model3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "import nltk\n",
    "import tensorflow as tf\n",
    "from gensim.models import Word2Vec\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torchtext.data.utils import get_tokenizer\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0059830c</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars. Cars have been around since they became ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>005db917</td>\n",
       "      <td>0</td>\n",
       "      <td>Transportation is a large necessity in most co...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>008f63e3</td>\n",
       "      <td>0</td>\n",
       "      <td>\"America's love affair with it's vehicles seem...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00940276</td>\n",
       "      <td>0</td>\n",
       "      <td>How often do you ride in a car? Do you drive a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00c39458</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars are a wonderful thing. They are perhaps o...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1373</th>\n",
       "      <td>fe6ff9a5</td>\n",
       "      <td>1</td>\n",
       "      <td>There has been a fuss about the Elector Colleg...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>ff669174</td>\n",
       "      <td>0</td>\n",
       "      <td>Limiting car usage has many advantages. Such a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>ffa247e0</td>\n",
       "      <td>0</td>\n",
       "      <td>There's a new trend that has been developing f...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>ffc237e9</td>\n",
       "      <td>0</td>\n",
       "      <td>As we all know cars are a big part of our soci...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>ffe1ca0d</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars have been around since the 1800's and hav...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1378 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  prompt_id                                               text  \\\n",
       "0     0059830c          0  Cars. Cars have been around since they became ...   \n",
       "1     005db917          0  Transportation is a large necessity in most co...   \n",
       "2     008f63e3          0  \"America's love affair with it's vehicles seem...   \n",
       "3     00940276          0  How often do you ride in a car? Do you drive a...   \n",
       "4     00c39458          0  Cars are a wonderful thing. They are perhaps o...   \n",
       "...        ...        ...                                                ...   \n",
       "1373  fe6ff9a5          1  There has been a fuss about the Elector Colleg...   \n",
       "1374  ff669174          0  Limiting car usage has many advantages. Such a...   \n",
       "1375  ffa247e0          0  There's a new trend that has been developing f...   \n",
       "1376  ffc237e9          0  As we all know cars are a big part of our soci...   \n",
       "1377  ffe1ca0d          0  Cars have been around since the 1800's and hav...   \n",
       "\n",
       "      generated  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "1373          0  \n",
       "1374          0  \n",
       "1375          0  \n",
       "1376          0  \n",
       "1377          0  \n",
       "\n",
       "[1378 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('Data/train_essays/train_essays.csv')\n",
    "\n",
    "# Display the data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Drop the last column from the test data\n",
    "test_df = test_df.iloc[:, :-1]\n",
    "\n",
    "# Create a directory to save the temporary data\n",
    "os.makedirs('Data/temporary', exist_ok=True)\n",
    "\n",
    "# Save the training and testing data\n",
    "train_df.to_csv('Data/temporary/Temp_Train_Data.csv', index=False) #change the path according to kaggel\n",
    "test_df.to_csv('Data/temporary/Temp_Test_Data.csv', index=False) #change the path according to kaggel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the training and testing data\n",
    "train_df = pd.read_csv('Data/temporary/Temp_Train_Data.csv') #change the path according to kaggel\n",
    "test_df = pd.read_csv('Data/temporary/Temp_Test_Data.csv') #change the path according to kaggel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Presidential election is held every after four...</td>\n",
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       "      <th>4</th>\n",
       "      <td>669e6f61</td>\n",
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       "      <td>How could we get the reduction of Greenhouse G...</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>cf8af518</td>\n",
       "      <td>1</td>\n",
       "      <td>Dear state senator, I believe that we shouldn'...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>f01dd0a6</td>\n",
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       "      <td>The modern automobile has been the axle to the...</td>\n",
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       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>9b753df1</td>\n",
       "      <td>1</td>\n",
       "      <td>The Electoral College is a process that should...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>cef0572a</td>\n",
       "      <td>0</td>\n",
       "      <td>There is a new age coming,and with that means ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  prompt_id                                               text  \\\n",
       "0     e0dbb2e7          0  Cars, they make life so much easier, or, do th...   \n",
       "1     3d75a33b          0  Now a days you see everyone with cars driving ...   \n",
       "2     6049a24f          1  Presidential election is held every after four...   \n",
       "3     cecf6e5e          1  I dont think that the electoral college should...   \n",
       "4     669e6f61          0  How could we get the reduction of Greenhouse G...   \n",
       "...        ...        ...                                                ...   \n",
       "1097  c7c1cf69          0  People all over the world are saying goodbye t...   \n",
       "1098  cf8af518          1  Dear state senator, I believe that we shouldn'...   \n",
