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        "<a href=\"https://colab.research.google.com/github/kavyajeetbora/recipe_recommender/blob/master/notebooks/food_recommender.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
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          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m898.8 kB/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: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.35.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.16.0+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.11.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (3.8.1)\n",
            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.20.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2.31.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (23.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (2.1.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.4.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.2.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=5ce0078e6ac43126c951601e6fe4502ff44bb983ff4f3cf23934a07d69f1ea19\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import torch\n",
        "from tqdm.notebook import tqdm\n",
        "import os"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2024-01-05T06:11:56.933712Z",
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          "iopub.status.idle": "2024-01-05T06:11:58.002172Z",
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          "shell.execute_reply": "2024-01-05T06:11:58.000812Z"
        },
        "trusted": true,
        "id": "lZ4pgC80VM5f"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3DR_xuxVWk8",
        "outputId": "5e91c85d-7222-472a-ef13-f6857b64b517"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read the data\n",
        "nutrition - calories (#), total fat (PDV), sugar (PDV) , sodium (PDV) , protein (PDV) , saturated fat (PDV) ,carbohydrates (PDV)"
      ],
      "metadata": {
        "id": "GAa9X-C1VM5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Projects/food_recommender/data_file/RAW_recipes.csv')\n",
        "df = df.reset_index(drop=True)\n",
        "df.head()"
      ],
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "execution": {
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          "iopub.execute_input": "2024-01-05T06:13:51.842450Z",
          "iopub.status.idle": "2024-01-05T06:13:57.915560Z",
          "shell.execute_reply.started": "2024-01-05T06:13:51.842412Z",
          "shell.execute_reply": "2024-01-05T06:13:57.914591Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "VHdQJiCOVM5m",
        "outputId": "ff39b5a5-0cd5-4713-e6e5-f2aa9aa48e31"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4.37 s, sys: 528 ms, total: 4.9 s\n",
            "Wall time: 8.04 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         name      id  minutes  \\\n",
              "0  arriba   baked winter squash mexican style  137739       55   \n",
              "1            a bit different  breakfast pizza   31490       30   \n",
              "2                   all in the kitchen  chili  112140      130   \n",
              "3                          alouette  potatoes   59389       45   \n",
              "4          amish  tomato ketchup  for canning   44061      190   \n",
              "\n",
              "   contributor_id   submitted  \\\n",
              "0           47892  2005-09-16   \n",
              "1           26278  2002-06-17   \n",
              "2          196586  2005-02-25   \n",
              "3           68585  2003-04-14   \n",
              "4           41706  2002-10-25   \n",
              "\n",
              "                                                tags  \\\n",
              "0  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "1  ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "2  ['time-to-make', 'course', 'preparation', 'mai...   \n",
              "3  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4  ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "\n",
              "                                    nutrition  n_steps  \\\n",
              "0       [51.5, 0.0, 13.0, 0.0, 2.0, 0.0, 4.0]       11   \n",
              "1   [173.4, 18.0, 0.0, 17.0, 22.0, 35.0, 1.0]        9   \n",
              "2  [269.8, 22.0, 32.0, 48.0, 39.0, 27.0, 5.0]        6   \n",
              "3   [368.1, 17.0, 10.0, 2.0, 14.0, 8.0, 20.0]       11   \n",
              "4   [352.9, 1.0, 337.0, 23.0, 3.0, 0.0, 28.0]        5   \n",
              "\n",
              "                                               steps  \\\n",
              "0  ['make a choice and proceed with recipe', 'dep...   \n",
              "1  ['preheat oven to 425 degrees f', 'press dough...   \n",
              "2  ['brown ground beef in large pot', 'add choppe...   \n",
