package main

import (
	"bufio"
	"encoding/csv"
	"encoding/gob"
	"fmt"
	"io/ioutil"
	"log"
	"math"
	"os"
	"path/filepath"
	"sort"
	"strings"

	"gorgonia.org/gorgonia"
	"gorgonia.org/tensor"
)

// ParsedData представляет структуру для входных и выходных данных
type ParsedData struct {
	Input  string
	Output string
}

// Константы для ограничения количества сообщений и размера словаря
const (
	maxTrainMessages = 10000
	maxVocabSize     = 500000
)

// LoadFiles загружает файлы из указанной директории
func LoadFiles(dirname string, maxMessages int) ([]ParsedData, error) {
	var data []ParsedData
	err := filepath.WalkDir(dirname, func(path string, d os.DirEntry, err error) error {
		if err != nil {
			return err
		}
		if d.IsDir() || len(data) >= maxMessages {
			return nil
		}

		if strings.HasSuffix(d.Name(), ".csv") {
			f, err := os.Open(path)
			if err != nil {
				return err
			}
			defer f.Close()

			r := csv.NewReader(f)
			records, err := r.ReadAll()
			if err != nil {
				return err
			}

			for i := 1; i < len(records) && len(data) < maxMessages; i++ { // Предполагается, что первая строка - заголовок
				record := records[i]
				if len(record) >= 2 {
					data = append(data, ParsedData{
						Input:  record[0],
						Output: record[1],
					})
				}
			}
		} else if strings.HasSuffix(d.Name(), ".txt") {
			content, err := ioutil.ReadFile(path)
			if err != nil {
				return err
			}
			lines := strings.Split(string(content), "\n")
			for _, line := range lines {
				if len(data) >= maxMessages {
					break
				}
				parts := strings.Split(line, ":")
				if len(parts) > 1 {
					data = append(data, ParsedData{
						Input:  strings.TrimSpace(parts[0]),
						Output: strings.TrimSpace(parts[1]),
					})
				}
			}
		}
		return nil
	})
	return data, err
}

// Tokenize разбивает текст на токены
func Tokenize(text string) []string {
	return strings.Fields(strings.TrimSpace(text))
}

// BuildVocab строит словарь из набора данных
func BuildVocab(data []ParsedData) map[string]int {
	vocab := make(map[string]int)
	wordFreq := make(map[string]int)

	for _, item := range data {
		tokens := append(Tokenize(item.Input), Tokenize(item.Output)...)
		for _, token := range tokens {
			wordFreq[token]++
		}
	}

	type kv struct {
		Key   string
		Value int
	}

	var freqs []kv
	for k, v := range wordFreq {
		freqs = append(freqs, kv{k, v})
	}

	sort.Slice(freqs, func(i, j int) bool {
		return freqs[i].Value > freqs[j].Value
	})

	if len(freqs) > maxVocabSize {
		freqs = freqs[:maxVocabSize]
	}

	for i, kv := range freqs {
		vocab[kv.Key] = i
	}
	return vocab
}

// Vectorize преобразует токены в вектор признаков
func Vectorize(tokens []string, vocab map[string]int) []float32 {
	vector := make([]float32, len(vocab))
	for _, token := range tokens {
		if idx, exists := vocab[token]; exists {
			vector[idx] += 1
		}
	}
	return vector
}

// Сохранение и загрузка весов модели
func SaveWeights(weights *tensor.Dense, filename string) error {
	f, err := os.Create(filename)
	if err != nil {
		return err
	}
	defer f.Close()

	encoder := gob.NewEncoder(f)
	return encoder.Encode(weights)
}

func LoadWeights(filename string) (*tensor.Dense, error) {
	f, err := os.Open(filename)
	if err != nil {
		return nil, err
	}
	defer f.Close()

	var weights tensor.Dense
	decoder := gob.NewDecoder(f)
	if err := decoder.Decode(&weights); err != nil {
		return nil, err
	}
	return &weights, nil
}

