Text_Summarizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "odtDOXFPqD9R"
   },
   "source": [
    "# Text Summarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wkdmx_wi4GYB"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.model_selection import train_test_split \n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.preprocessing import text, sequence\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "U7NttUam4Wjy",
    "outputId": "df4245a7-8616-4399-a122-6cee3c19b7e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "OaxOIKySpYSJ",
    "outputId": "99367edf-1ab6-4ab7-90ce-e44da3291b0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/Text Summarizer\n"
     ]
    }
   ],
   "source": [
    "cd drive/My\\ Drive/Text\\ Summarizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AvraIvkIqPfk"
   },
   "source": [
    "# Preparing and Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m7MHxmYnlcIn"
   },
   "outputs": [],
   "source": [
    "# !wget --header=\"Host: storage.googleapis.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\" --header=\"Accept-Language: en-IN,en-GB;q=0.9,en-US;q=0.8,en;q=0.7\" --header=\"Referer: https://www.kaggle.com/\" \"https://storage.googleapis.com/kaggle-data-sets/18%2F2157%2Fcompressed%2FReviews.csv.zip?GoogleAccessId=gcp-kaggle-com@kaggle-161607.iam.gserviceaccount.com&Expires=1592829433&Signature=Rcvu965Hen%2FP1g8g%2FXWoV3SQgI4FONM6EAig43QKnI8IVcOfX7vEr9mBZxez9S%2FIuE3cCixcweoyusegUYBvofd6Wnwooxft5z6G6yYAWtBnoUU7x7rH3%2BvEtDfmu4t8hMH7R%2Ftv9NML4BT9TylAmBq7hGWGDTnKn6nOdL8dWxugdV1X1zoJ4hizkuAE13CrPZPhMFDucTkmxOswb7Er9yyzCmkr8yzXzjm35eYON%2FWlhJ%2FjfnImIBJvQyWJ%2BLAer5NWepR5cX7afq5FRklDEQLs3JjA4SSxNdGZ%2BncUx824LCnQXjUZCPcGhFmlUkX9LNa6yOAj%2B50tQ1ZUjnnfdg%3D%3D\" -c -O '18_2157_compressed_Reviews.csv.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BXi1x6bUlzeT"
   },
   "outputs": [],
   "source": [
    "# import zipfile\n",
    "# with zipfile.ZipFile('Reviews.csv.zip', 'r') as zip_ref:\n",
    "#     zip_ref.extractall(\"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xupvYcR57v_R"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"Reviews.csv\",nrows=200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "Wo9OMuRW967i",
    "outputId": "0da3f2c5-2fe1-496b-d80f-a61f46911ff0"
   },
   "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>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40541</th>\n",
       "      <td>40542</td>\n",
       "      <td>B00020HHRW</td>\n",
       "      <td>A16QUBP9J1KSDM</td>\n",
       "      <td>Martha G. Huebert \"retired teacher\"</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1195171200</td>\n",
       "      <td>tea OK, postage way too high</td>\n",
       "      <td>The sage tea is OK, but I was surprised that f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77518</th>\n",
       "      <td>77519</td>\n",
       "      <td>B002EYBB3A</td>\n",
       "      <td>A1J5HIF41ENSMZ</td>\n",
       "      <td>Adam Wood</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>1276300800</td>\n",
       "      <td>High quality tuna that's easier on my conscience</td>\n",
       "      <td>I originally tried Wild Planet tuna back in 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>7683</td>\n",
       "      <td>B001E5DZTS</td>\n",
       "      <td>A3NE8L23J9GHC1</td>\n",
       "      <td>M. L. Lopez</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1190592000</td>\n",
       "      <td>My daughter loves this milk!</td>\n",
       "      <td>My 14 month old daughter can not tolerate cows...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185397</th>\n",
       "      <td>185398</td>\n",
       "      <td>B001AHFVHO</td>\n",
       "      <td>A3JV071KO43Q5X</td>\n",
       "      <td>Jeff Pittman \"Jeff\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1220140800</td>\n",
       "      <td>Somewhere between a Frito and a tortilla chip</td>\n",
       "      <td>...but I guess a little more like a tortilla c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66466</th>\n",
       "      <td>66467</td>\n",
       "      <td>B002HQFN9I</td>\n",
       "      <td>A1XV49PJK0AP7C</td>\n",
       "      <td>Bizobb</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1321920000</td>\n",
       "      <td>My favorite!</td>\n",
       "      <td>Level 4 is what used to be their \"Breakfast Bl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  ...                                               Text\n",
       "40541    40542  ...  The sage tea is OK, but I was surprised that f...\n",
       "77518    77519  ...  I originally tried Wild Planet tuna back in 20...\n",
       "7682      7683  ...  My 14 month old daughter can not tolerate cows...\n",
       "185397  185398  ...  ...but I guess a little more like a tortilla c...\n",
