Streamlit_Youtube_summary.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Streamlit-Youtube summary.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPp75tO4L3+I1M/TudXoPqq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Arjun-krish-10/NLP/blob/main/Streamlit_Youtube_summary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPBodbhGRlT_",
        "outputId": "c9a5c0bf-f554-43eb-b8bc-edf6bc6c9821"
      },
      "source": [
        "! pip install streamlit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMePJFyrRry5",
        "outputId": "0315da2b-e1f4-4d12-aa68-128fb52ca9c6"
      },
      "source": [
        "!pip install pyngrok==4.1.1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyngrok==4.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/a9/de2e15c92eb3aa4a2646ce3a7542317eb69ac47f667578ce8bf916320847/pyngrok-4.1.1.tar.gz\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyngrok==4.1.1) (0.16.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from pyngrok==4.1.1) (3.13)\n",
            "Building wheels for collected packages: pyngrok\n",
            "  Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyngrok: filename=pyngrok-4.1.1-cp37-none-any.whl size=15985 sha256=7847a17445cedd96310f9cf578d0c4545bbc4b75bea32d6b04a6a0fb94a191b8\n",
            "  Stored in directory: /root/.cache/pip/wheels/97/71/0d/1695f7c8815c0beb3b5d9b35d6eec9243c87e6070fbe3977fa\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-4.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOLFuHQ0EeMi",
        "outputId": "d71801ce-7b3c-43b9-e3ed-2b8405e7fb66"
      },
      "source": [
        "!pip install transformers "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/92/6153f4912b84ee1ab53ab45663d23e7cf3704161cb5ef18b0c07e207cef2/transformers-4.7.0-py3-none-any.whl (2.5MB)\n",
            "\u001b[K     |████████████████████████████████| 2.5MB 8.1MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/e2/df3543e8ffdab68f5acc73f613de9c2b155ac47f162e725dcac87c521c11/tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3MB 47.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (4.5.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
            "\u001b[K     |████████████████████████████████| 901kB 49.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from transformers) (3.13)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Collecting huggingface-hub==0.0.8\n",
            "  Downloading https://files.pythonhosted.org/packages/a1/88/7b1e45720ecf59c6c6737ff332f41c955963090a18e72acbcbeac6b25e86/huggingface_hub-0.0.8-py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\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",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\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: 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) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Installing collected packages: tokenizers, sacremoses, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.0.8 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fa9unQtwIOTu",
        "outputId": "bdb3e409-eda9-4393-fd0b-23988c101e77"
      },
      "source": [
        "!pip install youtube_transcript_api"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting youtube_transcript_api\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/24/581920922d80f6d6d2feb30787aa4d9e298db83cb081c1de1a63cc7cf121/youtube_transcript_api-0.4.1-py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from youtube_transcript_api) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->youtube_transcript_api) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->youtube_transcript_api) (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->youtube_transcript_api) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->youtube_transcript_api) (2.10)\n",
            "Installing collected packages: youtube-transcript-api\n",
            "Successfully installed youtube-transcript-api-0.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnlYYxVRIFic"
      },
      "source": [
        "# from transformers import pipeline\n",
        "# summary = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTcE6VarMWA6"
      },
      "source": [
        "import pickle\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IytVx3w0VVWm",
        "outputId": "aa3e0593-9b6e-4015-dec0-a065ad16020a"
      },
      "source": [
        "%%writefile app.py\n",
        "\n",
        "import streamlit as st\n",
        "import re\n",
        "import pandas as pd\n",
        "import math\n",
        "from youtube_transcript_api import YouTubeTranscriptApi\n",
        "import time\n",
        "import multiprocessing\n",
        "# from transformers import pipeline\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline,AutoModel\n",
        "import torch\n",
        "\n",
        "\n",
        "model_ids = {\n",
