nsfwapp/backend/rating.go

// backend\rating.go

package main

import (
	"math"
	"sort"
	"strings"
)

type aiRatingMeta struct {
	Score                  float64 `json:"score"`
	ActionScore            float64 `json:"actionScore"`
	Stars                  int     `json:"stars"`
	Segments               int     `json:"segments"`
	SegmentsPerMinute      float64 `json:"segmentsPerMinute"`
	FlaggedSeconds         float64 `json:"flaggedSeconds"`
	WeightedFlaggedSeconds float64 `json:"weightedFlaggedSeconds"`
	CoverageRatio          float64 `json:"coverageRatio"`
	WeightedCoverageRatio  float64 `json:"weightedCoverageRatio"`
	LongestSegmentSeconds  float64 `json:"longestSegmentSeconds"`
	AvgConfidence          float64 `json:"avgConfidence"`
}

const (
	// Normales Zusammenziehen sehr kurzer Pausen.
	ratingMaxSilentGapSec = 4.5

	// Größere Lücken dürfen überbrückt werden,
	// wenn beide Seiten stark genug / thematisch ähnlich sind.
	ratingBridgeStrongGapSec = 12.0

	ratingMinSegmentDurationSec = 2.5
)

func ratingClamp01(v float64) float64 {
	if v < 0 {
		return 0
	}
	if v > 1 {
		return 1
	}
	return v
}

func ratingRound(v float64, places int) float64 {
	p := math.Pow(10, float64(places))
	return math.Round(v*p) / p
}

func ratingSmoothStep(v float64) float64 {
	v = ratingClamp01(v)
	return v * v * (3 - 2*v)
}

func ratingSaturatingEvidence(value, scale float64) float64 {
	if value <= 0 || scale <= 0 {
		return 0
	}
	return ratingClamp01(1 - math.Exp(-value/scale))
}

func ratingEffectiveDurationSeconds(seconds float64) float64 {
	if seconds <= 0 {
		return 0
	}

	// Lange Segmente zählen, aber nicht linear unendlich stark.
	const kneeSeconds = 26.0
	return kneeSeconds * math.Log1p(seconds/kneeSeconds)
}

func normalizeSegmentLabel(label string) string {
	label = strings.ToLower(strings.TrimSpace(label))

	label = strings.TrimPrefix(label, "detector:")
	label = strings.TrimPrefix(label, "body:")
	label = strings.TrimPrefix(label, "object:")
	label = strings.TrimPrefix(label, "clothing:")
	label = strings.TrimPrefix(label, "position:")
	label = strings.TrimPrefix(label, "person:")

	return strings.TrimSpace(label)
}

func isPersonSegmentLabel(label string) bool {
	label = strings.ToLower(strings.TrimSpace(label))

	label = strings.TrimPrefix(label, "detector:")
	label = strings.TrimPrefix(label, "body:")
	label = strings.TrimPrefix(label, "object:")
	label = strings.TrimPrefix(label, "clothing:")
	label = strings.TrimPrefix(label, "position:")
	label = strings.TrimPrefix(label, "person:")

	switch label {
	case "person",
		"person_unknown",
		"person_male",
		"person_female",
		"male_person",
		"female_person",
		"people_male",
		"people_female":
		return true
	default:
		return false
	}
}

func isKnownPositionLabel(label string) bool {
	label = strings.ToLower(strings.TrimSpace(label))

	switch label {
	case "unknown",
		"missionary",
		"doggy",
		"doggystyle",
		"cowgirl",
		"reverse_cowgirl",
		"cunnilingus",
		"prone_bone",
		"standing",
		"standing_doggy",
		"spooning",
		"facesitting",
		"handjob",
		"blowjob",
		"boobjob",
		"toy_play",
		"fingering",
		"69":
		return true
	default:
		return false
	}
}

func positionSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))

