chatbot_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1784,
     "status": "ok",
     "timestamp": 1597238066361,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "tfjf3S2Zxry0",
    "outputId": "c34d80c5-177d-4630-b8d6-4c714a93e027"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# MOUNTING GOOGLE DRIVE\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 759
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12852,
     "status": "ok",
     "timestamp": 1597238077474,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "uO8iAOZRH-0Y",
    "outputId": "3263a85c-9435-44f5-b2e4-0a745664ea43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
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      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.18.5)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.0)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.1.0)\n",
      "Requirement already satisfied: en_vectors_web_lg==2.1.0 from https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz#egg=en_vectors_web_lg==2.1.0 in /usr/local/lib/python3.6/dist-packages (2.1.0)\n",
      "Requirement already satisfied: spacy<3.0.0,>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from en_vectors_web_lg==2.1.0) (2.2.4)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.18.5)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (2.0.3)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (49.2.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (4.41.1)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (3.0.2)\n",
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      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.0.2)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (0.7.1)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.1.3)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.0.0)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (0.4.1)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (2.23.0)\n",
      "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (7.4.0)\n",
      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.7.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (2020.6.20)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (1.24.3)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<3.0.0,>=2.1.0->en_vectors_web_lg==2.1.0) (3.1.0)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('en_vectors_web_lg')\n"
     ]
    }
   ],
   "source": [
    "# INSTALLING DEPENDENCY SUCH AS SPACY AND PRE-TRAINED WORD EMBEDDINGS\n",
    "\n",
    "!pip install spacy\n",
    "!python -m spacy download en_vectors_web_lg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 34281,
     "status": "ok",
     "timestamp": 1597238098925,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "Q1nVc3IWyz7P"
   },
   "outputs": [],
   "source": [
    "# IMPORTING THE NECESSARY LIBRARIES\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import re\n",
    "import spacy\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "import nltk\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, LSTM, GRU, Dense, Embedding\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras import backend as K\n",
    "import os\n",
    "import time\n",
    "nlp = spacy.load('en_vectors_web_lg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30232,
     "status": "ok",
     "timestamp": 1597238098927,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "uFeSwpNVyz7S"
   },
   "outputs": [],
   "source": [
    "# IMPORTING THE DATASETS FROM THE MOUNTED DRIVE\n",
    "\n",
    "lines = open(\"/content/gdrive/My Drive/chatbot_collab/movie_lines.txt\", encoding='utf-8', errors='ignore').read().split(\"\\n\")\n",
    "conversations = open(\"/content/gdrive/My Drive/chatbot_collab/movie_conversations.txt\", encoding='utf-8', errors='ignore').read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1931,
     "status": "ok",
     "timestamp": 1597238228034,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "hTVUb3QQyz7V"
   },
   "outputs": [],
   "source": [
    "# CREATING A DICT WHICH MAPS LINE ID TO LINE TEXT\n",
    "\n",
    "id2line = {}\n",
    "for line in lines:\n",
    "    _line = line.split(\" +++$+++ \")\n",
    "    if len(_line) == 5:\n",
    "        id2line[_line[0]] = _line[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2939,
     "status": "ok",
     "timestamp": 1597238233697,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "C7-M7H6Byz7Y"
   },
   "outputs": [],
   "source": [
    "# CLEANSING OF TEXT IN ID2LINE VALUES\n",
    "\n",
    "for i in id2line.keys():\n",
    "    id2line[i] = id2line[i].lower()\n",
    "    id2line[i] = str(\"<sos> \") + id2line[i].replace(\"i'm\", \"i am\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'ve\", \" have\").replace(\"'re\", \" are\").replace(\"'d\", \" would\").replace(\"won't\", \"will not\").replace(\"can't\", \"can not\").replace(\"n't\", \" not\").replace(\"'\", \"\") + str(\" <eos>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1839,
