sentiment_analysis_with_imdb_reviews.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP2oJMbLBD8UyWqTsnhifxH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UsamaGM/AI-ML/blob/main/sentiment_analysis_with_imdb_reviews.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install all required libraries"
      ],
      "metadata": {
        "id": "iHVVM3ha5U1B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7eYV-zB32431",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0469b2d-05dd-4672-9e7a-acc6586b7ed2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "%pip install tensorflow\n",
        "%pip install numpy\n",
        "%pip install matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Data\n",
        "### Import Libraries"
      ],
      "metadata": {
        "id": "HNqIrR2j5hzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from keras.datasets import imdb\n",
        "from keras.preprocessing import sequence"
      ],
      "metadata": {
        "id": "bsQMl-DR3Gh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set parameters and load dataset\n",
        "- VOCAB_SIZE: Total words in our vocabulary\n",
        "- MAXLEN: Maximum words in a single review\n",
        "- BATCH_SIZE: Number of inputs in each batch\n"
      ],
      "metadata": {
        "id": "Y3EJIhTe5u_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "VOCAB_SIZE = 88584\n",
        "\n",
        "MAXLEN = 256\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=VOCAB_SIZE)"
      ],
      "metadata": {
        "id": "olnPRmk53n3D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocess the data\n",
        "- Make all reviews to have a fixed size of 256 words.\n",
        "  - If a review has more words then trim it\n",
        "  - If a review has less words then pad with empty characters (zeros) in the beginning"
      ],
      "metadata": {
        "id": "ipvPnQLd51fq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = sequence.pad_sequences(train_data, MAXLEN)\n",
        "test_data = sequence.pad_sequences(test_data, MAXLEN)"
      ],
      "metadata": {
        "id": "6Jp4A3FJ303u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create and train the model\n",
        "### Create a model with 3 layers\n",
        "- First layer for word embeddings outputting 32 dimensional embedded words\n",
        "- Second Long Term Short Memory layer accepting the 32 dimensional word embeddings\n",
        "- Last output layer with single node as a review can be either positive or negative"
      ],
      "metadata": {
        "id": "_4T5DqgU57gN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(VOCAB_SIZE, 128),\n",
        "    tf.keras.layers.LSTM(128),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RL7psSxl36N_",
        "outputId": "90d996fa-b317-4b8d-917b-53197b13864e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, None, 128)         11338752  \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 128)               131584    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11475009 (43.77 MB)\n",
            "Trainable params: 11475009 (43.77 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compile the model\n",
        "- Used binary_crossentropy as loss function as our model has 2 possible outputs\n",
        "- Used rmsprop as optimizer function\n",
        "### Train the model\n",
        "- Set epochs as 5\n",
        "- Batch size = 32\n",
        "- Split train data as 80% for training and 20% for validation"
      ],
      "metadata": {
        "id": "kbH7WD1688la"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_data, train_labels, epochs=5, batch_size=BATCH_SIZE, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lqr9F4UY4DH-",
        "outputId": "47baeefe-99f1-4219-eaf2-65578a5b0e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "625/625 [==============================] - 41s 62ms/step - loss: 0.4951 - accuracy: 0.7531 - val_loss: 0.3585 - val_accuracy: 0.8508\n",
            "Epoch 2/5\n",
            "625/625 [==============================] - 14s 22ms/step - loss: 0.3047 - accuracy: 0.8818 - val_loss: 0.3373 - val_accuracy: 0.8558\n",
            "Epoch 3/5\n",
            "625/625 [==============================] - 12s 20ms/step - loss: 0.2452 - accuracy: 0.9076 - val_loss: 0.2938 - val_accuracy: 0.8774\n",
            "Epoch 4/5\n",
            "625/625 [==============================] - 10s 17ms/step - loss: 0.1870 - accuracy: 0.9316 - val_loss: 0.2946 - val_accuracy: 0.8734\n",
            "Epoch 5/5\n",
