package enhancement

import (
	"context"
	"fmt"
	"image"
	"image/color"
	"strings"
	"time"

	"git.leaktechnologies.dev/stu/VideoTools/internal/logging"
	"git.leaktechnologies.dev/stu/VideoTools/internal/player"
	"git.leaktechnologies.dev/stu/VideoTools/internal/utils"
)

// AIModel interface defines the contract for video enhancement models
type AIModel interface {
	Name() string
	Type() string // "basicvsr", "realesrgan", "rife", "realcugan"
	Load() error
	ProcessFrame(frame *image.RGBA) (*image.RGBA, error)
	Close() error
}

// SkinToneAnalysis represents detailed skin tone analysis for enhancement
type SkinToneAnalysis struct {
	DetectedSkinTones  []string // List of detected skin tones
	SkinSaturation     float64  // 0.0-1.0
	SkinBrightness     float64  // 0.0-1.0
	SkinWarmth         float64  // -1.0 to 1.0 (negative=cool, positive=warm)
	SkinContrast       float64  // 0.0-2.0 (1.0=normal)
	DetectedHemoglobin []string // Detected hemoglobin levels/characteristics
	IsAdultContent     bool     // Whether adult content was detected
	RecommendedProfile string   // Recommended enhancement profile
}

// ContentAnalysis represents video content analysis results
type ContentAnalysis struct {
	Type       string  // "general", "anime", "film", "interlaced", "adult"
	Quality    float64 // 0.0-1.0
	Resolution int64
	FrameRate  float64
	Artifacts  []string          // ["noise", "compression", "film_grain", "skin_tones"]
	Confidence float64           // AI model confidence in analysis
	SkinTones  *SkinToneAnalysis // Detailed skin analysis
}

// EnhancementConfig configures the enhancement process
type EnhancementConfig struct {
	Model             string                 // AI model name (auto, basicvsr, realesrgan, etc.)
	TargetResolution  string                 // target resolution (match_source, 720p, 1080p, 4K, etc.)
	QualityPreset     string                 // fast, balanced, high
	ContentDetection  bool                   // enable content-aware processing
	GPUAcceleration   bool                   // use GPU acceleration if available
	TileSize          int                    // tile size for memory-efficient processing
	PreviewMode       bool                   // enable real-time preview
	PreserveSkinTones bool                   // preserve natural skin tones (red/pink) instead of washing out
	SkinToneMode      string                 // off, conservative, balanced, professional
	AdultContent      bool                   // enable adult content optimization
	Parameters        map[string]interface{} // model-specific parameters
}

// EnhancementProgress tracks enhancement progress
type EnhancementProgress struct {
	CurrentFrame    int64
	TotalFrames     int64
	PercentComplete float64
	CurrentTask     string
	EstimatedTime   time.Duration
	PreviewImage    *image.RGBA
}

// EnhancementCallbacks for progress updates and UI integration
type EnhancementCallbacks struct {
	OnProgress      func(progress EnhancementProgress)
	OnPreviewUpdate func(frame int64, img image.Image)
	OnComplete      func(success bool, message string)
	OnError         func(err error)
}

// EnhancementModule provides unified video enhancement combining Filters + Upscale
// with content-aware processing and AI model management
type EnhancementModule struct {
	player       player.VTPlayer // Unified player for frame extraction
	config       EnhancementConfig
	callbacks    EnhancementCallbacks
	currentModel AIModel
	analysis     *ContentAnalysis
	progress     EnhancementProgress
	ctx          context.Context
	cancel       context.CancelFunc

	// Processing state
	active     bool
	inputPath  string
	outputPath string
	tempDir    string
}

// NewEnhancementModule creates a new enhancement module instance
func NewEnhancementModule(player player.VTPlayer) *EnhancementModule {
	ctx, cancel := context.WithCancel(context.Background())

	return &EnhancementModule{
		player: player,
		config: EnhancementConfig{
			Model:            "auto",
			TargetResolution: "match_source",
			QualityPreset:    "balanced",
			ContentDetection: true,
			GPUAcceleration:  true,
			TileSize:         512,
			PreviewMode:      false,
			Parameters:       make(map[string]interface{}),
		},
		callbacks: EnhancementCallbacks{},
		ctx:       ctx,
		cancel:    cancel,
		progress:  EnhancementProgress{},
	}
}

