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{ |
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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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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "56c86bae-1d3c-4c01-b5d6-c8879fec1954", |
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"metadata": {}, |
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"source": [ |
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"# Wiki Summarizer\n", |
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"\n", |
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"This Project takes the name of a topic as input, and checks if the corresponding wiki-page exists. If it does, it parses the web page, and outputs a summary created using the GPT-4o-mini model. \n", |
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"\n", |
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"Concepts used: \n", |
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"- Web Scraping via Beautiful Soup\n", |
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"- User and System Prompts\n", |
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"- Use of Open AI GPT-4o-mini via API key" |
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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": "4820830e-b3b4-426e-b1a2-518e7c7f6c1a", |
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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": "2cd7ad51-396c-45c5-9089-f7b21a19da50", |
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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": "689421a0-20a1-428b-a8b8-fa239fa6f633", |
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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": "401901ae-7639-4190-98fd-e69374084723", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def isWiki(url):\n", |
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" \"\"\"\n", |
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" Check whether a Wikipedia page exists for a given topic, and \n", |
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" returns a Boolean value.\n", |
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" \"\"\"\n", |
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" response = requests.get(url)\n", |
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"\n", |
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" if response.status_code != 200:\n", |
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" return False\n", |
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" \n", |
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" return True" |
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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": "7cdb14d3-05ea-4de2-a475-d49a5731692e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A class to represent a Webpage\n", |
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"\n", |
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"# Some websites need you to use proper headers when fetching them:\n", |
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"headers = {\n", |
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" \"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", |
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"}\n", |
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"\n", |
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"class Website:\n", |
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"\n", |
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" def __init__(self, url):\n", |
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" \"\"\"\n", |
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" Create this Website object from the given url using the BeautifulSoup library\n", |
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" \"\"\"\n", |
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" self.url = url\n", |
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" response = requests.get(url, headers=headers)\n", |
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" soup = BeautifulSoup(response.content, 'html.parser')\n", |
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" self.title = soup.title.string if soup.title else \"No title found\"\n", |
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
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" irrelevant.decompose()\n", |
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
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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": "7f6ed50e-0fb5-479e-9845-f62cf25980f7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_prompt = \"You are an educational assistant tasked with helping users understand topics\\\n", |
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"by providing succinct and clear summaries of requested data. Ignore navigation-related text\\\n", |
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"and provide answers in markdown format\"" |
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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": "b2d77dd9-a94f-49c1-a1be-11d157bd37fb", |
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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 wiki pages:\n", |
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"\n", |
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"def user_prompt_for(wiki):\n", |
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" user_prompt = f\"You are looking at a Wikipedia page titled {wiki.title}\"\n", |
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" user_prompt += \"\\nThe contents of this page is as follows; \\\n", |
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"please provide a short summary of this website in markdown.\\n\"\n", |
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" user_prompt += wiki.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": "0d23bcc4-1d89-4bd4-9809-d3a1819aa919", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def messages_for(wiki):\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(wiki)}\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": "971bd7fb-2ff8-4494-b386-de69a39c24ff", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def summarize(url):\n", |
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" website = Website(url)\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(website)\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": "a8fdf9f2-f49e-4d06-ac9e-dfcb8da33d60", |
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"metadata": {}, |
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|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(topic):\n", |
||||||
|
" url = f\"https://en.wikipedia.org/wiki/{topic}\"\n", |
||||||
|
" if isWiki(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))\n", |
||||||
|
" else:\n", |
||||||
|
" print('A Wikipedia page does not exist for this topic')\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4758ef0-9b7c-4d3e-9131-e3284dc76b6b", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"topic = input('Enter the name of Wikipedia page for which you would like a summary: ').strip()\n", |
||||||
|
"display_summary(topic)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"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": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
Loading…
Reference in new issue