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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YeongChaeYun7/OpenCV/blob/main/Durian_OCR_model_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# < 서류 이미지에서 텍스트 추출 OCR 프로젝트 >\n",
        "\n",
        "### 소속\n",
        "  한국폴리텍대학교 스마트금융과 7기\n",
        "\n",
        "### 팀원\n",
        "  윤영채, 이지은\n",
        "\n",
        "### 목표\n",
        "  다양한 형식의 이미지 파일에서 특정 텍스트 정보를 추출하는 시스템 개발\n",
        "\n",
        "### 데이터\n",
        "  (주)두리안정보기술사의 납품/검수 확인서(PDF, jpg) 및 제품정보(xlsx)\n",
        "\n",
        "### 목차\n",
        "1. 필요 패키지 및 라이브러리 연동\n",
        "2. 원본 데이터 확인\n",
        "3. PDF 텍스트 추출 라이브러리 비교 및 적용\n",
        "4. 데이터 전처리\n",
        "5. OCR 라이브러리 비교 및 적용\n",
        "6. 추출한 텍스트 데이터 구조화 및 저장\n",
        "7. 추출한 텍스트 데이터 유사도 검증"
      ],
      "metadata": {
        "id": "SwORoYGogJRZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 필요 패키지 및 라이브러리 연동"
      ],
      "metadata": {
        "id": "IIM1bdSEFCFI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install tesseract-ocr\n",
        "!sudo apt-get install tesseract-ocr-kor\n",
        "!pip -qqq install PyPDF2\n",
        "!pip -qqq install pdfminer.six\n",
        "!pip -qqq install pdfplumber\n",
        "!pip -qqq install pytesseract\n",
        "!pip -qqq install PyMuPDF # pymupdf와 같은 라이브러리 최신 버전\n",
        "!pip -qqq install easyocr\n",
        "!pip -qqq install keras-ocr\n",
        "!pip -qqq install --upgrade google-api-python-client # 2.133.0\n",
        "!pip -qqq install --upgrade google-cloud-vision # 3.7.2\n",
        "!pip -qqq install fuzzywuzzy"
      ],
      "metadata": {
        "id": "4R97GRMCE8-S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f32aca9-2237-49b8-dd16-54395c81cf9f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  tesseract-ocr-eng tesseract-ocr-osd\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
            "0 upgraded, 3 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 4,816 kB of archives.\n",
            "After this operation, 15.6 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n",
            "Fetched 4,816 kB in 1s (4,471 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-eng.\n",
            "(Reading database ... 121913 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
            "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
            "Selecting previously unselected package tesseract-ocr-osd.\n",
            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
            "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr-kor\n",
            "0 upgraded, 1 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 1,052 kB of archives.\n",
            "After this operation, 1,693 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-kor all 1:4.00~git30-7274cfa-1.1 [1,052 kB]\n",
            "Fetched 1,052 kB in 1s (1,558 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-kor.\n",
            "(Reading database ... 121960 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-kor_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
            "Unpacking tesseract-ocr-kor (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr-kor (1:4.00~git30-7274cfa-1.1) ...\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.6/232.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.9/57.9 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.8/15.8 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m908.3/908.3 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m68.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.3/42.3 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.0/40.0 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.9/11.9 MB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m459.6/459.6 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import PyPDF2\n",
        "import re\n",
        "import fitz  # PyMuPDF\n",
        "from pdfminer.high_level import extract_pages\n",
        "from pdfminer.layout import LTTextContainer\n",
        "import pdfplumber\n",
        "from PIL import Image\n",
        "import pytesseract\n",
        "import requests\n",
        "import easyocr\n",
        "import keras_ocr\n",
        "from google.cloud import vision\n",
        "from fuzzywuzzy import fuzz\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from difflib import SequenceMatcher\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "WBAOenH_BgSr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4909a7e8-c83f-45e8-bf49-0514445c6fc9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
            "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lBIz8XSAxQS",
        "outputId": "3414d259-3050-42ec-893f-9e8b0927c247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/Colab Notebooks\n"
          ]
        }
      ],
      "source": [
        "# 구글 드라이브 연동\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "%cd /content/drive/MyDrive/Colab Notebooks"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 원본 데이터 확인"
      ],
      "metadata": {
        "id": "CyaXCM5jGcOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def findTargetPaths(dataInputPath):\n",
        "    '''\n",
        "    경로 내에서 특정 확장자를 가지는 파일의 경로를 추출하는 함수\n",
        "    '''\n",
        "    paths = [] # 타겟 파일의 전체 경로를 저장할 리스트\n",
        "    extension_list=['png', 'jpeg', 'jpg', 'pdf']# 타겟 파일의 확장자\n",
        "\n",
        "    try:\n",
        "        for dirpath, dirnames, filenames in os.walk(dataInputPath): # 지정 경로 내의 하위 디렉토리 전체 순회\n",
        "            for filename in filenames:\n",
        "                if 'table' in filename:# 테이블 이미지 제외, 원본 이미지만 호출\n",
        "                  raise\n",
        "                if filename.split('.')[-1] in extension_list:\n",
        "                    paths.append(os.path.join(dirpath, filename)) # 파일의 전체 경로를 문자열으로 paths list에 저장\n",
        "                else:\n",
        "                  pass\n",
        "    except Exception as e:\n",
        "                print(e)\n",
        "    return paths"
      ],
      "metadata": {
        "id": "qFSX4rcMBmjN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 경로 내의 타겟 파일(png, jpeg, jpg, pdf) 확인\n",
        "dataInputPath = \"./durian_dataset\"\n",
        "paths = findTargetPaths(dataInputPath)\n",
        "# paths"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sS_vn4JEDYeq",
        "outputId": "29ecd8dc-7b48-4b59-fb85-ff3c0bbff613"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No active exception to reraise\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이미지 호출 함수\n",
        "def load_image(path, extension_list=['png', 'jpeg', 'jpg', 'pdf']):\n",
        "  '''\n",
        "  파일 경로를 입력받아 numpy.ndarray 타입의 이미지 변수로 변경해주는 함수\n",
        "  '''\n",
        "  extension = path.split('.')[-1]\n",
        "  filename = path.split('/')[-1]  # Exception 표시용\n",
        "\n",
        "  if extension not in extension_list:\n",
        "    raise ValueError(f'Unsupported extension: {filename}')\n",
        "\n",
        "  # png, jpeg, jpg 확장자 파일일 경우\n",
        "  if extension in ['png', 'jpeg', 'jpg'] :\n",
        "    flag = cv2.IMREAD_COLOR\n",
        "    img = cv2.imread(path, flag)\n",
        "\n",
        "  # pdf 확장자 파일일 경우\n",
        "  elif extension == 'pdf':\n",
        "    pdf_document = fitz.open(path)\n",
        "    page = pdf_document.load_page(0)  # 첫 번째 페이지만 호출 설정\n",
        "    pix = page.get_pixmap()\n",
        "    img = cv2.imdecode(np.frombuffer(pix.tobytes(), dtype=np.uint8), cv2.IMREAD_GRAYSCALE) # 흑백이미지로 호출하여 채널 1개만 사용\n",
        "  else:\n",
        "    raise ValueError(f'Unsupported extension: {filename}')\n",
        "\n",
        "  if img is None:\n",
        "    raise Exception(f'Image call failed: {filename}')\n",
        "\n",
        "  return img"
      ],
      "metadata": {
        "id": "W-sI-OYmB9ea"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "load_image(paths[0])"
      ],
      "metadata": {
        "id": "LsmR0pT53nme",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "ebde9330-f6bf-4c6a-aa17-3d402e5c7f86"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       ...,\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)"
            ],
            "text/html": [
              "<style>\n",
              "      .ndarray_repr .ndarray_raw_data {\n",
              "        display: none;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
              "        display: block;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
              "        display: none;\n",
              "      }\n",
              "      </style>\n",
              "      <div id=\"id-0d76a36b-6fa0-499b-ad8d-e13496e83712\" class=\"ndarray_repr\"><pre>ndarray (842, 596) <button style=\"padding: 0 2px;\">show data</button></pre><img 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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       ...,\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)</pre></div><script>\n",
              "      (() => {\n",
              "      const titles = ['show data', 'hide data'];\n",
              "      let index = 0\n",
              "      document.querySelector('#id-0d76a36b-6fa0-499b-ad8d-e13496e83712 button').onclick = (e) => {\n",
              "        document.querySelector('#id-0d76a36b-6fa0-499b-ad8d-e13496e83712').classList.toggle('show_array');\n",
              "        index = (++index) % 2;\n",
              "        document.querySelector('#id-0d76a36b-6fa0-499b-ad8d-e13496e83712 button').textContent = titles[index];\n",
              "        e.preventDefault();\n",
              "        e.stopPropagation();\n",
              "      }\n",
              "      })();\n",
              "    </script>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PDF 텍스트 추출 라이브러리 비교 및 적용\n",
        "- PyPDF2\n",
        "\n",
        "  PDF 병합, 분할, 메타데이터 수정 가능  \n",
        "  텍스트 추출 정확도 낮음  \n",
        "\n",
        "- PyMuPDF (fitz)\n",
        "\n",
        "  빠른 속도, PDF 렌더링 기능 포함  \n",
        "  텍스트 추출 정확도 낮음, 복잡한 레이아웃 처리 어려움  \n",
        "\n",
        "- pdfminer.six\n",
        "\n",
        "  PDF 구조 분석, 텍스트 레이아웃 보존  \n",
        "  속도 느림, 이미지 기반 PDF 처리 어려움\n",
        "\n",
        "- pdfplumber\n",
        "\n",
        "  텍스트 및 이미지 추출 가능, 다양한 PDF 포맷 지원  \n",
        "  텍스트 추출 정확도 낮음, 이미지 기반 PDF 처리 어려움  \n",
        "  "
      ],
      "metadata": {
        "id": "LiPUo-XQq_VK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##PyPDF2"
      ],
      "metadata": {
        "id": "MytHs6eGXxKY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_text_pypdf2(path):\n",
        "    with open(path, 'rb') as file:\n",
        "\n",
        "        reader = PyPDF2.PdfReader(file)\n",
        "        text_list = []\n",
        "\n",
        "        for page_num in range(len(reader.pages)):\n",
        "            page = reader.pages[page_num]\n",
        "            text = page.extract_text()\n",
        "            text_list.append(text.split('\\n'))\n",
        "    return text_list"
      ],
      "metadata": {
        "id": "QdZVmKBQXwUO"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = paths[0]\n",
        "print(extract_text_pypdf2(path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f-oJYwgYD6B",
        "outputId": "ffdafcd0-8c81-4a4a-aa84-f7cca839db74"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['내용 : 한국조세재정연구원 무선침입방지시스템(WIPS) 방화벽 장비 구입 ', '일자 : 2023-01-16', \"NO. MODEL DESCRIPTION Q'TY Serial REMARK\", '번호 모델명 품    명 수량 Number 비 고', '1 BLUEMAX WIPS 1000 WIPS 1', 'JTM9UA00700592 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001961', '3 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001962', '4 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001963', '5BLUEMAX NGF 200 BASIC UTM 1DSF22C12S14009', '6', '7', '내  역', '** 받으신 후 하단 인수자란에 서명하시어 FAX 부탁드립니다.   (FAX: 02-6455-3987)', '인계자', '** 상기와 같이  인계/설치하였음을 확인합니다.', '회    사 : ㈜ 두리안정보기술', '부    서 : 영 업 본 부', '성    명 : 신 기 수', '확인자', '** 상기와 같이  인수/설치하였음을 확인합니다.', '회    사 : 한국조세재정연구원', '부    서 : ', '성    명 :', '서    명 :DURIANIT co., LTD', '603ho, WOORIM W_CITY, 9-22, Pangyo-ro 255beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea ', ' TEL:82-2-542-4987, FAX:82-2-6455-3987', '납 품 / 검 수 확 인 서', 'Àü', '»ê', 'ÇÐ', '¼ú', 'Á¤', 'º¸', 'ÆÀ', '±è', '¹Î', '¿µ', '±è', '¹Î', '¿µ', '']]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PyPDF2/_cmap.py:142: PdfReadWarning: Advanced encoding /KSCms-UHC-H not implemented yet\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.figure(figsize=(20, 20))\n",
        "# plt.imshow(cv2.cvtColor(load_image(path), cv2.IMREAD_GRAYSCALE))\n",
        "# plt.title('org')\n",
        "# plt.axis('off')\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "BBBdjKqrmlzH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##PyMuPDF (fitz)"
      ],
      "metadata": {
        "id": "g7ONfMWWX0Jq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_text_pymupdf(path):\n",
        "    doc = fitz.open(path)\n",
        "    text_list = []\n",
        "    for page_num in range(len(doc)):\n",
        "        page = doc.load_page(page_num)\n",
        "        blocks = page.get_text(\"blocks\")\n",
        "        page_text = [block[4] for block in blocks]\n",
        "        text_list.append(page_text)\n",
        "    return text_list"
      ],
      "metadata": {
        "id": "jpS0QtIqZJtn"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(extract_text_pymupdf(path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5hKimCjZNQ4",
        "outputId": "37d8ac58-0439-451d-8e46-6c3815c54fd5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['내용 : 한국조세재정연구원 무선침입방지시스템(WIPS) 방화벽 장비 구입 \\n일자 : 2023-01-16\\n', \"NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\n\", '번호\\n모델명\\n품    명\\n수량\\nNumber\\n비 고\\n', '1\\nBLUEMAX WIPS 1000\\nWIPS\\n1\\n', 'JTM9UA0070059\\n', '2\\nBLUEMAX WIPS 602\\nWIPS\\n1\\nJTM8WA1001961\\n', '3\\nBLUEMAX WIPS 602\\nWIPS\\n1\\nJTM8WA1001962\\n', '4\\nBLUEMAX WIPS 602\\nWIPS\\n1\\nJTM8WA1001963\\n', '5\\nBLUEMAX NGF 200 BASIC\\nUTM\\n1\\n', 'DSF22C12S14009\\n', '6\\n', '7\\n', '내  역\\n', '** 받으신 후 하단 인수자란에 서명하시어 FAX 부탁드립니다.   (FAX: 02-6455-3987)\\n', '인계자\\n', '** 상기와 같이  인계/설치하였음을 확인합니다.\\n', '회    사 : ㈜ 두리안정보기술\\n', '부    서 : 영 업 본 부\\n', '성    명 : 신 기 수\\n', '확인자\\n', '** 상기와 같이  인수/설치하였음을 확인합니다.\\n', '회    사 : 한국조세재정연구원\\n', '부    서 : \\n', '성    명 :\\n', '서    명 :\\n', 'DURIANIT co.,LTD\\n', '603ho, WOORIM W_CITY, 9-22, Pangyo-ro 255beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea \\n', ' TEL:82-2-542-4987, FAX:82-2-6455-3987\\n', '납 품 / 검 수 확 인 서\\n', '전산학술정보팀\\n김민영\\n김민영\\n']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##pdfminer.six"
      ],
      "metadata": {
        "id": "QPkcmninX2Bh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_text_pdfminer(path):\n",
        "    text_list = []\n",
        "    for page_layout in extract_pages(path):\n",
        "        page_text = []\n",
        "        for element in page_layout:\n",
        "            if isinstance(element, LTTextContainer):\n",
        "                page_text.append(element.get_text())\n",
        "        text_list.append(page_text)\n",
        "    return text_list"
      ],
      "metadata": {
        "id": "b-afvipOXwPU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(extract_text_pdfminer(path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0X8D3Honz40",
        "outputId": "583a7d5d-047e-457b-e46b-ed80a720f0ed"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['DURIANIT co.,LTD\\n', '603ho, WOORIM W_CITY, 9-22, Pangyo-ro 255beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea \\n', ' TEL:82-2-542-4987, FAX:82-2-6455-3987\\n', '납 품 / 검 수 확 인 서\\n', '내용 : 한국조세재정연구원 무선침입방지시스템(WIPS) 방화벽 장비 구입 \\n일자 : 2023-01-16\\n', 'NO.\\n번호\\n', 'MODEL\\n모델명\\n', 'DESCRIPTION\\n품    명\\n', \"Q'TY\\n수량\\n\", 'Serial\\nNumber\\n', 'REMARK\\n비 고\\n', 'BLUEMAX WIPS 1000\\n', 'BLUEMAX WIPS 602\\n', 'BLUEMAX WIPS 602\\n', 'BLUEMAX WIPS 602\\n', 'BLUEMAX NGF 200 BASIC\\n', '1\\n', '2\\n', '3\\n', '4\\n', '5\\n', '6\\n', '7\\n내  역\\n', 'WIPS\\n', 'WIPS\\n', 'WIPS\\n', 'WIPS\\n', 'UTM\\n', '1\\n', '1\\n', '1\\n', '1\\n', '1\\n', 'DSF22C12S14009\\n', 'JTM8WA1001961\\n', 'JTM8WA1001962\\n', 'JTM8WA1001963\\n', 'JTM9UA0070059\\n', '** 받으신 후 하단 인수자란에 서명하시어 FAX 부탁드립니다.   (FAX: 02-6455-3987)\\n', '인계자\\n', '** 상기와 같이  인계/설치하였음을 확인합니다.\\n', '회    사 : ㈜ 두리안정보기술\\n', '부    서 : 영 업 본 부\\n', '성    명 : 신 기 수\\n', '확인자\\n', '** 상기와 같이  인수/설치하였음을 확인합니다.\\n', '회    사 : 한국조세재정연구원\\n', '부    서 : \\n', '성    명 :\\n', '서    명 :\\n', '전산학술정보팀\\n김민영\\n김민영\\n']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##pdfplumber"
      ],
      "metadata": {
        "id": "ALtkHtfjoV4y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_text_pdfplumber(path):\n",
        "    text_list = []\n",
        "    with pdfplumber.open(path) as pdf:\n",
        "        for page in pdf.pages:\n",
        "            page_text = page.extract_text()\n",
        "            if page_text:\n",
        "                text_list.append(page_text.split('\\n'))\n",
        "    return text_list"
      ],
      "metadata": {
        "id": "HVnWN3TMoZsx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(extract_text_pdfplumber(path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPqt3Eg4ofwN",
