Tweet_Emotion_Recognition.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KaziTanvir/Tweet-Emotion-Recognition-using-LSTM/blob/main/Tweet_Emotion_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp7D0ktn5eiG"
      },
      "source": [
        "## Tweet Emotion Recognition: Natural Language Processing with TensorFlow\n",
        "\n",
        "---\n",
        "\n",
        "Dataset: [Tweet Emotion Dataset](https://github.com/dair-ai/emotion_dataset)\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## Task 1: Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cprXxkrMxIgT"
      },
      "source": [
        "## Task 2: Setup and Imports\n",
        "\n",
        "1. Installing Hugging Face's nlp package\n",
        "2. Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5agZRy-45i0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "193c8f5a-01e9-4a72-e28f-f42410ce9a9a"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nlp in /usr/local/lib/python3.7/dist-packages (0.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlp) (1.21.6)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (2.23.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nlp) (1.3.5)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from nlp) (3.0.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from nlp) (0.3.5.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from nlp) (3.8.0)\n",
            "Requirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (6.0.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from nlp) (4.64.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2.10)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->nlp) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d131a7ca-96ca-45b3-8cdd-7490a3c41077"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nlp\n",
        "import random\n",
        "\n",
        "\n",
        "def show_history(h):\n",
        "    epochs_trained = len(h.history['loss'])\n",
        "    plt.figure(figsize=(16, 6))\n",
        "\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('accuracy'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation')\n",
        "    plt.ylim([0., 1.])\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('loss'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "def show_confusion_matrix(y_true, y_pred, classes):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
        "\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sp = plt.subplot(1, 1, 1)\n",
        "    ctx = sp.matshow(cm)\n",
        "    plt.xticks(list(range(0, 6)), labels=classes)\n",
        "    plt.yticks(list(range(0, 6)), labels=classes)\n",
        "    plt.colorbar(ctx)\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "print('Using TensorFlow version', tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow version 2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JsBpezExIga"
      },
      "source": [
        "## Task 3: Importing Data\n",
        "\n",
        "1. Importing the Tweet Emotion dataset\n",
        "2. Creating train, validation and test sets\n",
        "3. Extracting tweets and labels from the examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YHOvjAu5eiL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cce195d-59c5-436d-e443-e765bcfc6d16"
      },
      "source": [
        "dataset = nlp.load_dataset('emotion')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:nlp.builder:Using custom data configuration default\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0h541FxIgc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3425f9d4-081b-4c1f-feaf-7faa6ea5fb51"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 16000),\n",
              " 'validation': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000),\n",
              " 'test': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000)}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "train = dataset['train']\n",
        "test = dataset['test']\n",
        "val = dataset['validation']\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "def get_tweets(data):\n",
        "  tweets = [x['text'] for x in data]\n",
        "  labels = [x['label'] for x in data]\n",
        "  return tweets , labels"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR"
      },
      "source": [
        "tweets, labels = get_tweets(train)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3f490c6-fccf-4558-cbc8-8468c9c48bb8"
      },
      "source": [
        "tweets[10],labels[10]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i feel like i have to make the suffering i m seeing mean something',\n",
              " 'sadness')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcAflLv6xIgp"
      },
      "source": [
        "## Task 4: Tokenizer\n",
        "\n",
        "1. Tokenizing the tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfX5-ResxIgq"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif"
      },
      "source": [
        "tokenizer = Tokenizer(num_words = 10000, oov_token = '<UNK>')\n",
        "tokenizer.fit_on_texts(tweets)  "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.texts_to_sequences([tweets[10]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZT1FG8itN6q",
        "outputId": "01e679fb-4bcd-43d5-c61c-e54e1e098b26"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 3, 14, 2, 21, 5, 80, 6, 733, 2, 93, 544, 304, 84]]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets[10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "sa1UMiMutN2H",
        "outputId": "b1d01e23-786a-4ed3-dc88-0001c22c7a2f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i feel like i have to make the suffering i m seeing mean something'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3Bqm7b2xIgu"
      },
      "source": [
        "## Task 5: Padding and Truncating Sequences\n",
        "\n",
        "1. Checking length of the tweets\n",
        "2. Creating padded sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLvf_WFZxIgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "c62a84c2-340f-4f13-dcb2-4f2a7e9b7d45"
      },
      "source": [
        "lengths = [len(t.split(' ')) for t in tweets]\n",
        "plt.hist(lengths, bins = len(set(lengths)))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  8.,  45., 310., 488., 539., 625., 626., 629., 613., 636., 654.,\n",
              "        655., 608., 622., 609., 603., 559., 535., 530., 510., 452., 430.,\n",
              "        382., 385., 330., 327., 336., 317., 238., 208., 200., 200., 167.,\n",
              "        157., 141., 144., 120., 117., 117.,  88.,  96.,  72.,  66.,  66.,\n",
              "         65.,  52.,  37.,  43.,  30.,  32.,  31.,  26.,  30.,  20.,  23.,\n",
              "         14.,   9.,  10.,   4.,   4.,   4.,   1.,   4.,   1.]),\n",
              " array([ 2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14.,\n",
              "        15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,\n",
              "        28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.,\n",
              "        41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n",
              "        54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66.]),\n",
              " <a list of 64 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "max_len = 60 "
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9J_Iemf5eiq"
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0"
      },
      "source": [
        "def get_squences(tokenizer, tweets):\n",
        "  sqequences = tokenizer.texts_to_sequences(tweets)\n",
        "  padded = pad_sequences(sqequences, truncating='post', padding = 'post',maxlen = max_len)\n",
        "  return padded"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7"
      },
      "source": [
        "pts = get_squences(tokenizer, tweets)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pts[10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSx1E8zUvmTf",
        "outputId": "ba268ca4-0101-4a1c-8118-a3293b3ddfd6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2,   3,  14,   2,  21,   5,  80,   6, 733,   2,  93, 544, 304,\n",
              "        84,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BURhOX_KxIg8"
      },
      "source": [
        "## Task 6: Preparing the Labels\n",
        "\n",
        "1. Creating classes to index and index to classes dictionaries\n",
        "2. Converting text labels to numeric labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SufT2bpD5ejE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90d68abf-ed02-497c-c98c-096cf4404fae"
      },
      "source": [
        "classes = set(labels)\n",