// Сохранение и загрузка словаря
func SaveVocabulary(vocab map[string]int, filename string) error {
	f, err := os.Create(filename)
	if err != nil {
		return err
	}
	defer f.Close()

	encoder := gob.NewEncoder(f)
	return encoder.Encode(vocab)
}

func LoadVocabulary(filename string) (map[string]int, error) {
	f, err := os.Open(filename)
	if err != nil {
		return nil, err
	}
	defer f.Close()

	var vocab map[string]int
	decoder := gob.NewDecoder(f)
	if err := decoder.Decode(&vocab); err != nil {
		return nil, err
	}
	return vocab, nil
}

// sigmoid применяет сигмоиду к каждому элементу тензора
func sigmoid(x tensor.Tensor) tensor.Tensor {
	data := x.Data().([]float32)
	result := make([]float32, len(data))
	for i, v := range data {
		result[i] = 1 / (1 + float32(math.Exp(-float64(v))))
	}
	return tensor.New(tensor.Of(tensor.Float32), tensor.WithBacking(result), tensor.WithShape(x.Shape()...))
}

// trainModel обучает модель на данных из trainDir
func trainModel(trainDir string, maxTrainMessages int) {
	parsedData, err := LoadFiles(trainDir, maxTrainMessages)
	if err != nil {
		log.Fatalf("Error loading training files: %v", err)
	}

	fmt.Printf("Loaded %d training records\n", len(parsedData))

	vocab := BuildVocab(parsedData)

	// Create models directory if it doesn't exist
	if err := os.MkdirAll("models", os.ModePerm); err != nil {
		log.Fatalf("Error creating models directory: %v", err)
	}

	// Save vocabulary to models directory
	if err := SaveVocabulary(vocab, "models/vocab.gob"); err != nil {
		log.Fatalf("Error saving vocabulary: %v", err)
	}

	batchSize := 150
	totalSamples := len(parsedData)
	numEpochs := 10

	g := gorgonia.NewGraph()

	inputSize := len(vocab)
	w := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(inputSize, 1), gorgonia.WithInit(gorgonia.GlorotN(1.0)))

	x := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(batchSize, inputSize), gorgonia.WithName("x"))
	y := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(batchSize, 1), gorgonia.WithName("y"))

	yhat := gorgonia.Must(gorgonia.Mul(x, w))
	yhatSig := gorgonia.Must(gorgonia.Sigmoid(yhat)) // Apply sigmoid

	// Binary cross-entropy loss
	losses := gorgonia.Must(gorgonia.Sub(yhatSig, y))
	squaredLoss := gorgonia.Must(gorgonia.Square(losses))
	loss := gorgonia.Must(gorgonia.Mean(squaredLoss))

	// Create gradients
	_, err = gorgonia.Grad(loss, w)
	if err != nil {
		log.Fatalf("Failed to create gradients: %v", err)
	}

	// Initialize optimizer
	optimizer := gorgonia.NewAdamSolver(gorgonia.WithLearnRate(0.001))

	// Create the machine
	machine := gorgonia.NewTapeMachine(g, gorgonia.BindDualValues(w))
	defer machine.Close()

	for epoch := 0; epoch < numEpochs; epoch++ {
		var epochLoss float64
		for start := 0; start < totalSamples; start += batchSize {
			end := start + batchSize
			if end > totalSamples {
				end = totalSamples
			}

			batchSizeActual := end - start

			// **Skip the batch if it's not the full batch size**
			if batchSizeActual != batchSize {
				fmt.Printf("Skipping batch from %d to %d (size: %d)\n", start, end, batchSizeActual)
				continue
			}

			xValData := make([]float32, batchSizeActual*inputSize)
			yValData := make([]float32, batchSizeActual)

			for i := start; i < end; i++ {
				tokens := Tokenize(parsedData[i].Input)
				inputVec := Vectorize(tokens, vocab)
				copy(xValData[(i-start)*inputSize:(i-start+1)*inputSize], inputVec)

				// Parse Output as float32
				if parsedData[i].Output == "1" {
					yValData[i-start] = 1.0
				} else {
					yValData[i-start] = 0.0
				}
			}

			xVal := tensor.New(tensor.WithShape(batchSizeActual, inputSize), tensor.Of(tensor.Float32), tensor.WithBacking(xValData))
			yVal := tensor.New(tensor.WithShape(batchSizeActual, 1), tensor.Of(tensor.Float32), tensor.WithBacking(yValData))

