FitNOVA

Overview

FitNOVA is a full-stack web application built to personalize health and wellness journeys using advanced AI models. Designed with a user-first mindset, it delivers individualized workout and diet plans based on real user data—like age, goals, fitness level, and dietary restrictions. With real-time tracking, adaptive feedback loops, and rich analytics, the platform empowers users to stay consistent, stay motivated, and make smarter decisions about their fitness journey.

The project was developed as part of the Agile Software Engineering & DevOps coursework at RV University.

Key Objectives

Personalized Planning: Generate AI-driven workout and diet plans based on user metrics.
Progress Tracking: Enable users to monitor their calorie intake, workout completions, and fitness stats via intuitive dashboards.
AI Integration: Use Google’s Gemini and Flash 2.0 Lite models to generate structured plans tailored to individual needs.
Responsive UX: Design a fast and mobile-friendly interface with intuitive user flows.
Secure & Scalable: Ensure robust authentication, data privacy, and seamless performance under real-time workloads.

AI-Driven Innovation

Unlike traditional apps, this project uses prompt-engineered AI models to create fitness routines and meal plans in structured JSON. These responses are parsed and rendered on the frontend, enabling editable, adaptive, and context-aware plans.

Screenshot of AI-generated fitness plan — An AI-generated workout and diet plan structured in JSON and rendered in-app.

Technologies Used

Frontend

Next.js 14 (React 19) - App router with hybrid SSR
TypeScript - Type safety throughout the stack
Tailwind CSS + Shadcn/ui - Utility-first styling with pre-built accessible components
Chart.js - Dynamic data visualization for progress tracking

Backend

Supabase (PostgreSQL) - Managed, scalable database
Prisma ORM - Schema migrations and type-safe database queries
NextAuth.js - OAuth + Credentials-based authentication
Google Gemini APIs - Real-time AI-powered workout and diet generation
Serverless Functions - Hosted via Vercel for scalable APIs

DevOps

GitHub Actions - CI/CD with testing, linting, and Docker builds
Vercel - Seamless deployment for frontend + API

Development Approach

Agile Methodology

Sprint Planning: Defined user stories and deliverables for each sprint.
Daily Standups: Tracked blockers, task updates, and priorities.
Retrospectives: Iterated based on feedback and user testing.
CI/CD: GitHub Actions ensured builds, linting, type-checking, and deployment validations.

Core Functional Modules

User Authentication – Supports Google, Discord, GitHub, LinkedIn and Email/Password logins.

Authentication providers in FitNOVA — OAuth and credentials-based login options integrated using NextAuth.js

Onboarding System – Multi-step wizard to collect user health metrics and fitness goals.

AI-Powered Plan Generator – Uses structured prompts with Gemini APIs to return customized workout routines and diet plans.
Interactive Dashboard – Visualizes calorie stats, completion history, and editable plans.
Feedback-Driven AI – Captures user modifications and adherence data to refine future plans.
Test Coverage – >90% coverage across 75+ automated tests (unit, integration, and API).

Test coverage report showing over 90% coverage — Automated unit, integration, and API tests ensure reliability across the stack.

Validation – Zod schemas validate all forms both on client and server.
E2E Workflows – Simulated full user journeys from login to dashboard updates.
CI Workflows – Automatic test runs, coverage reports, and deployments on PRs.

CI workflow showing automated tests on PR creation — CI/CD pipeline via GitHub Actions validates every pull request.

Database Design

The database schema was modeled using Prisma, leveraging relational schemas to capture user profiles, workouts, meals, metrics, and AI-generated plans with proper associations and constraints.

You can view the full schema design here .

Challenges Faced

AI Model Tuning - Prompt engineering for structured and accurate outputs required rigorous testing.
Data Privacy - Ensuring GDPR compliance with Supabase RLS, JWT sessions, and encrypted storage.
Plan Adaptability - Implementing feedback loops that actually influence AI outputs and UX.
Tech Stack Friction - React 19 peer dependencies and SSR issues during build pipelines.

Conclusion

This project is more than just a fitness app—it's a data-driven, AI-personalized wellness companion. From secure authentication to intelligent recommendations, every component was engineered with precision, usability, and scalability in mind. It showcases how modern web tech and responsible AI can converge to create highly impactful, real-world applications.