{ "cells": [ { "cell_type": "markdown", "id": "c7d95a7f-205a-4262-a1af-4579489025ff", "metadata": {}, "source": [ "# Hello everyone." ] }, { "cell_type": "markdown", "id": "bc815dbc-acf7-45f9-a043-5767184c44c6", "metadata": {}, "source": [ "I completed the day 1, first LLM Experiment moments ago and found it really awesome. After the challenge was done, I wanted to chip in my two cents by making a PDF summarizer, basing myself on the code for the Website Summarizer. I want to share it in this contribution!\n", "### To consider:\n", "* To extract the contents of PDF files, I used the PyPDF2 library, which doesn't come with the default configuration of the virtual environment. To remedy the situation, you need to follow the steps:\n", " 1. Shut down Anaconda. Running `CTRL-C` in the Anaconda terminal should achieve this.\n", " 2. Run the following command, `pip install PyPDF2 --user`\n", " 3. Restart Jupyter lab with `jupyter lab`\n", "* To find PDF files online, you can add `filetype:url` on your browser query, i.e. searching the following can give you PDF files to add as input: `AI Engineering prompts filetype:pdf`!\n", "\n", "Without further ado, here's the PDF Summarizer!" ] }, { "cell_type": "code", "execution_count": null, "id": "06b63787-c6c8-4868-8a71-eb56b7618626", "metadata": {}, "outputs": [], "source": [ "# Import statements\n", "import os\n", "import requests\n", "from dotenv import load_dotenv\n", "from IPython.display import Markdown, display\n", "from openai import OpenAI\n", "from io import BytesIO\n", "from PyPDF2 import PdfReader" ] }, { "cell_type": "code", "execution_count": null, "id": "284ca770-5da4-495c-b1cf-637727a8609f", "metadata": {}, "outputs": [], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv()\n", "api_key = os.getenv('OPENAI_API_KEY')\n", "\n", "# Check the key\n", "\n", "if not api_key:\n", " print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", "elif not api_key.startswith(\"sk-proj-\"):\n", " print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", "elif api_key.strip() != api_key:\n", " print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", "else:\n", " print(\"API key found and looks good so far!\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d4c316d7-d9c9-4400-b03e-1dd629c6b2ad", "metadata": {}, "outputs": [], "source": [ "openai = OpenAI()\n", "\n", "# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", "# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, or try the below line instead:\n", "# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")" ] }, { "cell_type": "code", "execution_count": null, "id": "3a053092-f4f6-4156-8721-39353c8a9367", "metadata": {}, "outputs": [], "source": [ "# Step 0: Create article class\n", "class Article:\n", " def __init__(self, url):\n", " \"\"\"\n", " Create this Article object from the given url using the PyPDF2 library\n", " \"\"\"\n", " self.url = url \n", " response = requests.get(self.url)\n", " if response.status_code == 200:\n", " pdf_bytes = BytesIO(response.content)\n", " reader = PdfReader(pdf_bytes)\n", " \n", " text = \"\"\n", " for page in reader.pages:\n", " text += page.extract_text()\n", " \n", " self.text = text\n", " self.title = reader.metadata.get(\"/Title\", \"No title found\")\n", " else:\n", " print(f\"Failed to fetch PDF. Status code: {response.status_code}\")\n", " self.text = \"No text found\"\n", " self.title = \"No title found\"" ] }, { "cell_type": "code", "execution_count": null, "id": "adc528f2-25ca-47b5-896e-9d417ba0195f", "metadata": {}, "outputs": [], "source": [ "# Step 1: Create your prompts\n", "\n", "def craft_user_prompt(article):\n", " user_prompt = f\"You are looking at a research article titled {article.title}\\n Based on the body of the article, how are micro RNAs produced in the cell? State the function of the proteins \\\n", " involved. The body of the article is as follows.\"\n", " user_prompt += article.text\n", " return user_prompt\n", "\n", "# Step 2: Make the messages list\n", "def craft_messages(article):\n", " system_prompt = \"You are an assistant that analyses the contents of a research article and provide answers to the question asked by the user in 250 words or less. \\\n", " Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.\"\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "81ab896e-1ba9-4964-a477-2a0608b7036c", "metadata": {}, "outputs": [], "source": [ "# Step 3: Call OpenAI\n", "def summarize(url):\n", " article = Article(url)\n", " response = openai.chat.completions.create(\n", " model = \"gpt-4o-mini\",\n", " messages = craft_messages(article)\n", " )\n", " return response.choices[0].message.content" ] }, { "cell_type": "code", "execution_count": null, "id": "a7a98cdf-0d3b-477d-8e39-a6a4264b9feb", "metadata": {}, "outputs": [], "source": [ "# Step 4: Print the result of an example pdf\n", "summary = summarize(\"https://www.nature.com/articles/s12276-023-01050-9.pdf\")\n", "display(Markdown(summary))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }