Describe your project

Zestify is a comprehensive nutrition tracking application designed to help users manage their dietary habits and make healthier choices. With a focus on user engagement and personalized insights, Zestify empowers individuals to take control of their nutrition through a user-friendly interface and a variety of innovative features.

In-Scope of the Solution

1. An all-inclusive encyclopedia of nutrients.
2. Capabilities for tracking and logging meals that are easy to use.
3. Tailored suggestions according to the user's tastes.
4. Ideas for nutritious food substitutes.
5. Setting goals helps individuals stay motivated to meet their dietary goals.
6. Suggested healthy recipes based on user feedback.

Out of Scope of the Solution

1. There will be no meal planning or comprehensive recipe preparation instructions in the first edition.
2. Professional guidance on diet or meetings with nutritionists.
3. Integration for tracking physical activity with wearable fitness gadgets.
4. Meals or recipes can be shared via social networking capabilities.

Prospects for the Future

1. Meal Planning: To assist customers in creating balanced dinners, meal planning options and customized recipes should be added.
2. Fitness Integration: Working along with fitness trackers to offer a holistic perspective on diet and health.
3. Community elements: Adding social elements that let people exchange advice and firsthand knowledge to build a feeling of community.
4. AI-Driven Insights: Applying AI to offer more intelligent, individualised food suggestions predicated on user activity.
5. Multi-Language Support: Increasing the app's accessibility and growing its user base by incorporating multi-language support.

Zestify aims to empower users to take charge of their nutrition through easy tracking, personalized recommendations, and potential future enhancements, ensuring a holistic approach to dietary management.

Challenges we ran into

One of the biggest problems we ran into when developing Zestify was the dearth of datasets available for food item analysis. We manually scraped data and annotated photos to construct a custom dataset of 100 food products in order to solve this problem. Even though it took a while, this procedure was necessary to guarantee the accuracy and applicability of the data we employed.

Additionally, we had trouble developing precise object identification, which affected how well the app recognised and analysed product labels.

Creating appropriate substitute foods based on user-uploaded photos and their unique health circumstances presented another difficulty. To address this, we created a stronger recommendation system that takes into account the user's health as well as the features that were taken from the photos.