              "3  ['place potatoes in a large pot of lightly sal...   \n",
              "4  ['mix all ingredients& boil for 2 1 / 2 hours ...   \n",
              "\n",
              "                                         description  \\\n",
              "0  autumn is my favorite time of year to cook! th...   \n",
              "1  this recipe calls for the crust to be prebaked...   \n",
              "2  this modified version of 'mom's' chili was a h...   \n",
              "3  this is a super easy, great tasting, make ahea...   \n",
              "4  my dh's amish mother raised him on this recipe...   \n",
              "\n",
              "                                         ingredients  n_ingredients  \n",
              "0  ['winter squash', 'mexican seasoning', 'mixed ...              7  \n",
              "1  ['prepared pizza crust', 'sausage patty', 'egg...              6  \n",
              "2  ['ground beef', 'yellow onions', 'diced tomato...             13  \n",
              "3  ['spreadable cheese with garlic and herbs', 'n...             11  \n",
              "4  ['tomato juice', 'apple cider vinegar', 'sugar...              8  "
            ],
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              "      <td>arriba   baked winter squash mexican style</td>\n",
              "      <td>137739</td>\n",
              "      <td>55</td>\n",
              "      <td>47892</td>\n",
              "      <td>2005-09-16</td>\n",
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              "      <td>autumn is my favorite time of year to cook! th...</td>\n",
              "      <td>['winter squash', 'mexican seasoning', 'mixed ...</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a bit different  breakfast pizza</td>\n",
              "      <td>31490</td>\n",
              "      <td>30</td>\n",
              "      <td>26278</td>\n",
              "      <td>2002-06-17</td>\n",
              "      <td>['30-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[173.4, 18.0, 0.0, 17.0, 22.0, 35.0, 1.0]</td>\n",
              "      <td>9</td>\n",
              "      <td>['preheat oven to 425 degrees f', 'press dough...</td>\n",
              "      <td>this recipe calls for the crust to be prebaked...</td>\n",
              "      <td>['prepared pizza crust', 'sausage patty', 'egg...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>all in the kitchen  chili</td>\n",
              "      <td>112140</td>\n",
              "      <td>130</td>\n",
              "      <td>196586</td>\n",
              "      <td>2005-02-25</td>\n",
              "      <td>['time-to-make', 'course', 'preparation', 'mai...</td>\n",
              "      <td>[269.8, 22.0, 32.0, 48.0, 39.0, 27.0, 5.0]</td>\n",
              "      <td>6</td>\n",
              "      <td>['brown ground beef in large pot', 'add choppe...</td>\n",
              "      <td>this modified version of 'mom's' chili was a h...</td>\n",
              "      <td>['ground beef', 'yellow onions', 'diced tomato...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>alouette  potatoes</td>\n",
              "      <td>59389</td>\n",
              "      <td>45</td>\n",
              "      <td>68585</td>\n",
              "      <td>2003-04-14</td>\n",
              "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[368.1, 17.0, 10.0, 2.0, 14.0, 8.0, 20.0]</td>\n",
              "      <td>11</td>\n",
              "      <td>['place potatoes in a large pot of lightly sal...</td>\n",
              "      <td>this is a super easy, great tasting, make ahea...</td>\n",
              "      <td>['spreadable cheese with garlic and herbs', 'n...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amish  tomato ketchup  for canning</td>\n",
              "      <td>44061</td>\n",
              "      <td>190</td>\n",
              "      <td>41706</td>\n",
              "      <td>2002-10-25</td>\n",
              "      <td>['weeknight', 'time-to-make', 'course', 'main-...</td>\n",
              "      <td>[352.9, 1.0, 337.0, 23.0, 3.0, 0.0, 28.0]</td>\n",
              "      <td>5</td>\n",
              "      <td>['mix all ingredients&amp; boil for 2 1 / 2 hours ...</td>\n",
              "      <td>my dh's amish mother raised him on this recipe...</td>\n",
              "      <td>['tomato juice', 'apple cider vinegar', 'sugar...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lh1KFtNdTiNe",
        "outputId": "3e75fa6b-27ac-432b-c05a-8557dd65a884"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 231637 entries, 0 to 231636\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count   Dtype \n",
            "---  ------          --------------   ----- \n",
            " 0   name            231636 non-null  object\n",
            " 1   id              231637 non-null  int64 \n",
            " 2   minutes         231637 non-null  int64 \n",
