package services

import (
	"bytes"
	"encoding/base64"
	"fmt"
	"image"
	"regexp"
	"strings"
	"time"

	"github.com/trackeep/backend/models"
	"gorm.io/gorm"
)

// ComputerVisionService provides computer vision capabilities
type ComputerVisionService struct {
	db *gorm.DB
}

// NewComputerVisionService creates a new computer vision service
func NewComputerVisionService(db *gorm.DB) *ComputerVisionService {
	return &ComputerVisionService{db: db}
}

// ImageAnalysisRequest represents a request for image analysis
type ImageAnalysisRequest struct {
	ImageData    string `json:"image_data" binding:"required"`    // Base64 encoded image
	AnalysisType string `json:"analysis_type" binding:"required"` // ocr, objects, text, faces, all
	FileID       *uint  `json:"file_id,omitempty"`
}

// ImageAnalysisResponse represents the result of image analysis
type ImageAnalysisResponse struct {
	Success  bool                   `json:"success"`
	Analysis map[string]interface{} `json:"analysis"`
	Text     string                 `json:"text,omitempty"`
	Objects  []ObjectDetection      `json:"objects,omitempty"`
	Faces    []FaceDetection        `json:"faces,omitempty"`
	Metadata ImageMetadata          `json:"metadata"`
}

// ObjectDetection represents a detected object
type ObjectDetection struct {
	Name        string      `json:"name"`
	Confidence  float64     `json:"confidence"`
	BoundingBox BoundingBox `json:"bounding_box"`
}

// FaceDetection represents a detected face
type FaceDetection struct {
	Confidence  float64     `json:"confidence"`
	BoundingBox BoundingBox `json:"bounding_box"`
	Age         *int        `json:"age,omitempty"`
	Gender      *string     `json:"gender,omitempty"`
	Emotion     *string     `json:"emotion,omitempty"`
}

// BoundingBox represents coordinates of a detected object
type BoundingBox struct {
	X      int `json:"x"`
	Y      int `json:"y"`
	Width  int `json:"width"`
	Height int `json:"height"`
}

// ImageMetadata represents metadata about the analyzed image
type ImageMetadata struct {
	Width          int      `json:"width"`
	Height         int      `json:"height"`
	Format         string   `json:"format"`
	SizeBytes      int      `json:"size_bytes"`
	ColorSpace     string   `json:"color_space"`
	DominantColors []string `json:"dominant_colors"`
	TextDensity    float64  `json:"text_density"`
}

// AnalyzeImage performs computer vision analysis on an image
func (s *ComputerVisionService) AnalyzeImage(req ImageAnalysisRequest) (*ImageAnalysisResponse, error) {
	// Decode base64 image
	imageData, err := base64.StdEncoding.DecodeString(req.ImageData)
	if err != nil {
		return nil, fmt.Errorf("invalid base64 image data: %v", err)
	}

	// Parse image to get metadata
	img, format, err := image.Decode(bytes.NewReader(imageData))
	if err != nil {
		return nil, fmt.Errorf("failed to decode image: %v", err)
	}

	bounds := img.Bounds()
	response := &ImageAnalysisResponse{
		Success:  true,
		Analysis: make(map[string]interface{}),
		Metadata: ImageMetadata{
			Width:      bounds.Dx(),
			Height:     bounds.Dy(),
			Format:     format,
			SizeBytes:  len(imageData),
			ColorSpace: "RGB", // Simplified
		},
	}

	// Perform requested analysis types
	if req.AnalysisType == "ocr" || req.AnalysisType == "all" {
		text, err := s.extractText(imageData)
		if err == nil {
			response.Text = text
			response.Analysis["text"] = text
			response.Analysis["word_count"] = len(strings.Fields(text))
			response.Metadata.TextDensity = float64(len(text)) / float64(bounds.Dx()*bounds.Dy()) * 1000
		}
	}

	if req.AnalysisType == "objects" || req.AnalysisType == "all" {
		objects := s.detectObjects(imageData)
		response.Objects = objects
		response.Analysis["objects"] = objects
		response.Analysis["object_count"] = len(objects)
	}

	if req.AnalysisType == "faces" || req.AnalysisType == "all" {
		faces := s.detectFaces(imageData)
		response.Faces = faces
		response.Analysis["faces"] = faces
		response.Analysis["face_count"] = len(faces)
	}

	if req.AnalysisType == "text" || req.AnalysisType == "all" {
		// Extract readable text from image
		text, err := s.extractText(imageData)
		if err == nil {
			response.Analysis["readable_text"] = text
			response.Analysis["has_text"] = len(strings.TrimSpace(text)) > 0
		}
	}

	// Extract dominant colors
	colors := s.extractDominantColors(imageData)
	response.Metadata.DominantColors = colors

	// Save analysis to database if file ID is provided
	if req.FileID != nil {
		s.saveImageAnalysis(*req.FileID, response)
	}

	return response, nil
}

// extractText performs OCR on the image (simplified implementation)
func (s *ComputerVisionService) extractText(imageData []byte) (string, error) {
	// This is a simplified OCR implementation
	// In a real implementation, you would use:
	// - Tesseract OCR
	// - Google Cloud Vision API
	// - Azure Computer Vision
	// - AWS Textract

	// For demo purposes, we'll extract text from common patterns
	// This is just a placeholder implementation

	// Try to detect common text patterns in the image
	// In reality, this would require actual OCR processing

	// Simulate OCR by returning sample text based on image analysis
	text := `
	This is sample OCR text extracted from the image.
	In a real implementation, this would contain the actual
	text content found in the image using OCR technology.
	
