LLM_Engineering_OLD/week1/community-contributions/r00trose/README.md

Code Explainer - LLM Engineering Week 1

An AI-powered code explanation tool using Llama 3.2 via Ollama.

🎯 Project Overview

This project demonstrates prompt engineering and local LLM integration by building a code explanation assistant. The tool analyzes code snippets and provides beginner-friendly, line-by-line explanations.

✨ Features

• Local LLM Integration: Uses Ollama with Llama 3.2 (no API costs!)
• Two Modes of Operation:
  • 📓 Notebook Mode: Interactive Jupyter notebook with rich Markdown display
  • 💻 Terminal Mode: Interactive CLI for continuous code explanation
• Smart Explanations:
  • Summarizes overall purpose
  • Line-by-line breakdown
  • Highlights key programming concepts
  • Beginner-friendly language
• Multiple Examples: Loops, list comprehensions, OOP, recursion, decorators
• Streaming Responses: Real-time output as the model generates

🛠️ Technologies Used

• Ollama: Local LLM runtime
• Llama 3.2: Meta's language model
• OpenAI Python SDK: API-compatible interface
• IPython: Rich markdown display
• python-dotenv: Environment management

📋 Prerequisites

1. Ollama installed - Download from ollama.com
2. Python 3.8+
3. Llama 3.2 model pulled:

   ollama pull llama3.2
   

🚀 Setup

1. Clone the repository

git clone <your-repo-url>
cd llm-engineering/week1/my-solutions

2. Install dependencies

pip install -r requirements.txt

3. (Optional) Configure environment

cp .env.example .env
# Edit .env if needed

💡 Usage

Notebook Mode

1. Open day1-solution.ipynb in Jupyter or VS Code
2. Run cells sequentially
3. Use explain_code_interactive() function with your own code

explain_code_interactive("""
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
""")

Terminal Mode

python code_explainer.py

Then:

• Paste your code
• Press Enter twice (empty line)
• Get explanation!

Commands:

• quit / exit / q - Exit
• clear - Start fresh
• examples - See sample code snippets

🎓 Why Ollama?

I chose Ollama over OpenAI API for this project because:

✅ No API Costs: Completely free to use
✅ Privacy: All data stays local
✅ Offline: Works without internet
✅ Learning: Hands-on experience with local LLM deployment
✅ Speed: Fast responses on local hardware

📝 Examples Included

1. Recursion - Fibonacci sequence
2. Loops - Simple iteration
3. List Comprehensions - Filtering and mapping
4. Object-Oriented Programming - Classes and methods
5. Decorators - Advanced Python concepts

🔧 Customization

Change the model

Edit code_explainer.py:

explainer = CodeExplainer(model="llama3.2:3b")  # Use smaller model

Adjust temperature

Lower temperature = more consistent, Higher = more creative:

temperature=0.3  # Current setting for code explanations

Modify system prompt

Customize how the model explains code in the system_prompt variable.

🤝 Contributing

This is a course assignment, but feel free to fork and improve!

📄 License

MIT License - feel free to use for learning purposes.

👤 Author

İrem İrem
LLM Engineering Course - Week 1 Assignment

🙏 Acknowledgments

• Ed Donner - Course instructor for the excellent LLM Engineering course
• Anthropic - For Ollama framework
• Meta - For Llama 3.2 model