            " 3   contributor_id  231637 non-null  int64 \n",
            " 4   submitted       231637 non-null  object\n",
            " 5   tags            231637 non-null  object\n",
            " 6   nutrition       231637 non-null  object\n",
            " 7   n_steps         231637 non-null  int64 \n",
            " 8   steps           231637 non-null  object\n",
            " 9   description     226658 non-null  object\n",
            " 10  ingredients     231637 non-null  object\n",
            " 11  n_ingredients   231637 non-null  int64 \n",
            "dtypes: int64(5), object(7)\n",
            "memory usage: 21.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transform the data"
      ],
      "metadata": {
        "id": "10eR46uoVM5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Split the nutrition column to separate columns\n",
        "df[['calories','total fat (PDV)','sugar (PDV)','sodium (PDV)','protein (PDV)','saturated fat (PDV)','carbohydrates (PDV)']] = df.nutrition.str.split(\",\",expand=True)\n",
        "\n",
        "## Replace [] brackets after split\n",
        "df['calories'] =  df['calories'].apply(lambda x: x.replace('[',''))\n",
        "df['carbohydrates (PDV)'] =  df['carbohydrates (PDV)'].apply(lambda x: x.replace(']',''))\n",
        "\n",
        "## Convert the datatypes from str to float\n",
        "df[['calories','total fat (PDV)','sugar (PDV)','sodium (PDV)','protein (PDV)','saturated fat (PDV)','carbohydrates (PDV)']] = df[['calories','total fat (PDV)','sugar (PDV)','sodium (PDV)','protein (PDV)','saturated fat (PDV)','carbohydrates (PDV)']].astype('float')\n",
        "\n",
        "## Convert steps to list format\n",
        "df['steps'] = df['steps'].str.strip(\"[]\").str.replace('\"',\"\").str.split(\"',\").apply(lambda x: [y.replace(\"'\",\"\").strip() for y in x])\n",
        "\n",
        "## Drop unnecessary columns\n",
        "df.drop(['id', 'contributor_id','submitted','tags','nutrition'], axis=1,inplace = True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T12:57:35.554410Z",
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          "shell.execute_reply": "2024-01-04T12:57:38.402125Z"
        },
        "trusted": true,
        "id": "VeILMJWzVM5n"
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      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T12:59:21.239456Z",
          "iopub.execute_input": "2024-01-04T12:59:21.239854Z",
          "iopub.status.idle": "2024-01-04T12:59:21.247928Z",
          "shell.execute_reply.started": "2024-01-04T12:59:21.239821Z",
          "shell.execute_reply": "2024-01-04T12:59:21.246755Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yepac8lRVM5o",
        "outputId": "50d7e241-7526-4b83-814a-b92197870a3b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                    object\n",
              "minutes                  int64\n",
              "n_steps                  int64\n",
              "steps                   object\n",
              "description             object\n",
              "ingredients             object\n",
              "n_ingredients            int64\n",
              "calories               float64\n",
              "total fat (PDV)        float64\n",
              "sugar (PDV)            float64\n",
              "sodium (PDV)           float64\n",
              "protein (PDV)          float64\n",
              "saturated fat (PDV)    float64\n",
              "carbohydrates (PDV)    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T12:59:23.018401Z",
          "iopub.execute_input": "2024-01-04T12:59:23.019047Z",
          "iopub.status.idle": "2024-01-04T12:59:23.056503Z",
          "shell.execute_reply.started": "2024-01-04T12:59:23.019010Z",
          "shell.execute_reply": "2024-01-04T12:59:23.055477Z"
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        "trusted": true,
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          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "pf8H3xcuVM5o",
        "outputId": "ff2b078e-8787-453c-fc7c-4f88dcbc384d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         name  minutes  n_steps  \\\n",
              "0  arriba   baked winter squash mexican style       55       11   \n",
              "1            a bit different  breakfast pizza       30        9   \n",
              "2                   all in the kitchen  chili      130        6   \n",
              "3                          alouette  potatoes       45       11   \n",
              "4          amish  tomato ketchup  for canning      190        5   \n",
              "\n",
              "                                               steps  \\\n",
              "0  [make a choice and proceed with recipe, depend...   \n",
              "1  [preheat oven to 425 degrees f, press dough in...   \n",
              "2  [brown ground beef in large pot, add chopped o...   \n",
