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            "Collecting kiwipiepy\n",
            "  Downloading kiwipiepy-0.16.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting kiwipiepy-model~=0.16 (from kiwipiepy)\n",
            "  Downloading kiwipiepy_model-0.16.0.tar.gz (30.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.8/30.8 MB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from kiwipiepy) (1.25.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kiwipiepy) (4.66.2)\n",
            "Building wheels for collected packages: kiwipiepy-model\n",
            "  Building wheel for kiwipiepy-model (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kiwipiepy-model: filename=kiwipiepy_model-0.16.0-py3-none-any.whl size=30813256 sha256=4452fbafc1ba21605e917dd0c3b776d6ca7a74e3daa243191bb369fea188e9a3\n",
            "  Stored in directory: /root/.cache/pip/wheels/6b/03/2b/ac2c97cc65ebd9df3516f4b900adc2f0a744df8d1375b2e2ef\n",
            "Successfully built kiwipiepy-model\n",
            "Installing collected packages: kiwipiepy-model, kiwipiepy\n",
            "Successfully installed kiwipiepy-0.16.2 kiwipiepy-model-0.16.0\n"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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",
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            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
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              "vocab.txt:   0%|          | 0.00/263k [00:00<?, ?B/s]"
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            "음식 데이터 수 :  47\n",
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              "     name category                                        description\n",
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              "3  매콤땡초김밥       김밥  김밥 속에 매콤한 양념으로 조리한 땡초(고추)와 다양한 야채, 계란 등의 재료를 넣...\n",
              "4  참치마요김밥       김밥  참치와 마요네즈를 함께 넣어 감칠맛을 더한 김밥으로, 부드러운 식감과 진한 맛을 즐..."
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      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",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "    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-7ae116f3-e6f5-49f9-9c67-ac18b18208bc 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": "scraping_result",
              "summary": "{\n  \"name\": \"scraping_result\",\n  \"rows\": 47,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"\\uac08\\ube44\\ud0d5\",\n          \"\\ub3cc\\uc1a5\\ube44\\ube54\\ubc25\",\n          \"\\ub69d\\ubc30\\uae30\\ubd88\\uace0\\uae30\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\ubd84\\uc2dd\",\n          \"\\uacc4\\uc808\",\n          \"\\uc2dd\\uc0ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"\\uc18c\\uac08\\ube44\\uc640 \\ub2e4\\uc591\\ud55c \\uc57c\\ucc44\\ub97c \\ud568\\uaed8 \\ub053\\uc5ec \\ub9cc\\ub4e0 \\uc9c4\\ud55c \\uc721\\uc218\\ub85c, \\uace0\\uae30\\uc758 \\ud48d\\ubbf8\\uc640 \\uac74\\uac15\\uc5d0 \\uc88b\\uc740 \\uc601\\uc591\\uc744 \\ud568\\uaed8 \\uc990\\uae38 \\uc218 \\uc788\\ub294 \\ub300\\ud45c\\uc801\\uc778 \\ud55c\\uad6d\\uc758 \\uace0\\uae30\\uad6d\\uc694\\ub9ac\",\n          \"\\ub728\\uac70\\uc6b4 \\ub3cc\\uc1a5\\uc5d0 \\ubc25\\uacfc \\ub2e4\\uc591\\ud55c \\uc57c\\ucc44, \\uace0\\uae30, \\uacc4\\ub780 \\ub4f1\\uc744 \\ub123\\uc5b4 \\ube44\\ubcbc\\uba39\\ub294 \\ud55c\\uad6d\\uc758 \\uc804\\ud1b5 \\uc74c\\uc2dd\\uc73c\\ub85c, \\uace0\\uc18c\\ud558\\uace0 \\uc9c4\\ud55c \\ub9db\\uc744 \\ub290\\ub084 \\uc218 \\uc788\\ub294 \\ub300\\ud45c\\uc801\\uc778 \\ud55c\\uad6d\\uc758 \\ubc25 \\uc694\\ub9ac\",\n          \"\\ubd88\\uc5d0 \\uad6c\\uc6cc \\ub2f4\\uc740 \\uc18c\\uace0\\uae30\\uc640 \\ucc44\\uc18c\\ub97c \\uac01\\uc885 \\uc591\\ub150\\uacfc \\ud568\\uaed8 \\ub3c5\\ud2b9\\ud55c \\ub69d\\ubc30\\uae30\\uc5d0 \\ub2f4\\uc544 \\ub053\\uc5ec\\ub0b4\\uc5b4 \\uba39\\ub294 \\ud55c\\uad6d\\uc758 \\uc804\\ud1b5 \\uc74c\\uc2dd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from key_extraction import keywordExtractor\n",
        "from transformers import ElectraModel, ElectraTokenizerFast\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# load model and tokenizer\n",
        "name = \"monologg/koelectra-base-v3-discriminator\"\n",
        "model = ElectraModel.from_pretrained(name)\n",
        "tokenizer = ElectraTokenizerFast.from_pretrained(name)\n",
        "\n",
        "# load keywordExtractor\n",
        "key = keywordExtractor(model,tokenizer,dir='/content/eng_han.csv')\n",
        "\n",
        "# load scraping_data\n",
        "scraping_result = pd.read_csv('/content/binaryproject.csv')\n",
        "print('음식 데이터 수 : ', len(scraping_result))\n",
        "print('')\n",
        "scraping_result.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extract keywords\n",
        "docs_keywords = key.extract_keyword(scraping_result.iloc[[1]])\n",
        "\n",
        "# result\n",
        "result = pd.DataFrame(docs_keywords)\n",
        "print(result)\n",
        "print('키워드 추출 예시\\n')\n",
        "print('음식 : ', scraping_result.loc[1, 'name'])\n",
        "pd.DataFrame(result.keywords.values[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "pLyOcGnxt5i2",
        "outputId": "4bdc105f-396a-46c9-9eac-8e5b5bdf6800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   name  keywords\n",
            "0  치즈김밥  [치즈, 김밥]\n",
            "키워드 추출 예시\n",
            "\n",
            "음식 :  치즈김밥\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    0\n",
              "0  치즈\n",
              "1  김밥"
            ],
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              "  <div id=\"df-1a7c61e3-e437-43e8-91a3-afd55633c965\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "  <thead>\n",
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              "    </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",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "        document.querySelector('#df-1a7c61e3-e437-43e8-91a3-afd55633c965 button.colab-df-convert');\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "          + ' to learn more about interactive tables.';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    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",
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              "  }\n",
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              "    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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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      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-164dab61-0615-4c83-ad78-8a6ae7ad7b9a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"\\uae40\\ubc25\",\n          \"\\uce58\\uc988\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = []\n",