        "print(classes)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'surprise', 'joy', 'anger', 'love', 'sadness', 'fear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "630664d9-57c5-4cc4-a20d-3444f678876c"
      },
      "source": [
        "plt.hist(labels , bins=11)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNLF6rXL5ejN"
      },
      "source": [
        "class_to_index = dict((c,i) for i,c in enumerate(classes))\n",
        "index_to_class = dict((v,k) for k,v in class_to_index.items())"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc21b378-3a2e-4686-8315-105122db0324"
      },
      "source": [
        "class_to_index"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'surprise': 0, 'joy': 1, 'anger': 2, 'love': 3, 'sadness': 4, 'fear': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a884a89-45d8-44ac-bd23-9c03c5361cbe"
      },
      "source": [
        "index_to_class"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'surprise', 1: 'joy', 2: 'anger', 3: 'love', 4: 'sadness', 5: 'fear'}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR"
      },
      "source": [
        "names_to_ids = lambda labels: np.array([class_to_index.get(x) for x in labels])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef7685f3-8927-47d5-c3f4-7fecbc90912c"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[10])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-v0Mnh8xIhP"
      },
      "source": [
        "## Task 7: Creating the Model\n",
        "\n",
        "1. Creating the model\n",
        "2. Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpewXxPQ5eji"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Embedding(10000,16,input_length = max_len),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20, return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20)),\n",
        "    tf.keras.layers.Dense(6,activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    optimizer = 'adam',\n",
        "    metrics = ['accuracy']\n",
        ")\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EG3qlsvcy71K",
        "outputId": "a4f18230-8cd3-4194-e2fd-0392738ca045"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 60, 16)            160000    \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 60, 40)           5920      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 40)               9760      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense (Dense)               (None, 6)                 246       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "kyGzNdeGy7pg",
        "outputId": "225911fb-4c3b-4608-b086-fc0dba4b62a5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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F0aNH3d3dGW3c3Nz60OEw357DBEJqQF29enXWrFkGdnLo0KFPPvlE/6X0fK9hSEjIgwcPPDw85HI5y7Isy7a1tSkUisePH0ul0j50OMy35zCBr2oxgU4/9tvf5s+fX1dXN5Br1B+fz5dIJBKJ5Mknn+xzJ9ieQxuOpExAKBQa2IPul6URX7Qsy2ZmZh48eNBYHXbn+PHjfX4stufQhpDqhfb29tjYWBcXF4lEMn78+PT0dEJIYmKiTCbj8XiTJk1ycHAQCoUymczPzy8gIMDZ2VksFltaWr799tua/RQUFHh5eclkMolEEhAQcOHCBd2rIISwLLt7925PT0+RSCSXy9966y3NDnUsvXDhgouLC8MwH374ISEkOTlZJpNJpdLs7Ox58+ZZWFg4OTmlpqZqFvD+++97enpKJBJbW9vRo0e///774eHh/bA5tcP2hM5YYNnQ0NDQ0NAem23cuFEkEmVlZdXU1GzevJnH4129epVl2W3bthFCrly50tTUVFlZOXfuXELIyZMnKyoqmpqaoqOjCSE5OTlcJ4GBge7u7r/88otKpbp9+/bUqVPFYvG9e/d0r2LLli0Mw+zdu7empkahUCQlJRFCbty4wT1K91Luh3AOHDigbkwI+e677+rq6srLywMCAmQymVKp5Jbu2LGDz+dnZ2crFIpr1645ODjMnDnTiNuQZVnNc1Isy65bt+7WrVuaDbA9e7U9hzyEFMvqNyCam5ulUmlkZCQ3qVAoRCJRVFQU++8XVUNDA7fo008/JYSoX3g//vgjISQtLY2bDAwMnDBhgrrb3NxcQsjGjRt1rEKhUEil0jlz5qgfxb1Xcy8b3UvZbl5Uzc3N3CT3CiwoKOAmp0yZ8vTTT6u7+u///m8ej9fa2mqUbcjx8PDo9E6pNaSwPRFSHHzc01d+fr5CofDx8eEmJRKJo6NjXl5e15ZmZmaEkLa2Nm6SO2OiUqm0duvr6yuXy7mXVnerKCgoUCgUgYGBWnvQvbRHXLXq8lpaWliNq1ft7e1CoZDP5/et8+50OpLSp0Jsz2ELIaWvpqYmQsjWrVvVt/YUFRUpFArDexYKhdyY7m4VJSUlhBA7OzutD9e9tLdeeumla9euZWdnNzc3//TTT8ePH3/55Zf79UWVmJiozhGjGObbc+hBSOmLG7UJCQmaB6KXLl0ysNu2trbq6moXFxcdqxCLxYSQ1tZWrT3oXtpbcXFxs2fPXrFihYWFxcKFC8PDw3XcQ0QhbM+hByGlL+7Sktabkg1x+vTpjo4OPz8/Havw8fHh8Xhnz57V2oPupb11586dwsLCiooKlUpVXFycnJxsZWVllJ51e/To0cqVKw3vB9tz6EFI6UssFq9cuTI1NTU5Obm+vr69vb2kpOTRo0d96EqpVNbV1bW1tV2/fj06OtrV1XXFihU6VmFnZxcSEpKVlXX48OH6+vrc3FzNG210L+2t119/3cXFpbGxsc899BbLss3NzUePHrWwsOhbD9ieQ1z/nZMfRPS8ktLa2hoTE+Pi4iIQCLihfOfOncTERO5fOtzc3M6fP79r1y65XE4IcXBw+OKLL9LS0hwcHAghVlZWqampLMumpKTMmjXL3t5eIBDY2NgsWrSoqKhI9ypYlm1oaFi1apWNjY25ufn06dNjY2MJIU5OTjdv3tS99MCBA46OjoQQqVQaFBSUlJTEVTt27NjCwsKDBw9y0eDq6spdtv/+++9tbGzUw0MoFI4bN+7o0aNG2YbHjh3remlPbevWrSzLYnvqvz2HCYbFPyIREhYWRgjJzMw0dSGml5ycfP/+/YSEBG5SqVT+8Y9/TE5OrqmpkUgkOh6IbagVtqfh8L978H/Kysqio6M1T+KYmZm5uLioVCqVSqX7RQVdYXsaBc5Jwf+RSCRCofDw4cOPHz9WqVSlpaWHDh2KjY2NjIzs8wmj4Qzb0ygQUvB/5HL5qVOnbt++/eSTT0okEm9v75SUlF27dnH3fENvYXsaBT7uwX8ICAj45z//aeoqhg5sT8PhSAoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKohpACAaggpAKAaQgoAqIZvQfiXkpKSjIwMU1cxiHG/BIVtaCwlJSVOTk6mroIKCKl/uXz5ckREhKmrGPSwDY0oNDTU1CVQAd9xDsYUHh5OcDwFRoVzUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1gakLgMHt7Nmzly9fVk/m5eURQuLj49Vzpk2bNmPGDBNUBkMFw7KsqWuAQeyf//znCy+8IBQKebzOR+UdHR0qlerUqVNz5swxSW0wNCCkwCDt7e0ODg5VVVVal1pZWZWXlwsEOGCHvsM5KTAIn89fsmSJmZlZ10VmZmbLli1DQoGBEFJgqEWLFimVyq7zlUrlokWLBr4eGGLwcQ+MwNXVtbi4uNNMJyen4uJihmFMUhIMGTiSAiNYunSpUCjUnGNmZvbaa68hocBwOJICI7h79663t3enmbdu3fLx8TFJPTCUIKTAOLy9ve/evaue9PLy0pwE6DN83APjWL58ufoTn1AofO2110xbDwwZOJIC4yguLnZzc+OGE8MwDx48cHNzM3VRMBTgSAqMw8XFZfLkyTwej2GYKVOmIKHAWBBSYDTLly/n8Xh8Pn/ZsmWmrgWGDnzcA6OpqKh44oknCCG//fabg4ODqcuBoYI1kvT0dFM/FQCgRXp6urGyxcj/V4WoGtoiIiLWr1//zDPPdNfg7NmzDMM899xzA1kV0CYiIsKIvRk5pMLDw43bIVAlIiLimWee0bGX586dSwixsLAYwKKAOlSHFAxziCcwOlzdAwCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKohpACAaggpAKAaQgoAqIaQAgCqDWhITZkyhc/nT5w4sbsGX3/9tVwuP3HiRNdFq1atGjFiBMMwOTk5PTY2iv7uf8+ePfb29gzDfPzxx50Wffvtt5s2bdLRwCi+/PLL+Pj49vZ2I/Z59OhRd3d3RoNAILC1tX3++eePHTum2RL7msPta83t5ujouHTp0u66unnzZmRk5OjRo0Uika2t7YQJE9577z1uUWRkJKPTV199pbmid955R+sq9u3bxzAMj8fz8vI6d+5cf4yTXhnQkLp69eqsWbN0NGC7/y7jQ4cOffLJJ3o2Nor+7n/jxo0XL17sOn/btm379+/fvHlzdw2MJSgoSCwWBwYG1tbWGqvPkJCQBw8eeHh4yOVy7msVKyoq0tPTf/vtt5CQEM3vRMS+Jhr7WnO7lZWVff7551r7uXXrlr+/v6Oj4+nTp+vq6i5evDh37twzZ86oG5w6daq2tlalUj169IgQEhQUpFQqm5qaysvL//CHPxCNHUQIOXTokEql6rSK9vb2/fv3E0Jmz56dl5f33HPP9cc46RUTfNzT8dPb8+fPr6ure+WVV/Tpp1eN9dHc3Ozv799//etj165daWlpGRkZI0aM0PMhncrulXXr1k2YMOGll15qa2vrWw89srKyCgwM/OCDDwghGRkZ6vnY133Y13v27LG0tExMTHRzcxOLxU8++eS7774rkUi4pQzDPPvss3K5XCAQqOcIhUKpVGpnZzdp0iTNriZNmlRWVnb8+PFOqzh69OioUaM6zRyAcaKDCUJK/ROSvaUj3Yzi8OHD5eXl/boK3QoKCt55550//elPYrFY/0cZWHZcXFxOTk5iYmKfe9AH9wtX+r8VY19rVVVVVVdXV11drZ5jZmam/pSampoqlUq7e+zq1atffvll9WRUVBQh5KOPPurUbN++fRs2bOj68IEZJ1qZIKQKCgq8vLxkMplEIgkICLhw4QI3/8KFCy4uLgzDfPjhh9wclmV3797t6ekpEonkcvlbb72l7qRT4z//+c9SqXTEiBHl5eUbNmwYNWpUfn5+e3t7bGysi4uLRCIZP3685meNI0eOTJ48WSwWy2QyNze3d999d/369Rs2bCgsLGQYZsyYMVqL2bdv37hx40QikZWV1YIFC/Ly8rhFycnJMplMKpVmZ2fPmzfPwsLCyckpNTVVvbrz5897e3vL5XKxWOzr6/uPf/xD65bZv38/y7JBQUHdbbqzZ88+/fTTUqnUwsLC19e3vr6+U9mJiYkymYzH402aNMnBwUEoFMpkMj8/v4CAAGdnZ7FYbGlp+fbbb2v2aWVlNWPGjMTExH79yJObm0sImTFjBjeJfd3jvtZqypQpTU1Ns2fP/uGHH3r1wK5mz549bty406dP5+fnq2f+8MMPCoXihRde6Np+YMaJdsb6RQduWPTYLDAw0N3d/ZdfflGpVLdv3546dapYLL537x639NdffyWEHDhwgJvcsmULwzB79+6tqalRKBRJSUmEkBs3bnTXmBCybt26AwcOLFy48O7duxs3bhSJRFlZWTU1NZs3b+bxeFevXmVZNiEhgRCyc+fOqqqq6urqv/zlL0uWLGFZNiQkxMPDQ11qp/5jY2PNzMyOHDlSW1ubm5vr5+dna2tbVlamufbvvvuurq6uvLw8ICBAJpMplUpuaWZmZlxcXHV1dVVV1bRp02xsbLj59+/fJ4R89NFH3KS7u7u3t7fm5tJs0NjYaGFhER8f39zcXFZWtnDhwoqKiq5lb9u2jRBy5cqVpqamyspK7kvHT548WVFR0dTUFB0dTQjJycnRXMumTZs0N6wORL9fAdE8J6VQKL755htXV9cXXnihsbGxu82Lfd1pu2mlUCgmT57MvXK9vb3j4+Orqqq0tuTOSb366qvd7aBffvmF+wy+fv169fzg4OCUlJSGhgZCSGBgYKdHGX2c6MkEITVhwgT1JPcGu3HjRm5Sc6woFAqpVDpnzhx1Y+79SvfAbW5u5iabm5ulUmlkZCQ3qVAoRCJRVFSUUqm0tLScNWuWutu2tjbu/UHHwFUoFObm5ureWJb98ccfCSHbt2/XunbuNVZQUNB1C7z//vuEkPLycrZLBjEM88orr2g21mxw+/ZtQshXX33VqUOtIdXQ0MBNfvrpp4SQW7duaZadlpam2cNf//pXQshnn33WtdpO9A+pTu+Fvr6+n376aWtrq7oN9nWnfc3qEVIsyyqVyg8++MDLy4vbsPb29mfOnOnaTJ+Qqq2tlclkVlZWCoWCZdnCwkInJ6fW1tbuQsro40RPJr5PytfXVy6Xc1HVSUFBgUKhCAwM7FvP+fn5CoXCx8eHm5RIJI6Ojnl5ebm5ubW1tS+++KK6JZ/PX7dune7e7ty509jYqH4TI4RMmTLFzMzsypUrWtubmZkRQrpeOiH/PiXX9YIuN5R1nFNwd3e3t7dfunRpXFzcw4cPdRfcqRL1+U5u7Z0K41b6+PFjPfvUh/rFplKpSkpK3njjjejo6PHjx1dWVnZtjH2tP6FQGB0dfffu3cuXLy9YsKC8vDwsLKympqYPXcnl8sWLF9fU1KSlpRFCEhISoqKiuKejVX+ME32Y/mZOoVCodQeXlJQQQuzs7PrWbVNTEyFk69at6ptEioqKFApFfX09IcTS0rJXvXFnfM3NzTVnWlpacu85PTp58uTMmTPt7OxEIlGnU0JqLS0thBCRSNRdJxKJ5Pvvv58+ffqOHTvc3d0jIyObm5t78Ry6x10e4gowOoFAMGrUqJUrV+7Zsyc/P3/nzp1d22Bf98HUqVP//ve/r127tqKi4vTp033rhDt9/vHHH9fW1mZmZq5Zs0ZH434dJzqYOKTa2tqqq6tdXFy6LuKuerS2tvatZ27EJyQkaB43Xrp0aeTIkYQQre/nOnADvdMwra2tdXJy6vGxxQFY3i4AAB26SURBVMXFwcHBjo6OV65cqauri4+P19qMGwG6b5l76qmnTpw4UVpaGhMTk56evmfPnl48h+4plUp1Af3H19eXEPLzzz93XYR9rcO5c+e4M2uEkJCQkE43ASxbtowQolAo9Omqq4kTJ06bNu3HH39cvXp1WFiYlZWVjsYDM