AARA — Weight Loss • Wellness

The Intelligent Personal Mastery System

AARA is a high-performance, full-stack wellness application designed to simplify the journey toward weight loss and muscle gain. By synthesizing personalized nutrition, structured movement, and intelligent coaching into a unified iOS-style interface, AARA removes the friction of daily health management.

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📖 Table of Contents

• Project Overview
• Tech Stack
• Folder Structure
• System Architecture
• Database Schema
• Personalization Engine Logic
• API Endpoints
• Local Development
• Premium Feature System
• Future Scope

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🚀 Project Overview

• App Name: AARA
• Tagline: "Weight Loss • Wellness"
• Purpose: To provide a deterministic, goal-oriented path to physical transformation through automated planning and precise tracking.
• Target Users: Individuals seeking structured Indian-centric meal plans, guided workouts, and effortless consistency tracking.
• Key Features:
  • Personalized Daily Plans: Automated generation of meals and workouts based on region (North/South Indian), diet (Veg/Non-Veg), and primary goal.
  • iOS-Style Interface: A premium, airy UI designed for focus and clarity.
  • Smart Tracking: Hydration, weight logs, and meal consumption tracker.
  • AI Coach (Premium): An intelligent assistant that provides context-aware guidance based on user adherence data.
  • Gamification: XP, levels, and streak tracking to drive long-term engagement.

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🛠 Tech Stack

Tier	Technology	Purpose
Frontend	React + Vite	Fast, modern component-based UI.
Styling	Tailwind CSS	Utility-first design system with custom brand tokens.
Backend	Node.js + Express	Robust API and orchestration layer.
Database	PostgreSQL	Relational storage for complex health entities.
ORM	Drizzle ORM	Type-safe database interactions and schema management.
Auth	Passport.js	Secure session-based authentication (Local Strategy).
AI Engine	Google Gemini API	Powers the "Assistant" for context-aware health coaching.
Hosting	Render	Production-grade deployment for API and assets.

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📂 Folder Structure

Directory	Description
src/	Frontend React application code (pages, components, hooks, assets).
server/	Express backend server, API routes, and storage implementations.
shared/	Shared TypeScript interfaces and Drizzle schema definitions.
data/	Core dataset for the app (Meals, Workouts, and Indian-centric libraries).
scripts/	Maintenance, diagnostic, and utility scripts for the development lifecycle.

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🏛 System Architecture

AARA follows a modern Client-Server-Storage architecture optimized for horizontal scalability and data integrity.

• Client (React): Communicates with the API using TanStack Query for caching and state management.
• API Layer (Express): Handles routing, authentication middleware, and input validation via Zod.
• Personalization Engine: A logic module that processes user metrics to select deterministic meals and workouts from internal libraries.
• Persistence Layer: Ensures that once a plan is generated for a specific date, it is locked in the database (idempotency).
• Data Libraries: Static JSON repositories of high-quality Indian meals and structured workout routines.

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🗄 Database Schema

Architected for performance and deep relational tracking using Drizzle ORM.

• users: Core authentication identity.
• user_profiles: Health data (Age, Height, Weight, Region, Diet, Primary Goal).
• daily_plans: Root entity for a specific day. Tracks global targets (Calories, Protein, Water).
• meals: Planned macros, ingredients, and "Consumed Alternative" state.
• workouts: Exercises, duration, intensity, and structured sequences.
• activity_logs: Aggregated passive tracking data (Steps, Distance, Calories).
• user_gamification: Progression tracking (XP, Level, Streaks).

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🧠 Personalization Engine Logic

The engine uses a 3-Tier Fallback System to ensure plan availability:

1. Tier 1 (Strict): Matches exactly Goal AND Diet Type AND Region Preference.
2. Tier 2 (Relaxed): If Tier 1 yields no results, it ignores Region but prioritizes Goal/Diet.
3. Tier 3 (Minimum): Ultimate fallback providing best-matching options for the Diet Type.

Persistence Rule: Plans are generated once per day. The engine checks for an existing plan in the database before attempting to build a new one.

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📡 API Endpoints

Method	Endpoint	Description
POST	/api/register	User registration and gamification init.
POST	/api/login	Session-based authentication.
GET	/api/user/profile	Retrieve user health metrics.
GET	/api/user/stats	Weight history and consistency metrics.
POST	/api/plans/generate	Build/Fetch daily meal and workout plan.
GET	/api/plans/:date/meals	List meals for a specific date.
PATCH	/api/plans/water	Update daily hydration log.

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💻 Local Development

1. Prerequisites

• Node.js (v18+)
• PostgreSQL Database

2. Environment Setup

Create a .env file in the root directory:

DATABASE_URL=postgres://user:password@localhost:5432/aara
SESSION_SECRET=your_random_secret
GEMINI_API_KEY=your_google_gemini_key
NUTRITION_API_KEY=your_api_ninjas_key

3. Running the App

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

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🔒 Premium Feature System

AARA is pre-wired for a professional subscription model:

• Visual Indicators: Premium features like "Coach Chat" are visually greyed out with custom lock icons.
• Interaction Guard: Premium tabs trigger branded "Coming Soon" overlays instead of standard error messages.
• Role-Based Access: The royal_role field in the database allows for instant unlocking of features upon subscription.

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🗺 Future Scope

• Macro Optimization v2: Dynamic caloric adjustment based on active tracking.
• Expanded Meal Database: Global cuisines with calorie-matched Indian alternatives.
• Live AI Coaching: Real-time voice and text coaching via Gemini.
• Advanced Analytics: Interactive charts for trend analysis over weeks/months.

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Developed by the AARA Wellness Team © 2026