     "status": "ok",
     "timestamp": 1597238237419,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "GTGHj05dyz7a"
   },
   "outputs": [],
   "source": [
    "# CREATING LIST OF ALL THE CONVERSATIONS\n",
    "\n",
    "conversations_ids = []\n",
    "for conversation in conversations[:-1]:\n",
    "    _conversation = conversation.split(\" +++$+++ \")[-1][1:-1].replace(\"'\",\"\").replace(\" \",\"\")\n",
    "    conversations_ids.append(_conversation.split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6881,
     "status": "ok",
     "timestamp": 1597238245568,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "kZy4DicOyz7e"
   },
   "outputs": [],
   "source": [
    "# TOKENIZING AND EXTRACTING WORD_2_INDEX DICT\n",
    "\n",
    "t = Tokenizer(num_words=18000, oov_token='<unk>', filters='!\"#$%&()*+,-./:;=?@[\\\\]^_`{|}~\\t\\n')\n",
    "t.fit_on_texts(id2line.values())\n",
    "word_2_index_dict = t.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6331,
     "status": "ok",
     "timestamp": 1597238245570,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "MBE4Ttxcyz7n"
   },
   "outputs": [],
   "source": [
    "# CREATING A REVERSE DICTIONARY FOR THE OUTPUTS\n",
    "\n",
    "index_2_word_dict = {index : word  for word,index in word_2_index_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10447,
     "status": "ok",
     "timestamp": 1597238250218,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "h7KW4pAnyz7g"
   },
   "outputs": [],
   "source": [
    "# CREATING NEW DICT FOR MATCHING ID TO ENCODED LINES \n",
    "\n",
    "encoded_lines = t.texts_to_sequences(id2line.values())\n",
    "id_2_encoded_line = {}\n",
    "k = 0\n",
    "for i in id2line.keys():\n",
    "    id_2_encoded_line[i] = encoded_lines[k]\n",
    "    k += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9745,
     "status": "ok",
     "timestamp": 1597238250220,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "pbpTglSqyz7i"
   },
   "outputs": [],
   "source": [
    "# CREATING SEPARATELY THE QUESTION AND THE ANSWERS\n",
    "\n",
    "questions = []\n",
    "answers = []\n",
    "for conversation in conversations_ids:\n",
    "    for i in range(len(conversation) - 1):\n",
    "        questions.append(id_2_encoded_line[conversation[i]])\n",
    "        answers.append(id_2_encoded_line[conversation[i+1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8930,
     "status": "ok",
     "timestamp": 1597238250222,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "IT8PfBtdyz7l"
   },
   "outputs": [],
   "source": [
    "# SETTING THE 80 PERCENTILE LENGTH OF QUESTIONS AND ANSWERS AS MAX QUES AND ANS LEN\n",
    "\n",
    "len_of_all_questions = [len(ques) for ques in questions]\n",
    "len_of_all_answers = [len(ans) for ans in answers]\n",
    "\n",
    "max_ques_len = int(np.percentile(len_of_all_questions, 80))    #18\n",
    "max_ans_len = int(np.percentile(len_of_all_answers, 90))       #27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10038,
     "status": "ok",
     "timestamp": 1597238252052,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "6663K0Ijyz7p"
   },
   "outputs": [],
   "source": [
    "# GETTING THE QUESTION AND ANSWERS SORTED\n",
    "\n",
    "sorted_questions = []\n",
    "sorted_answers = []\n",
    "for i in range(3,max_ques_len + 1):\n",
    "    for j,k in enumerate(questions):\n",
    "        if i == len(k) and len(answers[j]) >= 3 and len(answers[j]) <= (max_ans_len):\n",
    "            sorted_questions.append(k)\n",
    "            sorted_answers.append(answers[j][1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10259,
     "status": "ok",
     "timestamp": 1597238252922,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "2ZPesbTHyz7s"
   },
   "outputs": [],
   "source": [
    "# PADDING THE SORTED QUESTIONS AND ANSWERS\n",
    "\n",
    "padded_sorted_ques = pad_sequences(sorted_questions, maxlen= max_ques_len, padding='pre')\n",
    "padded_sorted_ans = pad_sequences(sorted_answers, maxlen= max_ans_len , padding='post',truncating='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9652,
     "status": "ok",
     "timestamp": 1597238252924,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "UCHt-Q7XILhB"
   },
   "outputs": [],
   "source": [
    "# CREATING AN EMBEDDING MATRIX\n",
    "\n",
    "max_tokens = 18000\n",
    "embedding_matrix = np.random.randn(max_tokens, 300)\n",
    "for i in range(1,max_tokens):\n",
    "    if nlp.vocab[index_2_word_dict[i]].has_vector:\n",
    "        embedding_matrix[i] = nlp.vocab[index_2_word_dict[i]].vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9118,
     "status": "ok",
     "timestamp": 1597238252926,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "PUWoE8vfyz7x"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE PARAMETERS\n",