	switch label {
	case "doggy", "doggystyle", "standing_doggy":
		return 1.00
	case "cowgirl", "reverse_cowgirl":
		return 0.98
	case "missionary", "prone_bone":
		return 0.95
	case "blowjob", "cunnilingus", "69", "facesitting", "boobjob":
		return 0.94
	case "toy_play":
		return 0.88
	case "handjob", "fingering":
		return 0.84
	case "spooning":
		return 0.78
	case "standing":
		return 0.42
	default:
		return 0.00
	}
}

func bodyPartSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))

	switch label {
	case "pussy", "vulva":
		return 1.00
	case "penis":
		return 0.95
	case "anus":
		return 0.90
	case "breasts":
		return 0.80
	case "ass", "buttocks":
		return 0.66
	case "tongue":
		return 0.46
	default:
		return 0.00
	}
}

func objectSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))

	switch label {
	case "dildo", "vibrator", "strapon", "buttplug":
		return 0.86
	case "handcuffs", "blindfold", "collar":
		return 0.58
	case "shower":
		return 0.42
	case "towel":
		return 0.28
	default:
		return 0.00
	}
}

func clothingSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))

	switch label {
	case "lingerie":
		return 0.50
	case "panties", "bra":
		return 0.44
	case "bikini":
		return 0.30
	case "stockings", "heels":
		return 0.28
	case "skirt", "dress", "hotpants", "croptop":
		return 0.22
	default:
		return 0.00
	}
}

func personContextWeight(label string) float64 {
	if !isPersonSegmentLabel(label) {
		return 0
	}

	// Personen sind Kontext, aber nie alleiniger Rating-Treiber.
	return 0.18
}

func detectorSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))

	if isPersonSegmentLabel(label) {
		return personContextWeight(label)
	}

	if w := bodyPartSeverityWeight(label); w > 0 {
		return w
	}
	if w := objectSeverityWeight(label); w > 0 {
		return w
	}
	if w := clothingSeverityWeight(label); w > 0 {
		return w
	}
	if isKnownPositionLabel(label) {
		return positionSeverityWeight(label)
	}

	return 0.00
}

type ratingSignalSet struct {
	Position float64
	Body     float64
	Object   float64
	Clothing float64
	Person   float64

	HasPosition bool
	HasBody     bool
	HasObject   bool
	HasClothing bool
	HasPerson   bool
}

func normalizeRatingSignalLabel(label string) string {
	label = strings.ToLower(strings.TrimSpace(label))
	if label == "" || label == "unknown" {
		return ""
	}

	if strings.HasPrefix(label, "combo:") {
		return label
	}

	if strings.HasPrefix(label, "detector:") {
		return normalizeRatingSignalLabel(strings.TrimPrefix(label, "detector:"))
	}

	if strings.HasPrefix(label, "position:") {
		raw := strings.TrimPrefix(label, "position:")
		if isKnownPositionLabel(raw) && positionSeverityWeight(raw) > 0 {
			return "position:" + raw
		}
		return ""
	}

	if strings.HasPrefix(label, "body:") {
		raw := strings.TrimPrefix(label, "body:")
		if bodyPartSeverityWeight(raw) > 0 {
			return "body:" + raw
		}
		return ""
	}

	if strings.HasPrefix(label, "object:") {
		raw := strings.TrimPrefix(label, "object:")
		if objectSeverityWeight(raw) > 0 {
			return "object:" + raw
		}
		return ""
	}

	if strings.HasPrefix(label, "clothing:") {
		raw := strings.TrimPrefix(label, "clothing:")
		if clothingSeverityWeight(raw) > 0 {
			return "clothing:" + raw
		}
		return ""
	}

	if strings.HasPrefix(label, "person:") {
		raw := strings.TrimPrefix(label, "person:")
		if isPersonSegmentLabel(raw) {
			return "person:" + normalizeSegmentLabel(raw)
		}
		return ""
	}

	raw := normalizeSegmentLabel(label)
	if raw == "" || raw == "unknown" {
		return ""
	}

	if isPersonSegmentLabel(raw) {
		return "person:" + raw
	}
	if isKnownPositionLabel(raw) && positionSeverityWeight(raw) > 0 {
		return "position:" + raw
	}
	if bodyPartSeverityWeight(raw) > 0 {
		return "body:" + raw
	}
	if objectSeverityWeight(raw) > 0 {
		return "object:" + raw
	}
	if clothingSeverityWeight(raw) > 0 {
		return "clothing:" + raw
	}

