diff --git "a/final LSTM model.ipynb" "b/final LSTM model.ipynb"
new file mode 100644--- /dev/null
+++ "b/final LSTM model.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
\n",
+ "
Sharif University of Technology
\n",
+ "
Natural Language Processing
\n",
+ "
Final Project
\n",
+ "
Spoiler classification and summary generation
\n",
+ "
Authors: Ali Nikkhah, Ramtin Khoshnevis, Sarina Zahedi
\n",
+ "
(Equal Contribution)
\n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:16.919150Z",
+ "iopub.status.busy": "2024-08-16T01:08:16.918649Z",
+ "iopub.status.idle": "2024-08-16T01:08:19.653111Z",
+ "shell.execute_reply": "2024-08-16T01:08:19.651514Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:16.919108Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import nltk\n",
+ "from nltk.corpus import stopwords\n",
+ "from collections import Counter\n",
+ "import string\n",
+ "\n",
+ "# Download NLTK stopwords if not already downloaded\n",
+ "nltk.download('stopwords')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--------------------------------------------------------------------------------------------------------------------------------------\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **1. Load the Dataset**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:19.656416Z",
+ "iopub.status.busy": "2024-08-16T01:08:19.655928Z",
+ "iopub.status.idle": "2024-08-16T01:08:44.624368Z",
+ "shell.execute_reply": "2024-08-16T01:08:44.622674Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:19.656378Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import json\n",
+ "import torch\n",
+ "\n",
+ "\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "\n",
+ "# Load the dataset\n",
+ "file_path = '/kaggle/input/imdb-spoiler-dataset/IMDB_reviews.json'\n",
+ "data = []\n",
+ "with open(file_path, 'r') as file:\n",
+ " for line in file:\n",
+ " data.append(json.loads(line))\n",
+ "\n",
+ "df = pd.DataFrame(data)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **2. Exploratory Data Analysis (EDA)**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:44.626964Z",
+ "iopub.status.busy": "2024-08-16T01:08:44.626261Z",
+ "iopub.status.idle": "2024-08-16T01:08:45.316125Z",
+ "shell.execute_reply": "2024-08-16T01:08:45.314693Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:44.626923Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 573913 entries, 0 to 573912\n",
