Inspiration

We were inspired to utilize AI as a tool to help people achieve their health goals, in a time when it is harder than ever to understand nutrition and tracking calories.

What it does

NutriPic leverages computer vision and deep learning to identify foods in a meal and generate nutritional information based on the food type and quantity.

How we built it

The front-end is built with HTML/CSS/Javascript, with a functional back-end that primarily uses using Node.js v20.12.2 and Python using following libraries, packages, and modules Express - Local Server multer - Middleware sharp - Image Conversion node-fetch - Fetch API requests - Python HTTP python-dotenv - Python Environment Variables

Challenges we ran into

We were out of our comfort zone dealing with front-end development and using both client-side and server-side javascript to capture and convert user input to be sent across different endpoints.

Accomplishments that we're proud of

We are proud to have had many "firsts" with this project, all three of us had brand-new experiences with web development, servers, APIs, and insights completing this project and its many hurdles. Also as a first for us, we led this development with security in mind to ensure we don't forget about user privacy and safety.

What we learned

We learned a lot about how to develop client-side and server-side web applications using Node.js and Express.js, as well as gaining more experience with using third-party APIs and all the JSON handling that comes with it.

What's next for NutriPic

The complete vision for NutriPic would be a food and nutrition service that competes with the biggest names in the industry. The future of NutriPic includes managing a database of users, each with their own health and nutrition goals being tracked with history logs. NutriPic would expand its features to include more ways to track your nutrition and health to diversify its uses and provide its users a true end-to-end experience for nutrition tracking and improvment.

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