    "\n",
    "epochs = 300\n",
    "batch_size = 32\n",
    "rnn_size = 1024\n",
    "encoding_embedding_size = 300\n",
    "decoding_embedding_size = 300\n",
    "dropout_rate = 0.3\n",
    "steps_per_epoch = len(sorted_questions)//batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8761,
     "status": "ok",
     "timestamp": 1597238252929,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "bxDibXbuyz8w"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE ENCODER CLASS OF MODEL\n",
    "\n",
    "class Encoder(tf.keras.Model):\n",
    "    def __init__(self, max_tokens, embedding_dim, enc_units, batch_sz, dropout_rate, embedding_matrix):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.batch_sz = batch_sz\n",
    "        self.enc_units = enc_units\n",
    "        self.dropout_rate = dropout_rate\n",
    "        self.outputs = {}\n",
    "        self.states = {}\n",
    "        self.embedding = tf.keras.layers.Embedding(input_dim=max_tokens, output_dim= embedding_dim, weights = [embedding_matrix])\n",
    "        self.lstmb1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(self.enc_units,\n",
    "                                                                        return_sequences=True,\n",
    "                                                                        return_state=True,\n",
    "                                                                        dropout = self.dropout_rate,\n",
    "                                                                        go_backwards=True), merge_mode='sum')\n",
    "        \n",
    "        self.lstmb2 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(self.enc_units,\n",
    "                                                                        return_sequences=True,\n",
    "                                                                        return_state= True,\n",
    "                                                                         dropout=self.dropout_rate, go_backwards=True), merge_mode='sum')\n",
    "        \n",
    "        self.lstmb3 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(self.enc_units,\n",
    "                                                                        return_sequences=True,\n",
    "                                                                        return_state=True,\n",
    "                                                                        dropout=self.dropout_rate, go_backwards=True), merge_mode='sum')\n",
    "\n",
    "    \n",
    "    def call(self, x):\n",
    "        x = self.embedding(x)\n",
    "\n",
    "        x, hf, hb, cf, cb = self.lstmb1(x)\n",
    "        x, hf, hb, cf, cb = self.lstmb2(x, initial_state=[hf,hb,cf,cb])\n",
    "        self.outputs, self.states[\"for_h\"], self.states[\"for_c\"], self.states[\"bac_h\"], self.states[\"bac_c\"] = self.lstmb3(x, initial_state=[hf,hb,cf,cb])\n",
    "        return self.outputs, self.states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8278,
     "status": "ok",
     "timestamp": 1597238252930,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "OJC2G8Usyz8y"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE ATTENTION CLASS\n",
    "\n",
    "class BahdanauAttention(tf.keras.layers.Layer):\n",
    "  def __init__(self, units):\n",
    "    super(BahdanauAttention, self).__init__()\n",
    "    self.W1 = tf.keras.layers.Dense(units)\n",
    "    self.W2 = tf.keras.layers.Dense(units)\n",
    "    self.V = tf.keras.layers.Dense(1)\n",
    "\n",
    "  def call(self, query, values):\n",
    "    #query == states\n",
    "    #values == output\n",
    "    # query hidden state shape == (batch_size, hidden size)\n",
    "    # query_with_time_axis shape == (batch_size, 1, hidden size)\n",
    "    # values shape == (batch_size, max_len, hidden size)\n",
    "    # we are doing this to broadcast addition along the time axis to calculate the score\n",
    "    query_with_time_axis = tf.expand_dims(query, 1)\n",
    "\n",
    "    # score shape == (batch_size, max_length, 1)\n",
    "    # we get 1 at the last axis because we are applying score to self.V\n",
    "    # the shape of the tensor before applying self.V is (batch_size, max_length, units)\n",
    "    score = self.V(tf.nn.tanh(\n",
    "        self.W1(query_with_time_axis) + self.W2(values)))\n",
    "\n",
    "    # attention_weights shape == (batch_size, max_length, 1)\n",
    "    attention_weights = tf.nn.softmax(score, axis=1)\n",
    "\n",
    "    # context_vector shape after sum == (batch_size, hidden_size)\n",
    "    context_vector = attention_weights * values\n",
    "    context_vector = tf.reduce_sum(context_vector, axis=1)\n",
    "\n",
    "    return context_vector, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8503,
     "status": "ok",
