A deep learning service that identifies and tracks harmful algal blooms (HABs).
Switch branches/tags
Nothing to show
Clone or download
Pull request Compare This branch is 6 commits ahead, 28 commits behind johnneed:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



A service that tracks harmful algal blooms (HAB).

Getting started

Run the service using Docker

  1. You'll need a Docker Account (free) : https://docs.docker.com/
  2. Install Docker & Docker Compose following the official instructions for your platform: https://docs.docker.com/compose/install/
  3. Clone the repo.
  4. cd safetoswim
  5. docker-compose up (You may need to login on the CLI first)
  6. After setup, point your browser to http://localhost:5000/predict

To run the service...

  1. Start the server: Run flask_server.py from the 'servers' directory
  2. Call the service:
    1. In a browser go to localhost:5000/predict
    2. Or call post.bat in the 'clients' directory

To run the mobile client...

  1. Install Expo on your phone from Google Play or the iOS App Store.
  2. Install the Expo NPM module globally. https://expo.io/learn
  3. cd into app/safe-to-swim.
  4. On iOS :
    1. Execute exp start --send-to <your phone number or email>
    2. Open the link you receive via text message or email.
  5. On Android :
    1. Execute exp start
    2. Scan the resulting QR code with the Expo app on your phone.

To view the latest build of the mobile client

  1. Load the Expo app on your phone:

    iOS : https://itunes.apple.com/app/apple-store/id982107779

    Android : https://play.google.com/store/apps/details?id=host.exp.exponent

  2. Send yourself a link (iOS) or scan the barcode (Android) from this webpage : https://expo.io/@johnneed/safe-to-swim


  1. Identify algae blooms
    1. In Lake Champlain from user-uploaded images.
    2. From images uploaded by other users
  2. Serve data about presence of HABs through server API