Ques_Generation.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "zWOazi3cVSCV"
      },
      "source": [
        "# DATA-SAVING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYV509KTXewu",
        "outputId": "31302bd8-3623-435a-cb78-f8791576a34d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuUf8vLzXrH0",
        "outputId": "fd9b73db-a153-42a8-91bb-89a9d417baea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-1.18.3-py3-none-any.whl (311 kB)\n",
            "\u001b[K     |████████████████████████████████| 311 kB 4.1 MB/s \n",
            "\u001b[?25hCollecting xxhash\n",
            "  Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\n",
            "\u001b[K     |████████████████████████████████| 243 kB 54.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.3.5)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.5)\n",
            "Collecting huggingface-hub<1.0.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 6.7 MB/s \n",
            "\u001b[?25hCollecting aiohttp\n",
            "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 63.6 MB/s \n",
            "\u001b[?25hCollecting fsspec[http]>=2021.05.0\n",
            "  Downloading fsspec-2022.1.0-py3-none-any.whl (133 kB)\n",
            "\u001b[K     |████████████████████████████████| 133 kB 69.2 MB/s \n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
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            "Collecting async-timeout<5.0,>=4.0.0a3\n",
            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
            "Collecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n",
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            "Collecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
            "\u001b[K     |████████████████████████████████| 144 kB 71.6 MB/s \n",
            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
            "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
            "\u001b[K     |████████████████████████████████| 271 kB 67.9 MB/s \n",
            "\u001b[?25hCollecting aiosignal>=1.1.2\n",
            "  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
            "Collecting asynctest==0.13.0\n",
            "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.7.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, huggingface-hub, datasets\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.18.3 frozenlist-1.3.0 fsspec-2022.1.0 huggingface-hub-0.4.0 multidict-6.0.2 xxhash-2.0.2 yarl-1.7.2\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.62.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wPccKuWlYca_"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CANDkEtX9Q9",
        "outputId": "b260131c-50f1-4d24-b1c9-633fed791d54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/content/gdrive/MyDrive/ai_ques_gen/t5’: File exists\n",
            "mkdir: cannot create directory ‘/content/gdrive/MyDrive/ai_ques_gen/t5/dataset’: File exists\n",
            "mkdir: cannot create directory ‘/content/gdrive/MyDrive/ai_ques_gen/t5/model’: File exists\n",
            "mkdir: cannot create directory ‘/content/gdrive/MyDrive/ai_ques_gen/t5/tokenizer’: File exists\n"
          ]
        }
      ],
      "source": [
        "!mkdir '/content/gdrive/MyDrive/ai_ques_gen/t5'\n",
        "!mkdir '/content/gdrive/MyDrive/ai_ques_gen/t5/dataset'\n",
        "!mkdir '/content/gdrive/MyDrive/ai_ques_gen/t5/model'\n",
        "!mkdir '/content/gdrive/MyDrive/ai_ques_gen/t5/tokenizer'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMJ2cT96YNcq"
      },
      "outputs": [],
      "source": [
        "import datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ah7laVUCYP-k",
        "outputId": "f01ce850-b4e1-4a60-bb9b-28153e5240fc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\n",
            "Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\n"
          ]
        }
      ],
      "source": [
        "train_dataset=datasets.load_dataset('squad',split='train')\n",
        "valid_dataset=datasets.load_dataset('squad',split='validation')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "wgKQgq2kYVqp",
        "outputId": "19a66d1e-477c-4228-91c0-0c1f6d66ee74"
      },
      "outputs": [
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        "df_train=pd.DataFrame(columns=['context','answer','question'])\n",
        "df_validation=pd.DataFrame(columns=['context','answer','question'])\n",
        "df_train"
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            "100%|██████████| 87599/87599 [06:42<00:00, 217.60it/s]\n"
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        "longans=0;\n",
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        "for index,val in enumerate(tqdm(train_dataset)) :\n",
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            "100%|██████████| 10570/10570 [00:23<00:00, 450.95it/s]\n"
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        "  no_of_words=len(answer.split())\n",
        "  if no_of_words >=11:\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>10214</th>\n",
              "      <td>The pound-force has a metric counterpart, less...</td>\n",
              "      <td>kilogram-force</td>\n",
              "      <td>What is the metric term less used than the New...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10215</th>\n",
              "      <td>The pound-force has a metric counterpart, less...</td>\n",
              "      <td>kilopond</td>\n",
              "      <td>What is the kilogram-force sometimes reffered ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10216</th>\n",
              "      <td>The pound-force has a metric counterpart, less...</td>\n",
              "      <td>slug</td>\n",
              "      <td>What is a very seldom used unit of mass in the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10217</th>\n",
              "      <td>The pound-force has a metric counterpart, less...</td>\n",
              "      <td>kip</td>\n",
              "      <td>What seldom used term of a unit of force equal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10218</th>\n",
              "      <td>The pound-force has a metric counterpart, less...</td>\n",
              "      <td>sthène</td>\n",
              "      <td>What is the seldom used force unit equal to on...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10219 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-438a0543-54ae-4f14-b20e-f3a4c2b09743')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-438a0543-54ae-4f14-b20e-f3a4c2b09743 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-438a0543-54ae-4f14-b20e-f3a4c2b09743');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                 context  ...                                           question\n",
              "0      Super Bowl 50 was an American football game to...  ...  Which NFL team represented the AFC at Super Bo...\n",
              "1      Super Bowl 50 was an American football game to...  ...  Which NFL team represented the NFC at Super Bo...\n",