// AnalyzeContent performs intelligent content analysis using FFmpeg
func (m *EnhancementModule) AnalyzeContent(path string) (*ContentAnalysis, error) {
	logging.Debug(logging.CatEnhance, "Starting content analysis for: %s", path)

	// Use FFprobe to get video information
	cmd := utils.CreateCommand(m.ctx, utils.GetFFprobePath(),
		"-v", "error",
		"-select_streams", "v:0",
		"-show_entries", "stream=r_frame_rate,width,height,duration,bit_rate,pix_fmt",
		"-show_entries", "format=format_name,duration",
		path,
	)

	output, err := cmd.CombinedOutput()
	if err != nil {
		return nil, fmt.Errorf("content analysis failed: %w", err)
	}

	// Parse FFprobe output to extract video characteristics
	contentAnalysis := &ContentAnalysis{
		Type:       m.detectContentType(path, output),
		Quality:    m.estimateQuality(output),
		Resolution: 1920, // Default, will be updated from FFprobe output
		FrameRate:  30.0, // Default, will be updated from FFprobe output
		Artifacts:  m.detectArtifacts(output),
		Confidence: 0.8, // Default confidence
	}

	// TODO: Implement advanced skin tone analysis with melanin/hemoglobin detection
	// For now, use default skin analysis
	
	// Advanced skin analysis for Phase 2.5
	advancedSkinAnalysis := m.analyzeSkinTonesAdvanced(output)

	// Update content analysis with advanced skin tone information
	contentAnalysis.SkinTones = advancedSkinAnalysis

	logging.Debug(logging.CatEnhance, "Advanced skin analysis applied: %+v", advancedSkinAnalysis)
	return contentAnalysis, nil
}

// analyzeSkinTonesAdvanced performs sophisticated skin analysis for Phase 2.5
func (m *EnhancementModule) analyzeSkinTonesAdvanced(ffprobeOutput []byte) *SkinToneAnalysis {
	// Default analysis for when content detection is disabled
	if !m.config.ContentDetection {
		return &SkinToneAnalysis{
			DetectedSkinTones:    []string{"neutral"}, // Default tone
			SkinSaturation:       0.5,                 // Average saturation
			SkinBrightness:    0.5,                 // Average brightness
			SkinWarmth:         0.0,                 // Neutral warmth
			SkinContrast:         1.0,                 // Normal contrast
			DetectedHemoglobin: []string{"unknown"}, // Would be analyzed from frames
			IsAdultContent:      false,              // Default until frame analysis
			RecommendedProfile:  "balanced",          // Default enhancement profile
		}
	}
	
	// Parse FFprobe output for advanced skin analysis (placeholder for future use)
	_ = strings.Split(string(ffprobeOutput), "\n")
	
	// Initialize advanced analysis structure
	analysis := &SkinToneAnalysis{
		DetectedSkinTones:    []string{},    // Will be detected from frames
		SkinSaturation:       0.5,        // Average saturation
		SkinBrightness:    0.5,        // Average brightness
		SkinWarmth:         0.0,        // Neutral warmth
		SkinContrast:         1.0,        // Normal contrast
		DetectedHemoglobin:  []string{},    // Would be analyzed from frames
		IsAdultContent:      false,        // Default until frame analysis
		RecommendedProfile:  "balanced",        // Default enhancement profile
	}
	
	// TODO: Advanced frame-by-frame skin tone detection would use:
	// - frameCount for tracking processed frames
	// - skinToneHistogram for tone distribution
	// - totalSaturation, totalBrightness, totalWarmth, totalCoolness for averages
	// This will be implemented when video frame processing is added

	return analysis
}

// detectContentType determines if content is anime, film, or general
func (m *EnhancementModule) detectContentType(path string, ffprobeOutput []byte) string {
	// Simple heuristic-based detection
	pathLower := strings.ToLower(path)

	if strings.Contains(pathLower, "anime") || strings.Contains(pathLower, "manga") {
		return "anime"
	}

	// TODO: Implement more sophisticated content detection
	// Could use frame analysis, motion patterns, etc.
	return "general"
}

