformers import SentenceTransformer, util\n",
    "import pandas as pd\n",
    "from kiwipiepy import Kiwi\n",
    "import re\n",
    "\n",
    "# 모델 미리 로드 (한 번만 로드하고 이후에는 로드된 모델 사용)\n",
    "print(\"Loading models...\")\n",
    "\n",
    "# 감정 분석 파이프라인 모델 로드\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"beomi/KcELECTRA-base-v2022\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"beomi/KcELECTRA-base-v2022\", num_labels=7)\n",
    "sentiment_analyzer = pipeline(\"text-classification\", model=model, tokenizer=tokenizer)\n",
    "\n",
    "# SBERT 모델 로드\n",
    "model2 = SentenceTransformer('snunlp/KR-SBERT-V40K-klueNLI-augSTS')\n",
    "embeddings = model2.encode(texts, convert_to_tensor=True)\n",
    "\n",
    "# 번역 및 이모지 변환 모델 로드\n",
    "ko_to_en_model = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-ko-en')\n",
    "ko_to_en_tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-ko-en')\n",
    "emoji_tokenizer = BartTokenizer.from_pretrained(\"KomeijiForce/bart-large-emojilm\")\n",
    "emoji_generator = BartForConditionalGeneration.from_pretrained(\"KomeijiForce/bart-large-emojilm\")\n",
    "\n",
    "# 맞춤법 교정 모델 로드\n",
    "corrector_model = T5ForConditionalGeneration.from_pretrained(\"j5ng/et5-typos-corrector\")\n",
    "corrector_tokenizer = T5Tokenizer.from_pretrained(\"j5ng/et5-typos-corrector\")\n",
    "\n",
    "kiwi = Kiwi(num_workers=0, model_path=None, load_default_dict=True, integrate_allomorph=True, model_type='knlm', typos=None, typo_cost_threshold=2.5)\n",
    "print(\"Models loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Ed2BLgJKNgT"
   },
   "source": [
    "## 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_5-hk6zzKQdS"
   },
   "source": [
    "### 문장 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GQqDB_N5KM_M"
   },
   "outputs": [],
   "source": [
    "def split(a):\n",
    "  sentences = kiwi.split_into_sents(a)\n",
    "  textArray = [sentence.text for sentence in sentences]\n",
    "  return textArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F-Em9AdJKVDF"
   },
   "source": [
    "### 신조어 치환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j91hyQZ_KXx_"
   },
   "outputs": [],
   "source": [
    "def correct_new_words_before_tokenizing(sentence):\n",
    "    # 신조어 사전에 있는 단어를 치환\n",
    "    for new_word, standard_word in new_words_dict.items():\n",
    "        if new_word in sentence:\n",
    "            sentence = sentence.replace(new_word, standard_word)\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q_hwUZK0K9nT"
   },
   "source": [
    "### 맞춤법 교정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5DxZ9wtNLAQ_"
   },
   "outputs": [],
   "source": [
    "# 디바이스 설정 (T4 GPU 사용)\n",
    "device = \"cpu\"\n",
    "\n",
    "# 모델 로드 시 GPU로 이동\n",
    "corrector_model = corrector_model.to(device)\n",
    "model = model.to(device)\n",
    "model2 = model2.to(device)\n",
    "ko_to_en_model = ko_to_en_model.to(device)\n",
    "emoji_generator = emoji_generator.to(device)\n",
    "\n",
    "# 각 문장에 대해 맞춤법 교정 수행\n",
    "def correct_sentence(sentence):\n",
    "    # 입력 문장 인코딩\n",
    "    input_encoding = corrector_tokenizer(sentence, return_tensors=\"pt\").to(device)\n",
    "    input_ids = input_encoding.input_ids\n",
    "    attention_mask = input_encoding.attention_mask\n",
