Untitled3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# building a chatbot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import random\n",
    "import string\n",
    "\n",
    "import bs4 as bs\n",
    "import urllib.request\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have successfully imported pandas version 1.1.3\n",
      "You have successfully imported nltk version 3.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "\n",
    "print (\"You have successfully imported pandas version \"+pd.__version__)\n",
    "print (\"You have successfully imported nltk version \"+nltk.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have successfully loaded your csv file\n"
     ]
    }
   ],
   "source": [
    "# load the csv file that contains the tweets on natural disasters into a pandas dataframe\n",
    "df_raw = pd.read_csv('./analysis-public-place-assaults-sexual-assaults-and-robberies-2015-csv.csv', encoding='latin-1')\n",
    "\n",
    "print (\"You have successfully loaded your csv file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Area_unit_2013_code</th>\n",
       "      <th>Area_unit_2013_label</th>\n",
       "      <th>Victimisations_calendar_year_2015</th>\n",
       "      <th>Population_mid_point_2015</th>\n",
       "      <th>Rate_per_10000_population</th>\n",
       "      <th>Rate_ratio_NZ_average_rate</th>\n",
       "      <th>Urban_area_2013_code</th>\n",
       "      <th>Urban_area_2013_label</th>\n",
       "      <th>Urban_area_type</th>\n",
       "      <th>Territorial_authority_area_2013_code</th>\n",
       "      <th>Territorial_authority_area_2013_label</th>\n",
       "      <th>Region_2013_code</th>\n",
       "      <th>Region_2013_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>514102</td>\n",
       "      <td>Auckland Central West</td>\n",
       "      <td>550</td>\n",
       "      <td>15900</td>\n",
       "      <td>346</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4</td>\n",
       "      <td>Central Auckland Zone</td>\n",
       "      <td>Main urban area</td>\n",
       "      <td>76</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>2</td>\n",
       "      <td>Auckland Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>573101</td>\n",
       "      <td>Willis Street-Cambridge Terrace</td>\n",
       "      <td>447</td>\n",
       "      <td>8300</td>\n",
       "      <td>539</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>Wellington Zone</td>\n",
       "      <td>Main urban area</td>\n",
       "      <td>47</td>\n",
       "      <td>Wellington City</td>\n",
       "      <td>9</td>\n",
       "      <td>Wellington Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>529700</td>\n",
       "      <td>Hamilton Central</td>\n",
       "      <td>380</td>\n",
       "      <td>3170</td>\n",
       "      <td>1,199</td>\n",
       "      <td>22.9</td>\n",
       "      <td>6</td>\n",
       "      <td>Hamilton Zone</td>\n",
       "      <td>Main urban area</td>\n",
       "      <td>16</td>\n",
       "      <td>Hamilton City</td>\n",
       "      <td>3</td>\n",
       "      <td>Waikato Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>514103</td>\n",
       "      <td>Auckland Central East</td>\n",
       "      <td>375</td>\n",
       "      <td>13250</td>\n",
       "      <td>283</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Central Auckland Zone</td>\n",
       "      <td>Main urban area</td>\n",
       "      <td>76</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>2</td>\n",
       "      <td>Auckland Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>591500</td>\n",
       "      <td>Cathedral Square</td>\n",
       "      <td>305</td>\n",
       "      <td>570</td>\n",
       "      <td>5,351</td>\n",
       "      <td>102.3</td>\n",
       "      <td>22</td>\n",
       "      <td>Christchurch</td>\n",
       "      <td>Main urban area</td>\n",
       "      <td>60</td>\n",
       "      <td>Christchurch City</td>\n",
       "      <td>13</td>\n",
       "      <td>Canterbury Region</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index  Area_unit_2013_code             Area_unit_2013_label  \\\n",
       "0      1               514102            Auckland Central West   \n",
       "1      2               573101  Willis Street-Cambridge Terrace   \n",
       "2      3               529700                 Hamilton Central   \n",
       "3      4               514103            Auckland Central East   \n",
       "4      5               591500                 Cathedral Square   \n",
       "\n",
       "   Victimisations_calendar_year_2015  Population_mid_point_2015  \\\n",
       "0                                550                      15900   \n",
       "1                                447                       8300   \n",
       "2                                380                       3170   \n",
       "3                                375                      13250   \n",
       "4                                305                        570   \n",
       "\n",
       "   Rate_per_10000_population   Rate_ratio_NZ_average_rate   \\\n",
       "0                         346                          6.6   \n",
       "1                         539                         10.3   \n",
       "2                       1,199                         22.9   \n",
       "3                         283                          5.4   \n",
       "4                       5,351                        102.3   \n",
       "\n",
       "   Urban_area_2013_code  Urban_area_2013_label  Urban_area_type  \\\n",
       "0                     4  Central Auckland Zone  Main urban area   \n",
       "1                    20        Wellington Zone  Main urban area   \n",
       "2                     6          Hamilton Zone  Main urban area   \n",
       "3                     4  Central Auckland Zone  Main urban area   \n",