	return ""
}

func ratingSignalSetAddLabel(set *ratingSignalSet, label string) {
	label = normalizeRatingSignalLabel(label)
	if label == "" {
		return
	}

	switch {
	case strings.HasPrefix(label, "position:"):
		raw := strings.TrimPrefix(label, "position:")
		w := positionSeverityWeight(raw)
		if w > set.Position {
			set.Position = w
		}
		if w > 0 {
			set.HasPosition = true
		}

	case strings.HasPrefix(label, "body:"):
		raw := strings.TrimPrefix(label, "body:")
		w := bodyPartSeverityWeight(raw)
		if w > set.Body {
			set.Body = w
		}
		if w > 0 {
			set.HasBody = true
		}

	case strings.HasPrefix(label, "object:"):
		raw := strings.TrimPrefix(label, "object:")
		w := objectSeverityWeight(raw)
		if w > set.Object {
			set.Object = w
		}
		if w > 0 {
			set.HasObject = true
		}

	case strings.HasPrefix(label, "clothing:"):
		raw := strings.TrimPrefix(label, "clothing:")
		w := clothingSeverityWeight(raw)
		if w > set.Clothing {
			set.Clothing = w
		}
		if w > 0 {
			set.HasClothing = true
		}

	case strings.HasPrefix(label, "person:"):
		raw := strings.TrimPrefix(label, "person:")
		w := personContextWeight(raw)
		if w > set.Person {
			set.Person = w
		}
		if w > 0 {
			set.HasPerson = true
		}
	}
}

func ratingSignalSetFromLabel(label string) ratingSignalSet {
	label = strings.ToLower(strings.TrimSpace(label))

	var set ratingSignalSet

	if strings.HasPrefix(label, "combo:") {
		raw := strings.TrimPrefix(label, "combo:")
		for _, part := range strings.Split(raw, "+") {
			ratingSignalSetAddLabel(&set, part)
		}
		return set
	}

	ratingSignalSetAddLabel(&set, label)
	return set
}

func ratingSignalSetHasInterestingContent(set ratingSignalSet) bool {
	return set.HasPosition || set.HasBody || set.HasObject || set.HasClothing
}

func contextualSegmentSeverityWeight(label string) float64 {
	set := ratingSignalSetFromLabel(label)
	return contextualSegmentSeverityWeightFromSet(set)
}

func comboSegmentSeverityWeight(label string) float64 {
	set := ratingSignalSetFromLabel(label)
	return contextualSegmentSeverityWeightFromSet(set)
}

func contextualSegmentSeverityWeightFromSet(set ratingSignalSet) float64 {
	if !ratingSignalSetHasInterestingContent(set) {
		return 0
	}

	var labelParts []string

	if set.HasPosition {
		labelParts = append(labelParts, "position:x")
	}
	if set.HasBody {
		labelParts = append(labelParts, "body:x")
	}
	if set.HasObject {
		labelParts = append(labelParts, "object:x")
	}
	if set.HasClothing {
		labelParts = append(labelParts, "clothing:x")
	}
	if set.HasPerson {
		labelParts = append(labelParts, "person:x")
	}

	_ = labelParts

	var score float64

	if set.HasPosition {
		// Position soll das Hauptkriterium sein.
		// Sexuelle Körperteile und explizite Kleidung verstärken die Position.
		score =
			0.82*set.Position +
				0.12*set.Body +
				0.08*set.Clothing +
				0.015*set.Object +
				0.005*set.Person

		if set.HasBody {
			score += 0.030
		}
		if set.HasClothing {
			score += 0.020
		}
		if set.HasObject {
			score += 0.006
		}
		if set.HasPerson {
			score += 0.002
		}