              "2      Super Bowl 50 was an American football game to...  ...                Where did Super Bowl 50 take place?\n",
              "3      Super Bowl 50 was an American football game to...  ...                  Which NFL team won Super Bowl 50?\n",
              "4      Super Bowl 50 was an American football game to...  ...  What color was used to emphasize the 50th anni...\n",
              "...                                                  ...  ...                                                ...\n",
              "10214  The pound-force has a metric counterpart, less...  ...  What is the metric term less used than the New...\n",
              "10215  The pound-force has a metric counterpart, less...  ...  What is the kilogram-force sometimes reffered ...\n",
              "10216  The pound-force has a metric counterpart, less...  ...  What is a very seldom used unit of mass in the...\n",
              "10217  The pound-force has a metric counterpart, less...  ...  What seldom used term of a unit of force equal...\n",
              "10218  The pound-force has a metric counterpart, less...  ...  What is the seldom used force unit equal to on...\n",
              "\n",
              "[10219 rows x 3 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kjqqrjWQTn62",
        "outputId": "3ab0959c-4a3a-4f21-e792-9c4dfb950ada"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-9e148c56-95dc-4eae-afc2-8be88581de7e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>context</th>\n",
              "      <th>answer</th>\n",
              "      <th>question</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Architecturally, the school has a Catholic cha...</td>\n",
              "      <td>Saint Bernadette Soubirous</td>\n",
              "      <td>To whom did the Virgin Mary allegedly appear i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Architecturally, the school has a Catholic cha...</td>\n",
              "      <td>a copper statue of Christ</td>\n",
              "      <td>What is in front of the Notre Dame Main Building?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Architecturally, the school has a Catholic cha...</td>\n",
              "      <td>the Main Building</td>\n",
              "      <td>The Basilica of the Sacred heart at Notre Dame...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Architecturally, the school has a Catholic cha...</td>\n",
              "      <td>a Marian place of prayer and reflection</td>\n",
              "      <td>What is the Grotto at Notre Dame?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Architecturally, the school has a Catholic cha...</td>\n",
              "      <td>a golden statue of the Virgin Mary</td>\n",
              "      <td>What sits on top of the Main Building at Notre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>83643</th>\n",
              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
              "      <td>Oregon</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>83644</th>\n",
              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
              "      <td>Rangoon</td>\n",
              "      <td>What was Yangon previously known as?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83645</th>\n",
              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
              "      <td>Minsk</td>\n",
              "      <td>With what Belorussian city does Kathmandu have...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83646</th>\n",
              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
              "      <td>1975</td>\n",
              "      <td>In what year did Kathmandu create its initial ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83647</th>\n",
              "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
              "      <td>Kathmandu Metropolitan City</td>\n",
              "      <td>What is KMC an initialism of?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>83648 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e148c56-95dc-4eae-afc2-8be88581de7e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e148c56-95dc-4eae-afc2-8be88581de7e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                 context  ...                                           question\n",
              "0      Architecturally, the school has a Catholic cha...  ...  To whom did the Virgin Mary allegedly appear i...\n",
              "1      Architecturally, the school has a Catholic cha...  ...  What is in front of the Notre Dame Main Building?\n",
              "2      Architecturally, the school has a Catholic cha...  ...  The Basilica of the Sacred heart at Notre Dame...\n",
              "3      Architecturally, the school has a Catholic cha...  ...                  What is the Grotto at Notre Dame?\n",
              "4      Architecturally, the school has a Catholic cha...  ...  What sits on top of the Main Building at Notre...\n",
              "...                                                  ...  ...                                                ...\n",
              "83643  Kathmandu Metropolitan City (KMC), in order to...  ...  In what US state did Kathmandu first establish...\n",
              "83644  Kathmandu Metropolitan City (KMC), in order to...  ...               What was Yangon previously known as?\n",
              "83645  Kathmandu Metropolitan City (KMC), in order to...  ...  With what Belorussian city does Kathmandu have...\n",
              "83646  Kathmandu Metropolitan City (KMC), in order to...  ...  In what year did Kathmandu create its initial ...\n",
              "83647  Kathmandu Metropolitan City (KMC), in order to...  ...                      What is KMC an initialism of?\n",
              "\n",
              "[83648 rows x 3 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6k0A8cCsTqyu"
      },
      "outputs": [],
      "source": [
        "train_save_path= '/content/gdrive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_train_11.csv'\n",
        "validation_save_path= '/content/gdrive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_val_11.csv'\n",
        "\n",
        "df_train.to_csv(train_save_path,index=False)\n",
        "df_validation.to_csv(validation_save_path,index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qRq9BqmhVawE"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NhdY_ZM9Ve5r"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1pXo0VhqVgsm"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOhV372oVie5"
      },
      "source": [
        "# TOKENIZATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd4ZWzAPVjoO",
        "outputId": "79b75d5f-e867-40c6-dedd-948281406f42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc5xpRZnVqP4",
        "outputId": "7abb22e7-9a24-4cd6-fd16-bc287d7fedae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.17.0-py3-none-any.whl (3.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.63.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 49.8 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,>=0.11.1\n",