			// Set node values using gorgonia.Let
			if err := gorgonia.Let(x, xVal); err != nil {
				log.Fatalf("Error setting x value: %v", err)
			}
			if err := gorgonia.Let(y, yVal); err != nil {
				log.Fatalf("Error setting y value: %v", err)
			}

			if err := machine.RunAll(); err != nil {
				log.Fatalf("Machine run error during training: %v", err)
			}

			// Get loss value
			lossData := loss.Value().Data()
			lossVal, ok := lossData.(float32)
			if !ok {
				log.Fatalf("Unexpected type for loss value: %T", lossData)
			}

			epochLoss += float64(lossVal) * float64(batchSizeActual)

			// Update weights
			nodes := gorgonia.Nodes{w}
			err = optimizer.Step(gorgonia.NodesToValueGrads(nodes))
			if err != nil {
				log.Fatalf("Error during optimizer step: %v", err)
			}

			machine.Reset()
		}

		averageLoss := epochLoss / float64(totalSamples)
		fmt.Printf("Epoch %d/%d - Loss: %.4f\n", epoch+1, numEpochs, averageLoss)
	}

	fmt.Println("Model training complete")

	// Save weights to models directory
	if err := SaveWeights(w.Value().(*tensor.Dense), "models/model_weights.gob"); err != nil {
		log.Fatalf("Error saving model weights: %v", err)
	} else {
		fmt.Println("Model weights saved successfully.")
	}
}

// interactWithModel позволяет взаимодействовать с обученной моделью
func interactWithModel() {
	weightsTensor, err := LoadWeights("models/model_weights.gob")
	if err != nil {
		log.Fatalf("Error loading model weights: %v", err)
	}

	vocab, err := LoadVocabulary("models/vocab.gob")
	if err != nil {
		log.Fatalf("Error loading vocabulary: %v", err)
	}

	inputSize := len(vocab)
	reader := bufio.NewReader(os.Stdin)

	// Создаем граф для предсказания
	g := gorgonia.NewGraph()
	w := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(inputSize, 1), gorgonia.WithValue(weightsTensor))

	x := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(1, inputSize))
	yhat := gorgonia.Must(gorgonia.Mul(x, w))
	yhatSig := gorgonia.Must(gorgonia.Sigmoid(yhat)) // Применяем сигмоиду

	machine := gorgonia.NewTapeMachine(g, gorgonia.BindDualValues(w))
	defer machine.Close()

	for {
		fmt.Print("Ask a question (or type 'exit' to quit): ")
		text, _ := reader.ReadString('\n')
		text = strings.TrimSpace(text)

		if text == "exit" {
			break
		}

		tokens := Tokenize(text)
		inputVec := Vectorize(tokens, vocab)

		xValData := make([]float32, inputSize)
		copy(xValData, inputVec)
		xVal := tensor.New(tensor.WithShape(1, inputSize), tensor.Of(tensor.Float32), tensor.WithBacking(xValData))

		// Устанавливаем значение узла x с помощью gorgonia.Let
		if err := gorgonia.Let(x, xVal); err != nil {
			log.Fatalf("Error setting x value: %v", err)
		}

		if err := machine.RunAll(); err != nil {
			log.Fatalf("Machine run error during interaction: %v", err)
		}

		predictionsVal := yhatSig.Value().Data().([]float32)
		predictedProbability := predictionsVal[0]
		predictedOutput := 0
		if predictedProbability >= 0.5 {
			predictedOutput = 1
		}

		var response string
		if predictedOutput == 1 {
			response = "Positive Response"
		} else {
			response = "Negative Response"
		}

		fmt.Printf("Generated Response: %s (Probability: %.4f)\n", response, predictedProbability)
		machine.Reset()
	}
}