        "    # 'Distilbart 12-9': 'sshleifer/distilbart-cnn-12-6',\n",
        "    # 'Google xsum': 'google/pegasus-xsum',\n",
        "    # 'Distilbart 6-6': 'sshleifer/distilbart-cnn-6-6',\n",
        "    'Bart large CNN' : 'facebook/bart-large-cnn'\n",
        "}\n",
        "\n",
        "device = 0 if torch.cuda.is_available() else -1\n",
        "\n",
        "\n",
        "@st.cache(allow_output_mutation=True)\n",
        "def load_models():\n",
        "    # return {{id: AutoModelForSeq2SeqLM.from_pretrained(mod)} if id != 'Bart large CNN' else {id:AutoModel.from_pretrained(mod)} for id,mod in model_ids.items() }\n",
        "    return {id:AutoModel.from_pretrained(mod) for id,mod in model_ids.items() }\n",
        "\n",
        "models = load_models()\n",
        "\n",
        "@st.cache(allow_output_mutation=True, show_spinner=False)\n",
        "def load_tokenizer(tok_id):\n",
        "    return AutoTokenizer.from_pretrained(tok_id)\n",
        "\n",
        "@st.cache(allow_output_mutation=True, show_spinner=False)\n",
        "# def summary_fn1(nli_model_id):\n",
        "def summary_fn1():\n",
        "    # classifier = pipeline('summarization', model=models[nli_model_id], tokenizer=load_tokenizer(nli_model_id), device=device)\n",
        "    # return pipeline('summarization', model=models[nli_model_id], tokenizer=load_tokenizer(nli_model_id), device=device)\n",
        "    return pipeline('summarization', model='facebook/bart-large-cnn')#, tokenizer=load_tokenizer(nli_model_id), device=device)\n",
        "\n",
        "# models = load_models()\n",
        "classifier = summary_fn1()\n",
        "\n",
        "def summarizer(sequence,q,i):\n",
        "    outputs = classifier(sequence)\n",
        "    q.put({i:outputs[0]['summary_text']})\n",
        "    st.write(q)\n",
        "    return #outputs[0]['summary_text']\n",
        "\n",
        "# @st.cache\n",
        "# def_load_model():\n",
        "#     summary = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')\n",
        "# import pandas as pd\n",
        "# import re\n",
        "# # pd.set_option('display.max_colwidth',1000)\n",
        "# from transformers import pipeline\n",
        "# import math\n",
        "# # from tqdm import tqdm\n",
        "# # from multiprocessing.dummy import Pool\n",
        "# # pool = Pool(8)\n",
        "import concurrent.futures\n",
        "\n",
        "# def get_transcript(link):\n",
        "#   try:\n",
        "#     transcript = YouTubeTranscriptApi.get_transcript('link')\n",
        "#   except:\n",
        "#     st.except('Subtitles are disabled for this video')\n",
        "def text_fn(link,row = 500,start = 0 ,end = 0):\n",
        "    try:\n",
        "        index = re.search('v=',link).end()\n",
        "    except:\n",
        "        st.error('Enter valid link')\n",
        "        return\n",
        "    api_seq = link[index:]\n",
        "    try:\n",
        "        tr = YouTubeTranscriptApi.get_transcript(api_seq)\n",
        "    except:\n",
        "        st.error('Subtitles not present')\n",
        "        return\n",
        "    df = pd.DataFrame(tr)\n",
        "    # if (start != 0) & (end != 0):\n",
        "    #   df\n",
        "    text = ' '.join(df['text'])\n",
        "    split1 = math.ceil(len(text)/row)\n",
        "    \n",
        "    sum_text = (text[i*row : (i+1)*row] for i in range(split1))\n",
        "    return list(sum_text)\n",
        "# @st.cache(suppress_st_warning=True):\n",
        "# def summary_model():  \n",
        "#     summary = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')\n",
        "\n",
        "# def summary_fn1(q,text,i):\n",
        "# def summary_fn1(text):\n",
        "#     s = summary(text,min_length = 50)\n",
        "# #   q.put({i:s[0]['summary_text']})\n",
        "#     return s[0]['summary_text']\n",
        "\n",
        "# def main():\n",
        "def text_video():\n",
        "    # summary = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')\n",
        "    # model_desc = st.sidebar.selectbox('Model', list(model_ids.keys()), 0,key = 'mod')\n",
        "    # values = st.sidebar.slider('Word range', 500,2000,key='slider')\n",
        "    # st.title(\"Youtube summary\")\n",
        "    # st.subheader(\"Get your summary of the youtube video here\")\n",
        "    # link = st.text_input('Enter your youtube link',key = 'input')\n",
        "    # model_id = model_ids[model_desc]\n",
        "\n",
        "    # if st.button('Get summary'):\n",
        "        # st.slider()\n",
        "    text_vid = text_fn(link,row=values)\n",
        "    t_vid = text_vid\n",
        "    return t_vid\n",
        "        # final = []\n",
        "        # for i in range(len(text)):\n",
        "        #     final.append(summary_fn1(model_id,text[i]))\n",
        "        # sum_text = ' '.join(final)\n",
        "        # st.write(sum_text)\n",