        "for i in range(len(scraping_result)) :\n",
        "  docs_keywords = key.extract_keyword(scraping_result.iloc[[i]])\n",
        "  new_data.append(docs_keywords)\n",
        "print(new_data)\n",
        "for item in new_data:\n",
        "    item['name'] = item['name'][0]  # 'name' 필드에서 대괄호를 제거합니다.\n",
        "    item['keywords'] = [word for sublist in item['keywords'] for word in sublist]  # 'keywords' 리스트를 평탄화합니다.\n",
        "print(new_data)\n",
        "data_search = pd.DataFrame(new_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGGBYpb2rTLg",
        "outputId": "37efa268-efd4-46d2-8838-c37bd89568a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'name': ['야채김밥'], 'keywords': [['김밥']]}, {'name': ['치즈김밥'], 'keywords': [['치즈', '김밥']]}, {'name': ['소고기김밥'], 'keywords': [['소고기', '김밥']]}, {'name': ['매콤땡초김밥'], 'keywords': [['땡초', '김밥']]}, {'name': ['참치마요김밥'], 'keywords': [['김밥']]}, {'name': ['참치와사비김밥'], 'keywords': [['와사비', '참치', '김밥']]}, {'name': ['매콤오징어김밥'], 'keywords': [['오징어', '김밥']]}, {'name': ['신라면'], 'keywords': [['신라면']]}, {'name': ['콩나물라면'], 'keywords': [['콩나물']]}, {'name': ['떡라면'], 'keywords': [['라면']]}, {'name': ['만두라면'], 'keywords': [['라면', '육수', '만두']]}, {'name': ['치즈라면'], 'keywords': [['치즈', '라면']]}, {'name': ['국물떡볶이'], 'keywords': [['분식', '떡볶이', '국물']]}, {'name': ['유부라면'], 'keywords': [['라면']]}, {'name': ['후레쉬쫄면'], 'keywords': [[]]}, {'name': ['부산생생우동'], 'keywords': [['부산']]}, {'name': ['부산어묵탕'], 'keywords': [['부산']]}, {'name': ['김치만두'], 'keywords': [['김치', '만두']]}, {'name': ['고기만두'], 'keywords': [['고기', '만두']]}, {'name': ['만두국'], 'keywords': [['국물', '만두']]}, {'name': ['떡국+공기밥'], 'keywords': [['떡국', '국물']]}, {'name': ['떡만두국+공기밥'], 'keywords': [['만두국']]}, {'name': ['된장찌개'], 'keywords': [[]]}, {'name': ['순두부찌개'], 'keywords': [[]]}, {'name': ['돼지김치찌개'], 'keywords': [['김치']]}, {'name': ['참치김치찌개'], 'keywords': [['참치', '김치']]}, {'name': ['뚝배기불고기'], 'keywords': [['뚝배기']]}, {'name': ['갈비탕'], 'keywords': [['고기']]}, {'name': ['육개장'], 'keywords': [[]]}, {'name': ['짜장덮밥'], 'keywords': [['덮밥', '짜장']]}, {'name': ['부대찌개+라면사리'], 'keywords': [[]]}, {'name': ['햄야채볶음밥'], 'keywords': [['볶음밥', '야채']]}, {'name': ['김치볶음밥'], 'keywords': [['볶음밥', '김치']]}, {'name': ['참치볶음밥'], 'keywords': [['참치', '볶음밥', '야채']]}, {'name': ['카레라이스'], 'keywords': [[]]}, {'name': ['새우볶음밥'], 'keywords': [['볶음밥', '새우']]}, {'name': ['제육덮밥'], 'keywords': [['덮밥']]}, {'name': ['오징어덮밥'], 'keywords': [['오징어', '양념', '덮밥']]}, {'name': ['비빔밥'], 'keywords': [[]]}, {'name': ['돌솥비빔밥'], 'keywords': [['한국']]}, {'name': ['오므라이스'], 'keywords': [['요리']]}, {'name': ['등심돈까스'], 'keywords': [['돈까스', '등심']]}, {'name': ['생선까스'], 'keywords': [['생선', '요리']]}, {'name': ['치즈돈까스'], 'keywords': [['치즈', '돈까스', '요리']]}, {'name': ['수제왕돈까스'], 'keywords': [['돈까스']]}, {'name': ['물냉면'], 'keywords': [[]]}, {'name': ['비빔냉면'], 'keywords': [['냉면']]}]\n",
