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Source code for the streamlit based web application SexEst. The application uses machine learning models to predict the Sex of unknown skeletons based on their cranial and postcranial measurements.

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SexEst web application

This is the source code for the SexEst web application. The live version of the application can be found here.

The source code runs inside a docker container on your local machine or a web-server. Because the web application uses Docker, it is assumed that Docker is installed on your machine or the web server that will host the application. Instructions on how to install Docker on a Mac can be found here and for Ubuntu here. Additionally, still for Ubuntu, you may try using the following command in your terminal:

sudo apt-get install docker.io

Building the docker image

Once docker is setup on your machine, you may clone this repository to your local directory. You may do that by typing the following command in your terminal:

git clone https://github.com/cconsta1/SexEst.git

You can then navigate to the SexEst directory:

cd SexEst

Once the repository is cloned and you have naviagated inside the SexEst directory, you can build the Docker image by running the command:

docker build -f Dockerfile -t app:latest .

Running the docker image as a container locally

Once the image has been built, you can run a local instance of the SexEst web application by running:

docker run -p 8501:8501 app:latest &

If everything works, you can visit your web-app using the address http://localhost:8501/ in your browser.

Running the docker image as a container on a web server

Assuming that you have already installed docker on your webserver (sudo apt-get install docker.io should work), you can build the image in the same fashion as you would build it locally:

docker build -f Dockerfile -t app:latest .

You may then run the image as a container on the webserver:

docker run --restart always -p 80:8501 app:latest

We use the flag --restart and we set it to always to ensure that if the server goes down for whatever reason the image will be run automatically as soon as the server is back up again.

The web app will now be accessible from the IP address assigned to your webserver.

Cleanup

To check the status of your running container you may use the command:

docker ps

To stop a running container you can use the command:

docker stop <CONTAINER ID>,

where the <CONTAINER ID> can be found in the first column of the output of docker ps.

To remove all containers and all images (stop the containers first) you may use:

docker rm -vf $(docker ps -aq)

docker rmi -f $(docker images -aq)

Creating the model files

Example notebooks to produce the models needed within the the SexEst web application can be found here.

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Source code for the streamlit based web application SexEst. The application uses machine learning models to predict the Sex of unknown skeletons based on their cranial and postcranial measurements.

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