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            "  Downloading segtok-1.5.11-py3-none-any.whl (24 kB)\n",
            "Collecting pptree\n",
            "  Downloading pptree-3.1.tar.gz (3.0 kB)\n",
            "Requirement already satisfied: torch!=1.8,>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from flair) (1.11.0+cu113)\n",
            "Collecting deprecated>=1.2.4\n",
            "  Downloading Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown==3.12.2->flair) (2.23.0)\n",
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            "Collecting py4j\n",
            "  Downloading py4j-0.10.9.5-py2.py3-none-any.whl (199 kB)\n",
            "\u001b[K     |████████████████████████████████| 199 kB 59.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.7->flair) (1.3.0)\n",
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            "Collecting importlib-metadata<4.0.0,>=3.7.0\n",
            "  Downloading importlib_metadata-3.10.1-py3-none-any.whl (14 kB)\n",
            "Collecting overrides<4.0.0,>=3.0.0\n",
            "  Downloading overrides-3.1.0.tar.gz (11 kB)\n",
            "Collecting requests\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata<4.0.0,>=3.7.0->konoha<5.0.0,>=4.0.0->flair) (4.2.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata<4.0.0,>=3.7.0->konoha<5.0.0,>=4.0.0->flair) (3.8.0)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->flair) (3.1.0)\n",
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            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers>=4.0.0->flair) (0.12.1)\n",
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            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers>=4.0.0->flair) (0.0.53)\n",
            "Requirement already satisfied: wcwidth>=0.2.5 in /usr/local/lib/python3.7/dist-packages (from ftfy->flair) (0.2.5)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests->bpemb>=0.3.2->flair) (1.7.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers>=4.0.0->flair) (7.1.2)\n",
            "Building wheels for collected packages: gdown, mpld3, overrides, sqlitedict, langdetect, pptree, wikipedia-api\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gdown: filename=gdown-3.12.2-py3-none-any.whl size=9692 sha256=e96bc2117a4ec0a5812b0084229622db7c45acabfe0fad7fbf82f8e577d3dd4c\n",
            "  Stored in directory: /root/.cache/pip/wheels/ba/e0/7e/726e872a53f7358b4b96a9975b04e98113b005cd8609a63abc\n",
            "  Building wheel for mpld3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mpld3: filename=mpld3-0.3-py3-none-any.whl size=116702 sha256=2d3f0790f9064e04d618cfa4a90fae8e8abb7658e265214b3b01c45d59983a2b\n",
            "  Stored in directory: /root/.cache/pip/wheels/26/70/6a/1c79e59951a41b4045497da187b2724f5659ca64033cf4548e\n",
            "  Building wheel for overrides (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for overrides: filename=overrides-3.1.0-py3-none-any.whl size=10187 sha256=c976f6d4add87b15cae808c626156ddf578c27520e415fdb1b4d77b71de4d85c\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/0d/38/01a9bc6e20dcfaf0a6a7b552d03137558ba1c38aea47644682\n",
            "  Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sqlitedict: filename=sqlitedict-2.0.0-py3-none-any.whl size=15736 sha256=7f161fa8b394ff1d5eac3cca59c20bb18ecd6a138f82f7bbde166f724e1c86a8\n",
            "  Stored in directory: /root/.cache/pip/wheels/96/dd/2e/0ed4a25cb73fc30c7ea8d10b50acb7226175736067e40a7ea3\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993242 sha256=f27eb5f48261d1d78139cd2bb0a7ea5fb0bd8b67f797d0fbcc5c762c7ae2556b\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
            "  Building wheel for pptree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pptree: filename=pptree-3.1-py3-none-any.whl size=4629 sha256=3f9769157f927e20dfa2899a9061ae78239039e13049d9b24f38e3c419a83771\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/e8/7d/a9c3c19b4722608a0d8b05a38c36bc3f230c43becd2a46794b\n",
