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This project i a web application that uses machine learning back-end to identify your packed food and display info of all the nutrients in it and then by calculating your BMR it will tell you your daily calories intake you can take and its integrated with FitBit API (sample database is created to demonstrate not actual FitBit API). It will take …

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Foodify

Foodify

CREATE HEALTHY HABITS NOT RESTRICTIONS.

Overview

We came up with a web application using machine learning which keeps the tracks of user's calories consumption from a single snap of their meals. And warn's if the user exceed his/her calorie consumption limit of a day.

Requirements

  1. To run the machine learning testing file install all in requirements.txt

    pip install -r requirements.txt

  2. Install XAMPP

ML Model

To run the ML training and testing process go through steps in foodify_classifier folder.

Run App

  1. Import the fitbit.sql and foodify.sql files to database name fitbitapi and foodify respectively in phpmyadmin.
  2. Rename the foodify-master folder to foodify.
  3. Move the folder to xampp/htdocs.
  4. In browser goto URL and type http://localhost/foodify .

NOTE: Port No. may vary.

License

MIT

About

This project i a web application that uses machine learning back-end to identify your packed food and display info of all the nutrients in it and then by calculating your BMR it will tell you your daily calories intake you can take and its integrated with FitBit API (sample database is created to demonstrate not actual FitBit API). It will take …

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  • Python 62.0%
  • PHP 23.4%
  • TSQL 7.2%
  • CSS 5.2%
  • Hack 2.2%