        "        # starttime = time.time()\n",
        "        # processes = []\n",
        "        # unsorted_result = []\n",
        "        # q = multiprocessing.Queue()\n",
        "        # for i in range(len(text)):\n",
        "            \n",
        "        #     p = multiprocessing.Process(target=summary_fn1, args=(model_id,text[i],q,i))\n",
        "        #     processes.append(p)\n",
        "        #     p.start()\n",
        "            \n",
        "        # for process in processes:\n",
        "        #     process.join()\n",
        "        #     st.write(process)\n",
        "        #     st.write(q.get())\n",
        "        #     # unsorted_result.append(q.get())\n",
        "        #     # q.task_done()\n",
        "        #     # st.write(i)\n",
        "            \n",
        "        # print('Time taken = {} seconds'.format(time.time() - starttime))\n",
        "        # st.write(unsorted_result)\n",
        "        # st.write(len(text))\n",
        "        # stime = time.time()\n",
        "        # with concurrent.futures.ProcessPoolExecutor() as executor:\n",
        "        #     results = executor.map(summary_fn1,text)\n",
        "        # st.write(time.time() - stime)\n",
        "        # for result in results:\n",
        "        #     st.write(result)\n",
        "# def main():\n",
        "#     fin_text = text()\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    model_desc = st.sidebar.selectbox('Model', list(model_ids.keys()), 0)\n",
        "    values = st.sidebar.slider('Word range', 500,2000)\n",
        "    st.title(\"Youtube summary\")\n",
        "    st.subheader(\"Get your summary of the youtube video here\")\n",
        "    link = st.text_input('Enter your youtube link')\n",
        "    model_id = model_ids[model_desc]\n",
        "\n",
        "    if st.button('Get summary'):\n",
        "        # st.slider()\n",
        "        fin_text = text_fn(link,row=values)\n",
        "\n",
        "        fin_text = text_video()\n",
        "        starttime = time.time()\n",
        "        processes = []\n",
        "        unsorted_result = []\n",
        "        q = multiprocessing.Queue()\n",
        "        for i in range(len(fin_text)):\n",
        "                \n",
        "            p = multiprocessing.Process(target=summarizer, args=(fin_text[i],q,i))\n",
        "            processes.append(p)\n",
        "            p.start()\n",
        "                \n",
        "        for process in processes:\n",
        "            \n",
        "            st.write(process)\n",
        "            st.write(q.get())\n",
        "            process.join()\n",
        "        st.write('Time taken = {} seconds'.format(time.time() - starttime))\n",
        "        # stime = time.time()\n",
        "        # with concurrent.futures.ProcessPoolExecutor() as executor:\n",
        "        #     results = executor.map(summary_fn1,fin_text)\n",
        "        # st.write(time.time() - stime)\n",
        "        # for result in results:\n",
        "        #     st.write(result)\n",
        "    # main()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing app.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kGr6MLsrwhh"
      },
      "source": [
        "# !touch app.py\n",
        "# !streamlit run app.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJhm8W_qqDpS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ic0B1yudLXog",
        "outputId": "c116dd89-3cd6-4f12-dbc7-d0f9749bce8a"
      },
      "source": [
        "!ngrok authtoken 1rmvOrqc2OvfRyCBtYSBaPw7roC_35VqwkJoQoowoWpmbWH31"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "481DeQomLgXd",
        "outputId": "b547e993-1e82-4b51-f82f-69a145f9b24b"
      },
      "source": [
        "!nohup streamlit run app.py &"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nohup: appending output to 'nohup.out'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B17ucJBoWzCX"
      },
      "source": [
        "# !streamlit run --server.port 80 app.py >/dev/null"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6d93o3UL13C"
      },
      "source": [
        "from pyngrok import ngrok\n",
        "url = ngrok.connect(port=8501)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RxKFhE1DMGIi",
        "outputId": "395e4fcc-343f-43f8-f39f-d347c7bda946"
      },
      "source": [
        "url"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'http://737368347b7d.ngrok.io'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upqAEBFKMeWp"
      },
      "source": [
        "assert False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IP547v8QMUVY"
      },
      "source": [
        "# !cat content/nohup.out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5apFR8ySQGM"
      },
      "source": [
        "ngrok.kill()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}