Add Week 1 solutions - Day 1, 2, 4, 5 and Exercise
This commit is contained in:
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week1/my-solutions/day5-solution.ipynb
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385
week1/my-solutions/day5-solution.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Day 5 Solution - Business Solution: Company Brochure Generator\n",
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"\n",
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"This is my solution to the Day 5 assignment. I've implemented a comprehensive business solution that generates company brochures.\n",
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"\n",
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"## Features Implemented:\n",
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"- Intelligent link selection using LLM\n",
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"- Multi-page content aggregation\n",
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"- Professional brochure generation\n",
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"- Model comparison and optimization\n",
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"- Business-ready output formatting\n",
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"- Cost-effective processing strategies\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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Day 5 setup complete! Ready for business solution development.\n"
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]
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}
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],
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"source": [
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"# Day 5 Solution - Imports and Setup\n",
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"import os\n",
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"import json\n",
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"import ssl\n",
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"import requests\n",
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"from bs4 import BeautifulSoup\n",
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"from urllib.parse import urljoin\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI\n",
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"from dotenv import load_dotenv\n",
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"import ollama\n",
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"import time\n",
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"\n",
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"# Load environment variables\n",
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"load_dotenv(override=True)\n",
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"\n",
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"# SSL fix for Windows\n",
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"ssl._create_default_https_context = ssl._create_unverified_context\n",
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"os.environ['PYTHONHTTPSVERIFY'] = '0'\n",
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"os.environ['CURL_CA_BUNDLE'] = ''\n",
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"\n",
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"# Initialize clients\n",
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"openai = OpenAI()\n",
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"\n",
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"# Constants\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"\n",
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"print(\"Day 5 setup complete! Ready for business solution development.\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Enhanced Web Scraping Functions\n",
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"HEADERS = {\n",
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" \"User-Agent\": (\n",
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" \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n",
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" \"AppleWebKit/537.36 (KHTML, like Gecko) \"\n",
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" \"Chrome/117.0.0.0 Safari/537.36\"\n",
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" )\n",
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"}\n",
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"\n",
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"def fetch_website_contents(url, char_limit=2000):\n",
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" \"\"\"Fetch and clean website content\"\"\"\n",
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" try:\n",
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" response = requests.get(url, headers=HEADERS, timeout=10)\n",
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" response.raise_for_status()\n",
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" html = response.text\n",
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" except Exception as e:\n",
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" print(f\"Error fetching {url}: {e}\")\n",
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" return \"Error: Could not fetch website content\"\n",
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" \n",
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" soup = BeautifulSoup(html, \"html.parser\")\n",
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" \n",
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" # Remove script and style elements\n",
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" for script in soup([\"script\", \"style\"]):\n",
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" script.decompose()\n",
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" \n",
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" title = soup.title.get_text(strip=True) if soup.title else \"No title found\"\n",
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" text = soup.get_text()\n",
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" \n",
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" # Clean up whitespace\n",
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" lines = (line.strip() for line in text.splitlines())\n",
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" chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n",
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" text = ' '.join(chunk for chunk in chunks if chunk)\n",
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" \n",
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" return (f\"{title}\\\\n\\\\n{text}\").strip()[:char_limit]\n",
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"\n",
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"def fetch_website_links(url):\n",
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" \"\"\"Fetch all links from a website\"\"\"\n",
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" try:\n",
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" response = requests.get(url, headers=HEADERS, timeout=10)\n",
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" response.raise_for_status()\n",
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" html = response.text\n",
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" except Exception as e:\n",
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" print(f\"Error fetching links from {url}: {e}\")\n",
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" return []\n",
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" \n",
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" soup = BeautifulSoup(html, \"html.parser\")\n",
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" links = []\n",
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" \n",
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" for a in soup.select(\"a[href]\"):\n",
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" href = a.get(\"href\")\n",
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" if href:\n",
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" # Convert relative URLs to absolute\n",
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" if href.startswith((\"http://\", \"https://\")):\n",
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" links.append(href)\n",
