Amazon_Reviews_Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Amazon Reviews Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMlj0H/+O8aPflwFZMOQwBd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Amazon_Reviews_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "import string\n",
        "import spacy\n",
        "import nltk\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from spacy import displacy\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import confusion_matrix,classification_report,plot_confusion_matrix,accuracy_score\n",
        "from sklearn.metrics import plot_precision_recall_curve,plot_roc_curve\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from collections import Counter\n",
        "import warnings\n",
        "nltk.download(\"vader_lexicon\")\n",
        "warnings.filterwarnings('ignore')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRBIwMmv4wto",
        "outputId": "2f684413-5793-4a07-b65d-e47764f8ec7c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
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          "height": 363
        },
        "id": "-2C17eJE4g02",
        "outputId": "418116bb-9fe5-4e28-d541-24243ebe60ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pos</td>\n",
              "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>pos</td>\n",
              "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pos</td>\n",
              "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pos</td>\n",
              "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pos</td>\n",
              "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pos</td>\n",
              "      <td>an absolute masterpiece: I am quite sure any o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>neg</td>\n",
              "      <td>Buyer beware: This is a self-published book, a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>pos</td>\n",
              "      <td>Glorious story: I loved Whisper of the wicked ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>pos</td>\n",
              "      <td>A FIVE STAR BOOK: I just finished reading Whis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pos</td>\n",
              "      <td>Whispers of the Wicked Saints: This was a easy...</td>\n",
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            "text/plain": [
              "  label                                             review\n",
              "0   pos  Stuning even for the non-gamer: This sound tra...\n",
              "1   pos  The best soundtrack ever to anything.: I'm rea...\n",
              "2   pos  Amazing!: This soundtrack is my favorite music...\n",
              "3   pos  Excellent Soundtrack: I truly like this soundt...\n",
              "4   pos  Remember, Pull Your Jaw Off The Floor After He...\n",
              "5   pos  an absolute masterpiece: I am quite sure any o...\n",
              "6   neg  Buyer beware: This is a self-published book, a...\n",
              "7   pos  Glorious story: I loved Whisper of the wicked ...\n",
              "8   pos  A FIVE STAR BOOK: I just finished reading Whis...\n",
              "9   pos  Whispers of the Wicked Saints: This was a easy..."
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/amazonreviews.tsv'\n",
        "df = pd.read_csv(url,sep=\"\\t\")\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(5,5)})\n",
        "sns.histplot(df, x=\"label\", hue=\"label\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "LrXVCzFX5CLG",
        "outputId": "b9ee356c-5705-497b-b4d5-7c92b2f0f733"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFAioO928VZf",
        "outputId": "ab5225ec-3d67-4419-9fbb-26027e250ff1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label     0\n",
              "review    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "analysis_sa = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "c1K8Zrcu8X6y"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_text = 'the most exciting book i have ever read'\n",
        "print(analysis_sa.polarity_scores(random_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFApOdPs8c8k",
        "outputId": "c9a532a2-f21e-4e58-e620-54feca9918e7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'neg': 0.0, 'neu': 0.632, 'pos': 0.368, 'compound': 0.5413}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_text = 'This was the worst film to ever disgrace the screen.'\n",
        "print(analysis_sa.polarity_scores(random_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INrTemtN8fyn",
        "outputId": "55479ffc-0347-4d0f-a7b3-12f26d79b797"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'neg': 0.477, 'neu': 0.523, 'pos': 0.0, 'compound': -0.8074}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['score']= df['review'].apply(lambda rew: analysis_sa.polarity_scores(rew))\n",
        "df['compound']= df['score'].apply(lambda score: score['compound'])\n",
        "df['predict']= df['compound'].apply(lambda value: 'pos' if value >=0 else 'neg')"
      ],
      "metadata": {
        "id": "_Lp_JkQ38hoZ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "goHrj2j98jqp",
        "outputId": "b0b8ce13-5fa4-4ac8-9749-5d3c5bd8230c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
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              "      <td>pos</td>\n",
              "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
              "      <td>{'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...</td>\n",
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              "      <td>pos</td>\n",
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              "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
              "      <td>{'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...</td>\n",
              "      <td>0.9858</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pos</td>\n",
              "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
              "      <td>{'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...</td>\n",
              "      <td>0.9814</td>\n",
              "      <td>pos</td>\n",
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              "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
              "      <td>{'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...</td>\n",
