Smart Choice - Team 4 (Tech Tribe)

~ Empowering Healthier lives, One Scan at a Time

Problem Statement:

In today's market, numerous brands offer a plethora of ultra-processed food and consumer products, claiming to be beneficial for health. However, many of these products contain ingredients that are not only unhealthy but also potentially harmful. Despite extensive marketing efforts, consumers lack the necessary information to discern the true nature of these ingredients. As a result, they often unknowingly purchase products that may compromise their health and well-being. According to reports from the Times of India and the World Health Organization (WHO), India ranks high in diet-related death rates, indicating a pressing need for solutions to address poor food choices.

Solution:

To address this issue, we propose the development of a comprehensive web application equipped with advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This application will empower consumers to make informed decisions by providing them with instant access to detailed information about the ingredients present in various products. Key features of the solution include:

Ingredient Analysis: Users can scan or input product names to analyze ingredients using AI/ML for health effects.
Ingredient Classification: Categorization of ingredients (natural, minimally processed, ultra-processed) for informed choices.
Health Ratings: Assigning product health ratings based on ingredient quality for quick decision-making.

Impacts:

Reduced Health Care Costs: Improved health outcomes may lead to a decrease in healthcare expenditures related to diet-related illnesses such as obesity, diabetes, and heart disease.
Economic Impact: Encouraging the purchase of healthier products may shift market demand towards natural and minimally processed goods, benefiting industries producing healthier alternatives.

Technology Stack:

Frontend: React.js with various dependencies for image capture.
Backend: Flask Python framework with OpenCV, EasyOCR, NumPy, Pandas, and other libraries for image detection and analysis.
Database: Custom-built database consisting of different products and ratings.
Hosting: The application is hosted on an EC2 instance on AWS, with the prototype available at http://13.51.200.158:5173/.

Architecture:

The architecture of the system involves the following components:

User Interface: Developed using React.js, providing a user-friendly interface for interaction.
Backend Server: Implemented using Flask Python framework, handling requests from the frontend and performing ingredient analysis using AI/ML algorithms.
Image Processing: Utilising OpenCV and EasyOCR libraries for image capture and analysis.
Hosting: Deployed on an EC2 instance on AWS, providing scalability and reliability.

This architecture ensures seamless interaction between the frontend and backend components while leveraging advanced AI/ML algorithms to provide users with accurate and timely information about product ingredients and health ratings.

Overall, the proposed solution aims to empower consumers with the knowledge and tools they need to make informed decisions about their food choices, leading to improved health outcomes and a positive impact on both individuals and society as a whole.

List of Github Commits: https://github.com/Patel-Muhammad/ICIQ-ert7JsJK/commits/main/