            "  Building wheel for wikipedia-api (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia-api: filename=Wikipedia_API-0.5.4-py3-none-any.whl size=13477 sha256=e70b6eda387fc4c175a97d67e26aee2ac5165350154961082078ae59c3a68d96\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/24/56/58ba93cf78be162451144e7a9889603f437976ef1ae7013d04\n",
            "Successfully built gdown mpld3 overrides sqlitedict langdetect pptree wikipedia-api\n",
            "Installing collected packages: requests, importlib-metadata, sentencepiece, py4j, overrides, wikipedia-api, sqlitedict, segtok, pptree, mpld3, more-itertools, langdetect, konoha, janome, hyperopt, gdown, ftfy, deprecated, conllu, bpemb, flair\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 4.11.3\n",
            "    Uninstalling importlib-metadata-4.11.3:\n",
            "      Successfully uninstalled importlib-metadata-4.11.3\n",
            "  Attempting uninstall: more-itertools\n",
            "    Found existing installation: more-itertools 8.12.0\n",
            "    Uninstalling more-itertools-8.12.0:\n",
            "      Successfully uninstalled more-itertools-8.12.0\n",
            "  Attempting uninstall: hyperopt\n",
            "    Found existing installation: hyperopt 0.1.2\n",
            "    Uninstalling hyperopt-0.1.2:\n",
            "      Successfully uninstalled hyperopt-0.1.2\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\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",
            "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 3.10.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed bpemb-0.3.3 conllu-4.4.2 deprecated-1.2.13 flair-0.11.1 ftfy-6.1.1 gdown-3.12.2 hyperopt-0.2.7 importlib-metadata-3.10.1 janome-0.4.2 konoha-4.6.5 langdetect-1.0.9 more-itertools-8.8.0 mpld3-0.3 overrides-3.1.0 pptree-3.1 py4j-0.10.9.5 requests-2.27.1 segtok-1.5.11 sentencepiece-0.1.95 sqlitedict-2.0.0 wikipedia-api-0.5.4\n",
            "Requirement already satisfied: flashtext in /usr/local/lib/python3.7/dist-packages (2.7)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install flair\n",
        "!pip install flashtext"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "id": "C0RsAkNBObQK",
        "outputId": "47859772-08f9-4b94-8a52-dc2c384334fa"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2034631b-6050-48f5-9bfe-e496db7148ed\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2034631b-6050-48f5-9bfe-e496db7148ed\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Greeks.txt to Greeks.txt\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Greeks.txt': b'The Greek historian knew what he was talking about. The Nile River fed Egyptian civilization for hundreds of years. The Longest River the Nile is 4,160 miles long\\xe2\\x80\\x94the world\\xe2\\x80\\x99s longest river. It begins near the equator in Africa and flows north to the Mediterranean Sea. In the south the Nile churns with cataracts. A cataract is a waterfall. Near the sea the Nile branches into a delta. A delta is an area near a river\\xe2\\x80\\x99s mouth where the water deposits fine soil called silt. In the delta, the Nile divides into many streams. The river is called the upper Nile in the south and the lower Nile in the north. For centuries, heavy rains in Ethiopia caused the Nile to flood every summer. The floods deposited rich soil along the Nile\\xe2\\x80\\x99s shores. This soil was fertile, which means it was good for growing crops. Unlike the Tigris and Euphrates, the Nile River flooded at the same time every year, so farmers could predict when to plant their crops. Red Land, Black Land The ancient Egyptians lived in narrow bands of land on each side of the Nile. They called this region the black land because of the fertile soil that the floods deposited. The red land was the barren desert beyond the fertile region. Weather in Egypt was almost always the same. Eight months of the year were sunny and hot. The four months of winter were sunny but cooler. Most of the region received only an inch of rain a year. The parts of Egypt not near the Nile were a desert. Isolation The harsh desert acted as a barrier to keep out enemies. The Mediterranean coast was swampy and lacked good harbors. For these