From 7e73249dd67bcd0e91bf0c1e5f67aef62d82571c Mon Sep 17 00:00:00 2001 From: JacquieAM <69653489+JacquieAM@users.noreply.github.com> Date: Wed, 27 Aug 2025 18:01:50 -0500 Subject: [PATCH] Add website-summary notebook for community contribution --- .../jacquieAM/website-summary.ipynb | 329 ++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 week1/community-contributions/training-summary-translation-length/jacquieAM/website-summary.ipynb diff --git a/week1/community-contributions/training-summary-translation-length/jacquieAM/website-summary.ipynb b/week1/community-contributions/training-summary-translation-length/jacquieAM/website-summary.ipynb new file mode 100644 index 0000000..9c31463 --- /dev/null +++ b/week1/community-contributions/training-summary-translation-length/jacquieAM/website-summary.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "9ab446e4-219c-4589-aa8f-9386adcf5c60", + "metadata": {}, + "outputs": [], + "source": [ + "## Project Overview\n", + "This project combines web scraping with OpenAI’s GPT models to summarize online training content. It extracts material from Microsoft’s **Quantum Computing Fundamentals** learning path, cleans it, and generates concise summaries per lesson as well as an overall course summary. \n", + "\n", + "## Key Features\n", + "- Fetches and parses webpages using **requests** and **BeautifulSoup** \n", + "- Produces summaries in multiple languages (e.g., English, Spanish, or any language) and at varying levels of detail (short, medium, detailed) \n", + "- Summarizes individual lessons on demand or processes entire learning paths \n", + "- Presents results as clean, structured **Markdown** directly in the notebook \n", + "\n", + "## Tech Stack\n", + "- **Model**: GPT-4o-mini \n", + "- **Language**: Python \n", + "- **Libraries**: BeautifulSoup, OpenAI \n", + "\n", + "## Purpose\n", + "This project demonstrates how AI can streamline the understanding of technical documentation and online courses by generating multilingual, customizable summaries. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "import requests\n", + "from dotenv import load_dotenv\n", + "from bs4 import BeautifulSoup\n", + "from IPython.display import Markdown, display\n", + "from openai import OpenAI\n", + "\n", + "# If you get an error running this cell, then please head over to the troubleshooting notebook!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b87cadb-d513-4303-baee-a37b6f938e4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Load environment variables from .env file (not included)\n", + "\n", + "load_dotenv(override=True)\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\")\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\")\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\")\n", + "else:\n", + " print(\"API key found and looks good so far!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e793b2-6775-426a-a139-4848291d0463", + "metadata": {}, + "outputs": [], + "source": [ + "# A class to represent a Webpage\n", + "\n", + "# Some websites need you to use proper headers when fetching them:\n", + "headers = {\n", + " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", + "}\n", + "\n", + "class Website:\n", + "\n", + " def __init__(self, url):\n", + " \"\"\"\n", + " Create this Website object from the given url using the BeautifulSoup library\n", + " \"\"\"\n", + " self.url = url\n", + " response = requests.get(url, headers=headers)\n", + " soup = BeautifulSoup(response.content, 'html.parser')\n", + " self.title = soup.title.string if soup.title else \"No title found\"\n", + " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", + " irrelevant.decompose()\n", + " self.text = soup.body.get_text(separator=\"\\n\", strip=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "training_website = Website(\"https://learn.microsoft.com/en-us/training/paths/quantum-computing-fundamentals/\")\n", + "print(training_website.title)\n", + "print(training_website.text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abdb8417-c5dc-44bc-9bee-2e059d162699", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a system prompt function that can use different language and length \n", + "\n", + "def build_system_prompt(language=\"Spanish\", length=\"short\"):\n", + " return f\"\"\"You are an assistant that analyzes the contents of a website and provides a {length} summary, ignoring text that might be navigation related.\n", + " Respond in 20 words or less markdown, and respond in {language}.\n", + " \"\"\"\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "987c95a6-6618-4d22-a2c3-3038a9d3f154", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a function that writes a User Prompt that asks for summaries of websites:\n", + "\n", + "def user_prompt_for(website):\n", + " user_prompt = f\"You are looking at a website titled {website.title}\"\n", + " user_prompt += \"\\nThe contents of this website is as follows; \\\n", + "please provide a short summary in {language} of this website in markdown. \\\n", + "If it includes news or announcements, then summarize these too.\\n\\n\"\n", + " user_prompt += website.text\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a846c89-81d8-4f48-9d62-7744d76694e2", + "metadata": {}, + "outputs": [], + "source": [ + "print(user_prompt_for(training_website))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26448ec4-5c00-4204-baec-7df91d11ff2e", + "metadata": {}, + "outputs": [], + "source": [ + "print(user_prompt_for(training_website))" + ] + }, + { + "cell_type": "markdown", + "id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", + "metadata": {}, + "source": [ + "## And now let's build useful messages for GPT-4o-mini, using a function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def messages_for(website, language=\"Spanish\", length=\"short\"):\n", + " return [\n", + " {\"role\": \"system\", \"content\": build_system_prompt(language, length)},\n", + " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", + "metadata": {}, + "source": [ + "## Time to bring it together - the API for OpenAI is very simple!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "425214b8-c5c5-4d7a-8b79-f9e151c9d54f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", + "metadata": {}, + "outputs": [], + "source": [ + "#call the OpenAI API. \n", + "\n", + "def summarize(url, language=\"Spanish\", length=\"short\"):\n", + " website = Website(url)\n", + " response = openai.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=messages_for(website, language, length)\n", + " )\n", + " return response.choices[0].message.content\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c437357-d004-49f5-95c3-fce38aefcb5c", + "metadata": {}, + "outputs": [], + "source": [ + "#Summarize all the lessons in microsoft quantum computer training, having the option to summarize by lesson, or the training as a whole\n", + "\n", + "def summarize_training(path_url, language=\"Spanish\", length=\"short\"):\n", + " links = get_links_from_path(path_url)\n", + " print(f\"Found {len(links)} lessons\")\n", + "\n", + " all_summaries = []\n", + "\n", + " for link in links:\n", + " print(f\"Summarizing {link}...\")\n", + " summary = summarize(link, language, length)\n", + " all_summaries.append(f\"### {link}\\n{summary}\\n\")\n", + "\n", + " combined_prompt = \"Here are summaries of each lesson:\\n\\n\" + \"\\n\".join(all_summaries)\n", + " response = openai.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": build_system_prompt(language, length)},\n", + " {\"role\": \"user\", \"content\": \"Please summarize the entire training path based on these lesson summaries:\\n\\n\" + combined_prompt}\n", + " ]\n", + " )\n", + "\n", + " return \"\\n\".join(all_summaries) + \"\\n\\n## General Course Summary\\n\" + response.choices[0].message.content\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", + "metadata": {}, + "outputs": [], + "source": [ + "summarize(\"https://learn.microsoft.com/en-us/training/paths/quantum-computing-fundamentals/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d926d59-450e-4609-92ba-2d6f244f1342", + "metadata": {}, + "outputs": [], + "source": [ + "# A function to display this nicely in the Jupyter output, using markdown\n", + "\n", + "def display_summary(url):\n", + " summary = summarize(url)\n", + " display(Markdown(summary))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3018853a-445f-41ff-9560-d925d1774b2f", + "metadata": {}, + "outputs": [], + "source": [ + "display_summary(\"https://learn.microsoft.com/en-us/training/paths/quantum-computing-fundamentals/\")" + ] + } + ], + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}