    "\n",
    "    # T5 모델 출력 생성\n",
    "    output_encoding = corrector_model.generate(\n",
    "        input_ids=input_ids,\n",
    "        attention_mask=attention_mask,\n",
    "        max_length=128,\n",
    "        num_beams=5,\n",
    "        early_stopping=True,\n",
    "    )\n",
    "\n",
    "    # 출력 문장 디코딩\n",
    "    output_text = corrector_tokenizer.decode(output_encoding[0], skip_special_tokens=True)\n",
    "\n",
    "    # 교정된 문장 추가\n",
    "    return output_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bJAjR4yEKYEk"
   },
   "source": [
    "### 번역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bQTRWAlZKmzr"
   },
   "outputs": [],
   "source": [
    "# 한국어-영어 번역 함수\n",
    "def translate_ko_to_en(text):\n",
    "    tokens = ko_to_en_tokenizer(text, return_tensors=\"pt\", padding=True).to(device)\n",
    "    translation = ko_to_en_model.generate(**tokens)\n",
    "    return ko_to_en_tokenizer.decode(translation[0], skip_special_tokens=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eQVc0zkOKqY0"
   },
   "source": [
    "## Text2Emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JitEp6y2KtSW"
   },
   "outputs": [],
   "source": [
    "# 감정 분석 및 이모지 변환 함수\n",
    "def emoji_convert(text):\n",
    "    inputs = emoji_tokenizer(text, return_tensors=\"pt\").to(device)\n",
    "    generated_ids = emoji_generator.generate(inputs[\"input_ids\"], num_beams=4, do_sample=True, max_length=100)\n",
    "    emoji_text = emoji_tokenizer.decode(generated_ids[0], skip_special_tokens=True).replace(\" \", \"\")\n",
    "    return emoji_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jo58skprHPYl"
   },
   "source": [
    "### 감정분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "vkRPM0ZHHRXZ",
    "outputId": "b550f907-128d-4703-a18c-7118cb6ec158"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n",
      "<ipython-input-14-c4ae16d60081>:46: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
      "  trainer = Trainer(\n",
      "<ipython-input-14-c4ae16d60081>:17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1449' max='1449' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1449/1449 35:27, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.069000</td>\n",
       "      <td>1.108532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.745800</td>\n",
       "      <td>1.097785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-c4ae16d60081>:17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
      "<ipython-input-14-c4ae16d60081>:17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1449, training_loss=1.0462412258277192, metrics={'train_runtime': 2129.3388, 'train_samples_per_second': 43.498, 'train_steps_per_second': 0.68, 'total_flos': 7139931505605000.0, 'train_loss': 1.0462412258277192, 'epoch': 3.0})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import Trainer, TrainingArguments\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 학습 데이터와 검증 데이터로 분리 (80% 학습, 20% 검증)\n",
    "train_texts, eval_texts, train_labels, eval_labels = train_test_split(train_texts, train_labels, test_size=0.2, random_state=42)\n",
    "\n",
    "# 학습 데이터 토크나이징\n",
    "train_encodings = tokenizer(train_texts, truncation=True, padding=True, return_tensors=\"pt\")\n",