     "timestamp": 1597238253743,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "bnb4dEbKyz81"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE DECODER CLASS OF MODEL\n",
    "\n",
    "class Decoder(tf.keras.Model):\n",
    "  def __init__(self, max_tokens, embedding_dim, dec_units, batch_sz, dropout_rate, embedding_matrix):\n",
    "    super(Decoder, self).__init__()\n",
    "    self.batch_sz = batch_sz\n",
    "    self.dec_units = dec_units\n",
    "    self.dropout_rate = dropout_rate\n",
    "\n",
    "    self.embedding = tf.keras.layers.Embedding(input_dim=max_tokens, output_dim= embedding_dim, weights = [embedding_matrix])\n",
    "    self.lstm1 = tf.keras.layers.LSTM(self.dec_units,\n",
    "                                   return_sequences=True,\n",
    "                                   return_state=True,\n",
    "                                   dropout=self.dropout_rate)\n",
    "    self.lstm2 = tf.keras.layers.LSTM(self.dec_units,\n",
    "                                   return_sequences=True,\n",
    "                                   return_state=True,\n",
    "                                   dropout=self.dropout_rate)\n",
    "    self.lstm3 = tf.keras.layers.LSTM(self.dec_units,\n",
    "                                   return_sequences=True,\n",
    "                                   return_state=True,\n",
    "                                   dropout=self.dropout_rate)\n",
    "    \n",
    "    self.fc = tf.keras.layers.Dense(max_tokens)\n",
    "    self.attention = BahdanauAttention(self.dec_units)\n",
    "    \n",
    "\n",
    "  def call(self, x, enc_output, hidden):\n",
    "    # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n",
    "    x = self.embedding(x)\n",
    "\n",
    "    x, h, c = self.lstm1(x, initial_state = hidden)\n",
    "    x, h ,c = self.lstm2(x, initial_state= [h, c])\n",
    "    \n",
    "    # enc_output shape == (batch_size, max_length, hidden_size)\n",
    "    context_vector, attention_weights = self.attention(h, enc_output)\n",
    "\n",
    "    # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n",
    "    x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n",
    "\n",
    "    # applying lstm\n",
    "    x, h, c = self.lstm3(x, initial_state= [h, c])\n",
    "    states = [h, c]\n",
    "\n",
    "    x = tf.reshape(x, (-1, x.shape[2]))\n",
    "\n",
    "    # output shape == (batch_size, vocab)\n",
    "    x = self.fc(x)\n",
    "\n",
    "    return x, states,attention_weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1743,
     "status": "ok",
     "timestamp": 1597238270298,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "z4EV9Dj6yz84"
   },
   "outputs": [],
   "source": [
    "# CREATING THE DATASET\n",
    "dataset = tf.data.Dataset.from_tensor_slices((padded_sorted_ques, padded_sorted_ans))\n",
    "dataset = dataset.batch(batch_size, drop_remainder=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1009,
     "status": "ok",
     "timestamp": 1597238270301,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "V1yv0FEWyz88"
   },
   "outputs": [],
   "source": [
    "# CREATING OBJECTS OF MODEL CLASSES \n",
    "encoder = Encoder(max_tokens, encoding_embedding_size, rnn_size, batch_size, dropout_rate, embedding_matrix)\n",
    "decoder = Decoder(max_tokens, decoding_embedding_size, rnn_size, batch_size, dropout_rate, embedding_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 945,
     "status": "ok",
     "timestamp": 1597238271115,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "mW68HuDfyz9H"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE OPTIMIZER AND THE LOSS FUNCTION\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "def loss_function(real, pred):\n",
    "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
    "  loss_ = tf.keras.losses.sparse_categorical_crossentropy(y_true=real, y_pred=pred, from_logits=True)\n",
    "\n",
    "  mask = tf.cast(mask, dtype=loss_.dtype)\n",
    "  loss_ *= mask\n",
    "\n",
    "  return tf.reduce_mean(loss_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1009,
     "status": "ok",
     "timestamp": 1597238272056,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "JdId6KMGyz9O"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE PARAMETERS AND FUNCTION FOR PLOTTING ACCURACY HISTORY DURING TRAINING\n",
    "\n",
    "history={'loss':[]}\n",
    "smallest_loss = np.inf\n",
    "best_ep = 1\n",
    "\n",
    "def plot_history():\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.plot(best_ep,smallest_loss,'ro')\n",
    "    plt.plot(history['loss'],'b-')\n",
    "    plt.legend(['best','loss'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1669,
     "status": "ok",
     "timestamp": 1597238274275,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "Lgbb4jvi6srv"
   },
   "outputs": [],
   "source": [
    "# DEFINING THE CHECKPOINT AND MANAGER FOR OBJECT BASED SAVING\n",
