🐍 Python Learning Assistant

An interactive AI-powered Python tutor — built for all skill levels.
Adaptive explanations. Live code examples. Instant feedback. Powered by Google Gemini.

🌐 Live Demo · 🐛 Report Bug · ✨ Request Feature

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✨ Features

Feature	Description
🎯 Adaptive Learning	Tailored explanations for beginner, intermediate, and advanced learners
💻 Interactive Examples	Run code snippets directly inside the app
📚 50+ Question Bank	Pre-loaded with curated Python questions across all skill levels
⚡ Real-time Feedback	Instant AI-driven explanations and corrections
🗂️ Session History	Progress tracking that persists throughout your learning session

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🚀 Quick Start

Prerequisites

• Python 3.10+
• A free Gemini API key

Installation

# 1. Clone the repository
git clone https://github.com/yourusername/python-learning-assistant.git
cd python-learning-assistant

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate        # macOS / Linux
.\venv\Scripts\activate         # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add your API key
echo "GEMINI_API_KEY=your_api_key_here" > .env

# 5. Launch the app
streamlit run python-assistant.py

Then open http://localhost:8501 in your browser. 🎉

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📂 Project Structure

PYTHON-LEARNING-ASSISTANT/
├── .devcontainer/          # VSCode dev container configuration
├── .github/                # GitHub Actions workflows
├── venv/                   # Python virtual environment (gitignored)
├── .env                    # Environment variables (gitignored)
├── .gitignore
├── LICENSE
├── python-assistant.py     # Main Streamlit application
├── README.md
└── requirements.txt        # Python dependencies

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📚 Sample Questions

🟢 Beginner

• "Explain variables and data types in Python"
• "How do if-else statements work?"
• "What are lists and how do I use them?"

🟡 Intermediate

• "Explain OOP concepts with a class example"
• "How do I handle file I/O operations?"
• "What are decorators and when should I use them?"

🔴 Advanced

• "Explain metaclasses with real-world use cases"
• "How can I optimize Python code performance?"
• "Implement a custom context manager"

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🛠️ Development

Using the Dev Container (VSCode)

1. Open the project folder in VSCode
2. Press F1 → select "Remote-Containers: Reopen in Container"
3. The environment configures automatically — no manual setup needed

Dependencies (requirements.txt)

streamlit>=1.32.0
google-generativeai>=0.3.0
python-dotenv>=1.0.0

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🤝 Contributing

Contributions are welcome and appreciated! Here's how to get started:

1. Fork this repository
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Commit your changes: git commit -m 'feat: add your feature'
4. Push to your branch: git push origin feature/your-feature-name
5. Open a Pull Request

For major changes, please open an issue first to discuss what you'd like to change.

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📜 License

Distributed under the MIT License. See LICENSE for details.

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🙏 About the Author

Robina Mirbahar
🏆 Google Cloud Innovator Champion  |  👩‍💻 Women Techmakers Ambassador  |  🚀 Google Developer Expert

If you find this project useful, consider giving it a ⭐ — it helps others discover it too!

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