            "[{'name': '야채김밥', 'keywords': ['김밥']}, {'name': '치즈김밥', 'keywords': ['치즈', '김밥']}, {'name': '소고기김밥', 'keywords': ['소고기', '김밥']}, {'name': '매콤땡초김밥', 'keywords': ['땡초', '김밥']}, {'name': '참치마요김밥', 'keywords': ['김밥']}, {'name': '참치와사비김밥', 'keywords': ['와사비', '참치', '김밥']}, {'name': '매콤오징어김밥', 'keywords': ['오징어', '김밥']}, {'name': '신라면', 'keywords': ['신라면']}, {'name': '콩나물라면', 'keywords': ['콩나물']}, {'name': '떡라면', 'keywords': ['라면']}, {'name': '만두라면', 'keywords': ['라면', '육수', '만두']}, {'name': '치즈라면', 'keywords': ['치즈', '라면']}, {'name': '국물떡볶이', 'keywords': ['분식', '떡볶이', '국물']}, {'name': '유부라면', 'keywords': ['라면']}, {'name': '후레쉬쫄면', 'keywords': []}, {'name': '부산생생우동', 'keywords': ['부산']}, {'name': '부산어묵탕', 'keywords': ['부산']}, {'name': '김치만두', 'keywords': ['김치', '만두']}, {'name': '고기만두', 'keywords': ['고기', '만두']}, {'name': '만두국', 'keywords': ['국물', '만두']}, {'name': '떡국+공기밥', 'keywords': ['떡국', '국물']}, {'name': '떡만두국+공기밥', 'keywords': ['만두국']}, {'name': '된장찌개', 'keywords': []}, {'name': '순두부찌개', 'keywords': []}, {'name': '돼지김치찌개', 'keywords': ['김치']}, {'name': '참치김치찌개', 'keywords': ['참치', '김치']}, {'name': '뚝배기불고기', 'keywords': ['뚝배기']}, {'name': '갈비탕', 'keywords': ['고기']}, {'name': '육개장', 'keywords': []}, {'name': '짜장덮밥', 'keywords': ['덮밥', '짜장']}, {'name': '부대찌개+라면사리', 'keywords': []}, {'name': '햄야채볶음밥', 'keywords': ['볶음밥', '야채']}, {'name': '김치볶음밥', 'keywords': ['볶음밥', '김치']}, {'name': '참치볶음밥', 'keywords': ['참치', '볶음밥', '야채']}, {'name': '카레라이스', 'keywords': []}, {'name': '새우볶음밥', 'keywords': ['볶음밥', '새우']}, {'name': '제육덮밥', 'keywords': ['덮밥']}, {'name': '오징어덮밥', 'keywords': ['오징어', '양념', '덮밥']}, {'name': '비빔밥', 'keywords': []}, {'name': '돌솥비빔밥', 'keywords': ['한국']}, {'name': '오므라이스', 'keywords': ['요리']}, {'name': '등심돈까스', 'keywords': ['돈까스', '등심']}, {'name': '생선까스', 'keywords': ['생선', '요리']}, {'name': '치즈돈까스', 'keywords': ['치즈', '돈까스', '요리']}, {'name': '수제왕돈까스', 'keywords': ['돈까스']}, {'name': '물냉면', 'keywords': []}, {'name': '비빔냉면', 'keywords': ['냉면']}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_search.to_csv('data_for_search.csv', index = False)"
      ],
      "metadata": {
        "id": "ePxw6l9xywd6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# .dataframe tbody tr th {\n",
        "#     vertical-align: top;\n",
        "# }\n",
        "\n",
        "# .dataframe thead th {\n",
        "#     text-align: right;\n",
        "# }"
      ],
      "metadata": {
        "id": "y0yOl7Y9sgxK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_count = 2\n",
        "min_length = 2\n",
        "doc = scraping_result.iloc[1]\n",
        "\n",
        "print(f'음식 정보 \\n \\n {doc} \\n \\n')\n",
        "\n",
        "\n",
        "raw_data = key._convert_series_to_list(doc)\n",
        "print(raw_data)\n",
        "print(f'1. 음식 정보를 list로 통합 -> {len(raw_data)} 개 단어')\n",
        "print(f'\\n \\n {raw_data}.... \\n \\n')\n",
        "\n",
        "keyword_list = key._extract_keywords(raw_data)\n",
        "print(f'2. 형태소 분석기를 활용해 명사만을 추출 -> {len(keyword_list)} 개 단어')\n",
        "print(f'\\n \\n {keyword_list}.... \\n \\n')\n",
        "\n",
        "translated_keyword_list = key._map_english_to_korean(keyword_list)\n",
        "print(f'3. 영단어를 한글로 변환(ex python -> 파이썬) -> {len(translated_keyword_list)} 개 단어')\n",
        "print(f'\\n \\n {translated_keyword_list[:10]}.... \\n \\n')\n",
        "\n",
        "refined_keyword_list = key._eliminate_min_count_words(translated_keyword_list, min_count)\n",
        "print(f'4. 최소 3번이상 반복 사용되는 단어만 추출 -> {len(refined_keyword_list)} 개 단어')\n",
        "print(f'\\n \\n {refined_keyword_list[:10]}.... \\n \\n')\n",
        "\n",
        "result = list(filter(lambda x: len(x) >= min_length, refined_keyword_list))\n",
        "print(f'5. 단어 길이가 최소 한개 이상인 단어만 추출 -> {len(result)} 개 단어')\n",
