Python AI Coder

An AI-powered Python programming assistant built with Streamlit and Groq API.

Application Preview

Overview

Python AI Coder is a web-based assistant designed to help beginner and intermediate developers learn Python programming.

The application uses Large Language Models (LLMs) through the Groq API to provide:

• Python code examples
• Concept explanations
• Programming guidance
• Best practice recommendations
• Links to official documentation

The project was originally developed as part of the Data Science Academy Python Fundamentals Programme and was subsequently enhanced as a personal portfolio project.

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Features

• Interactive chat interface
• Python-focused AI assistant
• Conversation history management
• Secure API key input
• Python code generation
• Detailed explanations
• Documentation references
• Responsive Streamlit interface

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Technologies Used

• Python
• Streamlit
• Groq API
• OpenAI GPT OSS 20B
• Git
• GitHub
• Virtual Environments (venv)

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Project Structure

python-ai-coder/
│
├── app.py
├── requirements.txt
├── README.md
└── .gitignore

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Installation

Clone the repository:

git clone https://github.com/PauloF7319/python-ai-coder.git
cd python-ai-coder

Create a virtual environment:

python -m venv .venv

Activate the environment:

Windows

.venv\Scripts\activate

Linux / macOS

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

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Running the Application

streamlit run app.py

The application will be available at:

http://localhost:8501

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Example Questions

• How do I create a Hello World program in Python?
• How does a for loop work in Python?
• What is a lambda function?
• How can I use the map() function?
• What is the difference between a list and a tuple?

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Future Enhancements

• Chat export functionality
• Multiple model selection
• Conversation reset button
• Deployment to Streamlit Community Cloud
• Enhanced user interface
• Usage analytics dashboard

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Disclaimer

AI-generated responses may contain inaccuracies. Always validate code and technical recommendations before using them in production environments.

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Author

Paulo Faria

Software Development | Data Engineering | Analytics | AI Applications

GitHub: https://github.com/PauloF7319