chatbot_using_NLP.ipynb

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  "metadata": {
    "colab": {
      "name": "chatbot using NLP",
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      "authorship_tag": "ABX9TyO9+XNu/+QXwgvKFlNYewNh",
      "include_colab_link": true
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        "<a href=\"https://colab.research.google.com/github/Amankumar29/Chat-Bot/blob/master/chatbot_using_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "embubvwOONoT",
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        ""
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        ""
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      "outputs": []
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        ""
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    {
      "cell_type": "code",
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        "id": "fjjB3yg5Ngrn",
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      },
      "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')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dra5R6aYNv0D",
        "colab_type": "code",
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      },
      "source": [
        "\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofgWMMcMaxnB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42a17c79-e031-4ebf-c943-d4c7aaa31f5d"
      },
      "source": [
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('popular', quiet=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 2
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    {
      "cell_type": "code",
      "metadata": {
        "id": "pBQ7neYAnf9V",
        "colab_type": "code",
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      },
      "source": [
        "with open('chatbot.txt','r', encoding='utf8', errors ='ignore') as fin:\n",
        "    raw = fin.read().lower()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9WHpKvfpFgl",
        "colab_type": "code",
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      },
      "source": [
        "sent_tokens = nltk.sent_tokenize(raw) \n",
        "word_tokens = nltk.word_tokenize(raw)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrPFvQWPpQBe",
        "colab_type": "code",
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      "source": [
        "lemmer = nltk.stem.WordNetLemmatizer()\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",
        "\n",
        "def LemNormalize(text):\n",
        "    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzOGwfzLpVFn",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2_XUTXKpbj2",
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      "source": [
        "def response(user_response):\n",
        "    robo_response=''\n",
        "    sent_tokens.append(user_response)\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",
        "    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"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tW0jed4hphfM",
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          "height": 309
        },
        "outputId": "d9ff0751-380e-4475-e310-5811c150aa79"
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      "source": [
        "flag=True\n",
        "print(\"ROBO: My name is chatty!\")\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(\"ROBO: You are welcome..\")\n",
        "        else:\n",
        "            if(greeting(user_response)!=None):\n",
        "                print(\"ROBO: \"+greeting(user_response))\n",
        "            else:\n",
        "                print(\"ROBO: \",end=\"\")\n",
        "                print(response(user_response))\n",
        "                sent_tokens.remove(user_response)\n",
        "    else:\n",
        "        flag=False\n",
        "        print(\"ROBO: Bye! take care..\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ROBO: My name is chatty!\n",
            "what is Barbie?\n",
            "ROBO: hello barbie is an internet-connected version of the doll that uses a chatbot provided by the company toytalk,which previously used the chatbot for a range of smartphone-based characters for children.these characters' behaviors are constrained by a set of rules that in effect emulate a particular character and produce a storyline.\n",
            "Aeromaxico\n",
            "ROBO: I am sorry! I don't understand you\n",
            "what is Aeromaxico?\n",
            "ROBO: I am sorry! I don't understand you\n",
            "what is airline?\n",
            "ROBO: airlines klm and aeroméxico both announced their participation in the testing;both airlines had previously launched customer services on the facebook messenger platform.\n",
            "what is application?\n",
            "ROBO: according to forrester (2015), ai will replace 16 percent of american jobs by the end of the decade.chatbots have been used in applications such as customer service, sales and product education.\n",
            "toys\n",
            "ROBO: toys\n",
            "chatbots have also been incorporated into devices not primarily meant for computing such as toys.\n",
            "bye\n",
            "ROBO: Bye! take care..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "Uc5BH2Pkpkzg",
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}