Commut.r

Commut.r is an AI assistant for UTD commuters inspired by the stressful, and therefore endangering, task of trying to commute to a school with tens-of-thousands of commuters. Our app utilizes Nebula API data to make calculations to predict traffic and parking data. A voice driven AI assistant helps navigate drivers to the most optimal parking lot based on vacant parking spaces and proximity to their destination. The goal is to help UTD commuters make it safely to campus and not waste time finding parking spots. We faced many challenges in deciding on a coherent formula and NebulaApi endpoints as variables, for many combinations failed until we came to an efficient method. The model was built with AntiGravity as an ide and React as a frontend framework. We designed the frontend on Figma first, but translated it to react using the IDE models. The rest of the frontend was designed with Google Maps API json design formatting. Navigating multiple apis that used endpoints in two different languages proved to be more confusing than expected. We overcame this problem by splitting our backend architecture into a flask server and node server for our api services such as elevenlabs tts, openweatherapi, NebulaLabsApi, and GeminiApi.

Built With

• elevenlabs
• figma
• flask
• gemini-api
• google-antigravity
• google-cloud
• google-maps
• javascript
• nebula-labs-api
• node.js
• open-weather-api
• python
• react
• render-hosting