        "print(f'\\n \\n {result[:10]}.... \\n \\n')"
      ],
      "metadata": {
        "id": "fmXoWyYljutf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "199fc49e-1176-4123-d8e8-12fdd43b983b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "음식 정보 \n",
            " \n",
            " name                                                        치즈김밥\n",
            "category                                                      김밥\n",
            "description    전통적인 한국 김밥에 치즈를 추가하여 만든, 고소하고 부드러운 맛이 특징인 인기 있...\n",
            "Name: 1, dtype: object \n",
            " \n",
            "\n",
            "['치즈김밥', '김밥', '전통적인', '한국', '김밥에', '치즈를', '추가하여', '만든,', '고소하고', '부드러운', '맛이', '특징인', '인기', '있는', '변형', '김밥']\n",
            "1. 음식 정보를 list로 통합 -> 16 개 단어\n",
            "\n",
            " \n",
            " ['치즈김밥', '김밥', '전통적인', '한국', '김밥에', '치즈를', '추가하여', '만든,', '고소하고', '부드러운', '맛이', '특징인', '인기', '있는', '변형', '김밥'].... \n",
            " \n",
            "\n",
            "2. 형태소 분석기를 활용해 명사만을 추출 -> 13 개 단어\n",
            "\n",
            " \n",
            " ['치즈', '김밥', '김밥', '전통', '한국', '김밥', '치즈', '추가', '맛', '특징', '인기', '변형', '김밥'].... \n",
            " \n",
            "\n",
            "3. 영단어를 한글로 변환(ex python -> 파이썬) -> 13 개 단어\n",
            "\n",
            " \n",
            " ['치즈', '김밥', '김밥', '전통', '한국', '김밥', '치즈', '추가', '맛', '특징'].... \n",
            " \n",
            "\n",
            "4. 최소 3번이상 반복 사용되는 단어만 추출 -> 2 개 단어\n",
            "\n",
            " \n",
            " ['치즈', '김밥'].... \n",
            " \n",
            "\n",
            "5. 단어 길이가 최소 한개 이상인 단어만 추출 -> 2 개 단어\n",
            "\n",
            " \n",
            " ['치즈', '김밥'].... \n",
            " \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint\n",
        "doc = scraping_result.iloc[1]\n",
        "print(doc)\n",
        "print(f'-- 음식이름 -- \\n {doc.name} \\n \\n')\n",
        "\n",
        "keyword_list = key.extract_keyword_list(doc)\n",
        "\n",
        "print(f'음식에 대한 키워드 후보 : {len(result)} 개 단어')\n",
        "print(f'{result[:10]}.... \\n \\n')\n",
        "\n",
        "\n",
        "keyword_embedding = key.create_keyword_embedding(doc)\n",
        "doc_embedding = key.create_doc_embedding(doc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SD9Cly5P6IfP",
        "outputId": "9afea698-ad18-4e62-e4bd-986e1297ec5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name                                                        치즈김밥\n",
            "category                                                      김밥\n",
            "description    전통적인 한국 김밥에 치즈를 추가하여 만든, 고소하고 부드러운 맛이 특징인 인기 있...\n",
            "Name: 1, dtype: object\n",
            "-- 음식이름 -- \n",
            " 1 \n",
            " \n",
            "\n",
            "음식에 대한 키워드 후보 : 2 개 단어\n",
            "['치즈', '김밥'].... \n",
            " \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(doc)\n",
        "print(doc.name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvrIpqAuQp0z",
        "outputId": "0058d1c3-d51a-48db-d48e-1d2f7555a32a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name                                                        치즈김밥\n",
            "category                                                      김밥\n",
            "description    전통적인 한국 김밥에 치즈를 추가하여 만든, 고소하고 부드러운 맛이 특징인 인기 있...\n",
