A comprehensive Python tutorial project that demonstrates how to build a text analysis tool (BookBot) while learning to code collaboratively with Gemini CLI. This project showcases the power of AI-assisted development through hands-on learning.

GemiBook is an educational project that teaches you to:

• Build a complete Python application from scratch (BookBot - a text analysis tool)
• Learn AI-assisted development using Gemini CLI as your coding companion
• Master essential Python concepts including file I/O, data structures, and command-line interfaces
• Practice test-driven development with comprehensive test suites
• Experience modern development workflows with proper project structure and version control

BookBot is a Python program that analyzes novels and generates statistical reports on word and character usage. It demonstrates practical programming concepts while building something genuinely useful.

BookBot Features:

• 📖 Read and analyze text files (books, documents, etc.)
• 🔢 Count total words in any document
• 📊 Analyze character frequency with detailed statistics
• 📄 Generate beautifully formatted reports
• 🎯 Handle command-line arguments for different input files
• ✅ Comprehensive test coverage for all functionality

1. Python 3.x installed on your system
2. Node.js 20+ or Homebrew for Gemini CLI installation
3. A text editor or IDE (VS Code recommended)

git clone <your-repo-url>
cd gemibook

Follow the detailed installation guide in GEMINI_CLI_SETUP.md:

Option A: With Node.js

npm install -g @google/gemini-cli

Option B: With Homebrew

brew install gemini-cli

Set up your API key (choose one):

# Gemini API (recommended for learning)
export GEMINI_API_KEY="your-api-key-here"

# OR Vertex AI (for enterprise)
export GOOGLE_API_KEY="your-api-key-here"
export GOOGLE_GENAI_USE_VERTEXAI=true

cd gemibook
gemini

The Gemini CLI provides an interactive environment where you can ask questions, get code help, and learn step-by-step while building your BookBot.

Start your Gemini CLI session and begin with these prompts:

> I'm starting the BookBot tutorial. Can you help me understand the project structure and what we'll be building?

> Explain the learning objectives of this BookBot project and how each step builds on the previous one.

> What are the key Python concepts I'll learn while building BookBot?

Work through BOOKBOT_TUTORIAL.md with Gemini CLI assistance:

Step 1 - Environment Setup:

> Help me set up the basic Python environment and create the initial main.py file for BookBot

> I need to create a simple "Hello BookBot" program to test my Python setup. Show me the code and explain what it does.

Step 2 - File Reading:

> I need to implement the get_book_text() function that reads a file and returns its contents. Help me write this using Python's 'with' statement.

> My file reading isn't working. Here's my code: [paste your code]. Can you debug this and explain the fix?

Step 3 - Word Counting:

> Help me implement the count_words() function that takes text and returns the number of words using the .split() method.

> I want to test my word counting function. Can you help me write some test cases to verify it works correctly?

Step 4 - Code Organization:

> I need to refactor my code by moving the word counting function to a new stats.py file. Help me organize this properly and set up the imports.

> Explain the benefits of splitting code across multiple files and how Python imports work.

Step 5 - Character Analysis:

> Help me implement the count_characters() function that counts the frequency of each character in the text using a dictionary.

> I'm getting confused with dictionary operations in Python. Can you explain how to count characters step by step?

Step 6 - Data Processing:

> I need to create a get_character_report() function that converts my character dictionary to a sorted list. Help me implement this with proper sorting.

> My character sorting isn't working correctly. Here's my code: [paste code]. Can you fix this and explain the sorting logic?

Step 7 - Report Generation:

> Help me format the output into a beautiful report that matches the expected format in the tutorial.

> I want to add some polish to my report formatting. Can you suggest improvements and show me how to implement them?

Step 8 - Command Line Arguments:

> I need to add command-line argument support using sys.argv. Help me implement proper argument handling with error messages.

> My argument parsing has bugs. Here's the error I'm getting: [paste error]. Can you help me fix this?

> I want to run the test suite to validate my implementation. Help me understand the test files and run them properly.

> Some of my tests are failing. Here's the output: [paste test output]. Can you help me debug and fix the issues?

> Can you help me add additional test cases to make my BookBot more robust?

> My basic BookBot is working! Can you suggest some interesting extensions or improvements I could add?

> I want to add [specific feature like JSON export/GUI/multiple file support]. Help me design and implement this enhancement.

> Can you help me optimize my BookBot for better performance with large files?

gemibook/
├── README.md                 # This file - project overview and guide
├── BOOKBOT_TUTORIAL.md       # Detailed step-by-step tutorial
├── GEMINI_CLI_SETUP.md       # Gemini CLI installation guide
├── .gitignore               # Git ignore rules
├── skeleton/                # Starter code templates
│   ├── main.py             # Main program skeleton with TODOs
│   └── stats.py            # Statistics functions skeleton
├── tests/                  # Comprehensive test suites
│   ├── test_stats.py       # Unit tests for statistics functions
│   └── test_main.py        # Integration tests for main program
└── books/                  # Sample book data for testing
    └── sample.txt          # Sample text from Frankenstein

By completing this tutorial with Gemini CLI, you will:

• ✅ Master Python fundamentals: File I/O, dictionaries, functions, imports
• ✅ Understand data analysis: Text processing, character frequency, sorting
• ✅ Learn project organization: Multi-file projects, proper code structure
• ✅ Practice testing: Unit tests, integration tests, test-driven development
• ✅ Experience AI collaboration: How to effectively use AI assistants for coding
• ✅ Build something real: A functional text analysis tool you can use

The project includes comprehensive test suites to validate your work:

# Test individual functions
python3 tests/test_stats.py

# Test main program functionality  
python3 tests/test_main.py

# Test with sample data
python3 skeleton/main.py books/sample.txt

When your BookBot is complete, it should produce output like this:

============ BOOKBOT ============
Analyzing book found at books/sample.txt...
----------- Word Count ----------
Found 547 total words
--------- Character Count -------
e: 95
t: 73
a: 69
r: 56
n: 55
o: 54
i: 52
...
============= END ===============

Effective Prompting Strategies:

• Be specific about what you're trying to implement
• Share error messages and code snippets when debugging
• Ask for explanations, not just solutions
• Request test cases and validation approaches
• Seek code review and improvement suggestions

Learning Best Practices:

• Work through each step methodically
• Test frequently as you build
• Ask "why" questions to deepen understanding
• Experiment with variations and improvements
• Use Gemini CLI to explain unfamiliar concepts

After completing BookBot:

1. Explore Extensions: Add features like word frequency analysis, reading level calculation, or a GUI interface
2. Try Other Projects: Use Gemini CLI to tackle more complex programming challenges
3. Share Your Experience: Document your learning journey and share insights
4. Contribute: Improve this tutorial based on your experience

• Python Official Documentation
• Gemini CLI Documentation
• Project Gutenberg - Free books for testing
• Real Python Tutorials - Advanced Python learning

Ready to start your AI-assisted coding journey? Follow the setup guide and dive into the tutorial! 🚀

Happy coding with Gemini CLI! 🤖✨

This tutorial was created as part of exploring AI-assisted development workflows. I'm also building Cipher - an agentic memory system that helps AI assistants remember and learn from past conversations.

🌟 If you found this tutorial helpful, please consider:

• ⭐ Starring this repository to help others discover it
• ⭐ Checking out Cipher and giving it a star if you're interested in agentic memory systems
• 🤝 Sharing your feedback and suggestions for improving this tutorial

Your support helps me continue creating educational content and building tools that make AI-assisted development more accessible and effective!

Built with ❤️ using Gemini CLI