// estimateQuality estimates video quality from technical parameters
func (m *EnhancementModule) estimateQuality(ffprobeOutput []byte) float64 {
	// TODO: Implement quality estimation based on:
	// - Bitrate vs resolution ratio
	// - Compression artifacts
	// - Frame consistency
	return 0.7 // Default reasonable quality
}

// detectArtifacts identifies compression and quality artifacts
func (m *EnhancementModule) detectArtifacts(ffprobeOutput []byte) []string {
	// TODO: Implement artifact detection for:
	// - Compression blocking
	// - Color banding
	// - Noise patterns
	// - Film grain
	return []string{"compression"} // Default
}

// SelectModel chooses the optimal AI model based on content analysis
func (m *EnhancementModule) SelectModel(analysis *ContentAnalysis) string {
	if m.config.Model != "auto" {
		return m.config.Model
	}

	switch analysis.Type {
	case "anime":
		return "realesrgan-x4plus-anime" // Anime-optimized
	case "film":
		return "basicvsr" // Film restoration
	case "adult":
		// Adult content optimization - preserve natural tones
		if analysis.SkinTones != nil {
			switch m.config.SkinToneMode {
			case "professional", "conservative":
				return "realesrgan-x4plus-skin-preserve"
			case "balanced":
				return "realesrgan-x4plus-skin-enhance"
			default:
				return "realesrgan-x4plus-anime" // Fallback to anime model
			}
		}
		return "realesrgan-x4plus-skin-preserve" // Default for adult content
	default:
		return "realesrgan-x4plus" // General purpose
	}
}

// ProcessVideo processes video through the enhancement pipeline
func (m *EnhancementModule) ProcessVideo(inputPath, outputPath string) error {
	logging.Debug(logging.CatEnhance, "Starting video enhancement: %s -> %s", inputPath, outputPath)

	m.inputPath = inputPath
	m.outputPath = outputPath
	m.active = true

	// Analyze content first
	analysis, err := m.AnalyzeContent(inputPath)
	if err != nil {
		return fmt.Errorf("content analysis failed: %w", err)
	}

	m.analysis = analysis

	// Select appropriate model
	modelName := m.SelectModel(analysis)
	logging.Debug(logging.CatEnhance, "Selected model: %s for content type: %s", modelName, analysis.Type)

	// Load the AI model
	model, err := m.loadModel(modelName)
	if err != nil {
		return fmt.Errorf("failed to load model %s: %w", modelName, err)
	}

	m.currentModel = model
	defer model.Close()

	// Load video in unified player
	err = m.player.Load(inputPath, 0)
	if err != nil {
		return fmt.Errorf("failed to load video: %w", err)
	}
	defer m.player.Close()

	// Get video info
	videoInfo := m.player.GetVideoInfo()
	m.progress.TotalFrames = videoInfo.FrameCount
	m.progress.CurrentFrame = 0
	m.progress.PercentComplete = 0.0

	// Process frame by frame
	for m.active && m.progress.CurrentFrame < m.progress.TotalFrames {
		select {
		case <-m.ctx.Done():
			return fmt.Errorf("enhancement cancelled")
		default:
			// Extract current frame from player
			frame, err := m.extractCurrentFrame()
			if err != nil {
				logging.Error(logging.CatEnhance, "Frame extraction failed: %v", err)
				continue
			}

			// Apply AI enhancement to frame
			enhancedFrame, err := m.currentModel.ProcessFrame(frame)
			if err != nil {
				logging.Error(logging.CatEnhance, "Frame enhancement failed: %v", err)
				continue
			}

			// Update progress
			m.progress.CurrentFrame++
			m.progress.PercentComplete = float64(m.progress.CurrentFrame) / float64(m.progress.TotalFrames)
			m.progress.CurrentTask = fmt.Sprintf("Processing frame %d/%d", m.progress.CurrentFrame, m.progress.TotalFrames)

			// Send preview update if enabled
			if m.config.PreviewMode && m.callbacks.OnPreviewUpdate != nil {
				m.callbacks.OnPreviewUpdate(m.progress.CurrentFrame, enhancedFrame)
			}

			// Send progress update
			if m.callbacks.OnProgress != nil {
				m.callbacks.OnProgress(m.progress)
			}
		}
	}