// validateModel выполняет валидацию модели на данных из validationDir
func validateModel(validationDir string) {
	weightsTensor, err := LoadWeights("models/model_weights.gob")
	if err != nil {
		log.Fatalf("Error loading model weights: %v", err)
	}

	vocab, err := LoadVocabulary("models/vocab.gob")
	if err != nil {
		log.Fatalf("Error loading vocabulary: %v", err)
	}

	validationData, err := LoadFiles(validationDir, maxTrainMessages) // Используйте подходящее ограничение
	if err != nil {
		log.Fatalf("Error loading validation files: %v", err)
	}

	fmt.Printf("Loaded %d validation records\n", len(validationData))

	g := gorgonia.NewGraph()
	w := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(len(vocab), 1), gorgonia.WithValue(weightsTensor))

	x := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(1, len(vocab)))
	y := gorgonia.NewMatrix(g, tensor.Float32, gorgonia.WithShape(1, 1))

	yhat := gorgonia.Must(gorgonia.Mul(x, w))
	yhatSig := gorgonia.Must(gorgonia.Sigmoid(yhat)) // Применяем сигмоиду

	// Валидация не требует градиентов, поэтому не нужно вызывать Grad
	machine := gorgonia.NewTapeMachine(g, gorgonia.BindDualValues(w))
	defer machine.Close()

	var totalLoss float64
	correct := 0
	total := len(validationData)

	for idx, item := range validationData {
		inputTokens := Tokenize(item.Input)
		inputVec := Vectorize(inputTokens, vocab)

		xValData := make([]float32, len(vocab))
		copy(xValData, inputVec)
		xVal := tensor.New(tensor.WithShape(1, len(vocab)), tensor.Of(tensor.Float32), tensor.WithBacking(xValData))

		// Парсим Output как float32
		var expectedOutput float32
		if item.Output == "1" {
			expectedOutput = 1.0
		} else {
			expectedOutput = 0.0
		}

		yValData := []float32{expectedOutput}
		yVal := tensor.New(tensor.WithShape(1, 1), tensor.Of(tensor.Float32), tensor.WithBacking(yValData))

		// Устанавливаем значения узлов x и y с помощью gorgonia.Let
		if err := gorgonia.Let(x, xVal); err != nil {
			log.Fatalf("Error setting x value: %v", err)
		}
		if err := gorgonia.Let(y, yVal); err != nil {
			log.Fatalf("Error setting y value: %v", err)
		}

		if err := machine.RunAll(); err != nil {
			log.Fatalf("Machine run error during validation: %v", err)
		}

		predictionsVal := yhatSig.Value().Data().([]float32)
		predictedProbability := predictionsVal[0]
		predictedOutput := 0
		if predictedProbability >= 0.5 {
			predictedOutput = 1
		}

		// Вычисляем бинарную кросс-энтропию для текущего примера
		epsilon := float32(1e-7) // Для предотвращения log(0)
		p := float32(math.Min(math.Max(float64(predictedProbability), float64(epsilon)), 1-1e-7))
		lossVal := -expectedOutput*float32(math.Log(float64(p))) - (1-expectedOutput)*float32(math.Log(float64(1-p)))
		totalLoss += float64(lossVal)

		if predictedOutput == int(expectedOutput) {
			correct++
		}

		machine.Reset()

		// Опционально: вывод прогресса
		if (idx+1)%1000 == 0 || idx+1 == total {
			fmt.Printf("Validated %d/%d examples\n", idx+1, total)
		}
	}

	averageLoss := totalLoss / float64(total)
	accuracy := float64(correct) / float64(total) * 100
	fmt.Printf("Validation Loss: %.4f\n", averageLoss)
	fmt.Printf("Validation Accuracy: %.2f%%\n", accuracy)
}

// main - точка входа программы
func main() {
	fmt.Print("Enter mode of operation (t for training, v for validation, d for dialogue): ")
	scanner := bufio.NewScanner(os.Stdin)
	scanner.Scan()
	mode := scanner.Text()

	switch mode {
	case "t":
		trainModel("train", maxTrainMessages)
	case "v":
		validateModel("valid")
	case "d":
		interactWithModel()
	default:
		fmt.Println("Unknown mode. Use 't' for training, 'v' for validation, or 'd' for dialogue.")
	}
}


сделай так чтобы он выдавал не Generated Response: Positive Response (Probability: 0.5000)
а внятный ответ из буквс симбвалос и остапльного ответ ии должен составляться на базе обучнеий ии сам должен его составлять на базе обучненний