Overview

Snap & Eat is an innovative web application that revolutionizes nutrition tracking by simplifying the process to just a snapshot. As a project that won first place at a deep learning hackathon, Snap & Eat leverages cutting-edge deep learning technology to analyze and estimate the nutritional content of meals with impressive accuracy. Its primary goal is to enhance user engagement in healthy eating habits by making the tracking process effortless and visually engaging.

By providing meal suggestions based on individual income and local dining options, Snap & Eat aims to address the challenges associated with maintaining a balanced diet, especially in a world where nutritional awareness and health issues are increasingly prevalent.

Features

Intuitive Food Tracking: Users can effortlessly log meals by simply taking a picture, eliminating traditional manual entry.
Advanced Deep Learning Technology: Utilizes the ResNeXt-101 model, pretrained on extensive datasets for high accuracy in dish recognition.
Personalized Meal Suggestions: Recommends dishes based on dietary preferences and income, making healthy eating accessible to all.
Local Dining Recommendations: Provides users with nearby restaurants that serve the dishes they are interested in, enhancing the dining experience.
Seamless Web Application: Built using Flask and Node.js for a responsive and user-friendly experience.
AWS Integration: Offers a preconfigured Amazon Machine Image (AMI) to easily set up and deploy the application in the cloud.
Engaging Nutritional Insights: Displays nutritional information in a user-friendly manner to motivate healthier eating choices.
User-Friendly Interface: Designed to ensure ease of use, encouraging regular interaction for improved dietary habits.

Gabrielilharco

Snap N Eat