            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 33.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.6.0)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.49-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 50.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\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->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\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 huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.49 tokenizers-0.11.6 transformers-4.17.0\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.7/dist-packages (0.11.6)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.63.0)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 5.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n"
          ]
        }
      ],
      "source": [
        "!pip install -- transformers\n",
        "!pip install -- tokenizers\n",
        "!pip install -- tqdm\n",
        "!pip install -- sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t7Rhu4lPVzOa"
      },
      "outputs": [],
      "source": [
        "train_save_path= '/content/drive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_train_11.csv'\n",
        "validation_save_path= '/content/drive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_val_11.csv'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSA5xbawV-pc",
        "outputId": "e0354d35-62bb-43f5-80dc-98c2b1fefe65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 527 kB 5.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 398 kB 40.4 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 829 kB 41.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 31.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 45.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 94 kB 1.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 29.6 MB/s \n",
            "\u001b[?25h  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset,dataloader\n",
        "\n",
        "!pip install --quiet pytorch-lightning\n",
        "import pytorch_lightning as pl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TPniVIytzk8G"
      },
      "outputs": [],
      "source": [
        "from transformers import T5Tokenizer, T5ForConditionalGeneration,AdamW"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "f6a19fbface84bde8dc68b7520e6326c",
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            "cec08882e6fc43f980b2ebe97fa69d7c",
            "c152e51c088b4f4d9e6c22e673cf03c3",
            "3941b4a29ee4420c8bd580a262504f77",
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            "643a4da6332e4e128f603aa0b08ca2c7",
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            "44fff2c3a5774389b913d0a05bf5f3cc",
            "eec753fb19c144d6af2750a573b56f82",
            "01d720db85bc4dd1a2a73bf9fb556b5f",
            "54451f1acbd84470b59ddc3f1381befd",
            "27cac2d769af4994be78d2ed08aa4733",
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            "56219f1ce7124d1ba8e002ea6034b21b",
            "39ebafed4d9d4c22b131d7c4e4162ac3"
          ]
        },
        "id": "dv4QLtNvWJE5",
        "outputId": "6f01220f-b638-4604-e286-9bcd2f3e040b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6a19fbface84bde8dc68b7520e6326c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/773k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f9dddac786d470c8a3d01b216247647",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "abc75b5570a04fdebdd0885c48c55879",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/850M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "t5_tokenizer=T5Tokenizer.from_pretrained('t5-base')\n",
        "t5_model=T5ForConditionalGeneration.from_pretrained('t5-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "chtXWW0SWPm3"
      },
      "outputs": [],
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import copy\n",
        "class QuestionGenerationDataset():\n",
        "    def __init__(self, tokenizer, filepath, max_len_inp=512,max_len_out=96):\n",
        "        self.path = filepath\n",
        "\n",
        "        self.passage_column = \"context\"\n",
        "        self.answer = \"answer\"\n",
        "        self.question = \"question\"\n",
        "\n",
        "        self.data = pd.read_csv(self.path)\n",
        "        #self.data = pd.read_csv(self.path,nrows=1000)\n",
        "\n",
        "        self.max_len_input = max_len_inp\n",
        "        self.max_len_output = max_len_out\n",
        "        self.tokenizer = tokenizer\n",
        "        self.inputs = []\n",
        "        self.targets = []\n",
        "        self.skippedcount =0\n",
        "        self._build()\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.inputs)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        source_ids = self.inputs[index][\"input_ids\"].squeeze()\n",
        "        target_ids = self.targets[index][\"input_ids\"].squeeze()\n",
        "\n",
        "        src_mask = self.inputs[index][\"attention_mask\"].squeeze()  # might need to squeeze\n",
        "        target_mask = self.targets[index][\"attention_mask\"].squeeze()  # might need to squeeze\n",
        "\n",
        "        labels = copy.deepcopy(target_ids)\n",
        "        labels [labels==0] = -100\n",
        "\n",
        "        return {\"source_ids\": source_ids, \"source_mask\": src_mask, \"target_ids\": target_ids, \"target_mask\": target_mask,\"labels\":labels}\n",
        "\n",
        "    def _build(self):\n",
        "        for idx in tqdm(range(len(self.data))):\n",
        "            passage,answer,target = self.data.loc[idx, self.passage_column],self.data.loc[idx, self.answer], self.data.loc[idx, self.question]\n",
        "\n",
        "            input_ = \"context: %s  answer: %s </s>\" % (passage, answer)\n",
        "            target = \"question: %s </s>\" % (str(target))\n",
        "\n",
        "            # get encoding length of input. If it is greater than self.max_len skip it\n",
        "            test_input_encoding = self.tokenizer.encode_plus(input_,\n",
        "                                        truncation=False,\n",
        "                                        return_tensors=\"pt\")\n",
        "            \n",
        "            length_of_input_encoding = len(test_input_encoding['input_ids'][0])\n",
        "\n",
        "\n",
        "            if length_of_input_encoding > self.max_len_input:\n",
        "              self.skippedcount = self.skippedcount + 1\n",
        "              continue\n",
        "\n",
        "            # tokenize inputs\n",
        "            tokenized_inputs = self.tokenizer.batch_encode_plus(\n",
        "                [input_], max_length=self.max_len_input, pad_to_max_length=True, return_tensors=\"pt\"\n",
        "            )\n",
        "            # tokenize targets\n",