       "1099  f01dd0a6          0  The modern automobile has been the axle to the...   \n",
       "1100  9b753df1          1  The Electoral College is a process that should...   \n",
       "1101  cef0572a          0  There is a new age coming,and with that means ...   \n",
       "\n",
       "      generated  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "1097          0  \n",
       "1098          0  \n",
       "1099          0  \n",
       "1100          0  \n",
       "1101          0  \n",
       "\n",
       "[1102 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the training data\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>prompt_id</th>\n",
       "      <th>text</th>\n",
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       "      <th>0</th>\n",
       "      <td>70d7c567</td>\n",
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       "      <td>Can you imagine living in a place where there ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81977e6c</td>\n",
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       "      <td>Limiting car usage could have many advantages ...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>In this generation, our planet is getting filt...</td>\n",
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       "      <th>3</th>\n",
       "      <td>d33eca96</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars are a basic need for people today we use ...</td>\n",
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       "      <th>4</th>\n",
       "      <td>71f7131e</td>\n",
       "      <td>1</td>\n",
       "      <td>Why do we keep this despised method of choosin...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>271</th>\n",
       "      <td>62b480e1</td>\n",
       "      <td>0</td>\n",
       "      <td>The culture of the car has been coming to an e...</td>\n",
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       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>7405b110</td>\n",
       "      <td>0</td>\n",
       "      <td>Cars, though useful, have negative impacts on ...</td>\n",
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       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>47e743a7</td>\n",
       "      <td>0</td>\n",
       "      <td>The extensive use of car transportation is set...</td>\n",
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       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>95d41c3e</td>\n",
       "      <td>0</td>\n",
       "      <td>Do you know what pollution is? Have you ever t...</td>\n",
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       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>ee6fb060</td>\n",
       "      <td>1</td>\n",
       "      <td>The Electoral Colleage is not a Colleage like ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  prompt_id                                               text\n",
       "0    70d7c567          0  Can you imagine living in a place where there ...\n",
       "1    81977e6c          0  Limiting car usage could have many advantages ...\n",
       "2    e43869b8          0  In this generation, our planet is getting filt...\n",
       "3    d33eca96          0  Cars are a basic need for people today we use ...\n",
       "4    71f7131e          1  Why do we keep this despised method of choosin...\n",
       "..        ...        ...                                                ...\n",
       "271  62b480e1          0  The culture of the car has been coming to an e...\n",
       "272  7405b110          0  Cars, though useful, have negative impacts on ...\n",
       "273  47e743a7          0  The extensive use of car transportation is set...\n",
       "274  95d41c3e          0  Do you know what pollution is? Have you ever t...\n",
       "275  ee6fb060          1  The Electoral Colleage is not a Colleage like ...\n",
       "\n",
       "[276 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the testing data\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 00:00:01.30\n"
     ]
    }
   ],
   "source": [
    "# Define the function to preprocess the text\n",
    "def preprocess_text(text):\n",
    "    # Tokenize the text\n",
    "    tokenizer = get_tokenizer('basic_english')\n",
    "    words = tokenizer(text)\n",
    "    return words\n",
    "\n",
    "# Define a function to apply to the DataFrame\n",
    "\n",
    "\n",
    "def preprocess_df(df):\n",
    "    # Check if the 'text' column has already been tokenized\n",
    "    if isinstance(df['text'][0], list):\n",
    "        warnings.warn(\"The 'text' column has already been tokenized. Running this function again may lead to unexpected results.\")\n",
    "        return df['text']\n",
    "    # If not, tokenize the 'text' column\n",
    "    else:\n",
    "        preprocessed_texts = df['text'].apply(preprocess_text)\n",
    "        return preprocessed_texts\n",
    "\n",
    "# Start the timer\n",
    "start_time = time.time()\n",
    "\n",
    "# Preprocess the text\n",
    "df['text'] = preprocess_df(df)\n",
    "\n",
    "# Build a vocabulary\n",
    "vocab = build_vocab_from_iterator(df['text'].tolist(), specials=[\"<unk>\"])\n",
    "vocab.set_default_index(vocab[\"<unk>\"])\n",
    "\n",
    "# Convert words into numerical representations\n",
    "df['text'] = df['text'].apply(lambda text: [vocab[word] for word in text])\n",
    "\n",
    "# Define max_length as the length of the longest sequence\n",
    "max_length = max(df['text'].apply(len))\n",
    "\n",
    "# Pad the sequences\n",
    "df['text'] = df['text'].apply(lambda x: x + [vocab[\"<unk>\"]] * (max_length - len(x)) if len(x) < max_length else x[:max_length])\n",
    "\n",
    "# Convert the list of sequences into a torch tensor\n",
    "X = torch.tensor(df['text'].tolist())\n",
    "\n",
    "# y is target variable\n",
    "y = torch.tensor(df['generated'].tolist())\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# End the timer and print the elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "hours, rem = divmod(elapsed_time, 3600)\n",
    "minutes, seconds = divmod(rem, 60)\n",
    "print(f\"Elapsed time: {int(hours):02d}:{int(minutes):02d}:{seconds:05.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: torch.Size([1378, 1502])\n"