       "66466    66467  ...  Level 4 is what used to be their \"Breakfast Bl...\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L6zlayaw7wYw"
   },
   "outputs": [],
   "source": [
    "data.drop_duplicates(subset=['Text'],inplace=True)#dropping duplicates\n",
    "data.dropna(axis=0,inplace=True)#dropping na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "28RBr5cS-Vq5"
   },
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SgL8CVbV7wdX"
   },
   "outputs": [],
   "source": [
    "contraction_mapping = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\",\n",
    "                           \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\",\n",
    "                           \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",\n",
    "                           \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\",\n",
    "                           \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n",
    "                           \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\",\n",
    "                           \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\",\n",
    "                           \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\",\n",
    "                           \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\",\n",
    "                           \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n",
    "                           \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\",\n",
    "                           \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\",\n",
    "                           \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\",\n",
    "                           \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\",\n",
    "                           \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\",\n",
    "                           \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",\n",
    "                           \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\",\n",
    "                           \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n",
    "                           \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\",\n",
    "                           \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\",\n",
    "                           \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\n",
    "                           \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\",\n",
    "                           \"you're\": \"you are\", \"you've\": \"you have\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "Ro7GIIrrjUyT",
    "outputId": "62d7baf2-815e-4b22-955a-8ce1db014306"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk \n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vd7PAAET7wbm"
   },
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english')) \n",
    "\n",
    "def text_cleaner(text,num):\n",
    "    newString = text.lower()\n",
    "    newString = BeautifulSoup(newString, \"lxml\").text\n",
    "    newString = re.sub(r'\\([^)]*\\)', '', newString)\n",
    "    newString = re.sub('\"','', newString)\n",
    "    newString = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in newString.split(\" \")])    \n",
    "    newString = re.sub(r\"'s\\b\",\"\",newString)\n",
    "    newString = re.sub(\"[^a-zA-Z]\", \" \", newString) \n",
    "    newString = re.sub('[m]{2,}', 'mm', newString)\n",
    "    if(num==0):\n",
    "        tokens = [w for w in newString.split() if not w in stop_words]\n",
    "    else:\n",
    "        tokens=newString.split()\n",
    "    long_words=[]\n",
    "    for i in tokens:                                               \n",
    "        long_words.append(i)   \n",
    "    return \"startseq \" + (\" \".join(long_words)).strip() + \" endseq\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8GmwpStn-oml"
   },
   "outputs": [],
   "source": [
    "#call the function\n",
    "cleaned_text = []\n",
    "for t in data['Text']:\n",
    "    cleaned_text.append(text_cleaner(t,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "oaeZ7l7r_UPc",
    "outputId": "a2ea885c-d7d3-486f-8c23-ae75810cd730"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['startseq i have bought several of the vitality canned dog food products and have found them all to be of good quality the product looks more like a stew than a processed meat and it smells better my labrador is finicky and she appreciates this product better than most endseq',\n",
       " 'startseq product arrived labeled as jumbo salted peanuts the peanuts were actually small sized unsalted not sure if this was an error or if the vendor intended to represent the product as jumbo endseq',\n",