+ "Data columns (total 7 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 review_date 573913 non-null object\n",
+ " 1 movie_id 573913 non-null object\n",
+ " 2 user_id 573913 non-null object\n",
+ " 3 is_spoiler 573913 non-null bool \n",
+ " 4 review_text 573913 non-null object\n",
+ " 5 rating 573913 non-null object\n",
+ " 6 review_summary 573913 non-null object\n",
+ "dtypes: bool(1), object(6)\n",
+ "memory usage: 26.8+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tabulate import tabulate\n",
+ "import numpy as np\n",
+ "\n",
+ "# Basic info\n",
+ "info = df.info()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:45.319604Z",
+ "iopub.status.busy": "2024-08-16T01:08:45.319129Z",
+ "iopub.status.idle": "2024-08-16T01:08:51.515072Z",
+ "shell.execute_reply": "2024-08-16T01:08:51.513329Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:45.319552Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Describe the dataset\n",
+ "description = df.describe()\n",
+ "\n",
+ "\n",
+ "# Check for missing values\n",
+ "missing_values = df.isnull().sum()\n",
+ "\n",
+ "\n",
+ "# Distribution of spoiler vs. non-spoiler\n",
+ "spoiler_distribution = df['is_spoiler'].value_counts(normalize=True)\n",
+ "\n",
+ "# Length of reviews\n",
+ "df['review_length'] = df['review_text'].apply(len)\n",
+ "review_length_description = df['review_length'].describe()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:51.517647Z",
+ "iopub.status.busy": "2024-08-16T01:08:51.516981Z",
+ "iopub.status.idle": "2024-08-16T01:08:51.530667Z",
+ "shell.execute_reply": "2024-08-16T01:08:51.529217Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:51.517576Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Missing Values:\n",
+ "+----------------+----------------+\n",
+ "| Column | Missing Values |\n",
+ "+----------------+----------------+\n",
+ "| review_date | 0 |\n",
+ "| movie_id | 0 |\n",
+ "| user_id | 0 |\n",
+ "| is_spoiler | 0 |\n",
+ "| review_text | 0 |\n",
+ "| rating | 0 |\n",
+ "| review_summary | 0 |\n",
+ "+----------------+----------------+\n",
+ "\n",
+ "Spoiler vs. Non-Spoiler Distribution:\n",
+ "+------------+---------------------+\n",
+ "| Is Spoiler | Proportion |\n",