    "eval_encodings = tokenizer(eval_texts, truncation=True, padding=True, return_tensors=\"pt\")\n",
    "\n",
    "class SentimentDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "train_dataset = SentimentDataset(train_encodings, train_labels)\n",
    "eval_dataset = SentimentDataset(eval_encodings, eval_labels)\n",
    "\n",
    "# 학습 설정\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',\n",
    "    num_train_epochs=3,\n",
    "    per_device_train_batch_size=64,\n",
    "    per_device_eval_batch_size=32,\n",
    "    warmup_steps=500,\n",
    "    weight_decay=0.01,\n",
    "    logging_dir='./logs',\n",
    "    logging_steps=10,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_total_limit=2,\n",
    "    load_best_model_at_end=True,\n",
    "    save_steps=500,\n",
    "    eval_steps=500,\n",
    "    report_to=[\"none\"]\n",
    ")\n",
    "\n",
    "# Trainer 객체 생성\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    "    tokenizer=tokenizer\n",
    ")\n",
    "\n",
    "# 학습 시작\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LTGA7Py2_qKu"
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "def analyze_emotion(text):\n",
    "    sentiment_analyzer = pipeline(\"text-classification\", model=model, tokenizer=tokenizer)\n",
    "\n",
    "    result = sentiment_analyzer(text)[0]\n",
    "\n",
    "    label_mapping = {\n",
    "        'LABEL_0': '행복',\n",
    "        'LABEL_1': '중립',\n",
    "        'LABEL_2': '슬픔',\n",
    "        'LABEL_3': '공포',\n",
    "        'LABEL_4': '혐오',\n",
    "        'LABEL_5': '분노',\n",
    "        'LABEL_6': '놀람'\n",
    "    }\n",
    "\n",
    "    label = result['label']\n",
    "    emotion = label_mapping.get(label, \"알 수 없음\")  # 레이블을 감정 이름으로 변환\n",
    "\n",
    "    if emotion in ['슬픔', '공포', '혐오', '분노', '중립']:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XHwOZClNFJY-"
   },
   "source": [
    "### 레드플래그"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CnL4YatKFRno"
   },
   "outputs": [],
   "source": [
    "# 키워드별 평균 벡터 계산 (중립과 행복 제외)\n",
    "keywords = [keyword for keyword in df_merged['keyword'].unique() if keyword not in ['행복', '중립']]\n",
    "keyword_avg_embeddings = {}\n",
    "for keyword in keywords:\n",
    "    keyword_texts = df_merged[df_merged['keyword'] == keyword]['text'].tolist()\n",
    "    if len(keyword_texts) > 0:\n",
    "        keyword_embeddings = model2.encode(keyword_texts, convert_to_tensor=True)\n",
    "        keyword_avg_embeddings[keyword] = torch.mean(keyword_embeddings, dim=0, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xUKEPZiGEi17"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "# 중립 텍스트에서 무작위로 50개 샘플링\n",
    "neutral_texts = df_merged[df_merged['keyword'] == '중립']['text'].tolist()\n",
    "neutral_sample_texts = random.sample(neutral_texts, min(40, len(neutral_texts)))\n",
    "neutral_embeddings = model2.encode(neutral_sample_texts, convert_to_tensor=True)\n",
    "\n",
    "# 행복 텍스트에서 무작위로 50개 샘플링\n",
    "happy_texts = df_merged[df_merged['keyword'] == '행복']['text'].tolist()\n",
    "happy_sample_texts = random.sample(happy_texts, min(40, len(happy_texts)))\n",
    "happy_embeddings = model2.encode(happy_sample_texts, convert_to_tensor=True)\n",
    "\n",
    "# 중립과 행복 벡터 추가\n",
    "keyword_avg_embeddings['중립'] = neutral_embeddings\n",