        "            tokenized_targets = self.tokenizer.batch_encode_plus(\n",
        "                [target], max_length=self.max_len_output, pad_to_max_length=True,return_tensors=\"pt\"\n",
        "            )\n",
        "\n",
        "            self.inputs.append(tokenized_inputs)\n",
        "            self.targets.append(tokenized_targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "ca110d6aac0046e78f32c27d7864a7d8"
          ]
        },
        "id": "F4uwLrlNXoTu",
        "outputId": "9a8e0f5f-c192-4b9e-b7e1-ea0cd552ed7b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca110d6aac0046e78f32c27d7864a7d8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/83648 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:195: UserWarning: This sequence already has </s>. In future versions this behavior may lead to duplicated eos tokens being added.\n",
            "  f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added.\"\n",
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2277: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (513 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "train_dataset = QuestionGenerationDataset(t5_tokenizer, '/content/drive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_train_11.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "ef17907990084e4abc10ae36a5c03644"
          ]
        },
        "id": "DQiN9SmSZ2fb",
        "outputId": "a15bf3c5-0cb8-41aa-db60-75d36ccb098c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef17907990084e4abc10ae36a5c03644",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/10219 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:195: UserWarning: This sequence already has </s>. In future versions this behavior may lead to duplicated eos tokens being added.\n",
            "  f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added.\"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2277: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "validation_dataset = QuestionGenerationDataset(t5_tokenizer,'/content/drive/MyDrive/ai_ques_gen/t5/dataset/squad_t5_val_11.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QN26Rnx048TN"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxfhnbZwtEr5"
      },
      "outputs": [],
      "source": [
        "class T5FineTuner(pl.LightningModule):\n",
        "    def __init__(self,batchsize, t5model, t5tokenizer):\n",
        "        super(T5FineTuner, self).__init__()\n",
        "        self.batchsize = batchsize\n",
        "        self.model = t5model\n",
        "        self.tokenizer = t5tokenizer\n",
        "        print(self.parameters())\n",
        "\n",
        "\n",
        "    def forward( self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, lm_labels=None):\n",
        "         outputs = self.model(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            decoder_attention_mask=decoder_attention_mask,\n",
        "            labels=lm_labels,\n",
        "        )\n",
        "         \n",
        "         return outputs\n",
        "\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        outputs = self.forward(\n",
        "            input_ids=batch[\"source_ids\"],\n",
        "            attention_mask=batch[\"source_mask\"],\n",
        "            decoder_input_ids = batch[\"target_ids\"],\n",
        "            decoder_attention_mask=batch['target_mask'],\n",
        "            lm_labels=batch['labels']\n",
        "        )\n",
        "\n",
        "        loss = outputs[0]\n",
        "        self.log('train_loss',loss)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        outputs = self.forward(\n",
        "            input_ids=batch[\"source_ids\"],\n",
        "            attention_mask=batch[\"source_mask\"],\n",
        "            decoder_input_ids = batch[\"target_ids\"],\n",
        "            decoder_attention_mask=batch['target_mask'],\n",
        "            lm_labels=batch['labels']\n",
        "        )\n",
        "\n",
        "        loss = outputs[0]\n",
        "        self.log(\"val_loss\",loss)\n",
        "        return loss\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader (train_dataset, batch_size=4,num_workers=2)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader (validation_dataset, batch_size=4,num_workers=2)\n",
        "\n",
        "\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = AdamW(self.parameters(), lr=3e-4, eps=1e-8)\n",
        "        return optimizer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ILr_2EKz4ZB5"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yLWJekz30CqO"
      },
      "outputs": [],
      "source": [
        "import argparse\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SuQC161OzsFo"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "model = T5FineTuner(4,t5_model,t5_tokenizer)\n",
        "\n",
        "trainer = pl.Trainer(max_epochs = 1,gpus=1,progress_bar_refresh_rate=30)\n",
        "\n",
        "trainer.fit(model)\n",
        "\n",
        "print (\"Saving model\")\n",
        "save_path_model = '/content/gdrive/My Drive/ai_ques_gen/t5/model/'\n",
        "save_path_tokenizer = '/content/gdrive/My Drive/ai_ques_gen/t5/tokenizer/'\n",
        "model.model.save_pretrained(save_path_model)\n",
        "t5_tokenizer.save_pretrained(save_path_tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pwq8JoUHz7KD"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93gkISlFSlG6"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FP8gdOpOGlDb"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRrFlaseGl1u"
      },
      "source": [
        "# Keyphrase Extraction with summarization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Hijr5wtdW64",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca4a0be7-bb40-48d8-84a9-3008eeb6622a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYZJcJj0GqRQ",
        "outputId": "9d57f050-1363-4b94-d0a6-54634c2d3c20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 2.5 MB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 33.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 39.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 5.6 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet transformers==4.8.1\n",
        "!pip install --quiet sentencepiece==0.1.95\n",
        "!pip install --quiet nltk==3.2.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "5d93deb3283c4a53877a737860feed98"
          ]
        },
        "id": "IfID0uHZHALE",
        "outputId": "50ecc385-99c5-4431-fe25-c0b76f3c9f20"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "743ee9f446ff49ff9b9378e92937c8de"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36d6fd6a53bd4c00a4cfc496e69a9620"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f0fa46de1284b98ad9a4a0218d85ed1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccb6790045dd4a57b22b74578fc8d3b2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import T5ForConditionalGeneration,T5Tokenizer\n",
