This project is a FastAPI-based machine learning API that provides endpoints for model inference. The setup uses Poetry for dependency management and Alembic for database migrations.

curl -sSL https://install.python-poetry.org | python3 -

If you are using Bash, add the following to your ~/.profile or ~/.bashrc:

export PATH="$HOME/.local/bin:$PATH"

If you are using Fish, run:

fish_add_path $HOME/.local/bin

cd backend

poetry new app

pip3 install uvicorn gunicorn fastapi

cd backend/app

poetry add fastapi uvicorn gunicorn pydantic numpy pandas scikit-learn joblib alembic sqlalchemy psycopg2

cd backend/app

poetry shell

poetry install

alembic revision --autogenerate -m "Initial migration"

alembic upgrade head

Create a .env file in backend/app and fill it with values from .env.example.

Set user email and password in .env:

FIRST_SUPERUSER=
FIRST_SUPERUSER_PASSWORD=

Then, create the first superuser:

python3 initial_data.py
# or
poetry run python3 initial_data.py

To test WebSocket, use the following endpoint:

ws://ip:port/api/v1/utils/echo-client/

Paste the response in an HTML file and open it in a browser.

To start the FastAPI server, run:

uvicorn app.main:app --reload

• 📄 Swagger UI: http://127.0.0.1:8000/docs
• 📘 Redoc: http://127.0.0.1:8000/redoc