Skip to content

README.md

Latest commit

 

History

History
139 lines (98 loc) · 4.52 KB

README.md

File metadata and controls

139 lines (98 loc) · 4.52 KB

FAIRS: Fabulous Automated Intelligent Roulette System

Release Python Node.js License CI

1. Project Overview

FAIRS is a research web application for roulette training and inference experiments. It includes:

  • A FastAPI backend for dataset ingestion, training orchestration, checkpoint management, inference sessions, and persistence.
  • A React + Vite frontend for training and inference workflows.
  • An optional Tauri desktop shell for packaged Windows distribution.

Work in Progress: This project is still under active development. You may encounter bugs, issues, or incomplete features.

2. Runtime Modes

2.1 Local Mode (Default)

Run from repository root:

start_on_windows.bat

The launcher prepares local runtimes/dependencies and starts backend + frontend.

2.2 Desktop Mode (Tauri Packaging)

Prerequisites:

  1. Rust installed with default toolchain configured (rustup default stable).
  2. Local runtimes already prepared at least once:
start_on_windows.bat

Build desktop artifacts:

release\tauri\build_with_tauri.bat

Build output:

  • release/windows/installers
  • release/windows/portable

3. Configuration

Runtime profile files:

  • Template: settings/.env.example
  • Active profile: settings/.env
  • Database settings: settings/configurations.json

Initialize .env once:

copy /Y settings\.env.example settings\.env

Use .env to control host/port/runtime behavior and configurations.json for database mode/settings.

3.1 Database Initialization

Database backend selection is defined in settings/configurations.json (database.embedded_database).

  • SQLite (embedded_database=true):
    • The application initializes the database automatically on startup only when app/resources/database.db is missing.
    • Initialization creates schema objects and seeds required data.
    • If database.db already exists, startup skips initialization.
  • PostgreSQL (embedded_database=false):
    • The application does not initialize PostgreSQL automatically during startup.
    • Initialization is manual via:
setup_and_maintenance.bat

Select Initialize database to run app/scripts/initialize_database.py.

app/scripts/initialize_database.py can also initialize SQLite when SQLite mode is selected, but this is normally unnecessary because SQLite initialization is already handled automatically by app startup.

4. Typical Workflow

  1. Start the app: start_on_windows.bat
  2. Open the UI and upload or generate dataset data.
  3. Run training and manage checkpoints.
  4. Start inference sessions using a selected checkpoint.
  5. Optionally package a desktop build with Tauri when needed.

5. Testing

Run full automated tests:

run_tests.bat

Optional direct pytest commands:

uv run pytest -q app/tests/unit
uv run pytest -q app/tests/e2e

6. Setup and Maintenance

Use:

setup_and_maintenance.bat

Available maintenance actions include log cleanup, local uninstall/runtime cleanup, desktop build artifact cleanup, and database initialization.

7. Resources

  • Application data and artifacts: app/resources
  • Launcher-managed runtimes and environment: runtimes

8. User Documentation

Detailed operational guidance is available in:

  • assets/docs/USER_MANUAL.md
  • assets/docs/PACKAGING_AND_RUNTIME_MODES.md
  • assets/docs/ARCHITECTURE.md

9. Screenshots

Training Workspace

Desktop view of dataset upload, dataset selection, checkpoint panels, and the live training monitor.

Training overview

Mobile rendering of the training workspace for narrow viewports.

Training mobile workspace

Inference Workspace

Inference controls for checkpoint and dataset pairing with session playback history.

Inference overview

10. License

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