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" else:\n",
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" links.append(urljoin(url, href))\n",
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" \n",
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" return list(set(links)) # Remove duplicates\n",
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"\n",
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"print(\"Enhanced web scraping functions defined!\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Intelligent Link Selection\n",
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"def select_relevant_links(url, model=\"gpt-4o-mini\"):\n",
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" \"\"\"Use LLM to select relevant links for brochure generation\"\"\"\n",
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" print(f\"🔍 Analyzing links for {url}...\")\n",
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" \n",
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" # Get all links\n",
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" links = fetch_website_links(url)\n",
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" print(f\"Found {len(links)} total links\")\n",
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" \n",
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" # Create prompt for link selection\n",
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" link_system_prompt = \"\"\"\n",
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" You are provided with a list of links found on a webpage.\n",
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" You are able to decide which of the links would be most relevant to include in a brochure about the company,\n",
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" such as links to an About page, or a Company page, or Careers/Jobs pages.\n",
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" You should respond in JSON as in this example:\n",
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"\n",
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" {\n",
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" \"links\": [\n",
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" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
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" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n",
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" ]\n",
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" }\n",
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" \"\"\"\n",
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" \n",
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" user_prompt = f\"\"\"\n",
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" Here is the list of links on the website {url} -\n",
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" Please decide which of these are relevant web links for a brochure about the company, \n",
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" respond with the full https URL in JSON format.\n",
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" Do not include Terms of Service, Privacy, email links.\n",
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"\n",
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" Links (some might be relative links):\n",
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"\n",
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" {chr(10).join(links[:50])} # Limit to first 50 links to avoid token limits\n",
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" \"\"\"\n",
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" \n",
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" try:\n",
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" if model.startswith(\"gpt\"):\n",
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" response = openai.chat.completions.create(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": link_system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ],\n",
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" response_format={\"type\": \"json_object\"}\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" else:\n",
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" response = ollama.chat(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": link_system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]\n",
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" )\n",
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" result = response['message']['content']\n",
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" \n",
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" links_data = json.loads(result)\n",
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" print(f\"✅ Selected {len(links_data['links'])} relevant links\")\n",
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" return links_data\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"❌ Error selecting links: {e}\")\n",
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" return {\"links\": []}\n",
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"\n",
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"print(\"Intelligent link selection function defined!\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Content Aggregation\n",
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"def fetch_page_and_all_relevant_links(url, model=\"gpt-4o-mini\"):\n",
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" \"\"\"Fetch main page content and all relevant linked pages\"\"\"\n",
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" print(f\"📄 Fetching content for {url}...\")\n",
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" \n",
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" # Get main page content\n",
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" main_content = fetch_website_contents(url)\n",
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" \n",
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" # Get relevant links\n",
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" relevant_links = select_relevant_links(url, model)\n",
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" \n",
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" # Build comprehensive content\n",
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" result = f\"## Landing Page:\\\\n\\\\n{main_content}\\\\n## Relevant Links:\\\\n\"\n",
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" \n",
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" for link in relevant_links['links']:\n",
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" print(f\" 📄 Fetching {link['type']}: {link['url']}\")\n",
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" try:\n",
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" content = fetch_website_contents(link[\"url\"])\n",
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" result += f\"\\\\n\\\\n### Link: {link['type']}\\\\n\"\n",
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" result += content\n",
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" except Exception as e:\n",
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" print(f\" ❌ Error fetching {link['url']}: {e}\")\n",
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" result += f\"\\\\n\\\\n### Link: {link['type']} (Error)\\\\n\"\n",
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" result += f\"Error fetching content: {e}\"\n",
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" \n",
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" return result\n",
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"\n",
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"print(\"Content aggregation function defined!\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Professional Brochure Generation\n",
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"def create_company_brochure(company_name, url, model=\"gpt-4o-mini\", style=\"professional\"):\n",
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" \"\"\"Generate a professional company brochure\"\"\"\n",
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" print(f\"🏢 Creating brochure for {company_name}...\")\n",
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" \n",
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" # Get all content\n",
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" all_content = fetch_page_and_all_relevant_links(url, model)\n",
