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{
"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
}