066MnFInT59uqOjw8/Pr+siHx8fHo939uzZvvXMXcxS37Ks5ubmZm1tferUqV715uPjY25u/tNPP6nnXLlyRalUdrr3RKtbt26pVKqoqCh3d3exWNzdxXXujuS6urru+iktLeVe4XZ2djt37vTz89P6gu8DbqUODg5G6a07165dI4R4enp2XYR9rcO1a9dkMhn3d2tra6edzl2bGz9+vD5dacUdTGVlZb3xxhu6Ww7MOOnKBCGlVCrr6ura2tquX78eHR3t6uq6YsWKrs3s7OxCQkKysrIOHz5cX1+fm5t78OBB/dciFotXrlyZmpqanJxcX1/f3t5eUlLy6NEjkUi0efPmc+fORUdH//bbbx0dHQ0NDdyOt7a2Li0tffjwYUNDQ6dPoGKxeMOGDceOHfv888/r6+tv3bq1du3aJ554YvXq1T1Wwh0nfvvtty0tLffv3+/u1IZUKnV3d+c++GhVWlq6Zs2avLw8pVJ548aNoqKiadOm6S5bT9xKuSMdI2pubu7o6GBZtrS0NCUlZevWrba2tlpfCdjXWqlUqsePH585c0YdUoSQ4ODgjIyM2traurq67OzsP/7xj6+++qohIRUeHm5raxscHOzu7q67ZT+Nk54Z6wy8nlf3UlJSZs2aZW9vLxAIbGxsFi1aVFRUxC06cOCAo6MjIUQqlQYFBbEs29DQsGrVKhsbG3Nz8+nTp8fGxhJCnJycbt682alxfHw8dxTq7Ox85MgRrsPW1taYmBgXFxeBQMC9DO7cucMt+vDDD319fcVisVgs/t3vfpeUlMSy7PXr111dXSUSyfTp07du3dqpmI6Ojt27d48dO1YoFFpZWQUHB+fn53O9JSUlcecUx44dW1hYePDgQe6HfF1dXbm7K2JiYqytrS0tLcPCwribcTw8PNavX8+9KclksoULF7IsGx0dLRQKuUstLMvu3btXs8HDhw/9/f2trKz4fP7IkSO3bNnS1tbWqexNmzZxlbi5uZ0/f37Xrl1yuZwQ4uDg8MUXX6SlpXEdWllZpaamqnfK/PnzR40axQWKbqSnqzbHjh3remlPJBKNHTs2KiqquLgY+1rrvta63dSOHTvGNTt16lRERISHh4dIJDIzM/P09IyLi2tpadHcBfX19c8995y1tTUhhMfjjRkzZseOHV13kK2t7euvv87NfPvtty9evMj9rd4aPB7P29v7/Pnz/TFOemWgQwp0uH//vkAgUL/wBkZlZaVYLN6zZ48+jY07+IYzk+xrQ5hwnJj+6h6ojRkzZvv27du3b29sbBywlcbFxU2cOJG7yRMGjEn2tSFMOE4QUnTZtGlTWFhYZGSknmdVDbRv376cnJyvv/66z/9QCX02wPvaEKYdJwgp6uzYsSM6Olrr/UTGlZ2d3draeubMGd0XnqH/DNi+NoTJxwnDGunfBTMyMiIiIozVG9CJYZj09PTw8HBTFwJUM+44wZEUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFBNYNzuuvvmeRgyIiIiIiIiTF0FDCNG+6qWkpKSixcvGqUrGLy4H1/q8XdHYMjz9/fX50fA9GG0kAIghHBfIZSRkWHqQmDowDkpAKAaQgoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKohpACAaggpAKAaQgoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKohpACAaggpAKAaQgoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqAlMXAINbZWVlfX29erKpqYkQ8uDBA/UcCwsLW1tbE1QGQwXDsqypa4BB7PDhw6tWrdLR4NChQ7///e8HrB4YehBSYJCamhoHBweVSqV1qVAofPz4sZWV1QBXBUMJzkmBQaysrObOnSsQaDlvIBAI5s2bh4QCAyGkwFBLly5tb2/vOr+9vX3p0qUDXw8MMfi4B4ZqaWmxsbFRKBSd5kskksrKSqlUapKqYMjAkRQYSiwWBwcHC4VCzZlCoTAkJAQJBYZDSIERLF68uNO5c5VKtXjxYlPVA0MJPu6BEbS1tdnb29fU1KjnWFpalpeXdzq8AugDHEmBEQgEgsjISDMzM25SKBQuXrwYCQVGgZAC41i0aJFSqeT+VqlUixYtMm09MGTg4x4YB8uyTk5OpaWlhBBHR8fS0lKGYUxdFAwFOJIC42AYZunSpWZmZkKhcPny5UgoMBaEFBgN94kP1/XAuP7jvxkuXbq0b98+U5UCQ4C5uTkh5L333jN1ITCIvfnmm88884x68j+OpH799desrKwBLwmGDldXV1dXV62LsrKySkpKBrgeGHSysrJ+/fVXzTla/i80MzNzoOqBoaawsJAQ4uHh0XURwzBvvPFGeHj4gBcFg0nXs5n40jswJq3xBGAInDgHAKohpACAaggpAKAaQgoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKr1OqSmTJnC5/MnTpzYXYOvv/5aLpefOHGi66JVq1aNGDGCYZicnJweGxtFf/e/Z88ee3t7hmE+/vhj/R/V0dGRkJDg7+/fXYNvv/1206ZNfevc6LRW++WXX8bHx2v9dfU+O3r0qLu7O6NBIBDY2to+//zzx44d02yJMcbhxonmdnN0dNTx0/Y3b96MjIwcPXq0SCSytbWdMGGC+usJIyMjGZ2++uorzRW98847Wlexb98+hmF4PJ6Xl9e5c+eMMk56HVJXr16dNWuWjgY6ftnh0KFDn3zyiZ6NjaK/+9+4cePFixd79ZD79+8/99xzb775ZtffJeds27Zt//79mzdv7kPnRtddtUFBQWKxODAwsLa21ljrCgkJefDggYeHh1wuZ1mWZdmKior09PTffvstJCQkPT1d3RJjjGiME83tVlZW9vnnn2vt59atW/7+/o6OjqdPn66rq7t48eLcuXPPnDmjbnDq1Kna2lqVSvXo0SNCSFBQkFKpbGpqKi8v/8Mf/kA0dhAh5NChQ51+DpYQ0t7evn//fkLI7Nmz8/LynnvuOaOMkz5+n5SOr9mfP39+XV2dnv30qrE+mpubAwMD1TvV6P0b6ObNm9u3b1+7dm1TU5PWwb1r1660tLSbN2+KxWI9++z0lI1Id7Xr1q178ODBSy+9dO7cOYGgX76YzMrKKjAw8IMPPnjxxRczMjIiIiK4+RhjfRgne/bssbS0TExM5CaffPLJd999NyQkhJtkGObZZ5+VSqXq9gzDCIVCoVAolUonTZqk2dWkSZOuXbt2/PjxsLAwzflHjx4dNWpUUVGR5kzDx0kfz0n1+Xcf+/tHRA4fPlxeXt6vqzDEhAkTjh49umTJEpFI1HVpQUHBO++886c//Un/kUf68ynrrpYQEhcXl5OTox73/cTNzY0Qov9b8ZAfY30bJ1VVVXV1ddXV1eo5ZmZm6k+pqampmgnVyerVq19++WX1ZFRUFCHko48+6tRs3759GzZs6PpwA8dJH0OqoKDAy8tLJpNJJJKAgIALFy5w8y9cuODi4sIwzIcffsjNYVl29+7dnp6eIpFILpe/9dZb6k46Nf7zn/8slUpHjBhRXl6+YcOGUaNG5efnt7e3x8bGuri4SCSS8ePHax7zHzlyZPLkyWKxWCaTubm5vfvuu+vXr9+wYUNhYSHDMGPGjNFazL59+8aNGycSiaysrBYsWJCXl8ctSk5OlslkUqk0Ozt73rx5FhYWTk5Oqamp6tWdP3/e29tbLpeLxWJfX99//OMffdt0Ouzfv59l2aCgoO4anD179umnn5ZKpRYWFr6+vvX19Z2ecmJiokwm4/F4kyZNcnBwEAqFMpnMz88vICDA2dlZLBZbWlq+/fbbxirYyspqxowZiYmJ/fqRJzc3lxAyY8YMbhJjrMdxotWUKVOamppmz579ww8/9OqBXc2ePXvcuHGnT5/Oz89Xz/zhhx8UCsULL7zQtb2h44TVwO0etieBgYHu7u6//PKLSqW6ffv21KlTxWLxvXv3uKXcl6gfOHCAm9yyZQvDMHv37q2pqVEoFElJSYSQGzdudNeYELJu3boDBw4sXLjw7t27GzduFIlEWVlZNTU1mzdv5vF4V69eZVk2ISGBELJz586qqqrq6uq//OUvS5YsYVk2JCTEw8NDXWqn/mNjY83MzI4cOVJbW5ubm+vn52dra1tWVqa59u+++66urq68vDwgIEAmkymVSm5pZmZmXFxcdXV1VVXVtGnTbGxsuPn3798nhHz00Uc9bjdNU6dOnTBhQqeZ7u7u3t7emnM0O29sbLSwsIiPj29ubi4rK1