🔧 model-service

This application provides endpoints for ML predictions with the phishing detection model.

🐋 Running the app in a Docker container

1. docker build . -t flask_service
2. docker run -p 8080:8080 flask_service

📝 Functionality requirements for assignment A2

• Model tokenizer and encoder is hosted on google drive and downloaded during runtime.
• Flask is used to serve the model and it is runnable in a docker container.
• Utilises pre and postprocessing functions provided by lib-ml.
• Flasgger documentation is provided but does not seem to work from within a docker container right now.