       " 'startseq this is a confection that has been around a few centuries it is a light pillowy citrus gelatin with nuts in this case filberts and it is cut into tiny squares and then liberally coated with powdered sugar and it is a tiny mouthful of heaven not too chewy and very flavorful i highly recommend this yummy treat if you are familiar with the story of c s lewis the lion the witch and the wardrobe this is the treat that seduces edmund into selling out his brother and sisters to the witch endseq',\n",
       " 'startseq if you are looking for the secret ingredient in robitussin i believe i have found it i got this in addition to the root beer extract i ordered and made some cherry soda the flavor is very medicinal endseq',\n",
       " 'startseq great taffy at a great price there was a wide assortment of yummy taffy delivery was very quick if your a taffy lover this is a deal endseq']"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_text[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "PL9M3HiR_US6",
    "outputId": "1a50b4ad-3deb-47cf-eb8b-f580d8142c4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:336: UserWarning: \"http://www.amazon.com/gp/product/b007i7yygy/ref=cm_cr_rev_prod_title\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:273: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:336: UserWarning: \"http://www.amazon.com/gp/product/b000v9lq30/ref=cm_cr_rev_prod_title\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:336: UserWarning: \"http://www.amazon.com/gp/product/b001eq58fq/ref=cm_cr_rev_prod_title\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n"
     ]
    }
   ],
   "source": [
    "#call the function\n",
    "cleaned_summary = []\n",
    "for t in data['Summary']:\n",
    "    cleaned_summary.append(text_cleaner(t,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "3L5_1fho_UkJ",
    "outputId": "ec78b26f-395c-4c67-c5b8-934beb00be96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['startseq good quality dog food endseq',\n",
       " 'startseq not as advertised endseq',\n",
       " 'startseq delight says it all endseq',\n",
       " 'startseq cough medicine endseq',\n",
       " 'startseq great taffy endseq']"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_summary[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gAMpzV0en_FX"
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'text' : cleaned_text, 'summary' : cleaned_summary})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qPcVh4vjoYKn"
   },
   "outputs": [],
   "source": [
    "data.replace('', np.nan, inplace=True)\n",
    "data.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "c6ENjiBUG8fO",
    "outputId": "8dfcd38d-d599-439c-a272-9d347fdbff74"
   },
   "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>text</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>startseq i have bought several of the vitality...</td>\n",
       "      <td>startseq good quality dog food endseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>startseq product arrived labeled as jumbo salt...</td>\n",
       "      <td>startseq not as advertised endseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>startseq this is a confection that has been ar...</td>\n",
       "      <td>startseq delight says it all endseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>startseq if you are looking for the secret ing...</td>\n",
       "      <td>startseq cough medicine endseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>startseq great taffy at a great price there wa...</td>\n",
       "      <td>startseq great taffy endseq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text                                summary\n",
       "0  startseq i have bought several of the vitality...  startseq good quality dog food endseq\n",
       "1  startseq product arrived labeled as jumbo salt...      startseq not as advertised endseq\n",
       "2  startseq this is a confection that has been ar...    startseq delight says it all endseq\n",
       "3  startseq if you are looking for the secret ing...         startseq cough medicine endseq\n",
       "4  startseq great taffy at a great price there wa...            startseq great taffy endseq"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vy9_-ChZInUC",
    "outputId": "7e20ce0f-cecf-4ef6-8045-7890b6333191"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9814535047962956\n"
     ]
    }
   ],
   "source": [
    "cnt=0\n",
    "for i in data['text']:\n",
    "    if(len(i.split())<=300):\n",
    "        cnt=cnt+1\n",
    "print(cnt/len(data['text']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "g--b2KfZAPjF",
    "outputId": "faeb2db1-6740-4090-f7d5-7f413b9e41c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9989437095446897\n"
     ]
    }
   ],
   "source": [
    "cnt=0\n",
    "for i in data['summary']:\n",
    "    if(len(i.split())<=20):\n",
    "        cnt=cnt+1\n",
    "print(cnt/len(data['summary']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "huTKeZmD_UZi"