reasons, early Egyptians stayed close to home. Each year, Egyptian farmers watched for white birds called ibises, which flew up from the south. When the birds arrived, the annual flood waters would soon follow. After the waters drained away, farmers could plant seeds in the fertile soil. Agricultural Techniques By about 2400 B.C., farmers used technology to expand their farmland. Working together, they dug irrigation canals that carried river water to dry areas. Then they used a tool called a shaduf to spread the water across the fields. These innovative, or new, techniques gave them more farmland. Egyptian Crops Ancient Egyptians grew a large variety of foods. They were the first to grind wheat into flour and to mix the flour with yeast and water to make dough rise into bread. They grew vegetables such as lettuce, radishes, asparagus, and cucumbers. Fruits included dates, figs, grapes, and watermelons. Egyptians also grew the materials for their clothes. They were the first to weave fibers from flax plants into a fabric called linen. Lightweight linen cloth was perfect for hot Egyptian days. Men wore linen wraps around their waists. Women wore loose, sleeveless dresses. Egyptians also wove marsh grasses into sandals. Egyptian Houses Egyptians built houses using bricks made of mud from the Nile mixed with chopped straw. They placed narrow windows high in the walls to reduce bright sunlight. Egyptians often painted walls white to reflect the blazing heat. They wove sticks and palm trees to make roofs. Inside, woven reed mats covered the dirt floor. Most Egyptians slept on mats covered with linen sheets. Wealthy citizens enjoyed bed frames and cushions. Egyptian nobles had fancier homes with tree-lined courtyards for shade. Some had a pool filled with lotus blossoms and fish. Poorer Egyptians simply went to the roof to cool off after sunset. They often cooked, ate, and even slept outside. Egypt\\xe2\\x80\\x99s economy depended on farming. However, the natural resources of the area allowed other economic activities to develop too. The Egyptians wanted valuable metals that were not found in the black land. For example, they wanted copper to make tools and weapons. Egyptians looked for copper as early as 6000 B.C. '}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**If input files length is greater than 50 lines, code will not work and session will crash automatically**"
      ],
      "metadata": {
        "id": "TH-CV1bEmkV9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "\n",
        "f = open(\"Greeks.txt\",\"r\") # enter the name of file to read\n",
        "full_text = f.read()\n",
        "print(len(full_text))\n",
        "\n",
        "\n",
        "classifier = pipeline(\"summarization\")\n",
        "summary=classifier(full_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
          "referenced_widgets": [
            "cd8a0294c815453fa6b7686d0ac2f976",
            "7336b308b8df4c15ac0f384fdf52c5d2",
            "f1d221aab7294356aa7c4bce2d79299b",
            "4f90afdf187c41dd950071d15d34bf79",
            "2125af534b064de399b88e5c83e22af4",
            "36913c6bfe54491292859bde2dddb0df",
            "f1114b168de048ce8daa5ff8c4733499",
            "c8e93102bdc24119b8d59c5d1d451ab5",
            "c0586f96076143daa74f71d2a6d7b070",
            "e047067dd81c4168b477233a408e2875",
            "86eaabf3c88840db88798019f80069fb",
            "c616aae5d0a741918fd6748b7336dfa4",
            "4f6ff4e38c3a4ad98070350f74bc0d17",
            "ca64fdd5fa76462a8e20e03e15a72912",
            "d141412f0d7f490c85a6f0d9e0ca07bf",
            "5bde76c5506e470f96af2f589b294856",
            "7e35b876d79347db99237708b79eb091",
            "251142c65afe4d10ab184415ee9f7fb6",
            "ed097db880d84561a587ed6f7702cace",
            "10cca60f6b354cb49ba0c364ee5aba87",
            "ee410ff90be544d48325b4a091b51156",
            "65ace3d9af7547b0bcd606642c1542c8",
            "6b80ceb339d44d3cb9ecc05c6d5803d8",
            "c39eecb538d04b4989738d84f2d34b48",
            "b3dfd722efef434799f6e243d4a519da",
            "65a5ece410d84d5ebc14c551c2b0f442",