              "3  [place potatoes in a large pot of lightly salt...   \n",
              "4  [mix all ingredients& boil for 2 1 / 2 hours ,...   \n",
              "\n",
              "                                         description  \\\n",
              "0  autumn is my favorite time of year to cook! th...   \n",
              "1  this recipe calls for the crust to be prebaked...   \n",
              "2  this modified version of 'mom's' chili was a h...   \n",
              "3  this is a super easy, great tasting, make ahea...   \n",
              "4  my dh's amish mother raised him on this recipe...   \n",
              "\n",
              "                                         ingredients  n_ingredients  calories  \\\n",
              "0  ['winter squash', 'mexican seasoning', 'mixed ...              7      51.5   \n",
              "1  ['prepared pizza crust', 'sausage patty', 'egg...              6     173.4   \n",
              "2  ['ground beef', 'yellow onions', 'diced tomato...             13     269.8   \n",
              "3  ['spreadable cheese with garlic and herbs', 'n...             11     368.1   \n",
              "4  ['tomato juice', 'apple cider vinegar', 'sugar...              8     352.9   \n",
              "\n",
              "   total fat (PDV)  sugar (PDV)  sodium (PDV)  protein (PDV)  \\\n",
              "0              0.0         13.0           0.0            2.0   \n",
              "1             18.0          0.0          17.0           22.0   \n",
              "2             22.0         32.0          48.0           39.0   \n",
              "3             17.0         10.0           2.0           14.0   \n",
              "4              1.0        337.0          23.0            3.0   \n",
              "\n",
              "   saturated fat (PDV)  carbohydrates (PDV)  \n",
              "0                  0.0                  4.0  \n",
              "1                 35.0                  1.0  \n",
              "2                 27.0                  5.0  \n",
              "3                  8.0                 20.0  \n",
              "4                  0.0                 28.0  "
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              "      <td>arriba   baked winter squash mexican style</td>\n",
              "      <td>55</td>\n",
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              "      <td>51.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a bit different  breakfast pizza</td>\n",
              "      <td>30</td>\n",
              "      <td>9</td>\n",
              "      <td>[preheat oven to 425 degrees f, press dough in...</td>\n",
              "      <td>this recipe calls for the crust to be prebaked...</td>\n",
              "      <td>['prepared pizza crust', 'sausage patty', 'egg...</td>\n",
              "      <td>6</td>\n",
              "      <td>173.4</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>all in the kitchen  chili</td>\n",
              "      <td>130</td>\n",
              "      <td>6</td>\n",
              "      <td>[brown ground beef in large pot, add chopped o...</td>\n",
              "      <td>this modified version of 'mom's' chili was a h...</td>\n",
              "      <td>['ground beef', 'yellow onions', 'diced tomato...</td>\n",
              "      <td>13</td>\n",
              "      <td>269.8</td>\n",
              "      <td>22.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>alouette  potatoes</td>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "      <td>[place potatoes in a large pot of lightly salt...</td>\n",
              "      <td>this is a super easy, great tasting, make ahea...</td>\n",
              "      <td>['spreadable cheese with garlic and herbs', 'n...</td>\n",
              "      <td>11</td>\n",
              "      <td>368.1</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amish  tomato ketchup  for canning</td>\n",
              "      <td>190</td>\n",
              "      <td>5</td>\n",
              "      <td>[mix all ingredients&amp; boil for 2 1 / 2 hours ,...</td>\n",
              "      <td>my dh's amish mother raised him on this recipe...</td>\n",
              "      <td>['tomato juice', 'apple cider vinegar', 'sugar...</td>\n",
              "      <td>8</td>\n",
              "      <td>352.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</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-8b935a76-109f-4f87-ae5a-d2df1be7ec5a')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-8b935a76-109f-4f87-ae5a-d2df1be7ec5a 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-8b935a76-109f-4f87-ae5a-d2df1be7ec5a');\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-c64db855-e7e1-4e6c-981d-6df4e4a21eac\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c64db855-e7e1-4e6c-981d-6df4e4a21eac')\"\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-c64db855-e7e1-4e6c-981d-6df4e4a21eac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Export the data to a parquet file"