   },
   "outputs": [],
   "source": [
    "max_text_len=300\n",
    "max_summary_len=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k9czGE8VsjtV"
   },
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "08NtB1CiBge4"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data['text'], data['summary'],  train_size=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Za8Qua-xsmQG"
   },
   "source": [
    "# Tokenization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "svL71qyornui"
   },
   "outputs": [],
   "source": [
    "def pad_sequence(t, padding_var = 20):\n",
    "    t = sequence.pad_sequences(t, maxlen=padding_var, padding='post') \n",
    "    return t\n",
    "\n",
    "def build_data(train, test, padding_var):\n",
    "  print(\"=\"*100)\n",
    "  print(\"Fitting the Tokenizer...\")\n",
    "  tokenizer = Tokenizer(lower=True)\n",
    "  tokenizer.fit_on_texts(train)\n",
    "  print(\"Done...\")\n",
    "  print('Found %s unique tokens.' % len(tokenizer.word_counts))\n",
    "  print(\"=\"*100)\n",
    "  print()\n",
    "  print(\"Processing Text to sequence process\")\n",
    "  train = tokenizer.texts_to_sequences(train)\n",
    "  test = tokenizer.texts_to_sequences(test)\n",
    "  print(\"Done\")\n",
    "  print(\"=\"*100)\n",
    "  print()\n",
    "  print(\"Starting Padding Process...\")  \n",
    "  train = pad_sequence(train, padding_var)\n",
    "  test = pad_sequence(test, padding_var)\n",
    "  print(\"Done\")\n",
    "  return train, test, tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Usiy1bodsv5P"
   },
   "source": [
    "## Text Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "dggBkZDJr3LJ",
    "outputId": "6889a6e8-daa6-493f-ec25-94cf35031025"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "Fitting the Tokenizer...\n",
      "Done...\n",
      "Found 67960 unique tokens.\n",
      "====================================================================================================\n",
      "\n",
      "Processing Text to sequence process\n",
      "Done\n",
      "====================================================================================================\n",
      "\n",
      "Starting Padding Process...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, tokenizer_X = build_data(X_train, X_test, max_text_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZOiU86x3ss3k"
   },
   "source": [
    "## Summary Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "-zcutmh7r9sN",
    "outputId": "2401845c-b44a-439d-e498-835871d551db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "Fitting the Tokenizer...\n",
      "Done...\n",
      "Found 18422 unique tokens.\n",
      "====================================================================================================\n",
      "\n",
      "Processing Text to sequence process\n",
      "Done\n",
      "====================================================================================================\n",
      "\n",
      "Starting Padding Process...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "Y_train, Y_test, tokenizer_Y = build_data(Y_train, Y_test, max_summary_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uCGGt1ypsdZT",
    "outputId": "bde92244-921e-43da-fe2c-5bee4fff677f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67960, 18423)"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_encoder_tokens = len(tokenizer_X.word_counts)\n",
    "num_decoder_tokens = len(tokenizer_Y.word_counts)+1\n",
    "num_encoder_tokens, num_decoder_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HUPqyHYAs4SJ"
   },
   "source": [
    "# Processing Embedding Matrix¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "colab_type": "code",
    "id": "CpAfvGhhs2Co",
    "outputId": "8ecadf73-0d31-4273-9be2-5bfb0f560ac6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting en_core_web_lg==2.2.5\n",
      "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9MB)\n",
      "\u001b[K     |████████████████████████████████| 827.9MB 1.2MB/s \n",
      "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.2)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.41.1)\n",
      "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (47.3.1)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.6.0)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.3)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.2)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.18.5)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.2)\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>=2.2.2->en_core_web_lg==2.2.5) (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>=2.2.2->en_core_web_lg==2.2.5) (2020.4.5.2)\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>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\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>=2.2.2->en_core_web_lg==2.2.5) (2.9)\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>=2.2.2->en_core_web_lg==2.2.5) (1.6.1)\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>=2.2.2->en_core_web_lg==2.2.5) (3.1.0)\n",