              "      <td>0.9781</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pos</td>\n",
              "      <td>an absolute masterpiece: I am quite sure any o...</td>\n",
              "      <td>{'neg': 0.014, 'neu': 0.737, 'pos': 0.249, 'co...</td>\n",
              "      <td>0.9900</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>neg</td>\n",
              "      <td>Buyer beware: This is a self-published book, a...</td>\n",
              "      <td>{'neg': 0.124, 'neu': 0.806, 'pos': 0.069, 'co...</td>\n",
              "      <td>-0.8744</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>pos</td>\n",
              "      <td>Glorious story: I loved Whisper of the wicked ...</td>\n",
              "      <td>{'neg': 0.064, 'neu': 0.588, 'pos': 0.349, 'co...</td>\n",
              "      <td>0.9908</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>pos</td>\n",
              "      <td>A FIVE STAR BOOK: I just finished reading Whis...</td>\n",
              "      <td>{'neg': 0.113, 'neu': 0.712, 'pos': 0.174, 'co...</td>\n",
              "      <td>0.8353</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pos</td>\n",
              "      <td>Whispers of the Wicked Saints: This was a easy...</td>\n",
              "      <td>{'neg': 0.033, 'neu': 0.777, 'pos': 0.19, 'com...</td>\n",
              "      <td>0.8196</td>\n",
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            ],
            "text/plain": [
              "  label                                             review  ... compound  predict\n",
              "0   pos  Stuning even for the non-gamer: This sound tra...  ...   0.9454      pos\n",
              "1   pos  The best soundtrack ever to anything.: I'm rea...  ...   0.8957      pos\n",
              "2   pos  Amazing!: This soundtrack is my favorite music...  ...   0.9858      pos\n",
              "3   pos  Excellent Soundtrack: I truly like this soundt...  ...   0.9814      pos\n",
              "4   pos  Remember, Pull Your Jaw Off The Floor After He...  ...   0.9781      pos\n",
              "5   pos  an absolute masterpiece: I am quite sure any o...  ...   0.9900      pos\n",
              "6   neg  Buyer beware: This is a self-published book, a...  ...  -0.8744      neg\n",
              "7   pos  Glorious story: I loved Whisper of the wicked ...  ...   0.9908      pos\n",
              "8   pos  A FIVE STAR BOOK: I just finished reading Whis...  ...   0.8353      pos\n",
              "9   pos  Whispers of the Wicked Saints: This was a easy...  ...   0.8196      pos\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Overall accuracy is {}\\n'.format(accuracy_score(df['label'],df['predict'])*100))\n",
        "\n",
        "print(classification_report(df['label'],df['predict']))\n",
        "print(confusion_matrix(df['label'],df['predict']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDhEn12N8vaT",
        "outputId": "a5905f1c-7abb-4d93-e8cb-d0f0dba09b30"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall accuracy is 70.91\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         neg       0.86      0.51      0.64      5097\n",
            "         pos       0.64      0.91      0.75      4903\n",
            "\n",
            "    accuracy                           0.71     10000\n",
            "   macro avg       0.75      0.71      0.70     10000\n",
            "weighted avg       0.75      0.71      0.70     10000\n",
            "\n",
            "[[2623 2474]\n",
            " [ 435 4468]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label_num'] = pd.get_dummies(df['label'],drop_first=True)"
      ],
      "metadata": {
        "id": "wH6w5xDz82K1"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PredictReview:\n",
        "    \n",
        "    def vectorize(self,train_data,test_data):\n",
        "        \n",
        "        tfidf = TfidfVectorizer()\n",
        "        train = tfidf.fit_transform(train_data.values.astype('U'))\n",
        "        test = tfidf.transform(test_data.values.astype('U'))\n",
        "        \n",
        "        return train,test,tfidf\n",
        "    \n",
        "    def split(self,data,train_size=0.1,shuffle=101):\n",
        "        \n",
        "        input_data = data['review']\n",
        "        output_data = data['label_num']\n",
        "        train_data, test_data, train_output, test_output = train_test_split(input_data, output_data, test_size=train_size, random_state=shuffle)\n",
        "        return train_data, test_data, train_output, test_output\n",
        "    \n",
        "    def base(self,data):\n",
        "        \n",
        "        log_reg = LogisticRegression()\n",
        "        data = self.prepare_data_for_train(data)\n",
        "        train_data, test_data, train_output, test_output = self.split(data)\n",
        "        train,test,tfidf = self.vectorize(train_data,test_data)\n",
        "        log_reg.fit(train,train_output)\n",
        "        pred = log_reg.predict(test)\n",
        "        self.performance(pred,test,test_output,log_reg)\n",
        "        return log_reg,tfidf\n",
        "    \n",
        "   \n",
        "    def prepare_data_for_train(self,input_data):\n",
        "        \n",
        "        nlp = spacy.load('en_core_web_sm')\n",
        "        stopword = nltk.corpus.stopwords.words('english')\n",
        "        empty_list  = []\n",
        "        for text in input_data.review:\n",
        "            text = text.lower()\n",
        "            text = re.sub('\\[.*?\\]', '', text)\n",
        "            text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "            text = re.sub('<.*?>+', '', text)\n",
        "            text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "            text = re.sub('\\n', '', text)\n",
        "            text = re.sub('\\w*\\d\\w*', '', text)\n",
        "            text = re.sub(r'[^\\w\\s]', '',str(text))            \n",
        "            text=re.split(\"\\W+\",text)                          \n",
        "            text=[word for word in text if word not in stopword]\n",
        "            text = ' '.join(text)       \n",
        "            empty_list.append(text)\n",
        "        #input_data.drop('review',axis=1,inplace=True)\n",
