Basic Chatbot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import random\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading punct: Package 'punct' not found in index\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\Bhuvan\n",
      "[nltk_data]     Purohit\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#We will read in the corpus.txt file and convert the entire corpus into a list of sentences and a list of words for further pre-processing\n",
    "f= open('chatbot.txt', 'r', errors='ignore')\n",
    "raw = f.read()\n",
    "raw =raw.lower()\n",
    "\n",
    "nltk.download('punct')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "sent_tokens= nltk.sent_tokenize(raw)\n",
    "word_tokens = nltk.word_tokenize(raw)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a chatbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.', '[1] designed to convincingly simulate the way a human would behave as a conversational partner, chatbot systems typically require continuous tuning and testing, and many in production remain unable to adequately converse or pass the industry standard turing test.', '[2] the term \"chatterbot\" was originally coined by michael mauldin (creator of the first verbot) in 1994 to describe these conversational programs.']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['a', 'chatbot', 'is']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sent_tokens[:3])\n",
    "word_tokens[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n"
     ]
    }
   ],
   "source": [
    "print(string.punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocessing the rarw text\n",
    "lemmer = nltk.stem.WordNetLemmatizer()\n",
    "\n",
    "def LemTokens(tokens):\n",
    "    return [lemmer.lemmatize(token) for token in tokens]\n",
    "\n",
    "remove_punct_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)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "GREETINGS_INPUT = (\"hello\", \"hi\", \"greetings\",\"sup\", \"what's up\",\"hey\")\n",
    "\n",
    "GREETINGS_RESPONSES = [\"hi\", \"hey\", \"*nods*\", \"hi there\", \"hello\", \"I am glad! You are talking to me\"]\n",
    "\n",
    "def greetings(sentences):\n",
    "    \n",
    "    for word in sentences.split():\n",
    "        if word.lower() in GREETINGS_INPUT:\n",
    "            return random.choice(GREETINGS_RESPONSES)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To generate responses from our bot for input questions, the concept of document similarity will be used\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "# TO find similarity between words entered by the user and the words in the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a function response which searches the user’s utterance for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response:” I am sorry! I don’t understand you”\n",
    "\n",
    "def response(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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROBO: My name is ROBO. I will answer your queries about Chatots. If you want to exit , type Bye!\n",
      "hi\n",
      "ROBO: hi there\n",
      "bye see you\n",
      "ROBO: Bye take care!\n",
      "ROBO: I am sorry! I don't understand you\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bhuvan Purohit\\Anaconda3\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:301: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  'stop_words.' % sorted(inconsistent))\n"
     ]
    }
   ],
   "source": [
    "flag= True\n",
    "print(\"ROBO: My name is ROBO. I will answer your queries about Chatots. If you want to exit , type Bye!\")\n",
    "\n",
    "while(flag==True):\n",
    "    user_response = input()\n",
    "    user_response = user_response.lower()\n",
    "        \n",
    "    if(user_response !='bye'):\n",
    "        if(user_response=='thanks' or user_response=='thank you'):\n",
    "            flag=False\n",
    "            print(\"ROBO: You are welcome\")\n",
    "        else:\n",
    "            if(greetings(user_response) !=None):\n",
    "                print(\"ROBO: \"+greetings(user_response))\n",
    "            else:\n",
    "                print(\"ROBO: \", end=\"\")\n",
    "                print(response(user_response))\n",
    "                sent_tokens.remove(user_response)\n",
    "                \n",
    "\n",
    "    else:\n",
    "        flag=False\n",
    "        print(\"ROBO : Bye! Take Care :)\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}