	Common use cases:
	- Document scanning
	- Receipt processing
	- Business card reading
	- Screenshot text extraction
	`

	return strings.TrimSpace(text), nil
}

// detectObjects performs object detection on the image
func (s *ComputerVisionService) detectObjects(imageData []byte) []ObjectDetection {
	// This is a simplified object detection implementation
	// In a real implementation, you would use:
	// - YOLO (You Only Look Once)
	// - TensorFlow Object Detection API
	// - OpenCV DNN
	// - Cloud vision services

	// Simulate object detection with common objects
	objects := []ObjectDetection{
		{
			Name:        "document",
			Confidence:  0.95,
			BoundingBox: BoundingBox{X: 10, Y: 10, Width: 300, Height: 400},
		},
		{
			Name:        "text",
			Confidence:  0.88,
			BoundingBox: BoundingBox{X: 20, Y: 30, Width: 280, Height: 200},
		},
		{
			Name:        "logo",
			Confidence:  0.72,
			BoundingBox: BoundingBox{X: 250, Y: 20, Width: 50, Height: 50},
		},
	}

	return objects
}

// detectFaces performs face detection on the image
func (s *ComputerVisionService) detectFaces(imageData []byte) []FaceDetection {
	// This is a simplified face detection implementation
	// In a real implementation, you would use:
	// - OpenCV Face Detection
	// - Dlib
	// - FaceNet
	// - Cloud face detection services

	// Simulate face detection
	faces := []FaceDetection{
		{
			Confidence:  0.92,
			BoundingBox: BoundingBox{X: 100, Y: 80, Width: 120, Height: 150},
			Age:         func() *int { age := 28; return &age }(),
			Gender:      func() *string { gender := "male"; return &gender }(),
			Emotion:     func() *string { emotion := "happy"; return &emotion }(),
		},
	}

	return faces
}

// extractDominantColors extracts the dominant colors from the image
func (s *ComputerVisionService) extractDominantColors(imageData []byte) []string {
	// This is a simplified color extraction
	// In a real implementation, you would use:
	// - K-means clustering
	// - Color histogram analysis
	// - Median cut algorithm

	// Simulate dominant colors
	colors := []string{
		"#FFFFFF", // White
		"#333333", // Dark gray
		"#0066CC", // Blue
		"#FF6600", // Orange
		"#00CC66", // Green
	}

	return colors
}

// saveImageAnalysis saves the analysis results to the database
func (s *ComputerVisionService) saveImageAnalysis(fileID uint, analysis *ImageAnalysisResponse) error {
	// Convert analysis to JSON for storage
	analysisJSON := fmt.Sprintf(`{
		"text": "%s",
		"object_count": %d,
		"face_count": %d,
		"metadata": %+v
	}`, analysis.Text, len(analysis.Objects), len(analysis.Faces), analysis.Metadata)

	// Create or update file analysis record
	var fileAnalysis models.FileAnalysis
	err := s.db.Where("file_id = ?", fileID).First(&fileAnalysis).Error

	if err == gorm.ErrRecordNotFound {
		// Create new analysis record
		now := time.Now()
		fileAnalysis = models.FileAnalysis{
			FileID:       fileID,
			AnalysisType: "computer_vision",
			Results:      analysisJSON,
			Confidence:   0.85,
			ProcessedAt:  &now,
		}
		return s.db.Create(&fileAnalysis).Error
	} else if err == nil {
		// Update existing record
		fileAnalysis.Results = analysisJSON
		now := time.Now()
		fileAnalysis.ProcessedAt = &now
		return s.db.Save(&fileAnalysis).Error
	}

	return err
}

// ProcessDocumentImage processes a document image for text extraction and structure
func (s *ComputerVisionService) ProcessDocumentImage(imageData []byte) (*DocumentAnalysis, error) {
	// Extract text using OCR
	text, err := s.extractText(imageData)
	if err != nil {
		return nil, err
	}

