Responses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import random\n",
    "import string # to process standard python strings\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "[nltk_data] Downloading package punkt to /home/aakash/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/aakash/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "f=open(r'/home/aakash/Documents/project 1/solutions/GAD/poor performance.txt','r',errors = 'ignore')\n",
    "\n",
    "raw=f.read()\n",
    "\n",
    "raw=raw.lower()# converts to lowercase\n",
    "\n",
    "nltk.download('punkt') # first-time use only\n",
    "nltk.download('wordnet') # first-time use only\n",
    "\n",
    "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": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'chatbot', '(', 'also', 'known']"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent_tokens[:2]\n",
    "['a chatbot (also known as a talkbot, chatterbot, bot, im bot, interactive agent, or artificial conversational entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.',\n",
    " 'such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the turing test.']\n",
    "\n",
    "word_tokens[:2]\n",
    "['a', 'chatbot', '(', 'also', 'known']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmer = nltk.stem.WordNetLemmatizer()\n",
    "#WordNet is a semantically-oriented dictionary of English included in NLTK.\n",
    "\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",
    "def LemNormalize(text):\n",
    "    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poor_performance(user_response):\n",
    "    robo_response=''\n",
    "    sent_tokens.append(user_response)\n",
    "\n",
    "    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')\n",
    "    tfidf = TfidfVec.fit_transform(sent_tokens)\n",
    "    vals = cosine_similarity(tfidf[-1], tfidf)\n",
    "    idx=vals.argsort()[0][-2]\n",
    "    flat = vals.flatten()\n",
    "    flat.sort()\n",
    "    req_tfidf = flat[-2]\n",
    "\n",
    "    if(req_tfidf==0):\n",
    "        robo_response=robo_response+\"I am sorry! I don't understand you\"\n",
    "        return robo_response\n",
    "    else:\n",
    "        robo_response = robo_response+sent_tokens[idx]\n",
    "        return robo_response\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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