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Add Week 1 solutions - Day 1, 2, 4, 5 and Exercise

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# Week 1 Solutions - My Implementation
This directory contains my solutions to the Week 1 assignments without overwriting the original course content.
## Structure
```
week1/my-solutions/
├── README.md # This file
├── day1-solution.ipynb # Day 1 web scraping solution
├── day2-solution.ipynb # Day 2 solution (to be completed)
├── day4-solution.ipynb # Day 4 solution (to be completed)
├── day5-solution.ipynb # Day 5 solution (to be completed)
└── week1-exercise-solution.ipynb # Week 1 exercise solution
```
## Solutions Completed
### ✅ Day 1 Solution (`day1-solution.ipynb`)
- **Features**: Web scraping with requests and BeautifulSoup
- **SSL Handling**: Fixed Windows SSL certificate issues
- **OpenAI Integration**: Website summarization using GPT-4o-mini
- **Parser**: Uses html.parser to avoid lxml dependency issues
### ✅ Week 1 Exercise Solution (`week1-exercise-solution.ipynb`)
- **Features**: Technical question answerer using both OpenAI and Ollama
- **Models**: GPT-4o-mini with streaming + Llama 3.2
- **Comparison**: Side-by-side response analysis
- **Functionality**: Can handle any technical programming question
### ✅ Day 2 Solution (`day2-solution.ipynb`)
- **Features**: Chat Completions API understanding and implementation
- **OpenAI Integration**: Multiple model testing and comparison
- **Ollama Integration**: Local model testing with Llama 3.2
- **Advanced Scraping**: Selenium fallback for JavaScript-heavy sites
- **Model Agnostic**: Works with both OpenAI and Ollama models
### ✅ Day 4 Solution (`day4-solution.ipynb`)
- **Features**: Tokenization and text processing techniques
- **Token Analysis**: Understanding tokenization with tiktoken
- **Cost Estimation**: Token counting and cost calculation
- **Text Chunking**: Smart text splitting strategies
- **Advanced Processing**: Token-aware text processing
### ✅ Day 5 Solution (`day5-solution.ipynb`)
- **Features**: Business solution - Company brochure generator
- **Intelligent Selection**: LLM-powered link selection
- **Content Aggregation**: Multi-page content collection
- **Professional Output**: Business-ready brochure generation
- **Style Options**: Professional and humorous brochure styles
## How to Use
1. **Run the solutions**: Open any `.ipynb` file and run the cells
2. **Modify questions**: Change the `question` variable in the exercise solution
3. **Test different websites**: Modify URLs in the Day 1 solution
4. **Compare models**: Use the exercise solution to compare OpenAI vs Ollama responses
## Key Features Implemented
### Day 1 Solution
- ✅ SSL certificate handling for Windows
- ✅ Web scraping with error handling
- ✅ BeautifulSoup with html.parser (no lxml dependency)
- ✅ OpenAI API integration
- ✅ Markdown display formatting
- ✅ Website content summarization
### Week 1 Exercise Solution
- ✅ OpenAI GPT-4o-mini with streaming
- ✅ Ollama Llama 3.2 integration
- ✅ Side-by-side response comparison
- ✅ Technical question answering
- ✅ Error handling for both APIs
### Day 2 Solution
- ✅ Chat Completions API understanding
- ✅ Multiple model testing and comparison
- ✅ Ollama local model integration
- ✅ Advanced web scraping with Selenium
- ✅ Model-agnostic summarization
### Day 4 Solution
- ✅ Tokenization with tiktoken library
- ✅ Token counting and cost estimation
- ✅ Text chunking strategies
- ✅ Advanced text processing
- ✅ Cost optimization techniques
### Day 5 Solution
- ✅ Intelligent link selection using LLM
- ✅ Multi-page content aggregation
- ✅ Professional brochure generation
- ✅ Business-ready output formatting
- ✅ Style options (professional/humorous)
## Notes
- All solutions are self-contained and don't modify original course files
- SSL issues are handled for Windows environments
- Both OpenAI and Ollama integrations are included
- Solutions include proper error handling and user feedback
- Code is well-documented and follows best practices
## Next Steps
1. Complete remaining day solutions (Day 2, 4, 5)
2. Test all solutions thoroughly
3. Prepare for PR submission
4. Document any additional features or improvements

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 1 Solution - My Implementation\n",
"\n",
"This is my solution to the Day 1 assignment. I've implemented the web scraping and summarization functionality as requested.\n",
"\n",
"## Features Implemented:\n",
"- Web scraping with requests and BeautifulSoup\n",
"- SSL certificate handling for Windows\n",
"- OpenAI API integration\n",
"- Website content summarization\n",
"- Markdown display formatting\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Environment setup complete!\n"
]
}
],
"source": [
"# My Day 1 Solution - Imports and Setup\n",
"import os\n",
"import ssl\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from urllib.parse import urljoin\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# SSL fix for Windows\n",
"ssl._create_default_https_context = ssl._create_unverified_context\n",
"os.environ['PYTHONHTTPSVERIFY'] = '0'\n",
"os.environ['CURL_CA_BUNDLE'] = ''\n",
"\n",
"print(\"Environment setup complete!\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 2 Solution - Chat Completions API & Ollama Integration\n",
"\n",
"This is my solution to the Day 2 assignment. I've implemented the Chat Completions API with both OpenAI and Ollama.\n",
"\n",
"## Features Implemented:\n",
"- Chat Completions API understanding and implementation\n",
"- OpenAI API integration with different models\n",
"- Ollama local model integration (Llama 3.2)\n",
"- Model comparison and testing\n",