            "0483408074c345c6b3e3676dc9d1a923",
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            "23b0bd956c6e443f9d1ffde45c47c44c",
            "881434c32d904c52a21d6af614389db2",
            "46b0a0232aa442fd8a0f20018d3728f0",
            "0c91cb5500064a51bce4513aeaf21804",
            "22d07f1543db4b17aa37f21ea18dee7f",
            "0e57a25669c24494a30ded94c59fe591",
            "da4c94fc353d4aee9a2839d70426aacd",
            "bfe4b2084917457c91f7c1ccc54efbbe",
            "bd8d0b76b59846e2a8eb437e8da91b31",
            "b263e553cbb7458dbe6ba501ce7de4d8",
            "974f27119bd1491db6086de1ea45a423",
            "250670d1385c47e2bef6d29a384efab9",
            "45e9bd8834e7477b86c8c3699d5b1b78",
            "a65793580a324d14a7a68eaf9323a7e2",
            "78d59b1992e04a11b278b49a479c8a81",
            "eb870d282786437b8582cc847565afcf",
            "e887d494cd4242b58d3314e321a680c8",
            "0f96e01a248245e298e35517e6a2c428",
            "18c8b30d2d2143188dc977ccb5f2ad4e",
            "e5cafdbe66ae4b45972319eb21cb7f36",
            "acd465910a2d47a99f80d786755509f4",
            "d1e6349c6ed645d89dd3ab1f3606ef75",
            "6ba70c3dada04e5596ba43f9ecfe48b0",
            "8287e866f3164e00b053353a346837d3",
            "3889d7ee016c441fbe9470f539760850",
            "fa99bba7613848bd88c207b31f215ce7",
            "1dcdf6523f524be1a400c0799d9fbf58"
          ]
        },
        "id": "bLDbCybbOVPb",
        "outputId": "22d1e9e5-0a37-43c6-ca55-2ba114056795"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3817\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.76k [00:00<?, ?B/s]"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary_text=summary[0]['summary_text']"
      ],
      "metadata": {
        "id": "7fK9SUbEOiub"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XE0bAcIWSklI",
        "outputId": "60251ba7-abe0-4519-b339-ea648a31018b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk import pos_tag\n",
        "from nltk import RegexpParser\n",
        "from nltk import word_tokenize\n",
        "tokens=word_tokenize(summary_text)\n",
        "print(\"After Split:\",tokens)\n",
        "tokens_tag = pos_tag(tokens)\n",
        "print(\"After Token:\",tokens_tag)\n",
        "Nouns=[]\n",
        "for token,tag in tokens_tag:\n",
        "  if tag.startswith('N'):\n",
        "    Nouns.append(token)\n",
        "\n",
        "Nouns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgMKWjGhO-Jx",
        "outputId": "d18666d7-e31a-45e3-c48f-7cc03e92cd86"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Split: ['The', 'Nile', 'River', 'fed', 'Egyptian', 'civilization', 'for', 'hundreds', 'of', 'years', '.', 'The', 'ancient', 'Egyptians', 'lived', 'in', 'narrow', 'bands', 'of', 'land', 'on', 'each', 'side', 'of', 'the', 'Nile', '.', 'They', 'called', 'this', 'region', 'the', 'black', 'land', 'because', 'of', 'the', 'fertile', 'soil', 'that', 'the', 'floods', 'deposited', '.', 'Weather', 'in', 'Egypt', 'was', 'almost', 'always', 'the', 'same', '.', 'Eight', 'months', 'of', 'the', 'year', 'were', 'sunny', 'and', 'hot', '.', 'Four', 'months', 'of', 'winter', 'were', 'sunny', 'but', 'cooler', '.']\n",
            "After Token: [('The', 'DT'), ('Nile', 'NNP'), ('River', 'NNP'), ('fed', 'VBD'), ('Egyptian', 'JJ'), ('civilization', 'NN'), ('for', 'IN'), ('hundreds', 'NNS'), ('of', 'IN'), ('years', 'NNS'), ('.', '.'), ('The', 'DT'), ('ancient', 'JJ'), ('Egyptians', 'NNPS'), ('lived', 'VBN'), ('in', 'IN'), ('narrow', 'JJ'), ('bands', 'NNS'), ('of', 'IN'), ('land', 'NN'), ('on', 'IN'), ('each', 'DT'), ('side', 'NN'), ('of', 'IN'), ('the', 'DT'), ('Nile', 'NNP'), ('.', '.'), ('They', 'PRP'), ('called', 'VBD'), ('this', 'DT'), ('region', 'NN'), ('the', 'DT'), ('black', 'JJ'), ('land', 'NN'), ('because', 'IN'), ('of', 'IN'), ('the', 'DT'), ('fertile', 'JJ'), ('soil', 'NN'), ('that', 'IN'), ('the', 'DT'), ('floods', 'NNS'), ('deposited', 'VBN'), ('.', '.'), ('Weather', 'RBR'), ('in', 'IN'), ('Egypt', 'NNP'), ('was', 'VBD'), ('almost', 'RB'), ('always', 'RB'), ('the', 'DT'), ('same', 'JJ'), ('.', '.'), ('Eight', 'NNP'), ('months', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('year', 'NN'), ('were', 'VBD'), ('sunny', 'JJ'), ('and', 'CC'), ('hot', 'JJ'), ('.', '.'), ('Four', 'CD'), ('months', 'NNS'), ('of', 'IN'), ('winter', 'NN'), ('were', 'VBD'), ('sunny', 'JJ'), ('but', 'CC'), ('cooler', 'NN'), ('.', '.')]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Nile',\n",
