MangoTruth

Purpose:

MangoTruth is an open srouce AI detection system focused on:

• Ukrainian language
• Academic domain
• Explainable models

Contribution

New model

When adding new model, check out our training guidelines!

Ecosystem

Repository consists of:

• frontend(py) - user interface, authentication and requests routing
• core(go) - main application server that provides rest endpoints for both external integrations and frontend. Handles subscriptions, persistence, risk management, external integration and routes requests to execution venues
• compute(py) - compute engine of the system. Routes compute requests to detectors on both centralized and community servers
• detectors(py) - purely python code with implementation of known algorithms for AI detection implementing simple interface.

Supported models:

Installation from source

To set up installation on your environment follow these steps:

• Core:
  • Install go
  • Install rabbitMQ
  • Configure server using core/config/config-development.yml file. Extensive configuration reference coming soon
  • Install sqlboiler
  • Run sqlboiler --wipe --output .\core\pkg\storage\models psql
  • Run go build ./core/cmd/server
  • Run go run core/cmd/server/main.go
• Compute:
  • Optional create venv
  • pip install -r "compute/requirements.txt"
  • pip install -r "detectors/requirements.txt"
  • python "compute/main.py"
• Frontend:
  • Install Python
  • Optional create venv
  • pip install -r "frontend/requirements.txt"
  • python "frontend/app.py"

Running as container

Docker compose setup

Execute the following command from the project directory

 docker volume create storage
 docker-compose up --build -d

1) The front-end is accessible from http://localhost:7860/ 2) The back-end is accessible from http://localhost:8080/ or http://core:8080/

Docker setup

• Frontend
  1. Build docker image (Your PWD has to be in the frontend directory)

     docker build -t front-end-mango-truth .

  2. Run docker container

     docker run -d --name front-end-mango-truth -p 7860:7860 front-end-mango-truth 

  3. Access the Gradio front-end using http://127.0.0.1:7860/ or http://localhost:7860/
• Core (bash)
  1. Build docker image (Your PWD has to be in the project root directory)

     docker build --tag mango-truth-core:1.0.0 --file ./core/Dockerfile .

  2. Run docker container

     docker run -d --name core -p 8080:8080 -e COMPUTE_HOST='host.docker.internal' mango-truth-core:1.0.0

  3. Access core using http://127.0.0.1:8080/ or http://localhost:8080/
• Core (With makefile)
  • Follow MAKEFILE commands to run core+rabbitmq in separate docker network.
    • network
    • run-rabbitmq
    • run-core
    • run-postgres
  • Access core using http://127.0.0.1:8080/ or http://localhost:8080/
• Compute . Build docker image (Your PWD has to be in the project root directory)

   docker build --tag mango-truth-compute:1.0.0 --file ./compute/Dockerfile .

  2. Run docker container

     docker run -d --name compute -e RABBITMQ_HOST=host.docker.internal mango-truth-compute:1.0.0

Configuration

Generally, all variables mentioned here follow uppercase naming with "_" as delimiter for env variable and camelcase for .yml config files. Example:

Specifing SERVER_PORT=8080 in env is equivalent to specifying in corresponding config location.

server:
  port: 8080

Core

Config is resolved by path ./core/config/config-development.yml. TODO: Add option to override location.

Frontend

Compute

Documentation

API endpoints are available in core/docs/swagger.json file.

To generate docs for core endpoints run the following command:

cd core 
swag init -g ./cmd/server/main.go --output ./docs