🚴 Welcome to CYCLearn! 🚴‍♂️
Transforming Cyclist Training with Advanced Automation and Data Analysis

The CYCLearn project aims to develop an easy to use platform designed to analyze and visualize cyclists' training sessions data using data automation and analytics. This project targets enhancing training efficiency through predictive analysis and providing valuable insights into athlete performance. By integrating machine learning techniques with a user-friendly web platform, CYCLearn aims to to improve performance and health monitoring. This platfort leverages different technologies to offer a comprehensive solution for coaches and cyclists to monitor, evaluate, and improve their training regimens.

• Visualize Training Metrics: Provide detailed visualizations of various training metrics such as heart rate, altitude, distance, and speed to give users a comprehensive view of their performance.
• Analyze Performance: Use advanced machine learning algorithms to discover patterns and insights from the data.
• Predictive Analytics: Implement predictive analysis to foresee potential health risks and performance issues.
• Enhance Training Efficiency: Offer actionable insights and recommendations to improve training effectiveness.
• User-Friendly Interface: Provide an intuitive and easy-to-use interface for both coaches and cyclists.

Screenshots of the CYCLearn application:

Profile Overview

Cyclists Overview

Training Calendar

Training Metrics

Weather Data

Route Map

Hill Data

Altitude Over Time chart

Heart Rate Over Time chart

Speed Over Time chart

Training Plan

Training Template

Create Plan

My Plans

Health Monitoring

AST Monitor

Import Export

• Python
• PostgreSQL
• Poetry

1. Clone the repository:

   git clone https://github.com/yourusername/AST-Monitor-web.git
   cd AST-Monitor-web

2. Create a poetry environment and install dependencies:

   poetry install

3. Create a PostgreSQL database:

• Create a new PostgreSQL database.

• Navigate to the database/creatingDBscript.sql file and run the query to set up the database schema.

4. Create an environment file:

• In the root of the project, create a .env file with the following content:

  Copy code
  MAIL_USERNAME=cyclearninfo@gmail.com
  MAIL_PASSWORD=udnc oadv dxsh pwtv
  SQLALCHEMY_DATABASE_URI=postgres://YourUserName:YourPassword@YourHostname:YourPort/YourDatabaseName
  TEST_DATABASE_URL=postgres://YourUserName:YourPassword@YourHostname:YourPort/YourDatabaseName
  

• Insert valid uris for SQLALCHEMY_DATABASE_URI and TEST_DATABASE_URL

• TEST_DATABASE_URL should contain the same database as the SQLALCHEMY_DATABASE_URI, it's just meant for testing purposes

5. Starting the backend server

• Navigate to the ast_monitor_web/run.py and run it

• Node.js
• npm

1. Navigate to the frontend directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the application:

   npm start
   

1. Create a Coach and a Cyclist:

Use the application to create a coach and a cyclist.

2. Extract zip files from scripts and put it so the path is like this: scripts/Sport5Rider3.json

3. Run the population script:

• Navigate to the scripts/populateSessions.py file.
• At the bottom of the script, modify the insert_data(data_list, cyclist_id=1) line to change the cyclist's ID as needed.

  python scripts/populateSessions.py
  

Extract zip files from ast_monitor_web/csv/treci.zip and put it so the path is like this: ast_monitor_web/csv/treci.csv

• React
• Axios

• Python Flask
• PostgreSQL
• SQLAlchemy
• Flask-Mail

• AST-Monitor Integration
• GPS Data Visualization

Embark on an extraordinary cycling journey with CYCLearn! 🚴‍♂️🌟