{

public class TalkingStageBot

{

private static Dictionary<string, string> responses = new Dictionary<string, string>

{

{ "name", "your name" },

{ "location", "your location" },

{ "age", "your age" },

{ "occupation", "your job" },

{ "height", "your height" },

{ "physical activity", "your level of physical activities" },

{ "educational level", "your educational level" },

{ "drinking habit", "your drinking frequency" },

{ "smoking habit", "your smoking frequency" },

{ "gender identity", "your gender" },

{ "seeking", "what you seek from your partner" },

{ "want children", "if you want children or not" },

{ "star sign", "your horoscope sign" },

{ "politics", "how political are you" },

{ "religion", "your religion or lack of" },

{ "tribe", "your tribe if you have one" },

{ "hobbies", "your hobbies" },

{ "passion", "your passion" },

{ "dreams", "what you dream of doing or being" },

{ "expectations in a relationship", "what you expect in a relationship" },

{ "favorite food", "best food" },

{ "favorite color", "best color" },

{ "favorite animal", "best animal" },

{ "favorite movie", "best movie" },

{ "favorite book", "best book" },

{ "favorite music genre", "best music type" },

{ "favorite artist", "best musician" },

{ "favorite travel destination", "best place to travel" },

{ "favorite sport", "best sport" },

{ "favorite team", "best team" },

{ "favorite player", "best player" },

{ "favorite subject in school", "best subject" },

{ "favorite type of music", "best music type" },

{ "favorite type of food", "best food type" },

{ "favorite type of movie", "best movie type" },

{ "favorite type of book", "best book type" },

{ "favorite type of sport", "best sport type" },

{ "favorite type of travel", "best travel type" },

{ "initiates chat","do you prefer starting conversations or your partner?" }

};

private MLContext mlContext;

private ITransformer model;

private DataViewSchema modelSchema;

private bool isFirstInteraction;

public TalkingStageBot()

{

mlContext = new MLContext();

var modelPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "model.zip");

var trainingDataPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "training_data.csv");

System.Diagnostics.Debug.WriteLine($"Model path: {modelPath}");

System.Diagnostics.Debug.WriteLine($"Training data path: {trainingDataPath}");

if (!File.Exists(modelPath))

{

// Train and save the model if it doesn't exist

var trainingModel = new TrainingModel();

trainingModel.TrainAndSaveModel(trainingDataPath, modelPath);

}

// Load the model

model = mlContext.Model.Load(modelPath, out modelSchema);

// Set the first interaction flag

isFirstInteraction = true;

}

public (string response, bool isFirstInteraction) GetResponse(string questions)

{

// Provide information on the first interaction

if (isFirstInteraction)

{

isFirstInteraction = false;

return ("", true);

}

// Split input into individual questions

var questionList = questions.Split(new[] { '.', '?', ',', '!' }, StringSplitOptions.RemoveEmptyEntries);

var responsesSet = new HashSet<string>();

foreach (var question in questionList)

{

// Ensure lowercase for consistency

var lowerQuestion = question.ToLower().Trim();

// Check if any keywords directly match in the question

foreach (var keyword in responses.Keys)

{

if (lowerQuestion.Contains(keyword))

{

responsesSet.Add(responses[keyword]);

goto NextQuestion;

}

}

// If no direct keyword match, then use ML model prediction

var prediction = Predict(lowerQuestion);

if (prediction != null && !string.IsNullOrEmpty(prediction.PredictedLabel))

{

if (responses.TryGetValue(prediction.PredictedLabel, out var response))

{

responsesSet.Add(response);

}

}

else

{

responsesSet.Add("I don't have an answer for that.");

}

NextQuestion:

continue;

}

return (string.Join(", ", responsesSet), false);

}

private Prediction? Predict(string text)

{

var inputData = new List<InputData> { new InputData { Text = text } };

var inputDataView = mlContext.Data.LoadFromEnumerable(inputData);

var transformedData = model.Transform(inputDataView);

var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedData, reuseRowObject: false).ToList();

return predictions.FirstOrDefault();

}

public class InputData

{

[LoadColumn(0)] // Column index for 'Text' in training_data.csv

public string Text { get; set; } = string.Empty;

[LoadColumn(1)] // Column index for 'Label' in training_data.csv

public string Label { get; set; } = string.Empty;

}

public class Prediction

{

[ColumnName("PredictedLabel")]

public string PredictedLabel { get; set; } = string.Empty;

public VBuffer<float> Score { get; set; }

}

}