{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tYNpi1MX7FmW"
   },
   "source": [
    "<div style='text-align: center;'>\n",
    "    <img src='https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQzJzIHdangJTrH2mFXFgsLjuLCjpfXXwbxg&usqp=CAU' width='100'/>\n",
    "    <h1>Sharif University of Technology</h1>\n",
    "    <h2>Natural Language Processing</h2>\n",
    "    <h3>Final Project</h3>\n",
    "    <h4>Spoiler classification and summary generation</h4>\n",
    "    <p><strong>Authors:</strong> Ali Nikkhah, Ramtin Khoshnevis, Sarina Zahedi</p>\n",
    "    <p><strong>(Equal Contribution)</strong></p>\n",
    "</div>\n",
    "<hr/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "shi9eHWZaUNq",
    "outputId": "f70d8fff-e62f-4bc2-b0fe-5ec2d9592c89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets\n",
      "  Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
      "Collecting pyarrow>=15.0.0 (from datasets)\n",
      "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
      "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
      "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
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      "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n",
      "Collecting xxhash (from datasets)\n",
      "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
      "Collecting multiprocess (from datasets)\n",
      "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
      "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
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      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
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      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.3.5)\n",
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      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
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      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.12.2)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.7)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.7.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
      "Downloading datasets-2.21.0-py3-none-any.whl (527 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets\n",
      "  Attempting uninstall: pyarrow\n",
      "    Found existing installation: pyarrow 14.0.2\n",
      "    Uninstalling pyarrow-14.0.2:\n",
      "      Successfully uninstalled pyarrow-14.0.2\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
      "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3GvN0W_o5lCV",
    "outputId": "acf96d10-3d87-4369-9920-5ce825f16710"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.7.4)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
      "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
      "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n"
     ]
    }
   ],
   "source": [
    "!pip install kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YbqRqUMT7cuE",
    "outputId": "320e8152-397a-41fe-ee9f-4b3cfdb98037"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp: cannot stat 'kaggle.json': No such file or directory\n",
      "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n",
      "Dataset URL: https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset\n",
      "License(s): Attribution 4.0 International (CC BY 4.0)\n",
      "Downloading imdb-spoiler-dataset.zip to /content\n",
      "100% 330M/331M [00:18<00:00, 23.0MB/s]\n",
      "100% 331M/331M [00:18<00:00, 19.1MB/s]\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "!chmod 600 ~/.kaggle/kaggle.json\n",
    "\n",
    "# Download the dataset\n",
    "!kaggle datasets download -d rmisra/imdb-spoiler-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fAIBnB8I7jIv",
    "outputId": "ba9dc80c-26c0-4ade-f22e-a4f4405e1dea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  imdb-spoiler-dataset.zip\n",
      "  inflating: IMDB_movie_details.json  \n",
      "  inflating: IMDB_reviews.json       \n",
      "IMDB_movie_details.json  IMDB_reviews.json  imdb-spoiler-dataset.zip  sample_data\n"
     ]
    }
   ],
   "source": [
    "!unzip imdb-spoiler-dataset.zip\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
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     "height": 538
    },
    "id": "7dTJhfAd76Qu",
    "outputId": "bb68ad6b-8364-44ad-8ba7-1dca651499f9"
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   "outputs": [
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       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-8e53e93f-562f-4c0a-9edf-098685085d94 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "        review_date   movie_id    user_id  is_spoiler  \\\n",
       "0  10 February 2006  tt0111161  ur1898687        True   \n",