            "625/625 [==============================] - 16s 25ms/step - loss: 0.1431 - accuracy: 0.9491 - val_loss: 0.3446 - val_accuracy: 0.8624\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, 6)\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "BzhYVD7ExwAi",
        "outputId": "005f2acb-b06d-45b6-9273-beb121807754"
      },
      "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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J+QYgGxsbHD16FFOmTMHy5cvx+PFjuLu746efftKNwpSVWbNmoW7dulixYgX++9//onbt2hgxYgQ++eQT3X2qPDw84O/vj59++gk3btyAmZkZPDw8sGfPHt0VcD179kRSUhLWrVuHO3fuwNraGh07dsTs2bN1V5ERlYRCVKQ/PYmowurduzfOnTuX7/wUIqLKhnOAiCiP3I+tuHDhAnbv3o1OnToZpkFERKWMI0BElIednZ3u+VRXrlzB6tWrkZWVhTNnzqBx48aGbh4RUYlxDhAR5REQEIDNmzcjJSUFKpUKPj4++OSTTxh+iKjK4AgQERERyQ7nABEREZHsMAARERGR7HAOUD60Wi1u3ryJWrVqFen280RERGQ4Qgg8ePAA9vb2L73hJwNQPm7evAlHR0dDN4OIiIiK4dq1a3BwcHhhHQagfNSqVQuA9A00Nzc3cGuIiIioMNRqNRwdHXWf4y/CAJSPnNNe5ubmDEBERESVTGGmr3ASNBEREckOAxARERHJDgMQERERyQ7nABERUZnTaDR48uSJoZtBlVz16tWhVCpLZVsMQEREVGaEEEhJSUFaWpqhm0JVhKWlJWxtbUt8n74KEYBWrlyJBQsWICUlBR4eHli+fDnatm2bb92IiAiEhITolalUKjx+/Fj3WgiBsLAwrF27FmlpaWjfvj1Wr17NBzkSEZWznPBTr149mJmZ8eayVGxCCDx8+BC3bt0CANjZ2ZVoewYPQFu3bkVoaCjWrFkDb29vLFmyBP7+/khISEC9evXyXcfc3BwJCQm617n/Q82fPx/Lli3Dhg0b0LBhQ8yYMQP+/v74+++/YWJiUqb9ISIiiUaj0YWfOnXqGLo5VAWYmpoCAG7duoV69eqV6HSYwSdBL1q0CMOHD0dISAiaN2+ONWvWwMzMDOvWrStwHYVCAVtbW91iY2Oje08IgSVLlmD69Ono1asX3N3dsXHjRty8eRO7du0qhx4REREA3ZwfMzMzA7eEqpKcn6eSzikzaADKzs7GqVOn4OfnpyszMjKCn58fYmJiClwvIyMDTk5OcHR0RK9evXDu3Dnde4mJiUhJSdHbpoWFBby9vQvcZlZWFtRqtd5CRESlg6e9qDSV1s+TQQPQnTt3oNFo9EZwAMDGxgYpKSn5ruPm5oZ169bhhx9+wNdffw2tVot27drh+vXrAKBbryjbDA8Ph4WFhW4pq+eAaTRAdDSwebP0r0ZTJrshIiKilzD4KbCi8vHxQVBQEDw9PdGxY0fs2LEDdevWxeeff17sbU6bNg3p6em65dq1a6XYYsmOHYCzM/D668Dbb0v/OjtL5UREVPU5OztjyZIlha4fHR0NhUJR5lfQRUREwNLSskz3UREZNABZW1tDqVQiNTVVrzw1NRW2traF2kb16tXRqlUrXLx4EQB06xVlmyqVSvfcr7J4/teOHUD//sD/D1Lp3LghlTMEERG9WHmOoCsUihcus2bNKtZ2T548iREjRhS6frt27ZCcnAwLC4ti7Y9ezKAByNjYGF5eXoiKitKVabVaREVFwcfHp1Db0Gg0iIuL010O17BhQ9ja2uptU61W4/jx44XeZmnSaIDx4wEh8r6XUzZhAk+HEREVpLxH0JOTk3XLkiVLYG5urlc2adIkXV0hBJ4+fVqo7datW7dIE8KNjY1L5X43lD+DnwILDQ3F2rVrsWHDBsTHx2PkyJHIzMzU3esnKCgI06ZN09WfM2cO9u3bh8uXL+P06dN45513cOXKFbz77rsApOQ+YcIEfPTRR/jxxx8RFxeHoKAg2Nvbo3fv3uXev0OH8o78PE8I4No1qR4REekzxAj681cZW1hY6F15fP78edSqVQt79uyBl5cXVCoVDh8+jEuXLqFXr16wsbFBzZo10aZNGxw4cEBvu7lPgSkUCnz55Zfo06cPzMzM0LhxY/z444+693OfAss5VbV37140a9YMNWvWREBAAJKTk3XrPH36FOPGjYOlpSXq1KmDKVOmIDg4uMiff6tXr4arqyuMjY3h5uaGTZs26d4TQmDWrFlo0KABVCoV7O3tMW7cON37q1atQuPGjWFiYgIbGxv079+/SPsuLwYPQIGBgVi4cCFmzpwJT09PxMbGIjIyUjeJ+erVq3oH9/79+xg+fDiaNWuGbt26Qa1W4+jRo2jevLmuzuTJkzF27FiMGDECbdq0QUZGBiIjIw1yD6Dnml4q9YiI5KIij6BPnToV8+bNQ3x8PNzd3ZGRkYFu3bohKioKZ86cQUBAAHr06IGrV6++cDuzZ8/GwIEDcfbsWXTr1g2DBw/GvXv3Cqz/8OFDLFy4EJs2bcLvv/+Oq1ev6o1Iffrpp/jmm2+wfv16HDlyBGq1usi3gNm5cyfGjx+PiRMn4q+//sJ7772HkJAQHDx4EADw/fffY/Hixfj8889x4cIF7Nq1Cy1btgQA/PHHHxg3bhzmzJmDhIQEREZGokOHDkXaf7kRlEd6eroAINLT00u8rYMHhZD+q754OXiwxLsiIqpQHj16JP7++2/x6NGjYq1fEX5/rl+/XlhYWDzXpoMCgNi1a9dL123RooVYvny57rWTk5NYvHix7jUAMX36dN3rjIwMAUDs2bNHb1/379/XtQWAuHjxom6dlStXChsbG91rGxsbsWDBAt3rp0+figYNGohevXoVuo/t2rUTw4cP16szYMAA0a1bNyGEEJ999plo0qSJyM7OzrOt77//Xpibmwu1Wl3g/krqRT9XRfn8NvgIUFXn6ws4OAAFncJVKABHR6keERE9U5FH0Fu3bq33OiMjA5MmTUKzZs1gaWmJmjVrIj4+/qUjQO7u7rqva9SoAXNzc92jHvJjZmYGV1dX3Ws7Oztd/fT0dKSmpuo9SkqpVMLLy6tIfYuPj0f79u31ytq3b4/4+HgAwIABA/Do0SO4uLhg+PDh2Llzp24e1JtvvgknJye4uLhgyJAh+Oabb/Dw4cMi7b+8MACVMaUSWLpU+jp3CMp5vWSJVI+IiJ4p7KOeSvhIqGKpUaOG3utJkyZh586d+OSTT3Do0CHExsaiZcuWyM7OfuF2qlevrvdaoVBAq9UWqb7I7xxhGXJ0dERCQgJWrVoFU1NTjBo1Ch06dMCTJ09Qq1YtnD59Gps3b4adnR1mzpwJDw+PCvkwXAagctC3L7B9O1C/vn65g4NU3revYdpFRFSRVaYR9CNHjmDo0KHo06cPWrZsCVtbWyQlJZVrGywsLGBjY4OTJ0/qyjQaDU6fPl2k7TRr1gxHjhzRKzty5IjeXFtTU1P06NEDy5YtQ3R0NGJiYhAXFwcAqFatGvz8/DB//nycPXsWSUlJ+PXXX0vQs7Jh8IehykXfvkCvXtLVXsnJ0l8svr4c+SEiKkjOCHr//lLYeX6go6KNoDdu3Bg7duxAjx49oFAoMGPGjBeO5JSVsWPHIjw8HI0aNULTpk2xfPly3L9/v0iX0n/wwQcYOHAgWrVqBT8/P/z000/YsWOH7qq2iIgIaDQaeHt7w8zMDF9//TVMTU3h5OSEn3/+GZcvX0aHDh1gZWWF3bt3Q6vVws3Nray6XGwMQOVIqQQ6dTJ0K4iIKo+cEfTx4/UvhXdwkMJPRRlBX7RoEf7zn/+gXbt2sLa2xpQpUwzyXMkpU6YgJSUFQUFBUCqVGDFiBPz9/Yv01PTevXtj6dKlWLhwIcaPH4+GDRti/fr16PT/H2CWlpaYN28eQkNDodFo0LJlS/z000+oU6cOLC0tsWPHDsyaNQuPHz9G48aNsXnzZrRo0aKMelx8ClHeJw8rAbVaDQsLC6Snp5f6XaGJiOTi8ePHSExMRMOGDUt8GxKNhiPoxaHVatGsWTMMHDgQc+fONXRzSsWLfq6K8vnNESAiIqrwOIJeOFeuXMG+ffvQsWNHZGVlYcWKFUhMTMTbb79t6KZVOJwETUREVEUYGRkhIiICbdq0Qfv27REXF4cDBw6gWbNmhm5ahcMRICIioirC0dExzxVclD+OABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZWBTp06YcKECbrXzs7OWLJkyQvXUSgU2LVrV4n3XVrbeZFZs2bB09OzTPdRlhiAiIiIntOjRw8EBATk+96hQ4egUChw9uzZIm/35MmTGDFiREmbp6egEJKcnIyuXbuW6r6qGgYgIiKi5wwbNgz79+/H9eefvvr/1q9fj9atW8Pd3b3I261bty7MzMxKo4kvZWtrC5VKVS77qqwYgIiIiJ7z73//G3Xr1kVERIReeUZGBrZt24Zhw4bh7t27eOutt1C/fn2YmZmhZcuW2Lx58wu3m/sU2IULF9ChQweYmJigefPm2L9/f551pkyZgiZNmsDMzAwuLi6YMWMGnjx5AgCIiIjA7Nmz8eeff0KhUEChUOjanPsUWFxcHN544w2YmpqiTp06GDFiBDIyMnTvDx06FL1798bChQthZ2eHOnXqYPTo0bp9FYZWq8WcOXPg4OAAlUoFT09PREZG6t7Pzs7GmDFjYGdnBxMTEzg5OSE8PBwAIITArFmz0KBBA6hUKtjb22PcuHGF3ndx8FEYRERUboQAHj40zL7NzACF4uX1qlWrhqCgIERERODDDz+E4v9X2rZtGzQaDd566y1kZGTAy8sLU6ZMgbm5OX755RcMGTIErq6uaNu27Uv3odVq0bdvX9jY2OD48eNIT0/Xmy+Uo1atWoiIiIC9vT3i4uIwfPhw1KpVC5MnT0ZgYCD++usvREZG4sCBAwAACwuLPNvIzMyEv78/fHx8cPLkSdy6dQvvvvsuxowZoxfyDh48CDs7Oxw8eBAXL15EYGAgPD09MXz48Jd/0wAsXboUn332GT7//HO0atUK69atQ8+ePXHu3Dk0btwYy5Ytw48//ojvvvsODRo0wLVr13Dt2jUAwPfff4/Fixdjy5YtaNGiBVJSUvDnn38War/FJiiP9PR0AUCkp6cbuilERJXWo0ePxN9//y0ePXqkK8vIEEKKQeW/ZGQUvu3x8fECgDh48KCuzNfXV7zzzjsFrtO9e3cxceJE3euOHTuK8ePH6147OTmJxYsXCyGE2Lt3r6hWrZq4ceOG7v09e/YIAGLnzp0F7mPBggXCy8tL9zosLEx4eHjkqff8dr744gthZWUlMp77Bvzyyy/CyMhIpKSkCCGECA4OFk5OTuLp06e6OgMGDBCBgYEFtiX3vu3t7cXHH3+sV6dNmzZi1KhRQgghxo4dK9544w2h1WrzbOuzzz4TTZo0EdnZ2QXuL0d+P1c5ivL5zVNgREREuTRt2hTt2rXDunXrAAAXL17EoUOHMGzYMACARqPB3Llz0bJlS9SuXRs1a9bE3r17cfXq1UJtPz4+Ho6OjrC3t9eV+fj45Km3detWtG/fHra2tqhZsyamT59e6H08vy8PDw/UqFFDV9a+fXtotVokJCToylq0aAGlUql7bWdnh1u3bhVqH2q1Gjdv3kT79u31ytu3b4/4+HgA0mm22NhYuLm5Ydy4cdi3b5+u3oABA/Do0SO4uLhg+PDh2LlzJ54+fVqkfhYVAxAREZUbMzMgI8MwS1HnHw8bNgzff/89Hjx4gPXr18PV1RUdO3YEACxYsABLly7FlClTcPDgQcTGxsLf3x/Z2dml9r2KiYnB4MGD0a1bN/z88884c+YMPvzww1Ldx/OqV6+u91qhUECr1Zba9l999VUkJiZi7ty5ePToEQYOHIj+/fsDkJ5in5CQgFWrVsHU1BSjRo1Chw4dijQHqag4B4iIiMqNQgE8NxBRoQ0cOBDjx4/Ht99+i40bN2LkyJG6+UBHjhxBr1698M477wCQ5vT8888/aN68eaG23axZM1y7dg3Jycmws7MDABw7dkyvztGjR+Hk5IQPP/xQV3blyhW9OsbGxtBoNC/dV0REBDIzM3WjQEeOHIGRkRHc3NwK1d6XMTc3h729PY4cOaILiTn7eX5OlLm5OQIDAxEYGIj+/fsjICAA9+7dQ+3atWFqaooePXqgR48eGD16NJo2bYq4uDi8+uqrpdLG3BiAiIiI8lGzZk0EBgZi2rRpUKvVGDp0qO69xo0bY/v27Th69CisrKywaNEipKamFjoA+fn5oUmTJggODsaCBQugVqv1gk7OPq5evYotW7agTZs2+OWXX7Bz5069Os7OzkhMTERsbCwcHBxQq1atPJe/Dx48GGFhYQgODsasWbNw+/ZtjB07FkOGDIGNjU3xvjn5+OCDDxAWFgZXV1d4enpi/fr1iI2NxTfffAMAWLRoEezs7NCqVSsYGRlh27ZtsLW1haWlJSIiIqDRaODt7Q0zMzN8/fXXMDU1hZOTU6m1LzeeAiMiIirAsGHDcP/+ffj7++vN15k+fTpeffVV+Pv7o1OnTrC1tUXv3r0LvV0jIyPs3LkTjx49Qtu2bfHuu+/i448/1qvTs2dP/Pe//8WYMWPg6emJo0ePYsaMGXp1+vXrh4CAALz++uuoW7duvpfim5mZYe/evbh37x7atGmD/v37o3PnzlixYkXRvhkvMW7cOISGhmLixIlo2bIlIiMj8eOPP6Jx48YApCva5s+fj9atW6NNmzZISkrC7t27YWRkBEtLS6xduxbt27eHu7s7Dhw4gJ9++gl16tQp1TY+TyGEEGW29UpKrVbDwsIC6enpMDc3N3RziIgqpcePHyMxMRENGzaEiYmJoZtDVcSLfq6K8vnNESAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIqU7zWhkpTaf08MQAREVGZyLmz8ENDPf2UqqScn6fcd64uKt4IkYiIyoRSqYSlpaXueVJmZma6OykTFZUQAg8fPsStW7dgaWmp99yy4mAAIiKiMmNrawsAhX6oJtHLWFpa6n6uSoIBiIiIyoxCoYCdnR3q1atXpg+2JHmoXr16iUd+cjAAERFRmVMqlaX2wUVUGirEJOiVK1fC2dkZJiYm8Pb2xokTJwq13pYtW6BQKPI8f2Xo0KFQKBR6S0BAQBm0nIiIiCojgwegrVu3IjQ0FGFhYTh9+jQ8PDzg7+//0vPFSUlJmDRpEnx9ffN9PyAgAMnJybolvwfEERERkTwZPAAtWrQIw4cPR0hICJo3b441a9bAzMwM69atK3AdjUaDwYMHY/bs2XBxccm3jkqlgq2trW6xsrIqqy4QERFRJWPQAJSdnY1Tp07Bz89PV2ZkZAQ/Pz/ExMQUuN6cOXNQr149DBs2rMA60dHRqFevHtzc3DBy5EjcvXu3wLpZWVlQq9V6CxEREVVdBg1Ad+7cgUajgY2NjV65jY0NUlJS8l3n8OHD+Oqrr7B27doCtxsQEICNGzciKioKn376KX777Td07doVGo0m3/rh4eGwsLDQLY6OjsXvFBEREVV4leoqsAcPHmDIkCFYu3YtrK2tC6w3aNAg3dctW7aEu7s7XF1dER0djc6dO+epP23aNISGhupeq9VqhiAiIqIqzKAByNraGkqlEqmpqXrlqamp+d7k6NKlS0hKSkKPHj10ZVqtFgBQrVo1JCQkwNXVNc96Li4usLa2xsWLF/MNQCqVCiqVqqTdISIiokrCoKfAjI2N4eXlhaioKF2ZVqtFVFQUfHx88tRv2rQp4uLiEBsbq1t69uyJ119/HbGxsQWO2ly/fh13796FnZ1dmfWFiIiIKg+DnwILDQ1FcHAwWrdujbZt22LJkiXIzMxESEgIACAoKAj169dHeHg4TExM8Morr+itb2lpCQC68oyMDMyePRv9+vWDra0tLl26hMmTJ6NRo0bw9/cv174RERFRxWTwABQYGIjbt29j5syZSElJgaenJyIjI3UTo69evQojo8IPVCmVSpw9exYbNmxAWloa7O3t0aVLF8ydO5enuYiIiAgAoBBCCEM3oqJRq9WwsLBAeno6zM3NDd0cIiIiKoSifH4b/EaIREREROWNAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZKdCBKCVK1fC2dkZJiYm8Pb2xokTJwq13pYtW6BQKNC7d2+9ciEEZs6cCTs7O5iamsLPzw8XLlwog5YTERFRZWTwALR161aEhoYiLCwMp0+fhoeHB/z9/XHr1q0XrpeUlIRJkybB19c3z3vz58/HsmXLsGbNGhw/fhw1atSAv78/Hj9+XFbdICIiokrE4AFo0aJFGD58OEJCQtC8eXOsWbMGZmZmWLduXYHraDQaDB48GLNnz4aLi4vee0IILFmyBNOnT0evXr3g7u6OjRs34ubNm9i1a1cZ94aIiIgqA4MGoOzsbJw6dQp+fn66MiMjI/j5+SEmJqbA9ebMmYN69eph2LBhed5LTExESkqK3jYtLCzg7e39wm0SERGRfFQz5M7v3LkDjUYDGxsbvXIbGxucP38+33UOHz6Mr776CrGxsfm+n5KSottG7m3mvJdbVlYWsrKydK/VanVhu0BERESVkMFPgRXFgwcPMGTIEKxduxbW1taltt3w8HBYWFjoFkdHx1LbNhEREVU8Bh0Bsra2hlKpRGpqql55amoqbG1t89S/dOkSkpKS0KNHD12ZVqsFAFSrVg0JCQm69VJTU2FnZ6e3TU9Pz3zbMW3aNISGhupeq9VqhiAiIqIqzKAjQMbGxvDy8kJUVJSuTKvVIioqCj4+PnnqN23aFHFxcYiNjdUtPXv2xOuvv47Y2Fg4OjqiYcOGsLW11dumWq3G8ePH890mAKhUKpibm+stREREVHUZdAQIAEJDQxEcHIzWrVujbdu2WLJkCTIzMxESEgIACAoKQv369REeHg4TExO88soreutbWloCgF75hAkT8NFHH6Fx48Zo2LAhZsyYAXt7+zz3CyIiIiJ5MngACgwMxO3btzFz5kykpKTA09MTkZGRuknMV69ehZFR0QaqJk+ejMzMTIwYMQJpaWl47bXXEBkZCRMTk7LoAhEREVUyCiGEMHQjKhq1Wg0LCwukp6fzdBgREVElUZTP70p1FRgRERFRaWAAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItmpZugGyMm2bUB4OODurr/Uq2folhEREckLA1A5OnUKOHNGWp5nY5M3FDVrBqhUhmknERFRVacQQghDN6KiUavVsLCwQHp6OszNzUttuzduACdPAmfPPlsuXgTyOwJKJdC0ad5gVL8+oFCUWpOIiIiqjKJ8fjMA5aOsAlB+MjOBc+f0Q9HZs8D9+/nXt7LKG4patABq1CjTZhIREVV4DEAlVJ4BKD9CSKNFuUPR+fOARpO3vkIBNGqUNxg5OwNGnOZOREQyUZTP7wrx8bhy5Uo4OzvDxMQE3t7eOHHiRIF1d+zYgdatW8PS0hI1atSAp6cnNm3apFdn6NChUCgUektAQEBZd6PUKBSAgwPQrRswdSrw7bfAX39Jo0VnzgAbNgATJwJvvinNHxICuHAB+P57ICwM6NMHcHUFLCyAdu2A998HVq0CDh8G0tMN3TsiIiLDM/gk6K1btyI0NBRr1qyBt7c3lixZAn9/fyQkJKBePpdH1a5dGx9++CGaNm0KY2Nj/PzzzwgJCUG9evXg7++vqxcQEID169frXquqwIxilQrw9JSW56WmAnFx+qNF584BGRlATIy0PM/JKe9oUePG0rwjIiIiOTD4KTBvb2+0adMGK1asAABotVo4Ojpi7NixmDp1aqG28eqrr6J79+6YO3cuAGkEKC0tDbt27SpWmwx9Cqw0PHkijQrlPo127Vr+9U1MpLlEuYORtXX5tpuIiKi4ivL5bdARoOzsbJw6dQrTpk3TlRkZGcHPzw8xuYct8iGEwK+//oqEhAR8+umneu9FR0ejXr16sLKywhtvvIGPPvoIderUyXc7WVlZyMrK0r1Wq9XF7FHFUb060Ly5tAwa9Kz8/v28o0VxccDDh9Jl+qdO6W/Hzk4KQh4ez0KRmxtgbFy+/SEiIipNBg1Ad+7cgUajgY2NjV65jY0Nzp8/X+B66enpqF+/PrKysqBUKrFq1Sq8+eabuvcDAgLQt29fNGzYEJcuXcL//vc/dO3aFTExMVDmc54nPDwcs2fPLr2OVWBWVkCHDtKSQ6sFLl/OO1p06RKQnCwte/c+q1+9unSfotyjRba2vESfiIgqB4OeArt58ybq16+Po0ePwsfHR1c+efJk/Pbbbzh+/Hi+62m1Wly+fBkZGRmIiorC3LlzsWvXLnTq1Cnf+pcvX4arqysOHDiAzp0753k/vxEgR0fHSn0KrDQ8eCBNvs4djAoaILO2zhuKmjcHTE3Lt91ERCRPleYUmLW1NZRKJVJTU/XKU1NTYWtrW+B6RkZGaNSoEQDA09MT8fHxCA8PLzAAubi4wNraGhcvXsw3AKlUqioxSbq01aoF+PhISw4hgKtX84aif/4B7twBfv1VWnIYGQFNmuQNRg0acLSIiIgMx6AByNjYGF5eXoiKikLv3r0BSKM7UVFRGDNmTKG3o9Vq9UZwcrt+/Tru3r0LOzu7kjZZ9hQK6SoyJyegR49n5Y8eAX//rR+K/vwTuHtXun/R+fPAd989q29unjcUvfKKFLqIiIjKmsEvgw8NDUVwcDBat26Ntm3bYsmSJcjMzERISAgAICgoCPXr10d4eDgAab5O69at4erqiqysLOzevRubNm3C6tWrAQAZGRmYPXs2+vXrB1tbW1y6dAmTJ09Go0aN9C6Tp9Jlagp4eUlLDiGAlJS8o0Xx8dJptMOHpeV5Li55g5GLCy/RJyKqKpKSpHvTDRsmXVRjKAYPQIGBgbh9+zZmzpyJlJQUeHp6IjIyUjcx+urVqzB67nbGmZmZGDVqFK5fvw5TU1M0bdoUX3/9NQIDAwEASqUSZ8+exYYNG5CWlgZ7e3t06dIFc+fO5WmucqZQSFeR2dkBz2fP7GwgISFvMLp5U5qMffky8PwdDMzMpNGh50NRy5ZA7drl3iUiIioGIYADB4AVK4CffpJeZ2UBS5cark0Gvw9QRVQV7gNUGd25k/cS/b/+Ah4/zr++g0Pe0aImTaSr1IiIyPDUamDjRin4JCQ8K3/zTSA0FCjthzTwWWAlxABUcWg0wMWLeUeLkpLyr29sLF15ljsY5brTAhERlaHz54GVK4GICOmpBABQsyYwdCgwejTQtGnZ7JcBqIQYgCq+9PT8L9HP+Y+WW716eUNRs2bSHbCJiKjkNBrg55+l0Z4DB56Vu7kBY8YAQUHSBTBliQGohBiAKietFrhyJW8ounBBOt+cm1Ip/cfMHYwcHHiJPhFRYd29C3z1lTSx+coVqUyhkK4UHjMG8PMrv9+pDEAlxABUtTx8KD0cNvcl+vfv51/f0jL/S/Rr1CjXZhMRVWhnzkijPd9++2yuppUV8O67wMiRQMOG5d8mBqASYgCq+oSQrjrLPVp0/jzw9Gne+goF4OoqhSEvL2DAAKBx4/JvNxGRIWVnAzt2SMHnyJFn5Z6ewNix0rMnzcwM1jwGoJJiAJKvrCwpBOUORikpeev6+ADBwcDAgdJfPUREVVVyMvDFF8Dnn0tfA0C1akD//tJprnbtKsbUAQagEmIAotxu3Xp2if6+fdKi1UrvGRsDPXtKYcjfn5fhE1HVIAQQEwMsXw5s3/5sdNzWFnjvPWDECMDe3rBtzI0BqIQYgOhlkpOl894bNkjBKEe9esDbb0tXO3h6Voy/iIiIiuLRI2DzZuk015kzz8rbtZNGe/r1k/7wq4gYgEqIAYgKSwhpQvXGjcA330gjRTleeUUaFXr77Yr3VxIRUW5JScDq1cCXXwL37kllJibS77DRo4FXXzVo8wqFAaiEGICoOJ48kU6NbdwI/PCDNJ8IAIyMpLueBgcDvXoZdoIgEdHzhACiop49oiLn1L6TEzBqlPS8rjp1DNvGomAAKiEGICqp+/eBbdukU2RHjz4rr1VLmjQdFAS89poUjoiIytuDB88eUXH+/LPyzp2lq7n+/e/K+RDqMg9A165dg0KhgIODAwDgxIkT+Pbbb9G8eXOMGDGieK2uQBiAqDRdvCj9otm0Sf8RHg0bAkOGSEujRgZrHhHJSEKCFHo2bJBCECA9oiI4WDrN1ayZYdtXUmUegHx9fTFixAgMGTIEKSkpcHNzQ4sWLXDhwgWMHTsWM2fOLHbjKwIGICoLWi1w+LD0i2fbtme/fACgfXtpVGjgQOlGjEREpUWjAXbvlq7m2r//WXmTJtKk5uDgsn9ERXkpyud3sQbg//rrL7Rt2xYA8N133+GVV17B0aNH8c033yAiIqI4mySq8oyMgA4dpFvGp6RIk6b9/aXyI0eky0ptbYHAQOCXX/K/ISMRUWHduwcsWCCNMPfsKYWfnEdU7NsHxMdLp7uqSvgpqmIFoCdPnkClUgEADhw4gJ49ewIAmjZtiuScOyQRUYHMzKQrKyIjgWvXgPnzgRYtpInT330nnX+vXx8IDQViYw3dWiKqTGJjpcdR1K8PTJ4snXq3sgI++AC4dAn48Ufpwgy5z0EsVvdbtGiBNWvW4NChQ9i/fz8CAgIAADdv3kSdyjRdnKgCsLeXfjHFxQGnTgHjxwN160qX1C9eDLRqBXh4AJ999uwOrEREz3vyBNi6FfD1lX5nfPWV9HwuDw/psvbr16U/tAzxfK6KqlhzgKKjo9GnTx+o1WoEBwdj3bp1AID//e9/OH/+PHbs2FHqDS1PnANEhvbkiTQ6tHGj9NdadrZUbmQknTYLCpIuqTc1NWw7iciwUlKkR1SsWaP/iIp+/aT5Pe3by+uGrOVyGbxGo4FarYbVcw9BSkpKgpmZGerVq1ecTVYYDEBUkdy/L/1lt3GjdFv6HObm0qTp4GD5/ZIjkjMhgGPHnj2i4skTqdzGRppL+N578r35apkHoEePHkEIAbP/v6PblStXsHPnTjRr1gz+/v7Fa3UFwgBEFdU//0iX02/cCFy9+qzcxUW6nD4oSPqaiKqeR4+ALVuky9hPn35W7uMjTWauyI+oKC9lHoC6dOmCvn374v3330daWhqaNm2K6tWr486dO1i0aBFGjhxZ7MZXBAxAVNFptcDvv0tBaNs2ICPj2XuvvSaNCg0YAFhYGK6NRFQ6rlx59oiKu3elMpXq2SMqvLwM276KpMwvgz99+jR8fX0BANu3b4eNjQ2uXLmCjRs3YtmyZcXZJBEVgZER0KkTsG4dkJoKfP010KWLdBrs8GFg+HDpkvpBg4A9e3hJPVFlk/OIij59pFHdTz+Vwk+DBsC8edKk5nXrGH5KolgB6OHDh6hVqxYAYN++fejbty+MjIzwr3/9C1euXCnVBhLRi5mZAYMHA3v3SpfUf/op0Ly5dAXI1q1At26AgwMwcSJw9qyhW0tEL/LgAbBqlXRbDD8/YNcuacS3c2dg507g8mVgyhTA2trQLa38ihWAGjVqhF27duHatWvYu3cvunTpAgC4desWTxkRGVDOfT/++gv44w9g3DjpF2VqKrBokXRJrKendHl9aqqhW0tEORISpFtgODhIp7Xi44EaNaQHkp47Bxw4APTuXTmfz1VRFWsO0Pbt2/H2229Do9HgjTfewP7/v7d2eHg4fv/9d+zZs6fUG1qeOAeIqpInT6TTYBs3Sk97zrmkXqmULqkPDpbuEmtiYth2EsmNRiP931y+XLozc47GjZ89ooLz+IqmXC6DT0lJQXJyMjw8PGD0/7eTPHHiBMzNzdG0adPibLLCYACiqurePem02IYNwPHjz8otLKRHcAQFAe3a8ZJ6orJ07540f2fVKiAxUSpTKIDu3aWrufz8eJfm4iqXAJTj+vXrAKB7MnxVwABEcpCQIF1Sv2mT/iX1rq5SEBoyhHeNJSpNf/4pXcL+zTfSJe2A9IiKYcOAkSN5C4vSUOZXgWm1WsyZMwcWFhZwcnKCk5MTLC0tMXfuXGi12mI1mojKl5sb8NFH0l+gv/4KDB0qzTm4dAkIC5N+GXfsKN1SPz3d0K0lqpyePJGe79ehgzT/7ssvpfDj7g6sXStdzbVgAcOPIRRrBGjatGn46quvMHv2bLRv3x4AcPjwYcyaNQvDhw/Hxx9/XOoNLU8cASK5ysyUrjTZsEG6BDfnt4OJiXQ5blCQNDxfrZph20lU0aWkSAFnzRrg5k2pTKl89oiK117jqeayUOanwOzt7bFmzRrdU+Bz/PDDDxg1ahRu3LhR1E1WKAxARNJfpt98I4Wh+Phn5XZ20mX3QUFAy5aGax9RRSOENLduxQpp1CfnERX16j17REX9+oZtY1VX5gHIxMQEZ8+eRZMmTfTKExIS4OnpiUc5JzcrKQYgomeEkJ5Sv2EDsHnzszvRAtJTp4OCgLfekp5DRCRHOffcWr5c+r+S41//evaICpXKcO2TkzKfA+Th4YEVK1bkKV+xYgXc3d2Ls0kiqqAUCqB1a+mX+82b0o3Z+vQBqlcHzpwB/vtf6a/aHj2kx3I8fmzoFhOVj6tXgWnTpHv3DB0qhR+VSvr65Enp4cVvv83wU1EVawTot99+Q/fu3dGgQQP4+PgAAGJiYnDt2jXs3r1b95iMyoojQEQvd/fus0vqT5x4Vm5p+eySeh8fznOgqkUI4OBB6TTXDz9Id2kGAEdH6aaFw4YBdesato1yVi6Xwd+8eRMrV67E+fPnAQDNmjXDiBEj8NFHH+GLL74oziYrDAYgyo9GAxw6BCQnS/NgfH15V9Yc589LN1rctEmaO5SjUaNnl9Q7OxuseUQllpEh/XyvWAH8/fez8jfekCY19+jBiwMqgnK9D9Dz/vzzT7z66qvQaDSltUmDYACi3HbskG5T//yHu4MDsHQp0Lev4dpV0Wi1QHS0NCr0/ffSVWU5OnaUwlD//gD/W1Fl8c8/wMqVQEQEoFZLZTVqSD/Lo0dLz+yiioMBqIQYgOh5O3ZIH9q5/6fknNrZvp0hKD8ZGdL3buNG6T5DOd8/U1NpDlFwsPSAR46iUUWj1T57RMXevc/KGzeWQs/QoXxERUXFAFRCDECUQ6ORTt08P/LzPIVCGglKTOQH+YtcuwZ8/bU0MpSQ8Kzc3l66pD44mH9Jk+Hdvw+sXy+N+Fy+LJXlPKJizBjgzTf5iIqKrsyvAiOSi0OHCg4/gDSqce2aVI8K5ugoXS0THy/dJ2X0aKB2bemqsgULgFdeAby8gGXLgNu3Dd1akpuzZ4ERI6SrGSdOlMKPpaX09YUL0kOE/f0ZfqqaIk3Z6vuScf60tLRiNWLlypVYsGABUlJS4OHhgeXLl6Nt27b51t2xYwc++eQTXLx4EU+ePEHjxo0xceJEDBkyRFdHCIGwsDCsXbsWaWlpaN++PVavXo3GjRsXq30kX8nJpVtP7hQKoG1baVm0CPjlF+kU2c8/A6dPS8vEiUDXrtKo0L//zUuIqWw8eSLd0mHFCuD335+Vt2wp3btn8GDAzMxgzaNyUKQ8a2Fh8cLFyckJQUFBRWrA1q1bERoairCwMJw+fRoeHh7w9/fHrVu38q1fu3ZtfPjhh4iJicHZs2cREhKCkJAQ7H3uRO38+fOxbNkyrFmzBsePH0eNGjXg7++Px7xBCRWRnV3p1qNnjI2luUA7d0oBcvlyoE0b4OlT6S/u/v2l7+uoUcCxY3nnYBEVR2qq9Ay8hg2BgQOl8KNUAgMGSF//+ScwfDjDjywIA2vbtq0YPXq07rVGoxH29vYiPDy80Nto1aqVmD59uhBCCK1WK2xtbcWCBQt076elpQmVSiU2b95cqO2lp6cLACI9Pb3QbaCq6elTIRwchFAohJA+gvUXhUIIR0epHpWOc+eEmDpViPr19b/XTZoI8dFHQiQlGbqFVNlotUIcOybE4MFCVK/+7GeqXj0hZswQ4vp1Q7eQSktRPr8NekYzOzsbp06dgp+fn67MyMgIfn5+iImJeen6QghERUUhISEBHTp0AAAkJiYiJSVFb5sWFhbw9vYucJtZWVlQq9V6CxEg/WW4dKn0de4b+uW8XrKEE6BLU/PmQHg4cOUKsH+/dA8hMzPpcuTp06VJ6a+/Ll2W/OCBoVtLFdnjx9LE+7ZtpcdSfPONdOrrX/+SJuVfvQrMmcPnc8mVQQPQnTt3oNFoYJPrIUI2NjZISUkpcL309HTUrFkTxsbG6N69O5YvX44333wTAHTrFWWb4eHheqfyHB0dS9ItqmL69pUudc/9S9LBgZfAlyWlUnry/MaN0pO1IyKkm84pFNK9hkJCAFtbKSDt3y9dsUcESBcm/O9/0uT7oUOBP/6Q5pIFBz97RMXgwZxfJneV8r6VtWrVQmxsLDIyMhAVFYXQ0FC4uLigU6dOxdretGnTEBoaqnutVqsZgkhP375Ar168E7Sh1KolfXgFB0t/tedcUv/PP9LXX38tBdR33pFuUNe8uaFbTOVNCCkYr1ghTW5+/hEVI0cC777LR1SQPoMGIGtrayiVSqSmpuqVp6amwtbWtsD1jIyM0KhRIwCAp6cn4uPjER4ejk6dOunWS01Nhd1zM1NTU1Ph6emZ7/ZUKhVU/FOAXkKpBIqZsakUNWgg/XU/bZr0DLKNG6Wn1N+4AXz6qbS0bi0FoUGD+KFX1WVkSAF4xQrg3Lln5a+/Lt27p2dPPqKC8mfQU2DGxsbw8vJCVFSUrkyr1SIqKkr3kNXC0Gq1yMrKAgA0bNgQtra2ettUq9U4fvx4kbZJRBWbQgF4e0s3rUtOlh690auX9GH3xx/AuHHSjRZ795buSP3/vyKoirhwAfjvf6VT0SNHSuHHzAx4/33gr7+ku4/37cvwQwUz+I9GaGgogoOD0bp1a7Rt2xZLlixBZmYmQkJCAABBQUGoX78+wsPDAUjzdVq3bg1XV1dkZWVh9+7d2LRpE1avXg0AUCgUmDBhAj766CM0btwYDRs2xIwZM2Bvb4/evXsbqptEVIZUKunDrm9f6UaKW7ZIp8hOnZKe2P3DD9KNFwcNkkaG2rYt+Cn1OdcIabXPlpe9Lq065bVOZW/LgwfSPJ4cjRpJoz3BwdINDIkKw+ABKDAwELdv38bMmTORkpICT09PREZG6iYxX716FUbP3X4zMzMTo0aNwvXr12FqaoqmTZvi66+/RmBgoK7O5MmTkZmZiREjRiAtLQ2vvfYaIiMjYWJiUu79I6LyVbeudCO7sWOlUYGNG6VTJDdvAqtWSYupqVQ3vw9XqhwUCqBbNyn4dOnCuzRT0ZXqs8CqCj4LjKhq0WikUyIbNkinwx49Kt3tKxTSYmSkv+QuK6s6ctu3Uimd/nR1Ld3jSJVfUT6/DT4CRERU1pRK6UGWb74JZGZKdwMurQ/jnPBDRJULAxARyUqNGoCLi6FbQUSGxrOmREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7FSIArVy5Es7OzjAxMYG3tzdOnDhRYN21a9fC19cXVlZWsLKygp+fX576Q4cOhUKh0FsCAgLKuhtERERUSRg8AG3duhWhoaEICwvD6dOn4eHhAX9/f9y6dSvf+tHR0Xjrrbdw8OBBxMTEwNHREV26dMGNGzf06gUEBCA5OVm3bN68uTy6Q0RERJWAQgghDNkAb29vtGnTBitWrAAAaLVaODo6YuzYsZg6depL19doNLCyssKKFSsQFBQEQBoBSktLw65du4rVJrVaDQsLC6Snp8Pc3LxY2yAiIqLyVZTPb4OOAGVnZ+PUqVPw8/PTlRkZGcHPzw8xMTGF2sbDhw