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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/jakansha2001/CoVShorts-News-Summarization-Application-Based-on-Deep-NLP-Transformers-for-SARS-CoV-2/blob/master/5ModelWebsite_of_News_Article_Text_Summariser_App.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HB7vdfJdutO5"
      },
      "source": [
        "# Text Summariser Application"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgQ410kwqzkG"
      },
      "source": [
        "import os\n",
        "os.mkdir('templates')\n",
        "os.mkdir('static')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "e2dCtKQZsyZG",
        "outputId": "2624c308-5171-4ab4-9ad5-29a212c79f79"
      },
      "source": [
        "!pip install flask-ngrok"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting flask-ngrok\n",
            "  Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)\n",
            "Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.7/dist-packages (from flask-ngrok) (1.1.4)\n",
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            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (1.0.1)\n",
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            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=0.8->flask-ngrok) (2.0.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (3.0.4)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyjenam5nbSY",
        "outputId": "ab4746db-7b12-47a3-fa17-20fd15a47264"
      },
      "source": [
        "!pip install bert-extractive-summarizer"
      ],
      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Collecting bert-extractive-summarizer\n",
            "  Downloading bert_extractive_summarizer-0.8.1-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from bert-extractive-summarizer) (0.22.2.post1)\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (from bert-extractive-summarizer) (2.2.4)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.10.2-py3-none-any.whl (2.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.8 MB 6.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bert-extractive-summarizer) (1.0.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bert-extractive-summarizer) (1.19.5)\n",
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            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy->bert-extractive-summarizer) (1.0.5)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy->bert-extractive-summarizer) (57.4.0)\n",
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            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy->bert-extractive-summarizer) (4.6.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy->bert-extractive-summarizer) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy->bert-extractive-summarizer) (3.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (1.24.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (21.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (3.0.12)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (2019.12.20)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 45.4 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 29.0 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 67.2 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub>=0.0.12\n",
            "  Downloading huggingface_hub-0.0.16-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 5.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers->bert-extractive-summarizer) (2.4.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers->bert-extractive-summarizer) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers->bert-extractive-summarizer) (1.15.0)\n",
            "Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers, bert-extractive-summarizer\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed bert-extractive-summarizer-0.8.1 huggingface-hub-0.0.16 pyyaml-5.4.1 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Li7CnrmpndT9"
      },
      "source": [
        "#!pip install transformers==4.5.0 # common errors : make sure that you are installing the latest version of the Transformers library\n",
        "#!pip install transformers==2.2.0\n",
        "#!pip install sentencepiece"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhrAENh0nhHT",
        "outputId": "02f4d6b8-7caa-40a8-d778-a17c0bf4e97e"
      },
      "source": [
        "# !pip install spacy\n",
        "!pip install spacy"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (57.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.62.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.19.5)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (4.6.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.5.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/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/python3.7/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/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2021.5.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKF8ouKeOKlA"
      },
      "source": [
        "creating index.html file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mx-b7VHxtlKH"
      },
      "source": [
        "indextext='''\n",
        "<!DOCTYPE html>\n",
        "<html>\n",
        "  <head>\n",
        "    <meta charset=\"utf-8\" />\n",
        "    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n",
        "    <link\n",
        "      rel=\"stylesheet\"\n",
        "      href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\"\n",
        "      integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\"\n",
        "      crossorigin=\"anonymous\"\n",
        "    />\n",
        "    <link\n",
        "      rel=\"stylesheet\"\n",
        "      href=\"https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\"\n",
        "      crossorigin=\"anonymous\"\n",
        "    />\n",
        " \n",