        "outputId": "2261dfd6-2af0-4b2c-d126-627d2ace7708"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['DURIANIT', 'co.,', 'LTD', '603ho, WOORIM W_CITY, 9-22, Pangyo-ro 255beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea', 'TEL:82-2-542-4987, FAX:82-2-6455-3987', '납 품 / 검 수 확 인 서', '내용 : 한국조세재정연구원 무선침입방지시스템(WIPS) 방화벽 장비 구입', '일자 : 2023-01-16', \"NO. MODEL DESCRIPTION Q'TY Serial REMARK\", '번호 모델명 품 명 수량 Number 비 고', '1 BLUEMAX WIPS 1000 WIPS 1 DSF22C12S14009', '2 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001961', '3 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001962', '4 BLUEMAX WIPS 602 WIPS 1 JTM8WA1001963', '5 BLUEMAX NGF 200 BASIC UTM 1 JTM9UA0070059', '6', '7', '내 역', '** 받으신 후 하단 인수자란에 서명하시어 FAX 부탁드립니다. (FAX: 02-6455-3987)', '인계자', '** 상기와 같이 인계/설치하였음을 확인합니다.', '회 사 : ㈜ 두리안정보기술', '부 서 :영 업 본 부', '성 명 : 신 기 수', '확인자', '** 상기와 같이 인수/설치하였음을 확인합니다.', '회 사 : 한국조세재정연구원', '부 서 : 전산학술정보팀', '성 명 : 김민영', '서 명 : 김민영']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PDF OCR 라이브러리 적용 결과, 이미지 품질에 따라 인식 차이가 심함  \n",
        "요구하는 이미지 수준 이상의 데이터만 적용 가능  \n",
        "PDF 형식의 파일만 적용 가능해서 확장하는데 한계가 있음"
      ],
      "metadata": {
        "id": "5OVcW8qR5A5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리\n",
        "\n",
        "이미지 데이터의 평균 수준을 상향시키고자 데이터 전처리 적용  \n",
        "파일 이미지가 대상이라 노이즈를 줄이고 선명도를 최대한 올리고자 함  \n",
        "엣지 검출을 통해 목표 텍스트가 포함된 테이블 영역만 추출"
      ],
      "metadata": {
        "id": "XhQbkV5SD9zH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 원본 이미지 확인\n",
        "\n",
        "path = paths[0] # PDF 파일\n",
        "# path = paths[5] # jpg 파일\n",
        "\n",
        "image = load_image(path)\n",
        "\n",
        "# plt.figure(figsize=(20, 20))\n",
        "plt.imshow(cv2.cvtColor(image, cv2.IMREAD_GRAYSCALE))\n",
        "plt.title('org')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "KQ-OvyevB_7W",
        "outputId": "c0a23660-1767-4b6b-ffd4-646cab9cdc09"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sharpen_image(image):\n",
        "    '''\n",
        "    이미지의 잡음을 줄이고 선명하게 만드는 함수\n",
        "    '''\n",
        "    if len(image.shape) > 2:\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    # 가우시안 블러 처리 (잡음처리. 이미 흑백 문서라 약하게 설정)\n",
        "    blurred_image = cv2.GaussianBlur(image, (3, 3), 0)\n",
        "\n",
        "    # 중앙값 블러 처리(잡음처리22)\n",
        "    median_blurred_image = cv2.medianBlur(blurred_image, 5)\n",
        "\n",
        "    # 선명화 효과\n",
        "    sharpened_image = cv2.addWeighted(image, 2.0, median_blurred_image, -1.0, 0)\n",
        "\n",
        "    # CLAHE 적용. 대비도 높임\n",
        "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n",
        "    enhanced_image = clahe.apply(sharpened_image)\n",
        "\n",
        "    return enhanced_image"
      ],
      "metadata": {
        "id": "KxrB5sIVCPep"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = image.copy()\n",
        "sharpened_img = sharpen_image(img)\n",
        "\n",
        "# 결과 비교\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title('org')\n",
        "plt.imshow(img, cmap='gray')\n",
        "plt.axis('off')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title('sharp')\n",
        "plt.imshow(sharpened_img, cmap='gray')\n",
        "plt.axis('off')\n",
        "\n",
        "# plt.figure(figsize=(20, 20))\n",
        "# plt.imshow(cv2.cvtColor(sharpened_img, cv2.IMREAD_GRAYSCALE))\n",
        "# plt.title('sharp')\n",
        "# plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "collapsed": true,
        "id": "I3pvw49Olupe",
        "outputId": "a0f1b83c-d76f-41b3-e97b-62fc76e283a6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이미지 선명화 작업 후에 적용해야 정확한 영역 추출 가능\n",
        "def extract_table(img):\n",
        "    '''\n",
        "    이미지에서 테이블 영역만 추출하는 함수\n",
        "    '''\n",
        "    # 이미지 이진화\n",
        "    _, binary = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY_INV) # 이진화 처리된 이미지는 활용하지 않아 '_' 처리\n",
        "\n",
        "    # 수평선과 수직선 검출\n",
        "    kernel_h = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1))\n",
        "    kernel_v = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 40))\n",
        "\n",
        "    detect_horizontal = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel_h)\n",
        "    detect_vertical = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel_v)\n",
        "\n",
        "    # 수평선과 수직선 결합\n",
        "    mask = detect_horizontal + detect_vertical\n",
        "\n",
        "    # 테이블 검출\n",
        "    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\n",
        "    # 테이블 추출\n",
        "    table_cnt = None\n",
        "    for cnt in contours:\n",
        "        x, y, w, h = cv2.boundingRect(cnt)\n",
        "        if w > 100 and h > 10:  # 테이블 크기에 따라 조정\n",
        "            table_cnt = cnt\n",
        "            break\n",
        "\n",
        "    if table_cnt is not None:\n",
        "        x, y, w, h = cv2.boundingRect(table_cnt)\n",
        "        res = img[y:y+h, x:x+w]\n",
        "\n",
        "    else:\n",
        "        print(\"Table area not found\")\n",
        "    return res"
      ],
      "metadata": {
        "id": "Mc6YB07C8fd5"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = sharpened_img.copy()\n",
        "extract_table(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "HDm8tomeoXux",
        "outputId": "d73ad1f6-dd44-48ab-86dc-88ec56a8641f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 24,   3,   3, ...,  38,  66, 255],\n",
              "       [  3, 140, 152, ...,   2,   2, 255],\n",
              "       [  3, 255, 255, ...,   2,   2, 255],\n",
              "       ...,\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)"
            ],
            "text/html": [
              "<style>\n",
              "      .ndarray_repr .ndarray_raw_data {\n",
              "        display: none;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
              "        display: block;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
              "        display: none;\n",
              "      }\n",
              "      </style>\n",
              "      <div id=\"id-4f301ee3-8537-421e-95d7-df2cd073abc6\" class=\"ndarray_repr\"><pre>ndarray (186, 530) <button style=\"padding: 0 2px;\">show data</button></pre><img 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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[ 24,   3,   3, ...,  38,  66, 255],\n",
              "       [  3, 140, 152, ...,   2,   2, 255],\n",
              "       [  3, 255, 255, ...,   2,   2, 255],\n",
              "       ...,\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)</pre></div><script>\n",
              "      (() => {\n",
              "      const titles = ['show data', 'hide data'];\n",
              "      let index = 0\n",
              "      document.querySelector('#id-4f301ee3-8537-421e-95d7-df2cd073abc6 button').onclick = (e) => {\n",
              "        document.querySelector('#id-4f301ee3-8537-421e-95d7-df2cd073abc6').classList.toggle('show_array');\n",
              "        index = (++index) % 2;\n",
              "        document.querySelector('#id-4f301ee3-8537-421e-95d7-df2cd073abc6 button').textContent = titles[index];\n",
              "        e.preventDefault();\n",
              "        e.stopPropagation();\n",
              "      }\n",
              "      })();\n",
              "    </script>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # 테이블 추출 이미지 저장\n",
        "\n",
        "# # 테이블 이미지 저장 폴더 생성\n",
        "# table_image_path = os.path.join('/'.join(path.split('/')[:-1]), \"table_image\")\n",
        "# if not os.path.exists(table_image_path):\n",
        "#     os.makedirs(table_image_path)\n",
        "\n",
        "# # 저장\n",
        "# for i in range(len(paths)):\n",
        "#     path = paths[i]\n",
        "#     file_name = path.split('/')[-1].split('.')[-2] + \"_table.\" + path.split('.')[-1]\n",
        "#     file_save_path = table_image_path + \"/\" + file_name\n",
        "\n",
        "#     img = load_image(path)\n",
        "#     sharpen_img = sharpen_image(img)\n",
        "#     table_img = extract_table(sharpen_img)\n",
        "\n",
        "#     table_img = Image.fromarray(table_img)\n",
        "#     table_img.save(file_save_path)\n",
        "#     print(f\"Saved {file_save_path}\")"
      ],
      "metadata": {
        "id": "IMtdQuWLHuWJ",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_table(path):\n",
        "  '''\n",
        "  이미지의 호출부터 전처리, 테이블 영역 추출을 적용하는 함수\n",
        "  '''\n",
        "  img = load_image(path)\n",
        "  sharpen_img = sharpen_image(img)\n",
        "  table_img = extract_table(sharpen_img)\n",
        "  return table_img"
      ],
      "metadata": {
        "id": "x101Yhpoors_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = paths[0]\n",
        "show_table(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "XuAcONFho3jf",
        "outputId": "a520edda-0fd9-40e4-9093-ea356c843b96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 24,   3,   3, ...,  38,  66, 255],\n",
              "       [  3, 140, 152, ...,   2,   2, 255],\n",
              "       [  3, 255, 255, ...,   2,   2, 255],\n",
              "       ...,\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)"
            ],
            "text/html": [
              "<style>\n",
              "      .ndarray_repr .ndarray_raw_data {\n",
              "        display: none;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
              "        display: block;\n",
              "      }\n",
              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
              "        display: none;\n",
              "      }\n",
              "      </style>\n",
              "      <div id=\"id-7b61dfff-bbe2-4eda-a4fa-2ff25605d957\" class=\"ndarray_repr\"><pre>ndarray (186, 530) <button style=\"padding: 0 2px;\">show data</button></pre><img 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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[ 24,   3,   3, ...,  38,  66, 255],\n",
              "       [  3, 140, 152, ...,   2,   2, 255],\n",
              "       [  3, 255, 255, ...,   2,   2, 255],\n",
              "       ...,\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [  3,   3,   3, ...,   3,   3, 255],\n",
              "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)</pre></div><script>\n",
              "      (() => {\n",
              "      const titles = ['show data', 'hide data'];\n",
              "      let index = 0\n",
              "      document.querySelector('#id-7b61dfff-bbe2-4eda-a4fa-2ff25605d957 button').onclick = (e) => {\n",
              "        document.querySelector('#id-7b61dfff-bbe2-4eda-a4fa-2ff25605d957').classList.toggle('show_array');\n",
              "        index = (++index) % 2;\n",
              "        document.querySelector('#id-7b61dfff-bbe2-4eda-a4fa-2ff25605d957 button').textContent = titles[index];\n",
              "        e.preventDefault();\n",
              "        e.stopPropagation();\n",
              "      }\n",
              "      })();\n",
              "    </script>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# OCR 라이브러리 비교 및 적용\n",
        "\n",
        "- pytesseract  \n",
        "  Tesseract OCR 엔진을 Python에서 사용할 수 있게 해주는 오픈소스 라이브러리  \n",
        "  다양한 언어와 글꼴 인식 가능\n",
        "\n",
        "- EasyOCR  \n",
        "  PyTorch 기반의 오픈 소스 OCR 라이브러리  \n",
        "  80개 이상의 언어 지원 가능  \n",
        "  언어에 따른 인식률 차이가 있음\n",
        "\n",
        "- Keras-OCR  \n",
        "  Keras 및 TensorFlow를 기반으로 하는 오픈 소스 OCR 라이브러리  \n",
        "  커스터마이징이 용이\n",
        "  모델 실행 속도가 느림  \n",
        "\n",
        "- Google Vision AI    \n",
        "  Google의 클라우드 기반 OCR 서비스로, 이미지 내 텍스트 인식뿐만 아니라 다양한 이미지 분석 기능 제공  \n",
        "  클라우드 기반 서비스로, Google의 서버에서 이미지 분석을 수행하므로 인터넷 연결 필요  \n",
        "  높은 정확도를 보이지만 일정 횟수 이상은 유료 서비스로 전환됨 (월 1,000회까지 무료)\n",
        "\n"
      ],
      "metadata": {
        "id": "KXZsdgx1hfFB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## pytesseract  \n",
        "목표대상인 모델명, 시리얼에 한글이 해당되지 않아 별도 학글팩 설치하지 않음"
      ],
      "metadata": {
        "id": "wEZzV2eipMqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img = load_image(paths[5])\n",
        "sharpen_img = sharpen_image(img)\n",
        "table_img = extract_table(sharpen_img)\n",
        "high_img = cv2.resize(table_img, None, fx=2, fy=2, interpolation=cv2.INTER_LANCZOS4) # 해상도 증대\n",
        "\n",
        "# OCR 옵션 설정\n",
        "options = \"--psm 6 --oem 1 eng\"  # 영어 인식, LSTM 엔진 사용\n",
        "\n",
        "text = pytesseract.image_to_string(high_img, config=options)# .replace('\\n', ' ')\n",
        "print(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gg9fQjLlrCjk",
        "outputId": "da4fe9e4-b19b-443d-f07b-9c4fe8ccf37f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NO, MODEL DESCRIPTION Q'TY Serial REMARK\n",
            "was SuR = a Number Hy]\n",
            "\f\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##EasyOCR"
      ],
      "metadata": {
        "id": "R8sVO7MtyQ1b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/Colab Notebooks/durian_dataset/table_image/905_1566_7514d469c3f52f078889b33ebad1e008_table.jpg\"\n",
        "\n",
        "reader = easyocr.Reader(['en']) # 인식 객체 생성\n",
        "result = reader.readtext(path) # 인식 결과 저장\n",
        "\n",
        "# 텍스트 영역 좌표 추출\n",
        "boxes = []\n",
        "for bbox in result: # Iterate directly over result\n",
        "    boxes.append(bbox[0])\n",
        "\n",
        "# 이미지 읽기\n",
        "img = cv2.imread(path)\n",
        "\n",
        "# 인식 박스 표시\n",
        "for box in boxes:\n",
        "    top_left = tuple(box[0])\n",
        "    bottom_right = tuple(box[2])\n",
        "    img = cv2.rectangle(img, top_left, bottom_right, (0, 255, 0), 3)\n",
        "\n",
        "# 인식된 텍스트 표시\n",
        "text = result[0][1]\n",
        "top_left = tuple(result[0][0][0])\n",
        "font = cv2.FONT_HERSHEY_SIMPLEX\n",
        "img = cv2.putText(img, text, top_left, font, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
        "\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "k9czssTqt8pu",
        "outputId": "4b91cece-edc6-4723-c679-fc8f7e6f2964"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Downloading detection model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Downloading recognition model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return F.conv2d(input, weight, bias, self.stride,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# easyocr 사용하여 텍스트 인식\n",
        "reader = easyocr.Reader(['en'])  # 사용할 언어 설정\n",
        "easyocr_preds = reader.readtext(img)  # 이미지 데이터 직접 전달\n",
        "\n",
        "# 인식된 텍스트를 문자열로 변환\n",
        "recognized_texts = [prediction[1].strip() for prediction in easyocr_preds]\n",
        "\n",
        "# 문자열로 결합\n",
        "recognized_text_str = ' '.join(recognized_texts)\n",
        "\n",
        "# 불필요한 공백 및 특수문자 제거\n",
        "recognized_text_str = re.sub(r'[^\\w\\s\\.]+', '', recognized_text_str)  # 특수 문자 제거\n",
        "recognized_text_str = re.sub(r'\\s+', ' ', recognized_text_str)  # 불필요한 공백 제거\n",
        "\n",
        "print(recognized_text_str)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWUbGRCquzp3",
        "outputId": "31541b8e-c255-4733-dcff-cee1f190160b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NO. MODEL DESCRIPTION Serial REMARK Mr 58 6 3 Mumper HlZ VForce 1700U UTM UFW1 7002210758 VForce 1700U UTM UFW17002210631\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Keras-OCR"
      ],
      "metadata": {
        "id": "bTGJ7_tF8c4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table_img = show_table(path)"
      ],
      "metadata": {
        "id": "5lHz_s7Oxx3q"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table_img = cv2.cvtColor(table_img, cv2.COLOR_GRAY2BGR)\n",
        "pipeline = keras_ocr.pipeline.Pipeline()\n",
        "\n",
        "kerasocr_preds = pipeline.recognize([table_img])  # 인식할 이미지 리스트 전달\n",
        "\n",
        "# 결과 시각화\n",
        "fig, ax = plt.subplots(figsize=(15, 15))\n",
        "keras_ocr.tools.drawAnnotations(image=table_img, predictions=kerasocr_preds[0], ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "DpjIN-Xu8mtK",
        "outputId": "9b9e383d-07d3-4f69-b5a6-462536d0e813"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking for /root/.keras-ocr/craft_mlt_25k.h5\n",
            "Downloading /root/.keras-ocr/craft_mlt_25k.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking for /root/.keras-ocr/crnn_kurapan.h5\n",
            "Downloading /root/.keras-ocr/crnn_kurapan.h5\n",
            "1/1 [==============================] - 13s 13s/step\n",
            "1/1 [==============================] - 7s 7s/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 인식한 텍스트를 문자열로 변환\n",
        "recognized_texts = []\n",
        "for prediction in kerasocr_preds[0]:\n",
        "    recognized_texts.append(prediction[0])  # prediction[0]는 인식된 텍스트\n",
        "\n",
        "# 문자열로 결합\n",
        "recognized_text_str = ' '.join(recognized_texts)\n",
        "print(recognized_text_str)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WU_RHpWGDIa-",
