ACEest Fitness & Gym — V1.3

A comprehensive Flask API that models workout logging, diet planning, and progress tracking for ACEest Fitness and Gym, packaged with unit tests, Dockerfile, and a GitHub Actions CI pipeline.

Project Layout

.
├── app/
│   ├── __init__.py
│   ├── app.py            # Flask app (app factory: create_app())
│   └── templates/        # HTML templates
│       └── index.html    # Web UI interface
├── tests/
│   ├── conftest.py       # Test configuration
│   └── test_app.py       # Pytest unit tests
├── .github/workflows/
│   └── CI.yml            # GitHub Actions pipeline
├── ACEest_Fitness.py     # Original Tkinter app (V1.0)
├── ACEest_Fitness-V1.1.py # Enhanced version with categories
├── ACEest_Fitness-V1.2.py # Tabbed interface with 3 tabs
├── ACEest_Fitness-V1.2.1.py # Advanced version with 4 tabs + charts
├── ACEest_Fitness-V1.2.2.py # Enhanced UI styling and UX improvements
├── ACEest_Fitness-V1.2.3.py # Professional color palette and modern design
├── ACEest_Fitness-V1.3.py # Complete version with PDF reports, BMI/BMR
├── Dockerfile
├── kubernetes
 └── file-k8s-ab.yml
 └── file-k8s-shadow.yml
 └── k8s-blue-green.yml
 └── k8s-canary.yml
 └── k8s-deployment.yml
└── k8s-shadow.yml
├── requirements.txt
├── .gitignore
├── entrypoint.sh
├── jenkinsfile
├── sonar-project.properties
└── README.md

V1.3 - Complete Health Tracking (Final Tkinter Version)

• Major Additions:
  • User information section (Name, Regn-ID, Age, Gender, Height, Weight)
  • BMI (Body Mass Index) calculation
  • BMR (Basal Metabolic Rate) calculation
  • Calorie tracking using MET values
  • PDF report generation with ReportLab
  • Weekly workout summaries
  • Daily workout tracking
• Health Metrics:
  • Automatic BMI calculation from height/weight
  • Gender-specific BMR formulas (Mifflin-St Jeor equation)
  • Exercise-specific calorie burn estimates using MET values
• PDF Features:
  • Comprehensive weekly fitness reports
  • User health information summary
  • Detailed workout logs with calories
  • Professional table formatting
• Dependencies:
  • Requires reportlab==4.0.7 for PDF generation
  • All previous dependencies (matplotlib, tkinter)
• Usage:

  # Install dependencies including reportlab
  pip install -r requirements.txt
  
  # Run the Tkinter app
  python ACEest_Fitness-V1.3.py

• Important Note: V1.3 is a standalone desktop application and does NOT integrate with the Flask API. It represents the complete evolution of the Tkinter GUI versions (V1.0 → V1.3).

Features (V1.2.1+)

• ✅ Workout Logging - Track exercises with categories (Warm-up, Workout, Cool-down)
• ✅ Workout Chart - Personalized exercise recommendations
• ✅ Diet Chart - Goal-based diet plans (Weight Loss, Muscle Gain, Endurance)
• ✅ Progress Tracker - Visual progress with charts and statistics
• ✅ RESTful API - Complete API endpoints for all features
• ✅ Web UI - Modern tabbed interface with real-time updates
• ✅ Automated Testing - Comprehensive pytest coverage
• ✅ CI/CD Pipeline - GitHub Actions with Docker builds

API Endpoints

Endpoint	Method	Description
/	GET	API documentation
/health	GET	Health check
/workouts	POST	Add new workout session
/workouts	GET	List all workouts
/summary	GET	Get workout summary with motivation
/workout-chart	GET	Get workout recommendations
/diet-chart	GET	Get diet plans by goal
/progress	GET	Get progress statistics
/ui	GET	Web interface

Features (V1.2.1+)

• ✅ Workout Logging - Track exercises with categories (Warm-up, Workout, Cool-down)
• ✅ Workout Chart - Personalized exercise recommendations
• ✅ Diet Chart - Goal-based diet plans (Weight Loss, Muscle Gain, Endurance)
• ✅ Progress Tracker - Visual progress with charts and statistics
• ✅ RESTful API - Complete API endpoints for all features
• ✅ Web UI - Modern tabbed interface with real-time updates
• ✅ Automated Testing - Comprehensive pytest coverage
• ✅ CI/CD Pipeline - GitHub Actions with Docker builds