       "4                    22           Christchurch  Main urban area   \n",
       "\n",
       "   Territorial_authority_area_2013_code Territorial_authority_area_2013_label  \\\n",
       "0                                    76                              Auckland   \n",
       "1                                    47                       Wellington City   \n",
       "2                                    16                         Hamilton City   \n",
       "3                                    76                              Auckland   \n",
       "4                                    60                     Christchurch City   \n",
       "\n",
       "   Region_2013_code  Region_2013_label  \n",
       "0                 2    Auckland Region  \n",
       "1                 9  Wellington Region  \n",
       "2                 3     Waikato Region  \n",
       "3                 2    Auckland Region  \n",
       "4                13  Canterbury Region  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 Auckland\n",
       "1          Wellington City\n",
       "2            Hamilton City\n",
       "3                 Auckland\n",
       "4        Christchurch City\n",
       "5                 Auckland\n",
       "6                 Auckland\n",
       "7    Palmerston North City\n",
       "Name: Territorial_authority_area_2013_label, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw['Territorial_authority_area_2013_label'].head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before tokenization: Index                                                       101\n",
      "Area_unit_2013_code                                      524112\n",
      "Area_unit_2013_label                                     Mascot\n",
      "Victimisations_calendar_year_2015                            43\n",
      "Population_mid_point_2015                                  4410\n",
      " Rate_per_10000_population                                   98\n",
      " Rate_ratio_NZ_average_rate                                 1.9\n",
      "Urban_area_2013_code                                          5\n",
      "Urban_area_2013_label                    Southern Auckland Zone\n",
      "Urban_area_type                                 Main urban area\n",
      "Territorial_authority_area_2013_code                         76\n",
      "Territorial_authority_area_2013_label                  Auckland\n",
      "Region_2013_code                                              2\n",
      "Region_2013_label                               Auckland Region\n",
      "Name: 100, dtype: object\n"
     ]
    }
   ],
   "source": [
    "sample_tweet = df_raw.iloc[100]\n",
    "print('Before tokenization:', sample_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After tokenization: Index                                                       101\n",
      "Area_unit_2013_code                                      524112\n",
      "Area_unit_2013_label                                     Mascot\n",
      "Victimisations_calendar_year_2015                            43\n",
      "Population_mid_point_2015                                  4410\n",
      " Rate_per_10000_population                                   98\n",
      " Rate_ratio_NZ_average_rate                                 1.9\n",
      "Urban_area_2013_code                                          5\n",
      "Urban_area_2013_label                    Southern Auckland Zone\n",
      "Urban_area_type                                 Main urban area\n",
      "Territorial_authority_area_2013_code                         76\n",
      "Territorial_authority_area_2013_label                  Auckland\n",
      "Region_2013_code                                              2\n",
      "Region_2013_label                               Auckland Region\n",
      "Name: 100, dtype: object\n"
     ]
    }
   ],
   "source": [
    "sample_tweet = df_raw.iloc[100]\n",
    "print('After tokenization:', sample_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " '@',\n",
       " '#',\n",
       " 'http',\n",
       " ':']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop = nltk.corpus.stopwords.words('english')\n",
    "# Add a few more stop words we would like to remove here\n",
    "stop.append('@')\n",
    "stop.append('#')\n",
    "stop.append('http')\n",
    "stop.append(':')\n",
    "stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us take two sentences to be the knowledge base, and one more which is a question.\n",
    "Sentence_1='Workplace harassment is the belittling or threatening behavior directed at an individual worker or a group of workers'\n",
    "Sentence_2='Physical harassment in the workplace takes many forms'\n",
    "Question='In an informal survey among 50 employees in Singapore'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vocabulary = ['workplace', 'is', 'among', 'informal', 'forms', 'individual', 'group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_of_words = np.array([[1,1,1,1,0,0,0],\n",
    "                        [0,1,1,0,1,1,0],\n",
    "                        [0,1,1,1,0,0,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 0, 0, 0],\n",
       "       [0, 1, 1, 0, 1, 1, 0],\n",
       "       [0, 1, 1, 1, 0, 0, 1]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_of_words[:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 1 1 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "print (bag_of_words[-1,:].reshape(1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n",
    "plt.xlabel('workplace')\n",
    "plt.ylabel('gender ratio')\n",
    "plt.title('Histogram of sexual harassments')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import random\n",
    "import string # to process standard python strings\n",
    "import warnings\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "nltk.download('popular', quiet=True) # for downloading packages\n",
    "#nltk.download('punkt') # first-time use only\n",
