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
- Local Server
- Middleware
- Image Conversion
- Fetch API
- Python HTTP
- 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.
Built With
- api
- css
- express.js
- html
- javascript
- logmeal
- node.js
- python
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