Shared repository for Applied Machine Learning Group 2 Final Project.
Make sure to read through our Contribution Guidelines for instructions on environment setup and creating your first commit.
Below are instructions for running the project locally and within a Docker container.
To start the FastAPI service, run the following from the base directory of the repo:
make fastapiOnce 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.
To start the services in Docker, run the following:
docker-compose upOnce 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