u4cGFFRUXXp7xt2zZCyJUrV5qamiorK+fOnUsIOXnyZEVFRVNTU3R0NCEkJyfH8Go5mzZt0tyhOhBC0tPTe2ymeU5KoVB88803rq6uL7zwQmNjo7rNMB9jXcdJp+2mlUKhmDx5Mveq9/b2jo+Pr6qq0tqSOyf16quvdreDfvnllw8++IAQsn79evX84ODglJSUhoYGQkhgYGCnRxkyTvoYUppDlnuj27hxIzepuc8UCoVUKp0zZ466Mfe+oXsANTc3c5PNzc1SqTQyMpKbVCgUIpEoKipKqVRaWlrOmjVL3W1bWxuX0zoGkEKhMDc3V/fGsuyPP/5ICNm+fbvWtXNjvaCgoOsWeP/99wkh5eXlrPFCqrGxkWGYV155RXOmZue3b98mhHz11VedutIaUg0NDdzkp59+Sgi5deuW5lNOS0szsFq1v/71r4SQzz77rMdO9A+pTu+jvr6+n376aWtrq7rNcB5jWscJq0dIsSyrVCo/+OADLy8vbsPa29ufOXOmazN9Qqq2tlYmk1lZWSkUCpZlCwsLnZycWltbuwspQ8aJEe6T8vX1lcvlXFR1UlBQoFAoAgMD+9Zzfn6+QqHw8fHhJiUSiaOjY15eXm5ubm1t7Ysvvqhuyefz161bp7u3O3fuNDY2qt9MCCFTpkwxMzO7cuWK1vZmZmaEkK6XMMi/T8kZ9wI8Nxx1nBdwd3e3t7dfunRpXFzcw4cP9eyWexZtbW3cJFe51ifVN1zBjx8/NlaHhBD1i02lUpWUlLzxxhvR0dHjx4+vrKzs2ni4jbEex4kOQqEwOjr67t27ly9fXrBgQXl5eVhYWE1NTR+6ksvlixcvrqmpSUtLI4QkJCRERUVxT0crQ8aJcW7mFAqFWjc09ztrdnZ2feu2qamJELJ161b1zRpFRUUKhaK+vp4QYmlp2aveuDOv3K9XqllaWnLZ36OTJ0/OnDnTzs5OJBIZ8bSOWktLCyGku1PUhBCJRPL9999Pnz59x44d7u7ukZGRzc3NRi+jtyQSCfl38UYnEAhGjRq1cuXKPXv25Ofn79y5s2ub4TbGehwn+pg6derf//73tWvXVlRUnD59um+dcKfPP/7449ra2szMzDVr1uhobMg4MUJItbW1VVdXu7i4dF3EXX1obW3tW8/cyEtISNA89rt06dLIkSMJIVrfV3XgBlyn4VJbW+vk5NTjY4uLi4ODgx0dHa9cuVJXVxcfH9+rVeuD24u6j86eeuqpEydOlJaWxsTEpKen79mzx+hl9JZSqST/Lr7/+Pr6EkJ+/vnnrouG2xjTZ5yonTt3jjuzRggJCQlRH1Bzli1bRgjp7n69Hk2cOHHatGk//vjj6tWrw8LCrKysdDQ2ZJwYIaROnz7d0dHh5+fXdZGPjw+Pxzt79mzfeuYuSKlvHVZzc3OztrY+depUr3rz8fExNzf/6aef1HOuXLmiVCo73QOi1a1bt1QqVVRUlLu7u1gs7o+L3NxdxTruuCktLeVepXZ2djt37vTz89P6oh1gXMEODg79upZr164RQjw9PbsuGm5jrMdxounatWsymYz7u7W1tdOA4a7NjR8/Xp+utOIOprKyst544w3dLQ0ZJ30MKaVSWVdX19bWdv369ejoaFdX1xUrVnRtZmdnFxISkpWVdfjw4fr6+tzc3IMHD+q/FrFYvHLlytTU1OTk5Pr6+vb29pKSkkePHolEos2bN587dy46Ovq3337r6OhoaGjgdoC1tXVpaenDhw8bGho6fQIVi8UbNmw4duzY559/Xl9ff+vWrbVr1z7xxBOrV6/usRLuOPHbb79taWm5f/9+d6cYDCGVSt3d3XX8EHlpaemaNWvy8vKUSuWNGzeKioqmTZtGdD7lAcAVzB3pGFFzc3NHRwfLsqWlpSkpKVu3brW1tdX6ShhuY6zHccJRqVSPHz8+c+aMOqQIIcHBwRkZGbW1tXV1ddnZ2X/84x9fffVVQ0IqPDzc1tY2ODjY3d1dd0uDxonmUa6eV/dSUlJmzZplb28vEAhsbGwWLVpUVFTELTpw4ICjoyMhRCqVBgUFsSzb0NCwatUqGxsbc3Pz6dOnx8bGEkKcnJxu3rzZqXF8fDx3NOjs7HzkyBGuw9bW1piYGBcXF4FAwA3HO3fucIs+/PBDX19fsVgsFot/97vfJSUlsSx7/fp1V1dXiUQyffr0rVu3diqmo6Nj9+7dY8eOFQqFVlZWwcHB+fn5XG9JSUncub2xY8cWFhYePHjQwsKCEOLq6srdXRETE2NtbW1paRkWFsbdFOPh4bF+/XruzUEmky1cuLDHTXfp0qVnn332iSee4Da+o6Ojv7//2bNnuaXR0dFCoZC7XMKy7N69ezU7f/jwob+/v5WVFZ/PHzly5JYtW9ra2jo95U2bNnHPws3N7fz587t27ZLL5YQQBweHL774Ii0tjevQysoqNTXVwGo58+fPHzVqFBcoupGeru4dO3as66U9kUg0duzYqKio4uJirhnGWKdxonW7qR07doxrdurUqYiICA8PD5FIZGZm5unpGRcX19LSorkL6uvrn3vuOWtra0IIj8cbM2bMjh07uu4gW1vb119/nZv59ttvX7x4kftbvTV4PJ63t/f58+eNMk76ElLQT+7fvy8QCNQvHvpVVlaKxeI9e/bo07jHkAI9Dbdxgq9qociYMWO2b9++ffv2xsZGU9eil7i4uIkTJ3I3iMKAGW7jBCFlNHl5eTq+6SIyMlKfTjZt2hQWFhYZGdnf/7NqeLX79u3Lycn5+uuv+/yPnNBnAzZODGf4OOmXf14fnry8vFhj/Avbjh07Tp06tXPnzl27dhneW3cMrDY7O7u1tfXMmTN8Pt+IVYH+BmacGMgo44TRHKnct2EY5ZUG0AnDMOnp6eHh4aYuBKjWdZzg4x4AUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUE3LV7WEhYUNfB0wHCQkJGRmZpq6Chhk/iOknJ2dQ0NDTVUKDAHcD6Vo/jqmGoYW6CM0NNTZ2VlzDoNvjwIj4r4GKCMjw9SFwNCBc1IAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYZlWVPXAIPY3/72t8TExPb2dm6yoqKCEGJnZ8dN8vn89evXr1ixwlTlwRCAkAKD5Ofne3l56Whw9+5d3Q0AdMPHPTCIp6enr68vwzBdFzEM4+vri4QCAyGkwFDLly/n8/ld5wsEgtdee23g64EhBh/3wFClpaVOTk5dBxLDMMXFxU5OTiapCoYMHEmBoUaOHOnv78/j/cdY4vF4/v7+SCgwHEIKjGDZsmWdTksxDLN8+XJT1QNDCT7ugRFUV1c7ODi0tbWp5/D5/MePH9vY2JiwKhgacCQFRmBtbT1nzhyBQMBN8vn8OXPmIKHAKBBSYBxLly7t6Ojg/mZZdtmyZaatB4YMfNwD42hqarK1tW1paSGEiESiyspKc3NzUxcFQwGOpMA4ZDJZUFCQUCgUCAQLFixAQoGxIKTAaJYsWdLW1tbe3r548WJT1wJDh8DUBQAhhGRkZJi6BCNob28Xi8UsyzY2Ng6NZxQeHm7qEgDnpOig9X/fwOTw6qABPu7RIj09nR3MQkNDQ0NDv//++9OnT5u6FiNIT0839YiAf8HHPTCmGTNmmLoEGGoQUmBMnf6DD8BwGFIAQDWEFABQDSEFAFRDSAEA1RBSAEA1hBQAUA0hBQBUQ0gBANUQUgBANYQUAFANIQUAVENIAQDVEFKD0qpVq0aMGMEwTE5Ojqlr6YWjR4+6u7szGszMzOzt7WfOnLl79+6amhpTFwg0QkgNSocOHfrkk09MXUWvhYSEPHjwwMPDQy6Xsyzb0dFRXl6ekZExevTomJiYp5566qeffjJ1jUAdhBSYDMMwlpaWM2fOTElJycjIePz48fz58+vq6kxdF9AFITVYDbFvHA4NDV2xYkV5efnHH39s6lqALgipQYNl2d27d3t6eopEIrlc/tZbb2kubW9vj42NdXFxkUgk48eP5779Njk5WSaTSaXS7OzsefPmWVhYODk5paamqh919uzZp59+WiqVWlhY+Pr61tfXd9fVAFixYgUh5JtvvhkyzwiMw9TfJQ0sy7JEj+8437JlC8Mwe/furampUSgUSUlJhJAbN25wSzdu3CgSibKysmpqajZv3szj8a5evco9ihDy3Xff1dXVlZeXBwQEyGQypVLJsmxjY6OFhUV8fHxzc3NZWdnChQsrKip0dKUb9x3n+jxZ9TmpTrhAcXZ2puEZcUGmz9OB/obdQIUeQ0qhUEil0jlz5qjncIcPXEg1NzdLpdLIyEh1Y5FIFBUVxf77Jd3