Overview

Below is a high level overview of the project structure:

.
├── data                # Sample experimental data
├── active_learning     # Experiments in active learning
├── notebooks           # Notebooks for exploring data
├── services            # Implementations of different apis/services
│   ├── data_api        # Data API: For pulling signals for display
│   ├── event_api       # Event API: For storing event data and tags
│   ├── model_api       # Model API: For running and quering models
│   └── ui              # UI: the front end of the application
├── README.md           # This README doc
└── docker-compose.yml  # Master docker compose for running the application

Installation

1. Install docker and docker compose: https://docs.docker.com/engine/install/
2. Install and setup git lfs: https://git-lfs.com/

Setup

Configure git LFS and pull the model

git lfs install
git lfs pull

Build the relevant dataset for the ML model locally

uv venv --python 3.12.6 
source .venv/bin/activate
uv pip install -r ./scripts/requirements.txt
python -m scripts.build_dataset scripts/shots.csv

Run

Run the application by running the following command:

docker compose --env-file .env.dev up 

This will start the following services:

Service URL	Description
http://localhost:3001/	User Interface
http://localhost:8082/	MongoExpress Admin Panel
http://localhost:8000/	Event Database API
http://localhost:8001/	Model Runner API
http://localhost:8002/	Data API