/x5MkT1K5dW688Ojoa9erVg5ubG0aOHIm7d+8WuI2srCyo1Wq9hYiqHo0GiI4GNm+W/tVoDN0iIjIUgwagO3fuQKPRwMbGRq/cxsYGKSkphdrGlClTYG9vrxeiAgICsHHjRkRFReHTTz/Fb7/9hq5du0JTwG+78PBwWFhY6BZHR8fid4qIKqQdOwBnZ+D114G335b+dXaWyolIfqoZugElMW/ePGzZsgXR0dEwMTHRlQ8aNEj3dcuWLeHu7g5XV1dER0ejc+fOebYzbdo0hIaG6l6r1WqGIKIqZMcOoH9/IPcJ/xs3pPLt24G+fQ3TNiIyDIOOAFlbW0OpVCI1NVWvPDU1Fba2ti9cd+HChZg3bx727dsHd3f3F9Z1cXGBtbU1Ll68mO/7KpUK5ubmegsRVQ0aDTB+fN7wAzwrmzCBp8OI5MagAcjY2BheXl6IiorSlWm1WkRFRcHHx6fA9ebPn4+5c+ciMjISrVu3ful+rl+/jrt378LOzq5U2k1ElcehQ8D16wW/LwRw7ZpUj4jkw+CXwYeGhmLt2rXYsGED4uPjMXLkSGRmZiIkJAQAEBQUhGnTpunqf/rpp5gxYwbWrVsHZ2dnpKSkICUlBRkZGQCAjIwMfPDBBzh27BiSkpIQFRWFXr16oVGjRvD39zdIH4nIcJKTS7ceEVUNBp8DFBgYiNu3b2PmzJlISUmBp6cnIiMjdROjr169CiOjZzlt9erVyM7ORv/+/fW2ExYWhlmzZkGpVOLs2bPYsGED0tLSYG9vjy5dumDu3LlQqVTl2jciMrzCDvxygJhIXgx+H6CKiPcBIqo6NBrpaq8bN/KfB6RQAA4OQGIioFSWe/OIqBRVmvsAERGVNaUSWLpU+lqh0H8v5/WSJQw/RHLDAEREVV7fvtKl7vXr65c7OPASeCK5MvgcICKi8tC3L9Crl3S1V3KyNOfH15cjP0RyxQBERLKhVAKdOhm6FURUEfAUGBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJToUIQCtXroSzszNMTEzg7e2NEydOFFh37dq18PX1hZWVFaysrODn55envhACM2fOhJ2dHUxNTeHn54cLFy6UdTeIiIiokjB4ANq6dStCQ0MRFhaG06dPw8PDA/7+/rh161a+9aOjo/HWW2/h4MGDiImJgaOjI7p06YIbN27o6syfPx/Lli3DmjVrcPz4cdSoUQP+/v54/PhxeXWLiIiIKjCFEEIYsgHe3t5o06YNVqxYAQDQarVwdHTE2LFjMXXq1Jeur9FoYGVlhRUrViAoKAhCCNjb22PixImYNGkSACA9PR02NjaIiIjAoEGDXrpNtVoNCwsLpKenw9zcvGQdJCIionJRlM9vg44AZWdn49SpU/Dz89OVGRkZwc/PDzExMYXaxsOHD/HkyRPUrl0bAJCYmIiUlBS9bVpYWMDb27vQ2yQiIqKqrZohd37nzh1oNBrY2NjoldvY2OD8+fOF2saUKVNgb2+vCzwpKSm6beTeZs57uWVlZSErK0v3Wq1WF7oPREREVPkYfA5QScybNw9btmzBzp07YWJiUuzthIeHw8LCQrc4OjqWYiuJiIioojFoALK2toZSqURqaqpeeWpqKmxtbV+47sKFCzFv3jzs27cP7u7uuvKc9YqyzWnTpiE9PV23XLt2rTjdISIiokrCoAHI2NgYXl5eiIqK0pVptVpERUXBx8enwPXmz5+PuXPnIjIyEq1bt9Z7r2HDhrC1tdXbplqtxvHjxwvcpkqlgrm5ud5CREREVZdB5wABQGhoKIKDg9G6dWu0bdsWS5YsQWZmJkJCQgAAQUFBqF+/PsLDwwEAn376KWbOnIlvv/0Wzs7Ounk9NWvWRM2aNaFQKDBhwgR89NFHaNy4MRo2bIgZM2bA3t4evXv3NlQ3iYiIqAIxeAAKDAzE7du3MXPmTKSkpMDT0xORkZG6ScxXr16FkdGzgarVq1cjOzsb/fv319tOWFgYZs2aBQCYPHkyMjMzMWLECKSlpeG1115DZGRkieYJERERUdVh8PsAVUS8DxAREVHlU2nuA0RERERkCAY/BUZERFRYGg1w6BCQnAzY2QG+voBSaehWUWXEAERERJXCjh3A+PHA9evPyhwcgKVLgb59Ddcuqpx4CoyIiCq8HTuA/v31ww8A3Lghle/YYZh2UeXFAERERBWaRiON/OR3yU5O2YQJUj2iwmIAIiKiCu3QobwjP88TArh2TapHVFgMQEREVKElJ5duPSKAAYiIiCo4O7vSrUcEMAAREVEF5+srXe2lUOT/vkIBODpK9YgKiwGIiIgqNKVSutQdyBuCcl4vWcL7AVHRMAAREVGF17cvsH07UL++frmDg1TO+wBRUfFGiEREVCn07Qv06sU7QVPpYAAiIqJKQ6kEOnUydCuoKuApMCIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh2DB6CVK1fC2dkZJiYm8Pb2xokTJwqse+7cOfTr1w/Ozs5QKBRYsmRJnjqzZs2CQqHQW5o2bVqGPSAiIqLKxqABaOvWrQgNDUVYWBhOnz4NDw8P+Pv749atW/nWf/jwIVxcXDBv3jzY2toWuN0WLVogOTlZtxw+fLisukBERESVkEED0KJFizB8+HCEhISgefPmWLNmDczMzLBu3bp867dp0wYLFizAoEGDoFKpCtxutWrVYGtrq1usra3LqgtERERUCRksAGVnZ+PUqVPw8/N71hgjI/j5+SEmJqZE275w4QLs7e3h4uKCwYMH4+rVqy+sn5WVBbVarbcQERFR1WWwAHTnzh1oNBrY2NjoldvY2CAlJaXY2/X29kZERAQiIyOxevVqJCYmwtfXFw8ePChwnfDwcFhYWOgWR0fHYu+fiIiIKj6DT4IubV27dsWAAQPg7u4Of39/7N69G2lpafjuu+8KXGfatGlIT0/XLdeuXSvHFhMREVF5q2aoHVtbW0OpVCI1NVWvPDU19YUTnIvK0tISTZo0wcWLFwuso1KpXjiniIiIiEpOowEOHQKSkwE7O8DXF1AqDdMWg40AGRsbw8vLC1FRUboyrVaLqKgo+Pj4lNp+MjIycOnSJdjZ2ZXaNomIiKhoduwAnJ2B118H3n5b+tfZWSo3BIOeAgsNDcXatWuxYcMGxMfHY+TIkcjMzERISAgAICgoCNOmTdPVz87ORmxsLGJjY5GdnY0bN24gNjZWb3Rn0qRJ+O2335CUlISjR4+iT58+UCqVeOutt8q9f0RERCSFnP79gevX9ctv3JDKDRGCDHYKDAACAwNx+/ZtzJw5EykpKfD09ERkZKRuYvTVq1dhZPQso928eROtWrXSvV64cCEWLlyIjh07Ijo6GgBw/fp1vPXWW7h79y7q1q2L1157DceOHUPdunXLtW9EREQknfYaPx4QIu97QgAKBTBhAtCrV/meDlMIkV+T5E2tVsPCwgLp6ekwNzc3dHOIiIgqreho6XTXyxw8CHTqVLJ9FeXzu8pdBUZEREQVR3Jy6dYrLQxAREREVGYKew1SeV+rxABEREREZcbXF3BwkOb65EehABwdpXrliQGIiIiIyoxSCSxdKn2dOwTlvF6ypPzvB8QARERERGWqb19g+3agfn39cgcHqbxv3/Jvk0EvgyciIiJ56NtXutS9otwJmgGIiIiIyoVSWfJL3UsLT4ERERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHs8E7Q+RBCAADUarWBW0JERESFlfO5nfM5/iIMQPl48OABAMDR0dHALSEiIqKievDgASwsLF5YRyEKE5NkRqvV4ubNm6hVqxYUCkWpblutVsPR0RHXrl2Dubl5qW67ImD/Kr+q3kf2r/Kr6n1k/4pPCIEHDx7A3t4eRkYvnuXDEaB8GBkZwcHBoUz3YW5uXiV/sHOwf5VfVe8j+1f5VfU+sn/F87KRnxycBE1ERESywwBEREREssMAVM5UKhXCwsKgUqkM3ZQywf5VflW9j+xf5VfV+8j+lQ9OgiYiIiLZ4QgQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DUCn6/fff0aNHD9jb20OhUGDXrl0vXSc6OhqvvvoqVCoVGjVqhIiIiDJvZ3EVtX/R0dFQKBR5lpSUlPJpcBGFh4ejTZs2qFWrFurVq4fevXsjISHhpett27YNTZs2hYmJCVq2bIndu3eXQ2uLpzh9jIiIyHMMTUxMyqnFRbN69Wq4u7vrbrDm4+ODPXv2vHCdynT8gKL3sTIdv/zMmzcPCoUCEyZMeGG9ynYccxSmf5XtGM6aNStPe5s2bfrCdQxx/BiASlFmZiY8PDywcuXKQtVPTExE9+7d8frrryM2NhYTJkzAu+++i71795ZxS4unqP3LkZCQgOTkZN1Sr169Mmphyfz2228YPXo0jh07hv379+PJkyfo0qULMjMzC1zn6NGjeOuttzBs2DCcOXMGvXv3Ru/evfHXX3+VY8sLrzh9BKQ7tj5/DK9cuVJOLS4aBwcHzJs3D6dOncIff/yBN954A7169cK5c+fyrV/Zjh9Q9D4Clef45Xby5El8/vnncHd3f2G9yngcgcL3D6h8x7BFixZ67T18+HCBdQ12/ASVCQBi586dL6wzefJk0aJFC72ywMBA4e/vX4YtKx2F6d/BgwcFAHH//v1yaVNpu3XrlgAgfvvttwLrDBw4UHTv3l2vzNvbW7z33ntl3bxSUZg+rl+/XlhYWJRfo0qZlZWV+PLLL/N9r7Ifvxwv6mNlPX4PHjwQjRs3Fvv37xcdO3YU48ePL7BuZTyORelfZTuGYWFhwsPDo9D1DXX8OAJkQDExMfDz89Mr8/f3R0xMjIFaVDY8PT1hZ2eHN998E0eOHDF0cwotPT0dAFC7du0C61T2Y1iYPgJARkYGnJyc4Ojo+NLRhopCo9Fgy5YtyMzMhI+PT751KvvxK0wfgcp5/EaPHo3u3bvnOT75qYzHsSj9AyrfMbxw4QLs7e3h4uKCwYMH4+rVqwXWNdTx48NQDSglJQU2NjZ6ZTY2NlCr1Xj06BFMTU0N1LLSYWdnhzVr1qB169bIysrCl19+iU6dOuH48eN49dVXDd28F9JqtZgwYQLat2+PV155pcB6BR3DijrP6XmF7aObmxvWrVsHd3d3pKenY+HChWjXrh3OnTtX5g8NLo64uDj4+Pjg8ePHqFmzJnbu3InmzZvnW7eyHr+i9LGyHT8A2LJlC06fPo2TJ08Wqn5lO45F7V9lO4be3t6IiIiAm5sbkpOTMXv2bPj6+uKvv/5CrVq18tQ31PFjAKIy4+bmBjc3N93rdu3a4dKlS1i8eDE2bdpkwJa93OjRo/HXX3+98Lx1ZVfYPvr4+OiNLrRr1w7NmjXD559/jrlz55Z1M4vMzc0NsbGxSE9Px/bt2xEcHIzffvutwIBQGRWlj5Xt+F27dg3jx4/H/v37K/RE3+IqTv8q2zHs2rWr7mt3d3d4e3vDyckJ3333HYYNG2bAluljADIgW1tbpKam6pWlpqbC3Ny80o/+FKRt27YVPlSMGTMGP//8M37//feX/nVV0DG0tbUtyyaWWFH6mFv16tXRqlUrXLx4sYxaVzLGxsZo1KgRAMDLywsnT57E0qVL8fnnn+epW1mPX1H6mFtFP36nTp3CrVu39EaJNRoNfv/9d6xYsQJZWVlQKpV661Sm41ic/uVW0Y9hbpaWlmjSpEmB7TXU8eMcIAPy8fFBVFSUXtn+/ftfeC6/souNjYWdnZ2hm5EvIQTGjBmDnTt34tdff0XDhg1fuk5lO4bF6WNuGo0GcXFxFfY45qbVapGVlZXve5Xt+BXkRX3MraIfv86dOyMuLg6xsbG6pXXr1hg8eDBiY2PzDQeV6TgWp3+5VfRjmFtGRgYuXbpUYHsNdvzKdIq1zDx48ECcOXNGnDlzRgAQixYtEmfOnBFXrlwRQggxdepUMWTIEF39y5cvCzMzM/HBBx+I+Ph4sXLlSqFUKkVkZKShuvBCRe3f4sWLxa5du8SFCxdEXFycGD9+vDAyMhIHDhwwVBdeaOTIkcLCwkJER0eL5ORk3fLw4UNdnSFDhoipU6fqXh85ckRUq1ZNLFy4UMTHx4uwsDBRvXp1ERcXZ4guvFRx+jh79myxd+9ecenSJXHq1CkxaNAgYWJiIs6dO2eILrzQ1KlTxW+//SYSExPF2bNnxdSpU4VCoRD79u0TQlT+4ydE0ftYmY5fQXJfJVUVjuPzXta/ynYMJ06cKKKjo0ViYqI4cuSI8PPzE9bW1uLWrVtCiIpz/BiASlHOZd+5l+DgYCGEEMHBwaJjx4551vH09BTGxsbCxcVFrF+/vtzbXVhF7d+nn34qXF1dhYmJiahdu7bo1KmT+PXXXw3T+ELIr28A9I5Jx44ddf3N8d1334kmTZoIY2Nj0aJFC/HLL7+Ub8OLoDh9nDBhgmjQoIEwNjYWNjY2olu3buL06dPl3/hC+M9//iOcnJyEsbGxqFu3rujcubMuGAhR+Y+fEEXvY2U6fgXJHRCqwnF83sv6V9mOYWBgoLCzsxPGxsaifv36IjAwUFy8eFH3fkU5fgohhCjbMSYiIiKiioVzgIiIiEh2GICIiIhIdhiAiIiISHYYgIiIiEh2GICIiIhIdhiAiIiISHYYgIiIiEh2GICIiAqgUCiwa9cuQzeDiMoAAxARVUhDhw6FQqHIswQEBBi6aURUBfBp8ERUYQUEBGD9+vV6ZSqVykCtIaKqhCNARFRhqVQq2Nra6i1WVlYApNNTq1evRteuXWFqagoXFxds375db/24uDi88cYbMDU1RZ06dTBixAhkZGTo1Vm3bh1atGgBlUoFOzs7jBkzRu/9O3fuoE+fPjAzM0Pjxo3x448/6t67f/8+Bg8ejLp168LU1BSNGzfOE9iIqGJiACKiSmvGjBno168f/vzzTwwePBiDBg1CfHw8ACAzMxP+/v6wsrLCyZMnsW3bNhw4cEAv4KxevRqjR4/GiBEjEBcXhx9//BGNGjXS28fs2bMxcOBAnD17Ft26dcPgwYNx79493f7//vtv7NmzB/Hx8Vi9ejWsra3L7xtARMVX5o9bJSIqhuDgYKFUKkWNGjX0lo8//lgIIT3Z/v3339dbx9vbW4wcOVIIIcQXX3whrKysREZGhu79X375RRgZGYmUlBQhhBD29vbiww8/LLANAMT06dN1rzMyMgQAsWfPHiGEED169BAhISGl02EiKlecA0REFdbrr7+O1atX65XVrl1b97WPj4/eez4+PoiNjQUAxMfHw8PDAzVq1NC93759e2i1WiQkJEChUODmzZvo3LnzC9vg7u6u+7pGjRowNzfHrVu3AAAjR45Ev379cPr0aXTp0gW9e/dGu3btitVXIipfDEBEVGHVqFEjzymp0mJqalqoetWrV9d7rVAooNVqAQBdu3bFlStXsHv3buzfvx+dO3fG6NGjsXDhwlJvLxGVLs4BIqJK69ixY3leN2vWDADQrFkz/Pnnn8jMzNS9f+TIERgZGcHNzQ21atWCs7MzoqKiStSGunXrIjg4GF9//TWWLFmCL774okTbI6LywREgIqqwsrKykJKSoldWrVo13UTjbdu2oXXr1njttdfwzTff4MSJE/jqq68AAIMHD0ZYWBiCg4Mxa9Ys3L59G2PHjsWQIUNgY2MDAJg1axbef/991KtXD127dsWDBw9w5MgRjB07tlDtmzlzJry8vNCiRQtkZWXh559/1gUwIqrYGICIqMKKjIyEnZ2dXpmbmxvOnz8PQLpCa8uWLRg1ahTs7OywefNmNG/eHABgZmaGvXv3Yvz48WjTpg3MzMzQr18/LFq0SLet4OBgPH78GIsXL8akSZNgbW2N/v37F7p9xsbGmDZtGpKSkmBqagpfX19s2bKlFHpORGVNIYQQhm4EEVFRKRQK7Ny5E7179zZ0U4ioEuIcICIiIpIdBiAiIiKSHc4BIqJKiWfviagkOAJEREREssMARERERLLDAERERESywwBEREREssMARERERLLDAERERESywwBEREREssMARERERLLDAERERESy8381fdt/P/HvIAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate the model on test data to see how it performs"
      ],
      "metadata": {
        "id": "sM5lWdQv-B4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evaluate(test_data, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPcC4etM4eUo",
        "outputId": "fd3516ac-a6c6-4c6c-cda4-e97f32785ee9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 5s 7ms/step - loss: 0.3548 - accuracy: 0.8608\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lets now use our model to make predictions\n",
        "### Preprocessing the input text\n",
        "- Before feeding any review to our model, we need to convert the text into word embedding form\n",
        "- Here I have created a simple method to convert any given text into word embebbing form\n",
        "- Also, I have created a decode method to convert the encoded data back into simple words"
      ],
      "metadata": {
