pipeline {
    agent {
    	docker {
            image 'agakul/ium:mlflow'}
        }
        parameters {
    		buildSelector(
			defaultSelector: lastSuccessful(),
			description: 'Which build to use for copying artifacts',
			name: 'BUILD_SELECTOR'
            )
            string(
			defaultValue: '{\\"inputs\\": [[167.39999389648438, 72.18000030517578, 40.0, 21.0, 94.0], [162.3000030517578, 67.30000305175781, 18.0, 52.0, 219.0], [178.5, 90.5, 14.699999809265137, 45.0, 262.0], [180.89999389648438, 77.0999984741211, 25.399999618530273, 43.0, 224.0], [177.3000030517578, 88.4800033569336, 35.599998474121094, 18.0, 183.0]]}',
            description: 'Inputs',
            name: 'INPUT'
		)
        }
    stages {
		stage('Copy artifacts') {
			steps {
                copyArtifacts fingerprintArtifacts: true, projectName: 's444421-training/training_and_evaluation', selector: buildParameter('BUILD_SELECTOR')
			}
		}
        stage('Predict') {
            steps {
                sh "echo ${params.INPUT} > input_example.json"
				sh "python predict_444501.py"
            }
        }
    }
}