Food_Recipe_Classification_and_Recommendation_using_GPT_3.ipynb

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    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
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        "id": "4nWRWWI2QGyp",
        "outputId": "a33a0f88-a36b-4ff1-e46c-1b92711b321d"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: openai in /usr/local/lib/python3.8/dist-packages (0.26.5)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from openai) (3.8.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from openai) (4.64.1)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.8/dist-packages (from openai) (2.25.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.3.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (6.0.4)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (1.8.2)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (4.0.2)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (22.2.0)\n"
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      ],
      "source": [
        "!pip install openai"
      ]
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        "!pip install lazypredict"
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      "execution_count": 58,
      "outputs": [
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (2.2.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (4.64.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.0.2)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->lazypredict) (1.15.0)\n"
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    {
      "cell_type": "code",
      "source": [
        "#!/usr/bin/env python3\n",
        "\n",
        "#Import open AI OS and System Modules\n",
        "import openai,os,sys\n",
        "\n",
        "openai.api_key = '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'\n",
        "model_engine = \"text-davinci-003\"\n",
        "prompt = \"Write a blog on ChatGPT\"\n",
        "\n",
        "# Set the maximum number of tokens to generate in the response\n",
        "max_tokens = 1024\n",
        "\n",
        "# Generate a response\n",
        "completion = openai.Completion.create(\n",
        "    engine=model_engine,\n",
        "    prompt=prompt,\n",
        "    max_tokens=max_tokens,\n",
        "    temperature=0.5,\n",
        "    top_p=1,\n",
        "    frequency_penalty=0,\n",
        "    presence_penalty=0\n",
        ")\n",
        "\n",
        "# Print the response\n",
        "print(completion.choices[0].text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISS7RhjfiJEs",
        "outputId": "0ebbe463-9be0-4361-c52d-5b6c664ffe7d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "ChatGPT is a new and revolutionary chatbot technology that is taking the world of artificial intelligence (AI) by storm. It is a natural language processing (NLP) system that uses deep learning to understand and respond to user input. ChatGPT is an AI-powered chatbot that can interact with users in a natural and conversational way, providing them with information, advice, and assistance.\n",
            "\n",
            "ChatGPT is powered by a large-scale GPT-3 model, which is a type of deep learning algorithm. This model is trained on a large dataset of conversational data, which enables it to understand and respond to natural language queries. It also has the ability to learn from its interactions, allowing it to become more intelligent over time.\n",
            "\n",
            "ChatGPT is designed to be an easy-to-use and effective chatbot solution for businesses. It can be used to provide customer service, answer FAQs, and provide product recommendations. It can also be used to automate tasks, such as scheduling appointments or making reservations.\n",
            "\n",
            "ChatGPT is a powerful tool that can help businesses improve their customer service and increase their sales. It can be used to engage with customers in a more natural way, allowing them to get the answers they need quickly and easily. Additionally, it can be used to automate mundane tasks, freeing up employees to focus on more important tasks.\n",
            "\n",
            "Overall, ChatGPT is an exciting new technology that is revolutionizing the way businesses interact with their customers. It is a powerful tool that can help businesses improve their customer service and increase their sales.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('train (3).csv')\n",
        "df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 423
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        "outputId": "48b87433-ce9c-435e-d4b4-701c354782ff"
      },
      "execution_count": 60,
      "outputs": [
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              "      Id   category                                               text\n",
              "0      1      snack  \"RecipeName\": \" Reese's™ Peanut Butter Layer B...\n",
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              "2      3    dessert  \"RecipeName\": \"\"\"Easy Fruit Crisp \"Dump\" Desse...\n",