    "#nltk.download('wordnet') # first-time use only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_html = urllib.request.urlopen('https://en.wikipedia.org/wiki/Workplace_harassment')\n",
    "raw_html = raw_html.read()\n",
    "\n",
    "article_html = bs.BeautifulSoup(raw_html, 'lxml')\n",
    "\n",
    "article_paragraphs = article_html.find_all('p')\n",
    "\n",
    "article_text = ''\n",
    "\n",
    "for para in article_paragraphs:\n",
    "    article_text += para.text\n",
    "\n",
    "article_text = article_text.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=open('workplace harassment.txt','r',errors = 'ignore')\n",
    "raw=f.read()\n",
    "raw = raw.lower()# converts to lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences \n",
    "word_tokens = nltk.word_tokenize(raw)# converts to list of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmer = nltk.stem.WordNetLemmatizer()\n",
    "#WordNet is a semantically-oriented dictionary of English included in NLTK.\n",
    "def LemTokens(tokens):\n",
    "    return [lemmer.lemmatize(token) for token in tokens]\n",
    "remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)\n",
    "\n",
    "def LemNormalize(text):\n",
    "    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)\n",
    "def LemNormalize(text):\n",
    "    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict))) #see previous section 1.2.5 lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "GREETING_INPUTS = (\"hello\", \"hi\", \"greetings\", \"sup\", \"what's up\",\"hey\",)\n",
    "GREETING_RESPONSES = [\"hi\", \"hey\", \"*nods*\", \"hi there\", \"hello\", \"I am glad! You are talking to me\"]\n",
    "def greeting(sentence):\n",
    " \n",
    "    for word in sentence.split():\n",
    "        if word.lower() in GREETING_INPUTS:\n",
    "            return random.choice(GREETING_RESPONSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "article_text = re.sub(r'\\[[0-9]*\\]', ' ', article_text)\n",
    "article_text = re.sub(r'\\s+', ' ', article_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "article_sentences = nltk.sent_tokenize(article_text)\n",
    "article_words = nltk.word_tokenize(article_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_response(user_input):\n",
    "    cookie_response = ''\n",
    "    article_sentences.append(user_input)\n",
    "\n",
    "    word_vectorizer = TfidfVectorizer(tokenizer=get_processed_text, stop_words='english')\n",
    "    all_word_vectors = word_vectorizer.fit_transform(article_sentences)\n",
    "    similar_vector_values = cosine_similarity(all_word_vectors[-1], all_word_vectors)\n",
    "    similar_sentence_number = similar_vector_values.argsort()[0][-2]\n",
    "\n",
    "    matched_vector = similar_vector_values.flatten()\n",
    "    matched_vector.sort()\n",
    "    vector_matched = matched_vector[-2]\n",
    "\n",
    "    if vector_matched == 0:\n",
    "        cookie_response = cookie_response + \"I am sorry, I could not understand you\"\n",
    "        return cookie_response\n",
    "    else:\n",
    "        cookie_response = cookie_response + article_sentences[similar_sentence_number]\n",
    "        return cookie_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def greeting(sentence):\n",
    "    for word in sentence.split(): # Looks at each word in your sentence\n",
    "        if word.lower() in GREETING_INPUTS: # checks if the word matches a GREETING_INPUT\n",
    "            return random.choice(GREETING_RESPONSES) # replies with a GREETING_RESPONSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def response(user_response):\n",
    "    \n",
    "    cookie_response='' # initialize a variable to contain string\n",
    "    sent_tokens.append(user_response) #add user response to sent_tokens\n",
    "    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english') \n",
    "    tfidf = TfidfVec.fit_transform(sent_tokens) #get tfidf value\n",
    "    vals = cosine_similarity(tfidf[-1], tfidf) #get cosine similarity value\n",
    "    idx=vals.argsort()[0][-2] \n",
    "    flat = vals.flatten() \n",
    "    flat.sort() #sort in ascending order\n",
    "    req_tfidf = flat[-2] \n",
    "    \n",
    "    if(req_tfidf==0):\n",
    "        cookie_response=cookie_response+\"I am sorry! I don't understand you\"\n",
    "        return cookie_response\n",
    "    else:\n",
    "        cookie_response = cookie_response+sent_tokens[idx]\n",
    "        return cookie_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "def tell_time(sentence):\n",
    "    for word in sentence.split():\n",
    "        if word.lower() -- 'time':\n",
    "            currentdt - datetime.datetime.now()\n",
    "            return currentdt.strftime(\"%Y-%n-%d %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cookie: My name is cookie. I will answer your queries about Workplace Harassment. If you want to exit, type Bye!\n"
     ]
    }
   ],
   "source": [
    "flag=True\n",
    "print(\"cookie: My name is cookie. I will answer your queries about Workplace Harassment. If you want to exit, type Bye!\")\n",
    "while(flag==True):\n",
    "    user_response = input()\n",
    "    user_response=user_response.lower()\n",
    "    if(user_response!='bye'):\n",
    "        if(user_response=='thanks' or user_response=='thank you' ):\n",
    "            flag=False\n",
    "            print(\"cookie: You are welcome..\")\n",
    "        else:\n",
    "            if(greeting(user_response)!=None):\n",
    "                print(\"cookie: \"+greeting(user_response))\n",
    "            else:\n",
    "                print(\"cookie: \",end=\"\")\n",
    "                print(response(user_response))\n",
    "                sent_tokens.remove(user_response)\n",
    "    else:\n",
    "        flag=False\n",
    "        print(\"cookie: Bye! take care..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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