speechToText.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vufJiH7YOclr",
        "outputId": "53d244bf-90d1-4ecb-f0e9-521a36bb90f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Requirement 'PyAudio-0.2.11-cp310-cp310-win_amd64.whl' looks like a filename, but the file does not exist\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[31mERROR: PyAudio-0.2.11-cp310-cp310-win_amd64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install PyAudio-0.2.11-cp310-cp310-win_amd64.whl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install SpeechRecognition\n",
        "!pip install tensorflow==2.15.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2tzkm5UOhX0",
        "outputId": "9e7a7316-79c3-479f-922c-799636399d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting SpeechRecognition\n",
            "  Downloading SpeechRecognition-3.10.1-py2.py3-none-any.whl (32.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.8/32.8 MB\u001b[0m \u001b[31m44.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (2023.11.17)\n",
            "Installing collected packages: SpeechRecognition\n",
            "Successfully installed SpeechRecognition-3.10.1\n",
            "Collecting tensorflow==2.15.0\n",
            "  Downloading tensorflow-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (475.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (16.0.6)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.23.5)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (2.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (4.5.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.34.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.59.3)\n",
            "Collecting tensorboard<2.16,>=2.15 (from tensorflow==2.15.0)\n",
            "  Downloading tensorboard-2.15.1-py3-none-any.whl (5.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m72.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tensorflow-estimator<2.16,>=2.15.0 (from tensorflow==2.15.0)\n",
            "  Downloading tensorflow_estimator-2.15.0-py2.py3-none-any.whl (441 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.0/442.0 kB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting keras<2.16,>=2.15.0 (from tensorflow==2.15.0)\n",
            "  Downloading keras-2.15.0-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow==2.15.0) (0.42.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (3.5.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.0) (3.0.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15.0) (3.2.2)\n",
            "Installing collected packages: tensorflow-estimator, keras, tensorboard, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.14.0\n",
            "    Uninstalling tensorflow-estimator-2.14.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.14.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.14.0\n",
            "    Uninstalling keras-2.14.0:\n",
            "      Successfully uninstalled keras-2.14.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.14.1\n",
            "    Uninstalling tensorboard-2.14.1:\n",
            "      Successfully uninstalled tensorboard-2.14.1\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.14.0\n",
            "    Uninstalling tensorflow-2.14.0:\n",
            "      Successfully uninstalled tensorflow-2.14.0\n",
            "Successfully installed keras-2.15.0 tensorboard-2.15.1 tensorflow-2.15.0 tensorflow-estimator-2.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import speech_recognition as sr\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import layers\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "import nltk\n",
        "import string\n",
        "import warnings\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from wordcloud import WordCloud\n",
        "nltk.download('stopwords')\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('wordnet')\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMzfGMY-eNBD",
        "outputId": "6db8c1a6-5b9f-49f5-9ac3-a4730fe79bf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def main():\n",
        "  r = sr.Recognizer()\n",
        "\n",
        "  audio_path = \"/content/test1.aiff\"\n",
        "\n",
        "  #for microphone\n",
        "  # input = sr.Microphone()\n",
        "\n",
        "  #for file\n",
        "  input = sr.AudioFile(audio_path)\n",
        "\n",
        "  print(input)\n",
        "\n",
        "  with input as source:\n",
        "    #for microphone\n",
        "    # r.adjust_for_ambient_noise(source)\n",
        "    # print(\"Listening.....\")\n",
        "    # audio = r.listen(source)\n",
        "\n",
        "\n",
        "\n",
        "    #for file\n",
        "    audio = r.record(source)\n",
        "\n",
        "    print(\"Processing....\")\n",
        "\n",
        "    try:\n",
        "      text = r.recognize_google(audio)\n",
        "      print(\"Your input: \" + text)\n",
        "      return text\n",