        "outputId": "801089d6-a38d-4764-e26a-8fe0db2ef966"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "descrption serial remark qty nos modes az bi sd number bhi sed ufwi700210716 itoou utm 1 1 veorce 170ou ufwito0z16 z vforce utm 1 sies\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Google Vision AI"
      ],
      "metadata": {
        "id": "dh6W3tx8v0sU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# API Key 설정\n",
        "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/Colab Notebooks/durian_dataset/durian-240703-197a1294b8d2.json\""
      ],
      "metadata": {
        "id": "ezCQZxoqQiBL"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pdf 혹은 이미지 경로\n",
        "path = \"/content/drive/MyDrive/Colab Notebooks/durian_dataset/table_image/905_1566_7514d469c3f52f078889b33ebad1e008_table.jpg\""
      ],
      "metadata": {
        "id": "cE4hK-3IRhAD"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이미지 호출 함수\n",
        "def load_image(path, extension_list=['png', 'jpeg', 'jpg', 'pdf']):\n",
        "  extension = path.split('.')[-1]\n",
        "  filename = path.split('/')[-1]  # error 표시용\n",
        "  if extension not in extension_list:\n",
        "    raise ValueError(f'Unsupported extension: {filename}')\n",
        "\n",
        "  if extension in ['png', 'jpeg', 'jpg']:\n",
        "    flag = cv2.IMREAD_COLOR\n",
        "    img = cv2.imread(path, flag)\n",
        "  elif extension == 'pdf':\n",
        "    pdf_document = fitz.open(path)\n",
        "    page = pdf_document.load_page(0)  # 첫 번째 페이지만 호출\n",
        "    pix = page.get_pixmap()\n",
        "    img = cv2.imdecode(np.frombuffer(pix.tobytes(), dtype=np.uint8), cv2.IMREAD_GRAYSCALE)\n",
        "  else:\n",
        "    raise ValueError(f'Unsupported extension: {filename}')\n",
        "\n",
        "  if img is None:\n",
        "    raise Exception(f'Image call failed: {filename}')\n",
        "\n",
        "  return img"
      ],
      "metadata": {
        "id": "MCMZGdttRoSz"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_text(path):\n",
        "    client = vision.ImageAnnotatorClient()\n",
        "\n",
        "    # 이미지 처리만 할 경우\n",
        "    # with open(path, \"rb\") as image_file:\n",
        "    #     content = image_file.read()\n",
        "\n",
        "    # pdf 처리만 할 경우\n",
        "    content2 = load_image(path)\n",
        "    # plt.imshow(cv2.cvtColor(image, cv2.IMREAD_GRAYSCALE))\n",
        "    # plt.title('base')\n",
        "    # plt.axis('off')  # 축 제거\n",
        "    # plt.show()\n",
        "    content_bytes = cv2.imencode('.png', content2)[1].tobytes()\n",
        "    # print(type(content_bytes))\n",
        "\n",
        "    image = vision.Image(content=content_bytes)\n",
        "    response = client.text_detection(image=image)\n",
        "    texts = response.text_annotations\n",
        "    return texts[0].description"
      ],
      "metadata": {
        "id": "EltXalvTSAi7"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = detect_text(path)\n",
        "text.replace('\\n', ' ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "dbTa3CEBFHQR",
        "outputId": "862a441b-063b-4c84-9436-1b5b485a76fd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMARK 품명 수량 Number 비고 1 VForce 1700U UTM 1 UFW17002210758 2 VForce 1700U UTM 1 UFW17002210631 내역\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 추출한 텍스트 데이터 구조화 및 저장"
      ],
      "metadata": {
        "id": "UdB95AJFbSE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 파일명(혹은 경로)에서 forcasting_seq 추출 함수\n",
        "def forcasting_seq_extraction(string):\n",
        "    last_part = string.split('/')[-1]# '/'로 split하여 마지막 값을 가져옴\n",
        "    forcasting_seq = last_part.split('_')[1]# '_'로 split하여 두 번째 값을 가져옴\n",
        "    return forcasting_seq"
      ],
      "metadata": {
        "id": "W0LC1GXpIKQH"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 추출한 데이터를 저장할 dataframe 구조 생성\n",
        "doc_data = pd.DataFrame(columns=['forcasting_seq', 'text'])"
      ],
      "metadata": {
        "id": "pnnlF_BhYOWE"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def findTargetPaths(dataInputPath):\n",
        "    '''\n",
        "    경로 내에서 특정 확장자를 가지는 파일의 경로를 추출하는 함수\n",
        "    '''\n",
        "    paths = [] # 타겟 파일의 전체 경로를 저장할 리스트\n",
        "    extension_list=['png', 'jpeg', 'jpg', 'pdf']# 타겟 파일의 확장자\n",
        "\n",
        "    try:\n",
        "        for dirpath, dirnames, filenames in os.walk(dataInputPath): # 지정 경로 내의 하위 디렉토리 전체 순회\n",
        "            for filename in filenames:\n",
        "                if filename.split('.')[-1] in extension_list:\n",
        "                    paths.append(os.path.join(dirpath, filename)) # 파일의 전체 경로를 문자열으로 paths list에 저장\n",
        "                else:\n",
        "                  pass\n",
        "    except Exception as e:\n",
        "                print(e)\n",
        "    return paths"
      ],
      "metadata": {
        "id": "jDQrbN82FWTq"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataInputPath = \"./durian_dataset/table_image\"\n",
        "paths = findTargetPaths(dataInputPath)\n",
        "\n",
        "# doc_data에 파일명에서 추출한 forcasting_seq와 Google Vision AI로 추출한 text 입력\n",
        "for path in paths:\n",
        "  # Google Vision으로 text 추출\n",
        "  try:\n",
        "    text = detect_text(path)\n",
        "    text = text.replace('\\n', ' ')\n",
        "  except:\n",
        "    text = np.NAN\n",
        "\n",
        "  # forcasting_seq 추출\n",
        "  try:\n",
        "    forcasting_seq = int(forcasting_seq_extraction(path))\n",
        "  except:\n",
        "    forcasting_seq = np.NAN\n",
        "\n",
        "  # 데이터 입력\n",
        "  try:\n",
        "    new_row = {'forcasting_seq': forcasting_seq, 'text': text}\n",
        "    doc_data = pd.concat([doc_data, pd.DataFrame([new_row])], ignore_index=True)\n",
        "  except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "id": "MPapbRgERG-k"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 추출 데이터 저장\n",
        "# doc_data.to_excel('extract_GoogleVision_data.xlsx', index=False)\n",
        "\n",
        "# 파일로 추출한 데이터 호출\n",
        "doc_data_path = \"./extract_GoogleVision_data.xlsx\"\n",
        "doc_data = pd.read_excel(doc_data_path)"
      ],
      "metadata": {
        "id": "wBcH3l6fVGGT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRqy1AejhZod",
        "outputId": "a1d47580-7831-4801-da92-548626237b0f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53 entries, 0 to 52\n",
            "Data columns (total 2 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   forcasting_seq  53 non-null     int64 \n",
            " 1   text            52 non-null     object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 976.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TYjirf_NE4rP",
        "outputId": "c156241e-aedd-4d38-ca9e-42d2fe943642"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    forcasting_seq                                               text\n",
              "0             1437  NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...\n",
              "1             1465  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...\n",
              "2             1430  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...\n",
              "3             1464  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...\n",
              "4             1463  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...\n",
              "5             1462  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...\n",
              "6             1441  NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...\n",
              "7             1439  NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...\n",
              "8             1461  NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...\n",
              "9             1451  NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...\n",
              "10            1450  NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...\n",
              "11            1475  NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...\n",
              "12            1466  NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...\n",
              "13            1358  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...\n",
              "14            1536  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...\n",
              "15            1529  NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...\n",
              "16            1555  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...\n",
              "17            1566  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...\n",
              "18            1575  NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...\n",
              "19            1552  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...\n",
              "20            1505  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...\n",
              "21            1541  내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...\n",
              "22            1522  NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...\n",
              "23            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...\n",
              "24            1521  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...\n",
              "25            1519  NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...\n",
              "26            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...\n",
              "27            1444  NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...\n",
              "28            1497  NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...\n",
              "29            1489  NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...\n",
              "30            1532  NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...\n",
              "31            1456  NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...\n",
              "32            1221  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...\n",
              "33            1530  NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...\n",
              "34            1509  NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...\n",
              "35            1407  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...\n",
              "36            1474  NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...\n",
              "37            1442  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...\n",
              "38            1495  1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...\n",
              "39            1443  NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...\n",
              "40            1455  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...\n",
              "41            1472  NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...\n",
              "42            1471  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...\n",
              "43            1500  NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...\n",
              "44            1494  NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...\n",
              "45            1501  NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...\n",
              "46            1496  공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...\n",
              "47            1460  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...\n",
              "48            1448  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...\n",
              "49            1479  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...\n",
              "50            1480  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...\n",
              "51            1431  NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...\n",
              "52            1446                                                NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63a4dc0b-4352-4bb2-8389-39eb718b2097\" class=\"colab-df-container\">\n",
              "    <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>forcasting_seq</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1437</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1465</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1430</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1463</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1462</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1441</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1439</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1461</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1451</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1450</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1475</td>\n",
              "      <td>NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1466</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1358</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1536</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1529</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1555</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1566</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1575</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1552</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1505</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1541</td>\n",
              "      <td>내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1522</td>\n",
              "      <td>NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1521</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1519</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1444</td>\n",
              "      <td>NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1497</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1489</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1532</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1456</td>\n",
              "      <td>NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1221</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1530</td>\n",
              "      <td>NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1509</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1407</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1474</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1442</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1495</td>\n",
              "      <td>1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1443</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1455</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1472</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1471</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1500</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1494</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1501</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1496</td>\n",
              "      <td>공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1460</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1448</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1479</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1480</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1431</td>\n",
              "      <td>NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1446</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63a4dc0b-4352-4bb2-8389-39eb718b2097')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-63a4dc0b-4352-4bb2-8389-39eb718b2097 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-63a4dc0b-4352-4bb2-8389-39eb718b2097');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
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              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e4a2dccb-f7f9-427c-9491-2ec6dd992138 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_33681049-a10c-4cc1-ae86-d9a19bd8de18\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('doc_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_33681049-a10c-4cc1-ae86-d9a19bd8de18 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('doc_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "doc_data",
              "summary": "{\n  \"name\": \"doc_data\",\n  \"rows\": 53,\n  \"fields\": [\n    {\n      \"column\": \"forcasting_seq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1221,\n        \"max\": 1575,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1552,\n          1471,\n          1448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 51,\n        \"samples\": [\n          \"NO. MODEL DESCRIPTION QTY Serial \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 \\ud488\\uba85 \\uc218\\ub7c9 Number 1 License GATEONE-ZI/AM Node License 2 \\ub0b4\\uc5ed REMARK \\ube44\\uace0 \\uae30\\ubcf8 217 +\\ucd94\\uac00 2 219 Node\",\n          \"NO. MODEL \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 DESCRIPTION \\ud488\\uba85 QTY Serial REMARK \\uc218\\ub7c9 Number \\ube44\\uace0 1 License GATEONE-Z AM Node License 3 \\ub0b4\\ubd80 16(\\uae30\\uc900) 1 \\ub0b4\\uc5ed\",\n          \"NO. MODEL DESCRIPTION QTY Serial REMARK \\ubc88\\ud638 \\uc218\\ub7c9 Number \\ube44\\uace0 1 Secuway SSL U V2.1 SSL VPN\\ubcf4\\uc548\\uc194\\ub8e8\\uc158 1 SV2110NS23070041 2 3 4 5 6 7 \\ub0b4\\uc5ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 추출한 데이터의 유사도 연산 비교\n",
