224 lines
6.3 KiB
Plaintext
224 lines
6.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bfa3abd0-4e66-4117-96f9-7a71fbb6d0cb",
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"metadata": {},
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"source": [
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"# Powerpoint Slides Summarizer\n",
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"\n",
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"This converts a Power Point presentation into notes that a student can easily skim through.\n",
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"\n",
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"Concepts Used:\n",
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"- Converting Contents of PPT to text via python-pptx\n",
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"- User and System Prompts\n",
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"- Use of Open AI GPT-4o-mini via API key\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab95eb49-6a2d-4c7d-9057-78a2cd9364cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install python-pptx"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62715f16-7125-455e-98e7-5705871c0e4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff42eab7-789d-44f8-a5cc-64baeebf3224",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" 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",
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"elif api_key.strip() != api_key:\n",
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" 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",
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"else:\n",
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" print(\"API key found and looks good so far!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bce425c2-6d19-4c03-93ce-8930dabc61ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# creating an instance\n",
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"openai = OpenAI()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c0c75e30-3b38-4a89-b7d3-a41a6f5dc650",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pptx import Presentation\n",
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"\n",
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"class PowerPoint():\n",
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" def __init__(self,ppt):\n",
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" \"\"\"\n",
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" Creates a PowerPoint object, with name and text.\n",
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" \"\"\"\n",
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" self.ppt = ppt\n",
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" self.title = os.path.basename(ppt)\n",
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" self.text = self.extract_text()\n",
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"\n",
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" def extract_text(self):\n",
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" \"\"\"\n",
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" Extracts text from powerpoint.\n",
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" \"\"\"\n",
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" prs = Presentation(self.ppt)\n",
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" text_content = []\n",
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" \n",
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" for slide in prs.slides:\n",
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" for shape in slide.shapes:\n",
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" if hasattr(shape, \"text\"):\n",
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" text_content.append(shape.text)\n",
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" \n",
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" return \"\\n\".join(text_content)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1963a055-87f4-4e47-8456-cac4d4ac57fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"You are an assistant that analyzes the contents \\\n",
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"of a PowerPoint presentation, and provides a summary in the style of \\\n",
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"a cheat-sheet, for students to easily learn key concepts from.\\\n",
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"You are to ignore text that might be navigation-related\\\n",
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"and respond in Markdown.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ca600e90-7d3f-4fc7-a698-1b8f2925f81e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# A function that writes a User Prompt that asks for summaries of PowerPoints:\n",
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"\n",
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"def user_prompt_for(powerpoint):\n",
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" user_prompt = f\"You are looking at a website titled {powerpoint.title}\"\n",
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" user_prompt += \"\\nThe contents of this powerpoint are as follows; \\\n",
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"please provide a summary of the content in markdown. \\\n",
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"If it includes a question bank, add that along with short answers too.\\n\\n\"\n",
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" user_prompt += powerpoint.text\n",
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" return user_prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4fe19c56-9940-4528-b43a-c86798b215d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"def messages_for(powerpoint):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(powerpoint)}\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f7704da5-90b0-40af-bbb4-7d589309f180",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And now: call the OpenAI API. \n",
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"\n",
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"def summarize(powerpoint_path):\n",
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" powerpoint = PowerPoint(powerpoint_path)\n",
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" response = openai.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\n",
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" messages = messages_for(powerpoint)\n",
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" )\n",
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" return response.choices[0].message.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "49d1d0cf-fa4b-4bea-bd68-a834145070ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"def display_summary(url):\n",
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" summary = summarize(url)\n",
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" display(Markdown(summary))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "348078d1-e86f-4eb3-909d-33ab4ede984e",
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"metadata": {},
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"outputs": [],
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"source": [
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"ppt_file = \"Theoretical Perspectives on Media and Technology.pptx\" \n",
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"display_summary(ppt_file)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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