		// Standing ohne echten Kontext klar schwächer halten.
		if set.Position < 0.60 && !set.HasBody && !set.HasObject && !set.HasClothing {
			score = math.Min(score, 0.32)
		}

		return ratingClamp01(score)
	}

	score =
		0.54*set.Body +
			0.28*set.Clothing +
			0.16*set.Object +
			0.02*set.Person

	if set.HasBody && set.HasClothing {
		score += 0.05
	}
	if set.HasBody && set.HasObject {
		score += 0.04
	}
	if set.HasClothing && set.HasObject {
		score += 0.03
	}
	if set.HasPerson && (set.HasBody || set.HasObject || set.HasClothing) {
		score += 0.015
	}

	if set.HasClothing && !set.HasBody && !set.HasObject {
		score = math.Min(score, 0.38)
	}

	score = math.Min(score, 0.72)

	return ratingClamp01(score)
}

func segmentSeverityWeight(label string) float64 {
	label = strings.ToLower(strings.TrimSpace(label))
	if label == "" {
		return 0
	}

	if strings.HasPrefix(label, "combo:") {
		return comboSegmentSeverityWeight(label)
	}

	normalized := normalizeRatingSignalLabel(label)
	if normalized == "" {
		return 0
	}

	set := ratingSignalSetFromLabel(normalized)
	sev := contextualSegmentSeverityWeightFromSet(set)
	if sev > 0 {
		return sev
	}

	raw := normalizeSegmentLabel(normalized)
	if raw == "" {
		return 0
	}

	return detectorSeverityWeight(raw)
}

func ratingSegmentSeverity(label string) float64 {
	return segmentSeverityWeight(label)
}

func ratingSignalPriority(label string) int {
	label = normalizeRatingSignalLabel(label)

	switch {
	case strings.HasPrefix(label, "position:"):
		return 100
	case strings.HasPrefix(label, "body:"):
		return 80
	case strings.HasPrefix(label, "object:"):
		return 60
	case strings.HasPrefix(label, "clothing:"):
		return 40
	case strings.HasPrefix(label, "person:"):
		return 20
	default:
		return 0
	}
}

func buildRatingComboLabel(labels map[string]bool) string {
	if len(labels) == 0 {
		return ""
	}

	parts := make([]string, 0, len(labels))
	set := ratingSignalSet{}

	for label := range labels {
		normalized := normalizeRatingSignalLabel(label)
		if normalized == "" {
			continue
		}

		ratingSignalSetAddLabel(&set, normalized)
		parts = append(parts, normalized)
	}

	if !ratingSignalSetHasInterestingContent(set) {
		return ""
	}

	sort.SliceStable(parts, func(i, j int) bool {
		pi := ratingSignalPriority(parts[i])
		pj := ratingSignalPriority(parts[j])

		if pi != pj {
			return pi > pj
		}

		wi := ratingSegmentSeverity(parts[i])
		wj := ratingSegmentSeverity(parts[j])

		if wi != wj {
			return wi > wj
		}

		return parts[i] < parts[j]
	})

	deduped := make([]string, 0, len(parts))
	seen := map[string]bool{}

	for _, part := range parts {
		if part == "" || seen[part] {
			continue
		}
		seen[part] = true
		deduped = append(deduped, part)
	}

	if len(deduped) == 0 {
		return ""
	}

	if len(deduped) == 1 {
		// Person alleine wird oben schon verhindert.
		return deduped[0]
	}

	return "combo:" + strings.Join(deduped, "+")
}

func addRatingLabelsFromSegment(labels map[string]bool, label string) {
	label = strings.ToLower(strings.TrimSpace(label))
	if label == "" {
		return
	}

	if strings.HasPrefix(label, "combo:") {
		raw := strings.TrimPrefix(label, "combo:")
		for _, part := range strings.Split(raw, "+") {
			addRatingLabelsFromSegment(labels, part)
		}
		return
	}

	normalized := normalizeRatingSignalLabel(label)
	if normalized == "" {
		return
	}

	labels[normalized] = true
}

func ratingSignalSetFromLabels(labels map[string]bool) ratingSignalSet {
	var set ratingSignalSet

