Semantic_clustering.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Training Data**"
      ],
      "metadata": {
        "id": "RkYDdtf0Q-Ra"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas sentence-transformers pandarallel faiss-cpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOXLmG6iU7u_",
        "outputId": "36639279-d5df-495f-e577-e18c99568a2b"
      },
      "execution_count": 1,
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        {
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          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting pandarallel\n",
            "  Downloading pandarallel-1.6.5.tar.gz (14 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
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            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.42.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.5)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.3.1+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.23.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (9.4.0)\n",
            "Collecting dill>=0.3.1 (from pandarallel)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pandarallel) (5.9.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (24.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2024.6.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (4.12.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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            "Building wheels for collected packages: pandarallel\n",
            "  Building wheel for pandarallel (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/50/4f/1e/34e057bb868842209f1623f195b74fd7eda229308a7352d47f\n",
            "Successfully built pandarallel\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, faiss-cpu, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, pandarallel, nvidia-cusolver-cu12, sentence-transformers\n",
            "Successfully installed dill-0.3.8 faiss-cpu-1.8.0.post1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 pandarallel-1.6.5 sentence-transformers-3.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "4E4NUpNnVBNJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00648a6c-bc74-4135-8557-a2d447879520"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = [['I love the ambiance of this place!', 'positive'],\n",
        "        ['The service was terrible and slow.', 'negative'],\n",
        "        ['The food was absolutely delicious!', 'positive'],\n",
        "        ['I wouldn’t recommend this restaurant to anyone.', 'negative'],\n",
        "        ['The staff were very friendly and helpful.', 'positive'],\n",
        "        ['The product quality is not worth the price.', 'negative'],\n",
        "        ['I’m extremely satisfied with my purchase!', 'positive'],\n",
        "        ['The packaging was damaged when it arrived.', 'negative'],\n",
        "        ['This is the best experience I’ve ever had!', 'positive'],\n",
        "        ['The software keeps crashing and is very frustrating.', 'negative']]\n",
        "\n",
        "df_label = pd.DataFrame(data, columns=['text', 'category'])"
      ],
      "metadata": {
        "id": "kwAiTBQPy9BE"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "yG-uRwIJQ1Z5"
      },
      "outputs": [],
      "source": [
        "data = [['I love the ambiance of this place!'],\n",
        "        ['The service was terrible and slow.'],\n",
        "        ['The food was absolutely delicious!'],\n",
        "        ['I wouldn’t recommend this restaurant to anyone.'],\n",
        "        ['The staff were very friendly and helpful.'],\n",
        "        ['The product quality is not worth the price.'],\n",
        "        ['I’m extremely satisfied with my purchase!'],\n",
        "        ['The packaging was damaged when it arrived.'],\n",
        "        ['This is the best experience I’ve ever had!'],\n",
        "        ['The software keeps crashing and is very frustrating.']]\n",
        "\n",
        "df = pd.DataFrame(data, columns=['text'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "LpKIeRiMVFGV",
        "outputId": "edf2ae39-20ad-4236-99c4-9b0e26c88709"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text\n",
              "0                 I love the ambiance of this place!\n",
              "1                 The service was terrible and slow.\n",
              "2                 The food was absolutely delicious!\n",
              "3    I wouldn’t recommend this restaurant to anyone.\n",
              "4          The staff were very friendly and helpful.\n",
              "5        The product quality is not worth the price.\n",
              "6          I’m extremely satisfied with my purchase!\n",
              "7         The packaging was damaged when it arrived.\n",
              "8         This is the best experience I’ve ever had!\n",
              "9  The software keeps crashing and is very frustr..."