     ]
    }
   ],
   "source": [
    "print(f\"X shape: {X.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape: 1102\n",
      "Test data shape: 276\n",
      "Training labels shape: 1102\n",
      "Test labels shape: 276\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training data shape: {X_train.shape[0]}\")\n",
    "print(f\"Test data shape: {X_test.shape[0]}\")\n",
    "print(f\"Training labels shape: {y_train.shape[0]}\")\n",
    "print(f\"Test labels shape: {y_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence length: 1502\n",
      "Number of features: 1\n",
      "Epoch 1/10\n",
      " 1/18 [>.............................] - ETA: 1:19:36 - loss: 0.8040 - accuracy: 0.0312"
     ]
    }
   ],
   "source": [
    "# Convert PyTorch tensors to numpy arrays\n",
    "X_np = X.numpy()\n",
    "y_np = y.numpy()\n",
    "\n",
    "# Reshape X_np to be 3D (samples, timesteps, features)\n",
    "X_np = X_np.reshape((X_np.shape[0], X_np.shape[1], 1))\n",
    "\n",
    "# Define the model\n",
    "from keras.layers import Bidirectional\n",
    "# Get the sequence length and number of features from X\n",
    "sequence_length, num_features = X_np.shape[1], X_np.shape[2]\n",
    "print(f\"Sequence length: {sequence_length}\")\n",
    "print(f\"Number of features: {num_features}\")\n",
    "model_bidirectional = Sequential()\n",
    "model_bidirectional.add(Bidirectional(LSTM(100, return_sequences=True), input_shape=(sequence_length, num_features)))\n",
    "model_bidirectional.add(Dropout(0.2))\n",
    "model_bidirectional.add(Bidirectional(LSTM(100)))\n",
    "model_bidirectional.add(Dropout(0.2))\n",
    "model_bidirectional.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model_bidirectional.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Fit the model\n",
    "history = model_bidirectional.fit(X_np, y_np, epochs=10, batch_size=64, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 4s 474ms/step - loss: 0.0252 - accuracy: 0.9964\n",
      "Accuracy: 99.63768124580383%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model_bidirectional.evaluate(X_test, y_test)\n",
    "print(f'Accuracy: {accuracy * 100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+qVtvvVXbt2/X4MGDNXLkSB09etRl+wQAAACA1oLy8hZwqsqii6evNuS7d8zKUEiga/7PPGvWLF1//fWOn9u3b6+ePXs6fp49e7Y+/vhjffLJJ5owYcJZP2fMmDG6/fbbJUnPPPOM5s+fr40bN2rgwIEu2ScAAAAAtBacdKPB+vbt6/TziRMn9Mgjj+iiiy5S27ZtFRYWph9//PG8J92XXHKJ459DQ0MVERGhw4cPu2XPAAAAAGAkTrpbQHCAv3bMyjDsu10lNDTU6edHHnlEa9as0Z/+9CddeOGFCg4O1s0336zKyspzfk5AQIDTzyaTSVar1WX7BAAAAIDWgtDdAkwmk8tKvFuTf//73xozZoyGDRsmqebkOycnx9hNAQAAAEArQnk5mqxLly5asWKFtm3bpu+++0533HEHJ9YAAAAAcAZCN5ps7ty5ateuna644goNGTJEGRkZuvTSS43eFgAAAAC0GiabK2dK+ZCSkhJFRkaquLhYERERTr8rLy9Xdna2UlJSZDabDdqhd+DPEgAAAEBrdK5MeCZOugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4CaEbAAAAAAA3IXQDAAAAAOAmbYzeAODpbDab/vSPXdqVf8LorQAAAABeZe5tPRVhDjB6G81C6Aaa6cdDpXr5n3uM3gYAAADgdaqqrUZvodkI3UAz7S2sOeHuHB2qcf/V2eDdAAAAAN4jNMjzI6vn3wFgsNyik5Kk3ontdPtlnQzeDQAAAIDWhEZqkCSZTKZzvmbOnNmsz165cqXL9traZBeWSZKSO4QYvBMAAAAArQ0n3ZAkHTp0yPHPy5Yt0/Tp07Vr1y7HtbCwMCO25RFyi2pCd1JUqME7AQAAANDacNINSVJsbKzjFRkZKZPJ5HTt/fff10UXXSSz2axu3brplVdecby3srJSEyZMUFxcnMxms5KSkjRnzhxJUnJysiRp2LBhMplMjp+9SU5teXlKB0I3AAAAAGecdLcEm02qOmnMdweESCZTsz5iyZIlmj59uhYsWKDevXtr69atGjdunEJDQzV69GjNnz9fn3zyiT744AN16tRJ+/fv1/79+yVJmzZtUseOHbVw4UINHDhQ/v7+rrirVuNERbWOlFZIkjpRXg4AAADgFwjdLaHqpPRMvDHf/fhBKbB5J7AzZszQ888/r9/+9reSpJSUFO3YsUOvv/66Ro8erX379qlLly666qqrZDKZlJSU5HhvdHS0JKlt27aKjY1t1j5aI3tpefvQQEUGe/b8QAAAAACuR+jGOZWVlWnPnj268847NW7cOMf16upqRUZGSpLGjBmj66+/Xl27dtXAgQN14403asCAAUZtuUXlFNZUMNBEDQAAAEB9CN0tISCk5sTZqO9uhhMnamZQv/nmm0pLS3P6nb1U/NJLL1V2drY+++wzrV27VrfeeqvS09P10UcfNeu7PUFOkb1zOc9zAwAAAKiL0N0STKZml3gbJSYmRvHx8dq7d69Gjhx51nURERG67bbbdNttt+nmm2/WwIEDdfToUbVv314BAQGyWCwtuOuWYy8vT6ZzOQAAAIB6ELpxXk8++aQeeOABRUZGauDAgaqoqNC3336rY8eOadKkSZo7d67i4uLUu3dv+fn56cMPP1RsbKzatm0rqaaDeVZWlq688koFBQWpXbt2xt6QC9nLy5MoLwcAAABQD0aG4bzuuusu/fnPf9bChQvVo0cP/frXv9Y777yjlJQUSVJ4eLiee+459e3bV/369VNOTo7+/ve/y8+v5q/X888/rzVr1igxMVG9e/c28lZcjvJyAAAAAOdistlsNqM34YlKSkoUGRmp4uJiRUREOP2uvLxc2dnZSklJkdlsNmiH3qE1/1mWVVSr+4zVkqTvpg9QZAjdywEAAABfca5MeCZOuoEmyi2qKS1vFxJA4AYAAABQL0I30EQ0UQMAAABwPoRuoImyeZ4bAAAAwHkQuoEmyqVzOQAAAIDzIHQDTWTvXJ5CeTkAAACAsyB0u5HVajV6Cx6vNTfXt4fuJMrLAQAAAJxFG6M34I0CAwPl5+engwcPKjo6WoGBgTKZTEZvy+PYbDYdOXJEJpNJAQGtqzv4ycpqFZRUSJJSCN0AAAAAzoLQ7QZ+fn5KSUnRoUOHdPDgQaO349FMJpMuuOAC+fv7G70VJ/ZxYW0ZFwYAAADgHAjdbhIYGKhOnTqpurpaFovF6O14rICAgFYXuKXT48IoLQcAAABwLoRuN7KXRbe20mg0X07tSXcKncsBAAAAnAON1IAmyCnkpBsAAADA+RG6gSawdy5PjuKkGwAAAMDZEbqBJsgprCkvT+akGwAAAMA5ELqBRjpVaVF+SbkkQjcAAACAcyN0A42072jNKXdkcIDahQYavBsAAAAArRmhG2ik7Nomasl0LgcAAABwHoRuoJGY0Q0AAACgoQjdQCOd7lxO6AYAAABwbm2M3gDcxGaTqk4avQuvdOjwUQWrXKmRkirLjN4OAAAA4L0CQiSTyehdNAuh21tVnZSeiTd6F17pHUkyS/p77QsAAACAezx+UAr07ApTyssBAAAAAHATTrq9VUBIzb8VgkvtPnxCv1mwTuHmNtow5TqZPLzUBQAAAGjVAjx/YhCh21uZTB5fhtEaZReX6pTM6hIVKVNQmNHbAQAAANDKUV4ONEIO48IAAAAANAKhG2iEnKKajvDJHTy/zAUAAACA+xG6gUbIKayd0c1JNwAAAIAGIHQDjZBrP+mO4qQbAAAAwPkRuoEGKq+y6GDxKUmcdAMAAABoGEI30ED7j56UzSaFB7VR+9BAo7cDAAAAwAMQuoEGsjdRS4oKYT43AAAAgAYhdAMNRBM1AAAAAI1F6AYayD6jm9ANAAAAoKEI3UADne5cTugGAAAA0DCEbqCBsh3l5YwLAwAAANAwhG6gASqqT48LS6K8HAAAAEADEbqBBrCPCwsLaqOoMMaFAQAAAGgYQjfQADmFtePCOjAuDAAAAEDDEbqBBnB0LqeJGgAAAIBGIHQDDXB6XBhN1AAAAAA0HKEbaAD7uDCaqAEAAABoDEI30AD2k+4UyssBAAAANILhofvll19WcnKyzGaz0tLStHHjxrOuraqq0qxZs5Samiqz2ayePXtq1apVTmtKS0s1ceJEJSUlKTg4WFdccYU2bdrktGbMmDEymUxOr4EDB7rl/uD5KqutyjtmHxdGeTkAAACAhjM0dC9btkyTJk3SjBkztGXLFvXs2VMZGRk6fPhwveunTp2q119/XS+99JJ27Nihe+65R8OGDdPWrVsda+666y6tWbNGixYt0vfff68BAwYoPT1deXl5Tp81cOBAHTp0yPF677333Hqv8Fz7j52U1SaFBvorOizI6O0AAAAA8CAmm81mM+rL09LS1K9fPy1YsECSZLValZiYqPvvv1+TJ0+usz4+Pl5PPPGExo8f77g2fPhwBQcHa/HixTp16pTCw8P117/+VTfccINjTZ8+fTRo0CA99dRTkmpOuo8fP66VK1c2ee8lJSWKjIxUcXGxIiIimvw5aP2yfizQne9+q4vjIvT3B//L6O0AAAAAaAUamgkNO+murKzU5s2blZ6efnozfn5KT0/X+vXr631PRUWFzGaz07Xg4GCtW7dOklRdXS2LxXLONXZffPGFOnbsqK5du+ree+9VUVHROfdbUVGhkpISpxd8Q05tE7XkKErLAQAAADSOYaG7sLBQFotFMTExTtdjYmKUn59f73syMjI0d+5c7d69W1arVWvWrNGKFSt06NAhSVJ4eLj69++v2bNn6+DBg7JYLFq8eLHWr1/vWCPVlJb/5S9/UVZWlp599ll9+eWXGjRokCwWy1n3O2fOHEVGRjpeiYmJLvhTgCfIdYwLo4kaAAAAgMYxvJFaY7z44ovq0qWLunXrpsDAQE2YMEGZmZny8zt9G4sWLZLNZlNCQoKCgoI0f/583X777U5rRowYod/85jfq0aOHbrrpJn366afatGmTvvjii7N+95QpU1RcXOx47d+/3523ilYku5DQDQAAAKBpDAvdUVFR8vf3V0FBgdP1goICxcbG1vue6OhorVy5UmVlZcrNzdXOnTsVFhamzp07O9akpqbqyy+/1IkTJ7R//35t3LhRVVVVTmt+qXPnzoqKitLPP/981jVBQUGKiIhwesE3nJ7RTXk5AAAAgMYxLHQHBgaqT58+ysrKclyzWq3KyspS//79z/les9mshIQEVVdXa/ny5Ro6dGidNaGhoYqLi9OxY8e0evXqetfYHThwQEVFRYqLi2v6DcErVVZbdeBYTehmRjcAAACAxmpj5JdPmjRJo0ePVt++fXXZZZdp3rx5KisrU2ZmpiRp1KhRSkhI0Jw5cyRJGzZsUF5ennr16qW8vDzNnDlTVqtVjz32mOMzV69eLZvNpq5du+rnn3/Wo48+qm7dujk+88SJE3ryySc1fPhwxcbGas+ePXrsscd04YUXKiMjo+X/ENCqHagdFxYS6K/ocMaFAQAAAGgcQ0P3bbfdpiNHjmj69OnKz89Xr169tGrVKkdztX379jk9i11eXq6pU6dq7969CgsL0+DBg7Vo0SK1bdvWsaa4uFhTpkzRgQMH1L59ew0fPlxPP/20AgICJEn+/v7avn273n33XR0/flzx8fEaMGCAZs+eraAgQhWcnS4tD5XJZDJ4NwAAAAA8jaFzuj0Zc7p9w9vrsjXr0x0a9KtYvfq7PkZvBwAAAEAr0erndAOewD4uLInO5QAAAACagNANnEN2kb2JGp3LAQAAADQeoRs4B066AQAAADQHoRs4iyqLVQeOnZLEuDAAAAAATUPoBs7iwLFTslhtMgf4qSPjwgAAAAA0AaEbOIuc2tLyZMaFAQAAAGgiQjdwFrmFp0M3AAAAADQFoRs4i5zazuVJdC4HAAAA0ESEbuAsziwvBwAAAICmIHQDZ5FDeTkAAACAZiJ0A/U4c1xYMuXlAAAAAJqI0A3U4+DxU6quHRcWE242ejsAAAAAPBShG6hHdm1peVL7UPn5MS4MAAAAQNMQuoF65No7l3egtBwAAABA0xG6gXrYT7pTomiiBgAAAKDpCN1APXJrx4Ul0bkcAAAAQDMQuoF62MvL6VwOAAAAoDkI3cAvVFus2ne0NnRz0g0AAACgGQjdwC8cPF6uaqtNQW38FBvBuDAAAAAATUfoBn4h2/E8dwjjwgAAAAA0C6Eb+AWaqAEAAABwFUI38As5hTXPczMuDAAAAEBzEbqBX8g5o7wcAAAAAJqD0A38gj1007kcAAAAQHMRuoEzWKw27bePC6O8HAAAAEAzEbqBMxw8fkpVFpsC2/gpjnFhAAAAAJqJ0A2cwV5a3qk948IAAAAANB+hGzhDTiHPcwMAAABwHUI3cIacotrnuelcDgAAAMAFCN3AGXLtnctpogYAAADABQjdwBmyKS8HAAAA4EKEbqBWzbiwU5KkJMrLAQAAALgAoRuodfD4KVVarAr091N822CjtwMAAADACxC6gVq5tU3UEtsHy59xYQAAAABcgNAN1LLP6E6hiRoAAAAAFyF0A7XsM7qTaKIGAAAAwEUI3UAtZnQDAAAAcDVCN1CLGd0AAAAAXI3QDUiyWm3KPWo/6SZ0AwAAAHANQjcg6VBJuSqrrQrwNyku0mz0dgAAAAB4CUI3oNNN1BLbh6iNP/+1AAAAAOAapAtAp8eFUVoOAAAAwJUI3YCk3CKe5wYAAADgeoRuQFJ2ob1zOePCAAAAALgOoRvQ6XFhSZx0AwAAAHAhQjd8ntVqc5SXpxC6AQAAALgQoRs+L7+kXBXVVrXxMym+LePCAAAAALgOoRs+z965vBPjwgAAAAC4GAkDPi+nsKa0PKkDTdQAAAAAuBahGz6PJmoAAAAA3IXQDZ9nHxeWEkXoBgAAAOBahG74PHvncsrLAQAAALgaoRs+zWq1KfcoJ90AAAAA3IPQDZ9WUFqu8qqacWEJbYON3g4AAAAAL0Pohk+zdy6/oF0w48IAAAAAuBwpAz7N3rk8mdJyAAAAAG5A6IZPy7aHbsaFAQAAAHADQjd8Wm4hncsBAAAAuA+hGz4th/JyAAAAAG5E6IbPstlsp0M35eUAAAAA3IDQDZ91uLRC5VVW+fuZdEE7xoUBAAAAcD1CN3xWdmHNKfcF7YIVwLgwAAAAAG5A0oDPso8LS6K0HAAAAICbELrhs7JrO5en0LkcAAAAgJsQuuGzOOkGAAAA4G6EbvisnKLak27GhQEAAABwE0I3fJLNZjvjpJvycgAAAADuQeiGTzpSWqGTlRb5maQL2hG6AQAAALgHoRs+6fS4sBAFtuG/BgAAAADcg7QBn5Rb+zw3peUAAAAA3InQDZ+UU/s8N03UAAAAALgToRs+KYdxYQAAAABaAKEbPimnsKa8PJnycgAAAABuROiGzzlzXFgy5eUAAAAA3IjQDZ9z5ESFyhzjwoKN3g4AAAAAL0bohs+xdy6PbxusoDb+Bu8GAAAAgDcjdMPn2Gd007kcAAAAgLsRuuFzch2dy2miBgAAAMC9CN3wOTlF9s7lnHQDAAAAcC9CN3xOTm15OaEbAAAAgLsRuuFTasaF1Z50R1FeDgAAAMC9CN3wKYUnKnWiolomk5TYntANAAAAwL0MD90vv/yykpOTZTablZaWpo0bN551bVVVlWbNmqXU1FSZzWb17NlTq1atclpTWlqqiRMnKikpScHBwbriiiu0adMmpzU2m03Tp09XXFycgoODlZ6ert27d7vl/tC62JuoxUcyLgwAAACA+xkaupctW6ZJkyZpxowZ2rJli3r27KmMjAwdPny43vVTp07V66+/rpdeekk7duzQPffco2HDhmnr1q2ONXfddZfWrFmjRYsW6fvvv9eAAQOUnp6uvLw8x5rnnntO8+fP12uvvaYNGzYoNDRUGRkZKi8vd/s9w1j2JmqMCwMAAADQEkw2m81m1JenpaWpX79+WrBggSTJarUqMTFR999/vyZPnlxnfXx8vJ544gmNHz/ecW348OEKDg7W4sWLderUKYWHh+uvf/2rbrjhBseaPn36aNCgQXrqqadks9kUHx+vhx9+WI888ogkqbi4WDExMXrnnXc0YsSIBu29pKREkZGRKi4uVkRERHP+GNCC/rR6lxb882eNTOukp4f1MHo7AAAAADxUQzOhYSfdlZWV2rx5s9LT009vxs9P6enpWr9+fb3vqaiokNlsdroWHBysdevWSZKqq6tlsVjOuSY7O1v5+flO3xsZGam0tLSzfq/9u0tKSpxe8Dw5RXQuBwAAANByDAvdhYWFslgsiomJcboeExOj/Pz8et+TkZGhuXPnavfu3bJarVqzZo1WrFihQ4cOSZLCw8PVv39/zZ49WwcPHpTFYtHixYu1fv16xxr7ZzfmeyVpzpw5ioyMdLwSExObfO8wjiN0U14OAAAAoAUY3kitMV588UV16dJF3bp1U2BgoCZMmKDMzEz5+Z2+jUWLFslmsykhIUFBQUGaP3++br/9dqc1TTFlyhQVFxc7Xvv372/u7aCF2Ww25RbWjgvrQOdyAAAAAO5nWOiOioqSv7+/CgoKnK4XFBQoNja23vdER0dr5cqVKisrU25urnbu3KmwsDB17tzZsSY1NVVffvmlTpw4of3792vjxo2qqqpyrLF/dmO+V5KCgoIUERHh9IJnOVpWqVLGhQEAAABoQYaF7sDAQPXp00dZWVmOa1arVVlZWerfv/8532s2m5WQkKDq6motX75cQ4cOrbMmNDRUcXFxOnbsmFavXu1Yk5KSotjYWKfvLSkp0YYNG877vfBsOWeMCzMHMC4MAAAAgPu1MfLLJ02apNGjR6tv37667LLLNG/ePJWVlSkzM1OSNGrUKCUkJGjOnDmSpA0bNigvL0+9evVSXl6eZs6cKavVqscee8zxmatXr5bNZlPXrl31888/69FHH1W3bt0cn2kymTRx4kQ99dRT6tKli1JSUjRt2jTFx8frpptuavE/A7ScnNrS8iRKywEAAAC0EEND92233aYjR45o+vTpys/PV69evbRq1SpHk7N9+/Y5PYtdXl6uqVOnau/evQoLC9PgwYO1aNEitW3b1rGmuLhYU6ZM0YEDB9S+fXsNHz5cTz/9tAICAhxrHnvsMZWVlenuu+/W8ePHddVVV2nVqlV1up7Du+TSRA0AAABACzN0TrcnY06357n/va36v+8O6vHB3XT31alGbwcAAACAB2v1c7qBlmY/6U5iRjcAAACAFkLohk+w2WzKLqwJ3SmUlwMAAABoIYRu+IRjJ6tUWl4tSerEuDAAAAAALYTQDZ9welyYmXFhAAAAAFoMoRs+IaeQ57kBAAAAtDxCN3xCTlHNjO7kKErLAQAAALQcQjd8gv2kO5mTbgAAAAAtiNANn8C4MAAAAABGIHTDJ9jLyxkXBgAAAKAlEbrh9Y6VVar4VJUkxoUBAAAAaFmEbng9+7iw2AizggMZFwYAAACg5RC64fXsoZvO5QAAAABaGqEbXi+nsHZcGE3UAAAAALQwQje8Hp3LAQAAABiF0A2vl+3oXE55OQAAAICWReiG1+OkGwAAAIBRCN3wasdPVur4yZpxYUkdOOkGAAAA0LII3fBqObWl5TERQQoJbGPwbgAAAAD4GkI3vBql5QAAAACMROiGV8surAndKYRuAAAAAAYgdMOr5daWlyfRuRwAAACAAQjd8Go5RZx0AwAAADAOoRteLaeQZ7oBAAAAGIfQDa9VfLJKxxgXBgAAAMBAhG54LXtpecfwIIUGMS4MAAAAQMsjdMNr2UN3MqXlAAAAAAxC6IbXsncuT6ZzOQAAAACDELrhtWiiBgAAAMBohG54LcrLAQAAABiN0A2vlUN5OQAAAACDEbrhlYpPVeloWaUkyssBAAAAGIfQDa+0r/aUOyosSGGMCwMAAABgEEI3vFJ27fPcKZSWAwAAADAQoRteKZfO5QAAAABaAUI3vJK9iVpKFKEbAAAAgHEI3fBK9nFhSR0oLwcAAABgHEI3vFIuM7oBAAAAtAKEbnid0vIqFZ6wjwvjpBsAAACAcQjd8Dq5jnFhgQo3Bxi8GwAAAAC+jNANr5NDaTkAAACAVoLQDa+Tw7gwAAAAAK0EoRtexz4uLJnnuQEAAAAYjNANr2M/6U5mRjcAAAAAgxG64XVOn3QTugEAAAAYi9ANr3KiolqFJyokSUlRlJcDAAAAMBahG17FXlreITRQEYwLAwAAAGAwQje8in1GdxJN1AAAAAC0AoRueBXHjG6aqAEAAABoBQjd8CqOzuU0UQMAAADQChC64VUoLwcAAADQmhC64VWya8vLUygvBwAAANAKELrhNcoqqnWktHZcGOXlAAAAAFoBQje8hr20vH1ooCKDGRcGAAAAwHiEbngNe+dynucGAAAA0FoQuuE1HOPCKC0HAAAA0EoQuuE1GBcGAAAAoLUhdMNr5NQ+050cRXk5AAAAgNaB0A2vkUt5OQAAAIBWhtANr3CysloFJTXjwgjdAAAAAFoLQje8gn1cWNuQAEWGMC4MAAAAQOtA6IZXoIkaAAAAgNaI0A2v4GiixoxuAAAAAK0IoRtewd5ELYmTbgAAAACtCKEbXiG7trw8JYrQDQAAAKD1IHTDK9gbqSVRXg4AAACgFSF0w+OdqrQov6RcEifdAAAAAFoXQjc8Xu7RmtLyyOAAtQ0JNHg3AAAAAHAaoRseL6eQzuUAAAAAWidCNzxeTm3n8mRKywEAAAC0MoRueDzGhQEAAABorQjd8Hj28vKUKMrLAQAAALQuhG54vBxOugEAAAC0UoRueLTyKosOFdeMC0smdAMAAABoZQjd8Gi5RTWl5RHmNmoXEmDwbgAAAADAGaEbHu3MzuUmk8ng3QAAAACAM0I3PJq9czml5QAAAABaI0I3PFp2befy5A50LgcAAADQ+hC64dGY0Q0AAACgNSN0w6PlFJ5+phsAAAAAWhtCNzxWeZVFBx3jwigvBwAAAND6ELrhsfYfrXmeOzyojdqHBhq8GwAAAACoi9ANj5VdyLgwAAAAAK2b4aH75ZdfVnJyssxms9LS0rRx48azrq2qqtKsWbOUmpoqs9msnj17atWqVU5rLBaLpk2bppSUFAUHBys1NVWzZ8+WzWZzrBkzZoxMJpPTa+DAgW67R7hHblHNSXcSpeUAAAAAWqk2Rn75smXLNGnSJL322mtKS0vTvHnzlJGRoV27dqljx4511k+dOlWLFy/Wm2++qW7dumn16tUaNmyYvv76