              " 'River',\n",
              " 'civilization',\n",
              " 'hundreds',\n",
              " 'years',\n",
              " 'Egyptians',\n",
              " 'bands',\n",
              " 'land',\n",
              " 'side',\n",
              " 'Nile',\n",
              " 'region',\n",
              " 'land',\n",
              " 'soil',\n",
              " 'floods',\n",
              " 'Egypt',\n",
              " 'Eight',\n",
              " 'months',\n",
              " 'year',\n",
              " 'months',\n",
              " 'winter',\n",
              " 'cooler']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import wordnet\n",
        "def synonym_antonym_extractor(phrase):\n",
        "     \n",
        "     synonyms =[]\n",
        "     \n",
        "\n",
        "     for syn in wordnet.synsets(phrase):\n",
        "          for l in syn.lemmas():\n",
        "               synonyms.append(l.name())\n",
        "               if l.antonyms():\n",
        "                    synonyms.append(l.antonyms()[0].name())\n",
        "\n",
        "    #  print(set(synonyms))\n",
        "    #  print(set(antonyms))\n",
        "     return set(synonyms)\n",
        "\n",
        "\n",
        "similar_words={}\n",
        "keys=[]\n",
        "for i in Nouns :\n",
        "  distractors=synonym_antonym_extractor(phrase=i)\n",
        "  if len(distractors)>4:\n",
        "    similar_words[i]=distractors\n",
        "    keys.append(i)\n",
        "print(similar_words) \n",
        "keys"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGhMg9yvQEQM",
        "outputId": "77277459-5b8f-432b-a7fa-4074065a1932"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'hundreds': {'C', '100', 'hundred', 'century', 'one_C'}, 'years': {'eld', 'long_time', 'old_age', 'years', 'class', 'geezerhood', 'year', 'age', 'days', 'yr', 'twelvemonth'}, 'bands': {'lot', 'set', 'stripe', 'stria', 'circle', 'ring', 'striation', 'banding', 'isthmus', 'dance_band', 'dance_orchestra', 'band'}, 'land': {'demesne', 'realm', 'Land', 'state', 'shoot_down', 'set_ashore', 'domain', 'down', 'put_down', 'country', 'Edwin_Herbert_Land', 'terra_firma', 'kingdom', 'Din_Land', 'body_politic', 'bring', 'land', 'set_down', 'bring_down', 'shore', 'earth', 'farming', 'solid_ground', 'commonwealth', 'res_publica', 'ground', 'estate', 'landed_estate', 'soil', 'acres', 'nation', 'dry_land'}, 'side': {'face', 'incline', 'slope', 'English', 'side_of_meat', 'position', 'bottom', 'side'}, 'region': {'realm', 'region', 'neighborhood', 'area', 'part'}, 'soil': {'filth', 'dirty', 'territory', 'land', 'begrime', 'grunge', 'ground', 'clean', 'colly', 'grime', 'soil', 'dirt', 'bemire', 'grease', 'stain'}, 'floods': {'flood_lamp', 'swamp', 'overflow', 'ebbtide', 'inundate', 'oversupply', 'rising_tide', 'inundation', 'torrent', 'floodlight', 'photoflood', 'outpouring', 'glut', 'flowage', 'flood', 'flood_tide', 'alluvion', 'deluge'}, 'Eight': {'8', 'eight', 'octonary', 'ogdoad', 'Eight', 'eighter', 'eighter_from_Decatur', 'VIII', 'octad', 'octet', 'eight-spot', 'viii', 'Ashcan_School'}, 'cooler': {'ice_chest', 'cooler', 'nerveless', 'cool', 'tank', 'coolheaded', 'warm'}}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hundreds',\n",
              " 'years',\n",
              " 'bands',\n",
              " 'land',\n",
              " 'side',\n",
              " 'region',\n",
              " 'land',\n",
              " 'soil',\n",
              " 'floods',\n",
              " 'Eight',\n",
              " 'cooler']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flashtext import KeywordProcessor\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "def tokenize_sentences(text):\n",
        "    sentences = [sent_tokenize(text)]\n",
        "    sentences = [y for x in sentences for y in x]\n",
