Athlete Training Management System

Project Overview

A Flask-based web application for managing athletes, their training schedules, meals, supplements, and competitions.

Table of Contents

• Features
• Tech Stack
• Project Structure
• Installation
• API Documentation
• Database Schema
• Core Features
• Business Logic

Features

• 🏃‍♂️ Athlete Management
  • Profile tracking
  • Statistics monitoring
  • Performance metrics
• 📅 Training Schedule
  • Create/edit schedules
  • Assign trainers
• 🍽️ Meal Planning
  • Meal tracking
  • Nutritional calculations
  • Ingredient management
• 💊 Supplement Management
  • Dosage tracking
  • Intake scheduling
• 🏆 Competition Tracking
  • Event management
  • Results recording
• 👥 Staff Management
  • Trainer assignments
  • Doctor consultations

Tech Stack

• Backend: Python 3.12, Flask framework
• Database: MySQL
• ORM: SQLAlchemy
• Architecture: MVC pattern

Project Structure

app/
├── controller/     # Request handlers
├── dao/           # Database operations
├── domain/        # Data models
├── root/          # Route definitions
├── service/       # Business logic
└── config.py      # Configuration

Installation

1. Clone repository

git clone https://github.com/nezeracuk/database-labs-LPNU.git

2. Create virtual environment

python -m venv venv
.\venv\Scripts\activate

3. Install dependencies

pip install -r app/requirements.txt

4. Configure database

# filepath: app/config.py
SQLALCHEMY_DATABASE_URI = "mysql+pymysql://username:password@localhost/dbname"

5. Initialize database

python app.py

API Documentation

Endpoints

• /athlete - Athlete CRUD operations
• /competition - Competition management
• /meal - Meal and nutrition tracking
• /schedule - Training schedule management
• /trainer_doctor - Staff management
• /supplement - Supplement tracking

Database Schema

Core Tables

• athlete - Athlete information
• competition - Competition details
• meal - Meal records
• schedule - Training schedules
• trainer_doctor - Staff records
• supplement - Supplement information
• athlete_trainer - Relationship mapping
• athlete_statistics - Performance metrics

Core Features

Data Management

• Complete CRUD operations
• Relationship handling
• Data validation

Business Logic

• Energy calculations
• Statistics tracking
• Automated table creation
• Data validation triggers