	// Analyze document structure
	analysis := &DocumentAnalysis{
		Text:         text,
		WordCount:    len(strings.Fields(text)),
		LineCount:    len(strings.Split(text, "\n")),
		Language:     s.detectLanguage(text),
		DocumentType: s.detectDocumentType(text),
		Sections:     s.extractSections(text),
		Tables:       s.extractTables(text),
		Links:        s.extractLinks(text),
		Emails:       s.extractEmails(text),
		PhoneNumbers: s.extractPhoneNumbers(text),
	}

	return analysis, nil
}

// DocumentAnalysis represents the analysis of a document image
type DocumentAnalysis struct {
	Text         string            `json:"text"`
	WordCount    int               `json:"word_count"`
	LineCount    int               `json:"line_count"`
	Language     string            `json:"language"`
	DocumentType string            `json:"document_type"`
	Sections     []DocumentSection `json:"sections"`
	Tables       []DocumentTable   `json:"tables"`
	Links        []string          `json:"links"`
	Emails       []string          `json:"emails"`
	PhoneNumbers []string          `json:"phone_numbers"`
}

// DocumentSection represents a section in a document
type DocumentSection struct {
	Title   string `json:"title"`
	Content string `json:"content"`
	Level   int    `json:"level"`
}

// DocumentTable represents a table in a document
type DocumentTable struct {
	Headers []string   `json:"headers"`
	Rows    [][]string `json:"rows"`
}

// detectLanguage detects the language of the text
func (s *ComputerVisionService) detectLanguage(text string) string {
	// Simplified language detection
	// In a real implementation, you would use:
	// - Language detection libraries
	// - Machine learning models
	// - Cloud language detection services

	if strings.Contains(strings.ToLower(text), "the") && strings.Contains(strings.ToLower(text), "and") {
		return "en"
	} else if strings.Contains(text, "est") && strings.Contains(text, "que") {
		return "es"
	} else if strings.Contains(text, "und") && strings.Contains(text, "der") {
		return "de"
	}

	return "unknown"
}

// detectDocumentType detects the type of document
func (s *ComputerVisionService) detectDocumentType(text string) string {
	text = strings.ToLower(text)

	if strings.Contains(text, "invoice") || strings.Contains(text, "bill") {
		return "invoice"
	} else if strings.Contains(text, "receipt") || strings.Contains(text, "purchase") {
		return "receipt"
	} else if strings.Contains(text, "resume") || strings.Contains(text, "curriculum") {
		return "resume"
	} else if strings.Contains(text, "contract") || strings.Contains(text, "agreement") {
		return "contract"
	} else if strings.Contains(text, "report") || strings.Contains(text, "analysis") {
		return "report"
	}

	return "general"
}

// extractSections extracts document sections
func (s *ComputerVisionService) extractSections(text string) []DocumentSection {
	var sections []DocumentSection
	lines := strings.Split(text, "\n")

	for _, line := range lines {
		line = strings.TrimSpace(line)
		if line == "" {
			continue
		}

		// Simple section detection (headers followed by content)
		if len(line) < 100 && (strings.HasSuffix(line, ":") || strings.ToUpper(line) == line) {
			sections = append(sections, DocumentSection{
				Title:   line,
				Content: "",
				Level:   1,
			})
		}
	}

	return sections
}

// extractTables extracts tables from the text
func (s *ComputerVisionService) extractTables(text string) []DocumentTable {
	// Simplified table extraction
	// In a real implementation, this would be much more sophisticated
	var tables []DocumentTable

	// Look for tabular data patterns
	lines := strings.Split(text, "\n")
	for i, line := range lines {
		if strings.Contains(line, "\t") || strings.Contains(line, "  ") {
			// Potential table row
			if i > 0 && strings.Contains(lines[i-1], "\t") {
				// Multiple consecutive rows with tabs - likely a table
				table := DocumentTable{
					Headers: strings.Split(lines[i-1], "\t"),
					Rows:    [][]string{strings.Split(line, "\t")},
				}
				tables = append(tables, table)
			}
		}
	}

	return tables
}

// extractLinks extracts URLs from the text
func (s *ComputerVisionService) extractLinks(text string) []string {
	urlRegex := regexp.MustCompile(`https?://[^\s]+`)
	return urlRegex.FindAllString(text, -1)
}

// extractEmails extracts email addresses from the text
func (s *ComputerVisionService) extractEmails(text string) []string {
	emailRegex := regexp.MustCompile(`[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}`)
	return emailRegex.FindAllString(text, -1)
}

// extractPhoneNumbers extracts phone numbers from the text
func (s *ComputerVisionService) extractPhoneNumbers(text string) []string {
	phoneRegex := regexp.MustCompile(`\b\d{3}[-.]?\d{3}[-.]?\d{4}\b`)
	return phoneRegex.FindAllString(text, -1)
}

// CreateFileAnalysis creates a file analysis record
func (s *ComputerVisionService) CreateFileAnalysis(fileID uint, analysisType, results string, confidence float64) error {
	now := time.Now()
	fileAnalysis := models.FileAnalysis{
		FileID:       fileID,
		AnalysisType: analysisType,
		Results:      results,
		Confidence:   confidence,
		ProcessedAt:  &now,
	}

	return s.db.Create(&fileAnalysis).Error
}