    "\n",
    "checkpoint_dir = \"/content/gdrive/My Drive/chatbot_collab/model_checkpoints\"\n",
    "checkpoint = tf.train.Checkpoint(optimizer=optimizer,\n",
    "                                 encoder=encoder,\n",
    "                                 decoder=decoder)\n",
    "manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1388,
     "status": "ok",
     "timestamp": 1597238277800,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "ZXWe8b1Zyz9V"
   },
   "outputs": [],
   "source": [
    "# THIS IS THE TRAIN STEP OF THE MODEL\n",
    "\n",
    "def train_step(inp, targ):\n",
    "  loss = 0\n",
    "\n",
    "  with tf.GradientTape() as tape:\n",
    "    enc_output, enc_states = encoder(inp)\n",
    "\n",
    "    dec_input = tf.expand_dims([word_2_index_dict['<sos>']] * batch_size, 1)\n",
    "    dec_hidden = [ tf.add(enc_states[\"for_h\"], enc_states[\"bac_h\"]), tf.add(enc_states[\"for_c\"], \n",
    "                                enc_states[\"bac_c\"]) ]\n",
    "    \n",
    "    # Teacher forcing - feeding the target as the next input\n",
    "    for t in range(1, targ.shape[1]):\n",
    "      # passing enc_output to the decoder\n",
    "      predictions, dec_hidden,_ = decoder(dec_input, enc_output, dec_hidden)\n",
    "\n",
    "      loss += loss_function(targ[:, t], predictions)\n",
    "\n",
    "      dec_input = tf.expand_dims(targ[:, t], 1)\n",
    "\n",
    "  batch_loss = (loss / int(targ.shape[1]))\n",
    "\n",
    "  variables = encoder.trainable_variables + decoder.trainable_variables\n",
    "\n",
    "  gradients = tape.gradient(loss, variables)\n",
    "\n",
    "  optimizer.apply_gradients(zip(gradients, variables))\n",
    "\n",
    "  return batch_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1299,
     "status": "ok",
     "timestamp": 1597238279497,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "ykWuijzHFQF7"
   },
   "outputs": [],
   "source": [
    "# IMPORTING LIBRARIES AND DEFINING FUNCTION FOR PREPROCESSING THE ASKED QUESTIONS\n",
    "\n",
    "import unicodedata\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def unicode_to_ascii(s):\n",
    "    return ''.join(c for c in unicodedata.normalize('NFD', s)\n",
    "      if unicodedata.category(c) != 'Mn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1864,
     "status": "ok",
     "timestamp": 1597238282758,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "395gFKY9yz9l"
   },
   "outputs": [],
   "source": [
    "# FUNCTION FOR EVALUATING THE ANSWERS OF THE QUESTIONS ASKED (USED BY ANSWERING_QUES FUNCTION)\n",
    "\n",
    "def evaluate(sentence, token):\n",
    "  attention_plot = np.zeros((max_ans_len, max_ques_len))\n",
    "\n",
    "  sentence = unicode_to_ascii(sentence.lower())\n",
    "    \n",
    "  inp = str(\"<sos> \") + sentence.replace(\"i'm\", \"i am\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'ve\", \" have\").replace(\"'re\", \" are\").replace(\"'d\", \" would\").replace(\"won't\", \"will not\").replace(\"can't\", \"can not\").replace(\"n't\", \" not\").replace(\"'bout\", \"about\").replace(\"'til\", \"until\").replace(\"'\", \"\") + str(\" <eos>\")\n",
    "  inp = re.sub(r\"[-()\\\"#/@;:{}`$+=~|.!?,]\", \"\", inp)\n",
    "\n",
    "  inp = token.texts_to_sequences([inp])[0]\n",
    "  inp = pad_sequences([inp], maxlen=max_ques_len, padding='pre')\n",
    "  inp = tf.convert_to_tensor(inp)\n",
    "\n",
    "  result = ''\n",
    "\n",
    "  enc_out, enc_states = encoder(inp)\n",
    "  \n",
    "  dec_hidden = [ tf.add(enc_states[\"for_h\"], enc_states[\"bac_h\"]), tf.add(enc_states[\"for_c\"], \n",
    "                                enc_states[\"bac_c\"]) ]\n",
    "  dec_input = tf.expand_dims([word_2_index_dict['<sos>']], 1)\n",
    "\n",
    "  for t in range(max_ans_len):\n",
    "    predictions, dec_hidden, attention_weights = decoder(dec_input,\n",
    "                                                           enc_out,\n",
    "                                                          dec_hidden)\n",
    "\n",
    "    # storing the attention weights to plot later on\n",
    "    attention_weights = tf.reshape(attention_weights, [-1])\n",
    "    attention_plot[t] = attention_weights.numpy()\n",
    "\n",
    "    predicted_id = tf.argmax(predictions[0]).numpy()\n",
    "\n",
    "    if index_2_word_dict[predicted_id] == '<eos>':\n",
    "      return result, sentence, attention_plot\n",
    "\n",
    "    result += index_2_word_dict[predicted_id] + ' '\n",
    "    # the predicted ID is fed back into the model\n",
    "    dec_input = tf.expand_dims([predicted_id], 0)\n",
    "\n",
    "  return result, sentence, attention_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1064,
     "status": "ok",
     "timestamp": 1597238283713,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "1S_L36Biyz9o"
   },
   "outputs": [],
   "source": [
