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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": "fef36918-109d-41e3-8603-75ff81b42379", |
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"metadata": {}, |
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"source": [ |
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"# Solution for exercise day 2 - slight modification: model is a parameter also - display_summary(\"deepseek-r1:1.5b\",\"https://yoururl\")\n", |
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"\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": "b50349ac-93ea-496b-ae20-bd72a93bb138", |
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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 requests\n", |
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"from bs4 import BeautifulSoup\n", |
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"from IPython.display import Markdown, display" |
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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": "edd073c7-8444-4a0d-b84e-4b2ed0ee7f35", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Constants\n", |
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
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"#MODEL = \"llama3.2\"" |
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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": "2e3a6e1a-e4c7-4448-9852-1b6ba2bd8d66", |
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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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"# 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": "ae3752ca-3a97-4d6a-ac84-5b75ebfb50ed", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define the system prompt \n", |
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"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
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"and provides a short summary, ignoring text that might be navigation related. \\\n", |
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"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": "48b5240f-7617-4e51-a320-cba9650bec84", |
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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 websites:\n", |
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"\n", |
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"def user_prompt_for(website):\n", |
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" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
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" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
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"please provide a short summary of this website in markdown. \\\n", |
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"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
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" user_prompt += website.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": "6f7d84f0-60f2-4cbf-b4d1-173a79fe3380", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def messages_for(website):\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(website)}\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": "25520a31-c857-4ed5-86da-50dfe5fab7bb", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def summarize(model,url):\n", |
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" website = Website(url)\n", |
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" payload = {\n", |
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" \"model\": model,\n", |
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" \"messages\": messages_for(website),\n", |
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" \"stream\": False\n", |
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" }\n", |
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" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
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" return response.json()['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": "430776ed-8516-43a9-8a22-618d9080f2e1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A function to display this nicely in the Jupyter output, using markdown\n", |
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"def display_summary(model,url):\n", |
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" summary = summarize(model,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": "b2b05c1f-e4a2-4f65-bd6d-634d72e38b6e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#!ollama pull deepseek-r1:1.5b" |
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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": "01513f8a-15b7-4053-bfe4-44b36e5494d1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"deepseek-r1:1.5b\",\"https://www.ipma.pt\")" |
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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": "3.12.9" |
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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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