        "        input_data['review'] = empty_list\n",
        "        return input_data\n",
        "    \n",
        "    \n",
        "    def performance(self,_data,test_data,test_output,model):\n",
        "        \n",
        "        plt.figure(figsize=(12,12))\n",
        "       \n",
        "        print(plot_confusion_matrix(model,test_data,test_output))\n",
        "        print(plot_precision_recall_curve(model,test_data,test_output))\n",
        "        print(plot_roc_curve(model,test_data,test_output))\n",
        "        print('\\033[36m'+classification_report(_data,test_output))\n",
        "        print('\\033[4m'+'\\033[91m'+'\\033[1m' +'Overall accuracy is {}% 💣\\n'.format(round(accuracy_score(_data,test_output)*100),0))\n",
        "        plt.show()\n",
        "     \n",
        "        \n",
        "    def test_sample(self,text,tfidf,base_model):\n",
        "        \n",
        "        text = self.clean_df(text)\n",
        "        text_sample = tfidf.transform([text])\n",
        "        pred = base_model.predict(text_sample)\n",
        "        if pred[0] == 1:\n",
        "            return 'positive'\n",
        "        else:\n",
        "            return 'negative'\n",
        "        \n",
        "    def  clean_df(self,text):\n",
        "        text = text.lower()\n",
        "        nlp = spacy.load('en_core_web_sm')\n",
        "        stopword = nltk.corpus.stopwords.words('english')\n",
        "        text = re.sub(r'[^\\w\\s]', '',str(text))            \n",
        "        text=re.split(\"\\W+\",text)                          \n",
        "        text=[word for word in text if word not in stopword]\n",
        "        text = ' '.join(text)                              \n",
        "        return text\n"
      ],
      "metadata": {
        "id": "J49UfH4a86hr"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_predictor = PredictReview()"
      ],
      "metadata": {
        "id": "1jSqtDU29BK7"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chy4VdDP9JOo",
        "outputId": "4cf00c73-0ba6-42d1-98ec-5ff856515642"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (15,10)\n",
        "model,coverter = review_predictor.base(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FFOqeImo9DET",
        "outputId": "a0997417-2935-426f-e441-e68ffa8cdb97"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f3a9cc73ad0>\n",
            "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay object at 0x7f3a9cc52150>\n",
            "<sklearn.metrics._plot.roc_curve.RocCurveDisplay object at 0x7f3a9bc1fb90>\n",
            "\u001b[36m              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.86      0.87       517\n",
            "           1       0.85      0.88      0.87       483\n",
            "\n",
            "    accuracy                           0.87      1000\n",
            "   macro avg       0.87      0.87      0.87      1000\n",
            "weighted avg       0.87      0.87      0.87      1000\n",
            "\n",
            "\u001b[4m\u001b[91m\u001b[1mOverall accuracy is 87% 💣\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D0Na6LvmCURmSDrUGqcUwNL46X9G4qdwsu2qr3DIMQ/m5DlUW58id41Bell0/fXKNzARXNAUAQKL8AQDQr0AoKo8/rM7ukOrb/Gru8CsciauhzafGdr8Mw1BHd+iQr1NWkKWCPKdcDquOKncry2VVXpZD5UXZKnI7VZTnVJbTJsMwBiR3NBZXtz+iNk9QvkBEe1u86vSGZRiS3WrRGSfWqCDXOSDvBQBIH5Q/AEBG8wYikqSf//EjSZI/2HMJ5qG4s+2STB1dU6Cywiw57FaVFWapsihbedkOFbqdcmc7kpZ7e33Pthf/9YcP1dwRkGFIDW1+RWPxQ35uSUGWvnDciKRlAwAMTZQ/AEBG29vslSRt2NXRe2zsiHzFTVNFeU6NHZEvu92q0nxX70zdUFoZdNXmFlktPTOGlcXZKnK7ZLdaVFaUpbKCLGW77Cp2u1RS4FIsZurff/4Ol4sCQIai/AEAMtp1F0xWXYtP5UVZvZu/p4OHvnmKYjFT2S5bwgvVeHzhJKdKro9XP2339Kx8GghF1dIZlNNu1dTaYlWV5KQ6IgAMaenztxwAAElgs1o0qiIv1TEOW47LnuoIR8w0TcVicTV3+NXaFVSnNyRfMKo9zV75g1HFYnFtb/AoHI0rFosrGut/xnJHg0fXfnly0rJ6AxF5fGF1+8Nq94Rkt1k0YVShcrPS//cBQOag/AEAgAFjmqYi0bg69q1+Gth3D2Vnd0iGYWhbfZc8vrBsVou6EpiJrCjKljMWV7bLprEj8iVJ7hyHqktzleW0qSjPqQefWat4PPFLWU3TlC8YVZc3pA5vSP5gVHtbvOr2R2RI2lrXpUAoKqvFoubOQL+vc8YJNfrq6eMSfl8ASDXKHwAAOKR43FQgHFVbV1CN7X61dAYUCMXU3OFXQ7tf8bip1q6gItGDLzjjdFiV5bDKbrPohAllKsrPkkWmKouyVeh2Kj/HqYJch/IOY7Eci2HIlNTRHVJbV1DeYEQNrT61eYIyTWlvi1dtnqBsVouaO/ovc70Z7VZZLJLDZtVx40okSTlZdtWU5cplt8qd49Cvl61XJBpLOCMADAWUPwAAMphpmuryhdXaFVS7J6hOb1i7m7rlDUQUCse0rb7roJdbSpLdZpFpmiovzFZFUbasVkOlBVmqKslRttOmkoIsFbudctitsnxqO4vS0jy1tCS2qX1/6lp9qmv1adXmln7Pyc2yKxyJa8LIArlzHHLYrSovzFKx26WcLLuK3K6ejDarLJZDb7lhmlKnN6wtezvV1hVUIBxTa2dATR0BZbtsynbadOGptbLbhs7iQABA+QMAIEN8XOF+9+pmvbG6XntbvIf8nJHluYrFTeXnODSuukA2q6Eit0tVxTkqznfJ5bAmvOBMssw4plyb9nTq+PGlGlWRJ4fdquJ9ZS4v25FQmTtc/lBUa7a2as3W1v5zTSrXURXuAX9vAPisKH8AAGQI+ydKWmO7XxNHFcpptyov265RFXkqyHWq2O1S0b7SlC6u/tKkQX/P044boW5fWLUj8lVdmtMze5jnVG62XR9ub9eDT68VO2oAGGoofwAAZIhsl02PLJid6hjDwv878+hURwCAw8aF6AAAAACQAZj5AwAASIFYPK5wJK42T1BN7QF1dAcVicW1vc6jbn9YVqtFdptF//z5MRpZnn57UQIYeih/AAAASbBuR7taOgPa0+xVa1dQ8bipulafWjsDslgMBcOJbRUxvqaA8gdgQFD+AAAABtCe5p5VVP/wl+37PeewW1SY55TFYqi6NFdFbqdcDpvKCrNUVpClgtyeRWOcdqvCkZi+/tM3Bzs+gGGM8gcAADCAzjyxRis3NOv8k4+Sy2FVSb5LRW6XHDaLDCPxbSc+Xiz06Te26Y3VdbLbLPqXOeM0ZUxxcoIDGPYofwAAAAPIYbfqzvknHfHr2Kz/KIrhaFytXUFt3dtF+QPwmVH+AAAAhiCrxdJna475976WwjQAhgO2egAAAACADED5AwAAAIAMQPkDAAAAgAxA+QMAAEgDpqTn/7pTS5atl2mahzwfAD6N8gcAAJBG3vmoUdEY5Q/A4aP8AQAApIFHFszWhaeO2feRqVg8rvpWn1ZtbtGKDU3auKsjpfkADH1s9QAAAJAmPi541/zkzQM+f/8Ns+TOcQxmJABphPIHAACQJqbUlmjdzg4df3SprBZDRXkuja5ya1tdl15duUeRaDzVEQEMYZQ/AACANPHFE2v0xRNr9jve7glKkh54eq2ynFZ1ecNyOqyac3y1Pj+tarBjAhiiKH8AAABprqUzIEna2+KVJDlsFkVica3d1kb5A9CL8gcAAJDm5p0xXqcdN0LlhVmy26ySpNuW/E2S1NYVVGO7Xx5fWDsaPWps88tht8ppt+pf5oxVXjb3CAKZgvIHAACQ5iyGoerS3D7H9rb4tLelZzXQ/kw/pkxTa0sO+tqmaardE5LFYqgg1yHDMAYkM4DBR/kDAAAYhiaOKtTmPZ36/LQqjR2Rr7xsuyqKs1WSn6Xt9R798LH3FTcljz+spna/Gtr8Coai2ri7s+ceQkPa3eTt85pXnDNRs6ZWHnaWUCSmtq6g6lp9shiGxlS5VZjnHKgvFUCCKH8AAADD0He/ely/z3n8YUnSg0+vPehrjK8pULbTptwsu97+sKH38z4pGI6qozukxja/9rZ41eULq6M7pO0NHoUjMQVCsf0+59ixJbrxoqmH+RUBOFKUPwAAgAwzutItSZowskBFbpdGlOaopjRXRW6XCvOcynL2/RExHInp7Q8b9PQb29TQ5tPGXR3yBqIKR2MyzQO/R36OQ3arRRWVOTq6pkA2m6Gygmy9unK3ItH9CyGA5KP8AQAAZJj8HIceWTA74fM/eZvfOx82Ksdlk8NuUUm+SxNHFcrltKokP0s1ZbmqKMqWw26R1WI54Gs98uIG7W3xafHSj/RvZx6tHJf9SL8cAAmi/AEAAOCg7DarFvzr51Tkdio/xym77cDF7nCs2NCs044doQmjCgcgIYBEHPmfXAAAAAx742sKVJKfdcTF75EFs3XzQe5HBJA8lD8AAAAAyACUPwAAAADIANzzBwAAgEEV37dE6P/8ebOOHlkol8Oqc2aM2m+VUQADiz9hAAAAGFRd3p79Ave2+LS3xSdJGledr6m1JamMBQx7lD8AAAAMqpmTKxQMRzWltlgd3SHd87tVCkfiMk1Txif3lQAwoLjnDwAAAIPuC5+rVkl+ljy+iCTp4Wc/0sPPfpTiVMDwRvkDAABAykweXSRJynba1NwRSHEaYHij/AEAACBlnA6rHlkwW0ePLEh1FGDYo/wBAAAAQAag/AEAAABABqD8AQAAAEAGoPwBAAAAQAZgnz8AAACkXDRmqqHNp7fXNsgwpJqyXI0sz0t1LGBYofwBAAAg5T7c3iZJeuTFDZKkESU5uuvK6amMBAw7XPYJAACAlLvsrKM1tjpfV59/jKaMKVYkGk91JGDYofwBAAAg5U49doRuvfR4zZhUoQ+3t6m5M6Bv/9fb2lbflepowLBB+QMAAMCQMnNSuSSp0xtWfauv93jcNNXU4df7G5v17rpGbaujGAKHg3v+AAAAMKRcdf4kXXDKGN28+F395sWNevejRm3c3bnfeQ67RYv//bTBDwikKcofAAAAhhzzE4837u5UdWmu3Dl2leS7NK66QOt3dui99Y0pywekI8ofAAAAhpzSgizdccVJGlGSI4vF2O/5po5AClIB6Y3yBwAAgCGppiw31RGAYYUFXwAAAAAgA1D+AAAAACADUP4AAAAAIANQ/gAAAAAgA1D+AAAAACADUP4AAAAAIANQ/gAAAAAgA7DPHwAAANJaPG5qT7NXcdNUTVmubFbmN4ADofwBAAAg7Wyr65JpSlfc+1qf43NnjdbcWaNTlAoY2ih/AAAASDsuh1WSNLrSrWynVVUluXpjTZ38waia2v3a0eBRV2CvNu9ql9Nh1SlTqzRxVGGKUwOpRfkDAABA2rnhwqn7HfvT+3t6fx0I5Q+ZjvIHAACAYWXmpApNrS3W5PFlssRiuuM3K1MdCRgSKH8AAAAYFh5ZMLvPx6WleWpp6U5RGmDoofwBAAAgI7R0BrSzsVveQERZTqtOmlgui2GkOhYwaCh/AAAAGNaaOwNq7gzovXVNfY5XFedoZHleilIBg4/yBwAAgIww/ZhyTastVkd3SE+9sU2xuJnqSMCgovwBAABgWPv0vYB/39qaoiRAallSHQAAAAAAkHyUPwAAAADIAJQ/AAAAAMgAlD8AAAAAyACUPwAAAADIAJQ/AAAAAMgAg1b+duzYoa985Ss688wz9ZWvfEU7d+7c75yWlhZde+21Ov/883X22Wdr6dKlgxUPAAAAGaKhzS9JuuvR9/XMm9tSnAYYPINW/m6//XbNmzdPr7zyiubNm6fbbrttv3PuvfdeTZ48Wc8//7yeeOIJ/exnP1NDQ8NgRQQAAEAGmDiqsPfxjgZPCpMAg2tQyl9bW5vWr1+v8847T5J03nnnaf369Wpvb+9z3saNG3XKKadIkoqKijRhwgS99NJLgxERAAAAGWJURZ4eWTBbY0fkpzoKMKhsg/EmDQ0NKi8vl9VqlSRZrVaVlZWpoaFBRUVFvedNmjRJL774oqZMmaK9e/dq9erVqq6uPqz3Ki7OHdDsA6W0NC/VETCMMb6QTIwvJBtjDMl0sPFlt1vlsFsZg/jM0m3sDEr5S9SCBQt09913a+7cuaqqqtLMmTN7C2Oi2tq8isfNJCX8bEpL89TS0p3qGBimGF9IJsYXko0xhmQ61PjaUd+lYDim517fopOOKZPVwlqISNxQ/P+XxWIcdDJsUMpfZWWlmpqaFIvFZLVaFYvF1NzcrMrKyj7nFRUV6Sc/+Unvx1dddZXGjh07GBEBAACQYYLhmCTpV8vWq7QgS2OruQwUw9ug/PNGcXGxJk6cqGXLlkmSli1bpokTJ/a55FOSOjo6FI1GJUnvvvuuNm/e3HufIAAAADCQbv7qcTrzpBpJ0u9f26LHX92k/3t9qwKhaIqTAckxaJd9Lly4UAsWLNDDDz8st9utRYsWSeqZ3bvxxhs1ZcoUrV27Vj/60Y9ksVhUWFioxYsXKysra7AiAgAAIINMGFWoulafJGl7vUfb63tW/pwwskBTa0tSGQ1ICsM0zaF1g9wR4p4/ZBrGF5KJ8YVkY4whmRIZX7F4XPf9z2p9ZfY4BUJR/fTJNZKk2hFuOWxWXXb2BJUVMBmB/Q3F/38NiXv+AAAAgKHIarHolkuPlyR9uL2t9/i2up5ZwJ0NHsofhg3KHwAAACBpyphi3fDPUzRtXIn2NHl1x29XavHSdWr3hHTW9JGpjgccMdazBQAAAPY5bnypLIYhh/0fPya/sbpOkhSOxLS1rks7GjxD7jYjIBHM/AEAAACfUlmco0cWzNY3H3xLzZ0BXXHva32en3/uRJ08pbKfzwaGJmb+AAAAgH54/BFJUrHbpcmji3TacSMkSet3dqjLG0plNOCwMfMHAAAA9OPX//EFyexZRVGSOrpDemN1nd5d16iNuzv00+tPTnFCIHHM/AEAAAD9sBhGb/GTpMI8pyqKslXkdqqjO6Rdjd3c/4e0QfkDAAAADsPdV89Qu6fnks87frtSb66pS3EiIDGUPwAAAOAw3XDhFBXmOSVJH25vV1tXMMWJgEOj/AEAAACH6bhxpbr76hmSpDVbW3X7Iyv6PB8Kx9TaFZA/GE1FPOCAWPAFAAAA+Aycdqtqq9xq7w6pozuk3y/fop2N3ersDqm5M9B7zv03zpLTbv1M7xGPm+r0hmSzWeTOdgxkfGQgyh8AAADwGX3v/52g63/2piTp1ZV7eo8fO7ZEje1+Nbb79cqK3WrtCmpPs1dOu1WnH1+tEyaU7fdapmlqe4NH63d2qKUjoDVbW+UNRHqfv/mrx2nCqMLkf1EYtih/AAAAwBH41sXH6ok/bda8M8bpqAq37LaeO6vu/t0HUrv07Fs7+pxflOfUuJoC7aj3aFdTtzbsbNfmvV0HfO2RZbly2K3aWtelVZtbVFmSo/wcZgDx2RimaQ6rtWnb2rxDbrnd0tI8tbR0pzoGhinGF5KJ8YVkY4whmVI9vnY0eF9d/1QAACAASURBVHTXo+/r+gsma0xVvtw5dl113xsHPDc/16G8LLsmjirS1LHFGlWep9wsuyRp464O3fe/qyX1lMGFV5w0WF8CDiLV4+tALBZDxcW5/T7PzB8AAACQBKMr3XpkwewDPjdhZIFOmliumvJcVRXnKMvZ/4/l40cWKD/HoWgsrt3NXq3Y0KRjx5bI8RnvI0TmYuZvEAzFfxXA8MH4QjIxvpBsjDEk03AbX1fc+1rv46vPP0YzJlWkMA2G4vg61MwfWz0AAAAAaWDBv35Opx1bJUmKROMpToN0RPkDAAAA0sD4mgKdO/OoVMdAGqP8AQAAAEAGoPwBAAAAQAag/AEAAABABmCrBwAAACDNhSMx7WjwaMOuDjV1BGSzGjrzxJGqLut/5UdkHsofAAAAkGbW7WyXNxDRXz9qVF2r74DnFOY5leW0KW6aKsl3yTCMQU6JoYbyBwAAAKSJQCgqSVqxoVkrNjRLkmxWQxVF2TpxYrnGjchXdVmubnzgLS376y4t++suSdKFp45hpVBQ/gAAAIB0UVWaI0k6e/pITRtbolHleXI6rP2eP3l0kTbt6ZTHFxmsiBjCKH8AAABAmrAYhh5ZMPuQ533ynOt/9mYyIyGNsNonAAAAAGQAyh8AAAAAZADKHwAAAABkAMofAAAAAGQAyh8AAAAwjAVCPRvAt3YGUh0FKUb5AwAAAIa5rXVduuWX7ykYjqY6ClKIrR4AAACAYWzSUYXasKtTsbipm3/xrorzXcrPcej6C6bIbmMuKJPwuw0AAAAMY//+L8dp4qgCSZI3ENGuxm6t3damLm8oxckw2Jj5AwAAAIa56y6Yor9vbdWJE8v07kdNeuTFDamOhBSg/AEAAADDXJbTphmTKvoc27CrQ+auDuW47Prc+BIZhpGidBgslD8AAAAgg6zf2S5J+s1LG3uP3XPNDJUXZqcqEgYJ9/wBAAAAGeS040ZIki75wlidddJISVI0Gk9lJAwSZv4AAACADDK+pkCPLJgtSVq5sVmSZEoKhKJat6Nd0VhctSPyVVqQlcKUSAbKHwAAAJCh2j1BSdJtS1b0OT6ttlg3XTwtFZGQRJQ/AAAAIEPZrD13geW4bBpRkqPJY4q1YkOzIjEuAx2OKH8AAABAhppzfLXmHF/d59jabW0pSoNkY8EXAAAAAMgAh5z5a2tr01tvvaVNmzbJ4/HI7Xbr6KOP1sknn6zS0tLByAgAAABgkPmCEe1s7FZDq09Ou1UzJlXIbmPuKJ31W/62bdumBx54QH/72980adIkjRkzRiUlJfL5fHruued0zz33aPr06brxxhs1duzYwcwMAAAAIEm21nVJkm64/60+xwvznJo8pjgVkTBA+i1/CxYs0Pz58/WTn/xEDodjv+fD4bCWL1+u733ve3ryySeTGhIAAADA4DqqIk/HH10qh82q/12+RdG4mepIOEL9lr+nnnrqoJ/ocDh09tln6+yzzx7wUAAAAABS4+M9AD+2o8GToiQYaFy0CwAAAKBf3kBEkvTg02v1++VbUpwGR+Izl79wOKw5c+YMZBYAAAAAQ0xJvqv38asr92jT7g7FuQQ0LR3RzF9dXd1A5QAAAAAwBFUW5/S5FHTR/6zW2u3sBZiODrrVw8SJE/t9zjRNGYYx4IEAAAAADD2XnXW0/vzBXtW1+BSOxFIdB5/BQctffn6+7r777gNu5RAOh3X++ecnLRgAAACAoePUY0doXHWBvv/rv6U6Cj6jg5a/SZMmqaOjQyNHjtzvuXA4LNPkWl8AAAAgU4SjPTN+i5eu055mry48tTbFiXA4Dlr+FixYIJvtwKc4HA4tX748KaEAAAAADD1Zzn90gxfe3aXPjS/VqIo8WbgdLC0cdMGXcePGafTo0f0+P2LEiAEPBAAAAGBoKi/M7rP4y12Pvq8PNrWkMBEOB/v8AQAAADgs/3rGeGXvmwUMhKIpToNEUf4AAAAAHJY5x1frzvknpToGDhPlDwAAAAAyAOUPAAAAADJAwuWvsbExoWMAAAAAgKEn4fL3pS99ab9j55xzzoCGAQAAAAAkR8Llb+nSpfsde+GFFwY0DAAAAAAgORIuf5WVlQkdAwAAAAAMPbb+nnj66acTeoGLLrpowMIAAAAAAJKj3/J3oMs8P80wDMofAAAAAKSBfsvf448/Ppg5AAAAAABJlPA9fx0dHXr22Wf161//WpLU1NTEVg8AAAAAkCYSKn8rVqzQWWedpeeff14///nPJUm7du3SwoULk5kNAAAAQBqIRGNas6VVf/l7vbbs7Ux1HPSj38s+P+nuu+/W/fffr5kzZ+rEE0+UJE2bNk1r165NajgAAAAAQ1M8bkqSfvvSRv32pY29x3Oz7HrwplNSFQsHkVD5q6ur08yZMyX1LPIiSXa7XbFYLHnJAAAAAAxZH/cCSSotcOmUqVXa1ditj3a2pzAVDiah8ldbW6u33npLp5zyjwb/17/+VePHj09aMAAAAABDV3G+Sz+57p9UkOeUZV8R/P3yLSlOhYNJqPwtWLBA11xzjU477TQFg0Hddttteu211/Twww8nOx8AAACAIarI7Up1BByGhBZ8OfbYY/Xcc89p7NixuvDCC1VdXa2nn35aU6dOTXY+AAAAAMAASGjmT5LKy8t15ZVXqqOjQ4WFhX2u8QUAAAAADG0Jzfx5PB5997vf1dSpU3XyySdr6tSp+u53v6vOTpZxBQAAAIB0kFD5u+WWWxQKhfTss89q1apVevbZZxUOh3XrrbcmOx8AAAAAYAAkdNnne++9p3feeUcuV88NnbW1tbr33nv7rP4JAAAAABi6Epr5GzNmjOrq6vocq6+v1+jRo5MSCgAAAAAwsPqd+Xv66ad7H8+cOVNXXHGF5s6dq4qKCjU2Nuq5557T3LlzByUkAAAAAODI9Fv+li5d2ufjkSNHavXq1b0f19TU9PkYAAAAAELhmF5ZsVvBcEwVRdmafkx5qiNhn37L3+OPPz6YOQAAAACkuVdX7pEkPfna1t5jE0cVyp3jSFUkfEJC9/x9kmmaisfjvb8AAAAAQJIuOq1WknTVecfIZu2pGt986G29vmpvKmNhn4RW+2xqatKdd96p999/Xx6Pp89zGzZsSEowAAAAAOnlnBmjdM6MUZIkjz/cOwP4+Kub1dDu11fnjJNhGKmMmNESKn+33367XC6Xfvvb3+rSSy/VE088oYceekinnnpqsvMBAAAASENnnjRSZ540Ulfc+5ok6c/v71VDq08Ou1VFeS7NO4MiONgSuuxz9erVuvvuuzVx4kQZhqEJEyboRz/6kR555JFk5wMAAACQxn72jZNVmOeUJK3b2aHVW1q1fNVedQciKU6WeRIqfxaLRTZbzySh2+1We3u7srOz1dTUlNRwAAAAANJbfq5T3/jnKaooytYdV5ykr84Zl+pIGSuhyz6nTZumN998U2eccYZmzZqlb37zm3K5XJo8eXKy8wEAAABIc6Mr3br76hmSpM17OlOcJnMlVP7uu+++3pU9b731Vi1ZskR+v1+XXXZZUsMBAAAAAAZGQuXP7Xb3Pna5XLr++uuTFggAAADA8LW9vmf3gO/98j396xnjNWNSRYoTZY5+y98DDzyQ0AvcdNNNAxYGAAAAwPCWn9uz4bsvGNXG3Z2Uv0HUb/lrbGwczBwAAAAAMsAlXxirS74wVt/6r7dTHSXj9Fv+7rnnnsHMAQAAACCDdHnD+svf6+XxhXXdBZNlsya0EQGOAN9hAAAAACmzZmurOrtDqY6RESh/AAAAAAbdIwtm64pzJqY6Rkah/AEAAABABqD8AQAAAEAGSGifP0l655139MILL6i9vV2LFy/Whx9+KK/Xq5kzZyYzHwAAAABgACQ08/f4449r4cKFOuqoo7Ry5UpJPZu9J7oXIAAAAAAgtRIqf48++qh+85vf6Oqrr5bF0vMpY8aM0Y4dO5IaDgAAAAAwMBIqfz6fT5WVlZIkwzAkSdFoVHa7PXnJAAAAAAADJqHyd+KJJ+qXv/xln2OPPfaYpk+fnpRQAAAAAICBldCCL9///vf19a9/XU899ZR8Pp/OPPNM5eTk6L//+7+TnQ8AAAAAMAASKn9lZWV65plntHbtWtXX16uyslJTp07tvf8PAAAAAA5XNBaXJL2/qUUzJ1coP8eR4kTDW0Lt7be//a3a2to0bdo0nX322Tr22GMpfgAAAACOyNptbZKk/3t9q555c1uK0wx/CTW4FStWaM6cOfra176mZ555Rl6vN9m5AAAAAAxzXz19nPKy7Sp2OxWJxlMdZ9hLqPw9/PDDeuutt3Tuuedq6dKlmjVrlm644Qa9+uqryc4HAAAAYJgqLcjSAzeeIpuVqwoHQ8LfZbfbrYsvvliPPfaYXnzxRfl8Pt10003JzAYAAAAAGCAJLfjysffff18vvPCCXnnlFRUUFOiGG25IVi4AAAAAwABKqPwtWrRIL7/8sgzD0Nlnn60lS5Zo4sSJyc4GAAAAIANEY6Ya2/169q3tMgxDx44t0aiKvFTHGnYSKn+BQEA//vGPdcIJJyQ7DwAAAIAM0+YJqs0T1K7GbknSnmavvvHPU1KcavhJqPwtXLgwyTEAAAAAZCqHzaJwNK6vz52kF97dpXjcTHWkYanf8jd//nwtWbJEkjRv3jwZhnHA85544omE3mjHjh1asGCBOjs7VVBQoEWLFumoo47qc05bW5tuueUWNTQ0KBqNavr06fr+978vm+2wbk0EAAAAkEYWf+e0fzxeuk57mr36nz9t1sVfqJXdZk1dsGGm31b15S9/uffxxRdffMRvdPvtt2vevHmaO3euli5dqttuu02PPfZYn3MWL16s2tpa/fKXv1QkEtG8efP06quv6pxzzjni9wcAAACQPv78wV4FIzGdddJIVZXkpDrOsNBv+Tv//PN7H48ZM0bTpk3b75y1a9cm9CZtbW1av369fvOb30iSzjvvPN11111qb29XUVFR73mGYcjn8ykejyscDisSiai8vDzhLwYAAABAertz/kl64d1d+tv6Jr29tkFd3rC+dcn+XQSHL6F9/i6//PIDHr/yyisTepOGhgaVl5fLau2ZsrVarSorK1NDQ0Of86677jrt2LFDs2bN6v11/PHHJ/QeAAAAANJfdWmuvnb2BI2udGtURZ6isXiqIw0bB72ZLh6PyzTNPr8+tnv37t4yN1BefvllHX300Xr00Ufl8/l01VVX6eWXX9ZZZ52V8GsUF+cOaKaBUlrKUrVIHsYXkonxhWRjjCGZGF/p68HvfEE3P/SWHHbLkP19HKq5+nPQ8nfMMcf0LvRyzDHH9HnOYrHo61//ekJvUllZqaamJsViMVmtVsViMTU3N6uysrLPeb/73e909913y2KxKC8vT7Nnz9bf/va3wyp/bW3eIbc6UGlpnlpaulMdA8MU4wvJxPhCsjHGkEyMr/QXicYk0xySv49DcXxZLMZBJ8MOWv6WL18u0zT1b//2b/rd737Xe9wwDBUVFcnlciUUori4WBMnTtSyZcs0d+5cLVu2TBMnTuxzv58kVVdX6y9/+YumTp2qcDisd999V2eccUZC7wEAAAAA6N9By9+IESMkSa+//voRv9HChQu1YMECPfzww3K73Vq0aJEk6aqrrtKNN96oKVOm6NZbb9Xtt9+u888/X7FYTNOnT9cll1xyxO8NAAAAAJmu3/L3gx/8QHfddZck6eabb+73Be67776E3qi2tlZPPfXUfsd/9atf9T4eOXJk74qgAAAAAICB02/5q66u7n08cuTIQQkDAAAAAB/burdLkvTT36/WVedPkjvHkeJE6a3f8nfNNdf0Pv7GN74xKGEAAAAA4NPW7exQXYtX7pyiQ5+MfiW0z997772nPXv2SJJaWlr0H//xH7rlllvU0tKS1HAAAAAAMtfifz9V3/4KG7wPlITK3x133NG7p9+9996raDQqwzD0gx/8IKnhAAAAAGQuh90quzWhyoIEHHS1z481NTWpqqpK0WhUb7/9tl577TXZ7Xadcsopyc4HAAAAABgACZW/3Nxctba2asuWLaqtrVVOTo7C4bCi0Wiy8wEAAAAABkBC5e/SSy/VRRddpEgkoltvvVWStGrVKo0ZMyap4QAAAAAAAyOh8nf11VfrjDPOkNVq7d32oby8XD/84Q+TGg4AAAAAMDASKn+SVFNTo9WrV2vt2rUqLy/XcccdJ5st4U8HAAAAAKRQQu1t27ZtuvbaaxUMBlVZWamGhgY5nU4tXrxYtbW1yc4IAAAAADhCCZW/O+64Q5dcconmz58vwzAkSUuWLNHChQv1+OOPJzUgAAAAAODIJbRpxsaNG3X55Zf3Fj9Juuyyy7Rx48akBQMAAAAADJyEyl9ZWZlWrFjR59j777+vsrKypIQCAAAAAAyshC77/Na3vqXrrrtOp512mqqqqlRfX6833nhDP/7xj5OdDwAAAAAwABKa+ZszZ47++Mc/aty4cfL5fBo3bpz+8Ic/6PTTT092PgAAAADAADjozF8gENAvfvELbd68WZMmTdI111wjh8MxWNkAAAAAAAPkoDN/d955p15//XWNGTNGr7zyihYtWjRYuQAAAAAAA+ig5e+tt97SkiVLdPPNN+tXv/qVXn/99cHKBQAAAAAYQAe97NPv9/eu6FlZWSmv1zsooQAAAABAkmJxU5L049+vkTvbLpfDpm9eMk0VRdkpTpZ+Dlr+YrGY3nvvPZlmzzc8Go32+ViSZs6cmdyEAAAAADJWfauv97HHH5HHH1Fjm5/y9xkctPwVFxfr1ltv7f24oKCgz8eGYWj58uXJSwcAAAAgo83+XLXW7WjX1V+apMZ2v+569P1UR0pbBy1/r7322mDlAAAAAID9WCyGbrp4miTJMFIcJs0ltM8fAAAAACC99Vv+LrzwQr300ksKh8MHfD4cDuvFF1/UxRdfnLRwAAAAAICB0e9ln4sWLdKDDz6ohQsXatKkSRo9erRycnLk8/m0c+dOrVu3TjNmzNC99947mHkBAAAAAJ9Bv+Vv7NixevDBB9XS0qJ33nlHmzdvVkdHh9xut+bOnav77rtPxcXFg5kVAAAAQAYLhmKSpAefWauLT6vV2TNGpThRejnogi+SVFpaqi9/+cuDkQUAAAAA+mWx/GPFl3fXNVL+DhMLvgAAAABIC+NrCnTftTP1ufGlqY6Slih/AAAAANJGSX5WqiOkLcofAAAAgLSyanOL9rb49ODTa9XaFUh1nLRB+QMAAACQltZsbdW2Ok+qY6SNQy748rG3335bGzZskN/v73P8pptuGvBQAAAAANCf+2+cpT1NXv30yTWpjpJWEip/d955p1566SVNnz5dWVlcYwsAAAAgddzZDhW5namOkXYSKn/Lli3T0qVLVVlZmew8AAAAAHBIkWhckvTfz61Ta1dA5848KrWB0kBC9/wVFhYqLy8v2VkAAAAA4LD95e/1qY6QFhIqf5dffrm+853vaPXq1dqzZ0+fXwAAAAAw2EaW5+mOK07SjEnlqY6SNhK67HPhwoWSpDfeeKPPccMwtGHDhoHOBAAAAACHVFOWKyPVIdJIQuVv48aNyc4BAAAAAEiihLd6kKT6+no1NTWpoqKCxV8AAAAAII0kVP6am5v17W9/W2vWrFFBQYE6Ozs1bdo0/ed//qfKy7nGFgAAAACGuoQWfFm4cKEmTJigFStW6O2339aKFSs0ceJE3X777cnOBwAAAAAYAAnN/H3wwQd64IEHZLfbJUnZ2dm6+eabdcoppyQ1HAAAAABgYCQ085efn69t27b1ObZ9+3a53e6khAIAAACAREVjpmLxeKpjDHkJzfxdeeWV+trXvqaLLrpIVVVVqq+v1x/+8AfddNNNyc4HAAAAAP16d12TJOmaH7+pb39lmo45qijFiYauhMrfJZdcopqaGi1btkybNm1SWVmZfvrTn2rmzJnJzgcAAAAA/aouzdXeFq/ipqnWrmCq4wxpCW/1MHPmTMoeAAAAgCHlzvknqd0T1Hce/muqowx5/Za/X/ziF7r22mslSQ888EC/L8ClnwAAAAAw9PVb/hobGw/4GAAAAACQfvotf3fccUfv43vuuWdQwgAAAAAAkiOhe/62bt2qgoIClZSUyOfzacmSJbJYLJo/f76ysrKSnREAAAAAcIQS2ufv29/+tjwejyRp0aJFWrlypdasWaPbbrstqeEAAAAAAAMjoZm/uro6jRkzRqZp6k9/+pNeeOEFuVwuzZkzJ9n5AAAAAAADIKHy53Q65fV6tW3bNlVWVqqoqEjRaFShUCjZ+QAAAADgoEyz579PvrZVTrtV048pT22gISqh8nfeeefpsssuk8/n06WXXipJWr9+vaqrq5MaDgAAAAAOJRiOSpICoah+v3wL5a8fCZW/W2+9VW+//bZsNptmzJghSTIMQ7fccktSwwEAAADAoYwozdW5M0eppTOgjbs6Uh1nyEqo/EnSrFmz+nw8ZcqUAQ8DAAAAAJ/FhafW6rFXNqU6xpDWb/mbP3++lixZIkmaN2+eDMM44HlPPPFEcpIBAAAAAAZMv+Xvy1/+cu/jiy++eFDCAAAAAACSo9/yd/755/c+vuCCCwYlDAAAAAAgORLa5P2HP/yhVq1a1efYqlWr9KMf/SgpoQAAAAAAAyuh8rds2TJNnjy5z7HJkydr2bJlSQkFAAAAABhYCZU/wzBkfrxz4j6xWEzxeDwpoQAAAAAAAyuh8nfCCSfo/vvv7y178XhcDz30kE444YSkhgMAAAAADIyE9vn73ve+p2uuuUazZs1SVVWVGhoaVFpaqsWLFyc7HwAAAABgACRU/ioqKvTHP/5Ra9euVUNDgyorKzV16lRZLAlNHAIAAAAAUizh9haLxRSNRmWapo499lgFg0H5/f5kZgMAAAAADJCEZv42bdqka6+9Vg6HQ01NTTrnnHO0cuVK/fGPf9T999+f7IwAAAAAgCOU0MzfwoULdeONN+rll1+WzdbTF0888UR98MEHSQ0HAAAAAPj/7d17fM714//x567NluNss81WJJEzzfkwfBz6zGFL6UBy2EdJfYhUhE8OExUVRZSUiD6fPkh8GuUQMsp5teSUs9h5Y8xO1+H3h1y/9p0Ydl3Xtvfjfru5dV3v9/t6X89rXnQ9vd6HolGo8nf06FH16tVL0pXbPkhSuXLllJOT47hkAAAAAIAiU6jyd+edd2r//v35lsXFxal69eoOCQUAAAAAKFqFOudv5MiRGjp0qPr27au8vDzNnz9fX3zxhV577TVH5wMAAAAAFIFCzfx16tRJH3/8sdLS0tSiRQudPXtWc+bMUWhoqKPzAQAAAACKwA1n/iwWi8LCwrR27VpNnjzZCZEAAAAA4OYlp19WxuU8fbvztNo1qqqK5TxdHalYueHMn7u7u9zd3bm4CwAAAIBi7deT6ZKkZZuPauTsbdqw+4yLExUvhTrsc+DAgXrhhRe0a9cunT59WmfOnLH/AgAAAIDi4MXHm6hV/UD785Vbj7swTfFTqAu+XL2wy/bt2/Mtd3Nz08GDB4s+FQAAAADcpIY1/dSwpp/uq1ZZJ85laPehJFdHKlYKVf4OHTrk6BwAAAAAUCQ6hdypxLTLysmzKC0jWz4Vvez3Kzey65a/rKwsffDBBzpy5IgaNGigoUOHytOTkyYBAAAAFG/r/zjf7+V5PyikdhU93L6m7gqo4OJUrnXdc/6mTJmizZs3q2bNmlq3bp2mT5/urFwAAAAAcMs6hdxpfxz7W4omLtyltTtOKS0j24WpXOu65S8mJkaffPKJxowZowULFmjz5s3OygUAAAAAt