        "- fuzz\n",
        "  문자열 간 유사도를 연산 라이브러리  \n",
        "  Levenshtein 거리 알고리즘을 기반\n",
        "  간단한 문자열 유사도 비교에 효과적이지만, 긴 텍스트나 복잡한 구조에서는 성능이 떨어짐\n",
        "\n",
        "- Transformers\n",
        "  Hugging Face에서 제공하는 라이브러리로, 자연어 처리(NLP) 모델을 사용하여 텍스트 분석 및 변환을 수행  \n",
        "  매우 높은 정확도를 제공하지만, 설정과 사용이 비교적 복잡하고 더 많은 계산 자원이 필요\n",
        "\n",
        "- difflib\n",
        "  Python 표준 라이브러리의 일부로, 두 개의 시퀀스(주로 문자열)의 차이점을 비교하고 유사성을 계산하는 기능을 제공  \n",
        "\n",
        "\n",
        "간단한 문자열 유사도 비교: fuzz (FuzzyWuzzy)  \n",
        "고급 텍스트 분석: Transformers  \n",
        "기본적인 문자열 비교: difflib  "
      ],
      "metadata": {
        "id": "um72QPJRoMHg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "제품정보.xlsx에서 데이터 대조에 필요한 데이터를 추출하여 info_data(dataframe) 저장  \n",
        "info_data의 정보를 기반으로 추출한 텍스트의 정확도 검증"
      ],
      "metadata": {
        "id": "I3X-ZjU3ONMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 제품정보.xlsx 파일에서 필요 데이터 추출\n",
        "product_info_path = \"./durian_dataset/제품정보.xlsx\"\n",
        "info_data = pd.read_excel(product_info_path)\n",
        "info_data = info_data[['forcasting_seq', 'product_name', 'product_serial']]\n",
        "info_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hNcmdMPItOy",
        "outputId": "caaf0181-bcbb-4016-97d0-8aaeb287e985"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 122 entries, 0 to 121\n",
            "Data columns (total 3 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   forcasting_seq  122 non-null    int64 \n",
            " 1   product_name    122 non-null    object\n",
            " 2   product_serial  91 non-null     object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 3.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 추출한 forcasting_seq와 text\n",
        "df = doc_data.copy()\n",
        "df.head()"
      ],
      "metadata": {
        "id": "Sjv419m9UQ22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d53bdd73-301c-4367-ec53-9f66ebb1746e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   forcasting_seq                                               text\n",
              "0            1437  NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...\n",
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              "2            1430  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...\n",
              "3            1464  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...\n",
              "4            1463  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ..."
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            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 제품 정보의 데이터\n",
        "info_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UdfJGQB0ohBB",
        "outputId": "e19e11d2-bac8-4255-eb59-9b96042c593b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   forcasting_seq       product_name  product_serial\n",
              "0            1221       WAPPLES-2600  W2600I-2230003\n",
              "1            1358  BLUEMAX WIPS 1000  DSF22C12S14009\n",
              "2            1358   BLUEMAX WIPS 602  JTM8WA1001961 \n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e5c35ca3-f8e3-48a7-a2e0-c3e5f4167354 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e5c35ca3-f8e3-48a7-a2e0-c3e5f4167354');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dbd98f5b-6e4d-4776-bb84-b236fde57398\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dbd98f5b-6e4d-4776-bb84-b236fde57398')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dbd98f5b-6e4d-4776-bb84-b236fde57398 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "info_data",
              "summary": "{\n  \"name\": \"info_data\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"forcasting_seq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60,\n        \"min\": 1221,\n        \"max\": 1575,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1462,\n          1522,\n          1548\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 35,\n        \"samples\": [\n          \"BLUEMAX NGF 1510\",\n          \"BLUEMAX IPS 2000\",\n          \"BLUEMAX WIPS 624\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_serial\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 88,\n        \"samples\": [\n          \"-\",\n          \"W2600I-2230003\",\n          \"JTN6IB3010002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## fuzz\n",
        "- 문자열 간 유사성을 측정하기 위해 Levenshtein Distance(편집 거리)를 활용하는 라이브러리\n",
        "- 문자열 유사도 분석을 신속하게 수행할 수 있어 빠른 프로토타이핑에 적합\n",
        "-문맥적 유사도보다는 단순히 편집 거리에 기반하기 때문에 문맥적 의미를 파악하기 어려움"
      ],
      "metadata": {
        "id": "W00olv2XJJiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 저장 리스트\n",
        "names = []\n",
        "\n",
        "# 추출한 데이터가 포함된 데이터프레임 순회\n",
        "for idx, row in df.iterrows():\n",
        "    forcasting_seq = row['forcasting_seq']\n",
        "    text = row['text']\n",
        "    if pd.isna(text): # Check if text is NaN\n",
        "        names.append(np.NAN)\n",
        "        continue # Skip to the next iteration if text is NaN\n",
        "\n",
        "    # 해당 forcasting_seq의 product_name 리스트 추출\n",
        "    matching_names = info_data[info_data['forcasting_seq'] == forcasting_seq]['product_name'].tolist()\n",
        "\n",
        "    matched = False\n",
        "    similarity_list = [] # 유사도를 넣어서 여기서 유사도가 가장 높은 name 하나만 반환\n",
        "    for name in matching_names:\n",
        "        # 유사도 계산\n",
        "        similarity = fuzz.ratio(name, text)\n",
        "        if similarity >= 30:\n",
        "            similarity_list.append([similarity, name])\n",
        "            matched = True # 하나라도 기준을 맞춘 name이 있을 경우\n",
        "            break\n",
        "        else:\n",
        "            pass\n",
        "    if len(similarity_list) >= 1:\n",
        "        max_similarity = max(similarity_list, key=lambda x: x[0])\n",
        "        names.append(max_similarity[1])\n",
        "    else:\n",
        "        names.append(np.NAN)\n",
        "    if not matched: # 기준치 이상 값이 하나도 없을 경우\n",
        "        pass"
      ],
      "metadata": {
        "id": "uMs1Tdq2HwYH"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_fuzz = df.copy()\n",
        "match_data_fuzz['predict_name'] = ''\n",
        "match_data_fuzz['predict_serial'] = ''\n",
        "try:\n",
        "  if len(names) == len(match_data_fuzz):\n",
        "      match_data_fuzz['predict_name'] = names\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "dA5r4CLYdY3S"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "serials = []\n",
        "\n",
        "# match_data_fuzz['predict_name']의 각 값과 매칭되는 serial 찾기\n",
        "for idx, predict_name in enumerate(match_data_fuzz['predict_name']):\n",
        "    if pd.isna(predict_name):\n",
        "        # predict_name이 NaN인 경우\n",
        "        serials.append(np.nan)\n",
        "    else:\n",
        "        matched = False\n",
        "        text = match_data_fuzz.loc[idx, 'text']  # 현재 인덱스의 match_data['text'] 값\n",
        "        # info_data를 순회하며 product_name과 일치하는 product_serial 값을 찾음\n",
        "        for _, row in info_data.iterrows():\n",
        "            if row['product_name'] == predict_name:\n",
        "                # Convert 'product_serial' to string before calculating similarity\n",
        "                similarity = fuzz.ratio(str(row['product_serial']), text)\n",
        "                if similarity >= 30:\n",
        "                    serials.append(row['product_serial'])\n",
        "                    matched = True\n",
        "                    break\n",
        "        if not matched:\n",
        "            serials.append(np.nan)"
      ],
      "metadata": {
        "id": "HQi2_e4DrohI"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  if len(serials) == len(match_data_fuzz):\n",
        "      match_data_fuzz['predict_serial'] = serials\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "1AGYGSbpiSQ9"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 확인\n",
        "\n",
        "# 제품정보에 있는 모델명을 추출한 경우\n",
        "success = match_data_fuzz.dropna(subset=['predict_name']).shape[0]\n",
        "\n",
        "# predict_name만 있고 predict_serial이 없는 경우\n",
        "half_success = match_data_fuzz[match_data_fuzz['predict_name'].notna() & match_data_fuzz['predict_serial'].isna()].shape[0]\n",
        "\n",
        "# predict_name과 predict_serial 둘 다 없는 경우\n",
        "fail = match_data_fuzz.shape[0] - success\n",
        "\n",
        "# 결과 출력\n",
        "print(\"< fuzz >\")\n",
        "print(\"제품정보에 있는 모델명을 추출한 경우: \",f\"{success*100/len(match_data_fuzz):.2f}%\")\n",
        "print(\"제품정보에 있는 predict_serial이 없는 경우: \",f\"{half_success*100/len(match_data_fuzz):.2f}%\")\n",
        "print(\"제품정보에 있는 모델명을 추출 실패: \",f\"{fail*100/len(match_data_fuzz):.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajrgLdZtlOIC",
        "outputId": "65010347-883b-4cf7-c9b6-c86bcbabc7b7"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "< fuzz >\n",
            "제품정보에 있는 모델명을 추출한 경우:  26.42%\n",
            "제품정보에 있는 predict_serial이 없는 경우:  26.42%\n",
            "제품정보에 있는 모델명을 추출 실패:  73.58%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_fuzz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Fw8fjWpRT4SB",
        "outputId": "f0f9386e-bd9b-4c78-af44-96932cf3af16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    forcasting_seq                                               text  \\\n",
              "0             1437  NO.\\nMODEL\\n번호\\n모델명\\n1\\nPEA-40A\\n2\\n3\\n4\\n5\\n6...   \n",
              "1             1465  NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\n모델명\\n...   \n",
              "2             1430  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...   \n",
              "3             1464  NO.\\n번호\\nMODEL\\n모델명\\nNeobox MID43\\n1\\n2\\n3\\n4\\...   \n",
              "4             1463  NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\n모델명\\n...   \n",
              "5             1462  NO.\\n번호\\nMODEL\\n모델명\\nNeobox MID43\\n1\\n2\\n3\\n4\\...   \n",
              "6             1441  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQTY\\n품명\\n수량\\...   \n",
              "7             1439  NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREM...   \n",
              "8             1461  NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nQTY\\n수량\\n1\\n...   \n",
              "9             1451  NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n내역\\nMODEL\\n모델명\\n...   \n",
              "10            1450  NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\nIFO 2...   \n",
              "11            1475  NO.\\n번호\\n1\\nMODEL\\n모델명\\nPEA-40A\\nDESCRIPTION\\n...   \n",
              "12            1466  NO.\\n번호\\nMODEL\\n모델명\\nNeobox ID43\\n1\\n2\\n3\\n4\\n...   \n",
              "13            1358  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...   \n",
              "14            1536  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...   \n",
              "15            1529  NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQTY\\n모델명\\n품명\\n수량\\...   \n",
              "16            1555  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...   \n",
              "17            1566  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...   \n",
              "18            1575  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\n번호\\n모델명\\n품명\\n수량...   \n",
              "19            1552  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...   \n",
              "20            1505  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...   \n",
              "21            1541  내역\\nNO.\\nMODEL\\n번호\\n1\\n모델명\\nDESCRIPTION\\n품명\\nQ...   \n",
              "22            1522  NO.\\nMODEL\\nQ'TY\\n번호\\n모델명\\n수량\\n1\\nBLUEMAX WIPS...   \n",
              "23            1548  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...   \n",
              "24            1521  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...   \n",
              "25            1519  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\n번호\\n모델\\n...   \n",
              "26            1548  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...   \n",
              "27            1444  NO.\\n번호\\nMODEL\\n229\\nDESCRIPTION\\nQTY\\nSerial\\...   \n",
              "28            1497  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\n번호\\n모델명...   \n",
              "29            1489  NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQTY\\n수량\\n$\\n...   \n",
              "30            1532  NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQ'TY\\n모델명\\n품명\\n수량...   \n",
              "31            1456  NO.\\nMODEL\\n번호\\n모델명\\n1234567\\nSecuwaySSL VPN M...   \n",
              "32            1221  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...   \n",
              "33            1530  NO.\\nMODEL\\n번호\\n모델\\n1\\nLicense\\n내역\\nDESCRIPTIO...   \n",
              "34            1509  NO.\\nMODEL\\nDESCRIPTION\\nOTY\\nSerial\\n번호\\n모델명\\...   \n",
              "35            1407  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n수량\\n1\\n...   \n",
              "36            1474  NO.\\nMODEL\\n번호\\n모델명\\nDESCRIPTION\\n품명\\nQ'TY\\nSe...   \n",
              "37            1442  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n수량\\nSer...   \n",
              "38            1495  1\\nNO.\\n번호\\nMODEL\\n모델명\\nBLUEMAX NGF 100 BASIC\\...   \n",
              "39            1443  NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQTY\\n모델명\\n수량\\n1\\n...   \n",
              "40            1455  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...   \n",
              "41            1472  NO.\\nMODEL\\n번호\\n모델명\\nDESCRIPTION\\n품명\\nQTY\\nSer...   \n",
              "42            1471  NO.\\n번호\\n1\\n내역\\nMODEL\\n모델명\\nDESCRIPTION\\n품명\\nQ...   \n",
              "43            1500  NO.\\nMODEL\\n번호\\n모델명\\n1\\nLicense\\n내역\\nDESCRIPTI...   \n",
              "44            1494  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\n번호\\n모델명\\...   \n",
              "45            1501  NO.\\nMODEL\\nDESCRIPTION\\nOTY\\nSerial\\nREMARK\\n...   \n",
              "46            1496  공급자: 더망고\\n납품확인서\\n내용: 서울특별시데이터센터 재해복구시스템 환경 구축 ...   \n",
              "47            1460  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...   \n",
              "48            1448  NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...   \n",
              "49            1479  NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...   \n",
              "50            1480  NO.\\n번호\\n1\\n내역\\nMODEL\\n모델명\\nDESCRIPTION\\nGATEO...   \n",
              "51            1431  NO.\\n번호\\n1\\n2\\nMODEL\\n모델명\\nBLUEMAX NGF 300\\n3\\...   \n",
              "52            1446                                                NaN   \n",
              "\n",
              "                        predict_name  predict_serial  \n",
              "0                                NaN             NaN  \n",
              "1                                NaN             NaN  \n",
              "2                                NaN             NaN  \n",
              "3                                NaN             NaN  \n",
              "4                                NaN             NaN  \n",
              "5                                NaN             NaN  \n",
              "6                                NaN             NaN  \n",
              "7         GATEONE Z(AM) Node LIcense             NaN  \n",
              "8                                NaN             NaN  \n",
              "9                                NaN             NaN  \n",
              "10                               NaN             NaN  \n",
              "11                               NaN             NaN  \n",
              "12                               NaN             NaN  \n",
              "13                               NaN             NaN  \n",
              "14                               NaN             NaN  \n",
              "15                               NaN             NaN  \n",
              "16                               NaN             NaN  \n",
              "17                               NaN             NaN  \n",
              "18                               NaN             NaN  \n",
              "19                               NaN             NaN  \n",
              "20             BLUEMAX NGF 100 BASIC             NaN  \n",
              "21                               NaN             NaN  \n",
              "22                               NaN             NaN  \n",
              "23                               NaN             NaN  \n",
              "24             BLUEMAX NGF 100 BASIC             NaN  \n",
              "25                               NaN             NaN  \n",
              "26                               NaN             NaN  \n",
              "27                               NaN             NaN  \n",
              "28                               NaN             NaN  \n",