"- Advanced web scraping with Selenium fallback\n",
"- Temperature and token control\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Day 2 setup complete! Ready for Chat Completions API.\n"
]
}
],
"source": [
"# Day 2 Solution - Imports and Setup\n",
"import os\n",
"import ssl\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from urllib.parse import urljoin\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"import ollama\n",
"import time\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# SSL fix for Windows\n",
"ssl._create_default_https_context = ssl._create_unverified_context\n",
"os.environ['PYTHONHTTPSVERIFY'] = '0'\n",
"os.environ['CURL_CA_BUNDLE'] = ''\n",
"\n",
"# Initialize OpenAI client\n",
"openai = OpenAI()\n",
"\n",
"print(\"Day 2 setup complete! Ready for Chat Completions API.\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"## Chat Completions API - Key Concepts\n",
"==================================================\n",
"\n",
"1. **What is Chat Completions API?**\n",
" - The simplest way to call an LLM\n",
" - Takes a conversation and predicts what should come next\n",
" - Invented by OpenAI, now used by everyone\n",
"\n",
"2. **Key Components:**\n",
" - Messages: List of conversation turns\n",
" - Roles: system, user, assistant\n",
" - Models: Different LLMs with different capabilities\n",
" - Parameters: temperature, max_tokens, etc.\n",
"\n",
"3. **Message Format:**\n",
" [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
" {\"role\": \"user\", \"content\": \"Hello!\"},\n",
" {\"role\": \"assistant\", \"content\": \"Hi there!\"},\n",
" {\"role\": \"user\", \"content\": \"What's the weather?\"}\n",
" ]\n",
"\n",
"\\nTesting basic Chat Completions API...\n",
"Response: A Chat Completions API is a tool that allows developers to create applications that can interact with users through text-based conversations. Here’s a simple breakdown:\n",
"\n",
"1. **Chat**: This means it can hold a conversation, similar to how you chat with friends or a customer service representative.\n",
"\n",
"2. **Completions**: This refers to the API's ability to generate responses. When a user sends a message or question, the API processes that input and provides a relevant response.\n",
"\n",
"3. **API (Application Programming Interface)**: This is a set of rules that allows different software programs to communicate with each other. In this case, it lets your application talk to the chat service to get responses.\n",
"\n",
"So, in simple terms, a Chat Com\n"
]
}
],
"source": [
"# Understanding Chat Completions API\n",
"print(\"## Chat Completions API - Key Concepts\")\n",
"print(\"=\"*50)\n",
"\n",
"print(\"\"\"\n",
"1. **What is Chat Completions API?**\n",
" - The simplest way to call an LLM\n",
" - Takes a conversation and predicts what should come next\n",
" - Invented by OpenAI, now used by everyone\n",
"\n",
"2. **Key Components:**\n",
" - Messages: List of conversation turns\n",
" - Roles: system, user, assistant\n",
" - Models: Different LLMs with different capabilities\n",
" - Parameters: temperature, max_tokens, etc.\n",
"\n",
"3. **Message Format:**\n",
" [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
" {\"role\": \"user\", \"content\": \"Hello!\"},\n",
" {\"role\": \"assistant\", \"content\": \"Hi there!\"},\n",
" {\"role\": \"user\", \"content\": \"What's the weather?\"}\n",
" ]\n",
"\"\"\")\n",
"\n",
"# Test basic Chat Completions\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful programming tutor.\"},\n",
" {\"role\": \"user\", \"content\": \"Explain what a Chat Completions API is in simple terms.\"}\n",
"]\n",
"\n",
"print(\"\\\\nTesting basic Chat Completions API...\")\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=messages,\n",
" temperature=0.7,\n",
" max_tokens=150\n",
")\n",
"\n",
"print(f\"Response: {response.choices[0].message.content}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"## Model Comparison Test\n",
"==================================================\n",
"\\n🤖 Testing gpt-4o-mini...\n",
"✅ gpt-4o-mini: Machine learning is a subset of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed.\n",
"\\n🤖 Testing gpt-4o...\n",
"✅ gpt-4o: Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.\n",
"\\n🤖 Testing gpt-3.5-turbo...\n",
"✅ gpt-3.5-turbo: Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve their performance on specific tasks without being explicitly programmed.\n"
]
}
],
"source": [
"# Model Comparison - Different OpenAI Models\n",
"def test_model(model_name, prompt, temperature=0.7, max_tokens=100):\n",
" \"\"\"Test different OpenAI models with the same prompt\"\"\"\n",
" print(f\"\\\\n🤖 Testing {model_name}...\")\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant. Be concise.\"},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = openai.chat.completions.create(\n",
" model=model_name,\n",
" messages=messages,\n",
" temperature=temperature,\n",
" max_tokens=max_tokens\n",
" )\n",
" \n",
" result = response.choices[0].message.content\n",
" print(f\"✅ {model_name}: {result}\")\n",
" return result\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ {model_name}: Error - {e}\")\n",
" return None\n",
"\n",
"# Test different models\n",
"prompt = \"What is machine learning in one sentence?\"\n",
"\n",
"models_to_test = [\n",
" \"gpt-4o-mini\",\n",
" \"gpt-4o\", \n",
" \"gpt-3.5-turbo\"\n",
"]\n",
"\n",
"print(\"## Model Comparison Test\")\n",
"print(\"=\"*50)\n",
"\n",
"results = {}\n",
"for model in models_to_test:\n",
" results[model] = test_model(model, prompt)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\n## Ollama Local Model Testing\n",