      "Building wheels for collected packages: en-core-web-lg\n",
      "  Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for en-core-web-lg: filename=en_core_web_lg-2.2.5-cp36-none-any.whl size=829180944 sha256=d7b5ff0d1f90d1c7a377b489364c4ada42047d093e37fa3738890c19d364e373\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-cgydbab7/wheels/2a/c1/a6/fc7a877b1efca9bc6a089d6f506f16d3868408f9ff89f8dbfc\n",
      "Successfully built en-core-web-lg\n",
      "Installing collected packages: en-core-web-lg\n",
      "Successfully installed en-core-web-lg-2.2.5\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('en_core_web_lg')\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download en_core_web_lg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mRCjEgE8s9qw"
   },
   "outputs": [],
   "source": [
    "import en_core_web_lg\n",
    "text_word_vectos = en_core_web_lg.load() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QNHMqjn5tAhO"
   },
   "outputs": [],
   "source": [
    "def build_embedding_matrix(tokenizer):\n",
    "    embedding_matrix = np.zeros((len(tokenizer.word_counts) + 1, 300))\n",
    "\n",
    "    for word, i in tokenizer.word_index.items():  \n",
    "        embedding_matrix[i] = text_word_vectos(word).vector\n",
    "    \n",
    "    return embedding_matrix \n",
    "embedding_matrix_enc = build_embedding_matrix(tokenizer_X)\n",
    "embedding_matrix_dec = build_embedding_matrix(tokenizer_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "u7lVaHT7uLHW",
    "outputId": "57de22e8-b516-4dc7-aa85-ea6f467366fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((67961, 300), (18423, 300))"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix_enc.shape, embedding_matrix_dec.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nTutoTghuRtZ"
   },
   "source": [
    "# Defining The Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w17cvcD7uStR"
   },
   "outputs": [],
   "source": [
    "from attention import AttentionLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lLEvIw6iuVWB"
   },
   "outputs": [],
   "source": [
    "def build_model(latent_dim=256):\n",
    "\n",
    "    embedding_enc = Embedding(*embedding_matrix_enc.shape, weights=[embedding_matrix_enc], trainable=False, mask_zero=True, name = \"Encoder_Embedding\")\n",
    "    embedding_dec = Embedding(*embedding_matrix_dec.shape, weights=[embedding_matrix_dec], trainable=False, mask_zero=True, name = \"Decoder_Embedding\")\n",
    "    \n",
    "    \"\"\" Encoder LSTM\"\"\" \n",
    "    encoder_inputs = Input(shape=(max_text_len,), name=\"encoder_input\")\n",
    "    enc_emb = embedding_enc(encoder_inputs) \n",
    "    enc_output, forward_h, forward_c, backward_h, backward_c = Bidirectional(LSTM(latent_dim, return_sequences=True, return_state=True, name = \"Encoder_biLSTM\"))(enc_emb)\n",
    "    state_h = Concatenate()([forward_h, backward_h])\n",
    "    state_c = Concatenate()([forward_c, backward_c])\n",
    "     \n",
    "    encoder_states = [state_h, state_c]\n",
    "    \n",
    "\n",
    "    \"\"\" Decoder LSTM \"\"\" \n",
    "    decoder_inputs = Input(shape=(max_summary_len,), name=\"decoder_input\")\n",
    "    dec_emb_layer = embedding_dec(decoder_inputs)\n",
    "    dec_lstm = LSTM(2*latent_dim, return_sequences=True, return_state=True, name = \"Decoder_LSTM\")\n",
    "    \n",
    "    decoder_outputs, state_h, state_c = dec_lstm(dec_emb_layer,initial_state=encoder_states)\n",
    "     \n",
    "    decoder_states = [state_h, state_c] \n",
    "     \n",
    "    \"\"\" Attention Layer \"\"\"\n",
    "    attn_layer = AttentionLayer(name='attention_layer')\n",
    "    attn_out, attn_states = attn_layer([enc_output, decoder_outputs])\n",
    "    \n",
    "    decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])\n",
    "    \n",
    "    dense = Dense(num_decoder_tokens, activation='softmax', name='softmax_layer')\n",
    "    dense_time = TimeDistributed(dense, name='time_distributed_layer')\n",
    "    decoder_pred = dense_time(decoder_concat_input)\n",
    "\n",
    "    \"\"\" Defining Full Model \"\"\"\n",
    "    model = Model(inputs=[encoder_inputs, decoder_inputs], outputs = decoder_pred) \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \"\"\" Defining Inference Model \"\"\" \n",
    "    \n",
    "    # Encoder Model\n",
    "     \n",
    "    encoder_model = Model(inputs = encoder_inputs, outputs = [enc_output, encoder_states])\n",
    "     \n",
    "    \"\"\" Decoder Model \"\"\"\n",
    "     \n",
    "    decoder_states_inputs = [Input(shape=(2*latent_dim,), name = \"Decoder_inp_1\"), Input(shape=(2*latent_dim,), name = \"Decoder_inp_2\")]\n",