            "Name: 1, dtype: object\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "co_sim_score =key._calc_cosine_similarity(doc_embedding, keyword_embedding).flatten()\n",
        "\n",
        "keyword = dict(zip(keyword_list, co_sim_score))\n",
        "sorted_keyword = sorted(keyword.items(), key=lambda k: k[1], reverse=True)\n",
        "\n",
        "print(f'-- 키워드 추출 결과(20개 요약)--')\n",
        "pprint(sorted_keyword[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iv68PQ-w-AEl",
        "outputId": "daf31116-c897-4c08-952e-64f54baea640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- 키워드 추출 결과(20개 요약)--\n",
            "[('치즈', 0.8767234), ('김밥', 0.84581584)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/data_for_search.csv\")\n",
        "name = df[\"name\"].tolist()\n",
        "keywords = df[\"keywords\"].tolist()\n",
        "name[:5], keywords[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7y7JzlA_Lt8",
        "outputId": "7165a8d8-1033-4b5b-95e6-1fca1c8661e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['야채김밥', '치즈김밥', '소고기김밥', '매콤땡초김밥', '참치마요김밥'],\n",
              " [\"['김밥']\", \"['치즈', '김밥']\", \"['소고기', '김밥']\", \"['땡초', '김밥']\", \"['김밥']\"])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc.name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vy4c3BVNRlLp",
        "outputId": "418994bb-d52b-42a2-ce40-849b8b31ec56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 키워드 리스트\n",
        "\n",
        "def get_keywords_for_food(food_name, data):\n",
        "    matched_row = data[data['name'] == food_name]\n",
        "\n",
        "    if not matched_row.empty:\n",
        "        keywords_str = matched_row['keywords'].values[0]\n",
        "        keywords_list = eval(keywords_str)\n",
        "        return keywords_list\n",
        "    else:\n",
        "        return None  # If there is no matching food name, return None\n",
        "\n",
        "food_name_to_search = input()\n",
        "keywords = get_keywords_for_food(food_name_to_search, df)\n",
        "\n",
        "if keywords == 'None' :\n",
        "  print(\"일치하는 음식이 없습니다.\")\n",
        "\n",
        "print(keywords)\n",
        "df2 = pd.DataFrame(columns=['name', 'keywords'])\n",
        "df2['name'] = df['name']\n",
        "# 문자열 표현된 리스트를 실제 리스트로 변환\n",
        "df2['keywords'] = df['keywords'].apply(eval)\n",
        "\n",
        "# 주어진 키워드와 일치하거나 포함하는 음식명을 필터링\n",
        "matched_foods = df2[df2['keywords'].apply(lambda x: any(keyword in x for keyword in keywords))]\n",
        "\n",
        "# 상위 20개 음식명 선택 (중복 제거)\n",
        "top_foods = matched_foods['name'].drop_duplicates().head(20)\n",
        "excluded_food = food_name_to_search\n",
        "filtered_foods = matched_foods[(matched_foods['name'] != excluded_food)]\n",
        "\n",
        "# 상위 20개 음식명 선택 (중복 제거 후)\n",
        "top_excluded_foods = filtered_foods['name'].drop_duplicates().head(20)\n",
        "top_excluded_foods.tolist()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Y_JYw93NqOg",
        "outputId": "c6411a57-979c-4cd9-d7bd-6f6380ff74aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "치즈김밥\n",
            "['치즈', '김밥']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['야채김밥', '소고기김밥', '매콤땡초김밥', '참치마요김밥', '참치와사비김밥', '매콤오징어김밥', '치즈라면', '치즈돈까스']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}