ELI5

Inspiration

Many beginners and students struggle to understand Python code and programming concepts. We were inspired to create a tool that explains code in simple, age-appropriate language, making learning accessible and fun. Our goal was to bridge the gap between complex programming logic and human-friendly explanations that anyone can understand, regardless of their prior coding experience.

What it does

ELI5 Code Explainer allows users to paste Python code and select an age level for the explanation. The AI then generates clear, easy-to-understand descriptions of the code’s functionality, breaking down loops, functions, and logic into simple language. This helps students, beginners, and non-technical users quickly grasp programming concepts without feeling overwhelmed.

How we built it

We built ELI5 Code Explainer using Python as the core language and Streamlit to create a clean and interactive web interface. The application leverages the OpenAI API (v1 SDK) to generate AI-powered explanations in real-time. The tool can run locally or be hosted online through Streamlit Cloud, making it accessible to users across different platforms without the need for a complex backend or database.

Challenges we ran into

During development, we faced challenges learning the latest OpenAI SDK and adapting older code to work with it. Handling multi-line code inputs and ensuring the AI output remained concise, accurate, and age-appropriate also required careful prompt engineering. Additionally, designing a user-friendly interface that works for beginners and keeps interactions simple was a key challenge we overcame through iterative testing.

Accomplishments that we're proud of

We are proud to have created a fully functional AI-powered web application that can explain Python code instantly in beginner-friendly language. The Streamlit interface is clean and easy to navigate, making the tool accessible to users of all ages. This project demonstrates how AI can be leveraged to make coding education more inclusive and understandable for learners worldwide.

What we learned

Through this project, we learned the importance of prompt engineering and guiding AI to produce clear, human-readable explanations. We gained hands-on experience working with modern AI APIs and debugging version-related issues. Additionally, combining Python programming with web frameworks and AI services taught us how to integrate multiple technologies to build a cohesive, user-facing tool

What's next for ELI5

In the future, we plan to expand ELI5 Code Explainer to support other popular programming languages such as Java, JavaScript, and C++. This will allow a wider range of learners to benefit from simple, AI-powered explanations regardless of the language they are learning. We also aim to enhance the user interface with more interactive features, incorporate error detection and guidance for debugging, and deploy a public web version so that students and beginners worldwide can easily access and use the tool.

Built With

• and
• elevenlabs-(text-to-speech)
• google-cloud-(gemini)
• openai
• openai-api
• python
• streamlit
• the