       "1  6 September 2000  tt0111161  ur0842118        True   \n",
       "2     3 August 2001  tt0111161  ur1285640        True   \n",
       "3  1 September 2002  tt0111161  ur1003471        True   \n",
       "4       20 May 2004  tt0111161  ur0226855        True   \n",
       "\n",
       "                                         review_text  rating  \\\n",
       "0  In its Oscar year, Shawshank Redemption (writt...      10   \n",
       "1  The Shawshank Redemption is without a doubt on...      10   \n",
       "2  I believe that this film is the best story eve...       8   \n",
       "3  **Yes, there are SPOILERS here**This film has ...      10   \n",
       "4  At the heart of this extraordinary movie is a ...       8   \n",
       "\n",
       "                                  review_summary  \n",
       "0  A classic piece of unforgettable film-making.  \n",
       "1     Simply amazing. The best film of the 90's.  \n",
       "2               The best story ever told on film  \n",
       "3                     Busy dying or busy living?  \n",
       "4         Great story, wondrously told and acted  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "reviews_df = pd.read_json('IMDB_reviews.json' , lines=True)\n",
    "reviews_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tGnVLhg-N0Me",
    "outputId": "08cdf747-c2fe-4438-d09e-0bc46f34ddcf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of rows in the dataset: 573913\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total number of rows in the dataset: {reviews_df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ma4EVw06eMbK",
    "outputId": "6c4fcd54-d166-4bb0-b6c6-c6dfc863b8d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total reviews: 573913\n",
      "Number of spoiler reviews: 150924\n",
      "Number of non-spoiler reviews: 422989\n",
      "Average review length: 1460.5535246631457\n",
      "Median review length: 1052.0\n",
      "is_spoiler\n",
      "False    0.737026\n",
      "True     0.262974\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "print(\"Total reviews:\", len(reviews_df))\n",
    "print(\"Number of spoiler reviews:\", reviews_df['is_spoiler'].sum())\n",
    "print(\"Number of non-spoiler reviews:\", len(reviews_df) - reviews_df['is_spoiler'].sum())\n",
    "\n",
    "reviews_df['review_length'] = reviews_df['review_text'].apply(len)\n",
    "print(\"Average review length:\", reviews_df['review_length'].mean())\n",
    "print(\"Median review length:\", reviews_df['review_length'].median())\n",
    "\n",
    "print(reviews_df['is_spoiler'].value_counts(normalize=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2AtkJ_uGr5o-"
   },
   "outputs": [],
   "source": [
    "reviews_df = reviews_df[:175000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XLF5DCd9jdYo",
    "outputId": "326f551c-d069-4d4a-ab66-f82c9a302eb4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "428"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(reviews_df['movie_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "10Kz-73LrNZM",
    "outputId": "764c23d4-905d-47aa-92ce-c0c7fdd18cb2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-ad5e2d5c4829>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  reviews_df['review_length'] = reviews_df['review_text'].apply(len)\n"
     ]
    }
   ],
   "source": [
    "reviews_df['review_length'] = reviews_df['review_text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "GgxuKkb8e24K",
    "outputId": "31db9f6d-44f4-4ab3-ec3a-55dfb73f8c08"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Review length distribution\n",
    "sns.histplot(reviews_df['review_length'], bins=50, kde=True)\n",
    "plt.title('Distribution of Review Lengths')\n",
    "plt.xlabel('Review Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "q--A_BllfLkp",
    "outputId": "7f481aac-5743-4f7e-e17b-73c660570361"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot for review lengths by spoiler\n",
    "sns.boxplot(x='is_spoiler', y='review_length', data=reviews_df)\n",
    "plt.title('Review Length by Spoiler Status')\n",
    "plt.xlabel('Is Spoiler')\n",
    "plt.ylabel('Review Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "DSI6GMmYTwGm",
    "outputId": "ac155fef-bf01-43db-e57d-fcd909941535"
   },
   "outputs": [
    {
     "data": {
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       "type": "dataframe",
       "variable_name": "data"
      },
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       "      <th>3</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "        document.querySelector('#df-3f8c4880-9af7-4578-a0c6-7e5fe568ffb7 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-3f8c4880-9af7-4578-a0c6-7e5fe568ffb7');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-96a3c505-f1bc-4def-9bdb-9f0d844aa333 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                         review_text  is_spoiler\n",
