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WellTrack AI – Intelligent Health & Wellness Tracker

App Name: Health Wellness Tracker

GitHub Repository URL: Hadiqa13/WellTrack-AI-Final-Project-

Render Live App URL: https://welltrack-ai-final-project-rj7w.onrender.com

Container Image URL: https://hub.docker.com/layers/aliaafzal/welltrack-ai-final-project/latest

Overview

WellTrack AI is an AI-powered health and wellness tracking platform designed to help users monitor their daily lifestyle habits and receive intelligent, personalized feedback.

Unlike traditional tracking applications that only collect data, WellTrack AI transforms user inputs into actionable insights using Artificial Intelligence. By analyzing patterns in workouts, meals, and sleep, the system provides users with meaningful recommendations to improve their overall health and well-being.

The application is designed for students, professionals, and fitness-conscious individuals who want a simple yet powerful tool to better understand and optimize their daily routines.


What Makes This App Unique?

  • Combines tracking + AI insights in one system
  • Focuses on behavior analysis, not just data storage
  • Provides personalized recommendations instead of generic advice
  • Designed with real-world DevOps practices (CI/CD, Docker, Cloud)

Core Features

Health Tracking

  • Log workouts (type, duration, intensity)
  • Track meals and nutrition habits
  • Record sleep patterns and quality

Dashboard & Trends

  • Overview of daily and weekly activity
  • Visual summary of health patterns
  • Quick access to recent logs

AI-Powered Features (Gemini API)

  • AI Wellness Insights
    Analyze user data to generate summaries of health behavior

  • Personalized Goal Suggestions
    Recommend achievable goals based on user patterns

  • Trend Analysis
    Identify improvements or declines in habits over time

  • AI Coaching
    Provide actionable advice (sleep improvement, workout balance, etc.)


System Architecture

The application follows a modular architecture:

  • Frontend: User interface and interaction
  • Backend: REST API using Flask
  • Database: MongoDB Atlas for storing user data
  • AI Layer: Gemini API for intelligent recommendations
  • DevOps: Docker + GitHub Actions for CI/CD

Tech Stack

Layer Technology
Backend Flask (Python)
Frontend HTML, CSS, JavaScript (Bootstrap)
Database MongoDB Atlas
AI Gemini API
DevOps GitHub Actions
Container Docker
Deployment Render

Team Structure

Role Responsibility
Backend Developer Asma
Frontend Developer 1 Aliya
Frontend Developer 2 Eman
AI Engineer Hadiqa

Shared Responsibilities

All team members contribute to:

  • GitHub workflow (branches, PRs, reviews)
  • CI/CD pipeline setup
  • Docker configuration
  • Testing and debugging
  • Deployment

Development Timeline

Week 11 — Foundation

  • Project proposal and planning
  • GitHub repo and project board setup
  • Flask app skeleton with /health endpoint
  • MongoDB connection established
  • Docker container setup

Week 12 — Core Development

  • Implement workout, meal, sleep APIs
  • Integrate frontend with backend
  • Develop AI features (Gemini)
  • Write unit tests
  • Setup CI pipeline

Week 13 — Deployment

  • Configure CD pipeline
  • Deploy app to cloud
  • Final testing and debugging
  • Prepare presentation

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