{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "7bf0a6c9-ce7e-4610-90ae-75ae08d26cbf", "metadata": {}, "outputs": [], "source": [ "import ollama\n", "import requests\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display\n", "\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", "}" ] }, { "cell_type": "code", "execution_count": null, "id": "d414ed23-ed29-4600-a2d6-28b48c279221", "metadata": {}, "outputs": [], "source": [ "class Website:\n", "\n", " def __init__(self, url):\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 \"Website without title\"\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": "be9f3409-b4c8-428f-96b8-3720a8c3ad47", "metadata": {}, "outputs": [], "source": [ "def prompt_for(website):\n", " prompt = f\"Here is a website for you to summarize. Its title is {website.title}.\"\n", " prompt += \"\\nPlease provide a short summary of the website. If it contains any announcements, summarize those, too.\"\n", " prompt += f\"\\nHere is the Website:\\n\\n{website.text}\"\n", "\n", " return prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "2565ae0f-c5c5-44bc-98c2-f777f816b37f", "metadata": {}, "outputs": [], "source": [ "MODEL = \"llama3.2\"\n", "website = Website(\"https://edwarddonner.com\")\n", "\n", "def messages_for(website):\n", " messages = [\n", " {\"role\": \"system\", \"content\": \"You are a powerful, friendly, and helpful website summarization assistant. \\\n", " You are given a website and summarize its content succinctly. You format your answer in markdown.\"},\n", " {\"role\": \"user\", \"content\": prompt_for(website)}\n", " ]\n", "\n", " return messages" ] }, { "cell_type": "code", "execution_count": null, "id": "cdca9dc0-4ecc-494f-abee-d1ad2d373e0e", "metadata": {}, "outputs": [], "source": [ "def display_md_summary(website):\n", " response = ollama.chat(model=MODEL, messages=messages_for(website))\n", " display(Markdown(response['message']['content']))" ] }, { "cell_type": "code", "execution_count": null, "id": "e519091c-8fc6-442b-a99b-ac393a10cdcd", "metadata": {}, "outputs": [], "source": [ "display_md_summary(website)" ] } ], "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 }