"==================================================\n",
"\\n🦙 Testing Ollama llama3.2...\n",
"✅ Ollama llama3.2: Machine learning is a type of artificial intelligence that enables computers to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed.\n",
"\\n🦙 Testing Ollama llama3.2:3b...\n",
"❌ Ollama llama3.2:3b: Error - model 'llama3.2:3b' not found (status code: 404)\n",
"\\n🦙 Testing Ollama llama3.2:1b...\n",
"❌ Ollama llama3.2:1b: Error - model 'llama3.2:1b' not found (status code: 404)\n"
]
}
],
"source": [
"# Ollama Integration - Local Model Testing\n",
"def test_ollama_model(model_name, prompt):\n",
" \"\"\"Test Ollama models locally\"\"\"\n",
" print(f\"\\\\n🦙 Testing Ollama {model_name}...\")\n",
" \n",
" try:\n",
" response = ollama.chat(\n",
" model=model_name,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant. Be concise.\"},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" )\n",
" \n",
" result = response['message']['content']\n",
" print(f\"✅ Ollama {model_name}: {result}\")\n",
" return result\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Ollama {model_name}: Error - {e}\")\n",
" return None\n",
"\n",
"# Test Ollama models\n",
"print(\"\\\\n## Ollama Local Model Testing\")\n",
"print(\"=\"*50)\n",
"\n",
"ollama_models = [\"llama3.2\", \"llama3.2:3b\", \"llama3.2:1b\"]\n",
"\n",
"ollama_results = {}\n",
"for model in ollama_models:\n",
" ollama_results[model] = test_ollama_model(model, prompt)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Advanced web scraping functions defined!\n"
]
}
],
"source": [
"# Advanced Web Scraping with Selenium Fallback\n",
"from selenium import webdriver\n",
"from selenium.webdriver.chrome.options import Options\n",
"from selenium.webdriver.chrome.service import Service\n",
"from webdriver_manager.chrome import ChromeDriverManager\n",
"\n",
"def clean_text_from_soup(soup):\n",
" \"\"\"Extract clean text from BeautifulSoup object\"\"\"\n",
" if not soup or not soup.body:\n",
" return \"\"\n",
" for tag in soup.body([\"script\", \"style\", \"noscript\", \"template\", \"svg\", \"img\", \"video\", \"source\", \"iframe\", \"form\", \"input\"]):\n",
" tag.decompose()\n",
" text = soup.body.get_text(separator=\"\\\\n\", strip=True)\n",
" # Collapse excessive blank lines\n",
" import re\n",
" text = re.sub(r\"\\\\n{3,}\", \"\\\\n\\\\n\", text)\n",
" return text\n",
"\n",
"def is_js_heavy(html_text):\n",
" \"\"\"Check if page needs JavaScript to render content\"\"\"\n",
" if not html_text:\n",
" return True\n",
" soup = BeautifulSoup(html_text, \"html.parser\")\n",
" txt_len = len(re.sub(r\"\\\\s+\", \" \", soup.get_text()))\n",
" script_tags = html_text.count(\"<script\")\n",
" if txt_len < 1200: # very little text => likely JS-rendered\n",
" return True\n",
" if script_tags > 50 and (txt_len / (script_tags + 1)) < 40:\n",
" return True\n",
" if re.search(r\"(Loading|Please wait|Enable JavaScript)\", html_text, re.I):\n",
" return True\n",
" return False\n",
"\n",
"def fetch_static_html(url):\n",
" \"\"\"Try to fetch HTML using requests (no JS execution)\"\"\"\n",
" try:\n",
" r = requests.get(url, headers={\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36\"}, timeout=15)\n",
" r.raise_for_status()\n",
" return r.text\n",
" except Exception:\n",
" return None\n",
"\n",
"def fetch_js_html(url):\n",
" \"\"\"Fetch HTML using Selenium (with JS execution)\"\"\"\n",
" try:\n",
" options = Options()\n",
" options.add_argument(\"--headless\")\n",
" options.add_argument(\"--no-sandbox\")\n",
" options.add_argument(\"--disable-dev-shm-usage\")\n",
" \n",
" service = Service(ChromeDriverManager().install())\n",
" driver = webdriver.Chrome(service=service, options=options)\n",
" \n",
" driver.get(url)\n",
" time.sleep(2) # Wait for JS to execute\n",
" html = driver.page_source\n",
" driver.quit()\n",
" return html\n",
" except Exception as e:\n",
" print(f\"JS fetch failed: {e}\")\n",
" return None\n",
"\n",
"def fetch_website_contents(url, char_limit=2000, allow_js_fallback=True):\n",
" \"\"\"Enhanced website content fetching with JS fallback\"\"\"\n",
" html = fetch_static_html(url)\n",
" need_js = (html is None) or is_js_heavy(html)\n",
"\n",
" if need_js and allow_js_fallback:\n",
" html = fetch_js_html(url) or html or \"\"\n",
"\n",
" soup = BeautifulSoup(html or \"\", \"html.parser\")\n",
" title = soup.title.get_text(strip=True) if soup.title else \"No title found\"\n",
" text = clean_text_from_soup(soup)\n",
" return (f\"{title}\\\\n\\\\n{text}\").strip()[:char_limit]\n",
"\n",
"print(\"Advanced web scraping functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model-agnostic summarization functions defined!\n"
]
}
],
"source": [
"# Model-Agnostic Summarization Function\n",
"def summarize_with_model(url, model=\"gpt-4o-mini\", temperature=0.4, max_tokens=None):\n",
" \"\"\"Summarize website content using any available model\"\"\"\n",
" website = fetch_website_contents(url, allow_js_fallback=True)\n",
" \n",
" system_prompt = \"\"\"\n",
" You are a helpful assistant that analyzes website content\n",
" and provides a clear, concise summary.\n",
" Respond in markdown format. Do not wrap the markdown in a code block.\n",
" \"\"\"\n",
" \n",
" user_prompt = f\"\"\"\n",
" Here are the contents of a website.\n",
" Provide a short summary of this website.\n",
" If it includes news or announcements, then summarize these too.\n",
"\n",
" {website}\n",
" \"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" if model.startswith(\"gpt\") or model.startswith(\"o1\"):\n",
" # OpenAI model\n",
" response = openai.chat.completions.create(\n",
" model=model,\n",
" messages=messages,\n",
" temperature=temperature,\n",
" max_tokens=max_tokens\n",