	for label := range labels {
		ratingSignalSetAddLabel(&set, label)
	}

	return set
}

func ratingBridgeStrengthFromSet(set ratingSignalSet) float64 {
	strength := 0.0

	if set.HasPosition && set.Position > strength {
		strength = set.Position
	}
	if set.HasBody && set.Body > strength {
		strength = set.Body
	}
	if set.HasObject && set.Object > strength {
		strength = set.Object
	}
	if set.HasClothing && set.Clothing > strength {
		strength = set.Clothing
	}

	return strength
}

func ratingPositionSetFromLabels(labels map[string]bool) map[string]bool {
	out := map[string]bool{}

	for label := range labels {
		normalized := normalizeRatingSignalLabel(label)
		if strings.HasPrefix(normalized, "position:") {
			out[normalized] = true
		}
	}

	return out
}

func ratingPositionSetsConflict(a map[string]bool, b map[string]bool) bool {
	ap := ratingPositionSetFromLabels(a)
	bp := ratingPositionSetFromLabels(b)

	if len(ap) == 0 || len(bp) == 0 {
		return false
	}

	for label := range ap {
		if bp[label] {
			return false
		}
	}

	return true
}

func ratingLabelsBridgeCompatible(a map[string]bool, b map[string]bool) bool {
	if ratingPositionSetsConflict(a, b) {
		return false
	}

	for la := range a {
		na := normalizeRatingSignalLabel(la)
		if na == "" {
			continue
		}

		for lb := range b {
			nb := normalizeRatingSignalLabel(lb)
			if nb == "" {
				continue
			}

			if na == nb {
				return true
			}
		}
	}

	as := ratingSignalSetFromLabels(a)
	bs := ratingSignalSetFromLabels(b)

	// Wichtig:
	// Unterschiedliche Positionen NICHT pauschal verbinden.
	// Vorher hat "Doggy" + "Reverse Cowgirl" als kompatibel gegolten,
	// nur weil beide irgendeine Position waren.

	if as.HasBody && bs.HasBody {
		return true
	}
	if as.HasObject && bs.HasObject {
		return true
	}
	if as.HasClothing && bs.HasClothing {
		return true
	}

	return false
}

func mergeRatingActivitySegments(
	segments []aiSegmentMeta,
	maxSilentGapSec float64,
	minDurationSec float64,
) []aiSegmentMeta {
	if len(segments) == 0 {
		return nil
	}

	valid := make([]aiSegmentMeta, 0, len(segments))

	for _, s := range segments {
		if strings.TrimSpace(s.Label) == "" {
			continue
		}

		start := s.StartSeconds
		end := s.EndSeconds

		if end <= start && s.DurationSeconds > 0 {
			end = start + s.DurationSeconds
		}
		if end <= start {
			continue
		}

		labels := map[string]bool{}
		addRatingLabelsFromSegment(labels, s.Label)

		if buildRatingComboLabel(labels) == "" {
			continue
		}

		s.StartSeconds = start
		s.EndSeconds = end
		s.DurationSeconds = end - start

		if ratingSegmentSeverity(s.Label) <= 0 {
			continue
		}

		valid = append(valid, s)
	}

	if len(valid) == 0 {
		return nil
	}

	sort.SliceStable(valid, func(i, j int) bool {
		if valid[i].StartSeconds != valid[j].StartSeconds {
			return valid[i].StartSeconds < valid[j].StartSeconds
		}
		if valid[i].EndSeconds != valid[j].EndSeconds {
			return valid[i].EndSeconds < valid[j].EndSeconds
		}
		return valid[i].Label < valid[j].Label
	})

	type segmentBounds struct {
		start float64
		end   float64
		ok    bool
	}

	type activityBlock struct {
		start           float64
		end             float64
		scoreSum        float64
		scoreWeight     float64
		labels          map[string]bool
		positionWeights map[string]float64
		positionBounds  map[string]segmentBounds
		positionRanges  map[string][]segmentBounds
		marker          float64
		markerWeight    float64
	}

	var addRatingPositionSignal func(
		weights map[string]float64,
		bounds map[string]segmentBounds,
		ranges map[string][]segmentBounds,
		label string,
		weight float64,
		start float64,
		end float64,
	)