       "0  In its Oscar year, Shawshank Redemption (writt...        True\n",
       "1  The Shawshank Redemption is without a doubt on...        True\n",
       "2  I believe that this film is the best story eve...        True\n",
       "3  **Yes, there are SPOILERS here**This film has ...        True\n",
       "4  At the heart of this extraordinary movie is a ...        True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = reviews_df[['review_text', 'is_spoiler']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U8ss94fKF0ng",
    "outputId": "447aae8d-6038-4295-d0e7-07873814db51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "etofnLuVbIsH",
    "outputId": "4fd63613-3ded-46b2-83a3-c2ef2627945b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-331fbbe8856b>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['review_text'] = data['review_text'].apply(clean_text)\n"
     ]
    }
   ],
   "source": [
    "def clean_text(text):\n",
    "    text = text.lower()  # Convert to lowercase\n",
    "    text = text.replace('\\n', ' ')  # Replace newlines with spaces\n",
    "    text = text.replace('\\r', ' ')  # Replace carriage returns with spaces\n",
    "    text = ''.join([c if c.isalnum() or c.isspace() else ' ' for c in text])  # Remove special characters\n",
    "    text = ' '.join(text.split())  # Remove extra spaces\n",
    "    return text\n",
    "\n",
    "# Apply text cleaning to the dataset\n",
    "data['review_text'] = data['review_text'].apply(clean_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uLA4MoGFWcic",
    "outputId": "2488249f-6b26-4002-d54a-056b87116a2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 131250\n",
      "Validation set size: 21875\n",
      "Test set size: 21875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_data, temp_data = train_test_split(data, test_size=0.25, random_state=42)\n",
    "\n",
    "# Split the remainder into test and validation sets\n",
    "test_data, val_data = train_test_split(temp_data, test_size=0.5, random_state=42)\n",
    "\n",
    "# Display the sizes of each dataset\n",
    "print(\"Training set size:\", len(train_data))\n",
    "print(\"Validation set size:\", len(val_data))\n",
    "print(\"Test set size:\", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "vFswYX1OYD7W"
   },
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "import torch\n",
    "\n",
    "train_dataset = Dataset.from_pandas(train_data)\n",
    "val_dataset = Dataset.from_pandas(val_data)\n",
    "test_dataset = Dataset.from_pandas(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PAe4Amsln2MB",
    "outputId": "17d6ce1d-5e4c-4c55-f5dc-96a62fd176a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jnh68UU4fVNv"
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AMxuCcylYi8E",
    "outputId": "44c6fa5d-2a02-40ba-ba82-1aaf606ea377"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from optree->keras) (4.12.2)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras) (3.0.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install tensorflow\n",
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "MjJX0nS0YnPu"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "\n",
    "vocab_size = 8000\n",
    "max_length = 2000  # Maximum review length\n",
    "oov_tok = \"<OOV>\"\n",
    "\n",
    "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n",
    "tokenizer.fit_on_texts(train_data['review_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "-zSdWsUdZXbz"
   },
   "outputs": [],
   "source": [
    "def tokenize_and_pad(texts):\n",
    "    sequences = tokenizer.texts_to_sequences(texts)\n",
    "    padded_sequences = pad_sequences(sequences, maxlen=max_length, padding='post')\n",
    "    return padded_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "idHc4gFMZWK2"
   },
   "outputs": [],
   "source": [
    "X_train = tokenize_and_pad(train_data['review_text'])\n",
    "X_val = tokenize_and_pad(val_data['review_text'])\n",
    "X_test = tokenize_and_pad(test_data['review_text'])\n",
    "\n",
    "y_train = train_data['is_spoiler']\n",
    "y_val = val_data['is_spoiler']\n",
    "y_test = test_data['is_spoiler']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z_e8WL6IZgHq",
    "outputId": "231e57cc-5f26-4dd7-cd15-522328e43b18"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "\n",
    "from tensorflow.keras.layers import Bidirectional\n",
    "\n",
    "model = Sequential([\n",
    "    Embedding(vocab_size, 32, input_length=max_length),\n",
    "    Bidirectional(LSTM(64, return_sequences=True)),  # Using a bidirectional LSTM\n",
    "    Dropout(0.5),\n",
