Example The ML service is a web application that provides an API for interacting with a machine learning model. It allows users to send queries with prediction data and get results back.
Startup logic:
When launched, the application initializes FastAPI, which handles HTTP requests. The app also connects to the machine learning model and loads it into memory for use in making predictions.
.
├── Docker
│ └── frontend
| └── Dockerfile # Docker image for frontend app
| └── backend
| └── Dockerfile # Docker image for backend app
├── frontend
| └── tone_app.py # Streamlit app file for frontend part of app
├── docker-compose.yml # Docker container managing
├── pyproject.toml # Dependencies
└── src
├── app.py # Main app, FastAPI initializing
├── api # Package with API routes
│ ├── __init__.py
│ └── routes # Package with API routes
│ ├── __init__.py
│ ├── healthcheck.py # Route to check the srvice status
│ ├── predict.py # Route for model predictions
│ └── router.py # Main router
├── schemas # Package with data models
│ ├── __init__.py
│ ├── healthcheck.py # Model for service state responses
│ └── requests.py # Model for input requests to the API
└── services # Package with ML model
├── __init__.py
├── model.py # ML model with prediction
└── utils.py # Supporting utilities
docker-compose up --build
Web-server on
https://localhost:8000
UI on
https://localhost:8000/docs
App on
https://localhost:8501
pip install --no-cache-dir poetry
poetry install --no-dev
poetry run uvicorn src.app:app --host localhost --port 8000
and
poetry run streamlit run frontend/tone_app.py
