pipeline {
    agent any

    parameters {
        string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
    }

    stages {
        stage('Clone Repository') {
            steps {
                git url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
            }
        }
        stage('Download dataset') {
            steps {
                 withEnv(["KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", "KAGGLE_KEY=${env.KAGGLE_KEY}"]) {
                    sh "kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate --unzip"
                }
            }
        }
        stage('Process and Split Dataset') {
            agent {
                dockerfile {
                    filename 'Dockerfile'
                    reuseNode true
                }
            }
            steps {
                sh "chmod +x ./IUM_05-split.py"
                sh "python3 ./IUM_05-split.py"
                archiveArtifacts artifacts: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', onlyIfSuccessful: true
            }
        }
        stage("Run") {
            agent {
                dockerfile {
                    filename 'Dockerfile'
                    reuseNode true
                }
            }
            steps {
                sh "chmod +x ./IUM_05-model.py"
                sh "chmod +x ./IUM_05-predict.py"
                sh "python3 ./IUM_05-model.py"
                sh "python3 ./IUM_05-predict.py"
                archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
            }
        }

        stage('Sacred') {
            agent {
                dockerfile {
                    filename 'Dockerfile'
                    reuseNode true
                }
            }
              steps {
                sh 'chmod +x sacred/sacred_training_model.py'
                sh 'python3 sacred/sacred_training_model.py'
              }
        }

        stage('Archive Artifacts from Experiments') {
          steps {
            archiveArtifacts artifacts: 'sacred_runs/**/*.*', onlyIfSuccessful: true
            }
        }
    }
}