        "    # Remove any short sentences less than 20 letters.\n",
        "    sentences = [sentence.strip() for sentence in sentences if len(sentence) > 20]\n",
        "    # print(sentences)\n",
        "    return sentences\n",
        "\n",
        "def get_sentences_for_keyword(keywords, sentences):\n",
        "    keyword_processor = KeywordProcessor() # initializing keyword processor\n",
        "    keyword_sentences = {} #initializing dictionary\n",
        "    for word in keywords:\n",
        "        keyword_sentences[word] = [] # adding to the dictionary map\n",
        "\n",
        "        keyword_processor.add_keyword(word) # adding to the extract keywords method\n",
        "    for sentence in sentences:\n",
        "        keywords_found = keyword_processor.extract_keywords(sentence)# extract keywords in sentence\n",
        "        for key in keywords_found:\n",
        "            # print(sentence)\n",
        "            keyword_sentences[key].append(sentence)\n",
        "            # print(keyword_sentences)\n",
        "\n",
        "    for key in keyword_sentences.keys():\n",
        "        values = keyword_sentences[key]\n",
        "        values = sorted(values, key=len, reverse=True)\n",
        "        if len(values)>=1:\n",
        "          keyword_sentences[key] = values\n",
        "          # print((values))\n",
        "    return keyword_sentences\n",
        "\n",
        "sentences = tokenize_sentences(summary_text)\n",
        "keyword_sentence_mapping = get_sentences_for_keyword(keys, sentences)\n",
        "        \n",
        "keyword_sentence_mapping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Folv0P5MVn2D",
        "outputId": "91371843-be8c-4b12-f9d0-854e840298b5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Eight': ['Eight months of the year were sunny and hot.'],\n",
              " 'bands': ['The ancient Egyptians lived in narrow bands of land on each side of the Nile .'],\n",
              " 'cooler': ['Four months of winter were sunny but cooler .'],\n",
              " 'floods': ['They called this region the black land because of the fertile soil that the floods deposited .'],\n",
              " 'hundreds': ['The Nile River fed Egyptian civilization for hundreds of years .'],\n",
              " 'land': ['They called this region the black land because of the fertile soil that the floods deposited .',\n",
              "  'The ancient Egyptians lived in narrow bands of land on each side of the Nile .'],\n",
              " 'region': ['They called this region the black land because of the fertile soil that the floods deposited .'],\n",
              " 'side': ['The ancient Egyptians lived in narrow bands of land on each side of the Nile .'],\n",
              " 'soil': ['They called this region the black land because of the fertile soil that the floods deposited .'],\n",
              " 'years': ['The Nile River fed Egyptian civilization for hundreds of years .']}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# keyword_sentence_mapping['Sun'][0]"
      ],
      "metadata": {
        "id": "H2Lx7gucdBqL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import random\n",
        "'''\n",
        "for keyword in keyword_sentence_mapping:\n",
        "    # if len(keyword_sentence_mapping[keyword][0])>10:\n",
        "      if (len(keyword_sentence_mapping[keyword]))>=1:\n",
        "        wordsense = get_wordsense(keyword_sentence_mapping[keyword][0],keyword)\n",
        "        if wordsense:\n",
        "            distractors = get_distractors_wordnet(wordsense,keyword)\n",
        "            if len(distractors) ==0:\n",
        "                distractors = get_distractors_conceptnet(keyword)\n",
        "            if len(distractors) != 0:\n",
        "                key_distractor_list[keyword] = distractors\n",
        "        else:\n",
        "            \n",
        "            distractors = get_distractors_conceptnet(keyword)\n",
        "            if len(distractors) != 0:\n",
        "                key_distractor_list[keyword] = distractors\n",
