MyBot

hello.ai

good morning
good evening
good afternoon
hello there
###
speak("hi there");

IntentModel.cs

using SharpNL.DocumentCategorizer;
using SharpNL.Extensions;
using SharpNL.Tokenize;
using SharpNL.Utility;


[Serializable]
public class IntentModel : IObjectStream<DocumentSample>
{
    public string Intent { get; set; }
    public string[] Samples { get; set; }
    private int Position { get; set; } = 0;

    public DocumentSample Read()
    {
        if (Position >= Samples.Length)
            return null;
        string[] tokens = WhitespaceTokenizer.Instance.Tokenize(Samples[Position]);
        List<string> vector = new List<string>(tokens.Length);
        bool skip = false;
        foreach (string token in tokens)
        {
            if (token.StartsWith("<", StringComparison.Ordinal))
            {
                skip = !skip;
            }
            else if (!skip)
            {
                vector.Add(token);
            }
        }
        tokens = new string[vector.Count];
        vector.CopyTo(tokens);
        Position++;
        return new DocumentSample(Intent, tokens);
    }

    public void Reset() => Position = 0;
    public void Dispose() => Samples = null;
}

public class CompIntent<T> : IObjectStream<T>
{
    private IObjectStream<T>[] streams;
    private int streamIndex = 0;

    public CompIntent(IObjectStream<T>[] streams) => this.streams = streams;

    public T Read()
    {
        T obj = default(T);
        while (streamIndex < streams.Length && obj == null)
        {
            obj = streams[streamIndex].Read();
            if (obj == null)
            {
                streamIndex++;
            }
        }
        return obj;
    }

    public void Reset()
    {
        streamIndex = 0;
        streams.ForEach(x => x.Reset());
    }

    public void Dispose() => streams.ForEach(x => x.Dispose());
}

Program.cs

using Jint;
using SharpNL.DocumentCategorizer;
using SharpNL.Extensions;
using SharpNL.Utility;
using System.Speech.Recognition;
using System.Speech.Synthesis;

string[] data = Directory.GetFiles(Directory.GetCurrentDirectory(), "*.ai");
IObjectStream<DocumentSample>[] CategoryStreams = new IObjectStream<DocumentSample>[data.Length];
Dictionary<string, string> IntentSnippet = new Dictionary<string, string>();
data.ForEach((x, i) =>
{
    var d = File.ReadAllText(x).Split("###");
    string intentName = Path.GetFileNameWithoutExtension(x);
    string[] samples = d[0].Replace("\r", "").Split("\n");
    string code = d[1].Replace("\r", "").Replace("\n", Environment.NewLine);
    IntentSnippet.Add(intentName, code);
    CategoryStreams[i] = new IntentModel
    {
        Intent = intentName,
        Samples = samples
    };
});

var param = new TrainingParameters();
param.Set(Parameters.Iterations, "70");
param.Set(Parameters.Cutoff, "1");
DocumentCategorizerModel categorizerModel = DocumentCategorizerME.Train("vi",
    new CompIntent<DocumentSample>(CategoryStreams), param, new DocumentCategorizerFactory());

Engine engine = new Engine().SetValue("speak", new Action<string>(x =>
{
    SpeechSynthesizer synth = new SpeechSynthesizer();
    synth.SetOutputToDefaultAudioDevice();
    synth.Speak(x);
}));

SpeechRecognitionEngine recognizer = new SpeechRecognitionEngine(
    new System.Globalization.CultureInfo("en-US"));
recognizer.LoadGrammar(new DictationGrammar());
recognizer.SpeechRecognized += (sender, e) =>
{
    if(e.Result.Confidence > 0.25)
    {
        DocumentCategorizerME categorizer = new DocumentCategorizerME(categorizerModel);
        var outcome = categorizer.Categorize(e.Result.Text);
        var intent = categorizer.GetBestCategory(outcome);
        engine.Execute(IntentSnippet[intent]);
    }
};
recognizer.SetInputToDefaultAudioDevice();
recognizer.RecognizeAsync(RecognizeMode.Multiple);
while (true) { }

time.ai

what time is it
do you know the time
do you have the time
can you give me the time
###
speak("buy a watch you fool.");