+ "+------------+---------------------+\n",
+ "| False | 0.7370263437141169 |\n",
+ "| True | 0.26297365628588304 |\n",
+ "+------------+---------------------+\n",
+ "\n",
+ "Review Length Description:\n",
+ "+-----------+--------------------+\n",
+ "| Statistic | Value |\n",
+ "+-----------+--------------------+\n",
+ "| count | 573913.0 |\n",
+ "| mean | 1460.5535246631457 |\n",
+ "| std | 1125.577018615146 |\n",
+ "| min | 18.0 |\n",
+ "| 25% | 719.0 |\n",
+ "| 50% | 1052.0 |\n",
+ "| 75% | 1815.0 |\n",
+ "| max | 14963.0 |\n",
+ "+-----------+--------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Display the results\n",
+ "\n",
+ "print(\"\\nMissing Values:\")\n",
+ "print(tabulate(missing_values.items(), headers=[\"Column\", \"Missing Values\"], tablefmt=\"pretty\"))\n",
+ "\n",
+ "print(\"\\nSpoiler vs. Non-Spoiler Distribution:\")\n",
+ "print(tabulate(spoiler_distribution.items(), headers=[\"Is Spoiler\", \"Proportion\"], tablefmt=\"pretty\"))\n",
+ "\n",
+ "print(\"\\nReview Length Description:\")\n",
+ "print(tabulate(review_length_description.items(), headers=[\"Statistic\", \"Value\"], tablefmt=\"pretty\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-08-16T01:08:51.533626Z",
+ "iopub.status.busy": "2024-08-16T01:08:51.532623Z",
+ "iopub.status.idle": "2024-08-16T01:08:55.001037Z",
+ "shell.execute_reply": "2024-08-16T01:08:54.999471Z",
+ "shell.execute_reply.started": "2024-08-16T01:08:51.533561Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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bl5ic/aMf/SgNHDgwpZTS8uXLU/Xq1dP//d//lTj+oYcemi699NIyy3vNNdekiEj//Oc/c8teffXVEv1cRUGZoUOHpmOPPTb3urIyTpgwIRUWFpZo6xX5+OOPU0SkZ599NrdsS7WdVatWpRo1aqT77rsvt+zLL79MLVq0SNddd11uWb169UqEI8paVroPP+6449IBBxxQ7rHL6pc2NHPmzBQRuUm5lQUYxo8fnxo0aJC7T19//fWUl5eXCx2Upaw62twAQ1X7rw2PXVG/efvtt6dGjRrlnmNSSukPf/hDpZNWf/Ob36T8/PxUt27ddMghh6SRI0emoqKi3Ptb6pmqorJXdXJt8Xq1a9cucb/VqVMnVatWLdMAQ1k6duyYfv3rX5c4ZkXneccdd2x0jW6//fZKy1LWeDB27NgUESWCRmPGjElNmzbNvd7U8f9f//pXqlmzZi6wUFpxvd155525ZcX98vz588st/5FHHpkuuOCC3OsePXpsFNzdd9990yWXXJJSSmny5MmpWrVqaeHChWXuryptrrTu3bun0047rcSyH/3oR+l73/te7nW/fv3S4MGDyz2PlMqeGF2Vetnczx6bc66HHnroRgHFe++9NzVv3jyl9HWosHHjxumee+7JvX/cccflxpyqfHYpb+wvrbhurrnmmtyytWvXppYtW+ZCqaNGjUqHHXZYie3ee++9XEg5pW+vPyyt9LhSWkXjc1XqsSrPnVUJMFR2r2cRYKhKHZfVTqpSL4899liqV69e+uqrr9KcOXNSs