{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "7317c777-7a59-4719-842f-b3018aa7e73f", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import requests\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display\n", "import ollama" ] }, { "cell_type": "code", "execution_count": null, "id": "26b1489d-c043-4631-872b-e1e28fec9eed", "metadata": {}, "outputs": [], "source": [ "# Constants\n", "\n", "MODEL = \"llama3.2\"" ] }, { "cell_type": "code", "execution_count": null, "id": "a5630e12-40f5-40ea-996b-4b1a5d9c8697", "metadata": {}, "outputs": [], "source": [ "# A class to represent a Webpage\n", "\n", "class Website:\n", " \"\"\"\n", " A utility class to represent a Website that we have scraped\n", " \"\"\"\n", " url: str\n", " title: str\n", " text: str\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)\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": "510e0447-ed82-4337-b0aa-f9752b41711a", "metadata": {}, "outputs": [], "source": [ "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", "\n", "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", "and provides a short summary, ignoring text that might be navigation related. \\\n", "Respond in markdown.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "7a0926ae-8580-4f0a-8935-ce390b926074", "metadata": {}, "outputs": [], "source": [ "# 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 += \"The contents of this website is as follows; \\\n", "please provide a short summary 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": "963edaa9-daba-4fa1-8db6-518f22261ab0", "metadata": {}, "outputs": [], "source": [ "# See how this function creates exactly the format above\n", "\n", "def messages_for(website):\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "04c7a991-df38-4e73-8015-73684bdd7810", "metadata": {}, "outputs": [], "source": [ "# And now: call the Ollama function \n", "\n", "def summarize(url):\n", " website = Website(url)\n", " messages = messages_for(website)\n", " response = ollama.chat(model=MODEL, messages=messages)\n", " return response['message']['content']" ] }, { "cell_type": "code", "execution_count": null, "id": "b08efad7-7dbe-438e-898a-fc7ae7395149", "metadata": {}, "outputs": [], "source": [ "summarize(\"https://www.allrecipes.com/recipes/14485/healthy-recipes/main-dishes/chicken/\")" ] }, { "cell_type": "code", "execution_count": null, "id": "6ec180e8-4e2a-4e02-afc6-39a90a87bd7e", "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": "967b874a-af3a-494a-bb02-c83232d0f9a3", "metadata": {}, "outputs": [], "source": [ "display_summary(\"https://www.allrecipes.com/recipes/14485/healthy-recipes/main-dishes/chicken/\")" ] }, { "cell_type": "code", "execution_count": null, "id": "1148b8d0-1e44-4ea1-ba1f-44eb25e0af18", "metadata": {}, "outputs": [], "source": [] } ], "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 }