        "summary_model = T5ForConditionalGeneration.from_pretrained('t5-small')\n",
        "summary_tokenizer = T5Tokenizer.from_pretrained('t5-small')\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "summary_model = summary_model.to(device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8BmMW64HHx2",
        "outputId": "bf59c393-d1e2-46bb-e536-4847599f42d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/brown.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('brown')\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import wordnet as wn\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "def postprocesstext (content):\n",
        "  final=\"\"\n",
        "  for sent in sent_tokenize(content):\n",
        "    sent = sent.capitalize()\n",
        "    final = final +\" \"+sent\n",
        "  return final\n",
        "\n",
        "\n",
        "def summarizer(text,model,tokenizer):\n",
        "  text = text.strip().replace(\"\\n\",\" \")\n",
        "  text = \"summarize: \"+text\n",
        "  # print (text)\n",
        "  max_len = 512\n",
        "  encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
        "\n",
        "  input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
        "\n",
        "  outs = model.generate(input_ids=input_ids,\n",
        "                                  attention_mask=attention_mask,\n",
        "                                  early_stopping=True,\n",
        "                                  num_beams=3,\n",
        "                                  num_return_sequences=1,\n",
        "                                  no_repeat_ngram_size=2,\n",
        "                                  min_length = 75,\n",
        "                                  max_length=300)\n",
        "\n",
        "\n",
        "  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
        "  summary = dec[0]\n",
        "  summary = postprocesstext(summary)\n",
        "  summary= summary.strip()\n",
        "\n",
        "  return summary\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bb9__kyZHPZk",
        "outputId": "33e9014b-2ed7-4646-a197-aa764ef29292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▍                              | 10 kB 17.8 MB/s eta 0:00:01\r\u001b[K     |██▉                             | 20 kB 4.0 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 30 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |█████▋                          | 40 kB 6.9 MB/s eta 0:00:01\r\u001b[K     |███████                         | 51 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 71 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████▏                    | 81 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 92 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 102 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 112 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 122 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 133 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████▌            | 143 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 153 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 163 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▋        | 174 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 184 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 194 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 204 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 215 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 225 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 235 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 235 kB 4.9 MB/s \n",
            "\u001b[?25h  Building wheel for pke (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install --quiet git+https://github.com/boudinfl/pke.git@dc4d5f21e0ffe64c4df93c46146d29d1c522476b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "W5fZNpf8cb0F",
        "outputId": "5d869540-37cc-4eb3-ceb5-7a23c788aea9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rake-nltk\n",
            "  Downloading rake_nltk-1.0.6-py3-none-any.whl (9.1 kB)\n",
            "Collecting nltk<4.0.0,>=3.6.2\n",
            "  Downloading nltk-3.7-py3-none-any.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (4.63.0)\n",
            "Collecting regex>=2021.8.3\n",
            "  Downloading regex-2022.3.15-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n",
            "\u001b[K     |████████████████████████████████| 749 kB 40.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (7.1.2)\n",
            "Installing collected packages: regex, nltk, rake-nltk\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2019.12.20\n",
            "    Uninstalling regex-2019.12.20:\n",
            "      Successfully uninstalled regex-2019.12.20\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.2.5\n",
            "    Uninstalling nltk-3.2.5:\n",
            "      Successfully uninstalled nltk-3.2.5\n",
            "Successfully installed nltk-3.7 rake-nltk-1.0.6 regex-2022.3.15\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "nltk",
                  "regex"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "pip install rake-nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmGyyIF99Qea",
        "outputId": "8d3c04ed-e6ce-425d-e615-8abe0389331f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "from rake_nltk import Rake\n",
        "r = Rake()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muha4kFqHzOi",
        "outputId": "3ccc52dd-6061-4cbc-c26e-cc8dd94e347b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "import pke\n",
        "import traceback\n",
        "\n",
        "def get_nouns_multipartite(content):\n",
        "    out=[]\n",
        "    try:\n",
        "        extractor = pke.unsupervised.MultipartiteRank()\n",
        "        extractor.load_document(input=content)\n",
        "        #    not contain punctuation marks or stopwords as candidates.\n",
        "        pos = {'PROPN','NOUN'}\n",
        "        #pos = {'PROPN','NOUN'}\n",
        "        stoplist = list(string.punctuation)\n",
        "        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n",
        "        stoplist += stopwords.words('english')\n",
        "        extractor.candidate_selection(pos=pos, stoplist=stoplist)\n",
        "        # 4. build the Multipartite graph and rank candidates using random walk,\n",
        "        #    alpha controls the weight adjustment mechanism, see TopicRank for\n",
        "        #    threshold/method parameters.\n",
        "        extractor.candidate_weighting(alpha=1.1,\n",
        "                                      threshold=0.75,\n",
        "                                      method='average')\n",
        "        keyphrases = extractor.get_n_best(n=15)\n",
        "        \n",
        "\n",
        "        for val in keyphrases:\n",
        "            out.append(val[0])\n",
        "    except:\n",
        "        out = []\n",
        "        traceback.print_exc()\n",
        "\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GlzgrFg8pieg"
