summarize_the_book.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-jeobLKAX4-N"
      },
      "outputs": [],
      "source": [
        "!pip install langchain google-cloud-aiplatform google-auth PyPDF2 ratelimit backoff reportlab >/dev/null"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "NVyJGJSVYx1_"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import vertexai\n",
        "import warnings\n",
        "import backoff\n",
        "from langchain.chat_models import ChatVertexAI\n",
        "import PyPDF2\n",
        "import ratelimit\n",
        "from google.api_core import exceptions\n",
        "from tqdm import tqdm\n",
        "from vertexai.language_models import TextGenerationModel\n",
        "from reportlab.lib.styles import ParagraphStyle\n",
        "from reportlab.lib.pagesizes import letter\n",
        "from reportlab.lib.styles import getSampleStyleSheet\n",
        "from reportlab.platypus import SimpleDocTemplate, Paragraph\n",
        "from langchain.chat_models import ChatVertexAI\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "kTYG4NOQZec4"
      },
      "outputs": [],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import vertexai\n",
        "\n",
        "PROJECT_ID = \"your-project-id-here\"  # @param {type:\"string\"}\n",
        "vertexai.init(project=PROJECT_ID, location=\"us-central1\")"
      ],
      "metadata": {
        "id": "dCiurhG7peey"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "uuJ-GnWoysRk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_model = TextGenerationModel.from_pretrained(\"text-bison@002\")"
      ],
      "metadata": {
        "id": "bw5mXuJ0ysO5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def multiple_replace(string, rep_dict):\n",
        "    pattern = re.compile(\"|\".join([re.escape(k) for k in sorted(rep_dict,key=len,reverse=True)]), flags=re.DOTALL)\n",
        "    return pattern.sub(lambda x: rep_dict[x.group(0)], string)"
      ],
      "metadata": {
        "id": "IG6C6fyYJ8L9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_pdf(input_text, output_filename):\n",
        "    pdf = SimpleDocTemplate(output_filename, pagesize=letter)\n",
        "    styles = getSampleStyleSheet()\n",
        "    justified_style = ParagraphStyle(\n",
        "        \"Justified\",\n",
        "        parent=styles[\"Normal\"],\n",
        "        alignment=0,\n",
        "        spaceBefore=12,\n",
        "        spaceAfter=12,\n",
        "    )\n",
        "    story = []\n",
        "    # paragraphs = input_text.split('\\n')\n",
        "    for paragraph in input_text:\n",
        "        story.append(Paragraph(paragraph, justified_style))\n",
        "    pdf.build(story)\n"
      ],
      "metadata": {
        "id": "wej1ZBmm0BHY"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book_path = input('input book path here (local): ')\n",
        "reader = PyPDF2.PdfReader(book_path)\n",
        "pages = reader.pages\n",
        "page_texts = []\n",
        "replacements = {\"Crime and Punishment\":\"\", \"Free eBooks at Planet eBook.com\":\"\"}\n",
        "concatenated_text = \"\"\n",
        "\n",
        "for i in range(5,len(pages)):\n",
        "    text = multiple_replace(pages[i].extract_text().strip(),replacements)\n",
        "    page_texts.append(text)\n",
        "    concatenated_text += text"
      ],
      "metadata": {
        "id": "gpNzCg4iyrZa"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatVertexAI(temperature=0)\n",
        "num_tokens = llm.get_num_tokens(\" \".join(page_texts[0:10]))\n",
        "print (f\"Our prompt has {num_tokens} tokens\")"
      ],
      "metadata": {
        "colab": {
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          "height": 201,
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        "outputId": "33c48e21-dab7-42b6-c09e-da82c289c1f8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (3940 > 1024). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Our prompt has 3940 tokens\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum=0\n",
        "for p in page_texts[1:21]:\n",
        "  sum+=llm.get_num_tokens(p)\n",
        "\n",
        "print(f'average tokens on a page: {sum/20}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGW1LU7njOmn",
        "outputId": "967f1888-84db-44c3-f67c-8debce56485c"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average tokens on a page: 438.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"There are {len(concatenated_text)} characters in the pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4shZbky82XbH",
        "outputId": "c6f24165-55b2-47af-fa70-a52b6794c92e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1147238 characters in the pdf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CALL_LIMIT = 50\n",
        "ONE_MINUTE = 60\n",
        "FIVE_MINUTE = 10 * ONE_MINUTE\n",
        "def backoff_hdlr(details):\n",
        "    print(\n",
        "        \"Backing off {} seconds after {} tries\".format(\n",
        "            details[\"wait\"], details[\"tries\"]\n",
        "        )\n",
        "    )\n",
        "@backoff.on_exception(\n",
        "    backoff.expo,\n",
        "    (\n",
        "            exceptions.ResourceExhausted,\n",