    "    Bidirectional(LSTM(64)),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "VoQMMtlcb1kS"
   },
   "outputs": [],
   "source": [
    "model.build(input_shape=(None, max_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "id": "dd-3mlCWZj9I",
    "outputId": "938cd928-8d2b-4e6e-8aa2-e61efd302386"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2000</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2000</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">49,664</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2000</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2000\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │         \u001b[38;5;34m256,000\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2000\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │          \u001b[38;5;34m49,664\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2000\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m98,816\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,609</span> (1.54 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m404,609\u001b[0m (1.54 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,609</span> (1.54 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m404,609\u001b[0m (1.54 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sKMQE1YGZlyW",
    "outputId": "5048b049-a722-4d30-99f4-68f82684db51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1000s\u001b[0m 242ms/step - accuracy: 0.7216 - loss: 0.5929 - val_accuracy: 0.7301 - val_loss: 0.5770\n",
      "Epoch 2/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1042s\u001b[0m 243ms/step - accuracy: 0.7339 - loss: 0.5535 - val_accuracy: 0.7552 - val_loss: 0.5142\n",
      "Epoch 3/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1053s\u001b[0m 245ms/step - accuracy: 0.7586 - loss: 0.5097 - val_accuracy: 0.7632 - val_loss: 0.5039\n",
      "Epoch 4/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1036s\u001b[0m 244ms/step - accuracy: 0.7700 - loss: 0.4951 - val_accuracy: 0.7632 - val_loss: 0.5038\n",
      "Epoch 5/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1051s\u001b[0m 246ms/step - accuracy: 0.7813 - loss: 0.4803 - val_accuracy: 0.7639 - val_loss: 0.5083\n",
      "Epoch 6/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1042s\u001b[0m 246ms/step - accuracy: 0.7862 - loss: 0.4705 - val_accuracy: 0.7631 - val_loss: 0.5041\n",
      "Epoch 7/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1043s\u001b[0m 246ms/step - accuracy: 0.7963 - loss: 0.4563 - val_accuracy: 0.7644 - val_loss: 0.5167\n",
      "Epoch 8/8\n",
      "\u001b[1m4102/4102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1043s\u001b[0m 247ms/step - accuracy: 0.8045 - loss: 0.4437 - val_accuracy: 0.7601 - val_loss: 0.5149\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=8, batch_size=32, validation_data=(X_val, y_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UnGyGSeo4K4H",
    "outputId": "b7e5ee3f-938a-41ad-ce94-5cb6ebec26b7"
   },
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Save the model in HDF5 forma\n",
    "model.save('my_fine_tuned_model.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WeZv_N194s8z",
    "outputId": "722c0f6a-edc8-462e-ce05-c20618833fc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "TmLn0V0D452u"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "model_path = '/content/drive/MyDrive/LSTMModel'\n",
    "os.makedirs(model_path, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-J6G6pG35DmR",
    "outputId": "cd1e3364-1e0b-45c6-a1f6-65cdc71711e2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Save the model in HDF5 format\n",
    "model.save(os.path.join(model_path, 'my_fine_tuned_model.h5'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "etkORlK83kd-"
   },
   "outputs": [],
   "source": [
    "!nvidia-smi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "MOlCZi49dw9l"
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VPW3AaGFnAk4",
    "outputId": "eddb477d-8a13-44fe-df8a-5bd0c5d70398"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m684/684\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 104ms/step - accuracy: 0.7551 - loss: 0.5165\n",
      "Test Loss: 0.5159615874290466\n",
      "Test Accuracy: 0.7574856877326965\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Test Loss: {loss}\")\n",
    "print(f\"Test Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hRSvT5cB5QMp",
    "outputId": "64462d92-b4e0-4b03-be6b-42379bf65ca9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
      "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (71.0.4)\n",
      "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
      "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.0)\n",
      "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
      "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.7.1)\n",
