{
 "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
}