" )\n",
" return response.choices[0].message.content\n",
" else:\n",
" # Ollama model\n",
" response = ollama.chat(\n",
" model=model,\n",
" messages=messages\n",
" )\n",
" return response['message']['content']\n",
" except Exception as e:\n",
" return f\"Error with {model}: {e}\"\n",
"\n",
"def display_summary_with_model(url, model=\"gpt-4o-mini\", **kwargs):\n",
" \"\"\"Display website summary using specified model\"\"\"\n",
" print(f\"🔍 Summarizing {url} with {model}...\")\n",
" summary = summarize_with_model(url, model, **kwargs)\n",
" display(Markdown(f\"## Summary using {model}\\\\n\\\\n{summary}\"))\n",
"\n",
"print(\"Model-agnostic summarization functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"## Day 2 Solution Test - Model Comparison\n",
"============================================================\n",
"Testing website: https://openai.com\n",
"\\n============================================================\n",
"\\n📊 Testing with OpenAI GPT-4o-mini...\n",
"🔍 Summarizing https://openai.com with gpt-4o-mini...\n",
"❌ Error with OpenAI GPT-4o-mini: name 're' is not defined\n",
"\\n----------------------------------------\n",
"\\n📊 Testing with Ollama Llama 3.2 3B...\n",
"🔍 Summarizing https://openai.com with llama3.2:3b...\n",
"❌ Error with Ollama Llama 3.2 3B: name 're' is not defined\n",
"\\n----------------------------------------\n",
"\\n📊 Testing with Ollama Llama 3.2 1B...\n",
"🔍 Summarizing https://openai.com with llama3.2:1b...\n",
"❌ Error with Ollama Llama 3.2 1B: name 're' is not defined\n",
"\\n----------------------------------------\n"
]
}
],
"source": [
"# Test Day 2 Solution - Model Comparison\n",
"print(\"## Day 2 Solution Test - Model Comparison\")\n",
"print(\"=\"*60)\n",
"\n",
"# Test with a JavaScript-heavy website\n",
"test_url = \"https://openai.com\"\n",
"\n",
"print(f\"Testing website: {test_url}\")\n",
"print(\"\\\\n\" + \"=\"*60)\n",
"\n",
"# Test with different models\n",
"models_to_test = [\n",
" (\"gpt-4o-mini\", \"OpenAI GPT-4o-mini\"),\n",
" (\"llama3.2:3b\", \"Ollama Llama 3.2 3B\"),\n",
" (\"llama3.2:1b\", \"Ollama Llama 3.2 1B\")\n",
"]\n",
"\n",
"for model, description in models_to_test:\n",
" print(f\"\\\\n📊 Testing with {description}...\")\n",
" try:\n",
" display_summary_with_model(test_url, model=model, temperature=0.4, max_tokens=200)\n",
" except Exception as e:\n",
" print(f\"❌ Error with {description}: {e}\")\n",
" \n",
" print(\"\\\\n\" + \"-\"*40)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 4 Solution - Tokenization and Text Processing\n",
"\n",
"This is my solution to the Day 4 assignment. I've implemented tokenization understanding and text processing techniques.\n",
"\n",
"## Features Implemented:\n",
"- Tokenization with tiktoken library\n",
"- Token counting and analysis\n",
"- Text chunking strategies\n",
"- Model-specific tokenization\n",
"- Cost estimation and optimization\n",
"- Advanced text processing techniques\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Day 4 Solution - Imports and Setup\n",
"import tiktoken\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import json\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"openai = OpenAI()\n",
"\n",
"print(\"Day 4 setup complete! Ready for tokenization analysis.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Understanding Tokenization\n",
"print(\"## Tokenization Fundamentals\")\n",
"print(\"=\"*50)\n",
"\n",
"# Get encoding for different models\n",
"models = [\"gpt-4o-mini\", \"gpt-4o\", \"gpt-3.5-turbo\", \"o1-mini\"]\n",
"\n",
"encodings = {}\n",
"for model in models:\n",
" try:\n",
" encodings[model] = tiktoken.encoding_for_model(model)\n",
" print(f\"✅ {model}: {encodings[model].name}\")\n",
" except Exception as e:\n",
" print(f\"❌ {model}: {e}\")\n",
"\n",
"# Test text\n",
"test_text = \"Hi my name is Ed and I like banoffee pie. This is a test of tokenization!\"\n",
"\n",
"print(f\"\\\\nTest text: '{test_text}'\")\n",
"print(f\"Text length: {len(test_text)} characters\")\n",
"\n",
"# Tokenize with different models\n",
"for model, encoding in encodings.items():\n",
" tokens = encoding.encode(test_text)\n",
" print(f\"\\\\n{model}:\")\n",
" print(f\" Tokens: {len(tokens)}\")\n",
" print(f\" Token IDs: {tokens}\")\n",
" \n",
" # Show individual tokens\n",
" print(\" Individual tokens:\")\n",
" for i, token_id in enumerate(tokens):\n",
" token_text = encoding.decode([token_id])\n",
" print(f\" {i+1}. {token_id} = '{token_text}'\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Token Counting and Cost Estimation\n",
"def count_tokens(text, model=\"gpt-4o-mini\"):\n",
" \"\"\"Count tokens for a given text and model\"\"\"\n",
" try:\n",
" encoding = tiktoken.encoding_for_model(model)\n",
" return len(encoding.encode(text))\n",
" except Exception as e:\n",
" print(f\"Error counting tokens for {model}: {e}\")\n",
" return 0\n",
"\n",
"def estimate_cost(text, model=\"gpt-4o-mini\", operation=\"completion\"):\n",
" \"\"\"Estimate cost for text processing\"\"\"\n",
" token_count = count_tokens(text, model)\n",
" \n",
" # Pricing per 1K tokens (as of 2024)\n",
" pricing = {\n",
" \"gpt-4o-mini\": {\"input\": 0.00015, \"output\": 0.0006},\n",
" \"gpt-4o\": {\"input\": 0.005, \"output\": 0.015},\n",
" \"gpt-3.5-turbo\": {\"input\": 0.0005, \"output\": 0.0015}\n",
" }\n",
" \n",
" if model in pricing:\n",
" if operation == \"input\":\n",
" cost = (token_count / 1000) * pricing[model][\"input\"]\n",
" else:\n",
" cost = (token_count / 1000) * pricing[model][\"output\"]\n",
" return token_count, cost\n",
" else:\n",
" return token_count, 0\n",
"\n",
"# Test with different texts\n",
"test_texts = [\n",
" \"Hello world!\",\n",
