This model is designed accoding to the template in UniversalModelTemaplate. The model passes all the test cases in this application, and should work in the context of the server.
In the model directory, the code in model.py detects named-entities, i.e. names of people, locations and organizations using tranformers in Hugging Face. In the perdiction() function in model/model.py file, set only_person to True to detect only names of people.
config.py has some metadata about the ML model, e.g. input type, model name, and tags.
The requirements are added to requirements.py file in the model directory.
In the root directory of the project, run the command
docker-compose build debug and then docker-compose up debug. Open a web browser and navigate to
http://localhost:4650.
As you make changes to your model the results will appear on the web page showing the initialization status and a prediction result on a test image.
In the root directory of the project, run the command docker-compose build test and then
docker-compose up test. You will see the results of the test cases in your terminal. If all
test cases pass, then your model will work in the server environment.