2xAWB0tHNs537IVW45p68/nXJTI9a572Ofly5cVEBAgSQoKCtKlS5ecEgoAAAAAisLCsZ11Ij5DG/ec0c4DSbJYba6O5DLXLX8Wi0U7duyQzXblB2Q2m/M9l6Q2bdo4NiEAAAAA3IZ7gippSEQD7Tpo7Kt/Xrf8+fn5afz48fbnlStXzvfczc1N3333nePSAQAAAACKxHXL36ZNm5yVAwAAAADgQNe94AsAAAAAoHSg/AEAAACAAVD+AAAAAMAAKH8AAAAAYACUPwAAAAAwAMofAAAAABgA5Q8AAAAADIDyBwAAAAAGQPkDAAAAAAOg/AEAAACAAVD+AAAAAMAAKH8AAAAAYACUPwAAAAAwAMofAAAAABgA5Q8AAACAIVisNq358ZS2/nzO1VFcgvIHAAAAwFAWfXNIuXkWV8dwOsofAAAAAEN4oHk1tagbIEmyuTiLK1D+AAAAABjCE11rq0bViq6O4TKUPwAAAAAwAMofAAAAABgA5Q8AAAAADIDyBwAAAAAGQPkDAAAAAAOg/AEAAAAwjKu3eEhIvSyr1Vg3fKD8AQAAADCMmLh4SVLUot2K/vGkS7M4G+UPAAAAgGF0bBIsSfJwd9OlrDwXp3Euyh8AAAAAw+jWqroWju0sD3eTUi9ka/mWo1q2+ajOJF1ydTSH83DWG504cUJjx47V+fPnVblyZU2fPl01atTIt82YMWN0+PBh+/PDhw9r7ty56tKli7NiAgAAADCA7FyLYn9LkX678jwrx6xB3eq6NpSDOa38TZo0Sf369VOvXr20evVqTZw4UZ999lm+bWbMmGF/fOjQIQ0aNEjt27d3VkQAAAAABjMkvL6Wbzkqm630X/zFKYd9pqam6sCBAwoPD5ckhYeH68CBA0pLS/vL16xYsUIRERHy9PR0RkQAAAAABrJwbGctHNtZbRpWdXUUp3FK+YuPj1dgYKDc3d0lSe7u7goICFB8fPw1t8/NzdXXX3+tRx55xBnxAAAAAKDUc9phnzdj48aNCg4OVr169W76tX5+FRyQ6Pb5+1d0dQSUYowvOBLjC47GGIMjMb5QGCaTSXfc4XnT46WkjS+nlL+goCAlJibKYrHI3d1dFotFSUlJCgoKuub2X375mpUQzwAAIABJREFU5S3P+qWmXip2N2v096+o5OSLro6BUorxBUdifMHRGGNwJMYXCstqtSo7O/emxktxHF8mk9t1J8Occtinn5+f6tWrp+joaElSdHS06tWrJ19f3wLbJiQkaO/evYqIiHBGNAAAAAAwBKfd52/y5MlaunSpwsLCtHTpUkVFRUmShgwZol9++cW+3VdffaVOnTrJ29vbWdEAAAAAoNRz2jl/9957r5YvX15g+YIFC/I9f+6555wVCQAAAAAMw2kzfwAAAAAA16H8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAwNDOX8rVj78mav+JVFdHcSjKHwAAAADDyzNbNfO/P+v8pRxXR3EYyh8AAAAAQ6sZXEmVyntKksxmq4vTOA7lDwAAAIChvTqwuR7teK+rYzgc5Q8AAAAADIDyBwAAAAAGQPkDAAAAgD9YbDZXR3AYyh8AAAAAwzt8Jl2SNG7+Dq2KOe7iNI5B+QMAAABgeNX8K9gfJ6ZnuTCJ41D+AAAAABje31tW18KxnRXoU9bVURyG8gcAAAAABkD5AwAAAAADoPwBAAAAgAFQ/gAAAADAACh/AAAAAGAAHq4OAAAAAADFRWJ6lhLTsxToU1YPtrtHJpObqyMVGWb+AAAAAOD/+N/2k4pPzXR1jCJF+QMAAACAP/TrWlsdmgRJkmwuzlLUKH8AAAAA8Ieuzaup4T1+ro7hEJQ/AAAAADAAyh8AAAAAGADlDwAAAAD+JCvXLElaHXNCJxMyXJym6HCrBwAAAAD4k9/OXJAk7T2SrL1HkuVT0UuvDmwun4peLk52e5j5AwAAAIA/ebxzLdWoWtH+PP1ijtIuZrswUdFg5g8AAAAA/qRC2TKaGNlCkhR3LFXvLv/ZxYmKBjN/AAAAAGAAlD8AAAAAMADKHwAAAAAYAOUPAAAAAAyA8gcAAAAABkD5AwAAAAADoPwBAAAAgAFQ/gAAAADAACh/AAAAAGAAlD8AAAAAMADKHwAAAAAYAOUPAAAAAAyA8gcAAAAABkD5AwAAAAADoPwBAAAAgAFQ/gAAAADgL1isVknS1p/OKT4108Vpbg/lDwAAAAD+wm9nLkiSYuLi9cV3R12c5vZQ/gAAAADgL4S1rCZJCvIrJ7PF6uI0t4fyBwAAAAB/wbuClxaO7azyZcu4Ospto/wBAAAAgAFQ/gAAAADAACh/AAAAAGAAlD8AAAAAuIGjv1/QwVPpmrnsJ2Vk5ro6zi2h/AEAAABAIe0/nqazyZdcHeOWUP4AAAAA4AZee6qlBnar4+oYt8XD1QEAAAAAoLi707+CLmXluTrGbWHmDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwAAofwAAAABgAJQ/AAAAADAAyh8AAAAAGADlDwAAAAAMgPIHAAAAAAZA+QMAAAAAA6D8AQAAAIABUP4AAAAAwACcVv5OnDihPn36KCwsTH369NHJkyevud3atWsVERGh8PBwRUREKCUlxVkRAQAAAKDU8nDWG02aNEn9+vVTr169tHr1ak2cOFGfffZZvm1++eUXvf/++1q8eLH8/f118eJFeXp6OisiAAAAAJRaTpn5S01N1YEDBxQeHi5JCg8P14EDB5SWlpZvu0WLFmnw4MHy9/eXJFWsWFFeXl7OiAgAAAAApZpTyl98fLwCAwPl7u4uSXJ3d1dAQIDi4+PzbXfs2DGdOXNGTz75pB5++GHNmzdPNpvNGREBAAAAoFRz2mGfhWGxWHT48GF9+umnys3N1dNPP63g4GA99NBDhd6Hn18FBya8df7+FV0dAaUY4wuOxPiCozHG4EiMLxSlxIwcSZJ35XKSSt74ckr5CwoKUmJioiwWi9zd3WWxWJSUlKSgoKB82wUHB6tbt27y9PSUp6enunTpori4uJsqf6mpl2S1Fq/ZQn//ikpOvujqGCilGF9wJMYXHI0xBkdifKGonT9/WZJ04Y//FrfxZTK5XXcyzCmHffr5+alevXqKjo6WJEVHR6tevXry9fXNt114eLi2bdsmm82mvLw87dixQ3Xr1nVGRAAAAAAo1Zx2q4fJkydr6dKlCgsL09KlSxUVFSVJGjJkiH755RdJUs+ePeXn56cePXrooYceUq1atfToo486KyIAAAAAlFpOO+fv3nvv1fLlywssX7Bggf2xyWTSuHHjNG7cOGfFAgAAAABDcNrMHwAAAADAdSh/AAAAAGAAlD8AAAAAMADKHwAAAAAYAOUPAAAAAAyA8gcAAAAABkD5AwAAAAADcNp9/gAAAACgJMvMNkuS3vriJ2VbpZCavi5OdHOY+QMAAACAQriUlWd/bLPZXJjk1hhi5s9iMSs9PVlmc65L3j8pySSr1eqS90bp58jxZTK5q2zZCqpQwVtubm4OeQ8AAICSon3jIOXkWdS12V0KCKik5OSLro50UwxR/tLTk3XHHeVUvnxVl3yB9fAwyWym/MExHDW+bDabLBazLl48r/T0ZPn6BhT5ewAAAJQkbm5ueqB5NVfHuGWGOOzTbM5V+fKVmLkAboKbm5s8PMqocmU/5eZmuzoOAAAAbpMhyp8kih9wi9zcTJJK3jHtAAAAyM8w5Q8AAAAAjIzy5wKPPhqh48ePFsm+tm37XnPnvnfdbfbt26Ndu3bYn6ekJOv554fecN/Dhz+jxx7rpcjIfnriid5atOjj287rKIX9TDdr+fIv9O9/L8m37KuvVig0tLmOHDmUb/mff179+j2iZcv+U2Q5Tp8+paFD/6G+fXtr6NB/6MyZ09fcLjU1RWPHvqhBg/rqyScf1bp1a+3rPvlkvsLDH1BkZD9FRvbTO+9Mt6+bNGmcfvnl5yLLCwAAgOLHEBd8Kc1CQzsqNLTjdbeJjd2rrKwstWzZWpJUpYq/5syZX6j9v/DCy2rXrr1SUlLUv/+jatGitRo0aHjbuSXJbDbLw6NohuDNfKbCys7O1vLl/9GSJf/Nt3zNmv+pWbMWWrPmf7rvvrr51l39eSUmJmjgwD4KCWmm2rXvu+0sb7/9hnr3fkxhYT20bt1avfXW65o9+8MC282ZM0t169bXm2/OVHp6up56qr/uv7+pAgOrSpK6deup4cNfKPC6/v0j9d577+j99z+67awAAAAonih/xcg330TrP/9ZIjc3NwUH36UxY8bLx8dXeXl5mjlzhmJj98rHx0e1a9+ntLRUTZ06Q2vXfq0ffojR1KkzdPr0SU2bFqXs7GxZrRZ17x6hVq3aaPXqlbJardqzZ5e6dPm7unb9u55+eoDWrPlOkrR/f5zmzn1Ply9fliQNGzbSXhSvqlKliqpVu1uJiQlq0KChUlJS9O67M5SYmKCcnBx17RqmgQMHS5J+/jlW77zzptzc3BQS0lwxMVv01lvvqmbNWnr00Qh16fJ37du3WzVr1tLLL4/TRx/N008/7VVubp5q1aqll14ap3Llymn16pVatuzfKlPGUzabVVOmvKlq1apr5swZ2rdvt8qU8VS5cmX1wQcLFR9/Lt9n2rHjB82f/76sVqsqV/bR6NHjdddd1bRv3x7Nnj1T9es30K+//iLJTVFRr6tGjXsK/H5s2fKd7r+/qby87rAvO378qNLT0/Taa29qyJCBGjbsBXl6ehZ4bWBgVVWvfrfOnDl92+UvPT1NR44c0qxZcyVJXbuGadasGUpPT5ePj0++bY8e/U19+vSTJPtY2bRpo554ov9136N27TpKT0/TmTOnVa1a9dvKCwAAgOLJcOVv+y/x2hYX75B9hzYOUrtGQbf02uPHj+rDD9/XJ58sVZUqVbRgwQeaNestTZnyhlav/lKJiQlaunSZLBaLnn9+qAICCl52f+XKFQoN7aABA/4hScrIyFClSpXUq1dvZWVl2Wd84uPP2V+TkXFB48eP1rRpM9SoURNZLBZlZmYW2Pfp06eUkXFBISHNJElTp05UZOTTuv/+psrLy9PIkc+pXr36atKkqSZP/pcmT56mJk1C9P33m7VixRf59pWZmakFCz6TJC1a9LHKly9vfz5v3mwtWfKphg4dpnnz3tPnn3+pKlWqKDc3V1arVUePHlFs7B4tXbpcJpNJGRkZBbKmp6dp6tSJmjPnI91zT01FR69SVNSrWrBgsSTpxIljGj9+osaM+ZcWL/5Eixd/okmTphbYT2zsXtWv3yDfsujo1erePVxBQcGqVes+xcRsUZcufy/w2hMnjuvUqVOqVatWgXVLlizSd9+tL7BckkaNGq0mTULyLUtMTFSVKgFyd3eXJLm7u6tKFX8lJSUWKH916tTVxo3rVbdufcXHn9P+/XEKCgq2r//uu/XavXuHfH399NRTQ9WwYWP7ugYNGmnv3l2UPwAAgFLKcOWvuNq3b4/atGmnKlWqSJJ69eqtyMh+f6zbq27desjDw0MeHh7q2jVMcXGxBfZx//0hmjdvtrKzs9W0aXM1bdr8hu+7f/8vqlHjHjVq1ETSlWJRqVIl+/p3331bH3wwR6dPn9Tw4S/Ix8dHWVlZio3dq/Pnz9u3u3w5UydPnpSPj5+8vLzsBaZjx06qUKFivvfs1q2n/fH27VuVmZmpLVs2SZLy8nJVq1ZtSVLTpi00bdoktWvXXm3ahOrOO+9ScPBdMpvNevPN19S0aXO1bdu+wGf69df9uvfe+3TPPTUlST16PKh33pmuy5evlNrq1e+2H67ZoEEjbd8ec82fTXJyUr79m81mbdiwTh9+uPCP/UZozZr/5St/7777thYs+ECenp4aPXqcqlevUWC/AwZEasCAyGu+5+0aPnyU5syZqcjIfgoMrKpmzVraS+NDDz2iQYOekoeHh3bv3qGxY1/S558vl7d3ZUmSn18VJSUlOSQXAAAAXM9w5a9do1ufnSvu/va3LmrYsLF27dqhpUsXac2a/2nixNdua59Xz2GLi/tJo0YNU7NmLRUUFCQ3Nzd9/PFnBc7ZO3r0txvus1y5svbHNpv00ktj1axZiwLbvf76Wzp48Fft3btHI0Y8q5dfHqc2bdppyZJlio3dqz17dumDD+Zo4cKlN/WZPD297I9NJpMsFss1t/Py8lJubo79+bZt3ysz85JGjnxOkmS1WpWWlqrExAT7OXVXf17Xc7Mzf4GBgUpJSZLFYpG7u7ssFotSUpIVEBBY4PU+Pj75fs9ffnmEatRoJelKubuqRYvWCggI1PHjx+yzubm5OapUyfu62QEAAFBycbXPYqJp0+b68cftSk1NkSR9/fUqtWjRUpIUEtJM69d/I7PZrJycHG3atOGa+/j99zPy9fVTjx4R+sc/hujAgV8lSeXLl1dm5qVrvqZhw0Y6efKE9u+PkyRZLJZrHkrZuPH96t37cX388QcqV668mjQJ0dKli+zrExMTlJqaourV71Z2drbi4n6SJMXEbNGlSxf/8nOHhnbQf//7uXJyrtxE/MoM4gmZzWadO3dW9es31IABkWrZsrV+++2w0tPTlZ2drVat2ujZZ4erQoUKOnfubL59NmjQSMeOHdGpUyclXTmXsnbtOipXrvxf5riWmjVr6fTpU/bna9b8T6NGjdGKFV9rxYqvtXLlGvXoEaFvvom+qf0OGBCpRYv+fc1f/7f4SZKPj69q1bpPGzeukyRt3LhOtWvXKXDIpyRduHBeZrNZkrR3724dP35MDzzQTdKVmcyrfvvtsBIS4lW9+t32ZSdPnlStWrd/cRoAAAAUT4ab+SsuXnhhmP1wPElavPgLPfvscI0aNeyPC77cqdGjx0u6crje0aNH1L//46pcubLuvrvGNfe5adMGrV//rcqU8ZCbm5tGjnxJktShQyeNHz9akZH97Bd8uapSJW9NmzZDc+bMUnZ2ltzcTBo2bKRatGhVYP8DBw5W374P6ciRQ5o48TXNnj1TAwf2kSSVK1de48ZNlJ9fFU2aNFVvv/2G3NzcdP/9TeXj46vy5StcM3P//pH65JP5evrpgTKZTJLcNHjwEAUH36lp0ybr0qWLcnMzKTAwUM8+O1wJCQmaPn2qLBaLLBaLWrduqwYNGikxMcG+Tx8fH7366hRFRf1LFotFlSv73NIMaMeOnfTOO9P11FNDlZKSrNjYvZo4Mf+5gX//e3e9/nqUBg166qb3fzNGjx6vqVMn6dNPP1bFihU1YUKUfd2oUc/rqaeGqm7d+jpw4Fe9997bMplM8vaurOnTZ+qOO65csGb+/Lk6fPigTCZ3lSlTRhMmRNlnA7OysnTixLFrzsACAACgdHCz2Ww2V4coSqmpl2S15v9ICQmnVLXq3X/xCsfz8DDJbLbe1j4uX85UuXLllZubq7FjX1SnTl0VEfFQESUsWlezSlfOZZw2bbKWL//fH+WuZHnxxeF69tnhBW7pUJwUxfhatepLJScnaciQ56653tV/huA6/v4VlZz817P3wO1ijMGRGF9wpOI4vkwmN/n5XXvSRWLmr8QYOfKfysvLU25ujpo3b6nu3cNdHekvbdmySf/9779ls1nl6emlSZOmlsjiJ0mjRo3R779f+4bqpYm7u7v69490dQwAAAA4EDN/TlAUMzPAX3HG+HL1nyG4TnH8V02ULowxOBLjC45UHMfXjWb+SuZ0DAAAAADgphim/JWyCU7AaWw2qyQ3V8cAAADAbTJE+fPw8FRmZgYFELgJNptNZnOezp9PkafnHa6OAwAAgNtkiAu++Pj4Kz09WZcunXfJ+5tMJlmtnPMHx3Dk+DKZ3FW2bAVVqMDN3wEAAEo6Q5Q/d3cPVakS5LL3L44ng6L0YHwBAACgMAxx2CcAAAAAGB3lDwAAAAAMoNQd9mkyFc+rEhbXXCgdGF9wJMYXHI0xBkdifMGRitv4ulGeUneTdwAAAABAQRz2CQAAAAAGQPkDAAAAAAOg/AEAAACAAVD+AAAAAMAAKH8AAAAAYACUPwAAAAAwAMofAAAAABgA5Q8AAAAADIDyBwAAAAAGQPkrIidOnFCfPn0UFhamPn366OTJkwW2sVgsioqKUteuXfXAAw9o+fLlzg+KEqkw42vu3Lnq2bOnIiIi1Lt3b8XExDg/KEqswoyxq44fP64mTZpo+vTpzguIEq2w42vt2rWKiIhQeHi4IiIilJKS4tygKJEKM75SU1P1zDPPKCIiQt27d9fkyZNlNpudHxYlzvTp09W5c2fVqVNHR44cueY2Jeo7vg1FYsCAAbZVq1bZbDabbdWqVbYBAwYU2Oarr76yDR482GaxWGypqam29u3b286cOePsqCiBCjO+tm7dart8+bLNZrPZDh48aGvWrJktKyvLqTlRchVmjNlsNpvZbLb179/f9uKLL9refPNNZ0ZECVaY8RUXF2fr3r27LSkpyWaz2WwZGRm27Oxsp+ZEyVSY8TV16lT731m5ubm2Rx991LZmzRqn5kTJtHv3btu5c+dsnTp1sh0+fPia25Sk7/jM/BWB1NRUHThwQOHh4ZKk8PBwHThwQGlpafm2W7t2rR577DGZTCb5+vqqa9eu+vbbb10RGSVIYcdX+/btVbZsWUlSnTp1ZLPZdP78eafnRclT2DEmSR999JH+9re/qUaNGk5OiZKqsONr0aJFGjx4sPz9/SVJFStWlJeXl9PzomQp7Phyc3NTZmamrFarcnNzlZeXp8DAQFdERgnTvHlzBQUFXXebkvQdn/JXBOLj4xUYGCh3d3dJkru7uwICAhQfH19gu+DgYPvzoKAgJSQkODUrSp7Cjq8/W7VqlapXr66qVas6KyZKsMKOsUOHDmnbtm2KjIx0QUqUVIUdX8eOHdOZM2f05JNP6uGHH9a8efNks9lcERklSGHH1z//+U+dOHFCoaGh9l/NmjVzRWSUQiXpOz7lDyhldu3apffee0/vvPOOq6OgFMnLy9OECRMUFRVl/5IFFCWLxaLDhw/r008/1ZIlS7R161atXr3a1bFQSnz77beqU6eOtm3bpq1bt2rPnj3FdmYGcCTKXxEICgpSYmKiLBaLpCv/A0tKSiowRRwUFKRz587Zn8fHxzMzgxsq7PiSpNjYWI0ePVpz585VzZo1nR0VJVRhxlhycrJOnz6tZ555Rp07d9bixYu1bNkyTZgwwVWxUUIU9u+w4OBgdevWTZ6enqpQoYK6dOmiuLg4V0RGCVLY8bV06VI9+OCDMplMqlixojp37qydO3e6IjJKoZL0HZ/yVwT8/PxUr149RUdHS5Kio6NVr149+fr65tuuW7duWr58uaxWq9LS0rRx40aFhYW5IjJKkMKOr7i4OI0aNUqzZ89WgwYNXBEVJVRhxlhwcLB27typTZs2adOmTRo0aJAef/xxvfbaa66KjRKisH+HhYeHa9u2bbLZbMrLy9OOHTtUt25dV0RGCVLY8XXXXXdp69atkqTc3Fz9+OOPql27ttPzonQqSd/x3WwcUF8kjh07prFjxyojI0OVKlXS9OnTVbNmTQ0ZMkQjRoxQo0aNZLFYNGXKFG3fvl2SNGTIEPXp08fFyVESFGZ8PfLIIzp79my+E9hnzJihOnXquDA5SorCjLE/mzNnji5fvqxXXnnFRYlRkhRmfFmtVk2fPl1bt26VyWRSaGioXnnlFZlM/Ds1rq8w4+v06dOaNGmSUlJSZLFY1KpVK/3rX/+Sh4eHq+OjmJs6darWr1+vlJQU+fj4qHLlylqzZk2J/Y5P+QMAAAAAA+Cf0wAAAADAACh/AAAAAGAAlD8AAAAAMADKHwAAAAAYAOUPAAAAAAyA8gcAKBEGDBig5cuXS5JWrlypJ5544rrb9+3bVwcOHHBIlnPnzikkJMR+Y+lrCQkJ0ZkzZ4r0fXfu3KkOHToUatvC/Iwc8dr/a8mSJXrrrbeKZF8AgNtD+QMA3LTOnTurcePGCgkJUbt27TR27FhlZma6Opbdpk2bVL58edWvX1/SlfsSNmjQQCEhIWrevLn69u2r2NjYW95/cHCwYmNj5e7uLil/Mb0qNjZW1apVu/UPUYJMmDBBYWFhqlu3rlauXJlv3eOPP66vv/5aqampLkoHALiK8gcAuCUffvihYmNjtWrVKh04cEAfffSRqyPZffHFF+rVq1e+Zd27d1dsbKx+/PFHNW3aVM8//7y41W3RqFu3riZPnmwv23/m5eWlDh06aNWqVS5IBgD4M8ofAOC2+Pv7KzQ0VAcPHrQv++mnn9S3b181b95cDz74oHbu3Glfd/78eY0bN06hoaFq0aKF/vnPf0qSLly4oKFDh6p169Zq0aKFhg4dqoSEhJvOk5ubqx07dqhly5bXXF+mTBk9/PDDSk5OVnp6uhITE/Xss8+qZcuWeuCBB7Rs2TL7tnFxcerdu7eaNm2qtm3b6o033pAk/f7776pTp47MZrNmzZqlPXv2aMqUKQoJCdGUKVMkSXXq1NGpU6f0888/q127dvkOEd2wYYMiIiIkSVarVR999JG6du2qVq1aaeTIkTp//nyhPuvV14WEhKhHjx7asGFDvvU2m01TpkxRs2bN1K1bN/3444/2dRcvXtT48eMVGhqq9u3ba9asWdc9jPV6nnzySbVp00ZeXl7XXN+yZUtt2bLllvYNACg6lD8AwG1JSEhQTEyMqlevLklKTEzU0KFD9dxzz2nXrl165ZVXNGLECKWlpUmSxowZo6ysLK1Zs0Y//PCDIiMjJV0pQb1799bmzZu1efNmeXl52YvUzTh16pRMJpOqVq16zfW5ublauXKlgoKC5OvrqxdffFFVq1ZVTEyMZs+erZkzZ9pL0rRp0zRw4EDt27dPGzZsUPfu3Qvsb9SoUWrevLkmTpyo2NhYTZw4Md/6Jk2aqGzZstqxY4d92ddff20vf0uWLNHGjRu1dOlSxcTEyNvbu9Cfu1q1avr888+1d+9eDR8+XKNHj1ZSUpJ9fVxcnKpXr64dO3ZoxIgRGj58uL1Yjh07Vh4eHlq/fr1WrVql7du3Fzh09aqhQ4fe1szuvffeq8OHD9/y6wEARYPyBwC4JcOGDVNISIg6duwoX19fjRgxQpK0evVqdejQQR07dpTJZFK7du3UsGFDff/990pKStLWrVsVFRUlb29vlSlTxj5D5+Pjo7CwMJUtW1YVKlTQc889p927d990royMDJUvX77A8m+//VbNmzdXx44d9euvv+r9999XfHy89u3bp5dfflleXl6qV6+eHnvsMa1evVqS5OHhodOnTystLU3ly5fX/ffff0s/q549eyo6OlqSdOnSJW3dulU9e/aUdOUQ1VGjRqlq1ary9PTU8OHDtW7dOpnN5hvut3v37goMDJTJZFKPHj109913Ky4uzr7e19dXgwYNUpkyZdSjRw/dc8892rJli1JSUvT9999r/PjxKleunPz8/BQZGak1a9Zc833mz5+vZ5555pY+uySVL19eFy9evOXXAwCKhoerAwAASqa5c+eqbdu22rVrl1566SWlp6erUqVKOnfunL799ltt3rzZvq3ZbFarVq2UkJAgb29veXt7F9hfVlaW3njjDcXExOjChQuSpMzMTFnOOVwuAAAEXUlEQVQsFvuFVQrD29v7mhef6datm95+++18y37++Wd5e3urQoUK9mXBwcHav3+/pCszf7Nnz1b37t111113afjw4erUqVOhs1wVERGhvn37KioqShs2bFD9+vV15513Srpy5dBhw4bJZPr//x5rMpmUmpqqwMDA6+531apV+vTTT3X27FlJ0uXLl5Wenm5fHxgYKDc3t3yfLSkpSefOnZPZbFZoaKh9ndVqVVBQ0E1/tsLIzMxUxYoVHbJvAEDhUf4AALelZcuW6t27t6ZPn6558+YpKChIvXr10tSpUwtsm5SUpAsXLigjI0OVKlXKt27hwoU6ceKEli1bJn9/fx08eFAPPfTQTV+UpXr16rLZbEpMTLxheQoICNCFCxd06dIlewGMj4+3v65GjRqaOXOmrFar1q9frxEjRuQ7f7GwatWqpeDgYG3dulXR0dEKDw+3r6tatapef/11NWvW7Kb2efbsWb366qtatGiRQkJC5O7uXuAiN4mJibLZbPYCGB8fr86dO9tnGXfs2CEPD8d/FTh27Jjq1Knj8PcBAFwfh30CAG7boEGD9MMPP+jQoUN68MEHtXnzZsXExMhisSgnJ0c7d+5UQkKCAgIC1KFDB0VFRenChQvKy8uzH9qZmZkpLy8vVapUSefPn9f7779/S1k8PT3Vpk0b7dq164bbBgUFKSQkRDNnzlROTo4OHTqkFStW6MEHH5R05RDWtLQ0mUwme1n98wzdVVWqVLnhPf3Cw8O1ePFi7d69W926dbMvf+KJJ/Tuu+/aZ+/S0tK0cePGG2bPysqSm5ubfH19JUlffvmlfvvtt3zbpKWl6bPPPlNeXp6++eYbHTt2TB07dlRAQIDatWunN998U5cuXZLVatXp06cL9TO7ltzcXOXk5Mhms8lsNisnJ0dWq9W+fvfu3YW+PyEAwHEofwCA2+br66tevXpp7ty5CgoK0rx58zR//ny1adNGHTt21CeffGIvAzNmzJCHh4e6d++utm3bavHixZKuFMicnBy1bt1affr0Ufv27W85T9++fe3n7d3IzJkzdfbsWbVv317Dhw/X888/r7Zt20qSYmJi1LNnT4WEhGjatGmaNWuW7rjjjgL7GDhwoNatW6cWLVpcc8ZTulL+du/erdatW9sL29XXdu7cWYMHD1ZISIgef/zxfOft/ZVatWpp8ODB6tu3r9q2basjR46oadOm+bZp3LixTp06pdatW+vdd9/V7Nmz5ePjI+nK70NeXp569OihFi1aaMSIEUpOTr7mez399NP68MMP/zLLU089pcaNGys2NlYTJkxQ48aN7aU+JydH33//vR5++OEbfiYAgGO52bjJEQCgFOrbt68mTpx4zXvPwXmWLFmi+Ph4jRkzxtVRAMDwKH8AAAAAYAAc9gkAAAAABkD5AwAAAAADoPwBAAAAgAFQ/gAAAADAACh/AAAAAGAAlD8AAAAAMADKHwAAAAAYAOUPAAAAAAzg/wFQ2rD/zM9HNwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def quick_preictor(text):\n",