    "# FUNCTION FOR PLOTTING ATTENTION WEIGHTS DURING TRAINING\n",
    "\n",
    "def plot_attention(attention, sentence, predicted_sentence):\n",
    "  fig = plt.figure(figsize=(5,5))\n",
    "  ax = fig.add_subplot(1, 1, 1)\n",
    "  ax.matshow(attention, cmap='viridis')\n",
    "\n",
    "  fontdict = {'fontsize': 14}\n",
    "\n",
    "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
    "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n",
    "\n",
    "  ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "  ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1616,
     "status": "ok",
     "timestamp": 1597238287402,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "Xof1yztMyz9r"
   },
   "outputs": [],
   "source": [
    "# FUNCTION FOR ANSWERING THE QUESTION (USES EVALUATE FUNCTION)\n",
    "\n",
    "def answering_ques(sentence, token, train):\n",
    "  result, sentence, attention_plot = evaluate(sentence, token)\n",
    "  \n",
    "  print(result)\n",
    "\n",
    "  if(train == 1):\n",
    "    attention_plot = attention_plot[:len(result.split(' ')) - 1, :len(sentence.split(' '))]\n",
    "    plot_attention(attention_plot, sentence.split(' '), result.split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "O43t6M0Jyz9Z",
    "outputId": "95ec54a6-e577-43eb-8b89-0d26ac1b6b6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restored from /content/gdrive/My Drive/chatbot_collab/model_checkpoints/ckpt-43\n",
      "Restoring after 43 checkpoint\n",
      "\n",
      "\n",
      "\n",
      "Epoch 44 Batch 1 Loss 1.1638\n",
      "Epoch 44 Batch 101 Loss 1.1286\n",
      "Epoch 44 Batch 201 Loss 0.9856\n",
      "Epoch 44 Batch 301 Loss 1.0477\n",
      "Epoch 44 Batch 401 Loss 1.3305\n",
      "Epoch 44 Batch 501 Loss 1.5170\n",
      "Epoch 44 Batch 601 Loss 1.0939\n",
      "Epoch 44 Batch 701 Loss 1.0416\n",
      "Epoch 44 Batch 801 Loss 1.3773\n",
      "Epoch 44 Batch 901 Loss 1.4200\n",
      "Epoch 44 Batch 1001 Loss 1.0906\n",
      "Epoch 44 Batch 1101 Loss 1.1158\n",
      "Epoch 44 Batch 1201 Loss 1.3136\n",
      "Epoch 44 Batch 1301 Loss 0.8552\n",
      "Epoch 44 Batch 1401 Loss 0.9821\n",
      "Epoch 44 Batch 1501 Loss 0.8629\n",
      "Epoch 44 Batch 1601 Loss 1.4078\n",
      "Epoch 44 Batch 1701 Loss 1.1612\n",
      "Epoch 44 Batch 1801 Loss 0.8958\n",
      "Epoch 44 Batch 1901 Loss 1.0757\n",
      "Epoch 44 Batch 2001 Loss 0.8450\n",
      "Epoch 44 Batch 2101 Loss 1.2383\n",
      "Epoch 44 Batch 2201 Loss 1.3744\n",
      "Epoch 44 Batch 2301 Loss 1.2473\n",
      "Epoch 44 Batch 2401 Loss 1.2254\n",
      "Epoch 44 Batch 2501 Loss 1.5169\n",
      "Epoch 44 Batch 2601 Loss 0.9430\n",
      "Epoch 44 Batch 2701 Loss 0.9537\n",
      "Epoch 44 Batch 2801 Loss 1.3756\n",
      "Epoch 44 Batch 2901 Loss 1.2870\n",
      "Epoch 44 Batch 3001 Loss 1.4290\n",
      "Epoch 44 Batch 3101 Loss 1.3798\n",
      "Epoch 44 Batch 3201 Loss 1.0210\n",
      "Epoch 44 Batch 3301 Loss 1.2117\n",
      "Epoch 44 Batch 3401 Loss 0.7774\n",
      "Epoch 44 Batch 3501 Loss 1.0597\n",
      "Epoch 44 Batch 3601 Loss 0.9701\n",
      "Epoch 44 Batch 3701 Loss 0.8284\n",
      "Epoch 44 Batch 3801 Loss 0.9541\n",
      "Epoch 44 Batch 3901 Loss 1.2580\n",
      "Epoch 44 Batch 4001 Loss 1.3834\n",
      "Epoch 44 Batch 4101 Loss 1.3944\n",
      "Epoch 44 Batch 4201 Loss 0.9492\n",
      "Epoch 44 Batch 4301 Loss 1.1143\n",
      "Epoch 44 Batch 4401 Loss 1.1323\n",
      "Epoch 44 Batch 4501 Loss 1.1901\n",
      "Epoch 44 Batch 4601 Loss 1.2912\n",
      "Epoch 44 Batch 4701 Loss 1.3847\n",
      "Epoch 44 Batch 4801 Loss 1.0510\n",
      "Epoch 44 Batch 4901 Loss 1.3652\n",
      "Epoch 44 Batch 5001 Loss 1.0496\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44 Loss 1.1596\n",
      "Time taken for 1 epoch 7506.9240345954895 sec\n",
      "Best epoch so far:  44  smallest loss: tf.Tensor(1.1595545, shape=(), dtype=float32)\n",
      "Checkpoint saved at: /content/gdrive/My Drive/chatbot_collab/model_checkpoints/ckpt-44\n",
      "\n",
      "\n",
      "\n",
      "Ques:  Hello \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  how are you? \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's up? \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": 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QLQWHPjXLSg/QY3r+9+FtdEvBa3RLwaFbCg7dUnDoloJDtxT+D9fDSNmr72vgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's your name? \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  I am tired. \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": 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tUB6jnfSzEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  You're so smart. \n",
      "Ans :  is not a joke \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Epoch 45 Batch 1 Loss 1.1146\n",
      "Epoch 45 Batch 101 Loss 1.1400\n",
      "Epoch 45 Batch 201 Loss 0.9831\n",