	addRatingPositionSignal = func(
		weights map[string]float64,
		bounds map[string]segmentBounds,
		ranges map[string][]segmentBounds,
		label string,
		weight float64,
		start float64,
		end float64,
	) {
		label = strings.ToLower(strings.TrimSpace(label))
		if label == "" {
			return
		}

		if strings.HasPrefix(label, "combo:") {
			raw := strings.TrimPrefix(label, "combo:")
			for _, part := range strings.Split(raw, "+") {
				addRatingPositionSignal(weights, bounds, ranges, part, weight, start, end)
			}
			return
		}

		normalized := normalizeRatingSignalLabel(label)
		if !strings.HasPrefix(normalized, "position:") {
			return
		}

		if end < start {
			end = start
		}

		weights[normalized] += weight

		b := bounds[normalized]
		if !b.ok {
			b = segmentBounds{
				start: start,
				end:   end,
				ok:    true,
			}
		} else {
			if start < b.start {
				b.start = start
			}
			if end > b.end {
				b.end = end
			}
		}

		bounds[normalized] = b
		if end > start {
			ranges[normalized] = append(ranges[normalized], segmentBounds{
				start: start,
				end:   end,
				ok:    true,
			})
		}
	}

	effectiveRatingLabelsForBlock := func(
		labels map[string]bool,
		positionWeights map[string]float64,
	) (map[string]bool, string) {
		out := map[string]bool{}

		bestPosition := ""
		bestWeight := -1.0

		for label := range labels {
			normalized := normalizeRatingSignalLabel(label)
			if normalized == "" {
				continue
			}

			if strings.HasPrefix(normalized, "position:") {
				w := positionWeights[normalized]
				if w <= 0 {
					w = ratingSegmentSeverity(normalized)
				}

				if w > bestWeight {
					bestWeight = w
					bestPosition = normalized
				}

				continue
			}

			out[normalized] = true
		}

		if bestPosition != "" {
			out[bestPosition] = true
		}

		return out, bestPosition
	}

	segmentMarker := func(s aiSegmentMeta) float64 {
		if s.PreviewSeconds > 0 {
			return s.PreviewSeconds
		}
		if s.MarkerSeconds > 0 {
			return s.MarkerSeconds
		}
		if s.EndSeconds > s.StartSeconds {
			return (s.StartSeconds + s.EndSeconds) / 2
		}
		return s.StartSeconds
	}

	segmentMarkerWeight := func(s aiSegmentMeta) float64 {
		conf := ratingClamp01(s.Score)
		if conf <= 0 {
			conf = 0.50
		}

		sev := ratingSegmentSeverity(s.Label)
		if sev <= 0 {
			sev = 0.50
		}

		return conf * sev
	}

	newBlock := func(s aiSegmentMeta) activityBlock {
		labels := map[string]bool{}
		addRatingLabelsFromSegment(labels, s.Label)

		dur := s.EndSeconds - s.StartSeconds
		conf := ratingClamp01(s.Score)
		if conf <= 0 {
			conf = 0.50
		}

		positionWeights := map[string]float64{}
		positionBounds := map[string]segmentBounds{}
		positionRanges := map[string][]segmentBounds{}

		addRatingPositionSignal(
			positionWeights,
			positionBounds,
			positionRanges,
			s.Label,
			conf*math.Max(1, dur),
			s.StartSeconds,
			s.EndSeconds,
		)

		marker := segmentMarker(s)
		markerWeight := segmentMarkerWeight(s)

		return activityBlock{
			start:           s.StartSeconds,
			end:             s.EndSeconds,
			scoreSum:        conf * dur,
			scoreWeight:     dur,
			labels:          labels,
			positionWeights: positionWeights,
			positionBounds:  positionBounds,
			positionRanges:  positionRanges,
			marker:          marker,
			markerWeight:    markerWeight,
		}
	}

	coveredDuration := func(ranges []segmentBounds, start, end float64) float64 {
		if len(ranges) == 0 || end <= start {
			return 0
		}