API Endpoints

Endpoint	Method	Description
/	GET	API documentation
/health	GET	Health check
/workouts	POST	Add new workout session
/workouts	GET	List all workouts
/summary	GET	Get workout summary with motivation
/workout-chart	GET	Get workout recommendations
/diet-chart	GET	Get diet plans by goal
/progress	GET	Get progress statistics
/ui	GET	Web interface

1) Run Locally (No Docker)

Prereqs: Python 3.11+

# Create virtual environment
python -m venv .venv
source .venv/bin/activate    # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the app on http://localhost:8000
python -m app.app

Try it:

# Health check
curl http://localhost:8000/health

# Add a workout
curl -X POST http://localhost:8000/workouts \
  -H "Content-Type: application/json" \
  -d '{"category":"Workout","workout":"Running","duration":30}'

# Get all workouts
curl http://localhost:8000/workouts

# Get summary
curl http://localhost:8000/summary

# Get workout recommendations
curl http://localhost:8000/workout-chart

# Get diet plans
curl http://localhost:8000/diet-chart

# Get progress data
curl http://localhost:8000/progress

Run unit tests:

pytest -v

2) Run with Docker

docker build -t aceest-fitness:v1.2.1 .
docker run -p 8000:8000 aceest-fitness:v1.2.1

3) Git & GitHub (Suggested Workflow)

git init
git add .
git commit -m "feat: V1.2.1 - Add workout charts, diet plans, and progress tracking"
git tag v1.2.1
git branch -M main
git remote add origin https://github.com/2024tm93122/aceest-fitness.git
git push -u origin main
git push origin v1.2.1

app/ ├── init.py # Package initialization ├── app.py # Flask application with routes └── templates/ └── index.html # Web UI template

tests/ ├── conftest.py # Test configuration └── test_app.py # Unit tests

.github/workflows/ └── CI.yml # CI/CD pipeline configuration

- Install deps and run **pytest**
- Build Docker image and run a **smoke test** (health check + sample API calls)
- Test all new endpoints (workout-chart, diet-chart, progress)

# Run tests with coverage
pytest --cov=app tests/

# Run specific test file
pytest tests/test_app.py -v

Docker Development

# Build image
docker build -t aceest-fitness:dev .

# Run with volume mounting for development
docker run -p 8000:8000 -v $(pwd):/app aceest-fitness:dev

# View logs
docker logs <container_id>

The UI provides:

• Log Workouts Tab - Add and view workout sessions by category
• Workout Chart Tab - Browse recommended exercises
• Diet Chart Tab - Explore diet plans for different fitness goals
• Progress Tracker Tab - Visualize your progress with statistics and charts

Version History

Version	Features	Description
V1.0	Basic workout logging (Tkinter)	Simple desktop GUI with add/view workouts functionality
V1.1	Added categories, timestamps, motivation	Workout categories (Warm-up, Workout, Cool-down) with time tracking
V1.2	Tabbed interface with 3 tabs	Added Workout Chart and Diet Chart tabs (no progress tracker)
V1.2.1	Added 4th tab: Progress Tracker	Complete tabbed UI with visual charts using matplotlib
V1.2.2	Enhanced UI styling & UX	Improved theme, custom button styles, better visual design
V1.2.3	Professional color palette	Modern design with consistent color scheme and Inter font
V1.3	PDF reports & health metrics	User info, BMI/BMR calculation, calorie tracking, PDF export

Version Details

V1.2 - Tabbed Interface Introduction

• Added Features:
  • Tabbed notebook interface using ttk.Notebook
  • Workout Chart tab with exercise recommendations
  • Diet Chart tab with goal-based meal plans
  • Three main tabs: Log Workouts, Workout Chart, Diet Chart
• Key Differences from V1.1:
  • Moved from single window to multi-tab interface
  • Added static workout and diet recommendations
  • Improved organization and user navigation
• Note: V1.2 does NOT include the Progress Tracker tab (that's V1.2.1+)

V1.2.1 - Progress Visualization

• Added Features:
  • 4th tab: Progress Tracker with matplotlib charts
  • Bar chart showing time spent per category
  • Pie chart showing workout distribution
  • Dynamic chart updates after adding workouts
• Dependencies: Requires matplotlib for chart rendering
• Enhancements:
  • Auto-refresh charts when data changes
  • Visual feedback with color-coded categories
  • Professional chart styling