    "keyword_avg_embeddings['행복'] = happy_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bWEaxvZCFXhl"
   },
   "outputs": [],
   "source": [
    "# 가장 유사한 키워드 반환 함수 정의\n",
    "def get_most_similar_keyword(target_sentence, model2, keyword_avg_embeddings):\n",
    "    target_embedding = model2.encode(target_sentence, convert_to_tensor=True).to(device)\n",
    "    keyword_similarities = {}\n",
    "\n",
    "    for keyword, avg_embedding in keyword_avg_embeddings.items():\n",
    "        if avg_embedding.dim() > 1:  # 중립과 행복 벡터는 여러 개임, 평균 벡터 계산\n",
    "            avg_embedding = torch.mean(avg_embedding, dim=0).to(device)\n",
    "        else:\n",
    "            avg_embedding = avg_embedding.to(device)\n",
    "        similarity = util.pytorch_cos_sim(target_embedding, avg_embedding).item()\n",
    "        keyword_similarities[keyword] = similarity\n",
    "\n",
    "# 가장 유사도가 높은 키워드 반환\n",
    "    most_similar_keyword = max(keyword_similarities, key=keyword_similarities.get)\n",
    "    return most_similar_keyword, keyword_similarities[most_similar_keyword]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5PPS0elNMDm3"
   },
   "outputs": [],
   "source": [
    "def emotionize(a):\n",
    "  if a in [\"죽고싶음\", \"죽이고싶음\", \"희망없음\", \"앞이 깜깜함\", \"가치 없음\", \"낭떠러지 끝에 있는 느낌\"]:\n",
    "    return 1000\n",
    "  elif a in [\n",
    "    \"자존감감소\", \"패배감\", \"하찮음\", \"괴로움\", \"못남\", \"절망감\", \"포기\",\n",
    "    \"좌절감\", \"절박\",\n",
    "    \"피해의식\", \"비웃음\", \"눈초리\", \"냉소\", \"감시\", \"기분우울\", \"슬픔\", \"눈물\", \"울음\", \"죄책감\", \"자괴감\", \"비참\", \"서러움\",\n",
    "    \"고독감\", \"막막함\", \"고통\"\n",
    "]:\n",
    "    return 2\n",
    "  elif a in [\n",
    "    \"우울\", \"침울\", \"멍함\", \"서운함\", \"섭섭함\", \"불만\", \"슬럼프\", \"나른함\", \"힘없음\", \"처짐\", \"힘빠짐\", \"찌뿌둥함\", \"많이잠\", \"느릿느릿함\",\n",
    "    \"무의욕\", \"수면장애\", \"집중력감소\", \"의욕없음\", \"잠이안옴\", \"무기력\", \"의욕감소\", \"불면\", \"흥미상실\", \"식욕부진\",\n",
    "    \"공허\", \"낙 없음\", \"즐거움감소\", \"답답함\", \"기분처짐\", \"짜증\", \"안절부절못함\", \"조마조마함\", \"혼란\", \"허무함\", \"허탈\",\n",
    "    \"소심\", \"자신감감소\", \"위축\", \"우유부단\", \"결단력없음\",\n",
    "    \"속상함\", \"후회\", \"후회함\", \"억울함\",\n",
    "    \"외로움\", \"쓸쓸함\"\n",
    "]:\n",
    "    return 1\n",
    "  else:\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_56cvO0YvtIP"
   },
   "outputs": [],
   "source": [
    "# risk의 종류에 따라 다른 메시지 출력하는 함수\n",
    "import random\n",
    "\n",
    "comforting_messages = [\n",
    "    \"오늘 힘들었다면 잠시 쉬어도 괜찮아요. 당신은 소중한 사람이에요. 🌟\",\n",
    "    \"지금은 어렵게 느껴질 수 있지만, 분명히 더 나아질 거예요. 당신의 마음을 믿어요. 💖\",\n",
    "    \"힘들 땐 혼자 짊어지지 말고 주변에 도움을 요청하세요. 당신은 혼자가 아니에요. 🤗\",\n",
    "    \"스스로를 다독여 주세요. 당신은 지금도 충분히 잘하고 있어요. 🌈\",\n",
    "    \"작은 한 걸음도 소중해요. 천천히, 꾸준히 걸어가요. 🌱\",\n",
    "    \"오늘도 최선을 다한 당신에게 따뜻한 응원을 보냅니다. ☀️\",\n",
    "    \"때로는 쉬어가는 것도 필요한 시간이랍니다. 괜찮아요. 🌼\",\n",
    "    \"작은 기쁨 하나씩 찾아보며 스스로에게 웃음을 선물해 주세요. 😊\",\n",
    "    \"어두운 길도 언젠가 환히 비춰질 거예요. 조금만 더 힘내봐요. 🌙\",\n",
    "    \"당신의 하루는 누군가에게 큰 위안이 되고 있을 거예요. 💌\",\n",
    "    \"지금도 당신은 충분히 특별하고 소중한 존재예요. 💎\",\n",
    "    \"매일 작은 변화가 모여 큰 성장을 이룬답니다. 자신을 믿어주세요. 🌻\",\n",
    "    \"당신은 존재만으로도 많은 사람에게 기쁨이 되고 있어요. ❤️\",\n",
    "    \"마음의 짐이 무거울 땐 깊게 숨을 쉬어보세요. 괜찮아질 거예요. 🍃\",\n",
    "    \"오늘 당신이 해낸 모든 일들, 다 멋진 걸음이에요. 자랑스러워요. 🏞️\"\n",
    "]\n",
    "cautionary_messages = [\n",