              "3      4      snack  \"RecipeName\": \"3-Ingredient Crunchy Snack Mix\"...\n",
              "4      5  condiment  \"RecipeName\": \"5-Minute Poppy Seed Dressing\",\\...\n",
              "..   ...        ...                                                ...\n",
              "740  741  appetizer  \"RecipeName\": \"Witches’ Purses\",\\r\\n\"Ingredien...\n",
              "741  742    dessert  \"RecipeName\": \"Yellow Cupcakes\",\\r\\n\"Ingredien...\n",
              "742  743  condiment  \"RecipeName\": \"Yogurt with Apples (Sev Raita) ...\n",
              "743  744    dessert  \"RecipeName\": \"Zebra Crinkles \",\\r\\n\"Ingredien...\n",
              "744  745  appetizer  \"RecipeName\": \"Zesty Margarita Shrimp Cocktail...\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le1=LabelEncoder()\n",
        "df['category']=le1.fit_transform(df['category'])"
      ],
      "metadata": {
        "id": "Bv9BHJ8Tg469"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2v-CVSEDWkew",
        "outputId": "bf076567-ad6b-4767-936e-e0bd032ec6af"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  category                                               text\n",
              "0      1         4  \"RecipeName\": \" Reese's™ Peanut Butter Layer B...\n",
              "1      2         3  \"RecipeName\": \"\"\"\"Just Bugging You\" Cupcake Co...\n",
              "2      3         3  \"RecipeName\": \"\"\"Easy Fruit Crisp \"Dump\" Desse...\n",
              "3      4         4  \"RecipeName\": \"3-Ingredient Crunchy Snack Mix\"...\n",
              "4      5         2  \"RecipeName\": \"5-Minute Poppy Seed Dressing\",\\...\n",
              "..   ...       ...                                                ...\n",
              "740  741         0  \"RecipeName\": \"Witches’ Purses\",\\r\\n\"Ingredien...\n",
              "741  742         3  \"RecipeName\": \"Yellow Cupcakes\",\\r\\n\"Ingredien...\n",
              "742  743         2  \"RecipeName\": \"Yogurt with Apples (Sev Raita) ...\n",
              "743  744         3  \"RecipeName\": \"Zebra Crinkles \",\\r\\n\"Ingredien...\n",
              "744  745         0  \"RecipeName\": \"Zesty Margarita Shrimp Cocktail...\n",
              "\n",
              "[745 rows x 3 columns]"
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              "      <td>0</td>\n",
              "      <td>\"RecipeName\": \"Witches’ Purses\",\\r\\n\"Ingredien...</td>\n",
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              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>742</td>\n",
              "      <td>3</td>\n",
              "      <td>\"RecipeName\": \"Yellow Cupcakes\",\\r\\n\"Ingredien...</td>\n",
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              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>743</td>\n",
              "      <td>2</td>\n",
              "      <td>\"RecipeName\": \"Yogurt with Apples (Sev Raita) ...</td>\n",
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              "      <th>743</th>\n",
              "      <td>744</td>\n",
              "      <td>3</td>\n",
              "      <td>\"RecipeName\": \"Zebra Crinkles \",\\r\\n\"Ingredien...</td>\n",
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              "    <tr>\n",
              "      <th>744</th>\n",
              "      <td>745</td>\n",
              "      <td>0</td>\n",
              "      <td>\"RecipeName\": \"Zesty Margarita Shrimp Cocktail...</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resp = openai.Embedding.create(\n",
        "    input=[\"eating food\", \"I am hungry\", \"I am traveling\" , \"exploring new places\"],\n",
        "    engine=\"text-similarity-davinci-001\")"
      ],
      "metadata": {
        "id": "od-0lIFPWlY0"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resp['data'][0]['embedding']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "_fG4n7IYWvJO",
        "outputId": "349b7be5-07d7-4eac-dbb0-4bd110f08e3b"
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      "execution_count": 64,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def preprocess(text):\n",
        "    text = re.sub(r'[^\\w\\s\\']',' ', text)\n",
        "    text = re.sub(' +', ' ', text)\n",
        "    return text.strip().lower()"
      ],
      "metadata": {
        "id": "8VUEsuNCW1IJ"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'] = df['text'].map(preprocess)"
      ],
      "metadata": {
        "id": "gLx9UTG9Xzji"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Id',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "sIYYDWY5X3SX"
      },
      "execution_count": 67,
      "outputs": []
    },
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              "     category                                               text\n",
              "0           4  recipename reese's peanut butter layer bars \\r...\n",
              "1           3  recipename just bugging you cupcake cones \\r\\n...\n",
              "2           3  recipename easy fruit crisp dump dessert \\r\\n ...\n",
              "3           4  recipename 3 ingredient crunchy snack mix \\r\\n...\n",
