/atlas-madness

atlas-madness

Menstrual Cycle Wellness App

Project Description

This application uses machine learning to analyze a user's menstrual cycle data and generate personalized recommendations for yoga sequences, breathwork exercises, and meditation. It utilizes the power of MongoDB Atlas on Google Cloud for data storage and management, Google's BigQuery for data analysis, and TensorFlow Enterprise for model training and prediction.

This project was built as part of a hackathon, with the aim of using MongoDB Atlas and at least one Google Cloud Product.

Getting Started

These instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

  • Google Cloud SDK: Please follow this guide to download and install it.
  • TensorFlow Enterprise: Install it using pip. If you have Python installed, you can simply run pip install tensorflow.
  • MongoDB Atlas: A free-tier cluster on MongoDB Atlas is required. Follow this guide to set it up.
  • Python: Make sure you have Python 3 installed. You can download it from here.
  • Flask: Install it using pip. You can simply run pip install flask.
  • Code Editor: Any preferred code editor. If you don't have a preferred one, Visual Studio Code is recommended. You can download it from here.

Setup

  1. Clone this repository to your local machine. 
    git clone <repo-link>
  2. Navigate into the project directory. 
    cd <project-directory>
  3. Install the necessary Python packages. 
    pip install -r requirements.txt
  4. Run the local server. 
    python main.py

You should now have the server running and can navigate to localhost:5000 in your web browser to view the application.

Contributing

We welcome contributions from everyone. If you're interested in contributing, please fork this repository and make your changes, then create a pull request to this repository. We will review your contribution and, if no further changes are needed, your contribution will be merged!

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

We would like to thank our hackathon mentors and the open source community for their assistance and support during the development of this project.