Petal

This repository contains the code for my Master Thesis. Petal is a webservice for collecting and managing electrical signals from plants. An instantiation of the system can be found at plant.biolingo.org/. In the src directory the code for the web server can be found. The ml directory contains the code for the developed machine learning models.

Environment variables

Create .env file in root of directory The following env variables can/need to be configured

• PROFILE: "prod" on server; "dev" locally
• JWT_SECRET_KEY: secret key for generation of password hashes
• POSTGRES_USER: user of postgres database
• POSTGRES_PASSWORD: password for postgres database
• POSTGRES_DB: name of postgres database
• DROPBOX_APP_KEY: app key of dropbox developer app
• DROPBOX_APP_SECRET: app secret of dropbox developer app
• DROPBOX_REFRESH_TOKEN: refresh token for dropbox developer app
• DELETE_MEASUREMENTS_AFTER_STOP: whether measurements should be deleted after recording is stopped
• DELETE_OBSERVATIONS_AFTER_STOP: whether observations should be deleted after experiment is finished
• MERGE_OBSERVATIONS_THRESHOLD: threshold for merging observations in milliseconds

Running locally

Assumes environment variables have been configured

1. Create an environment: python -m venv venv
2. Activate environment: source venv/bin/activate
3. Install requirements: pip install -r requirements.txt
4. Run: bash run-debug.sh

Deployment

1. Build docker image with docker build --network=host . <repository/image_name>
2. Copy docker-compose.yml over to server
3. Adapt image field of plant-server service in docker-compose.yml to <repository/image_name>
4. Add .env file on server
5. On server run: docker-compose up