      },
      "outputs": [],
      "source": [
        "context = \"\"\"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
        "Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
        "system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
        "rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin.  In a recent tweet,\n",
        "Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
        "transaction, and hence was suspending vehicle purchases using the cryptocurrency.  A day later he again tweeted saying, “To be clear, I strongly\n",
        "believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”.  It triggered a downward spiral for Bitcoin value but\n",
        "the cryptocurrency has stabilised since.   A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
        "that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NziK4Akrpl3H"
      },
      "outputs": [],
      "source": [
        "stext='''Musk tweeted that his electric vehicle-making company tesla will not accept payments in bitcoin because of environmental concerns. He also said that\n",
        "the company was working with developers of dogecoin to improve system transaction efficiency. The world's largest cryptocurrency hit a two-month low,\n",
        "while doge coin rallied by about 20 percent. Musk has in recent months often tweeted in support of crypto, but rarely for bitcoin.'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P4lC87caH9UF"
      },
      "outputs": [],
      "source": [
        "def get_keywords(originaltext,summarytext):\n",
        "  r = Rake(min_length=1, max_length=2)\n",
        "  r.extract_keywords_from_text(summarytext)\n",
        "  z=[]\n",
        "  z=r.get_ranked_phrases()\n",
        "  z=list(z)\n",
        "\n",
        "\n",
        "  y = get_nouns_multipartite(originaltext)\n",
        "  important_keywords =[]\n",
        "  for keyword in y:\n",
        "\n",
        "    if keyword in z:\n",
        "     important_keywords.append(keyword)\n",
        "\n",
        "  return important_keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n9UKucnbIGZa"
      },
      "outputs": [],
      "source": [
        "question_model = T5ForConditionalGeneration.from_pretrained('/content/gdrive/MyDrive/ai_ques_gen/t5/model')\n",
        "question_tokenizer = T5Tokenizer.from_pretrained('/content/gdrive/MyDrive/ai_ques_gen/t5/tokenizer')\n",
        "question_model = question_model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w6JRr3ucdyO9"
      },
      "outputs": [],
      "source": [
        "def get_question(context,answer,model,tokenizer):\n",
        "  text = \"context: {} answer: {}\".format(context,answer)\n",
        "  encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
        "  input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
        "\n",
        "  outs = model.generate(input_ids=input_ids,\n",
        "                                  attention_mask=attention_mask,\n",
        "                                  early_stopping=True,\n",
        "                                  num_beams=5,\n",
        "                                  num_return_sequences=1,\n",
        "                                  no_repeat_ngram_size=2,\n",
        "                                  max_length=72)\n",
        "\n",
        "\n",
        "  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
        "\n",
        "\n",
        "  Question = dec[0].replace(\"question:\",\"\")\n",
        "  Question= Question.strip()\n",
        "  return Question"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0NvKxF1ejHZ",
        "outputId": "8c62a505-5301-4561-dd8b-5d9e7ea869d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sense2vec==1.0.2\n",
            "  Downloading sense2vec-1.0.2.tar.gz (54 kB)\n",
            "\u001b[K     |████████████████████████████████| 54 kB 1.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy<3.0.0,>=2.2.3 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (2.2.4)\n",
            "Requirement already satisfied: srsly>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (1.0.5)\n",
            "Requirement already satisfied: catalogue>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (0.9.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (1.21.5)\n",
            "Requirement already satisfied: importlib_metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from sense2vec==1.0.2) (4.11.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib_metadata>=0.20->sense2vec==1.0.2) (3.7.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib_metadata>=0.20->sense2vec==1.0.2) (3.10.0.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (4.63.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (3.0.6)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (2.0.6)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (0.4.1)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (7.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (57.4.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (1.0.6)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.2.3->sense2vec==1.0.2) (1.1.3)\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<3.0.0,>=2.2.3->sense2vec==1.0.2) (2021.10.8)\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<3.0.0,>=2.2.3->sense2vec==1.0.2) (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<3.0.0,>=2.2.3->sense2vec==1.0.2) (1.24.3)\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<3.0.0,>=2.2.3->sense2vec==1.0.2) (2.10)\n",
            "Building wheels for collected packages: sense2vec\n",
            "  Building wheel for sense2vec (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sense2vec: filename=sense2vec-1.0.2-py2.py3-none-any.whl size=35011 sha256=317f3f23725c4a597413e138ffcefbb62e8391f8139282dd38ba35a883c94b48\n",
            "  Stored in directory: /root/.cache/pip/wheels/cf/d3/93/fe8e871b410c5456a7b06be0f154ad6bab298462471551f39d\n",
            "Successfully built sense2vec\n",
            "Installing collected packages: sense2vec\n",
            "Successfully installed sense2vec-1.0.2\n"
          ]
        }
      ],
      "source": [
        "!pip install sense2vec==1.0.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRsSqnYjeriC",
        "outputId": "558b9279-8016-42f4-a795-b19d5421ad03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-27 10:07:47--  https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/50261113/52126080-0993-11ea-8190-8f0e295df22a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220327%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220327T100747Z&X-Amz-Expires=300&X-Amz-Signature=260a7464910e03019005a7592ea91e323ab377140114bf0bb4b907a5b9398454&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=50261113&response-content-disposition=attachment%3B%20filename%3Ds2v_reddit_2015_md.tar.gz&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-03-27 10:07:47--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/50261113/52126080-0993-11ea-8190-8f0e295df22a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220327%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220327T100747Z&X-Amz-Expires=300&X-Amz-Signature=260a7464910e03019005a7592ea91e323ab377140114bf0bb4b907a5b9398454&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=50261113&response-content-disposition=attachment%3B%20filename%3Ds2v_reddit_2015_md.tar.gz&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 600444501 (573M) [application/octet-stream]\n",