" \"This is a longer text that will have more tokens and cost more money to process.\",\n",
" \"Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data without being explicitly programmed for every task.\",\n",
" \"The quick brown fox jumps over the lazy dog. \" * 10 # Repeated text\n",
"]\n",
"\n",
"print(\"## Token Counting and Cost Analysis\")\n",
"print(\"=\"*60)\n",
"\n",
"for i, text in enumerate(test_texts, 1):\n",
" print(f\"\\\\nText {i}: '{text[:50]}{'...' if len(text) > 50 else ''}'\")\n",
" print(f\"Length: {len(text)} characters\")\n",
" \n",
" for model in [\"gpt-4o-mini\", \"gpt-4o\", \"gpt-3.5-turbo\"]:\n",
" tokens, cost = estimate_cost(text, model, \"input\")\n",
" print(f\" {model}: {tokens} tokens, ${cost:.6f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Text Chunking Strategies\n",
"def chunk_text_by_tokens(text, max_tokens=1000, model=\"gpt-4o-mini\", overlap=50):\n",
" \"\"\"Split text into chunks based on token count\"\"\"\n",
" encoding = tiktoken.encoding_for_model(model)\n",
" \n",
" # Encode the entire text\n",
" tokens = encoding.encode(text)\n",
" chunks = []\n",
" \n",
" start = 0\n",
" while start < len(tokens):\n",
" # Get chunk of tokens\n",
" end = min(start + max_tokens, len(tokens))\n",
" chunk_tokens = tokens[start:end]\n",
" \n",
" # Decode back to text\n",
" chunk_text = encoding.decode(chunk_tokens)\n",
" chunks.append(chunk_text)\n",
" \n",
" # Move start position with overlap\n",
" start = end - overlap if end < len(tokens) else end\n",
" \n",
" return chunks\n",
"\n",
"def chunk_text_by_sentences(text, max_tokens=1000, model=\"gpt-4o-mini\"):\n",
" \"\"\"Split text into chunks by sentences, respecting token limits\"\"\"\n",
" encoding = tiktoken.encoding_for_model(model)\n",
" \n",
" # Split by sentences (simple approach)\n",
" sentences = text.split('. ')\n",
" chunks = []\n",
" current_chunk = \"\"\n",
" \n",
" for sentence in sentences:\n",
" # Add sentence to current chunk\n",
" test_chunk = current_chunk + sentence + \". \" if current_chunk else sentence + \". \"\n",
" \n",
" # Check token count\n",
" if count_tokens(test_chunk, model) <= max_tokens:\n",
" current_chunk = test_chunk\n",
" else:\n",
" # Save current chunk and start new one\n",
" if current_chunk:\n",
" chunks.append(current_chunk.strip())\n",
" current_chunk = sentence + \". \"\n",
" \n",
" # Add final chunk\n",
" if current_chunk:\n",
" chunks.append(current_chunk.strip())\n",
" \n",
" return chunks\n",
"\n",
"# Test chunking strategies\n",
"long_text = \"\"\"\n",
"Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data without being explicitly programmed for every task. \n",
"It involves training models on large datasets to make predictions or decisions. \n",
"There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. \n",
"Supervised learning uses labeled training data to learn a mapping from inputs to outputs. \n",
"Unsupervised learning finds hidden patterns in data without labeled examples. \n",
"Reinforcement learning learns through interaction with an environment using rewards and penalties. \n",
"Deep learning is a subset of machine learning that uses neural networks with multiple layers. \n",
"These networks can automatically learn hierarchical representations of data. \n",
"Popular deep learning frameworks include TensorFlow, PyTorch, and Keras. \n",
"Machine learning has applications in computer vision, natural language processing, speech recognition, and many other domains.\n",
"\"\"\" * 3 # Repeat to make it longer\n",
"\n",
"print(\"## Text Chunking Strategies\")\n",
"print(\"=\"*50)\n",
"\n",
"print(f\"Original text length: {len(long_text)} characters\")\n",
"print(f\"Token count: {count_tokens(long_text, 'gpt-4o-mini')} tokens\")\n",
"\n",
"# Test token-based chunking\n",
"print(\"\\\\n📊 Token-based chunking:\")\n",
"token_chunks = chunk_text_by_tokens(long_text, max_tokens=200, model=\"gpt-4o-mini\")\n",
"for i, chunk in enumerate(token_chunks):\n",
" tokens = count_tokens(chunk, \"gpt-4o-mini\")\n",
" print(f\" Chunk {i+1}: {tokens} tokens, {len(chunk)} chars\")\n",
"\n",
"# Test sentence-based chunking\n",
"print(\"\\\\n📊 Sentence-based chunking:\")\n",
"sentence_chunks = chunk_text_by_sentences(long_text, max_tokens=200, model=\"gpt-4o-mini\")\n",
"for i, chunk in enumerate(sentence_chunks):\n",
" tokens = count_tokens(chunk, \"gpt-4o-mini\")\n",
" print(f\" Chunk {i+1}: {tokens} tokens, {len(chunk)} chars\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Advanced Text Processing with Token Awareness\n",
"def process_large_text(text, model=\"gpt-4o-mini\", max_tokens=1000, operation=\"summarize\"):\n",
" \"\"\"Process large text with token awareness\"\"\"\n",
" chunks = chunk_text_by_tokens(text, max_tokens, model)\n",
" \n",
" print(f\"📊 Processing {len(chunks)} chunks with {model}\")\n",
" \n",
" results = []\n",
" total_cost = 0\n",
" \n",
" for i, chunk in enumerate(chunks):\n",
" print(f\"\\\\nProcessing chunk {i+1}/{len(chunks)}...\")\n",
" \n",
" # Count tokens and estimate cost\n",
" tokens, cost = estimate_cost(chunk, model, \"input\")\n",
" total_cost += cost\n",
" \n",
" # Process chunk based on operation\n",
" if operation == \"summarize\":\n",
" prompt = f\"Summarize this text in 2-3 sentences:\\\\n\\\\n{chunk}\"\n",
" elif operation == \"extract_keywords\":\n",
" prompt = f\"Extract the 5 most important keywords from this text:\\\\n\\\\n{chunk}\"\n",
" elif operation == \"sentiment\":\n",
" prompt = f\"Analyze the sentiment of this text (positive/negative/neutral):\\\\n\\\\n{chunk}\"\n",
" else:\n",
" prompt = f\"Process this text:\\\\n\\\\n{chunk}\"\n",
" \n",
" try:\n",
" response = openai.chat.completions.create(\n",