        "    except Exception as e:\n",
        "      print(\"Error: \" + str(e))\n"
      ],
      "metadata": {
        "id": "y9CQPtpKOrJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdUwp7jXTBKP",
        "outputId": "46ff1397-feab-4e9c-e4c3-ac01bbbb452e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<speech_recognition.AudioFile object at 0x7982d6f65ba0>\n",
            "Processing....\n",
            "Your input: ladies and gentlemen I think my brain just committed suicide\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = pd.read_csv('/content/labeled_data.csv')"
      ],
      "metadata": {
        "id": "EYHQoDWfXFEU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pV1oSDboZozt",
        "outputId": "81626e42-1e04-4ba1-e25a-b1026e4a1266"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  count  hate_speech  offensive_language  neither  class  \\\n",
              "0           0      3            0                   0        3      2   \n",
              "1           1      3            0                   3        0      1   \n",
              "2           2      3            0                   3        0      1   \n",
              "3           3      3            0                   2        1      1   \n",
              "4           4      6            0                   6        0      1   \n",
              "\n",
              "                                               tweet  \n",
              "0  !!! RT @mayasolovely: As a woman you shouldn't...  \n",
              "1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...  \n",
              "2  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...  \n",
              "3  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
              "4  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00e3e004-0ab9-4851-89b3-bdd6044bf44d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech</th>\n",
              "      <th>offensive_language</th>\n",
              "      <th>neither</th>\n",
              "      <th>class</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</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-00e3e004-0ab9-4851-89b3-bdd6044bf44d')\"\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-00e3e004-0ab9-4851-89b3-bdd6044bf44d 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-00e3e004-0ab9-4851-89b3-bdd6044bf44d');\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-1df6a3e7-8687-40e3-8ecd-2a2f9b40871b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1df6a3e7-8687-40e3-8ecd-2a2f9b40871b')\"\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-1df6a3e7-8687-40e3-8ecd-2a2f9b40871b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGFk0lSfZp_x",
        "outputId": "011966f8-456d-4502-b810-dda7bedaf2f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0            0\n",
              "count                 0\n",
              "hate_speech           0\n",
              "offensive_language    0\n",
              "neither               0\n",
              "class                 0\n",
              "tweet                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahpabYGlZr51",
        "outputId": "8ae4d5d7-d5b0-4330-ff5a-0979a39bb2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24783 entries, 0 to 24782\n",
            "Data columns (total 7 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   Unnamed: 0          24783 non-null  int64 \n",
            " 1   count               24783 non-null  int64 \n",
            " 2   hate_speech         24783 non-null  int64 \n",
            " 3   offensive_language  24783 non-null  int64 \n",
            " 4   neither             24783 non-null  int64 \n",
            " 5   class               24783 non-null  int64 \n",
            " 6   tweet               24783 non-null  object\n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "values = df['class'].value_counts().values.tolist()\n",
        "categories = df['class'].value_counts().index.tolist()\n",
        "\n",
        "plt.pie(values, labels=categories, autopct='%1.1f%%')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "Nx0rcEopZwd7",
        "outputId": "2934948f-3cf0-44cb-a778-2deddffa60ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAybElEQVR4nO3dd3yV5f0+8Ovskb03hIQtG9nIEBEXirjqqKho3dVa/em31bbab7WtVvt1WxfWBdoKiChDQVEZYYVNQoAMQvY+OTn7+f0RPcoIhOTk3M+43q8XLyTjnCsSzpX7fp7n8+gkSZJAREQEQC86ABERyQdLgYiIglgKREQUxFIgIqIglgIREQWxFIiIKIilQEREQSwFIiIKYikQEVEQS4GIiIJYCkREFMRSICKiIJYCEREFsRSIiCiIpUBEREEsBSIiCmIpEBFREEuBiIiCWApERBTEUiAioiCWAhERBbEUiIgoiKVARERBLAUiIgpiKRARURBLgYiIglgKREQUxFIgIqIglgIREQWxFIiIKIilQEREQSwFIiIKYikQEVEQS4GIiIJYCkREFMRSICKiIJYCEREFsRSIiCiIpUBEREEsBSIiCmIpEBFREEuBiIiCWApERBTEUiAioiCWAhERBRlFByAKNUmSUN/qQVWzG1UtLtQ5PHD7/PD4AvD4AvD623/3+KUffvfD65Pg8be/z2zQw2o2wGo0wGbWw2o0IMJiRJTViGibCdFWE6JtRsTYTEiOssJs5M9WpB4sBVKciqY2HKppRUWTC1XNLlQ3u4IFUN3sRk2LGx5/ICxZ9DogOcqKrHgbMuPsyIpr/z0z3oasODvSYqwwGlgapBw6SZIk0SGITqbN48f+ymbsr2xBQWUL9lU0o6CqBY1Or+honWbU65AS3V4a/VOiMDQjBsMyY9EvORJ6vU50PKITsBRIFmpa3NhW2oB9Fc3YX9GC/ZXNKK13IqDS70672YAh6TEYmhmDYZntRZGdYIdOx6IgsVgKJERFUxs2HarHpsP12HS4DodqWkVHEi7aavyhJGIxrk88xuckwGoyiI5FGsNSoLBocXmx/mAdvj1Qg+8O1KK4zik6kuxZTXqMz0nAtP5JmD4wGb0TIkRHIg1gKVCPOVDVgi92V2JdYQ3yyxrhU+teUJj0SYzA1P5JmDYgiasI6jEsBQqp4tpWLNtxFJ/trEBBVYvoOKplNekxIScB0wYk48IhqUiOtoqORCrBUqBuK29sw2c/FMGu8ibRcTRHrwMm5CbgshEZuHBIKqKsJtGRSMFYCtQl1S0ufL6zAst2VmBbaQP4XSQPFqMeMwYlY+7ITEwfmAwDT3ulM8RSoE4LBCSs2V+NdzYU4/uiWtWeLqoWyVEWzB2ViavPzkROUqToOKQQLAU6rWaXFx9tLsO7G0tQwrOGFGlMdhyuG9cLlwxLh4lXWNMpsBSoQ0XVDixYfxiLt5Wj1eMXHYdCIC3Gilsm9cG143oh0sIpN3QilgIdQ5Lat4gWrC/Gd0W1PFagUlFWI64f1xu3TMrmmUt0DJYCAQBcXj8+zCvFO+uLeWGZhpgNeswZmY5fTclF32QedyCWguZ5/QEs2lyGF9cUobLZJToOCaLTATMGJuP2qbkYkx0vOg4JxFLQqEBAwpL8cvzzywMorefKgH5ydu84PHLhQJzNctAkloIGrdhdiWdXF6CwyiE6CsnYBWel4pELByI7kTOXtISloCHfHqjBMysLsOMIrzqmzjEZdLhhfG/cN6MfYu1m0XEoDFgKGrCttAF/X7EfGw/Vi45CChVtNeKec/ti3sRsWIwcxKdmLAUVq3W48eTn+/DJtnLRUUglsuJteGjWQFw6PF10FOohLAUVCgQkvL+pBE+vLECzyyc6DqnQiKxYPHbJYIzuHSc6CoUYS0FldpQ14tEluzmtlHqcXgfMm5iN/zdrIGxmbimpBUtBJZweH55ZWYgF6w9zUB2FVa94O/52xTBMyE0QHYVCgKWgAt8U1uD3i3fhSEOb6CikUTodcP24XvifCwchgjOVFI2loGCNTg8eX7YXi7fzQDLJQ0asDU/NHYop/ZNER6EuYiko1KZDdbhvYT5HU5AsXXN2Fn5/ySBE8y5wisNSUJhAQMLzaw7ghTVF8PPgAclYarQVT84dgnMHpoiOQmeApaAglU0u3L9oOy9CI0W5aWI2fnfRIJiNvLmPErAUFGLN/io8+PFO1Ld6REchOmPDM2Pw4nWjkBVvFx2FToOlIHMeXwB//WI/3l5/mDe8IUWLsZnw9JXDcP5ZqaKj0CmwFGSsuLYV9364nReikar8akoOHr5gIAx6negodBIsBZlasbsCD368Ew43x1SQ+kzqm4AXrh2F+AhOXpUbloIMvfrNQfxtxX5uF5GqZcTa8NovR2NIRozoKPQzLAUZ8fkDeGzpHnyYVyo6ClFYWIx6PDV3KOaOyhQdhX7AUpCJFpcXd72/Dd8eqBUdhSjsfjuzP+6d0U90DAJLQRbKG9twy9ubUVDVIjoKkTA3TuiNP80+C3oegBZK81eTrFu3DrNnz0Z6ejp0Oh2WLFkS1uffeaQRc176noVAmvfvDSW498PtcPv8oqNomuZLobW1FcOHD8dLL70U9udeuacS17y2ETUt7rA/N5EcLd9VgXlv5aHF5RUdRbO4ffQzOp0Oixcvxpw5c3r8ud749hCe/Hwf731AdBKD06Kx4JYxSI6yio6iOZpfKYjw4poD+N/lLASijuytaMYVr6zH4dpW0VE0h6UQZi+uOYBnVhWKjkEke2X1bbjylfXYUdYoOoqmsBTCiIVAdGbqWj249vWNyDvMycDhwlIIk5fWFrEQiLrA6fFj/oLN2HmkUXQUTWAphMFLa4vw9MoC0TGIFKvF7cONb+Vhf2Wz6Ciqp/lScDgcyM/PR35+PgDg8OHDyM/PR2lpaEZNsBCIQqPR6cUNb+Tx4HMP0/wpqV9//TWmT59+wtvnzZuHBQsWdOuxWQhEoZceY8VHd0xAZhxv2NMTNF8KPeXlr4vw9xUsBKKekJ1gx0d3TOB1DD1A89tHPeGjzWUsBKIeVFznxA1vbEIDb08bciyFEPumsAa/W7xLdAwi1SuscuBGjsQIOZZCCO0ub8Jd722Fj5cqE4XFrvImzF+wBR5fQHQU1WAphMiRBiduWbAZrR5OeCQKp7ziejy6hKvzUGEphIDD7cP8BVtQzWmnREJ8tOUI3v7+sOgYqsBS6KZAQMKvP9zO+yEQCfaX5fvwfRHvXNhdLIVuevLzfVizv1p0DCLN8wUk3P3BNpTU8eK27mApdMOizaV44zsuWYnkotHpxW3/3oJWt090FMViKXTR9tIGPLpkt+gYRHScwioHfrMoH7wut2tYCl3Q7PLi1wu3w+vnNx2RHK3aW4XnVnMqcVewFLrgfz7ZhbL6NtExiOgUXlhbhM93VYiOoTgshTP0waZSLN/JbzQiuZMk4MGPd+BQjUN0FEVhKZyBwqoWPPHZHtExiKiTnB4/7luYD6+fVzx3Fkuhk1xeP+75YBtcXn5zESnJrvImPLOKAyo7i6XQSY8v24vCKi5DiZToX+sOYT0vbOsUlkInLN9ZgQ/zQnMnNiIKP0kCHvhoBxqdHLV9OiyF0yird+KRT3aKjkFE3VTZ7MJjS3lM8HRYCqcgSRJ+sygfLS5eHUmkBst2HOVpqqfBUjiFD/JKsaWkQXQMIgqhR5fsRq2DE407wlLoQE2LG3/7Yr/oGEQUYvWtHvyed0fsEEuhA3/+bC+auW1EpEor91Th0x1HRceQJZbCSXx7oIbfMEQq9+TyfXB6+IPf8VgKx3F5/Zx+SqQBlc0uvLCmSHQM2WEpHOfFNUUoqXOKjkFEYfDmt4dRXMub8vwcS+FnDlS14F/rDomOQURh4vEH8MRne0XHkBWWwg8kScLvF++Gh4OziDRlzf5qrNlfJTqGbLAUfvDxliPIK64XHYOIBHhi2V54fPyBEGApAADaPH48zSmKRJpVXOfEG99x6xhgKQAAFqwvRk0Lr3Ak0rIX1xShssklOoZwmi+FZpcXr607KDoGEQnm9Pjx5Of7RMcQTvOl8Ma6Q2h0ekXHICIZ+HTHUewubxIdQyhNl0J9qwdvfV8sOgYRycjzXx0QHUEoTZfCy2uL4HDzMnci+snqfVXYV9EsOoYwmi2FyiYX3t1YIjoGEcmMJAEvrNHuakGzpfD8mgNw87xkIjqJL3ZX4kBVi+gYQmiyFErrnPh4S5noGEQkU+2rBW0Oy9NkKfzzy0J4/ZLoGEQkY5/tPIqDNQ7RMcLOKDpAuB1pcGKpQu+VcOSVW+Bvrj7h7ZEjL0bMuLkof3X+ST8v8bJHEDFw8mkfv27li3Dkr0DcubchesxlAADJ50XdiufhPLARhog4xJ9/F2zZI4Kf07Tpv/A31yB+5h1d+6KIZCogAS+tLcKzV48QHSWsNFcKC74vhj+gzFVC2rzngMBPx0E8tSWoXvQoIgZOgiEqEZl3v3vMx7fsWIHmvE9gyxl92sd2Fq6H+2gBDJHxJzyGp7IIqTc8g7ZDW1G77Glk3vMedDodvI2VcOxYibR5/wzJ10ckN5/mH8V9M/qhd0KE6Chho6ntI4fbh0WblXsswWCPgSEyLvirrSgPxtg0WLKGQqc3HPM+Q2QcnIUbYB8wGXqz7ZSP62upRf3q15B4yYOA/tifE7x1ZbD1HQdzUm9EjboYAWcTAm3tp+vVr3oZcdNugt5i77GvmUgkX0DCy2u1NfFAU6WwaHMZWlRyXYLk96J179eIHDYTOp3uhPe7K4vgrT6EyGHnn/pxpABqP3sW0ePmwpzU+4T3m5P7wH1kLwJeN1yHt8EQGQ+9LRqOPWuhM5ph7z8xZF8TkRx9sv2IpmajaaYU/AEJC9YfFh0jZJyFGxFwORAxZMZJ3+/YuQqmhCxYMwed8nGaN/4HOr0BUaMvPen7I4fOhCm5D46+eReaNnyExMseRsDlQNN37yP+vNvRsO5dlL92G6oWPQZfS223vy4iufH6JXy8Vbk7DGdKM6Xw1b4qlNW3iY4RMo6dq2DLGQ1jVMIJ7wt43Wjd+w0ih8085WO4K4vQvPVTJFx0/0lXGwCgMxiRcP6dyLzjTaTNew7WzLPQsOZNRI2eDU/VIbQd2IC0m1+AJX0gGr78V0i+NiK5WZhXBklS5rHIM6WZUlDT1cu+pmq4SnYgcvisk77fWfA9JK+7w1XEj9xlexBobUL5Kzej5O+XouTvl8LfXI2GtW/iyCu3nPRzXCU74a0rQdSoS+Aq3QlbztnQm62wD5wMV+mubn9tRHJUWu/Ed0XaWAlr4uyj4tpWVf2FOnathsEeA1vumJO/f+cq2PuOhcEec8rHiRgyHdbs4ce8rfqjPyDirHMROfS8Ez5e8nlQv/oVJM5+EDq9AZACkH48GSrghyTxCnFSrw/zSnFOvyTRMXqcJlYK720sgVpWfpIUgGPXl4gYMqP9hfk43oajcJft6XAVUf76HXAWrgcAGGzRMCdlH/MLeiMMEXEwJWSe8LmN6xfClnM2zCm5AABLxmA4C9fDU30YLds+gzXj1McviJRs9d4qTRxwVn0puLx+/GfbEdExQsZVnA9/c02HxwscO1fDEJUIa5+RJ32/r/4IAm7nGT+vp6YYzv3fImby9cG32QdOgi13DCrffxiemsOIO+9XZ/y4REqhlQPOOknlR0+W5pfjvoX5omMQkQr0TrDj6wendXhihhqofqWwbEeF6AhEpBIldeo/4KzqUmhq82JdYY3oGESkIh/mlYqO0KNUXQqr9lTC4+cZMUQUOqv3VqHOod4Dzqouhc92cuuIiELL65ewYk+l6Bg9RrWl0NDqwfcq3/sjIjG+2MVSUJwvdlfCp9AR2UQkbxsP1aGh1SM6Ro