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Front-end of Docker API

The trained model uses the Logistic Classifier that performed the best according to all the collected metrics and results.

  • The user can select one of the example data to be predicted by the model or fill out their own numeric data.
  • The user clicks predict.
  • The predicted class (B)enign or (M)alignant, class probabilty and how it relates to the trained model predictions on the PCA1 and PCA2 is displayed.
  • The PCA plot gives great insight how the newly classified datapoint relates and if it is close to the incorrectly predicted ones or where the two classes are difficult to distinguish extra care can be taken.

Deploy Docker-api Front-end Deploy Docker-api front-end

Pull the pre-build image for Docker Hub

Pull the latest build image

docker pull deusnexus/breast_cancer_classification:latest

Run the container

docker run --name breast_cancer_classification -p 8000:8000 deusnexus/breast_cancer_classification:latest

Open the API on localhost

http://127.0.0.1:8000

Building Image

Enter docker folder

cd docker-api

Build the image

docker build -t breast_cancer_classification:latest .

Run the container

docker run --name breast_cancer_classification -p 8000:8000 breast_cancer_classification:latest

Open the API Front-end

http://127.0.0.1:8000

Running API directly

Enter source/model_deployment directory

cd source/model_deployment

Create a local pythong venv

python3 -m venv venv

Activate the virtual environment

source venv/bin/activate

Install the required modules

pip3 install -r requirements.txt

Run the API

uvicorn main:app --reload

Open the API Front-end

http://127.0.0.1:8000