              "29             BLUEMAX NGF 100 BASIC             NaN  \n",
              "30                               NaN             NaN  \n",
              "31                               NaN             NaN  \n",
              "32                               NaN             NaN  \n",
              "33      GATEONE-Z (IAM) Node License             NaN  \n",
              "34      GATEONE-Z (IAM) Node License             NaN  \n",
              "35                               NaN             NaN  \n",
              "36                               NaN             NaN  \n",
              "37                               NaN             NaN  \n",
              "38                               NaN             NaN  \n",
              "39                               NaN             NaN  \n",
              "40                               NaN             NaN  \n",
              "41        GATEONE Z(AM) Node LIcense             NaN  \n",
              "42        GATEONE Z(AM) Node LIcense             NaN  \n",
              "43      GATEONE-Z (IAM) Node License             NaN  \n",
              "44      GATEONE-Z (IAM) Node License             NaN  \n",
              "45      GATEONE-Z (IAM) Node License             NaN  \n",
              "46                               NaN             NaN  \n",
              "47  SecuwaySSL U V2.1_Server 모듈_1000             NaN  \n",
              "48                               NaN             NaN  \n",
              "49      GATEONE-Z (IAM) Node License             NaN  \n",
              "50      GATEONE-Z (IAM) Node License             NaN  \n",
              "51                               NaN             NaN  \n",
              "52                               NaN             NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f2cdb40-6a24-464b-b87c-0b33a406316e\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>forcasting_seq</th>\n",
              "      <th>text</th>\n",
              "      <th>predict_name</th>\n",
              "      <th>predict_serial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1437</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델명\\n1\\nPEA-40A\\n2\\n3\\n4\\n5\\n6...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1465</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\n모델명\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1430</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nNeobox MID43\\n1\\n2\\n3\\n4\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1463</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\n모델명\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1462</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nNeobox MID43\\n1\\n2\\n3\\n4\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1441</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQTY\\n품명\\n수량\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1439</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREM...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1461</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nQTY\\n수량\\n1\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1451</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n내역\\nMODEL\\n모델명\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1450</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\n3\\n4\\n5\\nB\\n7\\n내역\\nMODEL\\nIFO 2...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1475</td>\n",
              "      <td>NO.\\n번호\\n1\\nMODEL\\n모델명\\nPEA-40A\\nDESCRIPTION\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1466</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nNeobox ID43\\n1\\n2\\n3\\n4\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1358</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1536</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1529</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQTY\\n모델명\\n품명\\n수량\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1555</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n품명\\n수량\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1566</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1575</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\n번호\\n모델명\\n품명\\n수량...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1552</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1505</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1541</td>\n",
              "      <td>내역\\nNO.\\nMODEL\\n번호\\n1\\n모델명\\nDESCRIPTION\\n품명\\nQ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1522</td>\n",
              "      <td>NO.\\nMODEL\\nQ'TY\\n번호\\n모델명\\n수량\\n1\\nBLUEMAX WIPS...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1521</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1519</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\n번호\\n모델\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQ'TY\\nSerial...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1444</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n229\\nDESCRIPTION\\nQTY\\nSerial\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1497</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\n번호\\n모델명...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1489</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\n모델명\\nDESCRIPTION\\nQTY\\n수량\\n$\\n...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1532</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQ'TY\\n모델명\\n품명\\n수량...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1456</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델명\\n1234567\\nSecuwaySSL VPN M...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1221</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1530</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델\\n1\\nLicense\\n내역\\nDESCRIPTIO...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1509</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nOTY\\nSerial\\n번호\\n모델명\\...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1407</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n수량\\n1\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1474</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델명\\nDESCRIPTION\\n품명\\nQ'TY\\nSe...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1442</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\n번호\\n모델명\\n수량\\nSer...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1495</td>\n",
              "      <td>1\\nNO.\\n번호\\nMODEL\\n모델명\\nBLUEMAX NGF 100 BASIC\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1443</td>\n",
              "      <td>NO.\\n번호\\nMODEL\\nDESCRIPTION\\nQTY\\n모델명\\n수량\\n1\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1455</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1472</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델명\\nDESCRIPTION\\n품명\\nQTY\\nSer...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1471</td>\n",
              "      <td>NO.\\n번호\\n1\\n내역\\nMODEL\\n모델명\\nDESCRIPTION\\n품명\\nQ...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1500</td>\n",
              "      <td>NO.\\nMODEL\\n번호\\n모델명\\n1\\nLicense\\n내역\\nDESCRIPTI...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1494</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\n번호\\n모델명\\...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1501</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nOTY\\nSerial\\nREMARK\\n...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1496</td>\n",
              "      <td>공급자: 더망고\\n납품확인서\\n내용: 서울특별시데이터센터 재해복구시스템 환경 구축 ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1460</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...</td>\n",
              "      <td>SecuwaySSL U V2.1_Server 모듈_1000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1448</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQ'TY\\nSerial\\nREMARK\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1479</td>\n",
              "      <td>NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1480</td>\n",
              "      <td>NO.\\n번호\\n1\\n내역\\nMODEL\\n모델명\\nDESCRIPTION\\nGATEO...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1431</td>\n",
              "      <td>NO.\\n번호\\n1\\n2\\nMODEL\\n모델명\\nBLUEMAX NGF 300\\n3\\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1446</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f2cdb40-6a24-464b-b87c-0b33a406316e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-8f2cdb40-6a24-464b-b87c-0b33a406316e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8f2cdb40-6a24-464b-b87c-0b33a406316e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1f3526a-babc-4040-b530-5692ba50410b button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_3983bada-6bf5-4454-bea1-64596718d445\">\n",
              "    <style>\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('match_data_fuzz')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
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              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('match_data_fuzz');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "match_data_fuzz",
              "summary": "{\n  \"name\": \"match_data_fuzz\",\n  \"rows\": 53,\n  \"fields\": [\n    {\n      \"column\": \"forcasting_seq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1221,\n        \"max\": 1575,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1552,\n          1471,\n          1448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 51,\n        \"samples\": [\n          \"NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\n\\ubc88\\ud638\\n\\ubaa8\\ub378\\uba85\\n\\ud488\\uba85\\n\\uc218\\ub7c9\\nNumber\\n1\\nLicense\\nGATEONE-ZI/AM Node License\\n2\\n\\ub0b4\\uc5ed\\nREMARK\\n\\ube44\\uace0\\n\\uae30\\ubcf8 217 +\\ucd94\\uac00 2\\n219 Node\",\n          \"NO.\\nMODEL\\n\\ubc88\\ud638\\n\\ubaa8\\ub378\\uba85\\nDESCRIPTION\\n\\ud488\\uba85\\nQTY\\nSerial\\nREMARK\\n\\uc218\\ub7c9\\nNumber\\n\\ube44\\uace0\\n1\\nLicense\\nGATEONE-Z AM Node License\\n3\\n\\ub0b4\\ubd80 16(\\uae30\\uc900) 1\\n\\ub0b4\\uc5ed\",\n          \"NO.\\nMODEL\\nDESCRIPTION\\nQTY\\nSerial\\nREMARK\\n\\ubc88\\ud638\\n\\uc218\\ub7c9\\nNumber\\n\\ube44\\uace0\\n1\\nSecuway SSL U V2.1\\nSSL VPN\\ubcf4\\uc548\\uc194\\ub8e8\\uc158\\n1\\nSV2110NS23070041\\n2\\n3\\n4\\n5\\n6\\n7\\n\\ub0b4\\uc5ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"BLUEMAX NGF 100 BASIC\",\n          \"SecuwaySSL U V2.1_Server \\ubaa8\\ub4c8_1000\",\n          \"GATEONE Z(AM) Node LIcense\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_serial\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformers\n",
        ": Sentence Embeddings (BERT, Sentence-BERT)\n",
        "- 문맥과 의미를 고려한 고급 NLP 모델로, 언어의 복잡한 패턴을 이해\n",
        "- 입력된 텍스트를 토큰화하고, 각 토큰의 임베딩을 계산\n",
        "- 문맥을 이해해야 하는 자연어 처리(NLP) 작업에 적합 (예: 텍스트 분류, 질의응답, 텍스트 생성 등\n",
        "- 단어의 의미적 유사도를 평가할 때 유용"
      ],
      "metadata": {
        "id": "TbSyR5DTJm3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1st.ver 추출된 단어와 유사도 값 반환\n",
        "\n",
        "# 코사인 유사도 계산 함수\n",
        "def cosine_similarity(v1, v2):\n",
        "    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n",
        "\n",
        "# 입력 문장과 대상 단어 설정\n",
        "sentence = \"NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역 DESCRIPTION QTY Serial REMARK 품명 수량 Number 비고 UTM 33\"\n",
        "target_word = \"UTM\"\n",
        "\n",
        "# BERT 모델과 토크나이저 로드\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
        "model = BertModel.from_pretrained('bert-base-multilingual-cased')\n",
        "\n",
        "# 문장을 단어 단위로 분리\n",
        "words = sentence.split()\n",
        "\n",
        "# 대상 단어의 토큰 ID 및 임베딩 추출\n",
        "target_inputs = tokenizer(target_word, return_tensors='pt')\n",
        "with torch.no_grad():\n",
        "    target_outputs = model(**target_inputs)\n",
        "    target_embedding = target_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "\n",
        "# 문장 내 각 단어와 대상 단어의 유사도 계산\n",
        "similarities = []\n",
        "for word in words:\n",
        "    word_inputs = tokenizer(word, return_tensors='pt')\n",
        "    with torch.no_grad():\n",
        "        word_outputs = model(**word_inputs)\n",
        "        word_embedding = word_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "    similarity = cosine_similarity(word_embedding.numpy(), target_embedding.numpy())\n",
        "    similarities.append([word, similarity])\n",
        "\n",
        "# 유사도 기준으로 내림차순 정렬\n",
        "similarities.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# # 상위 5개 유사 단어 출력\n",
        "# for word, similarity in similarities[:5]:\n",
        "#     print(f\"Word: {word}, Cosine similarity: {similarity}\")\n",
        "\n",
        "# 유사도가 가장 높은 단어 및 유사도 출력\n",
        "wordAbs = similarities[0][0]\n",
        "similarityAbs = similarities[0][1]\n",
        "\n",
        "print(similarities[0]) # 전체 출력: 리스트\n",
        "print(wordAbs, similarityAbs) # 개별 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381,
          "referenced_widgets": [
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        },
        "id": "YSdj11LlJmZo",
        "outputId": "c272a01d-4cb2-4d5b-f296-35e0f698c195"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a2aedfd7f6542cf8cbbfed516f92577"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9bad60bd97745ce880857b877ca50cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fed3de32a62b435e891f8c3aa2104886"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7ab2888f6a34db5bd9eaaf1588048f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c68f0c355e1c4208b48e5b14203ad97b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['UTM', 1.0]\n",
            "UTM 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd.ver 유사도 값 입력시 해당 유사도 값 이상에 해당하는 단어 반환\n",
        "def find_similar_word_in_sentence(sentence, target_word, threshold=0.7):\n",
        "    \"\"\"\n",
        "    주어진 문장에서 유사한 단어를 찾아 반환합니다.\n",
        "\n",
        "    Args:\n",
        "    - sentence (str): 비교할 문장\n",
        "    - target_word (str): 찾고자 하는 목표 단어\n",
        "    - threshold (float): 유사도 임계값 (기본값: 0.7)\n",
        "\n",
        "    Returns:\n",
        "    - similar_word (str): 유사한 단어 (목표 단어와 유사도가 임계값 이상인 경우)\n",
        "      유사한 단어가 없는 경우 None을 반환합니다.\n",
        "    \"\"\"\n",
        "\n",
        "    similar_word = None\n",
        "    max_ratio = 0\n",
        "\n",
        "    # BERT 모델과 토크나이저 로드\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
        "    model = BertModel.from_pretrained('bert-base-multilingual-cased')\n",
        "\n",
        "    # 문장을 단어 단위로 분리\n",
        "    words = sentence.split()\n",
        "\n",
        "    # 대상 단어의 토큰 ID 및 임베딩 추출\n",
        "    target_inputs = tokenizer(target_word, return_tensors='pt')\n",
        "    with torch.no_grad():\n",
        "        target_outputs = model(**target_inputs)\n",
        "        target_embedding = target_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "\n",
        "    # 코사인 유사도 계산 함수\n",
        "    def cosine_similarity(v1, v2):\n",
        "        return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n",
        "\n",
        "    # 문장 내 각 단어와 대상 단어의 유사도 계산\n",
        "    for word in words:\n",
        "        word_inputs = tokenizer(word, return_tensors='pt')\n",
        "        with torch.no_grad():\n",
        "            word_outputs = model(**word_inputs)\n",
        "            word_embedding = word_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "        similarity = cosine_similarity(word_embedding.numpy(), target_embedding.numpy())\n",
        "        if similarity > max_ratio:\n",
        "            max_ratio = similarity\n",
        "            similar_word = word\n",
        "\n",
        "    if max_ratio >= threshold:\n",
        "      return similar_word\n",
        "    else:\n",
        "      return None\n",
        "\n",
        "# 예시 문장\n",
        "sentence = \"NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역 DESCRIPTION QTY Serial REMARK 품명 수량 Number 비고 UTM 33\"\n",
        "target_word = \"UTM\"\n",
        "\n",
        "# 유사한 단어 찾기\n",
        "similar_word = find_similar_word_in_sentence(sentence, target_word)\n",
        "if similar_word:\n",
        "    print(f\"Found similar word: {similar_word}\")\n",
        "else:\n",
        "    print(\"No similar word found\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AORLJFXXKiCc",
        "outputId": "6b1fb1af-73d3-4734-f08c-f7077968bb3e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found similar word: UTM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 코사인 유사도 계산 함수\n",
        "def cosine_similarity(v1, v2):\n",
        "    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n",
        "\n",
        "# 입력 문장과 대상 단어를 인자로 받아 유사도가 가장 높은 단어와 유사도 값을 반환하는 함수\n",
        "def find_trans_similarity(sentence, target_word):\n",
        "    # BERT 모델과 토크나이저 로드\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
        "    model = BertModel.from_pretrained('bert-base-multilingual-cased')\n",
        "\n",
        "    # 문장을 단어 단위로 분리\n",
        "    words = sentence.split()\n",
        "\n",
        "    # 대상 단어의 토큰 ID 및 임베딩 추출\n",
        "    target_inputs = tokenizer(target_word, return_tensors='pt')\n",
        "    with torch.no_grad():\n",
        "        target_outputs = model(**target_inputs)\n",