        "    <title>NEWS ARTICLE TEXT SUMMARISER</title>\n",
        "  </head>\n",
        "  <body>\n",
        "    <h1>{% block title %}Home{% endblock %}</h1>\n",
        "    <nav class=\"navbar navbar-expand-lg navbar-dark bg-dark\">\n",
        "      <button\n",
        "        class=\"navbar-toggler\"\n",
        "        type=\"button\"\n",
        "        data-toggle=\"collapse\"\n",
        "        data-target=\"#navbar\"\n",
        "      >\n",
        "        <span class=\"navbar-toggler-icon\"></span>\n",
        "      </button>\n",
        "      <div class=\"collapse navbar-collapse\" id=\"navbar\">\n",
        "        <div class=\"navbar-nav\">\n",
        "          <a class=\"nav-item nav-link\" id=\"home\" href=\"/\">Choose Model</a>\n",
        "          <a class=\"nav-item nav-link\" id=\"input\" href=\"/enter-news-article\">Input Article</a>\n",
        "          <a class=\"nav-item nav-link\" id=\"summary\" href=\"/summary\">Summary</a>\n",
        "        </div>\n",
        "      </div>\n",
        "    </nav>\n",
        " \n",
        "    <div class=\"container\">{% block title2 %} {% endblock %} </div>\n",
        " \n",
        "    <div class=\"container\">{% block content %} {% endblock %}</div>\n",
        "    <script\n",
        "      src=\"https://code.jquery.com/jquery-3.2.1.slim.min.js\"\n",
        "      integrity=\"sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN\"\n",
        "      crossorigin=\"anonymous\"\n",
        "    ></script>\n",
        "    <script\n",
        "      src=\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js\"\n",
        "      integrity=\"sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q\"\n",
        "      crossorigin=\"anonymous\"\n",
        "    ></script>\n",
        "    <script\n",
        "      src=\"https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js\"\n",
        "      integrity=\"sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl\"\n",
        "      crossorigin=\"anonymous\"\n",
        "    ></script>\n",
        " \n",
        "  </body>\n",
        "</html>\n",
        "'''\n",
        "file1= open(\"templates/index.html\",\"w\")\n",
        "file1.write(indextext)\n",
        "file1.close()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IAygx_rrPUF3"
      },
      "source": [
        "creating home.html file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjwSZaqjObip"
      },
      "source": [
        " \n",
        "hometext='''\n",
        "{% extends \"index.html\" %} {% block title %}NEWS ARTICLE TEXT SUMMARISER-Choose Model{% endblock %} {% block content\n",
        "%}\n",
        "<h1> Welcome! Please select the language model! </h1>\n",
        "<form method=\"POST\">\n",
        "  <h3 align=\"center\">Choose from the following:</h3>\n",
        "   <br />\n",
        "  <input type=\"radio\" id=\"GPT-2\" name=\"mname\" value=\"GPT-2\">\n",
        "  <label for=\"GPT-2\">GPT-2</label><br>\n",
        "  <input type=\"radio\" id=\"BERT\" name=\"mname\" value=\"BERT\">\n",
        "  <label for=\"BERT\">BERT</label><br>\n",
        "  <input type=\"radio\" id=\"XLNet\" name=\"mname\" value=\"XLNet\">\n",
        "  <label for=\"XLNet\">XLNet</label><br>\n",
        "  <input type=\"radio\" id=\"BART\" name=\"mname\" value=\"BART\">\n",
        "  <label for=\"BART\">BART</label><br>\n",
        "  <input type=\"radio\" id=\"T5\" name=\"mname\" value=\"T5\">\n",
        "  <label for=\"T5\">T5</label>\n",
        "  <br />\n",
        "   <input type=\"submit\" name=\"S1\" value=\"Submit\">\n",
        "</form>\n",
        "{% endblock %}\n",
        "'''\n",
        "file1=open(\"templates/home.html\",\"w\")\n",
        "file1.write(hometext)\n",
        "file1.close()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZwVk5lBOolq"
      },
      "source": [
        "creating newsarticle.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4P6TJVwHOgvY"
      },
      "source": [
        "newstext='''\n",
        "{% extends \"index.html\" %} {% block title %}NEWS ARTICLE TEXT SUMMARISER-Enter News article{% endblock %} {% block content\n",
        "%}\n",
        "<h1> Now please enter the News article. </h1>\n",
        "<form method=\"POST\">\n",
        "  <h3 align=\"center\">The model being used is {{mname}}</h3>\n",
        "  <h4 align=\"left\">Enter here:</h4>\n",
        "  <div class=\"form-group\">\n",
        "    <input\n",
        "      type=\"text\"\n",
        "      class=\"form-control\"\n",
        "      id=\"newsbody\"\n",
        "      name=\"newsbody\"\n",
        "      placeholder=\"Enter article here...\"\n",
        "    />\n",
        "  </div>\n",
        "   <br />\n",
        "   <input type=\"submit\" name=\"S2\" value=\"Submit\">\n",
        "</form>\n",
        "{% endblock %}\n",
        "'''\n",
        "file1=open(\"templates/newsarticle.html\",\"w\")\n",
        "file1.write(newstext)\n",
        "file1.close()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8aMqgTiOlrN"
      },
      "source": [
        "creating summary.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4ur7Op4Ojb2"
      },
      "source": [
        "summarytext='''\n",
        "{% extends \"index.html\" %} {% block title %}NEWS ARTICLE TEXT SUMMARISER-Summary{% endblock %} {% block content\n",
        "%}\n",
        "<h1> Here's your summary! </h1>\n",
        "<br />\n",
        "<p>{{finalsummary}}</p>\n",
        " \n",
        "{% endblock %}\n",
        "'''\n",
        "file1=open(\"templates/summary.html\",\"w\")\n",
        "file1.write(summarytext)\n",
        "file1.close()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkhKFB5Yk5O8"
      },
      "source": [
        "import re"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5O4E0jyls-Kg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516,
          "referenced_widgets": [