	// Reassemble enhanced video from frames
	err = m.reassembleEnhancedVideo()
	if err != nil {
		return fmt.Errorf("video reassembly failed: %w", err)
	}

	// Call completion callback
	if m.callbacks.OnComplete != nil {
		m.callbacks.OnComplete(true, fmt.Sprintf("Enhancement completed using %s model", modelName))
	}

	m.active = false
	logging.Debug(logging.CatEnhance, "Video enhancement completed successfully")
	return nil
}

// loadModel instantiates and returns an AI model instance
func (m *EnhancementModule) loadModel(modelName string) (AIModel, error) {
	switch modelName {
	case "basicvsr":
		return NewBasicVSRModel(m.config.Parameters)
	case "realesrgan-x4plus":
		return NewRealESRGANModel(m.config.Parameters)
	case "realesrgan-x4plus-anime":
		return NewRealESRGANAnimeModel(m.config.Parameters)
	default:
		return nil, fmt.Errorf("unsupported model: %s", modelName)
	}
}

// Placeholder model constructors - will be implemented in Phase 2.2
func NewBasicVSRModel(params map[string]interface{}) (AIModel, error) {
	return &placeholderModel{name: "basicvsr"}, nil
}

func NewRealESRGANModel(params map[string]interface{}) (AIModel, error) {
	return &placeholderModel{name: "realesrgan-x4plus"}, nil
}

func NewRealESRGANAnimeModel(params map[string]interface{}) (AIModel, error) {
	return &placeholderModel{name: "realesrgan-x4plus-anime"}, nil
}

// placeholderModel implements AIModel interface for development
type placeholderModel struct {
	name string
}

func (p *placeholderModel) Name() string {
	return p.name
}

func (p *placeholderModel) Type() string {
	return "placeholder"
}

func (p *placeholderModel) Load() error {
	return nil
}

func (p *placeholderModel) ProcessFrame(frame *image.RGBA) (*image.RGBA, error) {
	// TODO: Implement actual AI processing
	return frame, nil
}

func (p *placeholderModel) Close() error {
	return nil
}

// extractCurrentFrame extracts the current frame from the unified player
func (m *EnhancementModule) extractCurrentFrame() (*image.RGBA, error) {
	// Interface with the unified player's frame extraction
	// The unified player should provide frame access methods

	// For now, simulate frame extraction from player
	// In full implementation, this would call m.player.ExtractCurrentFrame()

	// Create a dummy frame for testing
	frame := image.NewRGBA(image.Rect(0, 0, 1920, 1080))

	// Fill with a test pattern
	for y := 0; y < 1080; y++ {
		for x := 0; x < 1920; x++ {
			// Create a simple gradient pattern
			frame.Set(x, y, color.RGBA{
				R: uint8(x / 8),
				G: uint8(y / 8),
				B: uint8(255),
				A: 255,
			})
		}
	}

	return frame, nil
}

// reassembleEnhancedVideo reconstructs the video from enhanced frames
func (m *EnhancementModule) reassembleEnhancedVideo() error {
	// This will use FFmpeg to reconstruct video from enhanced frames
	// Implementation will use the temp directory for frame storage
	return fmt.Errorf("video reassembly not yet implemented")
}

// Cancel stops the enhancement process
func (m *EnhancementModule) Cancel() {
	if m.active {
		m.active = false
		m.cancel()
		logging.Debug(logging.CatEnhance, "Enhancement cancelled")
	}
}

// SetConfig updates the enhancement configuration
func (m *EnhancementModule) SetConfig(config EnhancementConfig) {
	m.config = config
}

// GetConfig returns the current enhancement configuration
func (m *EnhancementModule) GetConfig() EnhancementConfig {
	return m.config
}

// SetCallbacks sets the enhancement progress callbacks
func (m *EnhancementModule) SetCallbacks(callbacks EnhancementCallbacks) {
	m.callbacks = callbacks
}

// GetProgress returns current enhancement progress
func (m *EnhancementModule) GetProgress() EnhancementProgress {
	return m.progress
}

// IsActive returns whether enhancement is currently running
func (m *EnhancementModule) IsActive() bool {
	return m.active
}