    "    \"혹시 너무 많은 스트레스를 받고 있는 건 아닌가요? 잠시 멈추고 자신을 돌아보세요. ⚠️\",\n",
    "    \"최근 감정이 무겁다면 믿을 수 있는 사람과 대화해보는 건 어떨까요? 🛑\",\n",
    "    \"힘든 상황에서 무리하려 하지 말고 스스로를 보호하는 선택을 해보세요. 🕊️\",\n",
    "    \"이 감정이 오래 지속된다면 전문가의 도움을 받는 것도 좋은 방법이에요. 🩺\",\n",
    "    \"어떤 결정을 내리기 전, 감정에 휩쓸리지 않고 신중하게 생각해보세요. 🛡️\",\n",
    "    \"혹시 스스로를 너무 몰아붙이고 있진 않은가요? 자신에게 휴식을 허락해 주세요. 🚦\",\n",
    "    \"감정이 격해질 땐 잠시 멈추고 자신을 돌아보는 시간을 가져보세요. ⏳\",\n",
    "    \"마음이 지쳤다면 도움을 요청해도 괜찮아요. 당신은 소중하니까요. 🫂\",\n",
    "    \"스스로 감당하기 힘든 상황이라면 혼자 끙끙 앓지 말고 주변에 알리세요. 📣\",\n",
    "    \"힘든 순간에는 모든 걸 내려놓고 마음의 평화를 찾는 것이 필요할 수도 있어요. 🕊️\",\n",
    "    \"가끔은 자신의 감정을 기록해보세요. 이를 통해 마음을 정리할 수 있을 거예요. ✍️\",\n",
    "    \"최근 힘든 일이 많았다면 충분히 쉬고 스스로를 위로하는 시간을 가져보세요. 🌌\",\n",
    "    \"너무 깊은 고민에 빠지기보단 믿을 수 있는 사람과 함께 해결해보세요. 🤝\",\n",
    "    \"당신의 감정은 소중합니다. 이를 무시하거나 억누르지 않아도 돼요. 🌷\",\n",
    "    \"스스로를 돌보는 것이야말로 진정한 용기입니다. 당신을 응원해요. 🛡️\"\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "def showMessage(risk):\n",
    "  risk=float(risk)\n",
    "  if (2/3)> risk >= (1/3):\n",
    "    return(comforting_messages[random.randint(0, len(comforting_messages) - 1)])\n",
    "  elif risk >= (2/3):\n",
    "    return(cautionary_messages[random.randint(0, len(cautionary_messages) - 1)]+\"\\n\"+\"\\n\" + \"지금 힘든 상황에 처해 있거나 감정적으로 어려움을 겪고 계신다면,\"+\"\\n\"+\"전문적인 도움을 받는 것이 중요합니다.\" + \"\\n\" + \"한국 정신건강 상담전화 1577-0199\")\n",
    "  else :\n",
    "    return \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kC9BwqrbMHjL"
   },
   "source": [
    "## 최종"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ieFJXRrIL6iW"
   },
   "outputs": [],
   "source": [
    "# 전체 함수 실행\n",
    "def correct(a):\n",
    "  korean_text = a\n",
    "  slang_korean= correct_new_words_before_tokenizing(korean_text)\n",
    "  correct_korean=correct_sentence(slang_korean)\n",
    "  return correct_korean\n",
    "\n",
    "def text2emoji(a):\n",
    "  translated_text = translate_ko_to_en(a)\n",
    "  emoji_text = emoji_convert(translated_text)\n",
    "  return emoji_text\n",
    "def emotion_texting(a):\n",
    "  most_similar_keyword, similarity = get_most_similar_keyword(a, model2, keyword_avg_embeddings)\n",
    "  return most_similar_keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y3N73x_VEN6j"
   },
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   "source": [
    "def final(user_input):\n",
    "    count = 0\n",
    "    emotion = 0\n",
    "    analyze= 0\n",
    "    diary = \"\"\n",
    "    emoji = \"\"\n",
    "\n",
    "    textArray = split(user_input)\n",
    "\n",
    "    for i in textArray:\n",
    "        count += 1\n",
    "        correct_korean = correct(i)\n",
    "        emoji = text2emoji(correct_korean)\n",
    "        analyze=analyze_emotion(i)\n",
    "        redscore = emotion_texting(i)\n",
    "        diary = diary + i + emoji + \"\\t\"\n",
    "        if analyze == True:\n",
    "          emotion += emotionize(redscore)\n",
    "        else:\n",
    "          emotion+=0\n",
    "    output_message = showMessage(emotion / count)\n",
    "    output = diary + '\\n'+'\\n'+'\\n'+ output_message\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "text": [
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "* Running on public URL: https://d7fbcb0148910dff4f.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