      "Epoch 45 Batch 301 Loss 1.0107\n",
      "Epoch 45 Batch 401 Loss 1.2828\n",
      "Epoch 45 Batch 501 Loss 1.4366\n",
      "Epoch 45 Batch 601 Loss 1.0578\n",
      "Epoch 45 Batch 701 Loss 1.0494\n",
      "Epoch 45 Batch 801 Loss 1.3306\n",
      "Epoch 45 Batch 901 Loss 1.3568\n",
      "Epoch 45 Batch 1001 Loss 1.0690\n",
      "Epoch 45 Batch 1101 Loss 1.0897\n",
      "Epoch 45 Batch 1201 Loss 1.2967\n",
      "Epoch 45 Batch 1301 Loss 0.8428\n",
      "Epoch 45 Batch 1401 Loss 0.9395\n",
      "Epoch 45 Batch 1501 Loss 0.8256\n",
      "Epoch 45 Batch 1601 Loss 1.3814\n",
      "Epoch 45 Batch 1701 Loss 1.1191\n",
      "Epoch 45 Batch 1801 Loss 0.8853\n",
      "Epoch 45 Batch 1901 Loss 1.0375\n",
      "Epoch 45 Batch 2001 Loss 0.8426\n",
      "Epoch 45 Batch 2101 Loss 1.1967\n",
      "Epoch 45 Batch 2201 Loss 1.3494\n",
      "Epoch 45 Batch 2301 Loss 1.2195\n",
      "Epoch 45 Batch 2401 Loss 1.2011\n",
      "Epoch 45 Batch 2501 Loss 1.4898\n",
      "Epoch 45 Batch 2601 Loss 0.9228\n",
      "Epoch 45 Batch 2701 Loss 0.9388\n",
      "Epoch 45 Batch 2801 Loss 1.3315\n",
      "Epoch 45 Batch 2901 Loss 1.2532\n",
      "Epoch 45 Batch 3001 Loss 1.3754\n",
      "Epoch 45 Batch 3101 Loss 1.3429\n",
      "Epoch 45 Batch 3201 Loss 0.9928\n",
      "Epoch 45 Batch 3301 Loss 1.1524\n",
      "Epoch 45 Batch 3401 Loss 0.7869\n",
      "Epoch 45 Batch 3501 Loss 1.0431\n",
      "Epoch 45 Batch 3601 Loss 0.9810\n",
      "Epoch 45 Batch 3701 Loss 0.8184\n",
      "Epoch 45 Batch 3801 Loss 0.9434\n",
      "Epoch 45 Batch 3901 Loss 1.2244\n",
      "Epoch 45 Batch 4001 Loss 1.3171\n",
      "Epoch 45 Batch 4101 Loss 1.3495\n",
      "Epoch 45 Batch 4201 Loss 0.9467\n",
      "Epoch 45 Batch 4301 Loss 1.1230\n",
      "Epoch 45 Batch 4401 Loss 1.1325\n",
      "Epoch 45 Batch 4501 Loss 1.1228\n",
      "Epoch 45 Batch 4601 Loss 1.2526\n",
      "Epoch 45 Batch 4701 Loss 1.3571\n",
      "Epoch 45 Batch 4801 Loss 1.0420\n",
      "Epoch 45 Batch 4901 Loss 1.3742\n",
      "Epoch 45 Batch 5001 Loss 1.0482\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45 Loss 1.1372\n",
      "Time taken for 1 epoch 7691.095586299896 sec\n",
      "Best epoch so far:  45  smallest loss: tf.Tensor(1.1371653, shape=(), dtype=float32)\n",
      "Checkpoint saved at: /content/gdrive/My Drive/chatbot_collab/model_checkpoints/ckpt-45\n",
      "\n",
      "\n",
      "\n",
      "Ques:  Hello \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  how are you? \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's up? \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's your name? \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  I am tired. \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  You're so smart. \n",
      "Ans :  is not true \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Epoch 46 Batch 1 Loss 1.1403\n",
      "Epoch 46 Batch 101 Loss 1.1197\n",
      "Epoch 46 Batch 201 Loss 0.9609\n",
      "Epoch 46 Batch 301 Loss 1.0105\n",
      "Epoch 46 Batch 401 Loss 1.2789\n",
      "Epoch 46 Batch 501 Loss 1.4525\n",
      "Epoch 46 Batch 601 Loss 1.0499\n",
      "Epoch 46 Batch 701 Loss 1.0204\n",
      "Epoch 46 Batch 801 Loss 1.3123\n",
      "Epoch 46 Batch 901 Loss 1.3303\n",
      "Epoch 46 Batch 1001 Loss 1.0723\n",
      "Epoch 46 Batch 1101 Loss 1.0847\n",
      "Epoch 46 Batch 1201 Loss 1.2806\n",
      "Epoch 46 Batch 1301 Loss 0.8300\n",
      "Epoch 46 Batch 1401 Loss 0.9352\n",
      "Epoch 46 Batch 1501 Loss 0.8550\n",
      "Epoch 46 Batch 1601 Loss 1.3861\n",
      "Epoch 46 Batch 1701 Loss 1.0902\n",
      "Epoch 46 Batch 1801 Loss 0.9124\n",
      "Epoch 46 Batch 1901 Loss 1.0393\n",
      "Epoch 46 Batch 2001 Loss 0.8375\n",
      "Epoch 46 Batch 2101 Loss 1.1946\n",
      "Epoch 46 Batch 2201 Loss 1.3109\n",
      "Epoch 46 Batch 2301 Loss 1.2106\n",
      "Epoch 46 Batch 2401 Loss 1.1802\n",
      "Epoch 46 Batch 2501 Loss 1.4491\n",
      "Epoch 46 Batch 2601 Loss 0.9152\n",
      "Epoch 46 Batch 2701 Loss 0.9327\n",
      "Epoch 46 Batch 2801 Loss 1.2814\n",
      "Epoch 46 Batch 2901 Loss 1.2382\n",
      "Epoch 46 Batch 3001 Loss 1.3669\n",
      "Epoch 46 Batch 3101 Loss 1.3404\n",
      "Epoch 46 Batch 3201 Loss 1.0007\n",
      "Epoch 46 Batch 3301 Loss 1.1698\n",
      "Epoch 46 Batch 3401 Loss 0.7588\n",
      "Epoch 46 Batch 3501 Loss 1.0538\n",
      "Epoch 46 Batch 3601 Loss 0.9837\n",
      "Epoch 46 Batch 3701 Loss 0.7983\n",
      "Epoch 46 Batch 3801 Loss 0.9270\n",
      "Epoch 46 Batch 3901 Loss 1.2079\n",
      "Epoch 46 Batch 4001 Loss 1.3078\n",
      "Epoch 46 Batch 4101 Loss 1.3327\n",
      "Epoch 46 Batch 4201 Loss 0.9459\n",
      "Epoch 46 Batch 4301 Loss 1.1057\n",
      "Epoch 46 Batch 4401 Loss 1.1379\n",
      "Epoch 46 Batch 4501 Loss 1.1413\n",
      "Epoch 46 Batch 4601 Loss 1.2814\n",
      "Epoch 46 Batch 4701 Loss 1.3220\n",