              "4           2  recipename 5 minute poppy seed dressing \\r\\n i...\n",
              "..        ...                                                ...\n",
              "740         0  recipename witches purses \\r\\n ingredients \\r\\...\n",
              "741         3  recipename yellow cupcakes \\r\\n ingredients \\r...\n",
              "742         2  recipename yogurt with apples sev raita \\r\\n i...\n",
              "743         3  recipename zebra crinkles \\r\\n ingredients \\r\\...\n",
              "744         0  recipename zesty margarita shrimp cocktail \\r\\...\n",
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      "cell_type": "markdown",
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        "# Classification"
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      "metadata": {
        "id": "8ZyPV0-Zd0GP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = openai.Embedding.create(\n",
        "    input=list(df['text']),\n",
        "    engine=\"text-similarity-babbage-001\")"
      ],
      "metadata": {
        "id": "-l_AsVhAX9RH"
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      "execution_count": 69,
      "outputs": []
    },
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      "cell_type": "code",
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        "text['data'][0]['embedding']"
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        "colab": {
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        "id": "parkwmK3YiRd",
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      "execution_count": 70,
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              " -0.00861402414739132,\n",
              " -0.008459857665002346,\n",
              " -0.034436825662851334,\n",
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              " 0.04686645790934563,\n",
              " -0.0013260683044791222,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=[]\n",
        "for i in range(len(df)):\n",
        "  l1.append(text['data'][i]['embedding'])"
      ],
      "metadata": {
        "id": "EtBC4Kk7ZEpL"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        " \n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    l1,\n",
        "    df.category,\n",
        "    test_size = 0.2,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "YtzwZQ-IYnrw"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        " \n",
        "clf = RandomForestClassifier(n_estimators=100)\n",
        "clf.fit(X_train, y_train)\n",
        "preds = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "iRL9oY2XY14F"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,preds))  #Random Forest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZN3L06pZbNg",
        "outputId": "e3b2c413-337c-4051-a8c8-0a8760fe7002"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.91      0.87        33\n",
            "           1       0.89      0.96      0.93        26\n",
            "           2       0.88      0.77      0.82        30\n",
            "           3       1.00      0.94      0.97        33\n",
            "           4       0.89      0.93      0.91        27\n",
            "\n",
            "    accuracy                           0.90       149\n",
            "   macro avg       0.90      0.90      0.90       149\n",
            "weighted avg       0.90      0.90      0.90       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDeSMfMxc89b",
        "outputId": "33ef288c-0cef-4c20-f2dd-874142068535"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.7.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "classifier = XGBClassifier()\n",
        "classifier.fit(pd.DataFrame(X_train), y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2I72sx7dArW",
        "outputId": "50292ef6-7bec-4a6d-c2d1-e0aa0ce04ffe"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(objective='multi:softprob')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,preds))  #XGBoost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mW0ERKzdau9",
        "outputId": "643579fe-2308-4ab4-d3ca-550f8508921c"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.91      0.87        33\n",
            "           1       0.89      0.96      0.93        26\n",
            "           2       0.88      0.77      0.82        30\n",
            "           3       1.00      0.94      0.97        33\n",
            "           4       0.89      0.93      0.91        27\n",
            "\n",
            "    accuracy                           0.90       149\n",
            "   macro avg       0.90      0.90      0.90       149\n",
            "weighted avg       0.90      0.90      0.90       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Semantic Search"
      ],
      "metadata": {
        "id": "6Wti9sRsd56V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = openai.Embedding.create(\n",
        "    input=list(df['text']),\n",
        "    engine=\"text-search-babbage-query-001\")"
      ],
      "metadata": {
        "id": "Rp6PSd_GcqXi"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l1=[]\n",