    "    enc_output = Input(shape=(max_text_len, 2*latent_dim), name = \"encoder_output\")\n",
    "\n",
    "    # To predict the next word in the sequence, set the initial states to the states from the previous time step\n",
    "    decoder_outputs, state_h, state_c = dec_lstm(dec_emb_layer, initial_state=decoder_states_inputs)\n",
    "    decoder_states = [state_h, state_c]\n",
    "    \n",
    "    attn_out, attn_states = attn_layer([enc_output, decoder_outputs])\n",
    "    decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])\n",
    "    \n",
    "    decoder_pred = dense_time(decoder_concat_input)     \n",
    "    decoder_model = Model( [enc_output, decoder_states_inputs, decoder_inputs], [decoder_pred, attn_states, decoder_states])    \n",
    "    \n",
    "    \n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy')\n",
    "\n",
    "    return model, encoder_model, decoder_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N7KQ3zykuXM4"
   },
   "outputs": [],
   "source": [
    "model, encoder_model, decoder_model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "colab_type": "code",
    "id": "fyXFbpTcvSr0",
    "outputId": "710cb37d-8c95-472f-cde2-ea3127344c80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Architecture\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Model Architecture\")\n",
    "plot_model(model, to_file='model_architectures/model.png', show_shapes=True, show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "jg1KPX8MvgEF",
    "outputId": "453799dd-a946-4c06-a5d7-866b342c82e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Model Architecture\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Encoder Model Architecture\")\n",
    "plot_model(encoder_model, to_file='model_architectures/encoder_model.png', show_shapes=True, show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 591
    },
    "colab_type": "code",
    "id": "XefMjwExvgHp",
    "outputId": "8d0e11f2-8d62-42df-b1da-086153ea5415"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Model Architecture\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Decoder Model Architecture\")\n",
    "plot_model(decoder_model, to_file='model_architectures/decoder_model.png', show_shapes=True, show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "75BE_mU9vupJ"
   },
   "outputs": [],
   "source": [
    "def generate_batch(X, y, batch_size):\n",
    "    ''' Generate a batch of data '''\n",
    "    while True:\n",
    "        for j in range(0, len(X), batch_size):\n",
    "            encoder_input_data = np.array(X[j:j+batch_size])\n",
    "            decoder_input_data = np.array(y[j:j+batch_size])\n",
    "            decoder_target_data = np.zeros((encoder_input_data.shape[0], max_summary_len, num_decoder_tokens),dtype='float32')\n",
    "            for i, target_text in enumerate(y[j:j+batch_size]):\n",
    "                for t, index in enumerate(target_text):\n",
    "                    if t>0:\n",
    "                        # decoder target sequence (one hot encoded)\n",
    "                        # does not include the START_ token\n",
    "                        decoder_target_data[i, t - 1, index] = 1.\n",
    "           \n",
    "            yield([encoder_input_data, decoder_input_data], decoder_target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9HqQmOTqv9fa",
    "outputId": "65952300-beb7-4526-f581-ad27d9e0a1b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130267, 32567)"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_samples = len(X_train)\n",
    "val_samples = len(X_test)\n",
    "batch_size = 64\n",
    "epochs = 5\n",
    "train_samples, val_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D1b7hPaxwWN-"
   },
   "outputs": [],
   "source": [
    "filepath=\"model/weights-improvement-{epoch:02d}.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "JyQxr-t8wKz5",
    "outputId": "4e8e943e-111e-4867-a703-6e1812e900e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "2035/2035 [==============================] - 1712s 841ms/step - loss: 0.6750 - val_loss: 0.9622\n",
      "Epoch 2/5\n",
      "2035/2035 [==============================] - 1710s 841ms/step - loss: 0.5958 - val_loss: 0.9906\n",
      "Epoch 3/5\n",
      "2035/2035 [==============================] - 1715s 843ms/step - loss: 0.5302 - val_loss: 1.0241\n",
      "Epoch 4/5\n",
      "2035/2035 [==============================] - 1711s 841ms/step - loss: 0.4767 - val_loss: 1.0573\n",
      "Epoch 5/5\n",
      "2035/2035 [==============================] - 1732s 851ms/step - loss: 0.4324 - val_loss: 1.0939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f6d94951940>"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(generator = generate_batch(X_train, Y_train, batch_size = batch_size),\n",
