DSBA 6156: Applied Machine Learning

Shared repository for Applied Machine Learning Group 2 Final Project.

Getting Started

Make sure to read through our Contribution Guidelines for instructions on environment setup and creating your first commit.

Usage

Below are instructions for running the project locally and within a Docker container.

Local

To start the FastAPI service, run the following from the base directory of the repo:

make fastapi

Once the service has finished initializing, you can head on over to 127.0.0.1:8000/docs to view the interactive Swagger documentation.

To start the Streamlit service, open another terminal and run:

make streamlit

Once the Streamlit service has started, you can view the web app by going to 127.0.0.1:8501 in your browser.

Docker

To start the services in Docker, run the following:

docker-compose up

Once the services have finished initializing, you can head on over to 127.0.0.1:8000/docs and 127.0.0.1:8501 to view the Swagger documentation and Streamlit app, respectively.

To build the images locally, run:

# build both images
make docker-build

# build individually
make docker-build-ui
make docker-build-api

# build using docker compose
docker-compose -f docker-compose-dev.yml build

# build and run
docker-compose -f docker-compose-dev.yml up --build

License

MIT