using Microsoft.ML;

class Program
{
    static void Main(string[] args)
    {
        MLContext mlContext = new MLContext();

        string dataPath = "filmy.csv";
        IDataView dataView = mlContext.Data.LoadFromTextFile<FilmData>(
            path: dataPath,
            hasHeader: true,
            separatorChar: ','
        );

        var pipeline = mlContext.Transforms.Categorical
            .OneHotEncoding(
                outputColumnName: "GatunekEncoded",
                inputColumnName: "Gatunek"
            ).Append(mlContext.Transforms.Concatenate(
                "Features",
                "GatunekEncoded"
            )).Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
                labelColumnName: nameof(FilmData.Lubiany),
                featureColumnName: "Features"
            ));

        var model = pipeline.Fit(dataView);

        mlContext.Model.Save(model, dataView.Schema, "model.zip");

        System.Console.WriteLine("Model wytrenowany! 👌");

        var predEngine = mlContext.Model.CreatePredictionEngine<FilmData, FilmPrediction>(model);

        var nowyFilm = new FilmData
        {
            Tytul = "Testowy akcja",
            Gatunek = "Akcja"
        };

        var wynik = predEngine.Predict(nowyFilm);
        Console.WriteLine($"Czy film '{nowyFilm.Tytul}' będzie lubiany? {wynik.Prediction} (Score: {wynik.Score})");
    }
}