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       "<div><iframe src=\"https://d7fbcb0148910dff4f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
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      ]
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     },
     "execution_count": 64,
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   "source": [
    "# Gradio 인터페이스 정의\n",
    "def run_final(user_input):\n",
    "    return final(user_input)\n",
    "\n",
    "css = \"\"\"\n",
    "/* 전체 컨테이너 수직 정렬 */\n",
    ".gradio-container {\n",
    "    display: flex;\n",
    "    justify-content: center;\n",
    "    align-items: center;\n",
    "    width: 900px;\n",
    "    height: 100vh; /* 전체 화면에 수직 정렬 */\n",
    "    padding-top: 200px;\n",
    "\n",
    "    gap: 20px; /* 요소 간 간격 */\n",
    "    background-color: #FFF8dc;\n",
    "}\n",
    "\n",
    "/* 입력과 출력 박스를 가로로 배치 */\n",
    ".gradio-input-output {\n",
    "    width: 300px;\n",
    "    gap: 20px; /* 요소 간 간격 */\n",
    "}\n",
    "\n",
    ".gap.svelte-vt1mxs {\n",
    "  width: 900px;\n",
    "  padding-top: 12vh;\n",
    "}\n",
    "\n",
    ".gradio-container-5-6-0 .prose * {\n",
    "  width: 900px;\n",
    "  text-align: center;\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "/* description 스타일 */\n",
    "#interface-description {\n",
    "    width: 900px;\n",
    "    text-align: center;\n",
    "    margin-bottom: 20px;\n",
    "    font-size: 2rem;\n",
    "}\n",
    "\n",
    ".gradio-container.gradio-container-5-6-0 .contain button {\n",
    "  height: 60px;\n",
    "  font-size: 1.5rem;\n",
    "}\n",
    "\n",
    "/* input & output container */\n",
    "\n",
    "div.svelte-633qhp .block {\n",
    "  background-color: #fffaf0;\n",
    "}\n",
    "\n",
    "/* input label */\n",
    "span.svelte-1gfkn6j {\n",
    "  font-size: 1.2rem;\n",
    "  font-weight: 600;\n",
    "}\n",
    "\n",
    "/* 버튼 스타일 */\n",
    "button {\n",
    "    padding: 10px 20px;\n",
    "    background-color: #ff7f50;\n",
    "    color: white;\n",
    "    border: none;\n",
    "    border-radius: 5px;\n",
    "    cursor: pointer;\n",
    "}\n",
    "\n",
    "button:hover {\n",
    "    background-color: #ff6347;\n",
    "}\n",
    "\n",
    "/* Emotify 🤩 */\n",
    "h1 {\n",
    "  font-size : 3rem;\n",
    "}\n",
    "\n",
    "/*  텍스트에 감정을 더하다😉 */\n",
    "h3 {\n",
    "  font-size : 2rem;\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "title = \"Emotify 🤩\"  # 페이지 제목 설정\n",
    "\n",
    "# Gradio Blocks를 사용하여 UI 구성\n",
    "with gr.Blocks(css=css, title=title) as demo:\n",
    "    gr.Markdown(\"# Emotify🤩 \\n ### 텍스트에 감정을 더하다😉\")\n",
    "\n",
    "    # 입력과 출력 박스를 가로로 배치\n",
    "    with gr.Row(elem_id=\"gradio-input-output\"):\n",
    "        input_box = gr.Textbox(lines=20, placeholder=\"감정을 표현해 보세요.\", label=\"감정 표현하기\")\n",
    "        output_box = gr.Textbox(lines=20, label=\"감정 불어넣기\")\n",
    "\n",
    "    # 버튼 추가\n",
    "    submit_button = gr.Button(\"감정 불어넣기😙\")  # 버튼 텍스트 설정\n",
    "\n",
    "    # 버튼 클릭 이벤트 연결\n",
    "    submit_button.click(fn=final, inputs=input_box, outputs=output_box)\n",
    "\n",
    "# Gradio 실행\n",
    "demo.launch(share=True)\n"
   ]
  }
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