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" \n",
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" # Truncate if too long (to avoid token limits)\n",
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" if len(all_content) > 5000:\n",
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" all_content = all_content[:5000] + \"\\\\n\\\\n[Content truncated...]\"\n",
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" \n",
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" # Define brochure system prompt based on style\n",
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" if style == \"professional\":\n",
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" brochure_system_prompt = \"\"\"\n",
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" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
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" and creates a short brochure about the company for prospective customers, investors and recruits.\n",
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" Respond in markdown without code blocks.\n",
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" Include details of company culture, customers and careers/jobs if you have the information.\n",
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" \"\"\"\n",
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" elif style == \"humorous\":\n",
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" brochure_system_prompt = \"\"\"\n",
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" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
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" and creates a short, humorous, entertaining, witty brochure about the company for prospective customers, investors and recruits.\n",
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" Respond in markdown without code blocks.\n",
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" Include details of company culture, customers and careers/jobs if you have the information.\n",
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" \"\"\"\n",
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" else:\n",
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" brochure_system_prompt = \"\"\"\n",
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" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
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" and creates a short brochure about the company.\n",
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" Respond in markdown without code blocks.\n",
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" \"\"\"\n",
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" \n",
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" user_prompt = f\"\"\"\n",
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" You are looking at a company called: {company_name}\n",
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" Here are the contents of its landing page and other relevant pages;\n",
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" use this information to build a short brochure of the company in markdown without code blocks.\n",
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"\n",
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" {all_content}\n",
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" \"\"\"\n",
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" \n",
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" try:\n",
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" if model.startswith(\"gpt\"):\n",
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" response = openai.chat.completions.create(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": brochure_system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ],\n",
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" temperature=0.7,\n",
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" max_tokens=1000\n",
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" )\n",
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" brochure = response.choices[0].message.content\n",
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" else:\n",
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" response = ollama.chat(\n",
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" model=model,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": brochure_system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]\n",
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" )\n",
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" brochure = response['message']['content']\n",
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" \n",
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" print(f\"✅ Brochure generated successfully!\")\n",
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" return brochure\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"❌ Error generating brochure: {e}\")\n",
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" return f\"Error generating brochure: {e}\"\n",
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"\n",
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"def display_brochure(company_name, url, model=\"gpt-4o-mini\", style=\"professional\"):\n",
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" \"\"\"Display a company brochure\"\"\"\n",
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" brochure = create_company_brochure(company_name, url, model, style)\n",
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" display(Markdown(f\"# {company_name} Brochure\\\\n\\\\n{brochure}\"))\n",
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"\n",
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"print(\"Professional brochure generation functions defined!\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"# Test Day 5 Solution - Business Brochure Generator\n",
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"print(\"## Day 5 Solution Test - Business Brochure Generator\")\n",
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"print(\"=\"*60)\n",
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"\n",
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"# Test with different companies\n",
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"test_companies = [\n",
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" (\"Hugging Face\", \"https://huggingface.co\"),\n",
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" (\"OpenAI\", \"https://openai.com\"),\n",
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" (\"Anthropic\", \"https://anthropic.com\")\n",
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"]\n",
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"\n",
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"print(\"🏢 Testing brochure generation for different companies...\")\n",
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"\n",
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"for company_name, url in test_companies:\n",
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" print(f\"\\\\n{'='*50}\")\n",
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" print(f\"Testing: {company_name}\")\n",
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" print(f\"URL: {url}\")\n",
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" print('='*50)\n",
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" \n",
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" try:\n",
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" # Test with professional style\n",
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" print(f\"\\\\n📄 Generating professional brochure for {company_name}...\")\n",
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" display_brochure(company_name, url, model=MODEL_GPT, style=\"professional\")\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"❌ Error with {company_name}: {e}\")\n",
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" \n",
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" print(\"\\\\n\" + \"-\"*40)\n"
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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": ".venv",
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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": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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