      ],
      "metadata": {
        "id": "J9m_sAeGVwl9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_parquet('/content/drive/MyDrive/Colab Notebooks/Projects/food_recommender/data_file/RAW_recipes.parquet')"
      ],
      "metadata": {
        "id": "TY0impDVVwFr"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cleaning the ingredients column"
      ],
      "metadata": {
        "id": "BSK5gKi6VM5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "## First convert the string representation of list to list\n",
        "df['ingredients'] = df['ingredients'].str.strip(\"[]\").str.replace(\"'\",\"\") #.str.split(\",\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T13:04:32.481409Z",
          "iopub.execute_input": "2024-01-04T13:04:32.481853Z",
          "iopub.status.idle": "2024-01-04T13:04:33.905561Z",
          "shell.execute_reply.started": "2024-01-04T13:04:32.481817Z",
          "shell.execute_reply": "2024-01-04T13:04:33.904417Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FCdJdS_VM5p",
        "outputId": "e8f2ed70-80f6-468a-a4cc-8d444e9edca4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 344 ms, sys: 201 µs, total: 344 ms\n",
            "Wall time: 349 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ingredients'].iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "m9OVTj5wV6ds",
        "outputId": "27e61ab9-378f-432b-c4c2-7991d525ec47"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'winter squash, mexican seasoning, mixed spice, honey, butter, olive oil, salt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explode the series containing list to a single list:"
      ],
      "metadata": {
        "id": "dRuZkRYpVM5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ingredients = df['ingredients'].explode().str.lower().str.strip()\n",
        "print(f\"Around {ingredients.shape[0]} ingredients are there in the dataset\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T13:10:49.747739Z",
          "iopub.execute_input": "2024-01-04T13:10:49.748547Z",
          "iopub.status.idle": "2024-01-04T13:10:53.379899Z",
          "shell.execute_reply.started": "2024-01-04T13:10:49.748501Z",
          "shell.execute_reply": "2024-01-04T13:10:53.378646Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_eKAOw3VM5q",
        "outputId": "4a1b6daa-ac12-4d6e-8126-9d87846e1cb1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Around 231637 ingredients are there in the dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now find the unique ingredients:"
      ],
      "metadata": {
        "id": "f514u9CKVM5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unique_ingredients = ingredients.unique()\n",
        "print(f\"There around {unique_ingredients.shape[0]} are unique out of {ingredients.shape[0]}\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T13:10:55.335446Z",
          "iopub.execute_input": "2024-01-04T13:10:55.336208Z",
          "iopub.status.idle": "2024-01-04T13:10:56.128798Z",
          "shell.execute_reply.started": "2024-01-04T13:10:55.336166Z",
          "shell.execute_reply": "2024-01-04T13:10:56.127540Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_tXaf76VM5q",
        "outputId": "96f10fcf-120c-44fa-b6aa-12d7b47a17a1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There around 230475 are unique out of 231637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There might be some same words that mean the same but with different name.\n",
        "\n",
        "Try with some keywords to see if it exists like search for 'egg' or 'milk'"
      ],
      "metadata": {
        "id": "XcP575BGVM5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "keyword = 'bread'\n",
        "unique_word = ingredients[ingredients.str.contains(keyword.lower())].unique()\n",
        "if len(unique_word)> 0:\n",
        "    sample = \", \".join(np.random.choice(unique_word,10))\n",
        "    print(f\"There are around {len(unique_word)} unique ingredients containing the work 'f{keyword}'.\\n\\n Example:\\n\\n {sample}....\")\n",
        "else:\n",
        "    print(f\"No ingredients for the keyword: {keyword}\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-04T13:16:33.320405Z",
          "iopub.execute_input": "2024-01-04T13:16:33.320862Z",
          "iopub.status.idle": "2024-01-04T13:16:34.637023Z",
          "shell.execute_reply.started": "2024-01-04T13:16:33.320818Z",
          "shell.execute_reply": "2024-01-04T13:16:34.635824Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APvQHMYXVM5q",
        "outputId": "2a4de2b1-857f-485d-f54f-d2583d3306e3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are around 18673 unique ingredients containing the work 'fbread'.\n",
            "\n",