        "for i in range(len(df)):\n",
        "  l1.append(text['data'][i]['embedding'])"
      ],
      "metadata": {
        "id": "FtLnJoF9cyoZ"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from openai.embeddings_utils import get_embedding, cosine_similarity"
      ],
      "metadata": {
        "id": "NwG6KhTdcYyt"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def search_reviews(df,search_query, n):\n",
        "    embedding = get_embedding(\n",
        "        search_query,\n",
        "        engine=\"text-search-babbage-query-001\"\n",
        "    )\n",
        "    l2=[]\n",
        "    for i in l1:\n",
        "      l2.append(cosine_similarity(i,embedding))\n",
        "    df['similarities']=l2\n",
        "    top_n =df.sort_values(\"similarities\", ascending=False).head(n)\n",
        "    return top_n\n",
        "     "
      ],
      "metadata": {
        "id": "w_SM-j50cLVx"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, HTML\n",
        "res = search_reviews(df,\"Need a vegetarian cheesy potato dish which feels like both mashed potatos and french fries that can be made with 10 or less ingredients with a microwave\", n=5)\n",
        "display(HTML(res.to_html()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "doetSX9BchWx",
        "outputId": "82cc6e7b-5c89-4470-b149-2839e4a3a523"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>text</th>\n",
              "      <th>similarities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>0</td>\n",
              "      <td>recipename cheesy potato skins \\r\\n ingredients \\r\\n large potatoes 2 pounds \\r\\n tablespoons butter or margarine melted \\r\\n cup shredded colby monterey jack or cheddar cheese 4 ounces \\r\\n cup sour cream \\r\\n medium green onions sliced 1 2 cup \\r\\n \\r\\n prepmethods \\r\\n heat oven to 375 f pierce potatoes to allow steam to escape bake 1 hour to 1 hour 15 minutes or until tender let stand until cool enough to handle cut potatoes lengthwise into fourths carefully scoop out pulp leaving 1 4 inch shells save potato pulp for another use \\r\\n set oven control to broil place potato shells skin sides down on rack in broiler pan brush with butter \\r\\n broil with tops 4 to 5 inches from heat about 5 minutes or until crisp and brown sprinkle with cheese broil about 30 seconds or until cheese is melted serve hot with sour cream and onions</td>\n",
              "      <td>0.682734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>0</td>\n",
              "      <td>recipename sesame feta veggie dip \\r\\n ingredients \\r\\n cup reduced fat cottage cheese \\r\\n cup crumbled reduced fat herb and garlic feta cheese \\r\\n tablespoons sesame tahini paste from 1 lb jar \\r\\n tablespoon coarsely chopped fresh italian flat leaf parsley \\r\\n teaspoon garlic salt \\r\\n lb fresh cauliflower florets 2 cups \\r\\n lb fresh broccoli florets 2 cups \\r\\n lb fresh asparagus spears trimmed \\r\\n \\r\\n prepmethods \\r\\n in blender place dip ingredients cover blend on high speed until smooth spoon into small bowl cover refrigerate at least 15 minutes to blend flavors \\r\\n meanwhile in 4 quart dutch oven heat 2 quarts water to boiling add vegetables cook about 2 minutes or until crisp tender drain rinse with cold water pat dry \\r\\n serve dip with vegetables</td>\n",
              "      <td>0.670267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>0</td>\n",
              "      <td>recipename garden vegetable dip for a crowd \\r\\n ingredients \\r\\n cups plain fat free yogurt \\r\\n tablespoons chopped green bell pepper \\r\\n tablespoons chopped green onions \\r\\n tablespoons chopped radishes \\r\\n teaspoon celery salt \\r\\n bite size pieces vegetables breadsticks or tortilla chips \\r\\n \\r\\n prepmethods \\r\\n mix all ingredients except vegetables in small bowl using spoon \\r\\n refrigerate at least 1 hour until chilled \\r\\n serve with vegetables</td>\n",
              "      <td>0.667274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400</th>\n",
              "      <td>2</td>\n",
              "      <td>recipename irish cheese plate \\r\\n ingredients \\r\\n oz cashel blue cheese \\r\\n oz dubliner cheese sliced \\r\\n oz aged cheddar sliced \\r\\n soda bread sliced if desired \\r\\n assorted fruit if desired \\r\\n \\r\\n prepmethods \\r\\n onto serving plate arrange cheeses serve with soda bread and fruit</td>\n",
              "      <td>0.655876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>0</td>\n",
              "      <td>recipename mozzarella and basil with marinara sauce \\r\\n ingredients \\r\\n oz fresh mozzarella cheese cubed \\r\\n tablespoons chopped fresh basil leaves \\r\\n cups chunky marinara sauce \\r\\n french baguette slices \\r\\n \\r\\n prepmethods \\r\\n \\r\\nheat oven to 350 f in shallow 2 quart casserole place cheese cubes sprinkle with basil spoon marinara sauce around cheese \\r\\n \\r\\n \\r\\nbake uncovered 8 to 10 minutes or until cheese is hot and bubbly serve with baguette slices</td>\n",
              "      <td>0.653966</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}