            "Saving to: ‘s2v_reddit_2015_md.tar.gz’\n",
            "\n",
            "s2v_reddit_2015_md. 100%[===================>] 572.63M  39.7MB/s    in 12s     \n",
            "\n",
            "2022-03-27 10:08:00 (45.9 MB/s) - ‘s2v_reddit_2015_md.tar.gz’ saved [600444501/600444501]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4eGic0ietg6",
        "outputId": "2929dbd8-db70-4404-94c9-249c9f7944ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "./._s2v_old\n",
            "./s2v_old/\n",
            "./s2v_old/._freqs.json\n",
            "./s2v_old/freqs.json\n",
            "./s2v_old/._vectors\n",
            "./s2v_old/vectors\n",
            "./s2v_old/._cfg\n",
            "./s2v_old/cfg\n",
            "./s2v_old/._strings.json\n",
            "./s2v_old/strings.json\n",
            "./s2v_old/._key2row\n",
            "./s2v_old/key2row\n"
          ]
        }
      ],
      "source": [
        "!tar -xvf  s2v_reddit_2015_md.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmYl6vZWfSp5",
        "outputId": "84d53c9b-6dfd-4bb0-83c2-f0e6fd8374a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting strsim==0.0.3\n",
            "  Downloading strsim-0.0.3-py3-none-any.whl (42 kB)\n",
            "\u001b[K     |████████████████████████████████| 42 kB 523 kB/s \n",
            "\u001b[?25hInstalling collected packages: strsim\n",
            "Successfully installed strsim-0.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install strsim==0.0.3\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from similarity.normalized_levenshtein import NormalizedLevenshtein\n",
        "normalized_levenshtein = NormalizedLevenshtein()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xNwbuxyMevPn"
      },
      "outputs": [],
      "source": [
        "# load sense2vec vectors\n",
        "from sense2vec import Sense2Vec\n",
        "s2v = Sense2Vec().from_disk('s2v_old')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVk-C7yIewqn"
      },
      "outputs": [],
      "source": [
        "from collections import OrderedDict\n",
        "def sense2vec_get_words(word,s2v):\n",
        "    output = []\n",
        "    word = word.lower()\n",
        "    word = word.replace(\" \", \"_\")\n",
        "\n",
        "    sense = s2v.get_best_sense(word)\n",
        "    try:\n",
        "     most_similar = s2v.most_similar(sense, n=20)\n",
        "     # print (\"most_similar \",most_similar)\n",
        "     for each_word in most_similar:\n",
        "        append_word = each_word[0].split(\"|\")[0].replace(\"_\", \" \").lower()\n",
        "        if append_word.lower() != word:\n",
        "            output.append(append_word.title())\n",
        "    except:\n",
        "       output=[]       \n",
        "\n",
        "\n",
        "   \n",
        "    threshold=0.7\n",
        "    filtered_distractors_edit_distance_and_levenshtein_distance =[x for x in output if normalized_levenshtein.distance(x.lower(),word.lower())>threshold] \n",
        "    print(filtered_distractors_edit_distance_and_levenshtein_distance)\n",
        "    \n",
        "    final = [word.capitalize()]\n",
        "\n",
        "    for wrd in filtered_distractors_edit_distance_and_levenshtein_distance:\n",
        "      if wrd.lower() !=word.lower():\n",
        "        final.append(wrd.capitalize())\n",
        "    final = final[1:]\n",
        "  \n",
        "    return final[:5] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9QYib2YeyMS",
        "outputId": "9c33ef06-620c-43aa-ddcd-68b6f8d5d2a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 865 kB 5.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 211 kB 45.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 34.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 856 kB 32.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 61 kB 399 kB/s \n",
            "\u001b[K     |████████████████████████████████| 3.6 MB 32.0 MB/s \n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for flask-cachebuster (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install --quiet gradio==2.7.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G-6iAVm2i8j8"
      },
      "outputs": [],
      "source": [
        "def generate_question(context):\n",
        "  summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
        "  # np = getnounphrases(summary_text,sentence_transformer_model,3)\n",
        "  np =  get_keywords(context,summary_text)\n",
        "  print (\"\\n\\nNoun phrases\",np)\n",
        "  output=\"\"\n",
        "  for word in np:\n",
        "    ques = get_question(summary_text,word,question_model,question_tokenizer)\n",
        "    distractors = sense2vec_get_words(word,s2v)\n",
        "    # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
        "    output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
        "    output = output + \"<br>\"\n",
        "    output = output + \"<b style='color:green;'>\" + \"Ans: \" +word.capitalize()+  \"</b>\"+\"<br>\"\n",
        "    if len(distractors)>0:\n",
        "      for distractor in distractors[:4]:\n",
        "        output = output + \"<b style='color:brown;'>\" + distractor+  \"</b>\"+\"<br>\"\n",
        "    output = output + \"<br>\"\n",
        "\n",
        "  summary =\"Summary: \"+ summary_text\n",
        "  for word in np:\n",
        "    summary = summary.replace(word,\"<b>\"+word+\"</b>\" + \"<br>\")\n",
        "    summary = summary.replace(word.capitalize(),\"<b>\"+word.capitalize()+\"</b>\")\n",
        "  output = output + \"<p>\"+summary+\"</p>\"\n",
        "  output = output + \"<br>\"\n",
        "  return output "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-X7a_R3ci-UK"
      },
      "outputs": [],
      "source": [
        "context='''The initial sanctions are reportedly in response to Moscow recognising the breakaway republics of Donetsk and Luhansk in eastern Ukraine as independent and sending “peacekeeping” forces there.\n",
        "\n",
        "However it’s unclear how significant the sanctions will be. The US earlier said it would not be imposing a broader sanctions package on Russia as its actions on Monday had not constituted a further invasions.'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IKA_Qihe6PVa"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1DRD_Ok6P3h"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nslvMfs2e25l",
        "outputId": "6c477b68-24d6-41c7-cb32-47f22f759ef3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://40568.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7f4019af4450>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"500\"\n",
