InsightGuard is a simple API, that can be used to predict if message is considered as cyberbullying or not.

Models are trained in 5 languages: English, Polish, Japanese, Spanish, Catalan.

Project is easily scalable to perform predictions in other languages, and to create new features.

Project structure was generated using awesome fastapi_template.

This project uses poetry. It's a modern dependency management tool.

To run the project use this set of commands:

poetry install
poetry run python -m insightguard

This will start the server on the configured host.

You can find swagger documentation at /api/docs.

You can read more about poetry here: https://python-poetry.org/

You can start the project with docker using this command:

docker-compose -f deploy/docker-compose.yml --project-directory . up --build

If you want to develop in docker with autoreload add -f deploy/docker-compose.dev.yml to your docker command. Like this:

docker-compose -f deploy/docker-compose.yml -f deploy/docker-compose.dev.yml --project-directory . up

This command exposes the web application on port 8000, mounts current directory and enables autoreload.

But you have to rebuild image every time you modify poetry.lock or pyproject.toml with this command:

docker-compose -f deploy/docker-compose.yml --project-directory . build

This is my favourite way to run the project. It's a combination of docker and poetry.

You can start services with docker using this command:

docker-compose -f deploy/docker-compose.services.yml --project-directory . up -d

This command will start the database and redis.

Then you can run the project with poetry:

poetry run python -m insightguard

This application can be configured with environment variables.

You can create .env file in the root directory and place all environment variables here.

All environment variables should start with "INSIGHTGUARD_" prefix.

For example if you see in your "insightguard/settings.py" a variable named like random_parameter, you should provide the "INSIGHTGUARD_RANDOM_PARAMETER" variable to configure the value. This behaviour can be changed by overriding env_prefix property in insightguard.settings.Settings.Config.

An example of .env file:

INSIGHTGUARD_RELOAD="True"
INSIGHTGUARD_PORT="8000"
INSIGHTGUARD_ENVIRONMENT="dev"

INSIGHTGUARD_JWT_SECRET_KEY="secret_key"
INSIGHTGUARD_JWT_REFRESH_SECRET_KEY="refresh_token"

You can read more about BaseSettings class here: https://pydantic-docs.helpmanual.io/usage/settings/

If you want to migrate your database, you should run following commands:

# To run all migrations untill the migration with revision_id.
alembic upgrade "<revision_id>"

# To perform all pending migrations.
alembic upgrade "head"

If you want to revert migrations, you should run:

# revert all migrations up to: revision_id.
alembic downgrade <revision_id>

# Revert everything.
alembic downgrade base

To generate migrations you should run:

# For automatic change detection.
alembic revision --autogenerate

# For empty file generation.
alembic revision

If you want to run it in docker, simply run:

docker-compose -f deploy/docker-compose.yml --project-directory . run --rm api pytest -vv .
docker-compose -f deploy/docker-compose.yml --project-directory . down

For running tests on your local machine.

1. you need to start a database. For example with docker:

docker run -p "5432:5432" -e "POSTGRES_PASSWORD=insightguard" -e "POSTGRES_USER=insightguard" -e "POSTGRES_DB=insightguard" postgres:13.8-bullseye

2.Run the pytest.

pytest -vv .

If you want to contribute to this project, feel free to open a pull request.

This project is licensed under the terms of the MIT license.