9QbSl8tlOZN9IhIvnzBSSs2qvO1YIqS6G6xYWNh+pExyAiFftcpVtIqiyFL3ZVgjtHRNSTNhysg0Ml92f5OVWWAreOiKinefwBfKvC66BUVwrNLi+2lTaKjkFEGvDlvmrREUJOdaWw6VA9/Nw7IqIw+LqgGgGVvd6orhR4bQIRhUtdqwfbyxpFxwgp1ZXChoM864iIwuebAnVtIamqFGodbhRWt4iOQUQasrm4QXSEkFJVKWw4WKeaO6wRkTLklzXCp6LBm6oqhfUHeTyBiMKrzevHnqPNomOEjMpKgccTiCj8NhfXi44QMqophfLGNpTUnfm9h4mIumtriXqOK6imFNbzVFQiEmQLS0F+NnAAHhEJUtPiRkldq+gYIaGaUthd3iQ6AhFpmFpOTVVFKXh8ARyqUUdLE5EybS1Rx8FmVZRCUbWDd1kjIqG2cKUgH/sr1XOOMBEpU1GNQxX3V1BJKXC0BRGJJUnAYRVsY7MUiIhC5FCtQ3SEblNHKVRw+4iIxDtYzVIQrr7Vg+oWt+gYREQ4WMvtI+G4SiAiuVDDqfHKLwUeTyAimSiubYWk8Pn9ii+FApYCEclEm9ePo00u0TG6RfGlUFrPyahEJB9KP9is+FKoaGoTHYGIKOhQDUtBqAqFL9WISF0OKfwMJEWXQp3DDbdPPfdGJSLlU/rNvhRdClwlEJHc1Ld6REfoFpYCEVEIsRQEqnXwSmYikpdGJ0tBmFqOtyAimWn1+OH2+UXH6DJFl0KdwpdpRKRODa1e0RG6TNGlUMPtIyKSoQYFbyEpuhTqWApEJEMNCt7FUHQpqOHWd0SkPg1Obh8J4fUpexohEalTPbePxPD4eTUzEclPI7ePxPBwxAURyVBjG7ePhPBypUBEMqTk1yZFlwK3j4hIjvwB5R7vVHQpeLl9REQyFFDwLTmVXQp+5f6PJyL18in4tUnRpcDtIyKSI7+CVwpG0QG6imceUSgNiW7FvX1X4lObAaXeOtFxSOGS0s4BMEJ0jC5RbCko+eg+yc/BVivO378EswI+7MgajiVJmVjhOASHV9m3ViQxRqQMFh2hyxS7faTTiU5AatLmN8Ab3QsAMLxsB/64bTnWHi7Gk5ZcjIvpDx34DUedZ9AZREfoMsWWgt1shJ7/TimEGu19jvmz1duG2fvX4o38L/FFk4S7YoYiw54iKB0pCUtBkAizYne/SIaOGjI6fF9GfSnuzF+OL/ZswZtSCmbHDYXNYA1jOlISg56lIESEhaVAoVMYSDvtx+ggYWzxZjy5bTnWHKnEH+39MTw6NwzpSEm4UhAkwqLc//EkP/nOpDP6+EhXM67c8yXe27EWS1vNuCV2KJKs8T2UjpTEqFfuD6yKLoVIrhQohNY3dv0FPae6CL/Zvhyr9+/CS/pMzIw7Cya9KYTpSEmizFGiI3SZol9VuX1EoVTcZkUgLh76tvouP4ZB8mPKwfWYAqDRHo/luWOwROfE/paS0AUl2UuwJoiO0GWKXimwFCjUnFF9Tv9BnRTrrMf1u1bi453f4mNXJK6PHYZYc0zIHp/kK8HGUhCC20cUatXmXj3yuAMr9uKR7Z9hTVEBnjVlY0rsIEUfjKRTU/JKQdGvqjzQTKFWrEtHTg8+vsnvwczCdZgJoCY6FcuyR2JJoBGHW8t78Fm7r2pxFWqW1hzzNnOqGf3/2r/Dz6ldWYv6tfXw1nlhiDIg5uwYpFyZAr25/WfRxvWNqPxPJQKuAOLOiUPatT+d/eWp8aD4mWLk/ikXBpvy/p0n2hJFR+gyRZdCpIUH8ii09rhTcW6YniupuRK37PwCtwCKGK1hybAg+6Hs4J91ho6vHm3c0Iiqj6uQMT8D9r52uKvcKH+jHNABademwdfiQ/nb5ci8NROmJBNKnitBxKAIRI+IBgAcffcoUq5KUWQhAMrePlJ0KcTYWAoUWlscYk4pHV62A8PLduBhkw2rc8djqdWAvKYDkCCfaZs6vQ6m2M79m3MWOWHvZ0fshFgAgDnJjJhxMWg71AagfSVgsBkQM679GEvEoAi4j7qBEUDjxkboDDrEnK3M4y9RpiiYDWbRMbpM0ccUMuJsoiOQymxsjIEk8FRSOY/WcFe5sf/+/Sh4qABlr5bBU9fxzentfe1oK26D85ATAOCp9sCx04HIYZEAAEuKBQFPAG0lbfA5fGg73AZrlhX+Vj+qP6lG2g2nv5BQrpS8SgAAnSQpd/D39tIGXP7yetExSGUKUx+DufGg6BhBEnTYnH02Fscn4cvmA3D53WHP0LKzBQFXAJY0C7yNXlQvrYavwYe+/9u3wy2eutV1qFxY2b7a8QPx0+ORPi89+P7mrc2oWlwFySMhZkIMUi5PwZE3j8CaaYUt24aK9ysg+SUkz0lGzBjlrBpGp4zGggsWiI7RZYrePsqMs4uOQCrUaO+NZBmVwo+jNcYWA7+3RmNF7jgsMfqwozl8GaOG/XQxljXLCnuOHQUPFqAprwnxU0/ccnPsc6BmWQ3SbkyDPccOT7UHFe9XoHppNZIvSwYARI+ORvTo6ODntO5vhfuIG+k3pKPw4UJk3ZEFY4wRB584iIgBETBGK+PlSslnHgEK3z5KirLAZlLmgSiSr3JjlugIHWofrbFa+GgNQ4QBllQLPNUn30KqXlyN2ImxiJ8aD2uWFdGjo5FyZQpqltdAOslN7QPeAI7++yjS56XDU+2B5JcQMTACljQLLKkWOA86e/pLChmlbx8puhQAIJPHFSjEDvhSRUfoFJGjNfwuPzzVng4PPAfcgRNfXU7xalPzaQ0ih0bClm1rL42f3UNL8h37Z7lT+kpBGeuxU8iKt+NAtUN0DFKR/LZkXC06xBkIx2iNioUViB4RDVOCCb5GH6qWVAF6BM8eOvKvIzDGGZF6VXuhRo2IQt3KOlh7WWHPtcNT5UH1J9WIGhEF3XE3QnGVu9CU14S+T/QFAFjSLIAOqP+mHqYYE9wVbthylPPDn5KvUQBUUApcKVCofdcYJzpCl/04WuN6APvTBmNJag6WO0vQ6Gnq1uP66n0oe7UMfocfhigDIvpFIOexnOA+v6fOg5/fnC750mTodDpUf1INb4MXxigjokZEIeWKY8+kkiQJRxccReq1qdBb2pcSerMeGbdmoOLdCkheCWm/TIMpTjmnn6dEyONssa5S9NlHAPD6ukP4y+f7RMcglTkUdy/0bXWiY4SE12DG17njsSTChu+bCuGX/KIjqdrqK1cjNUIZW5Ano/iVQlY8VwoUeq1RfRClklJQ6mgNJYo0RSq6EABVHGjmaakUejVm+Z6B1B0/jtb4dPcGvOeLwxVxQxFpihAdSzVyYntyclZ4KL4UeiWwFCj0inUd369ZLYaX7cCfti3HmuISPGnti3Ex/aFDx/OM6PT6xvYNyeO89NJLyM7OhtVqxbhx45CXlxeSx+0MxZdCtNWEjFhuIVFo7XEni44QNjaPE7P3rZHlaA2lyY3p/v26Fy1ahAceeAB//OMfsW3bNgwfPhyzZs1CdXV1CBKenuJLAQCGZESf/oOIzsBmh7JPK+yqjPpS3Jm/HF/s2YI3pRRcEjcEVoNFdCzFCMVK4dlnn8Vtt92Gm2++GYMHD8arr74Ku92Ot956KwQJT08VpXBWunLmopAybGqMFjoYT7QfR2s8te1zrD1ShT/aB2B4dPd/Cla7AfEDuvX5Ho8HW7duxXnnnRd8m16vx3nnnYcNGzZ0N16nqKIUuFKgUHMH9PBG9xYdQxbkMlpD7lLsKd0ecVFbWwu/34+UlGO371JSUlBZWdmtx+4sVZQCVwrUExrtLIXjiRytIXeDEwaLjhASir9OAQBSoq1Iibagqjn8I4VJvcoNmdDO4eYzE47RGkoTilJITEyEwWBAVVXVMW+vqqpCamp4rn9QxUoBAEZkxYqOQCpT6FfujV7C6cfRGh/v/BYfuyJxfewwxJq1t3oPRSmYzWaMHj0aX331VfBtgUAAX331FSZMmNDtx+8M1ZTCyF7KnVdD8pTfps0zkLpjYMVePLL9M6wpKsCzpmxMiR0Eg04b4+1DtX30wAMP4PXXX8c777yDffv24c4770RraytuvvnmkDz+6ahi+wgARnKlQCH2fSMPpnbV8aM1Pv1htEaxSkdrpEWkhWw66jXXXIOamhr84Q9/QGVlJUaMGIEVK1accPC5pyh+IN6P2jx+DP3TSvhOcgMPoq5S02A8OcjPGoElSRlY6TgEh7dVdJyQmdtvLh6f+LjoGCGhmu0jm9mAwek8NZVCqzWqj+gIqjKiLF+VozUmpU8SHSFkVFMKADClX5LoCKQyNZZeoiOokppGaxh0BoxPHy86RsioqhSmDWApUGgdRrroCKqn9NEaQxOHItqsnl0KVZXCyF5xiLHxQhoKnb0eZf70qkRKHa0xMWOi6AghpapSMOh1OKcfTyOk0MlrUfZN2JVKSaM11HQ8AVBZKQDAtAG8BpVCJ0/jg/HkQM6jNWItsRiSOER0jJBSzXUKP5o2IAk6HaCOE21JtB8H45kbi0RH0Tw5jtYYnzYeep26frZW11cDIDHSgiEckEch1GDPFh2BjiOX0RqTMtS1dQSosBQAYDrPQqIQKjdkio5ApyBytMbEdHUdZAZUWgpTeVyBQuiAPzzTKal7fhyt8dL2lVhd24b7o85CdkTP3Wu7X1w/JNvV91qjylIYmRWLOLs8DkSR8m1v48pTaZKaKzF/5xdYtnsD3vXF44q4oYg0RYT0OSanTw7p48mFKktBr9dh+kD1NTiJsb6RE3iVrKdGa0zJnBKCdPKjylIAgCtGcR+YQqO0zYpAiCZgkjihHK2RFpGG0SmjQ5xQHlRbChNyEpAeYxUdg1SiNSpbdAQKoe6O1rg452LodMof5Hcyqi0FvV6Hy0f13EEm0pZqC+/XrEZdHa0xO2d2GNKJodpSALiFRKFTzMF4qtfZ0RqD4gchJzZHQMLwUHUp5CRFYmSvWNExSAX2uHnigpacOFpjcHC0xuxc9a4SABWOuTjeFaMysb20UXQMUrg8Bw80a9EJozX6jsesPheKjtWjVL1SAIDZw9JhNqr+y6QetrkxCpLBLDoGCRTrrMf1bl3I7sUsV6p/tYyxmzBzEGfiU/f8OBiPNG7UjaIT9DjVlwIAzOVZSBQCDTaWgqZFpQP9ZopO0eM0UQpT+ychMVI5t/cjeSo38mw2TRt5PaAPz6A9kTRRCkaDHjeM5w3YqXsKfWmiI5AoeiMwap7oFGGhiVIAgBsnZMNmUn/LU8/Jb1P3AUY6hbPmArFZolOEhWZKIT7CjKvO5vKfuu67BnneI5jCYNKvRScIG82UAgDcdk4ODHp1ziuhnnfEZeFgPC3KmQ6kDhWdImw0VQpZ8XZcOIQ3TKGuc0T1ER2Bwk1DqwRAY6UAAHdMPf2wK6KOVFt4woKmpA4Fcs8VnSKsNFcKQzJiMKlvgugYpFAcjKcxE7W1SgA0WAoAcPsUrhaoa/a4w3N1/LoSH2Z/6ET6P1qge7wZS/Z7T/iYfTV+XPqhEzF/bUbEk80Y87oDpU2BUz7uPze6MeBFB2x/aUbWcy34zQoXXD4p+P73d3qR9VwL4v7WjAdWuo753OLGAPq/4ECzWzr+YdUpJqv9rCONUf1AvJOZ0j8Jg9Kisa+iWXQUUpi8lvCcgdTqkTA8RY9bRpgw96O2E95/sD6AyW87MX+kCY9Pi0C0RYc9NX5YT/Ev+oNdXjzypRtvXWbDxCwDCusCuGlJG3Q64NlZVtQ6A7h1WRsWXGZDTpweF3/gxLl9DLikf/t00LuWu/DX8yyItmjkZI3xdwIG7b1Eau8r/sHtU3Jw/6J80TFIYfKaYiDZzND5PT36PBf2M+HCfqYf/nRiKfx+jQsX9TPi7zN/urtgbvypF/7ry3yY1MuA64a2P252rB7XDjFhU7kfAHCoQUKMRYdrhrS/f3ofA/bVBHBJf+DDXV6YDMDcQaYOH19VIpKB0TeJTiGEJrePAGD28HTkJkWIjkEK4w3o4BE8GC8gSVh+wIf+8XrMeq8VyU+3YNwbjpNuMf3cxCwjth71Iy9YAgF8XuTDRf3afzbsF6+H0ythe4Uf9W0SNpf7MSzFgIY2CY+tdeHFCzV0e9tpDwNmbb4+aLYUDHodHpo1QHQMUqAGW7bQ569uleDwAH/93o0Lco1Y9Us7Lh9owtxFbfim2Nfh51031IQnplsw+a1WmP7cjNznHZjW24DfndM+FyzOpsM7c2y4cUkbxr7uwI3DTZjV14gHV7lwz1gzDjcGMPI1B4a87MB/9p66gBQtPhcYdZPoFMJodvsIAC4YkoaRvWJ5Ex46I+WGDIi82iXww3HeywYY8ZsJ7S/oI1INWF/mx6tbPZiaffJ/1l8X+/Dktx68fLEV4zIMKKoP4L4VLvz5Gzcem9r+OJcPMuHyn20RfVPsw85qP164yIq+zzvw4RU2pEbqMPaNVkzpbUByhAp/rpzxmCaPJfxIhX+jZ+aRCwaKjkAKU+gXOxgv0a6DUQ8MTjp2ltegRD1Kmzo+M+ixtW78cpgJt44yY2iKAZcPMuHJGRY89Z0bAenEz3P7JNz1uQuvXWJDUX0AvgAwNduIAYkG9E/QY9MRf8i/NuEyRgNnXS46hVCaL4VxOQmYPiBJdAxSkPw2sd8vZoMOY9INKKg79vTTwvoAesd0fGaQ0yvh+CkvBl37G07SCfjfde3bU6PSDPAHAF/gpw/y+gG/Gs9MPe9x0QmE0+4a6Wd+d9EgfHug9phveqKOfN8Q1+PP4fBIKKr/6UX/cEMA+ZV+xNt06BWjx0MTzbjmP22Y0suA6X2MWFHkw7ICH76+yR78nBsXtyEjSoenzms/QDy7vxHPbvBgZJohuH302FoXZg8wnjATbG+NH4v2+LD99vaDrQMT9dDrdHhzmwepkTrsrw1gTLrKpg73nQn0OUd0CuFYCgD6pUThunG98O8NJaKjkAIccVkQiE+E3lnbY8+x5agf099xBv/8wCo3ADfmDTdhwRwbLh9kwquXSHjqOw9+vcKFAQl6/PdqGyb3+umfdGlTAHrdT5sBj06xQAcdHl3jQnmLhCS7DrP7G/GXGceeVSRJEn61zIVnZ1kQYW4vC5tJhwVzrLj7cxfcPuDFi6zIiFbRRoNOD5z3J9EpZEEnSSdbOGpPQ6sH0575Gk1tKj6rgkJmZ6/nEF29WXQMCpXh1wGXvyI6hSyoqOq7Jy7CjPtm9BMdgxSCg/FUxBoLzHxCdArZYCn8zI0TevOCNuqUwxIH46nGzCeASJ5s8iOWws8YDXo8eflQ6DQy2oW6bo8nPIPxqIf1mgiMulF0CllhKRxnXE4CbhwvdowByd/mFo5fVzyDGZj9T/CnwGOxFE7i4QsHIiveJjoGyVheUzQkg1l0DOqOSfcDSRx1czyWwknYzUb8be4w/gBBHWofjJctOgZ1VUJfYMqDolPIEkuhAxP7JuLasTzDhDrWYOM2o2Jd8hxgtIhOIUsshVP43UWDkBHLbSQ6uXJDpugI1BXDrwP6TBGdQrZYCqcQaTHiqblDRccgmSrwi5yVSl0S0wu44CnRKWSNpXAaU/on4eqz+RMhnWi7k+e2K4rOAFzxBmCLFZ1E1lgKnfDoJYORGq2hu05Rp6xv7PnBeBRCUx8Geo0TnUL2WAqdEG014emrhp0wdpi0rdxlQcCeKDoGdUbvSTzbqJNYCp10Tr8k3Dejv+gYJDOOqBzREeh0rLHA3H8BepWN+u4hLIUz8OsZfXlDHjpGtTlLdAQ6nUtfAGJ4XLCzWApnQKfT4blrRiAzjqepUrvDUoboCHQqo28CBl8qOoWisBTOUKzdjFdvGA2zkf/rCNjtThYdgTqSNBCYxdNPzxRf2bpgSEYMHr/0LNExSAbyHByMJ0u2OOAXHwBm++k/lo7BUuiia8f2wlWjuU+pdVs4GE9+9EbgqgVAQq7oJIrEUuiGP88ZgsFp0aJjkEAcjCdDF/wVyJkmOoVisRS6wWoy4NUbRiPaajz9B5NqNdg5GE82zp4PjL1NdApFYyl0U68EO16+fjRMBl7ZplXlem4jykKfKcCFfxedQvFYCiEwuV8inrlqOO+/oFEcjCcD8TnAVe8ABq7au4ulECKXjcjAoxcPFh2DBOBgPMEsMcC1iwB7vOgkqsBSCKH5k/vg9qkce6A13zXyxUgYgwW4+h0giSNoQoWlEGKPXDAQV4ziHrOWVLjMCNi5Wgg7vRG48i0gd7roJKrCUggxnU6Hv10xlDOSNMYR1Ud0BI3RAXNeAQZdIjqI6rAUeoDRoMfL14/GyF6xoqNQmFSZeT/vsLr4H8Cwq0WnUCWWQg+xmQ14a94Y5CZFiI5CYXBYShcdQTtmPgGMmS86hWqxFHpQXIQZ/54/DhmxnKqqdns8HIwXFlMeAibdJzqFqrEUelhGrA2Lbh+PXvEczKVmm5o5GK/HjbsTOPdR0SlUj6UQBplxdnx0+wTkJHIrSa02N0VBMlhEx1Cvs+cDF3AMdjiwFMIkNcaKhbePR7/kSNFRqAf4JT0H4/WUKQ8BlzwLjgwID5ZCGCVHWbHwV+M5WVWlGmw8Aym0dO03yeGWUVixFMIsIdKChbePx9hsXgWrNkeMvF9zyOiNwJyXgQl3iU6iOSwFAaKtJvx7/ljMGMgzVtSk0JsiOoI6GK3A1e8CI64TnUSTWAqCWE0GvPbL0Zgzgue3q8W2NpZ8t1migRv+Cwy8SHQSzWIpCGQ06PHcNSNw5zTeNlANvm+MEx1B2eyJwLxlQPZk0Uk0jaUgmE6nw8MXDMT//WIErCb+dShZhcsMPwfjdU3SIODWL4H0EaKTaB5fhWTishEZ+M8dE5EeYxUdhbqhNYqj089Yv1nArauBeA4VlAOWgowMyYjB0nsm4+ze3IZQqiozz0A6IxN/DVy7ELBEiU5CP2ApyExSlAUf3DYe147li4sScTBeJxltwNzXgfP/DOj5MiQn/NuQIbNRj6fmDsMTl50Fo55XcSrJbg7GO73Y3sD8VRx9LVMsBRm7cUI23rt1HOIjzKKjUCdtak4UHUHecmcAv/oaSBsmOgl1QCdJkiQ6BJ1aWb0T9y/Kx9aSBtFR6DQMugCK7POh87tFR5EXvRGY+ghwzm+5XSRz/NtRgKz49imrD57fHyYDt5PkjIPxTiI+t327aOpDLAQF4N+QQhj0Otxzbj8svmsSJ63KXIO9t+gI8jFqHnDHt0DGaNFJqJNYCgozJCMGy+6djJsnZXOSsEwd0WeIjiCePQH4xQfApc8D5u7fR+Spp57CmDFjEBUVheTkZMyZMwcFBQUhCErHYykokNVkwB9nn4X35o9DGi92k50Cf5roCGL1nQncuQEYeHHIHvKbb77B3XffjY0bN2L16tXwer04//zz0draGrLnoHY80KxwTW1ePLZkNz7dcVR0FPrBlSmVeKbpAdExws9kB857HBj3qx5/qpqaGiQnJ+Obb77BlClTevz5tIQrBYWLsZnw/LUj8fy1IxFnN4mOQwC+a9TgvTIGXATcvSkshQAATU1NAID4eA3+v+5hXCmoSJPTi3+sLsD7m0rhD/CvVaSDCffD0FotOkbPi+0FXPh3YMCFYXvKQCCASy+9FI2Njfjuu+/C9rxawZWCisTYTXjisiH47N7JGNuHP0GJ5IhU+XA3gxmY/ABw16awFgIA3H333di9ezcWLlwY1ufVCq4UVOzTHUfx1Of7UNHkEh1Fc1b3W4x+ZR+LjtEz+kwBLvoHkNQ/7E99zz33YOnSpVi3bh369FF58QrClYKKXTo8HV/9dirunp4Ls5F/1eF0SFLhGUiRKcDcN9pvhBPmQpAkCffccw8WL16MNWvWsBB6EFcKGlFS14o/f7YXX+7TwD63DNzb6zB+W/170TFCwxoLTPo1MO5OwGwXEuGuu+7CBx98gKVLl2LAgAHBt8fExMBmswnJpFYsBY35uqAaT68swJ6jzaKjqNrY2CZ85LpTdIzuMUUA4+9ov+eBLVZoFF0HV2q+/fbbuOmmm8IbRuVYChokSRJW7a3CP788gH0VLIeeYNAFUBRxK3Q+BR7PMZiB0TcDUx4EIjkKXGtYChomSRJW7K7EP788gIKqFtFxVKcg/XFY6hU0ikFnAIZfC0x7BIjlTZ60iqVAwZXDy18fxI6yRtFxVGND7gKkla8SHeP0DBZg6FXApPuEnFFE8mIUHYDE0+l0mHVWKmadlYrvi2rx8tdF+L6oTnQsxTuiz4Ssz0GyJwJj5gNjbuU2EQWxFOgYk/omYlLfROSXNeKd9cX4fFcF3L6A6FiKVOhPxRjRIU4maRAw4S5g6NWAiQMV6VjcPqJTanJ68d9tR7BwcykKqxyi4yjKFSlV+EfTb0TH+IEO6DsDmHA3kHuu6DAkYywF6rQtxfX4IK8Un++qgMvL1cPppFo82Ki7SWyIyFRg6JXAqBuBpAGn/3jSPJYCnbGmNi8WbzuCD/PKeNbSaRxM+A0MrVXhfVKTvf1eBsN+AeROB/SG8D4/KRpLgbpla0kD/rO1DKv3VqHW4REdR3Z29H4eMVUbw/BMOiB7MjD8F8DgywBLVBiek9SIpUAhEQhI2FbagFV7q7ByTyVK6pyiI8nCqn6L0b/HBuPpgNSh7SUw7BpeW0AhwVKgHlFQ2YJVeyqxam8VdpU3iY4jzGt9N2HWkf8L3QOaI4GcaUC/89t/Rcv6pFdSIJYC9bijjW1YvbcKq/ZWIu9wPbx+7XzL3Zt1GL+t6eZgvPgcoN8soP/5QO/JgNEcmnBEJ8FSoLByef3YUdaILSUN2PrDr6Y2r+hYPWZsbDM+ct1xZp8U0wvIGgNkjQNyZwCJfXsmHNFJsBRIKEmSUFTtwObiBmwpqcfWkgZVHY847WA8oxVIG9FeApljgayxQFRqWDMS/RxLgWSnusWFbSUN2FXehANVDhRVO1BS71Tsfaf3pz8Ba/3+9uMBif2B5EHtB4gzx7b/zu0gkhGWAimC2+fHoZpWHKh2oKS2FcV1TpTUtaKk3omaFrfoeEEmgw6ZcXb0TrAjOyEC2Ql2XJpaj/iEZCAmE+jgvgBEcsFSIMVrdftQ0eRCo9ODBqcXDa0eNBz3341O7w9v86DF5cPx3/QdvVQb9DrE2EyIs5sRF9H+e3yEGbF2M+LtJsRFtP/5x7enRFth0POFn5SLpUBEREG8mzsREQWxFIiIKIilQEREQSwFIiIKYikQEVEQS4GIiIJYCkREFMRSICKiIJYCEREFsRSIiCiIpUBEREEsBSIiCmIpEBFREEuBiIiCWApERBTEUiAioiCWAhERBbEUiIgoiKVARERBLAUiIgpiKRARURBLgYiIglgKREQUxFIgIqIglgIREQWxFIiIKIilQEREQSwFIiIKYikQEVEQS4GIiIJYCkREFMRSICKiIJYCEREFsRSIiCiIpUBEREEsBSIiCmIpEBFREEuBiIiCWApERBTEUiAioiCWAhERBbEUiIgoiKVARERBLAUiIgpiKRARURBLgYiIgv4/dWm8pGkiotQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pzcVnENBaLJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['tweet'] = df['tweet'].str.lower()\n",
        "punctuations_list = string.punctuation\n",
        "def remove_punctuations(text):\n",
        "\ttemp = str.maketrans('', '', punctuations_list)\n",
        "\treturn text.translate(temp)\n",
        "\n",
        "df['tweet']= df['tweet'].apply(lambda x: remove_punctuations(x))\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vSGwkzGtbXdi",
        "outputId": "9a6604e0-be78-4314-cfc6-1b518f310795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  count  hate_speech  offensive_language  neither  class  \\\n",
              "0           0      3            0                   0        3      2   \n",
              "1           1      3            0                   3        0      1   \n",
              "2           2      3            0                   3        0      1   \n",
              "3           3      3            0                   2        1      1   \n",
              "4           4      6            0                   6        0      1   \n",
              "\n",
              "                                               tweet  \n",
              "0   rt mayasolovely as a woman you shouldnt compl...  \n",
              "1   rt mleew17 boy dats coldtyga dwn bad for cuff...  \n",
              "2   rt urkindofbrand dawg rt 80sbaby4life you eve...  \n",
              "3     rt cganderson vivabased she look like a tranny  \n",
              "4   rt shenikaroberts the shit you hear about me ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-578227fb-118f-4e60-be4c-fd3b1725960a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech</th>\n",
              "      <th>offensive_language</th>\n",
              "      <th>neither</th>\n",
              "      <th>class</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>rt mayasolovely as a woman you shouldnt compl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt mleew17 boy dats coldtyga dwn bad for cuff...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt urkindofbrand dawg rt 80sbaby4life you eve...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>rt cganderson vivabased she look like a tranny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt shenikaroberts the shit you hear about me ...</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-578227fb-118f-4e60-be4c-fd3b1725960a')\"\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-578227fb-118f-4e60-be4c-fd3b1725960a 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-578227fb-118f-4e60-be4c-fd3b1725960a');\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-adda25c9-f59d-4f41-89c2-c61eea713bd3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-adda25c9-f59d-4f41-89c2-c61eea713bd3')\"\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-adda25c9-f59d-4f41-89c2-c61eea713bd3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopwords(text):\n",
        "\tstop_words = stopwords.words('english')\n",
        "\n",
        "\timp_words = []\n",
        "\n",
        "\tfor word in str(text).split():\n",
        "\n",
        "\t\tif word not in stop_words:\n",
        "\t\t\tlemmatizer = WordNetLemmatizer()\n",
        "\t\t\tlemmatizer.lemmatize(word)\n",
        "\n",
        "\t\t\timp_words.append(word)\n",
        "\n",
        "\toutput = \" \".join(imp_words)\n",
        "\n",
        "\treturn output\n",