2bN0rnnnpvrI0499dQSYa3Sqhpg6NChQ4nPNZdccknq0KFDufsty4bjQHGAtPg5NaWU5s+fnyIi14dPnjw5bbPNNiWeO5966qkKx6hLL7007bjjjunLL7/cpLIBAAAAW1a1Sn+iAQAAAKiSa6+9Nu6+++6YP3/+Ru/Nnz8/DjjggBLLDjjggFi8eHGsW7cut6xz5865v+fl5UWzZs3iww8/jIiII444IgoKCqKgoCA6duwYERF16tSJJ554IpYsWRKXXXZZFBQUxAUXXBD77bdfrF69OrevHXbYIbbffvvc627dusX69etj4cKF8a9//Sv+9re/lVm+ss6lLHPnzo1x48blyldQUBB9+/aN9evXx9KlS3Pr7bPPPlXa3yGHHBJz5syJV155JQYPHhwnn3xyHHvssRERsWTJkli9enX06dOnxPHuueeeKCoqioiILl26RIcOHeL++++PiIjp06fHhx9+GD/60Y/KLf+SJUuibt26uf01bNgwvvjii9w+e/ToEdOmTcvtr1evXnHwwQfHtGnTYubMmbF27dqN6nBTjR49Ou65555K17voootizpw5uT8nnnhiueuWrvNhw4bFVVddFQcccEAMHz483njjjU0uZ1Xb87dh/vz5sf/++5dY1q1bty2y7z59+sQuu+wS48ePj2233TYiIj7//PMoKiqKIUOGlGh/V111Va6tbErZu3XrFnl5ebllBxxwQKxatSref//9TS5vRf1HRMT48ePjgAMOiGbNmkVBQUFcdtll8e6775bYR5s2baJu3bq5182bNy+xj4iIhx56KH72s5/FM888Ez169Kj0HDfn+rRu3TqaNGmSez137txYtWpVNGrUqES9L126NFfvc+bMiUMPPbTSfZcuX1XacmV9V0ppk4674bWqU6dOFBYWblTPFR2zYcOGcdJJJ0Xfvn3jqKOOiltvvTVWrFhR4THnz58frVq1ilatWuWW7bbbblG/fv0SfX1F40Wxjh07RvXq1XOvN2wnb775Zqxbty7atWtX4lpNnz59k+6Rzp075/q4zz//PL766qvce2PGjIm99947mjRpEgUFBfH73/9+o7ZcURn79OkTrVu3jp122ilOOOGEuO+++0qMmVWxpdpOUVHRRuNHjRo1Yr/99qvyGFyeTb0nXn/99TjqqKNihx12iLp16+bu79J1W57+/ftH9erV4+GHH46IiHHjxsUhhxwSbdq0qXC7qj4bVKYq/VdpFfWbCxcujM6dO0etWrVy6+y3336VlmPo0KHxwQcfxH333RfdunWLP//5z9GxY8d45plnqnQeVX2mqqzP3xTjx48v8VwxZ86cLXZdyrNq1aq48MILo0OHDlG/fv0oKCiI+fPnb9TeKjrP+fPnb3SNvskzQH5+fuy888651xu2oc0Z/+fPnx9r1qyp9D7c8BybN28eEZE77rp162LUqFHRqVOnaNiwYRQUFMTkyZMrrKfSZZ8zZ060bNky2rVrV+bxq9rmSp/bN/nsUBUV1cvmfvbYnHOdO3dujBw5ssQ2p512WqxYsSJWr14d22yzTQwYMCDuu+++iPi6rTz66KNx/PHHR0TVPruUV97ybNjOt9lmm9hnn31ydT937tx49tlnSxxr1113jYiocBzOoj/c1HGlovG5KvW4JT4XbMln/U2xKXW8YTupSr0cdNBB8dlnn8Xs2bNj+vTp0aNHj+jZs2eJz7Y9e/b8xufQtWvXEp9runXrVuHnw8rGgfnz58c222wTe++9d26bXXfdNerXr597Xfx826JFixLHrcicOXPioIMOiho1amzOaQIAAABbyDZbuwAAAADw3+Lggw+Ovn37xqWXXhonnXTSZu2j9H+i5+Xlxfr16yMi4s4774x///vfZa638847x8477xynnnpq/PKXv4x27drF+PHj4+STT96scmyqVatWxRlnnBHDhg3b6L0ddtgh9/c6depUaX916tSJXXbZJSIi/vjHP8Yee+wRd911VwwZMiRWrVoVERFPPPFEiUm2ERE1a9bM/f3444+P+++/P37+85/H/fffH4cffng0atSo3PLvvffeuYlHGyqeRN2zZ88477zzYvHixTFv3rw48MADY8GCBTFt2rT4+OOPY5999on8/Pwqnd831bhx41z9VKZ0nZ966qnRt2/feOKJJ2LKlCkxevTouPHGG+Occ87JoqjfaUceeWRMmDAh5s2bF506dYqIyLW/P/zhDxtNkNpwsvTWUFH/8dJLL8Xxxx8fV155ZfTt2zfq1asXDz74YNx4441V3kexPffcM2bNmhV//OMfY5999ikxUWlLKd1uV61aFc2bN89NtNpQ8SSm2rVrb/FylFee0tq2bRt5eXmxYMGCKu2vKvVc2THHjh0bw4YNi6effjrGjx8fl112WTzzzDPRtWvXKpXhm6io/KtWrYrq1avH66+/vtE9UVBQUOb+2rZtGxFfT94rLn/NmjXL7OcefPDBuPDCC+PGG2+Mbt26Rd26deP666+PV155pcplrFu3bsyaNSumTZsWU6ZMiSuuuCJGjBgRM2fOLDEpbkuo6riXhU25Jz7//PPo27dv9O3bN+67775o0qRJvPvuu9G3b9/48ssvq7SPbbfdNk488cQYO3ZsHHPMMXH//ffHrbfeWul2peuoWrWvv3dpw2DQ2rVrK91PVe6rLbFNVdStWzeOOuqoOOqoo+Kqq66Kvn37xlVXXRV9+vSpdNuqPlNtybK3atVqo/styz41IuLCCy+MZ555Jm644YbYZZddonbt2vHDH/5wo/aW1TUqS1nHKm6HmzP+V7UONzxu8ZhafI7XX3993HrrrXHLLbdEp06dok6dOnHeeedtUj1VVo6qtrlvW0X1srmfPTbnXFetWhVXXnllHHPMMRu9VzzZ/Pjjj48ePXrEhx9+GM8880zUrl07Dj/88Nz2EZV/dimrvJtj1apVcdRRR8W111670XvFQZCybOl7bXPGlYrG502px4pUq1Zto+DphmPMf/KzfrEN20lV6qV+/fqxxx57xLRp0+Kll16KPn36xMEHHxwDBw6MRYsWxeLFiysMJVdWZ5urquPAlpb1+AYAAABUjQADAAAAbEHXXHNNdOnSJdq3b19ieYcOHWLGjBklls2YMSPatWtX5YkQpScklKdNmzaRn58fn3/+eW7Zu+++G3/7299y30z48ssvR7Vq1aJ9+/ZRWFgYLVq0iBkzZpSYuDBjxowqfbNmRMRee+0V8+bNq/Kk+k1RrVq1+MUvfhHnn39+DBo0KHbbbbeoWbNmvPvuuxVOtBg0aFBcdtll8frrr8dDDz0Uv/vd7yos//jx42O77baLwsLCMtfp1KlTNGjQIK666qro0qVLFBQURM+ePePaa6+Njz/+eIt8a+W3pVWrVnHmmWfGmWeeGZdeemn84Q9/iHPOOSf3KwOV/YrClmjPW0qHDh3iscceK7Hs5Zdf3iL7vuaaa6KgoCAOPfTQmDZtWuy2227RtGnTaNGiRbzzzju5b9atim233Xajeu3QoUNMmDAhUkq5iXkzZsyIunXrRsuWLau8n6p48cUXo3Xr1vHLX/4yt2z58uWbvJ+IrwNTN954Y/Ts2TOqV68ev/nNb8pdd0tdn7322is++OCD2Gabbcr9NvfOnTvH1KlTyw1ulXcNtkRbbtiwYfTt2zfGjBkTw4YN22gC4ieffLLFJ8ZHfB0m2XPPPePSSy+Nbt26xf333x9du3Yt91zfe++9eO+993K/wjBv3rz45JNPYrfddsutV9F4UdUyrVu3Lj788MM46KCDqrTNYYcdFg0bNoxrr7029+395ZkxY0Z07949zjrrrNyyzflG5G222SZ69+4dvXv3juHDh0f9+vXjr3/9a5kTVMuypdrOzjvvHNtuu23MmDEjWrduHRFfTwqcOXNmnHfeeVXeT1mK74krr7yy0nUXLFgQK1eujGuuuSbXPl577bVNPuapp54au+++e/z2t7+Nr776qsr1uaHi4OCKFSuiQYMGEfH1tyV/29q3bx9/+tOfYs2aNblJoDNnztzk/eTl5cWuu+4aL774YpXW3xLPVFUdz7emGTNmxEknnRRHH310RHw9CXfZsmWbtI8OHTrEvffeG1988UVuInlVxpjNGUs3Z/xv27Zt1K5dO6ZOnRqnnnrqJh2v2IwZM6Jfv37xk5/8JCK+nsC/aNGiEv12ZTp37hzvv/9+LFq0qMxfYdicNlfcBw4ePLhEWTelXBGb/1yzuffJ5my31157xcKFCyvcpnv37tGqVasYP358PPXUU/GjH/0oFwio6meXTfHyyy/HwQcfHBERX331Vbz++utx9tln58o7YcKEaNOmTWyzzZb5b+DN6Q83d1wpb3zu06dPpfVYlefOJk2axAcffFDi+XvDMWZzn/W/qc0dc6ravnr06BHPPvtsvPrqq3H11VdHw4YNo0OHDnH11VdH8+bNy/2Floiv6+ytt94qsWzOnDkbhV5Kh0lffvnlaNu2bbnPRZWNA7vuumuufe+7774R8XXY9ZNPPsmtU/x8u2LFilxAp7JxoHPnznH33XfH2rVr/QoDAAAAbEXVtnYBAAAA4L9Jp06d4vjjj4/bbrutxPILLrggpk6dGqNGjYpFixbF3XffHb/5zW/iwgsv/EbHGzFiRFx88cUxbdq0WLp0acyePTtOOeWUWLt2bYlv+a1Vq1YMHjw45s6dG88//3wMGzYsBgwYEM2aNYuIiIsuuiiuvfbaGD9+fCxcuDB+/vOfx5w5c+Lcc8+tUjkuueSSePHFF+Pss8+OOXPmxOLFi+PRRx/NTaT5pn70ox9F9erVY8yYMVG3bt248MIL42c/+1ncfffdUVRUFLNmzYpf//rXcffdd+e2adOmTXTv3j2GDBkS69atix/84Afl7v/444+Pxo0bR79+/eL555+PpUuXxrRp02LYsGHx/vvvR8TXExAPPvjguO+++3Jhhc6dO8eaNWti6tSplU5I+uc//xlz5syJefPmRcTXky/mzJkTH3zwQW6dSy+9NE488cTNraYqOe+882Ly5MmxdOnSmDVrVjz77LPRoUOHiIho3bp15OXlxeOPPx7/+Mc/ct/oWVpW7XlznHnmmbF48eK46KKLYuHChXH//ffHuHHjttj+b7jhhjj++OOjV69euW/Xv/LKK2P06NFx2223xaJFi+LNN9+MsWPHxk033VTuftq0aROvvPJKLFu2LD766KNYv359nHXWWfHee+/FOeecEwsWLIhHH300hg8fHueff37uG8jL2s+qVati6tSp8dFHH8Xq1aurdB5t27aNd999Nx588MEoKiqK2267rdKJ4hVp165dPPvsszFhwoQKJ1lvqevTu3fv6NatW/Tv3z+mTJkSy5YtixdffDF++ctf5ibDDR8+PB544IEYPnx4zJ8/P958880S3z7cpk2beO655+L//u//4qOPPoqILduWx4wZE+vWrYv99tsvJkyYEIsXL4758+fHbbfdFt26ddvk/VVk6dKlcemll8ZLL70Uy5cvjylTpsTixYtz93KbNm1i6dKlMWfOnPjoo49izZo10bt379wYNWvWrHj11VfjxBNPjB49esQ+++yT23dl40Vl2rVrF8cff3yceOKJMXHixFi6dGm8+uqrMXr06HjiiSfK3KagoCDuvPPOeOKJJ+LII4+MyZMnxzvvvBNvvPFGXHfddRHx/3/rcdu2beO1116LyZMnx6JFi+Lyyy/f5Enljz/+eNx2220xZ86cWL58edxzzz2xfv36Koc0IrZc26lTp0789Kc/jYsuuiiefvrpmDdvXpx22mmxevXqGDJkyCbtq7RLL700Zs6cGWeddVa88cYbsWDBgrj99ttz7X9DO+ywQ2y77bbx61//Ot5555147LHHYtSoUZt8zA4dOkTXrl3jkksuieOOO26zvuV4l112iVatWsWIESNi8eLF8cQTT2z0azHfhkGDBsX69evj9NNPj/nz58fkyZPjhhtuiIgo95dn5syZE/369YuHHnoo5s2bF0uWLIm77ror/vjHP0a/fv2qdNwt8Uy13XbbRe3atePpp5+Ov//97/Hpp59WedtvS9u2bWPixIkxZ86cmDt3bq6+N8WgQYMiLy8vTjvttJg3b148+eSTuWtUkbLGg6rY1PG/Vq1acckll8TFF18c99xzTxQVFcXLL78cd911V5WP2bZt23jmmWfixRdfjPnz58cZZ5wRf//736u8fcTXE5cPPvjgOPbYY+OZZ56JpUuXxlNPPRVPP/10RGxem7voooti3Lhxcfvtt8fixYvjpptuiokTJ25yH9imTZt44403YuHChfHRRx9V+VvdN/c+2ZztrrjiirjnnnviyiuvjLfffjvmz58fDz74YFx22WUl1hs0aFD87ne/i2eeeabExPeqfnbZFGPGjImHH344FixYEEOHDo2PP/44TjnllIiIGDp0aPzzn/+M4447LmbOnBlFRUUxefLkOPnkkzc71LQ5/eHmjCsVjc9VqceqPHf27Nkz/vGPf8R1110XRUVFMWbMmHjqqadKrLM5z/q77rrrN3q23pw6jqh6++rZs2dMnjw5ttlmm9h1111zy+67775KP8f26tUrXnvttbjnnnti8eLFMXz48I0CDRFfh2DPP//8WLhwYTzwwAPx61//usJ/T6hsHGjfvn0cfvjhccYZZ8Qrr7wSr7/+epx66qklni169+4d7dq1K/HsumFguyxnn312/Otf/4of//jH8dprr8XixYvj3nvvjYULF0ZExMMPP5yro2Klr++38bkdAAAA/tsJMAAAAMAWNnLkyI0mYO21117x//7f/4sHH3wwdt9997jiiiti5MiRcdJJJ32jY/Xo0SPeeeedOPHEE2PXXXeNI444Ij744IOYMmVKiYmYu+yySxxzzDHxve99Lw477LDo3Llz/Pa3v829P2zYsDj//PPjggsuiE6dOsXTTz8djz32WLRt27ZK5ejcuXNMnz49Fi1aFAcddFDsueeeccUVV+S+wfub2mabbeLss8+O6667Lj7//PMYNWpUXH755TF69Ojo0KFDHH744fHEE0/EjjvuWGK7448/PubOnRtHH310hZMo8/Pz47nnnosddtghjjnmmOjQoUMMGTIkvvjiixK/yNCjR49Yt25dLsBQrVq1OPjggyMvLy8OOOCACs/hscceiz333DOOPPLIiIj48Y9/HHvuuWeJX4ZYsWJFvPvuu5taPZtk3bp1MXTo0Fy9tWvXLtcWtt9++7jyyivj5z//eTRt2rTcyVxbsj2PGDGi3G/Ur4oddtghJkyYEI888kjsscce8bvf/S5+9atfbfb+ynLzzTfHgAEDolevXrFo0aI49dRT484774yxY8dGp06dokePHjFu3LiN2t+GLrzwwqhevXrstttu0aRJk3j33Xdj++23jyeffDJeffXV2GOPPeLMM8+MIUOGbDQhbkPdu3ePM888MwYOHBhNmjTJTe6uzA9+8IP42c9+FmeffXZ06dIlXnzxxbj88ss3uS421L59+/jrX/8aDzzwQFxwwQVlrrOlrk9eXl48+eSTcfDBB8fJJ58c7dq1ix//+MexfPnyaNq0aUR8PQnrz3/+czz22GPRpUuX6NWrV7z66qu5fYwcOTKWLVsWO++8c+4b3rdkW95pp51i1qxZccghh8QFF1wQu+++e/Tp0yemTp0at99++ybvryL5+fmxYMGCOPbYY6Ndu3Zx+umnx9ChQ+OMM86IiIhjjz02Dj/88DjkkEOiSZMm8cADD0ReXl48+uij0aBBgzj44IOjd+/esdNOO8X48eNL7Luy8aIqxo4dGyeeeGJccMEF0b59++jfv3/MnDkzdthhh3K3Ofroo+PFF1+M/Pz8OPHEE6N9+/bRq1ev+Otf/xoPPvhgfP/734+IiDPOOCOOOeaYGDhwYOy///6xcuXKEr/GUBX169ePiRMnRq9evaJDhw7xu9/9Lh544IHo2LFjlfexJdvONddcE8cee2yccMIJsddee8WSJUti8uTJuV8f2Fzt2rWLKVOmxNy5c2O//faLbt26xaOPPlrmN3I3adIkxo0bF3/+859jt912i2uuuaZKE8HLMmTIkPjyyy9zk2k3VY0aNeKBBx6IBQsWROfOnePaa6+Nq666arP29U0UFhbGpEmTYs6cOdGlS5f45S9/GVdccUVERO7b/ktr2bJltGnTJq688srYf//9Y6+99opbb701rrzyykonVBbbEs9U22yzTdx2221xxx13RIsWLaocntiS2rRpEyNGjCj3/ZtuuikaNGgQ3bt3j6OOOir69u0be+211yYdo6CgICZNmhRvvvlm7LnnnvHLX/6yRHCtPGWNB1WxOeP/5ZdfHhdccEFcccUV0aFDhxg4cGB8+OGHVT7mZZddFnvttVf07ds3evbsGc2aNYv+/ftXeftiEyZMiH333TeOO+642G233eLiiy/OTWbfnDbXv3//uPXWW+OGG26Ijh07xh133BFjx47d5F8kO+2006J9+/axzz77RJMmTTb6ZZvybO59sjnb9e3bNx5//PGYMmVK7LvvvtG1a9e4+eabc7+aU+z444+PefPmxfbbb7/R54KqfnapqmuuuSauueaa2GOPPeKFF16Ixx57LBo3bhwRkftlvXXr1sVhhx0WnTp1ivPOOy/q169fbkC2MpvTH27OuFLZ+FxZPVblubNDhw7x29/+NsaMGRN77LFHvPrqqxsFbzbnXl+4cOE3CottTh0Xq0r7Ouigg2L9+vUlwgo9e/Ys8dm2PH379o3LL788Lr744th3333js88+K3Py/oknnhj//ve/Y7/99ouhQ4fGueeeG6effnq5+63KODB27Nho0aJF9OjRI4455pg4/fTTY7vttsu9X61atXj44Ydzxz311FPj6quvrvB8GjVqFH/9619j1apV0aNHj9h7773jD3/4Q+7XGD799NNcmKFY6ev7bXxuBwAAgP92eSmltLULAQAAAGRnxIgR8cgjj8ScOXO2dlFgI4MHD468vLwt+qsJwOYxXrAljBo1Kv785z/HG2+8sbWLssXdd999cfLJJ8enn366Wb8u8b9i9erV0ahRo3jqqac2eUI78N3wXekPp02bFoccckh8/PHHUb9+/a1dnE3yXanjiK/DEF26dIlbbrllaxcFAAAA+I7Y+KuWAAAAAOBbkFKKadOmxQsvvLC1iwLAN7Rq1apYtmx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+ "text/plain": [
+ "