        "    answer = review_predictor.test_sample(text,coverter,model)\n",
        "    if answer == 'negative':\n",
        "        print('\\033[1m'+'\\033[91m'+'Prediction is : '+answer+\"\\n\")\n",
        "        print(text)\n",
        "    else:\n",
        "        print('\\033[1m'+'\\033[92m'+'Prediction is : '+answer+\"\\n\")\n",
        "        print(text)"
      ],
      "metadata": {
        "id": "Fh2P60eZ9EzZ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = 'Purchased this keyboard in September 2018 and it has already stopped working. Some keys do not work while others simply input a gibberish combination of characters. I also purchased the 4 year squaretrade warranty but since it is under the manufacturer warranty they wont allow me to make a claim.'\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFm3u10I9sqS",
        "outputId": "690e1485-f303-4577-bf36-38440173e7a5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[91mPrediction is : negative\n",
            "\n",
            "Purchased this keyboard in September 2018 and it has already stopped working. Some keys do not work while others simply input a gibberish combination of characters. I also purchased the 4 year squaretrade warranty but since it is under the manufacturer warranty they wont allow me to make a claim.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = 'Love it just have to sync it up with all the other RGB but love it. Big Razor fan'\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0S3LTMi9uo4",
        "outputId": "2c93ecf6-b6b8-4afa-c96e-502b80ce414d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[92mPrediction is : positive\n",
            "\n",
            "Love it just have to sync it up with all the other RGB but love it. Big Razor fan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I get cold really easy and this jacket is great if you want a warm one. I'm 5'10 and weigh 135lbs and the small fits me well. It has fleece lined pockets and a fleece lined hood. It has a good quality YKK zipper. The coat snaps shut in addition to zippering shut. It has elastic around the wrists for a nice tight fit that won't allow breezes to flow up your arm. There a fleece-line inside pocket about chest high big enough for a cell phone or wallet. It has a velcro enclosure.\"\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teQ4FW8f9yNQ",
        "outputId": "a5d6f00b-e68a-4799-b0a7-774d1dd48fa1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[92mPrediction is : positive\n",
            "\n",
            "I get cold really easy and this jacket is great if you want a warm one. I'm 5'10 and weigh 135lbs and the small fits me well. It has fleece lined pockets and a fleece lined hood. It has a good quality YKK zipper. The coat snaps shut in addition to zippering shut. It has elastic around the wrists for a nice tight fit that won't allow breezes to flow up your arm. There a fleece-line inside pocket about chest high big enough for a cell phone or wallet. It has a velcro enclosure.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Dont do it ! It will tell u steps and heartbeat .. But dont bother connecting it to your phone 👎 Not android anyways.\"\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pKeEkBw90pz",
        "outputId": "eea570bd-3798-4996-ae63-163e45d773ed"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[91mPrediction is : negative\n",
            "\n",
            "Dont do it ! It will tell u steps and heartbeat .. But dont bother connecting it to your phone 👎 Not android anyways.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I have owned two Samsung smartwatches, one Fitbit, and a new Apple watch 6 for wife, but I am looking for a simple, light, long battery life particularly with Alexa control function smart device. I finally made decision to try it. It took less than 5 minutes to setup EVERYTHING. The screen is beautiful, super crisp text. The Alexa function works perfectly even without the ability of speaker. I can read text message, and get phone call notification. It has a lot of basic health fitness tracker features that are very useful. I would give it 5+ stars.\"\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6LPuwJG94-X",
        "outputId": "bf451bf5-f221-4fea-d83b-df8f7ef9c51f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[92mPrediction is : positive\n",
            "\n",
            "I have owned two Samsung smartwatches, one Fitbit, and a new Apple watch 6 for wife, but I am looking for a simple, light, long battery life particularly with Alexa control function smart device. I finally made decision to try it. It took less than 5 minutes to setup EVERYTHING. The screen is beautiful, super crisp text. The Alexa function works perfectly even without the ability of speaker. I can read text message, and get phone call notification. It has a lot of basic health fitness tracker features that are very useful. I would give it 5+ stars.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"The WATCH is awesome! Does what i need it to do. Counts my steps and my calories while doing my workout.The battery life is great! Better than my previous watch. You can also change the strap with other ones used for other watches.\"\n",
        "quick_preictor(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWUUZEdN97Dv",
        "outputId": "d88a7044-4267-4c59-ee30-134f8a6f983c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[92mPrediction is : positive\n",
            "\n",
            "The WATCH is awesome! Does what i need it to do. Counts my steps and my calories while doing my workout.The battery life is great! Better than my previous watch. You can also change the strap with other ones used for other watches.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "W-sYqf9K98-C"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}