        "\n",
        "\n",
        "df['tweet'] = df['tweet'].apply(lambda text: remove_stopwords(text))\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wVLyPV9hbl6k",
        "outputId": "93f84fde-faf6-4521-bc58-db1f679dab25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  count  hate_speech  offensive_language  neither  class  \\\n",
              "0           0      3            0                   0        3      2   \n",
              "1           1      3            0                   3        0      1   \n",
              "2           2      3            0                   3        0      1   \n",
              "3           3      3            0                   2        1      1   \n",
              "4           4      6            0                   6        0      1   \n",
              "\n",
              "                                               tweet  \n",
              "0  rt mayasolovely woman shouldnt complain cleani...  \n",
              "1  rt mleew17 boy dats coldtyga dwn bad cuffin da...  \n",
              "2  rt urkindofbrand dawg rt 80sbaby4life ever fuc...  \n",
              "3           rt cganderson vivabased look like tranny  \n",
              "4  rt shenikaroberts shit hear might true might f...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4121cd4-c47e-438e-808d-c524687bc503\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech</th>\n",
              "      <th>offensive_language</th>\n",
              "      <th>neither</th>\n",
              "      <th>class</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>rt mayasolovely woman shouldnt complain cleani...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt mleew17 boy dats coldtyga dwn bad cuffin da...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt urkindofbrand dawg rt 80sbaby4life ever fuc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>rt cganderson vivabased look like tranny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rt shenikaroberts shit hear might true might f...</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-c4121cd4-c47e-438e-808d-c524687bc503')\"\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-c4121cd4-c47e-438e-808d-c524687bc503 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-c4121cd4-c47e-438e-808d-c524687bc503');\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-aa368280-6280-41ea-8238-0464e760785f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa368280-6280-41ea-8238-0464e760785f')\"\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-aa368280-6280-41ea-8238-0464e760785f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_2 = df[df['class'] == 2]\n",
        "class_1 = df[df['class'] == 1].sample(n=3500)\n",
        "class_0 = df[df['class'] == 0]\n",
        "\n",
        "sample_df = pd.concat([class_0, class_0, class_0, class_1, class_2], axis=0)\n"
      ],
      "metadata": {
        "id": "EIlJ7EINbuxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = sample_df['class'].value_counts().values.tolist()\n",
        "categories = sample_df['class'].value_counts().index.tolist()\n",
        "\n",
        "plt.pie(values, labels=categories, autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "zCCxBp8fb5rx",
        "outputId": "ba0e30eb-e4df-4f4b-8355-85833399c5e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "features = sample_df['tweet']\n",
        "target = sample_df['class']\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(features,\n",
        "\t\t\t\t\t\t\t\t\t\t\t\ttarget,\n",
        "\t\t\t\t\t\t\t\t\t\t\t\ttest_size=0.2,\n",
        "\t\t\t\t\t\t\t\t\t\t\t\trandom_state=22)\n",
        "X_train.shape, X_test.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BefYEeTBcCF1",
        "outputId": "2fd3eec4-0bca-4c7e-96fb-aa9428b25595"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9562,), (2391,))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "Y_train = to_categorical(Y_train, num_classes=3)\n",
        "Y_test = to_categorical(Y_test, num_classes=3)\n",
        "Y_train.shape, Y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTrvKZcAcRqg",
        "outputId": "a90477de-4703-4644-b0c1-dc751a942703"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9562, 3), (2391, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer()\n",
        "\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "vocab_size = len(tokenizer.word_index) + 1"
      ],
      "metadata": {
        "id": "jT1NlHCacaAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = max(len(seq) for seq in X_train)\n",
        "\n",
        "for x in X_test:\n",
        "    if len(x) > max_length:\n",
        "        print(f\"an outlier detected: {x}\")\n",
        "\n",
        "X_train = pad_sequences(X_train, maxlen = max_length)\n",
        "X_test = pad_sequences(X_test, maxlen = max_length)"
      ],
      "metadata": {