              "            src=\"https://40568.gradio.app\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Noun phrases ['aristotle', 'early church', 'middle ages', 'neoplatonism', 'catholic church']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Kant', 'Descartes', 'Plato', 'Hume', 'Spinoza', 'Aquinas', 'Nietzsche', 'Hegel', 'Wittgenstein', 'Thomas Aquinas', 'Spinoza', 'Socrates', 'Schopenhauer', 'Spinoza', 'Bertrand Russell', 'Kierkegaard']\n",
            "['Apostles', 'Christian Tradition', 'Church Fathers', 'New Testament', 'Apostles', 'Christian Faith', 'Scripture', 'Scripture', 'Scripture', 'Gospels', 'Scriptures', 'Theologians', 'Christian History', 'Apostles']\n",
            "['Roman Times', 'Roman Empire', 'Ancient Times', '1500S', 'Ancient World', 'Ancient Rome', 'Medieval Europe', '500 Years Ago', 'Medieval Period', 'Ancient Greece', '1800S', 'Medieval Europe', 'Antiquity', 'The 1600S', 'A Thousand Years Ago']\n",
            "['Greek Philosophy', 'Thelema', 'Kabbalah', 'Natural Philosophy', 'Advaita Vedanta', 'Vedas', 'Buddhism', 'Buddhism', 'Buddhism', 'Western Philosophy']\n",
            "[]\n",
            "\n",
            "\n",
            "Noun phrases ['aristotle', 'philosophy', 'age', 'biology', 'logic', 'lyceum']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Kant', 'Descartes', 'Plato', 'Hume', 'Spinoza', 'Aquinas', 'Nietzsche', 'Hegel', 'Wittgenstein', 'Thomas Aquinas', 'Spinoza', 'Socrates', 'Schopenhauer', 'Spinoza', 'Bertrand Russell', 'Kierkegaard']\n",
            "['Epistemology', 'Metaphysics', 'Social Theory', 'Social Science', 'Anthropology', 'Evolutionary Biology', 'Science', 'Academic Discipline', 'Critical Theory']\n",
            "['Certain Age', 'Current Age', 'Mid Twenties', 'Magical Age', 'Maturity Level', '18', '17 Or 18', 'Age Bracket', 'Mid 20S', 'Young']\n",
            "['Neuroscience', 'Other Sciences', 'Physical Science', 'Sciences', 'Physical Sciences']\n",
            "['Reasoning', 'Logical Reasoning', 'Argument', 'Reasoning', 'Whole Argument', 'Entire Argument', 'Arguement', 'Own Reasoning', 'Sound Reasoning', 'Analogy']\n",
            "[]\n",
            "\n",
            "\n",
            "Noun phrases ['modi', 'rss', 'india', 'member', 'bjp']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Kejriwal', 'Bjp', 'Kejriwal', 'Kejru', 'Rahul Gandhi', 'Kejriwal', 'Amit Shah', 'Swamy', 'Bjp', 'Aap', 'Kiran Bedi', 'Kejriwal', 'Kejriwal', 'Bjp']\n",
            "['Twitter', 'Ndtv', 'Feedly', 'Feedly', 'Vhp', 'Google Plus', 'G+', 'Twitter', 'Twitter', 'Ifttt', 'Feedly']\n",
            "['Bangladesh', 'Malaysia', 'Brunei', 'China', 'Sri Lanka', 'Pakistan', 'South Korea', 'South Africa', 'Nepal', 'Philippines', 'Zimbabwe', 'Bhutan', 'Southeast Asia', 'Myanmar', 'Singapore']\n",
            "[]\n",
            "['Kejriwal', 'Modi', 'Aap.', 'Kejriwal', 'Kejriwal', 'Modi', 'Aap.', 'Kiran Bedi', 'Bedi', 'Bjp Supporters', 'Delhi Elections', 'Modi', 'Rahul Gandhi', 'Kejri']\n",
            "\n",
            "\n",
            "Noun phrases ['confucius', 'importance', 'harmony']\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Lao Tzu', 'Buddha', 'Buddha', 'Socrates', 'Aristotle', 'Socrates', 'Socrates', 'Socrates', 'Zen Master', 'Joshu', 'Gautama Buddha', 'Socrates', 'Bhagavad Gita', 'Plato', 'Lao Tzu', 'Buddha', 'Greek Philosopher']\n",
            "['Paramount', 'Particular Aspect', 'Crucial Part', 'Key Factor', 'Other Aspects']\n",
            "['Togetherness', 'Oneness', 'Wholeness', 'Tranquility', 'Perfect Peace', 'Consonance', 'Interdependence', 'Coexistence', 'Tonality', 'Rhythms', 'Greater Whole', 'Universal Love', 'Transcending']\n",
            "\n",
            "\n",
            "Noun phrases ['aristotle', 'early church', 'middle ages', 'neoplatonism', 'catholic church']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1839: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  next_indices = next_tokens // vocab_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Kant', 'Descartes', 'Plato', 'Hume', 'Spinoza', 'Aquinas', 'Nietzsche', 'Hegel', 'Wittgenstein', 'Thomas Aquinas', 'Spinoza', 'Socrates', 'Schopenhauer', 'Spinoza', 'Bertrand Russell', 'Kierkegaard']\n",
            "['Apostles', 'Christian Tradition', 'Church Fathers', 'New Testament', 'Apostles', 'Christian Faith', 'Scripture', 'Scripture', 'Scripture', 'Gospels', 'Scriptures', 'Theologians', 'Christian History', 'Apostles']\n",
            "['Roman Times', 'Roman Empire', 'Ancient Times', '1500S', 'Ancient World', 'Ancient Rome', 'Medieval Europe', '500 Years Ago', 'Medieval Period', 'Ancient Greece', '1800S', 'Medieval Europe', 'Antiquity', 'The 1600S', 'A Thousand Years Ago']\n",
            "['Greek Philosophy', 'Thelema', 'Kabbalah', 'Natural Philosophy', 'Advaita Vedanta', 'Vedas', 'Buddhism', 'Buddhism', 'Buddhism', 'Western Philosophy']\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "import gradio as gr\n",
        "\n",
        "context = gr.inputs.Textbox(lines=20, placeholder=\"Enter paragraph/content here...\")\n",
        "output = gr.outputs.HTML(  label=\"Question and Answers\")\n",
        "\n",
        "def generate_question(context):\n",
        "  summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
        "  # np = getnounphrases(summary_text,sentence_transformer_model,3)\n",
        "  np =  get_keywords(context,summary_text)\n",
        "  print (\"\\n\\nNoun phrases\",np)\n",
        "  output=\"\"\n",
        "  for answer in np:\n",
        "    ques = get_question(summary_text,answer,question_model,question_tokenizer)\n",
        "    distractors = sense2vec_get_words(answer.capitalize(),s2v)\n",
        "    # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
        "    output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
        "    output = output + \"<br>\"\n",
        "    output = output + \"<b style='color:green;'>\" + \"Ans: \" +answer.capitalize()+  \"</b>\"+\"<br>\"\n",
        "    if len(distractors)>0:\n",
        "      for distractor in distractors[:4]:\n",
        "        output = output + \"<b style='color:brown;'>\" + distractor+  \"</b>\"+\"<br>\"\n",
        "    output = output + \"<br>\"\n",
        "\n",
        "  summary =\"Summary: \"+ summary_text\n",
        "  for answer in np:\n",
        "    summary = summary.replace(answer,\"<b>\"+answer+\"</b>\" + \"<br>\")\n",
        "    summary = summary.replace(answer.capitalize(),\"<b>\"+answer.capitalize()+\"</b>\")\n",
        "  output = output + \"<p>\"+summary+\"</p>\"\n",
        "  output = output + \"<br>\"\n",
        "  return output\n",
        "\n",
        "\n",
        "iface = gr.Interface(\n",
        "  fn=generate_question, \n",
        "  inputs=[context], \n",
        "  outputs=output)\n",
        "iface.launch(debug=True)"
      ]
    },
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        ""
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