      "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.12.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.7.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.6)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.16.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy scikit-learn tensorflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T6uO6ZVX50JF",
    "outputId": "dd48288d-9706-4e15-acd6-542171bafae4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('my_fine_tuned_model.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "E-YKdjrE6I34"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XVxaRfFB6XVo",
    "outputId": "a3f35aa5-225d-47a7-97f2-3ef3ed3b97ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m684/684\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 117ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions = (predictions > 0.5).astype(int)  # Threshold predictions for binary classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vy-eg8JG6e1j",
    "outputId": "69cb6c8e-4837-417a-81f2-6a8aec773ef1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " Non-Spoiler       0.79      0.91      0.84     15695\n",
      "     Spoiler       0.62      0.38      0.47      6180\n",
      "\n",
      "    accuracy                           0.76     21875\n",
      "   macro avg       0.70      0.64      0.66     21875\n",
      "weighted avg       0.74      0.76      0.74     21875\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(y_test, predictions, target_names=['Non-Spoiler', 'Spoiler'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4sHnLnuQ7BNS",
    "outputId": "f053ae15-8842-42f0-b7df-ef63914f85f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
      "Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "5-c0qcYn8GqF"
   },
   "outputs": [],
   "source": [
    "def prepare_input(text, tokenizer, max_length):\n",
    "    sequences = tokenizer.texts_to_sequences([text])\n",
    "    padded = pad_sequences(sequences, maxlen=max_length, padding='post')\n",
    "    return padded\n",
    "\n",
    "def predict_spoiler(text, tokenizer, model, max_length=1500):\n",
    "    prepared_text = prepare_input(text, tokenizer, max_length)\n",
    "    prediction = model.predict(prepared_text)\n",
    "    is_spoiler = (prediction > 0.5).astype(int)  # Threshold the prediction\n",
    "    return bool(is_spoiler)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Unh_FyLx8Ith",
    "outputId": "4d7b676f-bcb3-4ffb-f9f9-97d6e54f9a44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 966ms/step\n",
      "The text is a non-spoiler.\n"
     ]
    }
   ],
   "source": [
    "# Test the function\n",
    "input_text = \"Jack Ryan is on a working vacation in London with his family. He has retired from the CIA and is a Professor at the US Naval Academy. He is seen delivering a lecture at the Royal Naval Academy in London.Meanwhile, Ryan's wife Cathy and daughter Sally are sightseeing near Buckingham Palace. Sally and Cathy come upon a British Royal Guard, and Sally tries to get the guard to react by doing an improvised tap dance in front of him. She's impressed when the guard, trained to ignore distraction, doesn't react at all, and they leave.\"\n",
    "is_spoiler = predict_spoiler(input_text, tokenizer, model)\n",
    "print(f\"The text is a {'spoiler' if is_spoiler else 'non-spoiler'}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2bjajseSZpce",
    "outputId": "fd4681b2-a612-4453-bbc3-2e3552e67942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 406ms/step\n",
      "The text is a non-spoiler.\n"
     ]
    }
   ],
   "source": [
    "# Test the function\n",
    "input_text = \"Three years have passed since John Brennan (Russel Crowe) and his wife Lara (Elizabeth Banks) lost their son Luke (Tyler Simpkins) in a car accident. Three years later, John is a community college teacher who is teaching English. He tries to manage his job and raising Luke.\"\n",
    "is_spoiler = predict_spoiler(input_text, tokenizer, model)\n",
    "print(f\"The text is a {'spoiler' if is_spoiler else 'non-spoiler'}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "26-LoGDzZ6P1"
   },
   "outputs": [],
   "source": [
    "# Test the function\n",
    "input_text = \"Three years have passed since John Brennan (Russel Crowe) and his wife Lara (Elizabeth Banks) lost their son Luke (Tyler Simpkins) in a car accident. Three years later, John is a community college teacher who is teaching English. He tries to manage his job and raising Luke.\"\n",
    "is_spoiler = predict_spoiler(input_text, tokenizer, model)\n",
    "print(f\"The text is a {'spoiler' if is_spoiler else 'non-spoiler'}.\")"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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