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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": "9964872b-225d-4ced-93e4-fc5b279ec2ed", |
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"metadata": {}, |
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"source": [ |
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"# Webpage English summarizer with user inputs (url, ollama-based LLM) " |
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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": "4e49d399-d18c-4c91-8abc-cf3289e11e2f", |
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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\n", |
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"import ollama, time\n", |
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"from tqdm import tqdm" |
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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": "46e7d809-248d-41b8-80e1-36b210041581", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define system prompt.\n", |
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"\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 detailed summary, ignoring text that might be navigation related. \\\n", |
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"Respond in markdown, in English.\"" |
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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": "e8bf237f-591f-4c32-9415-5d5d4e2522b8", |
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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 detailed 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": "7d39ee6d-c670-41ba-a0b8-debd55bda8e3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# See how this function creates exactly the format above\n", |
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"\n", |
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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": "43e28ff5-2def-4a47-acdd-2e06c0666956", |
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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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"\n", |
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}" |
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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": "32f4f481-81a3-479d-817b-4e754d9af46d", |
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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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"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\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 = HEADERS\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": "f81cfd17-8208-4192-a59f-485ff3ea74e4", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# And now: call the ollama API wrapper and return the relevant component of the response\n", |
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"\n", |
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"def summarize(url):\n", |
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" website = Website(url)\n", |
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" response = ollama.chat(\n", |
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" model=MODEL,\n", |
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" messages = messages_for(website)\n", |
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" )\n", |
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" return response['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": "7a9eedc6-2183-473d-84ca-b10d40e2a1e6", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Ask the user the name of the url address\n", |
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"\n", |
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"url= str(input(\"\"\"\n", |
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"Please provide a valid url address:\n", |
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"https://\"\"\"))" |
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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": "5d012de2-0ef2-43db-9f51-fc7f989c3642", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Ask the user to select a valid model\n", |
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"\n", |
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"MODEL= str(input(\"\"\"\n", |
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"Please select a LLM:\n", |
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"(examples: llama3.2, deepseek-r1:1.5b)\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": "1ac8c02e-4a62-448b-a231-8c6f65891811", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's just make sure the model is loaded\n", |
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"\n", |
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"!ollama pull {MODEL}" |
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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": "0544541f-11a8-4eb7-8eb6-bc032ed6d0d1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"print('url: https://{0}\\nModel= {1}'.format(url, MODEL))" |
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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": "45518950-f2c9-43af-b897-4fe8fe48dfd8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"summary = summarize('https://'+ url)\n", |
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"for summ in tqdm(summary):\n", |
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" time.sleep(0.01)" |
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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": "02c0c15e-216d-47c7-843d-ac27af02820b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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": "985a3689-5827-4b15-b8d5-276f9b292afd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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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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