c3Mwt4qKtoKCAZdnbt28TQr766ivNFenoSjfDQ4plWe4sFQ3PCCFFD3zcGxwKCgoUCkVgYKDWpfn5+QqFwsfHh5uUSCSOjo55eXldW5qZmRFCVCoVIcTd3d3e3n7p0qVxcXEPHz7sbVdG19TUxLKshYVFr8qg+RmBUSCkBoeSkhJCiJ2dndalTU1NhJCtW7eq7z8qKipSKBS6+5RIJN9///306dN37Njh7u4eGRnZ3Nzct66M4t69e4QQLy8vMlSeERgFQmpwEIvFhJDW1latS7nwSkhI0DxIvnTpUo/dPvXUUydOnCgtLY2JiUlPT9+zZ0+fuzLc//7v/xJC5s2bR4bKMwKjQEgNDj4+Pjwe7+zZs1qXOjs7i8Xi3t59Xlpa+vPPPxNC7Ozsdu7c6efn9/PPP/etK8OVlZUlJCQ4OTn913/9FxkSzwiMBSE1ONjZ2YWEhGRlZR0+fLi+vj43N/fgwYPqpWKxeOXKlampqcnJyfX19e3t7SUlJY8ePdLdZ2lp6Zo1a/Ly8pRK5Y0bN4qKiqZNm9a3rnqLZdnGxsaOjg6WZSsqKtLT05999lk+n3/8+HHunNSge0bQj/rphDz0CtHjFoSGhoZVq1bZ2NiYm5tPnz49NjaWEOLk5HTz5k2WZVtbW2NiYlxcXAQCAZdod+7cSUpKkkqlhJCxY8cWFhYePHiQiwBXV9d79+49fPjQ39/fysqKz+ePHDlyy5YtbW1t3XXV41PQ5+rel19+OX78eKlUamZmxv2MKHc57+mnn96+fXtVVZVmY9M+I1zdowfDsqzpEhL+hWGY9PT08PBwUxfSd2FhYYSQzMxMUxdiHBkZGREREXh10AAf9wCAaggpAKAaQgoAqIaQAgCqIaQAgGoIKQCgGkIKAKiGkAIAqiGkAIBqCCkAoBpCCgCohpACAKohpACAaggpAKAaQgoAqIaQAgCqIaQAgGoCUxcA/zLYf7+E+9GtjIwMUxdiHIN9dwwl+PpgKjAMY+oSQAu8OmiAkAIAquGcFABQDSEFAFRDSAEA1RBSAEC1/w/6RzHD5NtZMwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HST_CHjxIhR"
      },
      "source": [
        "## Task 8: Training the Model\n",
        "\n",
        "1. Preparing a validation set\n",
        "2. Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff7F3hCK5ejm"
      },
      "source": [
        "val_tweets ,val_labels = get_tweets(val)\n",
        "val_seq = get_squences(tokenizer,val_tweets)\n",
        "val_labels = names_to_ids(val_labels)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69ab0c16-a43b-45e6-fe82-19ff964862d5"
      },
      "source": [
        "\n",
        "h = model.fit(\n",
        "    pts , train_labels,\n",
        "    validation_data = (val_seq,val_labels),\n",
        "    epochs =20,\n",
        "    callbacks = [\n",
        "        tf.keras.callbacks.EarlyStopping(monitor = 'val_accuracy', patience = 5)\n",
        "    ]\n",
        "    \n",
        ")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 20s 26ms/step - loss: 1.2803 - accuracy: 0.5088 - val_loss: 0.8047 - val_accuracy: 0.7315\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 7s 15ms/step - loss: 0.5416 - accuracy: 0.8234 - val_loss: 0.5766 - val_accuracy: 0.8150\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.2926 - accuracy: 0.9053 - val_loss: 0.3993 - val_accuracy: 0.8720\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1926 - accuracy: 0.9398 - val_loss: 0.3671 - val_accuracy: 0.8785\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1388 - accuracy: 0.9589 - val_loss: 0.3896 - val_accuracy: 0.8825\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1172 - accuracy: 0.9639 - val_loss: 0.4134 - val_accuracy: 0.8745\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1017 - accuracy: 0.9679 - val_loss: 0.3751 - val_accuracy: 0.8855\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0825 - accuracy: 0.9761 - val_loss: 0.4343 - val_accuracy: 0.8860\n",
            "Epoch 9/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0712 - accuracy: 0.9778 - val_loss: 0.4278 - val_accuracy: 0.8900\n",
            "Epoch 10/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0576 - accuracy: 0.9826 - val_loss: 0.4087 - val_accuracy: 0.8920\n",
            "Epoch 11/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0507 - accuracy: 0.9833 - val_loss: 0.4358 - val_accuracy: 0.8930\n",
            "Epoch 12/20\n",
            "500/500 [==============================] - 8s 16ms/step - loss: 0.0511 - accuracy: 0.9832 - val_loss: 0.4394 - val_accuracy: 0.8925\n",
            "Epoch 13/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0483 - accuracy: 0.9847 - val_loss: 0.4243 - val_accuracy: 0.8935\n",
            "Epoch 14/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0435 - accuracy: 0.9863 - val_loss: 0.4255 - val_accuracy: 0.8880\n",
            "Epoch 15/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0376 - accuracy: 0.9880 - val_loss: 0.4382 - val_accuracy: 0.9000\n",
            "Epoch 16/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0299 - accuracy: 0.9903 - val_loss: 0.4612 - val_accuracy: 0.8965\n",
            "Epoch 17/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0298 - accuracy: 0.9902 - val_loss: 0.5092 - val_accuracy: 0.8940\n",
