ExplainItSimple

Inspiration

Students struggle with dense textbook paragraphs and lose focus when studying. We wanted a tool that turns a paragraph into a friendly, bite-sized explanation targeted at a specific age and then reinforces learning with quick quiz questions.

What it does

ExplainItSimple is a Streamlit web app that accepts pasted academic text, detects the topic, and returns:

• A playful, age‑appropriate explanation (8–18 years)
• A set of 1–5 short, relevant quiz questions with answers
• A downloadable JSON with the explanation and quiz The app includes a lightweight "no-API" mode for a fast, reliable demo, and an optional Hosted LLM mode (HuggingFace or OpenAI) accessible via a toggle for higher-quality outputs when you provide an API key.

How we built it

Frontend: Streamlit for quick UI development and deployment Logic: Python text parsing + keyword detection for the lightweight mode; optional HTTP calls to HuggingFace or OpenAI when using Hosted mode Repo: https://github.com/Rivenator101/ExplainItSimple Live demo: https://explainitsimple-jzxhftdbbh2n6zsbobfeapp.streamlit.app/

Challenges we ran into

• API quotas and age/region restrictions forced multiple provider pivots (OpenAI → Gemini → HuggingFace → Replicate).
• Hosted endpoints changed and some required secrets, complicating reliable deployment under a tight deadline.
• Downloading large local models on Streamlit Cloud caused timeouts, so we prioritized a lightweight in‑app summarizer for stability.

Accomplishments that we're proud of

it working?? i started a bit late had alot to do and it actually working in the small time period i had was CRAZY

What we learned

In hackathons reliability often wins over maximum model quality. Designing a flexible architecture (lightweight default + optional hosted LLM) lets us demo instantly while keeping upgrade paths open.

Challenges

API quotas and age/region restrictions forced multiple provider pivots (OpenAI → Gemini → HuggingFace → Replicate). Hosted endpoints changed and some required secrets, complicating reliable deployment under a tight deadline. Downloading large local models on Streamlit Cloud caused timeouts, so we prioritized a lightweight in‑app summarizer for stability.

What's next for ExplainItSimple

Add a "teacher mode" with bulk-upload and summary export. Add a confidence/traceability view showing which sentences were used to build the explanation. Offer an option to select higher-quality hosted models for premium demos.

Built With

• python3x
• streamlit