News summarization & Topic Prediction.ipynb

{
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  "metadata": {
    "colab": {
      "name": "News summarization & Topic Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1fJClNYCRQh0OQ6zORPwam8fnpLO7FNb3",
      "authorship_tag": "ABX9TyPygfU00OkzmDEmRZ1h2tl9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pranshurastogi29/News-summarization-Topic-prediction/blob/main/News_summarization_%26_Topic_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch\n",
        "!pip install colorama\n",
        "!pip install transformers\n",
        "!pip install -q streamlit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUsEZbSPfdP2",
        "outputId": "384c8dd8-2f02-4632-b51a-9996b21c24ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "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",
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            "\u001b[?25h  Building wheel for blinker (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",
            "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.24 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.7.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.31.1 which is incompatible.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip ngrok-stable-linux-amd64.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OSKpxi_hMkP",
        "outputId": "f9353a0c-9e94-43bd-c153-fc16c3fa0437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-21 12:03:01--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 54.237.133.81, 18.205.222.128, 52.202.168.65, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|54.237.133.81|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13832437 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.19M  15.8MB/s    in 0.8s    \n",
            "\n",
            "2022-01-21 12:03:02 (15.8 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13832437/13832437]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "S0i52R58NJKC",
        "outputId": "fed89782-dd7d-409b-86e2-9f63e8057429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.10.0+cu111'"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle\n",
        "!mkdir ~/.kaggle\n",
        "%cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d pranshu29/plain-roberta-base-train\n",
        "!unzip plain-roberta-base-train.zip -d roberta_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvSnHNgh86rV",
        "outputId": "c14ed48e-ba67-4c29-ba35-e06d9c823220"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
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            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Downloading plain-roberta-base-train.zip to /content\n",
            " 96% 280M/291M [00:02<00:00, 134MB/s]\n",
            "100% 291M/291M [00:02<00:00, 127MB/s]\n",
            "Archive:  plain-roberta-base-train.zip\n",
            "  inflating: roberta_train/last-checkpoint.bin  \n",
            "  inflating: roberta_train/out.csv   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "from transformers import pipeline\n",
        "import time\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import joblib\n",
        "from transformers import AutoModel, AutoTokenizer, AutoConfig\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import pickle \n",
        "\n",
        "class ToxicSimpleNNModel(nn.Module):\n",
        "\n",
        "    def __init__(self, path):\n",
        "        super(ToxicSimpleNNModel, self).__init__()\n",
        "        self.backbone = AutoModel.from_pretrained(path)\n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "        self.linear = nn.Linear(in_features=self.backbone.pooler.dense.out_features*2,out_features=8)\n",
        "        \n",
        "    def forward(self, input_ids, attention_masks):\n",
        "        seq_x, _= self.backbone(input_ids=input_ids, attention_mask=attention_masks, return_dict=False)\n",
        "        apool = torch.mean(seq_x, 1)\n",
        "        mpool, _ = torch.max(seq_x, 1)\n",
        "        x = torch.cat((apool, mpool), 1)\n",
        "        x = self.dropout(x)\n",
        "        return self.linear(x)\n",
        "\n",
        "def load_topic_model(base_path, model_path):\n",
        "  net = ToxicSimpleNNModel(base_path)\n",
        "  net.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))\n",
        "  return net\n",
        "\n",
        "@st.cache(allow_output_mutation=True,suppress_st_warning=True)\n",
        "def Topic_generation_load(base_path, model_path, tokenizer_path):\n",
        "    print('loading topic_model')\n",
        "    with open(tokenizer_path , 'rb') as f: \n",