        "id": "syTQayGPkArE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "output_dim = 200\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "    layers.Embedding(vocab_size, output_dim, input_length=max_length),\n",
        "    layers.Bidirectional(layers.LSTM(16)),\n",
        "    layers.Dense(512, activation='relu', kernel_regularizer='l2'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Dropout(0.3),\n",
        "    layers.Dense(3, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "\t\t\toptimizer='adam',\n",
        "\t\t\tmetrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FV49bnW6chsO",
        "outputId": "806ecdcf-2afa-4b6c-9f88-4dfbfd8a1c94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 30, 200)           3940000   \n",
            "                                                                 \n",
            " bidirectional (Bidirection  (None, 32)                27776     \n",
            " al)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               16896     \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 512)               2048      \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3988259 (15.21 MB)\n",
            "Trainable params: 3987235 (15.21 MB)\n",
            "Non-trainable params: 1024 (4.00 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "es = EarlyStopping(patience=3,\n",
        "\t\t\t\tmonitor = 'val_accuracy',\n",
        "\t\t\t\trestore_best_weights = True)\n",
        "\n",
        "lr = ReduceLROnPlateau(patience = 2,\n",
        "\t\t\t\t\tmonitor = 'val_loss',\n",
        "\t\t\t\t\tfactor = 0.5,\n",
        "\t\t\t\t\tverbose = 0)"
      ],
      "metadata": {
        "id": "lBYvpOScdJUb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    batch_size = 64,\n",
        "    epochs=10,\n",
        "    # validation_data=(X_test, Y_test),\n",
        "    # callbacks = [lr, es]\n",
        "\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTdK8uDjdSvN",
        "outputId": "8908fbeb-0e6a-44f2-ca31-2b5aaa330f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "150/150 [==============================] - 21s 79ms/step - loss: 0.7554 - accuracy: 0.7826\n",
            "Epoch 2/10\n",
            "150/150 [==============================] - 11s 72ms/step - loss: 0.1575 - accuracy: 0.9585\n",
            "Epoch 3/10\n",
            "150/150 [==============================] - 12s 79ms/step - loss: 0.0570 - accuracy: 0.9881\n",
            "Epoch 4/10\n",
            "150/150 [==============================] - 12s 81ms/step - loss: 0.0376 - accuracy: 0.9924\n",
            "Epoch 5/10\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.0271 - accuracy: 0.9951\n",
            "Epoch 6/10\n",
            "150/150 [==============================] - 12s 80ms/step - loss: 0.0178 - accuracy: 0.9979\n",
            "Epoch 7/10\n",
            "150/150 [==============================] - 13s 84ms/step - loss: 0.0168 - accuracy: 0.9968\n",
            "Epoch 8/10\n",
            "150/150 [==============================] - 12s 82ms/step - loss: 0.0198 - accuracy: 0.9958\n",
            "Epoch 9/10\n",
            "150/150 [==============================] - 12s 78ms/step - loss: 0.0259 - accuracy: 0.9937\n",
            "Epoch 10/10\n",
            "150/150 [==============================] - 11s 75ms/step - loss: 0.0293 - accuracy: 0.9929\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_df = pd.DataFrame(history.history)\n",
        "history_df.loc[:, ['loss']].plot()\n",
        "history_df.loc[:, ['accuracy']].plot()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "GXQ3KnavdUbX",
        "outputId": "6b26d3b0-7279-4944-d3ac-0fc812705a3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "y_pred_probabilities = model.predict(X_test)\n",
        "y_pred_labels = np.argmax(y_pred_probabilities, axis=1)\n",
        "\n",
        "y_true_labels = np.argmax(Y_test, axis=1)\n",
        "\n",
        "accuracy = np.mean(y_pred_labels == y_true_labels)\n",
        "\n",
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpNRcJTKACV_",
        "outputId": "5f6c00d4-2307-43ca-97ce-80b0dc802583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 1s 5ms/step\n",
            "Accuracy: 89.96%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "input_text = \"This is just a piece of shit\"\n",
        "\n",
        "input_sequence = tokenizer.texts_to_sequences([input_text])\n",
        "input_sequence_padded = pad_sequences(input_sequence, maxlen=max_length, padding='post')\n",
        "\n",
        "predicted_probabilities = model.predict(input_sequence_padded)\n",
        "predicted_label = np.argmax(predicted_probabilities)\n",
        "\n",
        "print(f\"Predicted Class: {predicted_label}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFZTCuM1Eklj",
        "outputId": "49ddd36b-ab6f-4b67-e197-541fa090a5be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
            "Predicted Class: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I5OyL6sMFDdU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}