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BC_Hacks_2024

Hackathon At Bellevue College

MacroSnap

MacroSnap simplifies diet tracking by allowing users to snap a photo of their meal and instantly receive detailed nutritional information, including macros and calorie counts. It combines the power of image recognition with an easy-to-use mobile interface, enabling users to make healthier dietary choices effortlessly.

Inspiration

The inspiration for MacroSnap came from the everyday struggle of tracking nutrition and maintaining a balanced diet. With busy lives, manually logging meals and calculating nutritional values can be overwhelming. We envisioned an app that combines technology and simplicity to make nutrition tracking as easy as snapping a photo. MacroSnap empowers individuals to make healthier choices, bridging the gap between technology and well-being.

Features

  • Instant Nutritional Information: Snap a photo of your meal and receive detailed nutritional information, including macros and calorie counts.
  • Image Recognition: Uses TensorFlow for accurate image recognition of food items.
  • Comprehensive Nutritional Database: Access detailed information sourced from reliable datasets like Kaggle.
  • User-Friendly Interface: Designed with simplicity in mind to provide an intuitive experience for users.

Progress

  • Currently the program only detects fruits
  • The image detection is running off a webpage, which can present your calories and fact about the food you eat.
  • Logging Calories can showing results in still a work in progress, image mocks can be viewed here: https://devpost.com/software/macrosnap
  • image mocks are alos uploaded in the folder app presentation along with a power point

Built With

Installation

  1. Clone the repository:
    git clone https://github.com/your-username/macrosnap.git
    
    

Links:

Dependecies to install:

  • npx create-react-app macrosnap
  • cd macrosnap
  • npm start
  • npm install firebase
  • npm install firebase @material-ui/core @material-ui/icons @emotion/react @emotion/styled react-router-dom @tensorflow/tfjs @tensorflow-models/mobilenet

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Hackathon At Bellevue College

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