        "            ratelimit.RateLimitException,\n",
        "    ),  # Exceptions to retry on\n",
        "    max_time=FIVE_MINUTE,\n",
        "    on_backoff=backoff_hdlr,\n",
        ")\n",
        "@ratelimit.limits(\n",
        "    calls=CALL_LIMIT, period=ONE_MINUTE\n",
        ")\n",
        "def model_with_limit_and_backoff(**kwargs):\n",
        "    return generation_model.predict(**kwargs)"
      ],
      "metadata": {
        "id": "z6Ti5mmMG59U"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "initial_prompt_template = \"\"\"\n",
        "    This task is to summarize a book.\n",
        "    Write a concise summary of the following text. The text is being taken in chunks from the book 'crime and punishment'.\n",
        "\n",
        "    {text}\n",
        "\n",
        "    SUMMARY:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "0XKCPDJmH_YJ"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CHUNK_SIZE = 2\n",
        "initial_summary = []\n",
        "\n",
        "for i in tqdm(range(len(page_texts))):\n",
        "    pages_to_merge = [x for x in range(i, i + CHUNK_SIZE) if x < len(page_texts)]\n",
        "    extracted_texts = [page_texts[x] for x in pages_to_merge]\n",
        "    text = \"\\n\".join(extracted_texts)\n",
        "    prompt = initial_prompt_template.format(text=text)\n",
        "    summary = model_with_limit_and_backoff(prompt=prompt, max_output_tokens=1024).text\n",
        "    initial_summary.append(summary)\n",
        "    if pages_to_merge[-1] == len(reader.pages):\n",
        "        break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxxBcc905Pz-",
        "outputId": "fdd9bdcd-4f49-4135-895d-ca8b082deaea"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 762/762 [29:40<00:00,  2.34s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s=0\n",
        "for x in initial_summary:\n",
        "  s+=llm.get_num_tokens(x)\n",
        "print(f'total tokens in initial generated summaries: {s}')\n",
        "print(f'possible number of pages with these many tokens: {s/450}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFD8JQo2K0wG",
        "outputId": "7a32f110-1abc-4a66-9caa-090be1a7384e"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total tokens in initial generated summaries: 98259\n",
            "possible number of pages with these many tokens: 218.35333333333332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s=0\n",
        "for x in initial_summary[75:90]:\n",
        "  s+=llm.get_num_tokens(x)\n",
        "\n",
        "print(f'number of tokens in 15 summaries: {s}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSpUJHNUGQyl",
        "outputId": "c88781a6-2931-4774-c846-7a79c253ca67"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of tokens in 15 initial summaries: 1939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chunk_size = 30\n",
        "overlap = 2\n",
        "\n",
        "result_chunks = [\" \".join(initial_summary[i:i + chunk_size]) for i in range(0, len(initial_summary), chunk_size - overlap)]\n"
      ],
      "metadata": {
        "id": "FukupiKZJV6S"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_template = \"\"\"\n",
        "    This task is to summarize the already generated summaries of chunks the book 'crime and punishment'.\n",
        "    Write a concise summary of the following text.\n",
        "\n",
        "    {text}\n",
        "\n",
        "    CONCISE SUMMARY:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "GzjYBYx755_Q"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_list = []\n",
        "for s in tqdm(result_chunks):\n",
        "    prompt = prompt_template.format(text=s)\n",
        "    summary = model_with_limit_and_backoff(prompt=prompt, max_output_tokens=1000).text\n",
        "    summary_list.append(summary)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xTy5TXiP78D",
        "outputId": "67820db5-1424-4ed8-8e0a-ddcffbfad0c5"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28/28 [02:22<00:00,  5.09s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s=0\n",
        "for x in summary_list:\n",
        "  s+=llm.get_num_tokens(x)\n",
        "print(f'total tokens in finally generated summaries: {s}')\n",
        "print(f'possible number of pages with these many tokens: {s/440}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeK5ZbovE3IM",
        "outputId": "6bb8a778-de1e-41bd-c560-52ca13a80c5f"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total tokens in finally generated summaries: 7865\n",
            "possible number of pages with these many tokens: 17.875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_summary = \" \".join(summary_list)\n",
        "final_summary = final_summary.split()\n",
        "final_summary = [final_summary[i:i+250] for i in range(0, len(final_summary), 250)]\n",
        "final_summary = [\" \".join(x) for x in final_summary]"
      ],
      "metadata": {
        "id": "_NFGlR8uV7Dv"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_pdf(final_summary, \"summary.pdf\")"
      ],
      "metadata": {
        "id": "WNH2HYHWF1CW"
      },
      "execution_count": 193,
      "outputs": []
    }
  ],
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