      "Epoch 46 Batch 4801 Loss 1.0419\n",
      "Epoch 46 Batch 4901 Loss 1.3423\n",
      "Epoch 46 Batch 5001 Loss 1.0482\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46 Loss 1.1249\n",
      "Time taken for 1 epoch 7679.612992763519 sec\n",
      "Best epoch so far:  46  smallest loss: tf.Tensor(1.1248628, shape=(), dtype=float32)\n",
      "Checkpoint saved at: /content/gdrive/My Drive/chatbot_collab/model_checkpoints/ckpt-46\n",
      "\n",
      "\n",
      "\n",
      "Ques:  Hello \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  how are you? \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's up? \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  What's your name? \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  I am tired. \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ques:  You're so smart. \n",
      "Ans :  is the matter with you \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Epoch 47 Batch 1 Loss 1.1477\n"
     ]
    }
   ],
   "source": [
    "# TRAINING STEP\n",
    "\n",
    "ep = 1\n",
    "checkpoint.restore(manager.latest_checkpoint)    # Restoring the Checkpoints\n",
    "if manager.latest_checkpoint:\n",
    "  print(\"Restored from {}\".format(manager.latest_checkpoint))\n",
    "  print(\"Restoring after {} checkpoint\".format(manager.latest_checkpoint.split('-')[-1]))\n",
    "  print(\"\\n\\n\")\n",
    "  ep = int(manager.latest_checkpoint.split('-')[-1]) + 1\n",
    "                    \n",
    "else:\n",
    "  print(\"Initializing from scratch.\")\n",
    "\n",
    "for epoch in range(ep,epochs+1):\n",
    "  start = time.time()\n",
    "  total_loss = 0\n",
    "\n",
    "  for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):\n",
    "    batch_loss = train_step(inp, targ)\n",
    "    total_loss += batch_loss\n",
    "\n",
    "    if batch % 100 == 0:\n",
    "      print('Epoch {} Batch {} Loss {:.4f}'.format(epoch,\n",
    "                                                   batch + 1,\n",
    "                                                   batch_loss.numpy()))\n",
    "      \n",
    "  \n",
    "  epoch_loss = total_loss / steps_per_epoch\n",
    "  history['loss'].append(epoch_loss)\n",
    "  if epoch_loss < smallest_loss:\n",
    "        smallest_loss = epoch_loss\n",
    "        best_ep = epoch\n",
    "        \n",
    "  plot_history()\n",
    "  save_path = manager.save()\n",
    "\n",
    "  print('Epoch {} Loss {:.4f}'.format(epoch, total_loss / steps_per_epoch))\n",
    "  print('Time taken for 1 epoch {} sec'.format(time.time() - start))\n",
    "  print('Best epoch so far: ',best_ep,' smallest loss:',smallest_loss) \n",
    "  print(\"Checkpoint saved at: {}\".format(save_path))\n",
    "  print('\\n\\n')\n",
    "  sents = [\"Hello\",\"how are you?\",\"What's up?\",\"What's your name?\",\"I am tired.\",\"You're so smart.\"]\n",
    "  for s in sents:\n",
    "    print('Ques: ',s,'\\nAns :  ', end = '')\n",
    "    answering_ques(s,t,1)\n",
    "  \n",
    "  print('\\n\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 87095,
     "status": "ok",
     "timestamp": 1597239002939,
     "user": {
      "displayName": "Anuvrat Karanwal",
      "photoUrl": "",
      "userId": "00023598839116466965"
     },
     "user_tz": -330
    },
    "id": "Ux9njEjyKKiM",
    "outputId": "d3b4598e-8375-4745-9241-4949fecb3c43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Me  : hello...\n",
      "Ans : is the matter with you \n",
      "\n",
      "\n",
      "Me  : yes, it matters with me\n",
      "Ans : is the matter with you \n",
      "\n",
      "\n",
      "Me  : Shut up you fool... n just go to hell u rascal\n",
      "Ans : is the matter with you \n",
      "\n",
      "\n",
      "Me  : bye\n",
      "Ans : Are you sure you want to QUIT? If YES enter 'y' Else press any other key _____\n",
      "Me  : y\n",
      "Ans : Bye, Have a Nice Day...\n"
     ]
    }
   ],
   "source": [
    "# CHAT WITH THE BOT\n",
    "\n",
    "checkpoint.restore(manager.latest_checkpoint)\n",
    "while(True):\n",
    "  print('Me  : ',end = '')\n",
    "  ques = input()\n",
    "\n",
    "  if(ques.lower() == 'bye'):\n",
    "    print(\"Ans : Are you sure you want to QUIT? If YES enter 'y' Else press any other key _____\")\n",
    "    print(\"Me  : \",end = '')\n",
    "    ans = input()\n",
    "    \n",
    "    if(ans.lower() == 'y'):\n",
    "      print(\"Ans : Bye, Have a Nice Day...\")\n",
    "      break\n",
    "    \n",
    "    else:\n",
    "      answering_ques(ques,t,0)\n",
    "      print('\\n')\n",
    "      continue\n",
    "  \n",
    "  print('Ans : ', end = '')\n",
    "  answering_ques(ques,t,0)\n",
    "  print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CQxg8NpiKKcF"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "chatbot_code.ipynb",
   "provenance": []
  },
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}