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            "e2d46e18cd65420caa9b2884d9a51fe2",
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            "0efafdfadad64164929f33d68fbf8dc4",
            "93adb99dbe184c489a38191e76690f60",
            "6ac077cb0f1e4f039598cf33313fcc6d"
          ]
        },
        "outputId": "f5148a3c-dd8b-4e35-e99f-09a901b3ffcd"
      },
      "source": [
        "from flask import Flask,render_template, request\n",
        "from flask_ngrok import run_with_ngrok\n",
        "\n",
        "app = Flask(__name__)\n",
        "run_with_ngrok(app)\n",
        " \n",
        "class summary_generation:\n",
        "  def __init__(self,body,modelname,result):\n",
        "    self.body=body\n",
        "    self.modelname=modelname\n",
        "    self.result=result\n",
        "  \n",
        "  def bertSum(self):\n",
        "    from summarizer import Summarizer\n",
        "    bert_model = Summarizer()\n",
        "    return ''.join(bert_model(self.body, min_length=60))\n",
        " \n",
        "  def gptSum(self):\n",
        "    !pip install transformers==2.2.0\n",
        "    from summarizer import TransformerSummarizer\n",
        "    GPT2_model = TransformerSummarizer(transformer_type=\"GPT2\",transformer_model_key=\"gpt2-medium\")\n",
        "    return ''.join(GPT2_model(self.body, min_length=60))\n",
        " \n",
        "  def xlnetSum(self):\n",
        "    !pip install transformers==2.2.0\n",
        "    from summarizer import TransformerSummarizer\n",
        "    xlnet_model = TransformerSummarizer(transformer_type=\"XLNet\",transformer_model_key=\"xlnet-base-cased\")\n",
        "    return ''.join(xlnet_model(self.body, min_length=60))\n",
        "\n",
        "  def bartSum(self):\n",
        "    !pip install transformers==4.5.0\n",
        "    from transformers import pipeline\n",
        "    summarizer = pipeline('summarization', model='facebook/bart-large-cnn', tokenizer='facebook/bart-large-cnn')\n",
        "    summary=summarizer(self.body, min_length = round(0.1 * len(self.body.split(' '))), max_length = round(0.2 * len(self.body.split(' '))), do_sample=False)\n",
        "    return summary[0]['summary_text']\n",
        "  \n",
        "  def t5Sum(self):\n",
        "    !pip install transformers==4.5.0\n",
        "    !pip install sentencepiece\n",
        "    from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
        "    model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
        "    tokenizer = T5Tokenizer.from_pretrained('t5-base')\n",
        "    tokens_input = tokenizer.encode(self.body, return_tensors=\"pt\", max_length=512, truncation=True)\n",
        "    summary_ids = model.generate(tokens_input, min_length=60, max_length=180, length_penalty=4.0)\n",
        "    summary = tokenizer.decode(summary_ids[0])\n",
        "    summary = re.sub(r'<pad>','',summary)\n",
        "    summary = re.sub(r'<extra_id_0>','',summary)\n",
        "    summary = re.sub(r'</s>','',summary)\n",
        "    return summary\n",
        "  \n",
        "  def set_model(self,mname):\n",
        "    self.modelname=mname\n",
        " \n",
        "  def summarise(self):\n",
        "    if self.modelname == 'GPT-2':\n",
        "      self.result = self.gptSum()\n",
        "    elif self.modelname == 'BERT':\n",
        "      self.result = self.bertSum()\n",
        "    elif self.modelname == 'XLNet':\n",
        "     self.result = self.xlnetSum()\n",
        "    elif self.modelname == 'BART':\n",
        "     self.result = self.bartSum()\n",
        "    elif self.modelname == 'T5':\n",
        "     self.result = self.t5Sum()\n",
        " \n",
        "SG=summary_generation('','GPT-2','');\n",
        "finalsummary=''\n",
        "mname=''\n",
        " \n",
        "@app.route(\"/\",methods=['GET','POST'])\n",
        "def home():\n",
        "  if request.method== 'POST':\n",
        "    mname=request.form.get('mname')\n",
        "    SG.set_model(mname)\n",
        "  return render_template('home.html')\n",
        " \n",
        "@app.route(\"/enter-news-article\",methods=['GET','POST'])\n",
        "def input():\n",
        "  if request.method== 'POST':\n",
        "    SG.body=request.form['newsbody']\n",
        "    SG.summarise()\n",
        "  return render_template('newsarticle.html',mname=SG.modelname)\n",
        " \n",
        "@app.route(\"/summary\",methods=['GET','POST'])\n",
        "def summary():\n",
        "  return render_template('summary.html',finalsummary=SG.result)\n",
        " \n",
        "app.run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Running on http://11ee-35-231-140-32.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "127.0.0.1 - - [13/Sep/2021 12:06:43] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:06:44] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:06:45] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:07:15] \"\u001b[37mPOST / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:07:20] \"\u001b[37mGET /enter-news-article HTTP/1.1\u001b[0m\" 200 -\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0e50ed2a217c406cb4e0659a898add2d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "963be3e414894456941f636b9e8ea1b9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "837452a42dd24d23ac70d4582fef3b90",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5156f8f4900d4771845239687488d902",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e35cbdc3fbf242a88a6128c40f5a9758",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "127.0.0.1 - - [13/Sep/2021 12:08:18] \"\u001b[37mPOST /enter-news-article HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:11:35] \"\u001b[37mGET /summary HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Sep/2021 12:11:37] \"\u001b[37mGET /summary HTTP/1.1\u001b[0m\" 200 -\n"
          ]
        }
      ]
    }
  ]
}