            "Epoch 18/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0402 - accuracy: 0.9873 - val_loss: 0.4421 - val_accuracy: 0.8970\n",
            "Epoch 19/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0370 - accuracy: 0.9877 - val_loss: 0.4438 - val_accuracy: 0.9015\n",
            "Epoch 20/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0268 - accuracy: 0.9912 - val_loss: 0.4688 - val_accuracy: 0.8930\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdsJyMTLxIhX"
      },
      "source": [
        "## Task 9: Evaluating the Model\n",
        "\n",
        "1. Visualizing training history\n",
        "2. Prepraring a test set\n",
        "3. A look at individual predictions on the test set\n",
        "4. A look at all predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENCfvXeLxIhX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "99a3443c-b444-4941-d34c-bee3ac3b9144"
      },
      "source": [
        "show_history(h)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWuzoz8uxIha"
      },
      "source": [
        "test_tweets, test_labels = get_tweets(test)\n",
        "test_seq = get_squences(tokenizer,test_tweets)\n",
        "test_labels = names_to_ids(test_labels)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vRVJ_2SxIhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8ef5579-684b-4e53-90a6-bd175d6da792"
      },
      "source": [
        "_ = model.evaluate(test_seq, test_labels)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 8ms/step - loss: 0.5164 - accuracy: 0.8835\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15e49497-bdce-4264-ae89-553384c7f222"
      },
      "source": [
        "i = random.randint(0, len(test_labels)-1)\n",
        "\n",
        "print('Sentence : ', test_tweets[i])\n",
        "print('Emotion : ',index_to_class[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_seq[i],axis =0))[0]\n",
        "pred_class = index_to_class[np.argmax(p).astype('uint8')]\n",
        "\n",
        "print('Prediction Emotion : ',pred_class)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence :  im thinking well i could be a bit smaller but for health reasons and i should see a doctor more regularly because im feeling crappy\n",
            "Emotion :  sadness\n",
            "Prediction Emotion :  sadness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHl5SVCFxIhh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e1e8fca-44a7-4f37-e10c-74cd00d78369"
      },
      "source": [
        "i = random.randint(0, len(test_labels)-1)\n",
        "\n",
        "print('Sentence : ', test_tweets[i])\n",
        "print('Emotion : ',index_to_class[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_seq[i],axis =0))[0]\n",
        "pred_class = index_to_class[np.argmax(p).astype('uint8')]\n",
        "\n",
        "print('Prediction Emotion : ',pred_class)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence :  i feel the presence of the divine with you when you are buried inside me smiling down at me your sweat dripping into my eager mouth\n",
            "Emotion :  joy\n",
            "Prediction Emotion :  joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC8YQ0OexIhj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f06e72f1-6d64-4135-a4b6-3eb02f9050e3"
      },
      "source": [
        "i = random.randint(0, len(test_labels)-1)\n",
        "\n",
        "print('Sentence : ', test_tweets[i])\n",
        "print('Emotion : ',index_to_class[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_seq[i],axis =0))[0]\n",
        "pred_class = index_to_class[np.argmax(p).astype('uint8')]\n",
        "\n",
        "print('Prediction Emotion : ',pred_class)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence :  i am truly unfortunate the majority of the time i m usually drained but i obtain it hard to get from bed i really feel restless and others\n",
            "Emotion :  fear\n",
            "Prediction Emotion :  sadness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = np.argmax(model.predict(test_seq), axis=-1)"
      ],
      "metadata": {
        "id": "WZfQMnlO5Oz7"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_confusion_matrix(test_labels,y_predict, list(classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZQM5SguG5dlE",
        "outputId": "ab1a62fc-c558-44db-91da-0dbb4bbd44e4"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}