a/Xu3VuS9Oyzz+rVV1/Vu+++q+7du+vbb79VZmamIiMj9cADDzg+a+DAgVq4cKHj56CgIPffMFwqp7ZzeQpN1AAAAAC0UoaedM+dO1fjxo1TZmamLr74Yr322msKCQnR22+/Xe/6RYsW6fHHH9fgwYPVuXNn3XvvvRo8eLCef/55x5qvv/5aQ4cO1Q033KDk5GTdfPPNGjBgQJ0T9KCgIMXGxjpe7dq1c+u9wvVyGBcGAAAAoJUzLHRXVlZq8+bNSk9PP70ZPz+lp6dr/fr19b6noqJCZrPZ6VpwcLDWrVvn+PmKK65QVlaWfvrpJ0nSd999p3Xr1mnQoEFO7/viiy/UsWNHde3aVffee6+KiorOud+KigqVlJQ4vWCsnMKa8nI6lwMAAABorQwrLy8sLJTFYlFMTIzT9ZiYGO3cubPe92RkZGju3Lm6+uqrlZqaqqysLK1YsUIWi8WxZvLkySopKVG3bt3k7+8vi8Wip59+WiNHjnSsGThwoH77298qJSVFe/bs0eOPP65BgwZp/fr18vf3r/e758yZoyeffNIFdw5XqBkXdkoSM7oBAAAAtF6GPtPdWC+++KLGjRunbt26yWQyKTU1VZmZmU7l6B988IGWLFmipUuXqnv37tq2bZsmTpyo+Ph4jR49WpI0YsQIx/oePXrokksuUWpqqr744gtdd9119X73lClTNGnSJMfPJSUlSkxMdNOd4nwOHDspm00KC2qjDowLAwAAANBKGVZeHhUVJX9/fxUUFDhdLygoUGxsbL3viY6O1sqVK1VWVqbc3Fzt3LlTYWFh6ty5s2PNo48+qsmTJ2vEiBHq0aOHfv/73+uhhx7SnDlzzrqXzp07KyoqSj///PNZ1wQFBSkiIsLpBeM4SsujQhgXBgAAAKDVMix0BwYGqk+fPsrKynJcs1qtysrKUv/+/c/5XrPZrISEBFVXV2v58uUaOnSo43cnT56Un5/zbfn7+8tqtZ718w4cOKCioiLFxcU18W7Q0miiBgAAAMATGFpePmnSJI0ePVp9+/bVZZddpnnz5qmsrEyZmZmSpFGjRikhIcFxSr1hwwbl5eWpV69eysvL08yZM2W1WvXYY485PnPIkCF6+umn1alTJ3Xv3l1bt27V3LlzNXbsWEnSiRMn9OSTT2r48OGKjY3Vnj179Nhjj+nCCy9URkZGy/8hoEnsoZsmagAAAABaM0ND92233aYjR45o+vTpys/PV69evbRq1SpHc7V9+/Y5nVqXl5dr6tSp2rt3r8LCwjR48GAtWrRIbdu2dax56aWXNG3aNN133306fPiw4uPj9Yc//EHTp0+XVHPqvX37dr377rs6fvy44uPjNWDAAM2ePZtZ3R7kdOdyTroBAAAAtF4mm81mM3oTnqikpESRkZEqLi7m+W4DXPXs5zpw7JQ+vKe/+iW3N3o7AAAAAHxMQzOhYc90A01VUW3RweM148KSKC8HAAAA0IoRuuFx9h89JatNCg30V3QYjwQAAAAAaL0I3fA4uWd0LmdcGAAAAIDWjNANj5NdWBO6U6JoogYAAACgdSN0w+PkFtV0Lud5bgAAAACtHaEbHuf0jG5OugEAAAC0boRueBxH6Ka8HAAAAEArR+iGR6mstirvWM24sGTKywEAAAC0coRueJQDx07KapNCAv0VHc64MAAAAACtG6EbHiWHcWEAAAAAPAihGx4lp7Cmczml5QAAAAA8AaEbHoUmagAAAAA8CaEbHiWniJNuAAAAAJ6D0A2PksuMbgAAAAAehNANj1FlseqAfVwY5eUAAAAAPAChGx7jwLFTslhtCg7wV0fGhQEAAADwAIRueIycQvu4sBDGhQEAAADwCIRueIwcnucGAAAA4GEI3fAYubWdy5Oi6FwOAAAAwDMQuuExsmvLy1M46QYAAADgIQjd8Bj2cWFJhG4AAAAAHoLQDY9QZbFqf+24sBTGhQEAAADwEIRueIS82nFh5gA/xoUBAAAA8BiEbngEe+fypPah8vNjXBgAAAAAz0Dohkewz+hOpnM5AAAAAA9C6IZHyKkdF8aMbgAAAACehNANj2DvXJ5MEzUAAAAAHoTQDY9gP+lO6kB5OQAAAADPQehGq1dtsWr/UcrLAQAAAHgeQjdavbzjp1RttSmojZ9iI8xGbwcAAAAAGozQjVbvzNJyxoUBAAAA8CSEbrR6jiZqlJYDAAAA8DCEbrR62YV0LgcAAADgmQjdaPVy6VwOAAAAwEMRutHq5dSedKdQXg4AAADAwxC60apVW6zaf6z2pJvycgAAAAAehtCNVu1QcbmqLDYFtvFTHOPCAAAAAHgYQjdaNXsTtaT2jAsDAAAA4HkI3WjV7OPCknieGwAAAIAHanToTk5O1qxZs7Rv3z537Adwkl1Y8zx3ShSdywEAAAB4nkaH7okTJ2rFihXq3Lmzrr/+er3//vuqqKhwx94ATroBAAAAeLQmhe5t27Zp48aNuuiii3T//fcrLi5OEyZM0JYtW9yxR/iwnNrQnUzoBgAAAOCBmvxM96WXXqr58+fr4MGDmjFjhv785z+rX79+6tWrl95++23ZbDZX7hM+yGK1af/RU5KkZMrLAQAAAHigNk19Y1VVlT7++GMtXLhQa9as0eWXX64777xTBw4c0OOPP661a9dq6dKlrtwrfMzB46dUabEq0N9PcZHBRm8HAAAAABqt0aF7y5YtWrhwod577z35+flp1KhReuGFF9StWzfHmmHDhqlfv34u3Sh8T25RTRO1Th1C5M+4MAAAAAAeqNGhu1+/frr++uv16quv6qabblJAQECdNSkpKRoxYoRLNgjfle14npvScgAAAACeqdGhe+/evUpKSjrnmtDQUC1cuLDJmwIkKbeQzuUAAAAAPFujG6kdPnxYGzZsqHN9w4YN+vbbb12yKUA6o3N5FKEbAAAAgGdqdOgeP3689u/fX+d6Xl6exo8f75JNAZKUU/tMN+XlAAAAADxVo0P3jh07dOmll9a53rt3b+3YscMlmwIsVpv2OUI3J90AAAAAPFOjQ3dQUJAKCgrqXD906JDatGnyBDLAyaHimnFhAf4mxbdlXBgAAAAAz9To0D1gwABNmTJFxcXFjmvHjx/X448/ruuvv96lm4Pvso8LS2zPuDAAAAAAnqvRR9N/+tOfdPXVVyspKUm9e/eWJG3btk0xMTFatGiRyzcI35Rd27k8hdJyAAAAAB6s0aE7ISFB27dv15IlS/Tdd98pODhYmZmZuv322+ud2Q00RW4R48IAAAAAeL4mPYQdGhqqu+++29V7ARwcncuj6FwOAAAAwHM1ufPZjh07tG/fPlVWVjpd/81vftPsTQE5teXldC4HAAAA4MkaHbr37t2rYcOG6fvvv5fJZJLNZpMkmUw1za4sFotrdwifY7XalHuUcWEAAAAAPF+ju5c/+OCDSklJ0eHDhxUSEqL//Oc/+uqrr9S3b1998cUXbtgifE1+Sbkqq+3jwsxGbwcAAAAAmqzRJ93r16/X559/rqioKPn5+cnPz09XXXWV5syZowceeEBbt251xz7hQ+yl5YntQtTGv9H/XggAAAAAWo1GJxqLxaLw8HBJUlRUlA4ePChJSkpK0q5du1y7O/gkexO1pA40UQMAAADg2Rp90v2rX/1K3333nVJSUpSWlqbnnntOgYGBeuONN9S5c2d37BE+Jqd2XFhyFM9zAwAAAPBsjQ7dU6dOVVlZTSiaNWuWbrzxRv3Xf/2XOnTooGXLlrl8g/A9dC4HAAAA4C0aHbozMjIc/3zhhRdq586dOnr0qNq1a+foYA40R65jRjehGwAAAIBna9Qz3VVVVWrTpo1++OEHp+vt27cncMMlrFbb6fJynukGAAAA4OEaFboDAgLUqVMnZnHDbQpKy1VRbVUbP5MS2gYbvR0AAAAAaJZGdy9/4okn9Pjjj+vo0aPu2A98XLZ9XFh7xoUBAAAA8HyNfqZ7wYIF+vnnnxUfH6+kpCSFhjo/d7tlyxaXbQ6+J5dxYQAAAAC8SKND90033eSGbQA1Tj/PTRM1AAAAAJ6v0aF7xowZ7tgHIOnMcWGcdAMAAADwfDw0i1bFUV7OuDAAAAAAXqDRJ91+fn7nHA9GZ3M01ZnjwlIoLwcAAADgBRoduj/++GOnn6uqqrR161a9++67evLJJ122Mfiew6UVKq+yyt/PpIR2jAsDAAAA4PkaHbqHDh1a59rNN9+s7t27a9myZbrzzjtdsjH4Hvsp9wXtghXAuDAAAAAAXsBlyebyyy9XVlaWqz4OPuh0EzVKywEAAAB4B5eE7lOnTmn+/PlKSEhwxcfBR+XUNlGjczkAAAAAb9Ho8vJ27do5NVKz2WwqLS1VSEiIFi9e7NLNwbfk2md007kcAAAAgJdodOh+4YUXnEK3n5+foqOjlZaWpnbt2rl0c/At2ZSXAwAAAPAyjQ7dY8aMccM24OtsNtvpGd2UlwMAAADwEo1+pnvhwoX68MMP61z/8MMP9e6777pkU/A9h0srdKrKIn8/ky5oR+gGAAAA4B0aHbrnzJmjqKioOtc7duyoZ555xiWbgu+xdy5PaBuswDaMCwMAAADgHRqdbvbt26eUlJQ615OSkrRv3z6XbAq+x15aThM1AAAAAN6k0aG7Y8eO2r59e53r3333nTp06OCSTcH3ZNs7l/M8NwAAAAAv0ujQffvtt+uBBx7QP//5T1ksFlksFn3++ed68MEHNWLEiEZv4OWXX1ZycrLMZrPS0tK0cePGs66tqqrSrFmzlJqaKrPZrJ49e2rVqlVOaywWi6ZNm6aUlBQFBwcrNTVVs2fPls1mc6yx2WyaPn264uLiFBwcrPT0dO3evbvRe4fr2MeFJdG5HAAAAIAXaXTonj17ttLS0nTdddcpODhYwcHBGjBggK699tpGP9O9bNkyTZo0STNmzNCWLVvUs2dPZWRk6PDhw/Wunzp1ql5//XW99NJL2rFjh+655x4NGzZMW7dudax59tln9eqrr2rBggX68ccf9eyzz+q5557TSy+95Fjz3HPPaf78+Xrttde0YcMGhYaGKiMjQ+Xl5Y3944CLZBfWlJenRHHSDQAAAMB7mGxnHgE3wu7du7Vt2zYFBwerR48eSkpKavRnpKWlqV+/flqwYIEkyWq1KjExUffff78mT55cZ318fLyeeOIJjR8/3nFt+PDhCg4O1uLFiyVJN954o2JiYvTWW2/Vu8Zmsyk+Pl4PP/ywHnnkEUlScXGxYmJi9M477zT4tL6kpESRkZEqLi5WREREo+8dp9lsNnWfsVonKy3KevjXSo0OM3pLAAAAAHBODc2ETW4T3aVLF91yyy268cYbmxS4KysrtXnzZqWnp5/ejJ+f0tPTtX79+nrfU1FRIbPZ7HQtODhY69atc/x8xRVXKCsrSz/99JOkmmfN161bp0GDBkmSsrOzlZ+f7/S9kZGRSktLO+v32r+7pKTE6QXXOHKiQicrLfIzSYmMCwMAAADgRRoduocPH65nn322zvXnnntOt9xyS4M/p7CwUBaLRTExMU7XY2JilJ+fX+97MjIyNHfuXO3evVtWq1Vr1qzRihUrdOjQIceayZMna8SIEerWrZsCAgLUu3dvTZw4USNHjpQkx2c35nulmlFpkZGRjldiYmKD7xXnllNbWp7QjnFhAAAAALxLoxPOV199pcGDB9e5PmjQIH311Vcu2dTZvPjii+rSpYu6deumwMBATZgwQZmZmfLzO30bH3zwgZYsWaKlS5dqy5Ytevfdd/WnP/1J7777brO+e8qUKSouLna89u/f39zbQa0cR+dymqgBAAAA8C5tGvuGEydOKDAwsM71gICARpVcR0VFyd/fXwUFBU7XCwoKFBsbW+97oqOjtXLlSpWXl6uoqEjx8fGaPHmyOnfu7Fjz6KOPOk67JalHjx7Kzc3VnDlzNHr0aMdnFxQUKC4uzul7e/Xqddb9BgUFKSgoqMH3h4bLKSR0AwAAAPBOjT7p7tGjh5YtW1bn+vvvv6+LL764wZ8TGBioPn36KCsry3HNarUqKytL/fv3P+d7zWazEhISVF1dreXLl2vo0KGO3508edLp5FuS/P39ZbVaJUkpKSmKjY11+t6SkhJt2LDhvN8L98gtqikvT2JGNwAAAAAv0+iT7mnTpum3v/2t9uzZo2uvvVaSlJWVpaVLl+qjjz5q1GdNmjRJo0ePVt++fXXZZZdp3rx5KisrU2ZmpiRp1KhRSkhI0Jw5cyRJGzZsUF5ennr16qW8vDzNnDlTVqtVjz32mOMzhwwZoqefflqdOnVS9+7