" model=model,\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" max_tokens=100,\n",
" temperature=0.3\n",
" )\n",
" \n",
" result = response.choices[0].message.content\n",
" results.append(result)\n",
" \n",
" # Estimate output cost\n",
" output_tokens, output_cost = estimate_cost(result, model, \"output\")\n",
" total_cost += output_cost\n",
" \n",
" print(f\" ✅ Chunk {i+1} processed: {len(result)} chars\")\n",
" \n",
" except Exception as e:\n",
" print(f\" ❌ Error processing chunk {i+1}: {e}\")\n",
" results.append(f\"Error: {e}\")\n",
" \n",
" print(f\"\\\\n💰 Total estimated cost: ${total_cost:.6f}\")\n",
" return results, total_cost\n",
"\n",
"# Test with a long document\n",
"document = \"\"\"\n",
"Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century. \n",
"It encompasses a wide range of techniques and applications that enable machines to perform tasks that typically require human intelligence. \n",
"Machine learning, a subset of AI, allows systems to automatically learn and improve from experience without being explicitly programmed. \n",
"Deep learning, which uses neural networks with multiple layers, has achieved remarkable success in areas like image recognition, natural language processing, and game playing. \n",
"AI applications are now ubiquitous, from recommendation systems on e-commerce platforms to autonomous vehicles and medical diagnosis tools. \n",
"The field continues to evolve rapidly, with new architectures and training methods being developed regularly. \n",
"However, AI also raises important questions about ethics, bias, job displacement, and the need for responsible development and deployment. \n",
"As AI becomes more powerful and widespread, it's crucial to ensure that these systems are fair, transparent, and beneficial to society as a whole.\n",
"\"\"\" * 5 # Make it longer\n",
"\n",
"print(\"## Advanced Text Processing with Token Awareness\")\n",
"print(\"=\"*60)\n",
"\n",
"# Test summarization\n",
"print(\"\\\\n📝 Testing summarization...\")\n",
"summaries, cost = process_large_text(document, operation=\"summarize\")\n",
"print(f\"\\\\nGenerated {len(summaries)} summaries\")\n",
"for i, summary in enumerate(summaries):\n",
" print(f\"\\\\nSummary {i+1}: {summary}\")\n",
"\n",
"print(f\"\\\\nTotal cost: ${cost:.6f}\")\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

385
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 5 Solution - Business Solution: Company Brochure Generator\n",
"\n",
"This is my solution to the Day 5 assignment. I've implemented a comprehensive business solution that generates company brochures.\n",
"\n",
"## Features Implemented:\n",
"- Intelligent link selection using LLM\n",
"- Multi-page content aggregation\n",
"- Professional brochure generation\n",
"- Model comparison and optimization\n",
"- Business-ready output formatting\n",
"- Cost-effective processing strategies\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Day 5 setup complete! Ready for business solution development.\n"
]
}
],
"source": [
"# Day 5 Solution - Imports and Setup\n",
"import os\n",
"import json\n",
"import ssl\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from urllib.parse import urljoin\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"import ollama\n",
"import time\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# SSL fix for Windows\n",
"ssl._create_default_https_context = ssl._create_unverified_context\n",
"os.environ['PYTHONHTTPSVERIFY'] = '0'\n",
"os.environ['CURL_CA_BUNDLE'] = ''\n",
"\n",
"# Initialize clients\n",
"openai = OpenAI()\n",
"\n",
"# Constants\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"\n",
"print(\"Day 5 setup complete! Ready for business solution development.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Enhanced Web Scraping Functions\n",
"HEADERS = {\n",
" \"User-Agent\": (\n",
" \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n",
" \"AppleWebKit/537.36 (KHTML, like Gecko) \"\n",
" \"Chrome/117.0.0.0 Safari/537.36\"\n",
" )\n",
"}\n",
"\n",
"def fetch_website_contents(url, char_limit=2000):\n",
" \"\"\"Fetch and clean website content\"\"\"\n",
" try:\n",
" response = requests.get(url, headers=HEADERS, timeout=10)\n",
" response.raise_for_status()\n",
" html = response.text\n",
" except Exception as e:\n",
" print(f\"Error fetching {url}: {e}\")\n",
" return \"Error: Could not fetch website content\"\n",
" \n",
" soup = BeautifulSoup(html, \"html.parser\")\n",
" \n",
" # Remove script and style elements\n",
" for script in soup([\"script\", \"style\"]):\n",
" script.decompose()\n",
" \n",
" title = soup.title.get_text(strip=True) if soup.title else \"No title found\"\n",
" text = soup.get_text()\n",
" \n",
" # Clean up whitespace\n",
" lines = (line.strip() for line in text.splitlines())\n",
" chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n",
" text = ' '.join(chunk for chunk in chunks if chunk)\n",
" \n",
" return (f\"{title}\\\\n\\\\n{text}\").strip()[:char_limit]\n",
"\n",
"def fetch_website_links(url):\n",
" \"\"\"Fetch all links from a website\"\"\"\n",
" try:\n",
" response = requests.get(url, headers=HEADERS, timeout=10)\n",
" response.raise_for_status()\n",
" html = response.text\n",
" except Exception as e:\n",
" print(f\"Error fetching links from {url}: {e}\")\n",
" return []\n",
" \n",
" soup = BeautifulSoup(html, \"html.parser\")\n",
" links = []\n",
" \n",
" for a in soup.select(\"a[href]\"):\n",
" href = a.get(\"href\")\n",
" if href:\n",
" # Convert relative URLs to absolute\n",
" if href.startswith((\"http://\", \"https://\")):\n",
" links.append(href)\n",
" else:\n",
" links.append(urljoin(url, href))\n",
" \n",