        "'''\n",
        "index = 1\n",
        "print (\"#############################################################################\")\n",
        "print (\"                   MCQ GENERATION FROM TEXT                                   \")\n",
        "print (\"#############################################################################\\n\\n\")\n",
        "for each in keys:\n",
        "    choices=[]\n",
        "\n",
        "    sentence = keyword_sentence_mapping[each][0]\n",
        "    # print('sentence is ',sentence)\n",
        "    pattern = re.compile(each, re.IGNORECASE)\n",
        "    output = pattern.sub( \" _______ \", sentence)\n",
        "    print (\"%s)\"%(index),output)\n",
        "    \n",
        "    dups=list(similar_words[each])\n",
        "    # print(dups)\n",
        "    choices=dups[:3]\n",
        "    choices.append(each)\n",
        "    # choices = [each.capitalize()] + similar_words[each]\n",
        "    print(choices)\n",
        "    top4choices = choices[:4]\n",
        "    random.shuffle(top4choices)\n",
        "    optionchoices = ['a','b','c','d']\n",
        "    for idx,choice in enumerate(top4choices):\n",
        "          print (\"\\t\",optionchoices[idx],\")\",\" \",choice)\n",
        "    print (\"\\nMore options: \", dups[3:20],\"\\n\")\n",
        "    index = index + 1\n",
        "    print('correct answers is :',each,'\\n')\n",
        "\n",
        "    if index>10:\n",
        "      break\n",
        "    '''\n",
        "    # print('count is ',sentence.lower().count(each.lower()))\n",
        "    if sentence.lower().count(each.lower())==1 :\n",
        "      pattern = re.compile(each, re.IGNORECASE)\n",
        "      output = pattern.sub( \" _______ \", sentence)\n",
        "      print (\"%s)\"%(index),output)\n",
        "      choices = [each.capitalize()] + key_distractor_list[each]\n",
        "      top4choices = choices[:4]\n",
        "      random.shuffle(top4choices)\n",
        "      optionchoices = ['a','b','c','d']\n",
        "      for idx,choice in enumerate(top4choices):\n",
        "          print (\"\\t\",optionchoices[idx],\")\",\" \",choice)\n",
        "      print (\"\\nMore options: \", choices[4:20],\"\\n\")\n",
        "      index = index + 1\n",
        "      print('correct answers is :',each,'\\n') '''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fu498qqWYUDI",
        "outputId": "cfe81843-d8dd-4ee2-ba05-467707866e59"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#############################################################################\n",
            "                   MCQ GENERATION FROM TEXT                                   \n",
            "#############################################################################\n",
            "\n",
            "\n",
            "1) The Nile River fed Egyptian civilization for  _______  of years .\n",
            "['C', '100', 'hundred', 'hundreds']\n",
            "\t a )   C\n",
            "\t b )   100\n",
            "\t c )   hundreds\n",
            "\t d )   hundred\n",
            "\n",
            "More options:  ['century', 'one_C'] \n",
            "\n",
            "correct answers is : hundreds \n",
            "\n",
            "2) The Nile River fed Egyptian civilization for hundreds of  _______  .\n",
            "['eld', 'long_time', 'old_age', 'years']\n",
            "\t a )   long_time\n",
            "\t b )   years\n",
            "\t c )   eld\n",
            "\t d )   old_age\n",
            "\n",
            "More options:  ['years', 'class', 'geezerhood', 'year', 'age', 'days', 'yr', 'twelvemonth'] \n",
            "\n",
            "correct answers is : years \n",
            "\n",
            "3) The ancient Egyptians lived in narrow  _______  of land on each side of the Nile .\n",
            "['lot', 'set', 'stripe', 'bands']\n",
            "\t a )   lot\n",
            "\t b )   set\n",
            "\t c )   stripe\n",
            "\t d )   bands\n",
            "\n",
            "More options:  ['stria', 'circle', 'ring', 'striation', 'banding', 'isthmus', 'dance_band', 'dance_orchestra', 'band'] \n",