dtXXrVs2dO1djx46VJJlMJk2cOFFPPfWUunTpopSUFE2bNk3x8fG66aabGvvHARegvBwAAACAt2p06B4yZIhWrlypZ555Rh999JGCg4PVs2dPff7552rfvn2jPuu2227TkSNHNH36dOXn56tXr15atWqVo8nZvn37nE6ty8vLNXXqVO3du1dhYWEaPHiwFi1apLZt2zrWvPTSS5o2bZruu+8+HT58WPHx8frDH/6g6dOnO9Y89thjKisr0913363jx4/rqquu0qpVq+p0Rof72Wy20+XlUYRuAAAAAN6lyXO67UpKSvTee+/prbfe0ubNm2WxWFy1t1aNOd2ucaS0Qv2eXis/k/Tj7IEKauNv9JYAAAAA4LzcPqf7q6++0ujRoxUfH6/nn39e1157rb755pumfhx8VG5taXl822ACNwAAAACv06jy8vz8fL3zzjt66623VFJSoltvvVUVFRVauXJlo5qoAXbZdC4HAAAA4MUafNI9ZMgQde3aVdu3b9e8efN08OBBvfTSS+7cG3wAncsBAAAAeLMGn3R/9tlneuCBB3TvvfeqS5cu7twTfEh2bXl5Ck3UAAAAAHihBp90r1u3TqWlperTp4/S0tK0YMECFRYWunNv8AH2Z7qTKC8HAAAA4IUaHLovv/xyvfnmmzp06JD+8Ic/6P3331d8fLysVqvWrFmj0tJSd+4TXshmsym3sKa8PCWK8nIAAAAA3qfR3ctDQ0M1duxYrVu3Tt9//70efvhh/fGPf1THjh31m9/8xh17hJcqKqtUaUW1TCbpgnaEbgAAAADep8kjwySpa9eueu6553TgwAG99957rtoTfIRjXFhksMwBjAsDAAAA4H2aFbrt/P39ddNNN+mTTz5xxcfBR2TXlpYnU1oOAAAAwEu5JHQDTUETNQAAAADejtANw+TUzuhOZkY3AAAAAC9F6IZhcgprTrqTOekGAAAA4KUI3TCEzWZTTm15eXIUoRsAAACAdyJ0wxBHyypVWl4zLqxTe8rLAQAAAHgnQjcMYX+eOy7CzLgwAAAAAF6L0A1D0LkcAAAAgC8gdMMQjiZqPM8NAAAAwIsRumEIxoUBAAAA8AWEbhgil87lAAAAAHwAoRstzmazKZsZ3QAAAAB8AKEbLe74ySqVlFdLYlwYAAAAAO9G6EaLy64tLY+LNCs4kHFhAAAAALwXoRst7vS4ME65AQAAAHg3QjdaXE6hvXM5z3MDAAAA8G6EbrS4HDqXAwAAAPARhG60OGZ0AwAAAPAVhG60uJxCTroBAAAA+AZCN1rU8ZOVKj5VJYlxYQAAAAC8H6EbLcpeWh4TEaSQwDYG7wYAAAAA3IvQjRblKC2nczkAAAAAH0DoRotydC4ndAMAAADwAYRutCiaqAEAAADwJYRutCjGhQEAAADwJYRutKjc2vLyJMrLAQAAAPgAQjdaTPHJKh07WTMuLDmKk24AAAAA3o/QjRZjb6LWMZxxYQAAAAB8A6EbLcbRuZwmagAAAAB8BKEbLSankCZqAAAAAHwLoRsthiZqAAAAAHwNoRstJrs2dKdQXg4AAADARxC60WJya2d0J1FeDgAAAMBHELrRIopPVeloWaUkyssBAAAA+A5CN1qE/Xnu6PAghQUxLgwAAACAbyB0o0XkFNG5HAAAAIDvIXSjReQU1s7oprQcAAAAgA8hdKNF5NSWlyfTuRwAAACADyF0o0XQuRwAAACALyJ0o0VQXg4AAADAFxG64XYl5VUqqh0XRnk5AAAAAF9C6Ibb5RbWlJZHhTEuDAAAAIBvIXTD7RxN1HieGwAAAICPIXTD7XJrQ3cSz3MDAAAA8DGEbrhddm15eUoUJ90AAAAAfAuhG27HSTcAAAAAX0XohtvlFNlPugndAAAAAHwLoRtuVVpepcITFZKkTjRSAwAAAOBjCN1wq9zaU+4OoYGKMAcYvBsAAAAAaFmEbriVY1wYpeUAAAAAfBChG25lP+lOorQcAAAAgA8idMOtcgprT7rpXA4AAADABxG64VaUlwMAAADwZYRuuJV9XFgy5eUAAAAAfBChG25zoqJaR0prxoUlUV4OAAAAwAcRuuE2ubWl5e1DAxUZzLgwAAAAAL6H0A23oXM5AAAAAF9H6IbbZNd2Lk+htBwAAACAjyJ0w23s5eU8zw0AAADAVxG64TaOzuVRlJcDAAAA8E2EbrhNTm15eTIn3QAAAAB8FKEbbnGyslqHa8eFEboBAAAA+CpCN9wip7CmtLxdSIAiQxgXBgAAAMA3EbrhFjRRAwAAAABCN9zE0USNGd0AAAAAfBihG27haKIWxUk3AAAAAN9F6IZb5BTRuRwAAAAACN1wC0fo5qQbAAAAgA8jdMPlTlZWq6DEPi6MZ7oBAAAA+C5CN1xu39GaJmqRwQFqGxJo8G4AAAAAwDiEbrgcTdQAAAAAoAahGy7HuDAAAAAAqEHohss5TrrpXA4AAADAx7WK0P3yyy8rOTlZZrNZaWlp2rhx41nXVlVVadasWUpNTZXZbFbPnj21atUqpzXJyckymUx1XuPHj3es+e///u86v7/nnnvcdo++5HTnck66AQAAAPg2w0P3smXLNGnSJM2YMUNbtmxRz549lZGRocOHD9e7furUqXr99df10ksvaceOHbrnnns0bNgwbd261bFm06ZNOnTokOO1Zs0aSdItt9zi9Fnjxo1zWvfcc8+570Z9SG5teXkSJ90AAAAAfJzhoXvu3LkaN26cMjMzdfHFF+u1115TSEiI3n777XrXL1q0SI8//rgGDx6szp07695779XgwYP1/PPPO9ZER0crNjbW8fr000+VmpqqX//6106fFRIS4rQuIiLCrffqC05VWnSouFySlELoBgAAAODjDA3dlZWV2rx5s9LT0x3X/Pz8lJ6ervXr19f7noqKCpnNZqdrwcHBWrdu3Vm/Y/HixRo7dqxMJpPT75YsWaKoqCj96le/0pQpU3Ty5Mlm3hHs48IizG3UNiTA4N0AAAAAgLHaGPnlhYWFslgsiomJcboeExOjnTt31vuejIwMzZ07V1dffbVSU1OVlZWlFStWyGKx1Lt+5cqVOn78uMaMGeN0/Y477lBSUpLi4+O1fft2/c///I927dqlFStW1Ps5FRUVqqiocPxcUlLSiDv1HfbnuVOiQuv8Sw4AAAAA8DWGhu6mePHFFzVu3Dh169ZNJpNJqampyszMPGs5+ltvvaVBgwYpPj7e6frdd9/t+OcePXooLi5O1113nfbs2aPU1NQ6nzNnzhw9+eSTrr0ZL2TvXM7z3AAAAABgcHl5VFSU/P39VVBQ4HS9oKBAsbGx9b4nOjpaK1euVFlZmXJzc7Vz506FhYWpc+fOddbm5uZq7dq1uuuuu867l7S0NEnSzz//XO/vp0yZouLiYsdr//795/1MX8SMbgAAAAA4zdDQHRgYqD59+igrK8txzWq1KisrS/379z/ne81msxISElRdXa3ly5dr6NChddYsXLhQHTt21A033HDevWzbtk2SFBcXV+/vg4KCFBER4fRCXY4Z3VGcdAMAAACA4eXlkyZN0ujRo9W3b19ddtllmjdvnsrKypSZmSlJGjVqlBISEjRnzhxJ0oYNG5SXl6devXopLy9PM2fOlNVq1WOPPeb0uVarVQsXLtTo0aPVpo3zbe7Zs0dLly7V4MGD1aFDB23fvl0PPfSQrr76al1yySUtc+NeKreI8nIAAAAAsDM8dN922206cuSIpk+frvz8fPXq1UurVq1yNFfbt2+f/PxOH8iXl5dr6tSp2rt3r8LCwjR48GAtWrRIbdu2dfrctWvXat++fRo7dmyd7wwMDNTatWsdAT8xMVHDhw/X1KlT3Xqv3q68yqKDtePCKC8HAAAAAMlks9lsRm/CE5WUlCgyMlLFxcWUmtf6qaBUA174SuHmNto+YwDdywEAAAB4rYZmQkOf6YZ3cTzP3YFxYQAAAAAgEbrhQvYZ3TRRAwAAAIAahG64DOPCAAAAAMAZoRsuQ+dyAAAAAHBG6IbL5BTWnHSnRHHSDQAAAAASoRsuUjMu7JQkTroBAAAAwI7QDZfYf/SkbDYpPKiNOoQGGr0dAAAAAGgVCN1wCXsTtaSoEMaFAQAAAEAtQjdcgiZqAAAAAFAXoRsukV1YE7pTCN0AAAAA4EDohkvk2svLmdENAAAAAA6EbrhETm15eUoUJ90AAAAAYEfoRrNVVFt08DjjwgAAAADglwjdaLb9R0/JapNCA/0VFca4MAAAAACwI3Sj2XJqm6glR4UyLgwAAAAAzkDoRrPZn+dOprQcAAAAAJwQutFsdC4HAAAAgPoRutFsjpNuOpcDAAAAgBNCN5qN8nIAAAAAqB+hG81SWW1V3rGacWHJUZSXAwAAAMCZCN1olv3HTspqk0IC/RUdFmT0dgAAAACgVSF0o1lya0vLkzowLgwAAAAAfonQjWbJLqzpXJ5CaTkAAAAA1EHoRrOcedINAAAAAHBG6EazZBfWhO4UQjcAAAAA1EHoRrPkFtWUlyd1oLwcAAAAAH6J0I0mq6y26sCxmtCdHMVJNwAAAAD8EqEbTXagdlxYcIC/OoYzLgwAAAAAfonQjSY7s7SccWEAAAAAUBehG02WU9u5PIXScgAAAACoF6EbTZZTyLgwAAAAADgXQjeaLKe2vDyZzuUAAAAAUC9CN5rMXl5O53IAAAAAqB+hG01SZbHqwLFTkqRkyssBAAAAoF6EbjRJ3rFTslhtMgf4MS4MAAAAAM6C0I0mybaXlncIlZ8f48IAAAAAoD6EbjRJrqNzOU3UAAAAAOBsCN1oEkfncpqoAQAAAMBZEbrRJDlnlJcDAAAAAOpH6EaT5NaedFNeDgAAAABnR+hGo1VbrNp/tCZ0p1BeDgAAAABnRehGo+UdP6Vqq01BbfwUE242ejsAAAAA0GoRutFo2YWMCwMAAACAhiB0o9F4nhsAAAAAGobQjUZzdC7neW4AAAAAOCdCNxotp5BxYQAAAADQEIRuNJq9vDyZ8nIAAAAAOCdCNxql2mLV/mO1z3RTXg4AAAAA50ToRqMcPF6uKotNgW38FBfBuDAAAAAAOBdCNxrF3kQtqX0I48IAAAAA4DwI3WgUOpcDAAAAQMMRutEoOYU0UQMAAACAhiJ0o1Fy7eXljAsDAAAAgPMidKNRsmtDdwrl5QAAAABwXoRuNJjFatP+o7XjwigvBwAAAIDzInSjwQ4eP+UYFxYfGWz0dgAAAACg1SN0o8Hsncs7MS4MAAAAABqE0I0GyymiczkAAAAANAahGw2WU1g7o5vO5QAAAADQIIRuNJhjXBidywEAAACgQQjdaLDs2pPuFE66AQAAAKBBCN1okJpxYackMS4MAAAAABqK0I0GOVR8SpUWqwL9/RTflnFhAAAAANAQhG40SE5hTefyxPbB8mdcGAAAAAA0CKEbDWKf0U3ncgAAAABoOEI3GsTRuZzQDQAAAAANRuhGg2TXlpenRNFEDQAAAAAaitCNBuGkGwAAAAAaj9CN87Jabco9aj/pJnQDAAAAQEMRunFeh0rKVVltVYC/SXGRZqO3AwAAAAAeg9CN88otrCktT2wXojb+/JUBAAAAgIYiQeG8su3jwigtBwAAAIBGIXTjvHKLap7nTupA53IAAAAAaAxCN84ru7a8nCZqAAAAANA4hG6cF+PCAAAAAKBpCN04J6vV5igvT6a8HAAAAAAahdCNc8ovKVdFtVVt/ExKaBts9HYAAAAAwKMQunFOObWl5YntGRcGAAAAAI1FisI5UVoOAAAAAE1H6MY55RTSRA0AAAAAmorQjXOyl5dz0g0AAAAAjUfoxjnlFNaWlzOjGwAAAAAarVWE7pdfflnJyckym81KS0vTxo0bz7q2qqpKs2bNUmpqqsxms3r27KlVq1Y5rUlOTpbJZKrzGj9+vGNNeXm5xo8frw4dOigsLEzDhw9XQUGB2+7RE1mtNuUetZ90E7oBAAAAoLEMD93Lli3TpEmTNGPGDG3ZskU9e/ZURkaGDh8+XO/6qVOn6vXXX9dLL72kHTt26J577tGwYcO0detWx5pNmzbp0KFDjteaNWskSbfccotjzUMPPaT/+7//04cffqgvv/xSBw8e1G9/+1v33qyHOVxaofIqq/z9TEpox7gwAAAAAGgsk81msxm5gbS0NPXr108LFiyQJFmtViUmJur+++/X5MmT66yPj4/XE0884XRqPXz4cAUHB2vx4sX1fsfEiRP16aefavfu3TKZTCouLlZ0dLSWLl2qm2++WZK0c+dOXXTRRVq/fr0uv/