            " Example:\n",
            "\n",
            " boneless chicken breasts, eggs, milk, italian seasoned breadcrumbs, parmesan cheese, curry powder, salt, fresh ground pepper, seasoning salt, butter, onion, water, chicken-flavored vegetarian seasoning, carrots, red bell pepper, fresh tomatoes, diced green chilies, fresh garlic, ground cumin, dark red kidney beans, great northern beans, vegetarian refried beans, tomato paste, oregano, salt, cornbread batter, cooking spray, pasta, salt, broccoli, butter, breadcrumbs, parmesan cheese, nonfat milk, flour, onion, low-fat cheddar cheese, dijon mustard, pepper, butter, eggs, tomatoes, scallions, feta cheese, dried oregano, salt & freshly ground black pepper, pita breads, white bread, margarine, parmesan cheese, sesame seeds, butter, onion, celery, haricot beans, salt, cayenne pepper, water, bay leaf, flour, oil, bell pepper, carrot, duck legs, andouille sausage, chicken broth, duck, dry breadcrumbs, parmesan cheese, parsley, olive oil, beer, water, olive oil, sugar, garlic salt, bread flour, all-purpose flour, yeast, garlic powder, brown button mushrooms, garlic butter, whole grain bread, coarse grain mustard, sour cream, fresh chives, fresh mushrooms, red onion, extra virgin olive oil, frozen artichoke hearts, ham slices, chicken breasts, margarine, all-purpose flour, skim milk, swiss cheese, salt, black pepper, bread, eggs, seasoned bread crumbs, cream cheese, salt, nutmeg, frozen spinach, half-and-half, cheddar cheese....\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentence encoder model\n",
        "\n",
        "\n",
        "Selecting the encoder model: https://www.sbert.net/docs/pretrained_models.html"
      ],
      "metadata": {
        "id": "i4-5qHJ5WHfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624,
          "referenced_widgets": [
            "b97181a2faa74a6db990ab45381cdaac",
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            "1414da9963c54927bc8dd5055008405b",
            "2d3a810bb123451eb81b6ced05155ec5",
            "4d27e4508f6c4bc8acde68dc3b5d8f28",
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        "id": "WYhb3ncyWHKY",
        "outputId": "35f03127-c3dd-47f5-b4fa-c590c2448de1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf189c722a754c88a1237d084d4cdd98"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.14 s, sys: 283 ms, total: 1.43 s\n",
            "Wall time: 6.11 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = model.to(device)\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UywgacodgeN",
        "outputId": "9be35dd2-a542-4750-fde0-22c3541bab21"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SentenceTransformer(\n",
              "  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n",
              "  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
              "  (2): Normalize()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding the ingredients"
      ],
      "metadata": {
        "id": "1U_sRzbZk9AN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "## encode all the ingredients:\n",
        "embedding = model.encode(df['ingredients'].values)"
      ],
      "metadata": {
        "id": "LIpiBPJE9ZUN",
        "outputId": "4c0d1283-0f98-4cc3-90ac-47f550085c8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 57s, sys: 2.08 s, total: 1min 59s\n",
            "Wall time: 1min 48s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_final = df.copy()\n",
        "df_final['embedding'] = list(zip(embedding))\n",
        "df_final['embedding'] = df_final['embedding'].apply(lambda x: x[0])\n",
        "df_final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "F8FcLEjLpwAm",
        "outputId": "a8a4c6c5-c0ae-4530-ae2a-ac9c8cad89d4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         name  minutes  n_steps  \\\n",
              "0  arriba   baked winter squash mexican style       55       11   \n",
              "1            a bit different  breakfast pizza       30        9   \n",
              "2                   all in the kitchen  chili      130        6   \n",
              "3                          alouette  potatoes       45       11   \n",
              "4          amish  tomato ketchup  for canning      190        5   \n",
              "\n",
              "                                               steps  \\\n",
              "0  [make a choice and proceed with recipe, depend...   \n",
              "1  [preheat oven to 425 degrees f, press dough in...   \n",
              "2  [brown ground beef in large pot, add chopped o...   \n",
              "3  [place potatoes in a large pot of lightly salt...   \n",
              "4  [mix all ingredients& boil for 2 1 / 2 hours ,...   \n",
              "\n",
              "                                         description  \\\n",
              "0  autumn is my favorite time of year to cook! th...   \n",