        "      tokenizer = pickle.load(f)\n",
        "    model = load_topic_model(base_path, model_path)\n",
        "    return model , tokenizer\n",
        "\n",
        "@st.cache(allow_output_mutation=True,suppress_st_warning=True)\n",
        "def load_summarization_model():\n",
        "    print('loading summarization model')\n",
        "    summarization_pipe = pipeline('summarization', model = 'sshleifer/distilbart-cnn-6-6')\n",
        "    print('sentiment model loading')\n",
        "    return summarization_pipe\n",
        "\n",
        "def get_summarization(text, summarization, max_lenght):\n",
        "  return summarization(text, max_length=max_lenght)[0]['summary_text']\n",
        "\n",
        "\n",
        "def predict_topic(text, tokenizer):\n",
        "  encoded = tokenizer.encode_plus(\n",
        "            text, \n",
        "            add_special_tokens=True, \n",
        "            max_length=512, \n",
        "            pad_to_max_length=True\n",
        "        )\n",
        "  tokens = torch.tensor(encoded['input_ids']).unsqueeze(0)\n",
        "  attention_masks = torch.tensor(encoded['attention_mask']).unsqueeze(0)\n",
        "  outputs = model(tokens, attention_masks)\n",
        "  topics = nn.functional.sigmoid(outputs).detach().numpy()\n",
        "  _ , topics = torch.topk(torch.tensor(topics), dim = 1, k = 3)\n",
        "  topics = np.array(topics)\n",
        "  top_dic = {'0':'business','1':'elections','2':'entertainment',\n",
        "             '3':'news','4':'opinion','5':'sci-tech','6':'society',\n",
        "             '7':'sport'}\n",
        "  l = []\n",
        "  for i in topics[0]:\n",
        "    l.append(top_dic[str(i)])\n",
        "  return l\n",
        "\n",
        "summarization_pipe = load_summarization_model()\n",
        "model , tokenizer = Topic_generation_load('/content/drive/MyDrive/tiny-bert' ,'model.bin', 'tokenizer.obj')\n",
        "\n",
        "st.title('News Summary Generation and Topic Prediction')\n",
        "\n",
        "st.markdown('Here you can enter the News in first text box and can get news summary around the subject')\n",
        "\n",
        "text = st.text_input('Enter News here:',key=0)\n",
        "\n",
        "if st.checkbox('Start Generate Summary'):\n",
        "    st.write('uncheck the box if you are done')\n",
        "    option = st.sidebar.selectbox(label='Max_Lenght',options=['20','40','50'])\n",
        "    summary = get_summarization(text, summarization_pipe, int(option))\n",
        "    st.write('Final summary ' + summary)\n",
        "else: pass\n",
        "\n",
        "st.markdown('Once done you can get the top Topics the news relate too')\n",
        "\n",
        "if st.button('Predict Topics'):\n",
        "  l = predict_topic(text, tokenizer)\n",
        "  st.markdown('Top topics related are '+' , '.join(l))\n",
        "else: pass\n"
      ],
      "metadata": {
        "id": "253rGR7EhVFt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ac8bb0e-60bb-4bc2-b49e-d391c55f835f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!./ngrok authtoken 21yJFX87jVygzsrMEb2rePp0j1G_K3UkVeGWRtiBDRmuGFRJ"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p69NL7jxBQv8",
        "outputId": "a24a5a22-088c-42ad-d4eb-a07d4499414b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_ipython().system_raw('./ngrok http 8501 &')"
      ],
      "metadata": {
        "id": "SVqBlD0GhfgC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    'import sys, json; print(\"Execute the next cell and the go to the following URL: \" +json.load(sys.stdin)[\"tunnels\"][0][\"public_url\"])'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YfCIBfq-ob1",
        "outputId": "04408d65-5e10-4ed2-e9e1-d07fd0ed5c94"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execute the next cell and the go to the following URL: https://3cff-34-83-152-139.ngrok.io\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run \"app.py\""
      ],
      "metadata": {
        "id": "wxPZZH12hjZk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c404e20-cc22-4765-ef5b-46a051f893f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.2:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://34.83.152.139:8501\u001b[0m\n",
            "\u001b[0m\n",
            "loading summarization model\n",
            "sentiment model loading\n",
            "loading topic_model\n",
            "Some weights of the model checkpoint at /content/drive/MyDrive/tiny-bert were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Wt3UMCtsJuMY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}