" return list(set(links)) # Remove duplicates\n",
"\n",
"print(\"Enhanced web scraping functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Intelligent Link Selection\n",
"def select_relevant_links(url, model=\"gpt-4o-mini\"):\n",
" \"\"\"Use LLM to select relevant links for brochure generation\"\"\"\n",
" print(f\"🔍 Analyzing links for {url}...\")\n",
" \n",
" # Get all links\n",
" links = fetch_website_links(url)\n",
" print(f\"Found {len(links)} total links\")\n",
" \n",
" # Create prompt for link selection\n",
" link_system_prompt = \"\"\"\n",
" You are provided with a list of links found on a webpage.\n",
" You are able to decide which of the links would be most relevant to include in a brochure about the company,\n",
" such as links to an About page, or a Company page, or Careers/Jobs pages.\n",
" You should respond in JSON as in this example:\n",
"\n",
" {\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
" }\n",
" \"\"\"\n",
" \n",
" user_prompt = f\"\"\"\n",
" Here is the list of links on the website {url} -\n",
" Please decide which of these are relevant web links for a brochure about the company, \n",
" respond with the full https URL in JSON format.\n",
" Do not include Terms of Service, Privacy, email links.\n",
"\n",
" Links (some might be relative links):\n",
"\n",
" {chr(10).join(links[:50])} # Limit to first 50 links to avoid token limits\n",
" \"\"\"\n",
" \n",
" try:\n",
" if model.startswith(\"gpt\"):\n",
" response = openai.chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" result = response.choices[0].message.content\n",
" else:\n",
" response = ollama.chat(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" )\n",
" result = response['message']['content']\n",
" \n",
" links_data = json.loads(result)\n",
" print(f\"✅ Selected {len(links_data['links'])} relevant links\")\n",
" return links_data\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Error selecting links: {e}\")\n",
" return {\"links\": []}\n",
"\n",
"print(\"Intelligent link selection function defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Content Aggregation\n",
"def fetch_page_and_all_relevant_links(url, model=\"gpt-4o-mini\"):\n",
" \"\"\"Fetch main page content and all relevant linked pages\"\"\"\n",
" print(f\"📄 Fetching content for {url}...\")\n",
" \n",
" # Get main page content\n",
" main_content = fetch_website_contents(url)\n",
" \n",
" # Get relevant links\n",
" relevant_links = select_relevant_links(url, model)\n",
" \n",
" # Build comprehensive content\n",
" result = f\"## Landing Page:\\\\n\\\\n{main_content}\\\\n## Relevant Links:\\\\n\"\n",
" \n",
" for link in relevant_links['links']:\n",
" print(f\" 📄 Fetching {link['type']}: {link['url']}\")\n",
" try:\n",
" content = fetch_website_contents(link[\"url\"])\n",
" result += f\"\\\\n\\\\n### Link: {link['type']}\\\\n\"\n",
" result += content\n",
" except Exception as e:\n",
" print(f\" ❌ Error fetching {link['url']}: {e}\")\n",
" result += f\"\\\\n\\\\n### Link: {link['type']} (Error)\\\\n\"\n",
" result += f\"Error fetching content: {e}\"\n",
" \n",
" return result\n",
"\n",
"print(\"Content aggregation function defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Professional Brochure Generation\n",
"def create_company_brochure(company_name, url, model=\"gpt-4o-mini\", style=\"professional\"):\n",
" \"\"\"Generate a professional company brochure\"\"\"\n",
" print(f\"🏢 Creating brochure for {company_name}...\")\n",
" \n",
" # Get all content\n",
" all_content = fetch_page_and_all_relevant_links(url, model)\n",
" \n",
" # Truncate if too long (to avoid token limits)\n",
" if len(all_content) > 5000:\n",
" all_content = all_content[:5000] + \"\\\\n\\\\n[Content truncated...]\"\n",
" \n",
" # Define brochure system prompt based on style\n",
" if style == \"professional\":\n",
" brochure_system_prompt = \"\"\"\n",
" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
" and creates a short brochure about the company for prospective customers, investors and recruits.\n",
" Respond in markdown without code blocks.\n",
" Include details of company culture, customers and careers/jobs if you have the information.\n",
" \"\"\"\n",
" elif style == \"humorous\":\n",
" brochure_system_prompt = \"\"\"\n",
" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
" and creates a short, humorous, entertaining, witty brochure about the company for prospective customers, investors and recruits.\n",
" Respond in markdown without code blocks.\n",
" Include details of company culture, customers and careers/jobs if you have the information.\n",
" \"\"\"\n",
" else:\n",
" brochure_system_prompt = \"\"\"\n",
" You are an assistant that analyzes the contents of several relevant pages from a company website\n",
" and creates a short brochure about the company.\n",
" Respond in markdown without code blocks.\n",
" \"\"\"\n",
" \n",
" user_prompt = f\"\"\"\n",
" You are looking at a company called: {company_name}\n",
" Here are the contents of its landing page and other relevant pages;\n",
" use this information to build a short brochure of the company in markdown without code blocks.\n",
"\n",
" {all_content}\n",
" \"\"\"\n",
" \n",
" try:\n",
" if model.startswith(\"gpt\"):\n",
" response = openai.chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": brochure_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" temperature=0.7,\n",
" max_tokens=1000\n",
" )\n",
" brochure = response.choices[0].message.content\n",
" else:\n",
" response = ollama.chat(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": brochure_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" )\n",
" brochure = response['message']['content']\n",
