This code builds a AI4EU (acumos) component that executes the processing defined in a specific python function.

The input and return value are always a .mat file binary coded.

Data to the service is passed and returned through variables stored inside the .mat files (loaded and saved with scipy.io.loadmat/savemat )

Edit file src/simplebox_service.py and place your edits in function run_codigo

def run_codigo(datafile):
    """
    Reads all variables from a MATLAB .mat file,

    Parameters:
    datafile (bytes pointer): Data sent through grpc (matfile message - data field)

    Returns:
    new_file_pointer (io.BytesIO): In-memory file-like object containing the output in a .mat file.
    """
    #Load the mat file using scipy.io.loadmat
    mat_data=loadmat(io.BytesIO(datafile))
    
    # SPECIFIC CODE STARTS HERE - 
    # exemple (assume there is a variable im in the mat file
    # image=mat_data["im"]



    # SPECIFIC CODE ENDS HERE

    # create file to return data
    f=io.BytesIO()

    # WRITE RETURNING DATA TO MAT FILE - be carefull with the variable naming
    #example (create a variable im ):  savemat(f,{"im":-image})
    return f.getvalue()

Pull the docker image :

docker pull jpcosteira/genericbox

or run the script

#you may edit the script and rename de image
bash buildme

• Standalone:
  1. assign a port number from the host that maps to port 8061 exposed by the docker container (mandatory for AI4EU pipelines).
  2. Launch the component:

  3. $ docker run --rm -it -p 8061:8061 --name servicename -v pathtoexternal/externalfile.py=/workspace/external.py jpcosteira/genericbox:<specific tag>-latest```

1. Use a grpc enabled code to test and/or interact with the service. See notebook test/test_image_generic.ipynb

• Running the service in a pipeline: Follow the configuration and deployment rules of maestro the pipeline orchestrator maestro@github

python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. X.proto