C#

I have this class User:

    [Serializable()]
    [XmlRoot("Users")]
    public class Users
    {
        [XmlArrayItem(typeof(User))]
        public List<User> UsersList { get; set; }
    }


    [Serializable()]
    public class User
    {
        [System.Xml.Serialization.XmlAttribute("ID")]
        public string ID { get; set; }

        [System.Xml.Serialization.XmlElementAttribute("Name")]
        public string Name { get; set; }

        [System.Xml.Serialization.XmlElementAttribute("Username")]
        public string Username { get; set; }

        [System.Xml.Serialization.XmlElementAttribute("Password")]
        public string Password { get; set; }

        [System.Xml.Serialization.XmlElementAttribute("Email")]
        public string Email { get; set; } = string.Empty;
    }
	
	
And this:
  private void Serialization()
        {
            var path = @"D:\Projects\repo\xml\users.xml";
            var newUsers = new Users();
            newUsers.UsersList = new List<User>();
            
                var user = new User
                ();
                user.Email = txtEmail.Text;
                user.Name = txtName.Text;
                user.Password = txtPass.Text;
                user.ID =txtID.Text;
                newUsers.UsersList.Add(user);

            XmlSerializer serializer = new XmlSerializer(typeof(Users));
            using (TextWriter writer =  new StreamWriter(path,true))
            {
                serializer.Serialize(writer, newUsers);
            }
        }

And it saves this xml:


<?xml version="1.0" encoding="utf-8"?>
<Users xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
	<UsersList>
		<User ID="A">
			<Name>a</Name>
			<Password>a</Password>
			<Email>a</Email>
		</User>
	</UsersList>
</Users><?xml version="1.0" encoding="utf-8"?>
<Users xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
	<UsersList>
		<User ID="SSSS">
			<Name>s</Name>
			<Password>s</Password>
			<Email>ss</Email>
		</User>
	</UsersList>
</Users>


But I want to save the xml file in this format:

<?xml version="1.0" encoding="utf-8"?>
<Users xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
	<UsersList>
		<User ID="A">
			<Name>a</Name>
			<Password>a</Password>
			<Email>a</Email>
		</User>
		<User ID="SSSS">
			<Name>s</Name>
			<Password>s</Password>
			<Email>ss</Email>
		</User>
	</UsersList>
The most important thing is that I have a windows form with some textboxes and every time I run the application and register a new user I want it to pe added in <UsersList> not overwritten over the existing ones

<pre lang="C#">

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Numpy;
using Python.Runtime;
using Keras;
using Keras.Layers;
using Keras.Models;
using Keras.Optimizers;
using Keras.losses;

namespace SQLInjectionDetection
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load CSV file
            var trainData = File.ReadAllLines("tokens.csv")
                .Select(l => l.Split(','))
                .Select(s => new { Token = s[0], Label = int.Parse(s[1]) })
                .ToList();

            // Shuffle trainData
            var random = new Random();
            trainData = trainData.OrderBy(d => random.Next()).ToList();

            // Split trainData into training and validation sets
            var splitIndex = (int)(trainData.Count * 0.8);
            var trainDataSubset = trainData.Take(splitIndex).ToList();
            var testDataSubset = trainData.Skip(splitIndex).ToList();

            // Define vocabulary and tokenize trainData
            var vocabulary = new HashSet<char>(trainDataSubset.SelectMany(d => d.Token).Distinct());
            var tokenToIndex = vocabulary.Select((c, i) => new { Token = c, Index = i }).ToDictionary(t => t.Token, t => t.Index);
            var maxSequenceLength = trainDataSubset.Max(d => d.Token.Length);
            var trainTokenized = Tokenize(trainDataSubset, tokenToIndex, maxSequenceLength);
            var testTokenized = Tokenize(testDataSubset, tokenToIndex, maxSequenceLength);

            // Build RNN model
            using (Py.GIL())
            {
                dynamic keras = Py.Import("keras");
                dynamic np = Py.Import("numpy");
                var input = new Input(shape: 1000);
                var embedding = new Embedding(vocabulary.Count, 32).Apply(input);
                var lstm = new LSTM(32).Apply(embedding);
                var output = new Dense(1, activation: keras.activations.sigmoid).Apply(lstm);
                var model = new Model(inputs: input, outputs: output);
                model.Compile(optimizer: new Adam(), loss: new BinaryCrossentropy(), metrics: new[] { "accuracy" });

                // Train model
                var trainX = trainTokenized.Item1;
                var trainY = trainTokenized.Item2;
                var testX = testTokenized.Item1;
                var testY = testTokenized.Item2;
                model.Fit(trainX, trainY, batchSize: 32, epochs: 10, validationData: (testX, testY));

                // Take user input and make prediction
                Console.Write("Enter user input: ");
                var userInput = Console.ReadLine();
                var inputTokenized = TokenizeInput(userInput, tokenToIndex, maxSequenceLength);
                var prediction = model.Predict(inputTokenized).GetData<float>()[0, 0];
                Console.WriteLine($"Prediction: {(prediction > 0.5 ? "Malicious" : "Safe")} (Score: {prediction:F4})");

                // Evaluate model
                var testMetrics = model.Evaluate(testX, testY);
                Console.WriteLine($"Test loss: {testMetrics[0]:F4}");
                Console.WriteLine($"Test accuracy: {testMetrics[1]:F4}");
            }
        }

        private static (NDarray, NDarray) Tokenize(List<dynamic> data, Dictionary<char, int> tokenToIndex, int maxSequenceLength)
        {
            var numExamples = data.Count;
            var X = np.zeros((numExamples, maxSequenceLength));
            var Y = np.zeros((numExamples, 1));
            for (var i = 0; i < numExamples; i++)
            {
                var tokens = data[i].Token;
                var label = data[i].Label;
                Y[i] = label;
                for (var j = 0; j < tokens.Length; j++)
                {
                    var token = tokens[j];
                    var index = tokenToIndex[token];
                    X[i, j] = index;
                }
            }
            return (X, Y);
        }

    }
}

public async Task SomeFunc()
{
	await Task.Run(() =>
	{
		// Some Sync method calls here like..
		var reader = com.ExecuteReader();
		...
	}

}
 
// Or

public async Task SomeFunc()
{
	// Some Async method calls here like..
	var reader = await com.ExecuteReaderAsync();
	....
}