Web_app_NLP_summarizer.ipynb

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      "authorship_tag": "ABX9TyMvzrQ5fQpPORE+2Gy/y1yZ",
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    "kernelspec": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/devshah21/NLP_summarizer/blob/main/Web_app_NLP_summarizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "W7CdTxAvvZzO",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "14285428-5ac6-4587-f0f4-0588a2e33e90"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n",
            "2023-01-09 14:57:22.696692: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting en-core-web-sm==3.4.1\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.5.0,>=3.4.0 in /usr/local/lib/python3.8/dist-packages (from en-core-web-sm==3.4.1) (3.4.4)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (21.3)\n",
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            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n"
          ]
        }
      ],
      "source": [
        "!pip install -U spacy\n",
        "!python -m spacy download en_core_web_sm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "from spacy import displacy\n",
        "from string import punctuation\n",
        "import nltk\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "metadata": {
        "id": "jMOLZwCxwNcp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27827f36-dd1a-4d37-8c50-9c1647f53fa0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n",
            "[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 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]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wrds = STOP_WORDS"
      ],
      "metadata": {
        "id": "evM-vOM2zpvA"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sampletext = input()"
      ],
      "metadata": {
        "id": "aXuGFJ5DzyVc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e49094f2-415d-49d7-f66c-baaf5554c28e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Backpropagation is an algorithm that is used for training neural networks; the fundamental idea behind neural networks is that each neuron receives an input from other neurons and they use that as an input to compute an output which is transmitted to the other neurons in the next layer. The entire process of training a neural network is highly dependent on adjusting weights and biases of the connections between neurons. In order to do this, backpropagation is employed. Backpropagation is an efficient algorithm for learning the weights and biases of a neural network. It works by using the chain rule of calculus to compute the gradient of the error function with respect to the network’s weights and biases. The gradient is a vector that points in the direction of steepest increase in the error function, and can be used to update the weights and biases in a way that reduces the error.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_tokenize(sampletext)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ig89pvWb1JHo",
        "outputId": "adcaae9c-06ed-4167-9707-1ea7a7048469"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " '.']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "from nltk import pos_tag\n",
        "nltk.download('universal_tagset')\n",
        "from yellowbrick.text import PosTagVisualizer\n",
        "\n",
        "tokenizing = word_tokenize(sampletext)\n",
        "taging = pos_tag(tokenizing, tagset = \"universal\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MK-DEMRO1cG9",
        "outputId": "e4ac27b1-73fa-4196-814b-c616bd59b94c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/universal_tagset.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "punctuation = punctuation + '\\n'"
      ],
      "metadata": {
        "id": "hLU7gFvFNlUP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordcount = {}\n",
        "for word in tokenizing:\n",
        "  if word not in wrds:\n",
        "    if word not in wordcount.keys():\n",
        "      wordcount[word] = 1\n",
        "    else:\n",
        "      wordcount[word] += 1"
      ],
      "metadata": {
        "id": "LsGIXlC6OnP9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "highestoccurence = max(wordcount.values())\n",
        "for word in wordcount:\n",
        "  wordcount[word] = wordcount[word]/highestoccurence\n"
      ],
      "metadata": {
        "id": "6BcwLOx2vj44"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import sent_tokenize\n",
        "sentences = sent_tokenize(sampletext)\n",
        "\n",
        "\n",
        "# Calculate the score for each sentence\n",
        "sentence_scores = {}\n",
        "for sentence in sentences:\n",
        "    tokens = word_tokenize(sentence)\n",
        "    sentence_score = 0\n",
        "    for token in tokens:\n",
        "        if token in wordcount:\n",
        "            sentence_score += wordcount[token]\n",
        "    # Add the score for the sentence to the dictionary\n",
        "    sentence_scores[sentence] = sentence_score"
      ],
      "metadata": {
        "id": "Sx4ybcUbx_Es"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from heapq import nlargest"
      ],
      "metadata": {
        "id": "rd_MjEVezNcM"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "sentencecount = int(len(sentences))*0.25\n",
        "sentencecount = math.ceil(sentencecount)"
      ],
      "metadata": {
        "id": "RjjJPU9ozjSB"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_summary = nlargest(sentencecount, sentence_scores, key = sentence_scores.get)"
      ],
      "metadata": {
        "id": "SZG7wueA0Cna"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary = \" \".join(list_summary)"
      ],
      "metadata": {
        "id": "XhUk_Xtl1KMP"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYr0g7wyDNb4",
        "outputId": "0139a193-0dae-4715-a9ee-a54a9ba15c2a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/14.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/14.2 MB\u001b[0m \u001b[31m131.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/14.2 MB\u001b[0m \u001b[31m143.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m153.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m153.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 KB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m63.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr"
      ],
      "metadata": {
        "id": "0rVqq_CdDJ6P"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def greet(Input_text):\n",
        "  word_tokenize(Input_text)\n",
        "  tokenizing = word_tokenize(Input_text)\n",
        "  taging = pos_tag(tokenizing, tagset = \"universal\")\n",
        "  wordcount = {}\n",
        "  for word in tokenizing:\n",
        "    if word not in wrds:\n",
        "      if word not in wordcount.keys():\n",
        "        wordcount[word] = 1\n",
        "      else:\n",
        "        wordcount[word] += 1\n",
        "  highestoccurence = max(wordcount.values())\n",
        "  for word in wordcount:\n",
        "    wordcount[word] = wordcount[word]/highestoccurence\n",
        "  from nltk.tokenize import sent_tokenize\n",
        "  sentences = sent_tokenize(Input_text)\n",
        "  # Calculate the score for each sentence\n",
        "  sentence_scores = {}\n",
        "  for sentence in sentences:\n",
        "      tokens = word_tokenize(sentence)\n",
        "      sentence_score = 0\n",
        "      for token in tokens:\n",
        "          if token in wordcount:\n",
        "              sentence_score += wordcount[token]\n",
        "      # Add the score for the sentence to the dictionary\n",
        "      sentence_scores[sentence] = sentence_score\n",
        "  from heapq import nlargest\n",
        "  import math\n",
        "  sentencecount = int(len(sentences))*0.25\n",
        "  sentencecount = math.ceil(sentencecount)\n",
        "  list_summary = nlargest(sentencecount, sentence_scores, key = sentence_scores.get)\n",
        "  summary = \" \".join(list_summary)\n",
        "  return summary"
      ],
      "metadata": {
        "id": "A3PMHeL3DWhY"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\").launch(share = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "69yIP9ndDdYz",
        "outputId": "c9753b5e-c342-4ef9-ac3a-ab8370a2eedb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://fcc902fa-0ae4-488c.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://fcc902fa-0ae4-488c.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}