zy8+67pKREkZGRKi4uVkRERFNuvdVbv6dIt7/5jZI7hOiLR68xejsAAAAA0Go0NBMaetJdWVmpzZs3Kz093XHNz89P6enpWr9+fb3vqaiokNlsdroWHBysdevWnfU7Fi9erLFjx8pkMkmSNm/erKqqKqfv7datmzp16nTW7/VFuUV0LgcAAACA5jA0dBcWFspisSgmJsbpekxMjPLz8+t9T0ZGhubOnavdu3fLarVqzZo1WrFihQ4dOlTv+pUrV+r48eMaM2aM41p+fr4CAwPVtm3bBn9vRUWFSkpKnF7eLrs2dKfQRA0AAAAAmsTwZ7ob68UXX1SXLl3UrVs3BQYGasKECcrMzJSfX/238tZbb2nQoEGKj49v1vfOmTNHkZGRjldiYmKzPs8T5NZ2Lk9iXBgAAAAANImhoTsqKkr+/v51uoYXFBQoNja23vdER0dr5cqVKisrU25urnbu3KmwsDB17ty5ztrc3FytXbtWd911l9P12NhYVVZW6vjx4w3+3ilTpqi4uNjx2r9/fyPu1DOdntHNSTcAAAAANIWhoTswMFB9+vRRVlaW45rValVWVpb69+9/zveazWYlJCSourpay5cv19ChQ+usWbhwoTp27KgbbrjB6XqfPn0UEBDg9L27du3Svn37zvq9QUFBioiIcHp5M5vNptwiZnQDAAAAQHO0MXoDkyZN0ujRo9W3b19ddtllmjdvnsrKypSZmSlJGjVqlBISEjRnzhxJ0oYNG5SXl6devXopLy9PM2fOlNVq1WOPPeb0uVarVQsXLtTo0aPVpo3zbUZGRurOO+/UpEmT1L59e0VEROj+++9X//79G9S53BccLq3QqSqL/P1MuoBxYQAAAADQJIaH7ttuu01HjhzR9OnTlZ+fr169emnVqlWO5mr79u1zel67vLxcU6dO1d69exUWFqbBgwdr0aJFdZqirV27Vvv27dPYsWPr/d4XXnhBfn5+Gj58uCoqKpSRkaFXXnnFbffpabILa0rLL2gXrAB/j3v0HwAAAABaBcPndHsqb5/TvWzTPv3P8u919f+L1l/GXmb0dgAAAACgVfGIOd1ovXLsz3PTuRwAAAAAmozQjXrlFNK5HAAAAACai9CNejlOuqM46QYAAACApiJ0o46acWE1J91JnHQDAAAAQJMRulHHkdIKnay0yM8kJbbjpBsAAAAAmorQjTrspeUJ7YIV2Ia/IgAAAADQVCQq1EETNQAAAABwDUI36sgpInQDAAAAgCsQulFHbm15eRIzugEAAACgWQjdqCO7trw8JYqTbgAAAABoDkI3nDAuDAAAAABch9ANJ0dOVKjMPi6sfbDR2wEAAAAAj0bohhP789zxbYMV1Mbf4N0AAAAAgGcjdMMJ48IAAAAAwHUI3XDiGBcWRedyAAAAAGguQjec5NSWl3PSDQAAAADNR+iGE8rLAQAAAMB1CN1wqBkXVnvSTXk5AAAAADQboRsORWWVOlFRLZNJuqAdoRsAAAAAmovQDQd7aXl8ZLDMAYwLAwAAAIDmInTDIYfScgAAAABwKUI3HHJrx4Ul0UQNAAAAAFyC0A2H7Nry8hRCNwAAAAC4BKEbDvbO5UkdKC8HAAAAAFcgdENSzbgweyO1lChOugEAAADAFQjdkCQdLatUae24sMT2nHQDAAAAgCsQuiHpdOfyuAgz48IAAAAAwEUI3ZB0ekZ3MqXlAAAAAOAyhG5IYlwYAAAAALgDoRuSpOza8vKUKJ7nBgAAAABXIXRDEifdAAAAAOAOhG7IZrMp2/5MN6EbAAAAAFyG0A0dO1ml0vJqSVJSB8rLAQAAAMBVCN1QTm1peVwk48IAAAAAwJUI3Tg9LozScgAAAABwKUI3lFPbuTyZzuUAAAAA4FKEbtC5HAAAAADchNANyssBAAAAwE0I3aC8HAAAAADchNDt446frFTxqSpJUlJ7TroBAAAAwJUI3T4uu7a0PDbCrOBAxoUBAAAAgCsRun1cbm1peVIHSssBAAAAwNUI3T7OftKdEkVpOQAAAAC4GqHbxzEuDAAAAADch9Dt4xydyykvBwAAAACXI3T7uJzak+5kyssBAAAAwOUI3T7s+MlKHT9ZOy6Mk24AAAAAcDlCtw+zl5bHRAQpJLCNwbsBAAAAAO9D6PZhNFEDAAAAAPcidPuwnEKaqAEAAACAOxG6fRhN1AAAAADAvQjdPswRuikvBwAAAAC3IHT7sJxC+zPdlJcDAAAAgDsQun1U8ckqHasdF8ZJNwAAAAC4B6HbR+UerTnljg4PUmgQ48IAAAAAwB0I3T4qu7a0PIVTbgAAAABwG0K3j8otqhkXxvPcAAAAAOA+hG4fxbgwAAAAAHA/QrePsncup4kaAAAAALgPodtHUV4OAAAAAO5H6PZBJeVVKiqrlER5OQAAAAC4E6HbB+UW1pxyR4UFKYxxYQAAAADgNoRuH+RookZpOQAAAAC4FaHbBzmaqFFaDgAAAABuRej2QTm1TdQ46QYAAAAA9yJ0+yBmdAMAAABAyyB0+6DcImZ0AwAAAEBLIHT7mNLyKhWeqBkXxoxuAAAAAHAvQrePyS2yjwsLVLg5wODdAAAAAIB3I3T7GPvz3EmUlgMAAACA2xG6fYx9XBil5QAAAADgfoRuH2MfF5bCSTcAAAAAuB2h28fYO5cnMS4MAAAAANyO0O1jsgs56QYAAACAlkLo9iEnKqpVeKJCktSJZ7oBAAAAwO0I3T7EXlrePjRQkcGMCwMAAAAAdyN0+5Cc2tLyZE65AQAAAKBFELp9iH1GdzLPcwMAAABAiyB0+xD7jO5kOpcDAAAAQIsgdPuQ3NoZ3UmUlwMAAABAiyB0+xDKywEAAACgZRG6fURZRbUOl9aMCyN0AwAAAEDLMDx0v/zyy0pOTpbZbFZaWpo2btx41rVVVVWaNWuWUlNTZTab1bNnT61atarOury8PP3ud79Thw4dFBwcrB49eujbb791/H7MmDEymUxOr4EDB7rl/loLe2l5u5AARYYwLgwAAAAAWkIbI7982bJlmjRpkl577TWlpaVp3rx5ysjI0K5du9SxY8c666dOnarFixfrzTffVLdu3bR69WoNGzZMX3/9tXr37i1JOnbsmK688kpdc801+uyzzxQdHa3du3erXbt2Tp81cOBALVy40PFzUFCQe2/WYI7ScpqoAQAAAECLMTR0z507V+PGjVNmZqYk6bXXXtPf/vY3vf3225o8eXKd9YsWLdITTzyhwYMHS5LuvfderV27Vs8//7wWL14sSXr22WeVmJjoFKhTUlLqfFZQUJBiY2PdcVutEs9zAwAAAEDLM6y8vLKyUps3b1Z6evrpzfj5KT09XevXr6/3PRUVFTKbzU7XgoODtW7dOsfPn3zyifr27atbbrlFHTt2VO/evfXmm2/W+awvvvhCHTt2VNeuXXXvvfeqqKjIRXfWOuUW0rkcAAAAAFqaYaG7sLBQFotFMTExTtdjYmKUn59f73syMjI0d+5c7d69W1arVWvWrNGKFSt06NAhx5q9e/fq1VdfVZcuXbR69Wrde++9euCBB/Tuu+861gwcOFB/+ctflJWVpWeffVZffvmlBg0aJIvFctb9VlRUqKSkxOnlSbJrT7pTKC8HAAAAgBZjaHl5Y7344osaN26cunXrJpPJpNTUVGVmZurtt992rLFarerbt6+eeeYZSVLv3r31ww8/6LXXXtPo0aMlSSNGjHCs79Gjhy655BKlpqbqiy++0HXXXVfvd8+ZM0dPPvmkG+/OvXJrQ3cS5eUAAAAA0GIMO+mOioqSv7+/CgoKnK4XFBSc9Vnr6OhorVy5UmVlZcrNzdXOnTsVFhamzp07O9bExcXp4osvdnrfRRddpH379p11L507d1ZUVJR+/vnns66ZMmWKiouLHa/9+/c35DZbhZOV1SoosY8Lo7wcAAAAAFqKYaE7MDBQffr0UVZWluOa1WpVVlaW+vfvf873ms1mJSQkqLq6WsuXL9fQoUMdv7vyyiu1a9cup/U//fSTkpKSzvp5Bw4cUFFRkeLi4s66JigoSBEREU4vT2EfF9Y2JEBtQwIN3g0AAAAA+A5D53RPmjRJb775pt599139+OOPuvfee1VWVuboZj5q1ChNmTLFsX7Dhg1asWKF9u7dq3/9618aOHCgrFarHnvsMceahx56SN98842eeeYZ/fzzz1q6dKneeOMNjR8/XpJ04sQJPfroo/rmm2+Uk5OjrKwsDR06VBdeeKEyMjJa9g+ghVBaDgAAAADGMPSZ7ttuu01HjhzR9OnTlZ+fr169emnVqlWO5mr79u2Tn9/pfy9QXl6uqVOnau/evQoLC9PgwYO1aNEitW3b1rGmX79++vjjjzVlyhTNmjVLKSkpmjdvnkaOHClJ8vf31/bt2/Xuu+/q+PHjio+P14ABAzR79myvndWdXdu5PIXScgAAAABoUSabzWYzehOeqKSkRJGRkSouLm71peaTl2/X+5v268Hruuih6/+f0dsBAAAAAI/X0ExoaHk5WkZObXl5chQn3QAAAADQkgjdPiCntrw8mWe6AQAAAKBFEbq93KlKi/JLyiURugEAAACgpRG6vVzu0ZrS8sjgALULZVwYAAAAALQkQreXO11azvPcAAAAANDSCN1ejhndAAAAAGAcQreXO925nNANAAAAAC2N0O3lKC8HAAAAAOMQur1cDuXlAAAAAGAYQrcXK6+y6FBxzbiwFMrLAQAAAKDFEbq92L6jNaXl4eY2ahcSYPBuAAAAAMD3ELq9WHZhTWl5SlSoTCaTwbsBAAAAAN9D6PZijAsDAAAAAGMRur1YThGdywEAAADASIRuL5ZTW16ezEk3AAAAABiC0O3Fcu0n3VGcdAMAAACAEQjdXqq8yqKDxackcdINAAAAAEYhdHup/UdPymaTwoPaqH1ooNHbAQAAAACf1MboDcA9rDbp2m4dFejvx7gwAAAAADAIodtLdY0N19tj+hm9DQAAAADwaZSXAwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG7SxugNeCqbzSZJKikpMXgnAAAAAICWZs+C9mx4NoTuJiotLZUkJSYmGrwTAAAAAIBRSktLFRkZedbfm2zni+Wol9Vq1cGDBxUeHi6TyWT0dupVUlKixMRE7d+/XxEREUZvB3AJ/l7DG/H3Gt6Iv9fwRvy9xplsNptKS0sVHx8vP7+zP7nNSXcT+fn56YILLjB6Gw0SERHB/yjA6/D3Gt6Iv9fwRvy9hjfi7zXsznXCbUcjNQAAAAAA3ITQDQAAAACAmxC6vVhQUJBmzJihoKAgo7cCuAx/r+GN+HsNb8Tfa3gj/l6jKWikBgAAAACAm3DSDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQuj2Ui+//LKSk5NlNpuVlpamjRs3Gr0loFnmzJmjfv36KTw8XB07dtRNN92kXbt2Gb0twGX++Mc/ymQyaeLEiUZvBWi2vLw8/e53v1OHDh0UHBysHj166NtvvzV6W0CTWSwWTZs2TSkpKQoODlZqaqpmz54t2mOhIQjdXmjZsmWaNGmSZsyYoS1btqhnz57KyMjQ4cOHjd4a0GRffvmlxo8fr2+++UZr1qxRVVWVBgwYoLKyMqO3BjTbpk2b9Prrr+uSSy4xeitAsx07dkxXXnmlAgIC9Nlnn2nHjh16/vnn1a5dO6O3BjTZs88+q1dffVULFizQjz/+qGeffVbPPfecXnrpJaO3Bg9A93IvlJaWpn79+mnBggWSJKvVqsTERN1///2aPHmywbsDXOPIkSPq2LGjvvzyS1199dVGbwdoshMnTujSSy/VK6+8oqeeekq9evXSvHnzjN4W0GSTJ0/Wv//9b/3rX/8yeiuAy9x4442KiYnRW2+95bg2fPhwBQcHa/HixQbuDJ6Ak24vU1lZqc2bNys9Pd1xzc/PT+np6Vq/fr2BOwNcq7i4WJLUvn17g3cCNM/48eN1ww03OP3vNuDJPvnkE/Xt21e33HKLOnbsqN69e+vNN980eltAs1xxxRXKysrSTz/9JEn67rvvtG7dOg0aNMjgncETtDF6A3CtwsJCWSwWxcTEOF2PiYnRzp07DdoV4FpWq1UTJ07UlVdeqV/96ldGbwdosvfff19btmzRpk2bjN4K4DJ79+7Vq6++qkmTJunxxx/Xpk2b9MADDygwMFCjR482entAk0yePFklJSXq1q2b/P39ZbFY9PTTT2vkyJFGbw0egNANwOOMHz9eP