    "  steps_per_epoch = len(X_train)//batch_size,\n",
    "  epochs=epochs,\n",
    "  validation_data = generate_batch(X_test, Y_test, batch_size = batch_size),\n",
    "  validation_steps = len(X_test)//batch_size , callbacks = callbacks_list\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KfaibdiLwYhP"
   },
   "outputs": [],
   "source": [
    "model.save('model/final_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G7jvotqnxBFO"
   },
   "outputs": [],
   "source": [
    "# saving\n",
    "import pickle\n",
    "with open('model/tokenizer_X.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer_X, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "# saving\n",
    "with open('model/tokenizer_Y.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer_Y, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9DZO_LwlqXza"
   },
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NE2D-7bpqV89"
   },
   "outputs": [],
   "source": [
    "# saving\n",
    "import pickle\n",
    "with open('model/tokenizer_X.pickle', 'rb') as handle:\n",
    "    tokenizer_X = pickle.load(handle)\n",
    "# saving\n",
    "with open('model/tokenizer_Y.pickle', 'rb') as handle:\n",
    "    tokenizer_Y = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9VkXemlrxMpf"
   },
   "outputs": [],
   "source": [
    "def sents2sequences(tokenizer, sentences, pad_length=None, padding_type='post'):\n",
    "    encoded_text = tokenizer.texts_to_sequences(sentences)\n",
    "    preproc_text = pad_sequences(encoded_text, padding=padding_type, maxlen=pad_length)\n",
    "    return preproc_text\n",
    "\n",
    "stop_words = set(stopwords.words('english')) \n",
    "def text_cleaner(text,num):\n",
    "    newString = text.lower()\n",
    "    newString = BeautifulSoup(newString, \"lxml\").text\n",
    "    newString = re.sub(r'\\([^)]*\\)', '', newString)\n",
    "    newString = re.sub('\"','', newString)\n",
    "    newString = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in newString.split(\" \")])    \n",
    "    newString = re.sub(r\"'s\\b\",\"\",newString)\n",
    "    newString = re.sub(\"[^a-zA-Z]\", \" \", newString) \n",
    "    newString = re.sub('[m]{2,}', 'mm', newString)\n",
    "    if(num==0):\n",
    "        tokens = [w for w in newString.split() if not w in stop_words]\n",
    "    else:\n",
    "        tokens=newString.split()\n",
    "    long_words=[]\n",
    "    for i in tokens:                                               \n",
    "        long_words.append(i)   \n",
    "    return \"startseq \" + (\" \".join(long_words)).strip() + \" endseq\"\n",
    "\n",
    "def decode_sequence(text, tokenizer_text = tokenizer_X, tokenizer_Y=tokenizer_Y):\n",
    "    # Text to sequence\n",
    "    #text = text_cleaner(text,1)\n",
    "    #print(text)\n",
    "    text = pad_sequence(tokenizer_text.texts_to_sequences([text]), 300)\n",
    "\n",
    "    # Encode the input as state vectors. \n",
    "    enc_output, encoder_states = encoder_model.predict(text) \n",
    "    decoder_states = encoder_states\n",
    "\n",
    "    # Generate empty target sequence of length 1.\n",
    "    target_seq = np.zeros((1,1))\n",
    "\n",
    "    # Populate the first character of target sequence with the start character.\n",
    "    target_seq[0, 0] = tokenizer_Y.word_index['startseq']\n",
    "     \n",
    "    attention_weights = []\n",
    "    stop_condition = False\n",
    "    decoded_sentence = ''\n",
    "    \n",
    "    while True:\n",
    "        decoder_pred, attn_states, decoder_states = decoder_model.predict([enc_output, decoder_states, target_seq])\n",
    "\n",
    "        dec_ind = np.argmax(decoder_pred, axis=-1)[0, 0]\n",
    "        if dec_ind == tokenizer_Y.word_index['endseq'] or len(decoded_sentence.split()) > 20:\n",
    "            break \n",
    "             \n",
    "        target_seq = np.zeros((1,1))\n",
    "        target_seq[0, 0] = dec_ind\n",
    "        attention_weights.append((dec_ind, attn_states))\n",
    "        decoded_sentence += tokenizer_Y.index_word[dec_ind] + ' '\n",
    "\n",
    "    return decoded_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "3DaGBV_2xed2",
    "outputId": "859585f6-e17e-4741-fa1a-a32d3b0bddb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: i have bought several of the vitality canned dog food products and have found them all to be of good quality the product looks more like a stew than a processed meat and it smells better my labrador is finicky and she appreciates this product better than most\n",
      "\n",
      "====================================================================================================\n",
      "Acutal Summary: good quality dog food\n",
      "\n",
      "====================================================================================================\n",
      "Text: good quality dog food \n"