              "1  this recipe calls for the crust to be prebaked...   \n",
              "2  this modified version of 'mom's' chili was a h...   \n",
              "3  this is a super easy, great tasting, make ahea...   \n",
              "4  my dh's amish mother raised him on this recipe...   \n",
              "\n",
              "                                         ingredients  n_ingredients  calories  \\\n",
              "0  winter squash, mexican seasoning, mixed spice,...              7      51.5   \n",
              "1  prepared pizza crust, sausage patty, eggs, mil...              6     173.4   \n",
              "2  ground beef, yellow onions, diced tomatoes, to...             13     269.8   \n",
              "3  spreadable cheese with garlic and herbs, new p...             11     368.1   \n",
              "4  tomato juice, apple cider vinegar, sugar, salt...              8     352.9   \n",
              "\n",
              "   total fat (PDV)  sugar (PDV)  sodium (PDV)  protein (PDV)  \\\n",
              "0              0.0         13.0           0.0            2.0   \n",
              "1             18.0          0.0          17.0           22.0   \n",
              "2             22.0         32.0          48.0           39.0   \n",
              "3             17.0         10.0           2.0           14.0   \n",
              "4              1.0        337.0          23.0            3.0   \n",
              "\n",
              "   saturated fat (PDV)  carbohydrates (PDV)  \\\n",
              "0                  0.0                  4.0   \n",
              "1                 35.0                  1.0   \n",
              "2                 27.0                  5.0   \n",
              "3                  8.0                 20.0   \n",
              "4                  0.0                 28.0   \n",
              "\n",
              "                                           embedding  \n",
              "0  [-0.008845049, 0.009866926, 0.028063796, 0.101...  \n",
              "1  [-0.054575536, 0.027983457, 0.065263726, 0.032...  \n",
              "2  [-0.10401253, -0.024388006, 0.06653514, 0.0346...  \n",
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              "      <td>arriba   baked winter squash mexican style</td>\n",
              "      <td>55</td>\n",
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              "      <td>[make a choice and proceed with recipe, depend...</td>\n",
              "      <td>autumn is my favorite time of year to cook! th...</td>\n",
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              "      <td>30</td>\n",
              "      <td>9</td>\n",
              "      <td>[preheat oven to 425 degrees f, press dough in...</td>\n",
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              "      <td>130</td>\n",
              "      <td>6</td>\n",
              "      <td>[brown ground beef in large pot, add chopped o...</td>\n",
              "      <td>this modified version of 'mom's' chili was a h...</td>\n",
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              "      <td>alouette  potatoes</td>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "      <td>[place potatoes in a large pot of lightly salt...</td>\n",
              "      <td>this is a super easy, great tasting, make ahea...</td>\n",
              "      <td>spreadable cheese with garlic and herbs, new p...</td>\n",
              "      <td>11</td>\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_final.info()"
      ],
      "metadata": {
        "id": "jhTD5KEMyXKX",
        "outputId": "f56db90f-6450-4802-fb8e-569eaae55612",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 231637 entries, 0 to 231636\n",
            "Data columns (total 15 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   name                 231636 non-null  object \n",
            " 1   minutes              231637 non-null  int64  \n",
            " 2   n_steps              231637 non-null  int64  \n",
            " 3   steps                231637 non-null  object \n",
            " 4   description          226658 non-null  object \n",
            " 5   ingredients          231637 non-null  object \n",
            " 6   n_ingredients        231637 non-null  int64  \n",
            " 7   calories             231637 non-null  float64\n",
            " 8   total fat (PDV)      231637 non-null  float64\n",
            " 9   sugar (PDV)          231637 non-null  float64\n",
            " 10  sodium (PDV)         231637 non-null  float64\n",
            " 11  protein (PDV)        231637 non-null  float64\n",
            " 12  saturated fat (PDV)  231637 non-null  float64\n",
            " 13  carbohydrates (PDV)  231637 non-null  float64\n",
            " 14  embedding            231637 non-null  object \n",
            "dtypes: float64(7), int64(3), object(5)\n",