        "        target_embedding = target_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "\n",
        "    # 문장 내 각 단어와 대상 단어의 유사도 계산\n",
        "    similarities = []\n",
        "    for word in words:\n",
        "        word_inputs = tokenizer(word, return_tensors='pt')\n",
        "        with torch.no_grad():\n",
        "            word_outputs = model(**word_inputs)\n",
        "            word_embedding = word_outputs.last_hidden_state[:, 0, :].squeeze(0)\n",
        "        similarity = cosine_similarity(word_embedding.numpy(), target_embedding.numpy())\n",
        "        similarities.append([word, similarity])\n",
        "\n",
        "    # 유사도 기준으로 내림차순 정렬\n",
        "    similarities.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # 유사도가 가장 높은 단어 및 유사도 값 반환\n",
        "    most_similar_word = similarities[0][0]\n",
        "    highest_similarity = similarities[0][1]\n",
        "\n",
        "    return highest_similarity"
      ],
      "metadata": {
        "id": "uaka72ZmRjMN"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 저장 리스트\n",
        "names = []\n",
        "\n",
        "for idx, row in df.iterrows():\n",
        "    forcasting_seq = row['forcasting_seq']\n",
        "    text = row['text']\n",
        "    if pd.isna(text): # Check if text is NaN\n",
        "        names.append(np.NAN)\n",
        "        continue\n",
        "\n",
        "    matching_names = info_data[info_data['forcasting_seq'] == forcasting_seq]['product_name'].tolist()\n",
        "\n",
        "    matched = False\n",
        "    similarity_list = []\n",
        "    for name in matching_names:\n",
        "        similarity = find_trans_similarity(text, name) * 100\n",
        "        if similarity >= 30:\n",
        "            similarity_list.append([similarity, name])\n",
        "            # print(similarity)\n",
        "            matched = True\n",
        "            break\n",
        "        else:\n",
        "            pass\n",
        "    if len(similarity_list) >= 1:\n",
        "        max_similarity = max(similarity_list, key=lambda x: x[0])\n",
        "        names.append(max_similarity[1])\n",
        "    else:\n",
        "        names.append(np.NAN)\n",
        "    if not matched:\n",
        "        pass"
      ],
      "metadata": {
        "id": "WzZSMgI0RlIj"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_trans = df.copy()\n",
        "match_data_trans['predict_name'] = ''\n",
        "match_data_trans['predict_serial'] = ''\n",
        "try:\n",
        "  if len(names) == len(match_data_trans):\n",
        "      match_data_trans['predict_name'] = names\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "mCLKGk1oRlIr"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "serials = []\n",
        "\n",
        "for idx, predict_name in enumerate(match_data_trans['predict_name']):\n",
        "    if pd.isna(predict_name):\n",
        "        serials.append(np.nan)\n",
        "    else:\n",
        "        matched = False\n",
        "        text = match_data_trans.loc[idx, 'text']\n",
        "        for _, row in info_data.iterrows():\n",
        "            if row['product_name'] == predict_name:\n",
        "                similarity = find_trans_similarity(text, str(row['product_serial'])) * 100\n",
        "                if similarity >= 30:\n",
        "                    serials.append(row['product_serial'])\n",
        "                    matched = True\n",
        "                    break\n",
        "        if not matched:\n",
        "            serials.append(np.nan)"
      ],
      "metadata": {
        "id": "l0h5_-vHRlIr"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  if len(serials) == len(match_data_trans):\n",
        "      match_data_trans['predict_serial'] = serials\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "8DeWsmfPRlIr"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 확인\n",
        "\n",
        "# 제품정보에 있는 모델명을 추출한 경우\n",
        "success = match_data_trans.dropna(subset=['predict_name']).shape[0]\n",
        "\n",
        "# predict_name만 있고 predict_serial이 없는 경우\n",
        "half_success = match_data_trans[match_data_trans['predict_name'].notna() & match_data_trans['predict_serial'].isna()].shape[0]\n",
        "\n",
        "# predict_name과 predict_serial 둘 다 없는 경우\n",
        "fail = match_data_trans.shape[0] - success\n",
        "\n",
        "# 결과 출력\n",
        "print(\"< transformers >\")\n",
        "print(\"제품정보에 있는 모델명을 추출한 경우: \",f\"{success*100/len(match_data_trans):.2f}%\")\n",
        "print(\"제품정보에 있는 predict_serial이 없는 경우: \",f\"{half_success*100/len(match_data_trans):.2f}%\")\n",
        "print(\"제품정보에 있는 모델명을 추출 실패: \",f\"{fail*100/len(match_data_trans):.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de167df7-5ad0-4813-a03b-a8f2f1ac5c7a",
        "id": "me7s7dypRlIr"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "< transformers >\n",
            "제품정보에 있는 모델명을 추출한 경우:  98.11%\n",
            "제품정보에 있는 predict_serial이 없는 경우:  39.62%\n",
            "제품정보에 있는 모델명을 추출 실패:  1.89%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_trans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hal5wah1T6WM",
        "outputId": "c561c680-76ac-476c-f9d3-7782ae153c3f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    forcasting_seq                                               text  \\\n",
              "0             1437  NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...   \n",
              "1             1465  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...   \n",
              "2             1430  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...   \n",
              "3             1464  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...   \n",
              "4             1463  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...   \n",
              "5             1462  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...   \n",
              "6             1441  NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...   \n",
              "7             1439  NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...   \n",
              "8             1461  NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...   \n",
              "9             1451  NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...   \n",
              "10            1450  NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...   \n",
              "11            1475  NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...   \n",
              "12            1466  NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...   \n",
              "13            1358  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...   \n",
              "14            1536  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "15            1529  NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...   \n",
              "16            1555  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...   \n",
              "17            1566  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "18            1575  NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...   \n",
              "19            1552  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "20            1505  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "21            1541  내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...   \n",
              "22            1522  NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...   \n",
              "23            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "24            1521  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "25            1519  NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...   \n",
              "26            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "27            1444  NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...   \n",
              "28            1497  NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...   \n",
              "29            1489  NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...   \n",
              "30            1532  NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...   \n",
              "31            1456  NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...   \n",
              "32            1221  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...   \n",
              "33            1530  NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...   \n",
              "34            1509  NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...   \n",
              "35            1407  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...   \n",
              "36            1474  NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...   \n",
              "37            1442  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...   \n",
              "38            1495  1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...   \n",
              "39            1443  NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...   \n",
              "40            1455  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "41            1472  NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...   \n",
              "42            1471  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...   \n",
              "43            1500  NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...   \n",
              "44            1494  NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...   \n",
              "45            1501  NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...   \n",
              "46            1496  공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...   \n",
              "47            1460  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...   \n",
              "48            1448  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "49            1479  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "50            1480  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...   \n",
              "51            1431  NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...   \n",
              "52            1446                                                NaN   \n",
              "\n",
              "                        predict_name    predict_serial  \n",
              "0                            PEA-40A      N0040A002928  \n",
              "1                       Neobox M104a               NaN  \n",
              "2         GATEONE Z(AM) Node LIcense               NaN  \n",
              "3                       Neobox M104a               NaN  \n",
              "4                       Neobox M104a               NaN  \n",
              "5                       Neobox M104a               NaN  \n",
              "6              BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "7         GATEONE Z(AM) Node LIcense               NaN  \n",
              "8                            PEA-40A      N0040A002928  \n",
              "9                   BLUEMAX IPS 2000     JTN6IB3010002  \n",
              "10                          MFD 2300      TSCD52000345  \n",
              "11                           PEA-40A      N0040A002928  \n",
              "12                           PEA-40A      N0040A002928  \n",
              "13                 BLUEMAX WIPS 1000    DSF22C12S14009  \n",
              "14                            FG-60F   FGT60FTK2207254  \n",
              "15                  BLUEMAX NGF 1510               NaN  \n",
              "16                            FG-60F   FGT60FTK2207254  \n",
              "17                      VForce 1700U    UFW17002210631  \n",
              "18             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "19             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "20             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "21                               HDD                 -  \n",
              "22                  BLUEMAX WIPS 624     DVMCWA1050407  \n",
              "23                   VForce UTM 1406    NZL14062010623  \n",
              "24             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "25                               SSO               NaN  \n",
              "26                   VForce UTM 1406    NZL14062010623  \n",
              "27             BLUEMAX NGF 300 BASIC      TSCD62002326  \n",
              "28        GATEONE Z(AM) Node LIcense               NaN  \n",
              "29             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "30             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "31                   SecuwaySSL U100  SV2110LS23070031  \n",
              "32                      WAPPLES-2600    W2600I-2230003  \n",
              "33      GATEONE-Z (IAM) Node License               NaN  \n",
              "34      GATEONE-Z (IAM) Node License               NaN  \n",
              "35                  BLUEMAX NGF 2100        KSE0490586  \n",
              "36                    GATEONE-Z V3.0               NaN  \n",
              "37            BLUEMAX NGF 1500 BASIC      TSCD82000363  \n",
              "38             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "39            BLUEMAX NGF 1500 BASIC      TSCD82000363  \n",
              "40                i-oneNet V5.0 자료전송               NaN  \n",
              "41        GATEONE Z(AM) Node LIcense               NaN  \n",
              "42        GATEONE Z(AM) Node LIcense               NaN  \n",
              "43      GATEONE-Z (IAM) Node License               NaN  \n",
              "44      GATEONE-Z (IAM) Node License               NaN  \n",
              "45      GATEONE-Z (IAM) Node License               NaN  \n",
              "46        GATEONE Z(AM) Node LIcense               NaN  \n",
              "47  SecuwaySSL U V2.1_Server 모듈_1000  SV2110MS23070041  \n",
              "48             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "49      GATEONE-Z (IAM) Node License               NaN  \n",
              "50      GATEONE-Z (IAM) Node License               NaN  \n",
              "51             BLUEMAX NGF 300 BASIC      TSCD62002326  \n",
              "52                               NaN               NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6dd3d857-f97f-426d-810c-8be98625e6bb\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>forcasting_seq</th>\n",
              "      <th>text</th>\n",
              "      <th>predict_name</th>\n",
              "      <th>predict_serial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1437</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1465</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1430</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1463</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1462</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1441</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1439</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1461</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1451</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...</td>\n",
              "      <td>BLUEMAX IPS 2000</td>\n",
              "      <td>JTN6IB3010002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1450</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...</td>\n",
              "      <td>MFD 2300</td>\n",
              "      <td>TSCD52000345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1475</td>\n",
              "      <td>NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1466</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1358</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX WIPS 1000</td>\n",
              "      <td>DSF22C12S14009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1536</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>FG-60F</td>\n",
              "      <td>FGT60FTK2207254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1529</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX NGF 1510</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1555</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...</td>\n",
              "      <td>FG-60F</td>\n",
              "      <td>FGT60FTK2207254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1566</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce 1700U</td>\n",
              "      <td>UFW17002210631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1575</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1552</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1505</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1541</td>\n",
              "      <td>내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...</td>\n",
              "      <td>HDD</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1522</td>\n",
              "      <td>NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...</td>\n",
              "      <td>BLUEMAX WIPS 624</td>\n",
              "      <td>DVMCWA1050407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce UTM 1406</td>\n",