     ]
    }
   ],
   "source": [
    "index = 0\n",
    "print(\"Text: {}\".format(data.iloc[index]['text'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Acutal Summary: {}\".format(data.iloc[index]['summary'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Text: {}\".format(decode_sequence(data.iloc[index]['text'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "MbkI3DPXxcv0",
    "outputId": "0351d34c-94f3-49ec-8143-b8f9464fd2be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: product arrived labeled as jumbo salted peanuts the peanuts were actually small sized unsalted not sure if this was an error or if the vendor intended to represent the product as jumbo\n",
      "\n",
      "====================================================================================================\n",
      "Acutal Summary: not as advertised\n",
      "\n",
      "====================================================================================================\n",
      "Text: warning about roasted peanuts \n"
     ]
    }
   ],
   "source": [
    "index = 1\n",
    "print(\"Text: {}\".format(data.iloc[index]['text'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Acutal Summary: {}\".format(data.iloc[index]['summary'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Text: {}\".format(decode_sequence(data.iloc[index]['text'][9:-7])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "r0oKbdZO2n2s",
    "outputId": "a4158296-5ba2-422f-8dfa-0a04c34edc8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: this is a confection that has been around a few centuries it is a light pillowy citrus gelatin with nuts in this case filberts and it is cut into tiny squares and then liberally coated with powdered sugar and it is a tiny mouthful of heaven not too chewy and very flavorful i highly recommend this yummy treat if you are familiar with the story of c s lewis the lion the witch and the wardrobe this is the treat that seduces edmund into selling out his brother and sisters to the witch\n",
      "\n",
      "====================================================================================================\n",
      "Acutal Summary: delight says it all\n",
      "\n",
      "====================================================================================================\n",
      "Text: chewy delight \n"
     ]
    }
   ],
   "source": [
    "index = 2\n",
    "print(\"Text: {}\".format(data.iloc[index]['text'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Acutal Summary: {}\".format(data.iloc[index]['summary'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Text: {}\".format(decode_sequence(data.iloc[index]['text'][9:-7])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "4abGDhnL2nw0",
    "outputId": "58f19da6-de0b-47d7-8dc1-7985504eda21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: if you are looking for the secret ingredient in robitussin i believe i have found it i got this in addition to the root beer extract i ordered and made some cherry soda the flavor is very medicinal\n",
      "\n",
      "====================================================================================================\n",
      "Acutal Summary: cough medicine\n",
      "\n",
      "====================================================================================================\n",
      "Text: cough medicine\n"
     ]
    }
   ],
   "source": [
    "index = 3\n",
    "print(\"Text: {}\".format(data.iloc[index]['text'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Acutal Summary: {}\".format(data.iloc[index]['summary'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Text: {}\".format(\"decode_sequence(data.iloc[index]['text'][9:-7])\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "lWVPgIrB2nuv",
    "outputId": "1c4b8780-7548-4b00-8240-4d4bca4ffbeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: great taffy at a great price there was a wide assortment of yummy taffy delivery was very quick if your a taffy lover this is a deal\n",
      "\n",
      "====================================================================================================\n",
      "Acutal Summary: great taffy\n",
      "\n",
      "====================================================================================================\n",
      "Text: great taffy \n"
     ]
    }
   ],
   "source": [
    "index = 4\n",
    "print(\"Text: {}\".format(data.iloc[index]['text'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Acutal Summary: {}\".format(data.iloc[index]['summary'][9:-7]))\n",
    "print()\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(\"Text: {}\".format(decode_sequence(data.iloc[index]['text'][9:-7])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5Zygr6uUJzkQ"
   },
   "outputs": [],
   "source": []
  }
 ],
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