DescribeModel mit einem AWS-SDK verwenden

class Models:

    @staticmethod
    def describe_model(lookoutvision_client, project_name, model_version):
        """
        Shows the performance metrics for a trained model.

        :param lookoutvision_client: A Boto3 Amazon Lookout for Vision client.
        :param project_name: The name of the project that contains the desired model.
        :param model_version: The version of the model.
        """
        response = lookoutvision_client.describe_model(
            ProjectName=project_name, ModelVersion=model_version
        )
        model_description = response["ModelDescription"]
        print(f"\tModel version: {model_description['ModelVersion']}")
        print(f"\tARN: {model_description['ModelArn']}")
        if "Description" in model_description:
            print(f"\tDescription: {model_description['Description']}")
        print(f"\tStatus: {model_description['Status']}")
        print(f"\tMessage: {model_description['StatusMessage']}")
        print(f"\tCreated: {str(model_description['CreationTimestamp'])}")

        if model_description["Status"] in ("TRAINED", "HOSTED"):
            training_start = model_description["CreationTimestamp"]
            training_end = model_description["EvaluationEndTimestamp"]
            duration = training_end - training_start
            print(f"\tTraining duration: {duration}")

            print("\n\tPerformance metrics\n\t-------------------")
            print(f"\tRecall: {model_description['Performance']['Recall']}")
            print(f"\tPrecision: {model_description['Performance']['Precision']}")
            print(f"\tF1: {model_description['Performance']['F1Score']}")

            training_output_bucket = model_description["OutputConfig"]["S3Location"][
                "Bucket"
            ]
            prefix = model_description["OutputConfig"]["S3Location"]["Prefix"]
            print(f"\tTraining output: s3://{training_output_bucket}/{prefix}")