              "      <td>NZL14062010623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1521</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1519</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...</td>\n",
              "      <td>SSO</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce UTM 1406</td>\n",
              "      <td>NZL14062010623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1444</td>\n",
              "      <td>NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...</td>\n",
              "      <td>BLUEMAX NGF 300 BASIC</td>\n",
              "      <td>TSCD62002326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1497</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1489</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1532</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1456</td>\n",
              "      <td>NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...</td>\n",
              "      <td>SecuwaySSL U100</td>\n",
              "      <td>SV2110LS23070031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1221</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...</td>\n",
              "      <td>WAPPLES-2600</td>\n",
              "      <td>W2600I-2230003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1530</td>\n",
              "      <td>NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1509</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1407</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...</td>\n",
              "      <td>BLUEMAX NGF 2100</td>\n",
              "      <td>KSE0490586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1474</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...</td>\n",
              "      <td>GATEONE-Z V3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1442</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...</td>\n",
              "      <td>BLUEMAX NGF 1500 BASIC</td>\n",
              "      <td>TSCD82000363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1495</td>\n",
              "      <td>1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1443</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...</td>\n",
              "      <td>BLUEMAX NGF 1500 BASIC</td>\n",
              "      <td>TSCD82000363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1455</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>i-oneNet V5.0 자료전송</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1472</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1471</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1500</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1494</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1501</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1496</td>\n",
              "      <td>공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1460</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...</td>\n",
              "      <td>SecuwaySSL U V2.1_Server 모듈_1000</td>\n",
              "      <td>SV2110MS23070041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1448</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1479</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1480</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1431</td>\n",
              "      <td>NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...</td>\n",
              "      <td>BLUEMAX NGF 300 BASIC</td>\n",
              "      <td>TSCD62002326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1446</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6dd3d857-f97f-426d-810c-8be98625e6bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6dd3d857-f97f-426d-810c-8be98625e6bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6dd3d857-f97f-426d-810c-8be98625e6bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba47597b-5643-43b7-8d67-5ce46ae57689 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_051dce88-5505-4dac-83f8-bdeaaa591487\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('match_data_trans')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "        document.querySelector('#id_051dce88-5505-4dac-83f8-bdeaaa591487 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('match_data_trans');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "match_data_trans",
              "summary": "{\n  \"name\": \"match_data_trans\",\n  \"rows\": 53,\n  \"fields\": [\n    {\n      \"column\": \"forcasting_seq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1221,\n        \"max\": 1575,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1552,\n          1471,\n          1448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 51,\n        \"samples\": [\n          \"NO. MODEL DESCRIPTION QTY Serial \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 \\ud488\\uba85 \\uc218\\ub7c9 Number 1 License GATEONE-ZI/AM Node License 2 \\ub0b4\\uc5ed REMARK \\ube44\\uace0 \\uae30\\ubcf8 217 +\\ucd94\\uac00 2 219 Node\",\n          \"NO. MODEL \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 DESCRIPTION \\ud488\\uba85 QTY Serial REMARK \\uc218\\ub7c9 Number \\ube44\\uace0 1 License GATEONE-Z AM Node License 3 \\ub0b4\\ubd80 16(\\uae30\\uc900) 1 \\ub0b4\\uc5ed\",\n          \"NO. MODEL DESCRIPTION QTY Serial REMARK \\ubc88\\ud638 \\uc218\\ub7c9 Number \\ube44\\uace0 1 Secuway SSL U V2.1 SSL VPN\\ubcf4\\uc548\\uc194\\ub8e8\\uc158 1 SV2110NS23070041 2 3 4 5 6 7 \\ub0b4\\uc5ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"SecuwaySSL U100\",\n          \"VForce 1700U\",\n          \"PEA-40A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_serial\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"N0040A002928\",\n          \"JTN4UA0060190\",\n          \" FGT60FTK2207254\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## difflib\n",
        "- 단순한 문자열 비교 알고리즘으로, 문맥을 고려하지 않고 문자 일치 여부에 따라 유사도를 계산\n",
        "- 두 문자열 간의 유사도를 편집 거리(Edit Distance) 기반으로 계산\n",
        "- 단순한 문자열 매칭, 텍스트 비교, 차이점 찾기 등 단순한 문자열 조작 작업에 적합\n",
        "- 텍스트의 단순한 일치 여부를 평가할 때 유용"
      ],
      "metadata": {
        "id": "Rq0wnY5UJqNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1st.ver 추출된 단어와 유사도 값 반환\n",
        "\n",
        "# 예시 문장\n",
        "sentence = \"NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역 DESCRIPTION QTY Serial REMARK 품명 수량 Number 비고 UTM 33\"\n",
        "target_word = \"UTM\"\n",
        "\n",
        "words = sentence.split()  # 문장을 단어로 분할\n",
        "\n",
        "# 유사도가 가장 높은 단어 및 유사도 출력\n",
        "similarities = []\n",
        "for word in words:\n",
        "    ratio = SequenceMatcher(None, target_word, word).ratio()\n",
        "    similarities.append([word, ratio])\n",
        "\n",
        "# 유사도 기준으로 내림차순 정렬\n",
        "similarities.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# # 상위 5개 유사 단어 출력\n",
        "# for word, similarity in similarities[:5]:\n",
        "#     print(f\"Word: {word}, Cosine similarity: {similarity}\")\n",
        "\n",
        "# 유사도가 가장 높은 단어 및 유사도 출력\n",
        "wordAbs = similarities[0][0]\n",
        "similarityAbs = similarities[0][1]\n",
        "\n",
        "print(similarities[0]) # 전체 출력: 리스트\n",
        "print(wordAbs, similarityAbs) # 개별 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IufRgUcjLUYI",
        "outputId": "47f0e014-bdd9-4b20-9b21-d56a0094532b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['UTM', 1.0]\n",
            "UTM 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2nd.ver 유사도 값 입력시 해당 유사도 값 이상에 해당하는 단어 반환\n",
        "\n",
        "def find_similar_word_in_sentence(sentence, target_word, threshold=0.7):\n",
        "    \"\"\"\n",
        "    주어진 문장에서 유사한 단어를 찾아 반환합니다.\n",
        "\n",
        "    Args:\n",
        "    - sentence (str): 비교할 문장\n",
        "    - target_word (str): 찾고자 하는 목표 단어\n",
        "    - threshold (float): 유사도 임계값 (기본값: 0.7)\n",
        "\n",
        "    Returns:\n",
        "    - similar_word (str): 유사한 단어 (목표 단어와 유사도가 임계값 이상인 경우)\n",
        "      유사한 단어가 없는 경우 None을 반환합니다.\n",
        "    \"\"\"\n",
        "\n",
        "    similar_word = None\n",
        "    max_ratio = 0\n",
        "\n",
        "    words = sentence.split()  # 문장을 단어로 분할\n",
        "\n",
        "    for word in words:\n",
        "        ratio = SequenceMatcher(None, target_word, word).ratio()\n",
        "        if ratio > max_ratio:\n",
        "            max_ratio = ratio\n",
        "            similar_word = word\n",
        "\n",
        "    if max_ratio >= threshold:\n",
        "        return similar_word\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "# 예시 문장\n",
        "sentence = \"NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역 DESCRIPTION QTY Serial REMARK 품명 수량 Number 비고 UTM 33\"\n",
        "target_word = \"UTM\"\n",
        "\n",
        "# 유사한 단어 찾기\n",
        "similar_word = find_similar_word_in_sentence(sentence, target_word)\n",
        "if similar_word:\n",
        "    print(f\"Found similar word: {similar_word}\")\n",
        "else:\n",
        "    print(\"No similar word found\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXl5AHdwLUVe",
        "outputId": "de52348d-9523-43e6-d579-e0958f50c76c"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found similar word: UTM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_difflib_similarity(sentence, target_word):\n",
        "    words = sentence.split()  # 문장을 단어로 분할\n",
        "\n",
        "    # 유사도가 가장 높은 단어 및 유사도 출력\n",
        "    similarities = []\n",
        "    for word in words:\n",
        "        ratio = SequenceMatcher(None, target_word, word).ratio()\n",
        "        similarities.append([word, ratio])\n",
        "\n",
        "    # 유사도 기준으로 내림차순 정렬\n",
        "    similarities.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # 유사도가 가장 높은 단어 및 유사도 추출\n",
        "    wordAbs = similarities[0][0]\n",
        "    similarityAbs = similarities[0][1]\n",
        "\n",
        "    return similarityAbs"
      ],
      "metadata": {
        "id": "UHu9IZL-Po5z"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 저장 리스트\n",
        "names = []\n",
        "\n",
        "for idx, row in df.iterrows():\n",
        "    forcasting_seq = row['forcasting_seq']\n",
        "    text = row['text']\n",
        "    if pd.isna(text): # Check if text is NaN\n",
        "        names.append(np.NAN)\n",
        "        continue\n",
        "\n",
        "    matching_names = info_data[info_data['forcasting_seq'] == forcasting_seq]['product_name'].tolist()\n",
        "\n",
        "    matched = False\n",
        "    similarity_list = []\n",
        "    for name in matching_names:\n",
        "        similarity = find_difflib_similarity(text, name) * 100\n",
        "        if similarity >= 30:\n",
        "            similarity_list.append([similarity, name])\n",
        "            # print(similarity)\n",
        "            matched = True\n",
        "            break\n",
        "        else:\n",
        "            pass\n",
        "    if len(similarity_list) >= 1:\n",
        "        max_similarity = max(similarity_list, key=lambda x: x[0])\n",
        "        names.append(max_similarity[1])\n",
        "    else:\n",
        "        names.append(np.NAN)\n",
        "    if not matched:\n",
        "        pass"
      ],
      "metadata": {
        "id": "rIBR1c1IGsxg"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_difflib = df.copy()\n",
        "match_data_difflib['predict_name'] = ''\n",
        "match_data_difflib['predict_serial'] = ''\n",
        "try:\n",
        "  if len(names) == len(match_data_difflib):\n",
        "      match_data_difflib['predict_name'] = names\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "icGy9XQ0QcPN"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "serials = []\n",
        "\n",
        "for idx, predict_name in enumerate(match_data_difflib['predict_name']):\n",
        "    if pd.isna(predict_name):\n",
        "        serials.append(np.nan)\n",
        "    else:\n",
        "        matched = False\n",
        "        text = match_data_difflib.loc[idx, 'text']\n",
        "        for _, row in info_data.iterrows():\n",
        "            if row['product_name'] == predict_name:\n",
        "                similarity = find_difflib_similarity(text, str(row['product_serial'])) * 100\n",
        "                if similarity >= 30:\n",
        "                    serials.append(row['product_serial'])\n",
        "                    matched = True\n",
        "                    break\n",
        "        if not matched:\n",
        "            serials.append(np.nan)"
      ],
      "metadata": {
        "id": "LFcIkR7ZGwls"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  if len(serials) == len(match_data_difflib):\n",
        "      match_data_difflib['predict_serial'] = serials\n",
        "  else:\n",
        "      raise\n",
        "except Exception as e:\n",
        "  print(e)"
      ],
      "metadata": {
        "id": "I-it8CwzQcPV"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 확인\n",
        "\n",
        "# 제품정보에 있는 모델명을 추출한 경우\n",
        "success = match_data_difflib.dropna(subset=['predict_name']).shape[0]\n",
        "\n",
        "# predict_name만 있고 predict_serial이 없는 경우\n",
        "half_success = match_data_difflib[match_data_difflib['predict_name'].notna() & match_data_difflib['predict_serial'].isna()].shape[0]\n",
        "\n",
        "# predict_name과 predict_serial 둘 다 없는 경우\n",
        "fail = match_data_difflib.shape[0] - success\n",
        "\n",
        "# 결과 출력\n",
        "print(\"< difflib >\")\n",
        "print(\"제품정보에 있는 모델명을 추출한 경우: \",f\"{success*100/len(match_data_difflib):.2f}%\")\n",
        "print(\"제품정보에 있는 predict_serial이 없는 경우: \",f\"{half_success*100/len(match_data_difflib):.2f}%\")\n",
        "print(\"제품정보에 있는 모델명을 추출 실패: \",f\"{fail*100/len(match_data_difflib):.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e71e262-462c-4e10-c305-cef1b6866a1b",
        "id": "-f0IBLCdQcPV"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "< difflib >\n",
            "제품정보에 있는 모델명을 추출한 경우:  98.11%\n",
            "제품정보에 있는 predict_serial이 없는 경우:  43.40%\n",
            "제품정보에 있는 모델명을 추출 실패:  1.89%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "match_data_difflib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "See6bzW1UGzh",
        "outputId": "af9b75b7-5822-4e8e-a790-4dfdb1b98393"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    forcasting_seq                                               text  \\\n",
              "0             1437  NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...   \n",
              "1             1465  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...   \n",
              "2             1430  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...   \n",
              "3             1464  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...   \n",
              "4             1463  NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...   \n",
              "5             1462  NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...   \n",
              "6             1441  NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...   \n",
              "7             1439  NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...   \n",
              "8             1461  NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...   \n",
              "9             1451  NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...   \n",
              "10            1450  NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...   \n",
              "11            1475  NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...   \n",
              "12            1466  NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...   \n",
              "13            1358  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...   \n",
              "14            1536  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "15            1529  NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...   \n",
              "16            1555  NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...   \n",
              "17            1566  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "18            1575  NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...   \n",
              "19            1552  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "20            1505  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "21            1541  내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...   \n",
              "22            1522  NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...   \n",
              "23            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "24            1521  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "25            1519  NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...   \n",
              "26            1548  NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...   \n",
              "27            1444  NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...   \n",
              "28            1497  NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...   \n",
              "29            1489  NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...   \n",
              "30            1532  NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...   \n",