        "id": "d-wv_yWb-O1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_index = imdb.get_word_index()\n",
        "\n",
        "def encode_text(text):\n",
        "  tokens = tf.keras.preprocessing.text.text_to_word_sequence(text)\n",
        "  tokens = [word_index[word] if word in word_index else 0 for word in tokens]\n",
        "  return sequence.pad_sequences([tokens], MAXLEN)[0]\n",
        "\n",
        "reverse_word_index = {value: key for (key, value) in word_index.items()}\n",
        "\n",
        "def decode_text(integers):\n",
        "  PAD = 0\n",
        "  text = \"\"\n",
        "  for num in integers:\n",
        "    if num != PAD:\n",
        "      text += reverse_word_index[num] + \" \"\n",
        "  return text[:-1]"
      ],
      "metadata": {
        "id": "hPcAo0-H5Jem"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lets finally make predictions with our model\n",
        "- Here I have defined a method which takes text as input, encodes it, performs predictions with model, and prints the result"
      ],
      "metadata": {
        "id": "mKxOsjtE_iTh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(text):\n",
        "  encoded_text = encode_text(text)\n",
        "  pred = model.predict(np.expand_dims(encoded_text, 0))[0]\n",
        "\n",
        "  print(pred)\n",
        "  if(pred > 0.5):\n",
        "    print(\"Positive review\")\n",
        "  else:\n",
        "    print(\"Negative review\")"
      ],
      "metadata": {
        "id": "--ZPCKYp_QbW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review1 = \"Everyone has done a great job with this movie. Really enjoyed it. Definitely recommended to everyone.\"\n",
        "predict(review1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWG6bhD4BSmG",
        "outputId": "d889cbba-785d-44d6-f71c-08818d9f9ed0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 364ms/step\n",
            "[0.8649821]\n",
            "Positive review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review2 = \"One of the worst movies I have ever watched.\"\n",
        "predict(review2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qv5UgiMFCIMx",
        "outputId": "d0155d2b-d8dd-4ec2-efcc-1007c4dceb79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "[0.40100977]\n",
            "Negative review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review3 = \"One of the best movies ever made. Christopher Nolan never disappoints.\"\n",
        "predict(review3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tro7XxM6GiWH",
        "outputId": "c8882141-538a-4444-a0e4-44ad9dca05a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "[0.7085494]\n",
            "Positive review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review3 = \"Do not watch this movie, It is a complete waste of time.\"\n",
        "predict(review3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5Mr_SyaCfEt",
        "outputId": "975f8b05-d2b0-4021-fbb0-36445c826d4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n",
            "[0.17121255]\n",
            "Negative review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review4 = \"One of the weakest entries in the J-horror remake sweepstakes, One Missed Call is undone by bland performances and shopworn shocks.\"\n",
        "predict(review4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmXjynMAEOBS",
        "outputId": "4f282b57-56bd-41db-d718-191b6b4fed23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "[0.30014524]\n",
            "Negative review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review5 = \"This movie deserves an oscar. What a plot, directions, acting and everything. Go watch this movie and enjoy the drama, suspense and thrill that is packed into this well-done movie.\"\n",
        "predict(review5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4_S2NADycGg",
        "outputId": "0acae701-0256-4f31-aea8-6ddc486f2203"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "[0.9174098]\n",
            "Positive review\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review6 = \"Seriously? This is what everyone was waiting for for the past 2 years? Really annoying, cannot even bear a watch.\"\n",
        "predict(review6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCb225NizBMP",
        "outputId": "2dcc8b68-79a3-431d-af5b-d4e0ca426ebf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "[0.5489196]\n",
            "Positive review\n"
          ]
        }
      ]
    }
  ]
}