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      "execution_count": 6,
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        {
          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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      "source": [
        "def generate_embeddings(query):\n",
        "  embeddings = model.encode(query)\n",
        "  return embeddings"
      ],
      "metadata": {
        "id": "koPqUwJzWD3z"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate embeddings for all the products descriptions - approx 3 min to complete\n",
        "\n",
        "from pandarallel import pandarallel\n",
        "\n",
        "pandarallel.initialize(progress_bar=True, nb_workers=8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsvsIiJhWGV5",
        "outputId": "02087ff1-e852-4a32-e751-0d4121d19873"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO: Pandarallel will run on 8 workers.\n",
            "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Generate Embeddings for all the products\n",
        "df['Text_Embedding'] = df['text'].parallel_apply(generate_embeddings)"
      ],
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      "execution_count": 9,
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              "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=2), Label(value='0 / 2'))), HBox(c…"
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            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
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    {
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      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "nXWCxXePWIxW",
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      "execution_count": 10,
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              "                                              text  \\\n",
              "0               I love the ambiance of this place!   \n",
              "1               The service was terrible and slow.   \n",
              "2               The food was absolutely delicious!   \n",
              "3  I wouldn’t recommend this restaurant to anyone.   \n",
              "4        The staff were very friendly and helpful.   \n",
              "\n",
              "                                      Text_Embedding  \n",
              "0  [0.031904228, 0.03573211, 0.07044881, 0.020851...  \n",
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              "  <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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"This is the best experience I\\u2019ve ever had!\",\n          \"The service was terrible and slow.\",\n          \"The product quality is not worth the price.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text_Embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import faiss                   # make faiss available"
      ],
      "metadata": {
        "id": "8ebJn_apWqkO"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ncentroids = 2\n",
        "niter = 30\n",
        "verbose = True\n",
        "d = 384\n",
        "kmeans = faiss.Kmeans(d, ncentroids, niter=niter, verbose=verbose)\n",
        "kmeans.train(df['Text_Embedding'].to_list())"
      ],
      "metadata": {
        "id": "N3Z4KDF6EYL2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fa16c10-0d10-445d-e4fc-59244b35346e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.070149898529053"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.centroids.shape)\n",
        "print(kmeans.centroids[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dJHFhBmRa6G",
        "outputId": "a3085617-627d-4ff9-ff78-378679f884a1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 384)\n",
            "[-4.84317681e-03  1.24052698e-02  2.90986057e-02 -2.03665365e-02\n",
            " -3.58103812e-02 -3.23633328e-02 -3.91662344e-02  9.72917490e-03\n",
            " -1.15435887e-02 -2.88396608e-03  1.01477467e-02  4.91911173e-02\n",
            "  9.52033047e-03  1.55684203e-02 -2.66565010e-02 -2.51307357e-02\n",
            "  8.28880519e-02 -4.25428823e-02  1.44768124e-02  1.99353416e-03\n",
            " -2.62457058e-02 -1.11181661e-02 -1.56391952e-02  3.27581502e-02\n",
            " -2.50892453e-02 -1.33982990e-02 -2.60737389e-02  3.44852451e-04\n",
            " -1.48956701e-02 -3.67165916e-02 -1.46787036e-02  2.20413674e-02\n",
            " -4.14152537e-03 -1.26406606e-02  2.41413284e-02 -9.80912801e-03\n",
            "  3.33294943e-02 -2.36754045e-02 -2.62889080e-02  2.56552435e-02\n",
            "  1.45991798e-03  2.33527049e-02 -1.37302754e-02 -1.69169847e-02\n",
            "  1.95294134e-02 -5.37581109e-02 -3.02131381e-03 -2.71758456e-02\n",
            "  8.97781998e-02 -3.76443379e-04 -1.66797712e-02 -3.44264880e-02\n",
            "  2.38980129e-02 -4.92203683e-02  1.22691980e-02  6.24720473e-03\n",
            " -3.97338569e-02  3.73760760e-02  1.76548529e-02  7.05070645e-02\n",
            "  1.65554918e-02 -4.55376953e-02 -3.65725718e-02  2.47146301e-02\n",
            "  5.12296706e-02  3.54759395e-03 -1.07919509e-02 -5.90841733e-02\n",
            " -4.31272294e-03 -7.83882570e-04 -5.17479032e-02  9.71044227e-03\n",
            "  2.38778945e-02  5.02179675e-02 -7.60842348e-03  8.45203083e-03\n",
            "  3.52285728e-02 -7.04937428e-02 -2.85555292e-02  7.01389927e-03\n",
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        "D, I = kmeans.index.search(np.array(df['Text_Embedding'].to_list()), 1)"
      ],
      "metadata": {
        "id": "ljqrW-T8ReMo"
      },
      "execution_count": 14,
      "outputs": []
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        "outputId": "7745af35-78ec-41bd-88f1-2343913b1a8a"
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      "execution_count": 15,
      "outputs": [
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          "data": {
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        "outputId": "37ac5b88-5907-48bf-f1f7-4faf87f1a606"
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      "execution_count": 16,
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        {
          "output_type": "execute_result",
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              "                                                text  category\n",
              "0                 I love the ambiance of this place!  positive\n",
              "1                 The service was terrible and slow.  negative\n",
              "2                 The food was absolutely delicious!  positive\n",
              "3    I wouldn’t recommend this restaurant to anyone.  negative\n",
              "4          The staff were very friendly and helpful.  positive\n",
              "5        The product quality is not worth the price.  negative\n",
              "6          I’m extremely satisfied with my purchase!  positive\n",
              "7         The packaging was damaged when it arrived.  negative\n",
              "8         This is the best experience I’ve ever had!  positive\n",
              "9  The software keeps crashing and is very frustr...  negative"
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              "      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-294a99a4-2c9e-4b30-b050-53c22bf832c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a61792ec-bd67-49d8-8f3f-70fa823c00e4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_label')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a61792ec-bd67-49d8-8f3f-70fa823c00e4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_label');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_label",
              "summary": "{\n  \"name\": \"df_label\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"This is the best experience I\\u2019ve ever had!\",\n          \"The service was terrible and slow.\",\n          \"The product quality is not worth the price.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    }
  ]
}