            "memory usage: 26.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Export the embedded recipes to parquet files"
      ],
      "metadata": {
        "id": "NUOEWYWQvlWL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "partitions = np.linspace(0, df_final.shape[0], 30, dtype=int)\n",
        "for ix in range(len(partitions)-1):\n",
        "    i,j = partitions[ix], partitions[ix+1]\n",
        "    dfx = df_final.iloc[i:j,:]\n",
        "    dfx.to_parquet(rf'/content/drive/MyDrive/Colab Notebooks/Projects/food_recommender/data_file/Exports/recipes_{ix}.parquet')"
      ],
      "metadata": {
        "id": "CIndXZuNbUe1"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finally calculate the cosine similarity"
      ],
      "metadata": {
        "id": "vwSV43MLvVkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cosine_similarity(vec1,vec2):\n",
        "    '''\n",
        "    Returns the cosine similarity between two vectors of n dimension\n",
        "    '''\n",
        "    denom = np.sqrt(np.sum(np.square(vec1))) * np.sqrt(np.sum(np.square(vec2)))\n",
        "    return np.round(np.dot(vec1,vec2) / denom * 100, 2)"
      ],
      "metadata": {
        "id": "UiEI934PYhhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = 3\n",
        "data = df_final.iloc[index]\n",
        "recipe, vector = data['name'], data['embedding']\n",
        "\n",
        "print(\"Name of the dish:\", recipe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4Vl8kaLYrLa",
        "outputId": "4d17485b-644f-416f-a0a1-c589a0240838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name of the dish: alouette  potatoes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "df_result = df_final.copy()\n",
        "df_result['similarity'] = df_result['embedding'].apply(lambda x : cosine_similarity(vector, x))\n",
        "df_result.sort_values(by=\"similarity\", ascending=False).iloc[1:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "DESZB3g_ZHEz",
        "outputId": "6fa17be5-05be-4d7a-c79c-4cbc0114d39b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 9.29 s, sys: 530 ms, total: 9.82 s\n",
            "Wall time: 10.3 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     name  minutes  n_steps  \\\n",
              "189645  smoked mozzarella and penne salad       30        5   \n",
              "39949     cheesy arrabbiata over linguine       32        6   \n",
              "9704      asparagus and goat cheese salad        2       10   \n",
              "\n",
              "                                                    steps  \\\n",
              "189645  ['process the dressing ingredients in food pro...   \n",
              "39949   ['in a large pan , heat 2 tbsp of oil over med...   \n",
              "9704    ['heat 2 tablespoons oil in 12-inch nonstick s...   \n",
              "\n",
              "                                              description  \\\n",
              "189645  from the whole foods market cookbook.  i buy t...   \n",
              "39949   we love arrabbiata, the hotter the better for ...   \n",
              "9704                                        not tried yet   \n",
              "\n",
              "                                              ingredients  n_ingredients  \\\n",
              "189645  ['parmesan cheese', 'fresh parsley', 'mayonnai...             11   \n",
              "39949   ['olive oil', 'onions', 'shallot', 'red pepper...             14   \n",
              "9704    ['virgin olive oil', 'red bell pepper', 'aspar...             10   \n",
              "\n",
              "        calories  total fat (PDV)  sugar (PDV)  sodium (PDV)  protein (PDV)  \\\n",
              "189645     262.4             19.0          5.0          14.0           20.0   \n",
              "39949      557.3             25.0         59.0          15.0           32.0   \n",
              "9704       321.7             44.0          9.0           8.0           20.0   \n",
              "\n",
              "        saturated fat (PDV)  carbohydrates (PDV)  \\\n",
              "189645                 25.0                  9.0   \n",
              "39949                  13.0                 29.0   \n",
              "9704                   43.0                  2.0   \n",
              "\n",
              "                                                embedding  similarity  \n",
              "189645  [-0.09707589, -0.01945921, 0.018470693, 0.0582...       92.82  \n",
              "39949   [-0.09360438, 0.002902513, 0.023475569, 0.0308...       92.73  \n",
              "9704    [-0.0630732, 0.005144814, 0.0011174467, 0.0333...       92.68  "
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\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-addb50cf-de37-409e-a0f2-51b50f33a56f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}