/zwg9atW2f0VoAm279/vx588EGtWbNGZrPZ6O0ALmO1WtW3b18988wzkqTevXvrhx9+0GuvvUbohsf64IMPtGTJEi1dulTdu3fXtm3bNHHiRMXHx/P3GudF6PYyUVFR8vf3V0FBgdP1goICxcbGGrQrwHUmTJigTz/9VF999ZUuuOACo7cDNNnmzZt1+PBhXXrppY5rFotFX331lRYsWKCKigr5+/sbuEOgaeLi4nTxxRc7Xbvooou0fPlyg3YENN+jjz6qyZMna8SIEZKkHj16KDc3V3PmzCF047x4ptvLBAYGqk+fPsrKynJcs1qtysrKUv/+/Q3cGdA8NptNEyZM0Mcff6zPP/9cKSkpRm8JaJbrrrtO33//vbZt2+Z49e3bVyNHjtS2bdsI3PBYV155ZZ2Rjj/99JOSkpIM2hHQfCdPnpSfn3N08vf3l9VqNWhH8CScdHuhSZMmafTo0erbt68uu+wyzZs3T2VlZcrMzDR6a0CTjR8/XkuXLtVf//pXhYeHKz8/X5IUGRmp4OBgg3cHNF54eHidngShoaHq0KEDvQrg0R566CFdccUVeuaZZ3Trrbdq48aNeuONN/TGG28YvTWgyYYMGaKnn35anTp1Uvfu3bV161bNnTtXY8eONXpr8ACMDPNSCxYs0P/+7/8qPz9fvXr10vz585WWlmb0toAmM5lM9V5fuHChxowZ07KbAdzkv//7vxkZBq/w6aefasqUKdq9e7dSUlI0adIkjRs3zuhtAU1WWlqqadOm6eOPP9bhw4cVHx+v22+/XdOnT1dgYKDR20MrR+gGAAAAAMBNeKYbAAAAAAA3IXQDAAAAAOAmhG4AAAAAANyE0A0AAAAAgJsQugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4CaEbAAC0KJPJpJUrVxq9DQAAWgShGwAAHzJmzBiZTKY6r4EDBxq9NQAAvFIbozcAAABa1sCBA7Vw4UKna0FBQQbtBgAA78ZJNwAAPiYoKEixsbFOr3bt2kmqKf1+9dVXNWjQIAUHB6tz58766KOPnN7//fff69prr1VwcLA6dOigu+++WydOnHBa8/bbb6t79+4KCgpSXFycJkyY4PT7wsJCDRs2TCEhIerSpYs++eQT9940AAAGIXQDAAAn06ZN0/Dhw/Xdd99p5MiRGjFihH788UdJUllZmTIyMtSuXTtt2rRJH374odauXesUql999VWNHz9ed999t77//nt98sknuvDCC52+48knn9Stt96q7du3a/DgwRo5cqSOHj3aovcJAEBLMNlsNpvRmwAAAC1jzJgxWrx4scxms9P1xx9/XI8//rhMJpPuuecevfrqq47fXX755br00kv1yiuv6M0339T//M//aP/+/QoNDZUk/f3vf9eQIUN08OBBxcTEKCEhQZmZmXrqqafq3YPJZNLUqVM1e/ZsSTVBPiwsTJ999hnPlgMAvA7PdAMA4GOuueYap1AtSe3bt3f8c//+/Z1+179/f23btk2S9OOPP6pnz56OwC1JV155paxWq3bt2iWTyaSDBw/quuuuO+ceLrnkEsc/h4aGKiIiQocPH27qLQEA0GoRugEA8DGhoaF1yr1dJTg4uEHrAgICnH42mUyyWq3u2BIAAIbimW4AAODkm2++qfPzRRddJEm66KKL9N1336msrMzx+3//+9/y8/NT165dFR4eruTkZGVlZbXongEAaK046QYAwMdUVFQoPz/f6VqbNm0UFRUlSfrwww/Vt29fXXXVVVqyZIk2btyot956S5I0cuRIzZgxQ6NHj9bMmTN15MgR3X///fr973+vmJgYSdLMmTN1zz33qGPHjho0aJBKS0v173//W/fff3/L3igAAK0AoRsAAB+zatUqxcXFOV3r2rWrdu7cKamms/j777+v++67T3FxcXrvvfd08cUXS5JCQkK0evVqPfjgg+rXr59CQkI0fPhwzZ071/FZo0ePVnl5uV544QU98sgjioqK0s0339xyNwgAQCtC93IAAOBgMpn08ccf66abbjJ6KwAAeAWe6QYAAAAAwE0I3QAAAAAAuAnPdAMAAAeeOgMAwLU46QYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE3+PysiU9fifLoaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the directories\n",
    "os.makedirs('images/Recurrent Neural Network (RNN)', exist_ok=True)\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.savefig('images/Recurrent Neural Network (RNN)/accuracy.png', dpi=600)\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.savefig('images/Recurrent Neural Network (RNN)/loss.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\engine\\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "model_rnn.save('my_model.h5') #change the path according to kaggel\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the model\n",
    "from keras.models import load_model\n",
    "model = load_model('my_model.h5') #change the path according to kaggel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 3s 237ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       275\n",
      "           1       1.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           1.00       276\n",
      "   macro avg       1.00      0.50      0.50       276\n",
      "weighted avg       1.00      1.00      0.99       276\n",
      "\n",
      "ROC AUC: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X_test is the test data and y_test are the true labels\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)  # Convert probabilities to binary outputs\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(y_test, y_pred, zero_division=1))\n",
    "\n",
    "# Print the ROC AUC score\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "print(f'ROC AUC: {roc_auc}')\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.title('Confusion matrix')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')\n",
    "plt.tight_layout()\n",
    "plt.savefig('images/Recurrent Neural Network (RNN)/confusion_matrix.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 2s 244ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70d7c567</td>\n",
       "      <td>0.001602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81977e6c</td>\n",
       "      <td>0.001467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e43869b8</td>\n",
       "      <td>0.001331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d33eca96</td>\n",
       "      <td>0.001379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71f7131e</td>\n",
       "      <td>0.002195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>62b480e1</td>\n",
       "      <td>0.001516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>7405b110</td>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>47e743a7</td>\n",
       "      <td>0.001597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>95d41c3e</td>\n",
       "      <td>0.001435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>ee6fb060</td>\n",
       "      <td>0.001845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  predictions\n",
       "0    70d7c567     0.001602\n",
       "1    81977e6c     0.001467\n",
       "2    e43869b8     0.001331\n",
       "3    d33eca96     0.001379\n",
       "4    71f7131e     0.002195\n",
       "..        ...          ...\n",
       "271  62b480e1     0.001516\n",
       "272  7405b110     0.001500\n",
       "273  47e743a7     0.001597\n",
       "274  95d41c3e     0.001435\n",
       "275  ee6fb060     0.001845\n",
       "\n",
       "[276 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array of IDs\n",
    "ids = df['id'].values\n",
    "\n",
    "# Split the IDs into training and test sets\n",
    "_, ids_test = train_test_split(ids, test_size=0.2, random_state=42)\n",
    "\n",
    "# Generate predictions\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# If predictions is not a 1D array, flatten it\n",
    "if predictions.ndim != 1:\n",
    "    predictions = predictions.flatten()\n",
    "\n",
    "# Create a new DataFrame with IDs and predictions\n",
    "result_df = pd.DataFrame({\n",
    "    'id': ids_test,\n",
    "    'predictions': predictions\n",
    "})\n",
    "\n",
    "# Display the DataFrame\n",
    "result_df"
   ]
  }
 ],
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