V1.2.2 - Enhanced UI & UX

• Improvements over V1.2.1:
  • Implemented "clam" theme for modern look
  • Custom Ttk button styling with active states
  • Tab change event binding for auto-refresh
  • Scrollable summary window with text formatting
  • Enhanced color scheme (#f0f0f0 backgrounds)
  • Better tab labels ("💡 Workout Plan", "🥗 Diet Guide")
  • Improved chart container with proper cleanup
  • More descriptive status messages and feedback
• Focus Areas:
  • Visual hierarchy and spacing improvements
  • Better user feedback mechanisms
  • Professional appearance
  • Enhanced readability in all views
• Technical Changes:
  • Added on_tab_change() event handler
  • Custom button style configurations
  • Improved summary window with scrollbar
  • Better chart embedding and cleanup

V1.2.3 - Professional Design

• Design Enhancements:
  • Defined color palette constants (PRIMARY, SECONDARY, BACKGROUND)
  • Inter font family for modern typography
  • Consistent spacing and padding throughout
  • Card-based layout with subtle shadows
  • Better visual hierarchy with color usage
• Key Features:
  • Clean, minimalist design philosophy
  • Improved button styling with hover states
  • Enhanced form layouts with better alignment
  • Professional summary views with formatted text
  • Optimized chart colors for accessibility

V1.3 - Complete Health Tracking

• Major Additions:
  • User information section (Name, Regn-ID, Age, Gender, Height, Weight)
  • BMI (Body Mass Index) calculation
  • BMR (Basal Metabolic Rate) calculation
  • Calorie tracking using MET values
  • PDF report generation with ReportLab
  • Weekly workout summaries
  • Daily workout tracking
• Health Metrics:
  • Automatic BMI calculation from height/weight
  • Gender-specific BMR formulas
  • Exercise-specific calorie burn estimates
• PDF Features:
  • Comprehensive weekly fitness reports
  • User health information summary
  • Detailed workout logs with calories
  • Professional table formatting

Technology Stack

Backend

• Flask 3.0.3 - Web framework
• Gunicorn 22.0.0 - WSGI HTTP Server
• Python 3.11 - Programming language

Testing

• Pytest 8.3.2 - Testing framework
• Requests 2.32.3 - HTTP library for testing

Visualization

• Matplotlib 3.9.0 - Charts and graphs (V1.2.1+)

DevOps

• Docker - Containerization
• GitHub Actions - CI/CD pipeline
• Git - Version control

Development Progression

The project demonstrates incremental development across 8 versions:

1. V1.0: Foundation - Basic CRUD operations
2. V1.1: Structure - Added categories and timestamps
3. V1.2: Organization - Tabbed interface (3 tabs)
4. V1.2.1: Visualization - Progress tracking with charts
5. V1.2.2: Polish - UI/UX enhancements
6. V1.2.3: Design - Professional styling
7. V1.3: Completion - Health metrics and PDF reports

Each version builds upon the previous, showcasing:

• Iterative development practices
• Feature expansion
• UI/UX improvements
• Professional maturity

Notes

• The original provided Tkinter script was translated into HTTP endpoints so the app can be tested and containerized easily.
• The in-memory workout store resets on each restart; persistence is out of scope for this assignment but can be added later.
• Added conftest.py to resolve the module not found error which was failing the GitHub Work Action.
• V1.2 introduces tabbed interface with workout and diet recommendations (3 tabs).
• V1.2.1 extends V1.2 by adding visual progress tracking (4 tabs with matplotlib charts).
• V1.2.2 focuses on UI/UX polish without adding new functionality.
• V1.2.3 implements a professional design system with consistent styling.
• V1.3 adds comprehensive health tracking and reporting capabilities.
• All Tkinter versions are preserved to demonstrate incremental feature development.

Assignment Context

This project is part of the Introduction to DevOps (CSIZG514/SEZG514) course assignment, demonstrating:

Currently Available

• ✅ Flask web application development
• ✅ Version control with Git/GitHub
• ✅ Unit testing with Pytest
• ✅ Containerization with Docker
• ✅ CI/CD pipeline with GitHub Actions
• ✅ Progressive feature development
• ✅ RESTful API design
• ✅ Automated testing and quality assurance

Currently In Progress

• ⏳ Jenkins CI/CD pipeline setup
• ⏳ SonarQube code quality analysis
• ⏳ Docker Hub registry integration
• ⏳ Kubernetes deployment (Minikube/Cloud)
• ⏳ Advanced deployment strategies:
  • Blue-Green Deployment
  • Canary Release
  • Rolling Update
  • Shadow Deployment
  • A/B Testing