            "\n",
            "correct answers is : bands \n",
            "\n",
            "4) They called this region the black  _______  because of the fertile soil that the floods deposited .\n",
            "['demesne', 'realm', 'Land', 'land']\n",
            "\t a )   realm\n",
            "\t b )   Land\n",
            "\t c )   demesne\n",
            "\t d )   land\n",
            "\n",
            "More options:  ['state', 'shoot_down', 'set_ashore', 'domain', 'down', 'put_down', 'country', 'Edwin_Herbert_Land', 'terra_firma', 'kingdom', 'Din_Land', 'body_politic', 'bring', 'land', 'set_down', 'bring_down', 'shore'] \n",
            "\n",
            "correct answers is : land \n",
            "\n",
            "5) The ancient Egyptians lived in narrow bands of land on each  _______  of the Nile .\n",
            "['face', 'incline', 'slope', 'side']\n",
            "\t a )   incline\n",
            "\t b )   slope\n",
            "\t c )   side\n",
            "\t d )   face\n",
            "\n",
            "More options:  ['English', 'side_of_meat', 'position', 'bottom', 'side'] \n",
            "\n",
            "correct answers is : side \n",
            "\n",
            "6) They called this  _______  the black land because of the fertile soil that the floods deposited .\n",
            "['realm', 'region', 'neighborhood', 'region']\n",
            "\t a )   realm\n",
            "\t b )   region\n",
            "\t c )   neighborhood\n",
            "\t d )   region\n",
            "\n",
            "More options:  ['area', 'part'] \n",
            "\n",
            "correct answers is : region \n",
            "\n",
            "7) They called this region the black  _______  because of the fertile soil that the floods deposited .\n",
            "['demesne', 'realm', 'Land', 'land']\n",
            "\t a )   realm\n",
            "\t b )   Land\n",
            "\t c )   land\n",
            "\t d )   demesne\n",
            "\n",
            "More options:  ['state', 'shoot_down', 'set_ashore', 'domain', 'down', 'put_down', 'country', 'Edwin_Herbert_Land', 'terra_firma', 'kingdom', 'Din_Land', 'body_politic', 'bring', 'land', 'set_down', 'bring_down', 'shore'] \n",
            "\n",
            "correct answers is : land \n",
            "\n",
            "8) They called this region the black land because of the fertile  _______  that the floods deposited .\n",
            "['filth', 'dirty', 'territory', 'soil']\n",
            "\t a )   filth\n",
            "\t b )   dirty\n",
            "\t c )   territory\n",
            "\t d )   soil\n",
            "\n",
            "More options:  ['land', 'begrime', 'grunge', 'ground', 'clean', 'colly', 'grime', 'soil', 'dirt', 'bemire', 'grease', 'stain'] \n",
            "\n",
            "correct answers is : soil \n",
            "\n",
            "9) They called this region the black land because of the fertile soil that the  _______  deposited .\n",
            "['flood_lamp', 'swamp', 'overflow', 'floods']\n",
            "\t a )   swamp\n",
            "\t b )   overflow\n",
            "\t c )   flood_lamp\n",
            "\t d )   floods\n",
            "\n",
            "More options:  ['ebbtide', 'inundate', 'oversupply', 'rising_tide', 'inundation', 'torrent', 'floodlight', 'photoflood', 'outpouring', 'glut', 'flowage', 'flood', 'flood_tide', 'alluvion', 'deluge'] \n",
            "\n",
            "correct answers is : floods \n",
            "\n",
            "10)  _______  months of the year were sunny and hot.\n",
            "['8', 'eight', 'octonary', 'Eight']\n",
            "\t a )   octonary\n",
            "\t b )   8\n",
            "\t c )   Eight\n",
            "\t d )   eight\n",
            "\n",
            "More options:  ['ogdoad', 'Eight', 'eighter', 'eighter_from_Decatur', 'VIII', 'octad', 'octet', 'eight-spot', 'viii', 'Ashcan_School'] \n",
            "\n",
            "correct answers is : Eight \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-9VQHn8uc1ae"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}