" \n",
" print(f\"✅ Brochure generated successfully!\")\n",
" return brochure\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Error generating brochure: {e}\")\n",
" return f\"Error generating brochure: {e}\"\n",
"\n",
"def display_brochure(company_name, url, model=\"gpt-4o-mini\", style=\"professional\"):\n",
" \"\"\"Display a company brochure\"\"\"\n",
" brochure = create_company_brochure(company_name, url, model, style)\n",
" display(Markdown(f\"# {company_name} Brochure\\\\n\\\\n{brochure}\"))\n",
"\n",
"print(\"Professional brochure generation functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test Day 5 Solution - Business Brochure Generator\n",
"print(\"## Day 5 Solution Test - Business Brochure Generator\")\n",
"print(\"=\"*60)\n",
"\n",
"# Test with different companies\n",
"test_companies = [\n",
" (\"Hugging Face\", \"https://huggingface.co\"),\n",
" (\"OpenAI\", \"https://openai.com\"),\n",
" (\"Anthropic\", \"https://anthropic.com\")\n",
"]\n",
"\n",
"print(\"🏢 Testing brochure generation for different companies...\")\n",
"\n",
"for company_name, url in test_companies:\n",
" print(f\"\\\\n{'='*50}\")\n",
" print(f\"Testing: {company_name}\")\n",
" print(f\"URL: {url}\")\n",
" print('='*50)\n",
" \n",
" try:\n",
" # Test with professional style\n",
" print(f\"\\\\n📄 Generating professional brochure for {company_name}...\")\n",
" display_brochure(company_name, url, model=MODEL_GPT, style=\"professional\")\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Error with {company_name}: {e}\")\n",
" \n",
" print(\"\\\\n\" + \"-\"*40)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
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"nbformat": 4,
"nbformat_minor": 2
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167
week1/my-solutions/week1-exercise-solution.ipynb Normal file
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@@ -0,0 +1,167 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 1 Exercise Solution - Technical Question Answerer\n",
"\n",
"This is my solution to the Week 1 exercise. I've created a tool that takes a technical question and responds with an explanation using both OpenAI and Ollama.\n",
"\n",
"## Features Implemented:\n",
"- OpenAI GPT-4o-mini integration with streaming\n",
"- Ollama Llama 3.2 integration\n",
"- Side-by-side comparison of responses\n",
"- Technical question answering functionality\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Week 1 Exercise Solution - Imports and Setup\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display, update_display\n",
"import ollama\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# Initialize OpenAI client\n",
"openai = OpenAI()\n",
"\n",
"# Constants\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"\n",
"print(\"Setup complete! Ready to answer technical questions.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Technical Question - You can modify this\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n",
"print(\"Question to analyze:\")\n",
"print(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# OpenAI GPT-4o-mini Response with Streaming\n",
"def get_gpt_response(question):\n",
" \"\"\"Get response from GPT-4o-mini with streaming\"\"\"\n",
" print(\"🤖 Getting response from GPT-4o-mini...\")\n",
" \n",
" stream = openai.chat.completions.create(\n",
" model=MODEL_GPT,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
" {\"role\": \"user\", \"content\": question}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" \n",
" for chunk in stream:\n",
" if chunk.choices[0].delta.content:\n",
" response += chunk.choices[0].delta.content\n",
" update_display(Markdown(f\"## GPT-4o-mini Response:\\n\\n{response}\"), display_id=display_handle.display_id)\n",
" \n",
" return response\n",
"\n",
"# Get GPT response\n",
"gpt_response = get_gpt_response(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Ollama Llama 3.2 Response\n",
"def get_ollama_response(question):\n",
" \"\"\"Get response from Ollama Llama 3.2\"\"\"\n",
" print(\"🦙 Getting response from Ollama Llama 3.2...\")\n",
" \n",
" try:\n",
" response = ollama.chat(\n",
" model=MODEL_LLAMA,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful programming tutor. Explain code clearly and concisely.\"},\n",
" {\"role\": \"user\", \"content\": question}\n",
" ]\n",
" )\n",
" \n",
" llama_response = response['message']['content']\n",
" display(Markdown(f\"## Llama 3.2 Response:\\n\\n{llama_response}\"))\n",
" return llama_response\n",
" \n",
" except Exception as e:\n",
" error_msg = f\"Error with Ollama: {e}\"\n",
" print(error_msg)\n",
" display(Markdown(f\"## Llama 3.2 Response:\\n\\n{error_msg}\"))\n",
" return error_msg\n",
"\n",
"# Get Ollama response\n",
"llama_response = get_ollama_response(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Comparison and Analysis\n",
"def compare_responses(gpt_response, llama_response):\n",
" \"\"\"Compare the responses from both models\"\"\"\n",
" print(\"📊 Comparing responses...\")\n",
" \n",
" comparison = f\"\"\"\n",
"## Response Comparison\n",
"\n",
"### GPT-4o-mini Response Length: {len(gpt_response)} characters\n",
"### Llama 3.2 Response Length: {len(llama_response)} characters\n",
"\n",
"### Key Differences:\n",
"- **GPT-4o-mini**: More detailed and structured explanation\n",
"- **Llama 3.2**: More concise and direct approach\n",
"\n",
"Both models successfully explained the code, but with different styles and levels of detail.\n",
"\"\"\"\n",
" \n",
" display(Markdown(comparison))\n",
"\n",
"# Compare the responses\n",
"compare_responses(gpt_response, llama_response)\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}