              "31            1456  NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...   \n",
              "32            1221  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...   \n",
              "33            1530  NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...   \n",
              "34            1509  NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...   \n",
              "35            1407  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...   \n",
              "36            1474  NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...   \n",
              "37            1442  NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...   \n",
              "38            1495  1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...   \n",
              "39            1443  NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...   \n",
              "40            1455  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "41            1472  NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...   \n",
              "42            1471  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...   \n",
              "43            1500  NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...   \n",
              "44            1494  NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...   \n",
              "45            1501  NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...   \n",
              "46            1496  공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...   \n",
              "47            1460  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...   \n",
              "48            1448  NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...   \n",
              "49            1479  NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...   \n",
              "50            1480  NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...   \n",
              "51            1431  NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...   \n",
              "52            1446                                                NaN   \n",
              "\n",
              "                        predict_name    predict_serial  \n",
              "0                            PEA-40A      N0040A002928  \n",
              "1                       Neobox M104a               NaN  \n",
              "2         GATEONE Z(AM) Node LIcense               NaN  \n",
              "3                       Neobox M104a               NaN  \n",
              "4                       Neobox M104a               NaN  \n",
              "5                       Neobox M104a               NaN  \n",
              "6              BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "7         GATEONE Z(AM) Node LIcense               NaN  \n",
              "8                            PEA-40A      N0040A002928  \n",
              "9                   BLUEMAX IPS 2000     JTN6IB3010002  \n",
              "10                          MFD 2300      TSCD52000345  \n",
              "11                           PEA-40A      N0040A002928  \n",
              "12                           PEA-40A               NaN  \n",
              "13                 BLUEMAX WIPS 1000    DSF22C12S14009  \n",
              "14                            FG-60F   FGT60FTK2207254  \n",
              "15                  BLUEMAX NGF 1510               NaN  \n",
              "16                            FG-60F   FGT60FTK2207254  \n",
              "17                      VForce 1700U    UFW17002210631  \n",
              "18             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "19             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "20             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "21                               HDD               NaN  \n",
              "22                  BLUEMAX WIPS 624     DVMCWA1050407  \n",
              "23                   VForce UTM 1406    NZL14062010623  \n",
              "24             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "25                               SSO               NaN  \n",
              "26                   VForce UTM 1406    NZL14062010623  \n",
              "27             BLUEMAX NGF 300 BASIC      TSCD62002326  \n",
              "28        GATEONE Z(AM) Node LIcense               NaN  \n",
              "29             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "30             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "31                   SecuwaySSL U100  SV2110LS23070031  \n",
              "32                      WAPPLES-2600    W2600I-2230003  \n",
              "33      GATEONE-Z (IAM) Node License               NaN  \n",
              "34      GATEONE-Z (IAM) Node License               NaN  \n",
              "35                  BLUEMAX NGF 2100        KSE0490586  \n",
              "36                    GATEONE-Z V3.0               NaN  \n",
              "37            BLUEMAX NGF 1500 BASIC      TSCD82000363  \n",
              "38             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "39            BLUEMAX NGF 1500 BASIC      TSCD82000363  \n",
              "40                i-oneNet V5.0 자료전송               NaN  \n",
              "41        GATEONE Z(AM) Node LIcense               NaN  \n",
              "42        GATEONE Z(AM) Node LIcense               NaN  \n",
              "43      GATEONE-Z (IAM) Node License               NaN  \n",
              "44      GATEONE-Z (IAM) Node License               NaN  \n",
              "45      GATEONE-Z (IAM) Node License               NaN  \n",
              "46        GATEONE Z(AM) Node LIcense               NaN  \n",
              "47  SecuwaySSL U V2.1_Server 모듈_1000  SV2110MS23070041  \n",
              "48             BLUEMAX NGF 100 BASIC     JTN4UA0060190  \n",
              "49      GATEONE-Z (IAM) Node License               NaN  \n",
              "50      GATEONE-Z (IAM) Node License               NaN  \n",
              "51             BLUEMAX NGF 300 BASIC      TSCD62002326  \n",
              "52                               NaN               NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6db245b-37fd-4ea4-b6db-c85573b1fa40\" class=\"colab-df-container\">\n",
              "    <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>forcasting_seq</th>\n",
              "      <th>text</th>\n",
              "      <th>predict_name</th>\n",
              "      <th>predict_serial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1437</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 PEA-40A 2 3 4 5 6 7 내역 DESC...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1465</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox MID43...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1430</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 Licen...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1463</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL 모델명 Neobox M104 ...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1462</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox MID43 1 2 3 4 5 B 7 내역...</td>\n",
              "      <td>Neobox M104a</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1441</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1439</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY Serial REMARK 모델...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1461</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 QTY 수량 1 Serial Number...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1451</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 6 7 내역 MODEL 모델명 DESCRIPTION ...</td>\n",
              "      <td>BLUEMAX IPS 2000</td>\n",
              "      <td>JTN6IB3010002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1450</td>\n",
              "      <td>NO. 번호 1 2 3 4 5 B 7 내역 MODEL IFO 2300 BLUEWAX...</td>\n",
              "      <td>MFD 2300</td>\n",
              "      <td>TSCD52000345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1475</td>\n",
              "      <td>NO. 번호 1 MODEL 모델명 PEA-40A DESCRIPTION Q'TY 품명...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>N0040A002928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1466</td>\n",
              "      <td>NO. 번호 MODEL 모델명 Neobox ID43 1 2 3 4 5 B 7 내역 ...</td>\n",
              "      <td>PEA-40A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1358</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX WIPS 1000</td>\n",
              "      <td>DSF22C12S14009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1536</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>FG-60F</td>\n",
              "      <td>FGT60FTK2207254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1529</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 품명 수량 1 BLUEM...</td>\n",
              "      <td>BLUEMAX NGF 1510</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1555</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 품명 수량 1 FG-60...</td>\n",
              "      <td>FG-60F</td>\n",
              "      <td>FGT60FTK2207254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1566</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce 1700U</td>\n",
              "      <td>UFW17002210631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1575</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY 번호 모델명 품명 수량 1 BLUE...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1552</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1505</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1541</td>\n",
              "      <td>내역 NO. MODEL 번호 1 모델명 DESCRIPTION 품명 Q'TY Seri...</td>\n",
              "      <td>HDD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1522</td>\n",
              "      <td>NO. MODEL Q'TY 번호 모델명 수량 1 BLUEMAX WIPS 624 2 ...</td>\n",
              "      <td>BLUEMAX WIPS 624</td>\n",
              "      <td>DVMCWA1050407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce UTM 1406</td>\n",
              "      <td>NZL14062010623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1521</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1519</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델 수량 1 Hy...</td>\n",
              "      <td>SSO</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1548</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION Q'TY Serial REMAR...</td>\n",
              "      <td>VForce UTM 1406</td>\n",
              "      <td>NZL14062010623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1444</td>\n",
              "      <td>NO. 번호 MODEL 229 DESCRIPTION QTY Serial REMARK...</td>\n",
              "      <td>BLUEMAX NGF 300 BASIC</td>\n",
              "      <td>TSCD62002326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1497</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial 번호 모델명 품명 수량...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1489</td>\n",
              "      <td>NO. 번호 MODEL 모델명 DESCRIPTION QTY 수량 $ BLUEMAX ...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1532</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION Q'TY 모델명 품명 수량 Serial...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1456</td>\n",
              "      <td>NO. MODEL 번호 모델명 1234567 SecuwaySSL VPN Manage...</td>\n",
              "      <td>SecuwaySSL U100</td>\n",
              "      <td>SV2110LS23070031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1221</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 품명...</td>\n",
              "      <td>WAPPLES-2600</td>\n",
              "      <td>W2600I-2230003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1530</td>\n",
              "      <td>NO. MODEL 번호 모델 1 License 내역 DESCRIPTION GATEO...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1509</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1407</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 1 BLUEMAX ...</td>\n",
              "      <td>BLUEMAX NGF 2100</td>\n",
              "      <td>KSE0490586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1474</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 Q'TY Serial RE...</td>\n",
              "      <td>GATEONE-Z V3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1442</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY 번호 모델명 수량 Serial Num...</td>\n",
              "      <td>BLUEMAX NGF 1500 BASIC</td>\n",
              "      <td>TSCD82000363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1495</td>\n",
              "      <td>1 NO. 번호 MODEL 모델명 BLUEMAX NGF 100 BASIC DESCR...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1443</td>\n",
              "      <td>NO. 번호 MODEL DESCRIPTION QTY 모델명 수량 1 BLUEWAX ...</td>\n",
              "      <td>BLUEMAX NGF 1500 BASIC</td>\n",
              "      <td>TSCD82000363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1455</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>i-oneNet V5.0 자료전송</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1472</td>\n",
              "      <td>NO. MODEL 번호 모델명 DESCRIPTION 품명 QTY Serial REM...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1471</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION 품명 QTY Seria...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1500</td>\n",
              "      <td>NO. MODEL 번호 모델명 1 License 내역 DESCRIPTION GATE...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1494</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial 번호 모델명 품명 수량 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1501</td>\n",
              "      <td>NO. MODEL DESCRIPTION OTY Serial REMARK 번호 모델 ...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1496</td>\n",
              "      <td>공급자: 더망고 납품확인서 내용: 서울특별시데이터센터 재해복구시스템 환경 구축 사업...</td>\n",
              "      <td>GATEONE Z(AM) Node LIcense</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1460</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 수량 ...</td>\n",
              "      <td>SecuwaySSL U V2.1_Server 모듈_1000</td>\n",
              "      <td>SV2110MS23070041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1448</td>\n",
              "      <td>NO. MODEL DESCRIPTION Q'TY Serial REMARK 번호 모델...</td>\n",
              "      <td>BLUEMAX NGF 100 BASIC</td>\n",
              "      <td>JTN4UA0060190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1479</td>\n",
              "      <td>NO. MODEL DESCRIPTION QTY Serial REMARK 번호 모델명...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1480</td>\n",
              "      <td>NO. 번호 1 내역 MODEL 모델명 DESCRIPTION GATEONE-Z /A...</td>\n",
              "      <td>GATEONE-Z (IAM) Node License</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1431</td>\n",
              "      <td>NO. 번호 1 2 MODEL 모델명 BLUEMAX NGF 300 3 4 5 6 7...</td>\n",
              "      <td>BLUEMAX NGF 300 BASIC</td>\n",
              "      <td>TSCD62002326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1446</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6db245b-37fd-4ea4-b6db-c85573b1fa40')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6db245b-37fd-4ea4-b6db-c85573b1fa40');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a467f797-7563-4bab-8fae-8f1545a27bc8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a467f797-7563-4bab-8fae-8f1545a27bc8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a467f797-7563-4bab-8fae-8f1545a27bc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_483957ad-de9d-44c6-b914-77e3255e4c08\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('match_data_difflib')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_483957ad-de9d-44c6-b914-77e3255e4c08 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('match_data_difflib');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "match_data_difflib",
              "summary": "{\n  \"name\": \"match_data_difflib\",\n  \"rows\": 53,\n  \"fields\": [\n    {\n      \"column\": \"forcasting_seq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1221,\n        \"max\": 1575,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1552,\n          1471,\n          1448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 51,\n        \"samples\": [\n          \"NO. MODEL DESCRIPTION QTY Serial \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 \\ud488\\uba85 \\uc218\\ub7c9 Number 1 License GATEONE-ZI/AM Node License 2 \\ub0b4\\uc5ed REMARK \\ube44\\uace0 \\uae30\\ubcf8 217 +\\ucd94\\uac00 2 219 Node\",\n          \"NO. MODEL \\ubc88\\ud638 \\ubaa8\\ub378\\uba85 DESCRIPTION \\ud488\\uba85 QTY Serial REMARK \\uc218\\ub7c9 Number \\ube44\\uace0 1 License GATEONE-Z AM Node License 3 \\ub0b4\\ubd80 16(\\uae30\\uc900) 1 \\ub0b4\\uc5ed\",\n          \"NO. MODEL DESCRIPTION QTY Serial REMARK \\ubc88\\ud638 \\uc218\\ub7c9 Number \\ube44\\uace0 1 Secuway SSL U V2.1 SSL VPN\\ubcf4\\uc548\\uc194\\ub8e8\\uc158 1 SV2110NS23070041 2 3 4 5 6 7 \\ub0b4\\uc5ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"SecuwaySSL U100\",\n          \"VForce 1700U\",\n          \"PEA-40A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_serial\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"TSCD62002326\",\n          \"W2600I-2230003\",\n          \"N0040A002928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    }
  ]
}