		clipped := make([]segmentBounds, 0, len(ranges))
		for _, r := range ranges {
			rangeStart := math.Max(start, r.start)
			rangeEnd := math.Min(end, r.end)
			if rangeEnd > rangeStart {
				clipped = append(clipped, segmentBounds{
					start: rangeStart,
					end:   rangeEnd,
					ok:    true,
				})
			}
		}
		if len(clipped) == 0 {
			return 0
		}

		sort.SliceStable(clipped, func(i, j int) bool {
			if clipped[i].start != clipped[j].start {
				return clipped[i].start < clipped[j].start
			}
			return clipped[i].end < clipped[j].end
		})

		total := 0.0
		currentStart := clipped[0].start
		currentEnd := clipped[0].end

		for _, r := range clipped[1:] {
			if r.start <= currentEnd {
				if r.end > currentEnd {
					currentEnd = r.end
				}
				continue
			}

			total += currentEnd - currentStart
			currentStart = r.start
			currentEnd = r.end
		}

		return total + currentEnd - currentStart
	}

	finishBlock := func(b activityBlock) (aiSegmentMeta, bool) {
		dur := b.end - b.start
		if dur <= 0 {
			return aiSegmentMeta{}, false
		}

		effectiveLabels, bestPosition := effectiveRatingLabelsForBlock(b.labels, b.positionWeights)
		label := buildRatingComboLabel(effectiveLabels)
		if label == "" {
			return aiSegmentMeta{}, false
		}

		sev := ratingSegmentSeverity(label)

		start := b.start
		end := b.end

		// Wenn der finale Block eine Position trägt, soll der Block
		// wirklich auf die echte Positions-Zeit gekürzt werden —
		// nicht nur am Anfang, sondern auch am Ende.
		if bestPosition != "" {
			if bounds, ok := b.positionBounds[bestPosition]; ok && bounds.ok {
				if bounds.start > start {
					start = bounds.start
				}
				if bounds.end < end {
					end = bounds.end
				}
			}
		}

		if end < start {
			end = start
		}

		dur = end - start
		if dur <= 0 {
			return aiSegmentMeta{}, false
		}

		ratingDuration := dur
		if bestPosition != "" {
			if evidenceDuration := coveredDuration(
				b.positionRanges[bestPosition],
				start,
				end,
			); evidenceDuration > 0 {
				ratingDuration = evidenceDuration
			}
		}

		// Kurze Segmente dürfen bleiben, wenn sie stark genug sind.
		if ratingDuration < minDurationSec && sev < 0.72 {
			return aiSegmentMeta{}, false
		}

		score := 0.0
		if b.scoreWeight > 0 {
			score = b.scoreSum / b.scoreWeight
		}
		if score <= 0 {
			score = 0.50
		}

		marker := b.marker
		if marker < start || marker > end {
			marker = (start + end) / 2
		}

		return aiSegmentMeta{
			Label:           label,
			Score:           ratingClamp01(score),
			RatingIntensity: ratingClamp01(sev * ratingConfidenceWeight(score)),
			StartSeconds:    start,
			EndSeconds:      end,
			DurationSeconds: ratingDuration,
			AutoSelected:    true,
			Position:        segmentPositionFromAnalyzeLabel(label),
			Tags:            segmentTagsFromAnalyzeLabel(label),
			MarkerSeconds:   marker,
			PreviewSeconds:  marker,
		}, true
	}

	out := make([]aiSegmentMeta, 0, len(valid))
	cur := newBlock(valid[0])

	for i := 1; i < len(valid); i++ {
		n := valid[i]

		gap := n.StartSeconds - cur.end
		nextLabels := map[string]bool{}
		addRatingLabelsFromSegment(nextLabels, n.Label)
		positionConflict := ratingPositionSetsConflict(cur.labels, nextLabels)

		// A short gap may connect supporting signals or the same position, but
		// must not turn consecutive different positions into one long segment.
		shouldBridge := gap <= maxSilentGapSec && !positionConflict

		if !shouldBridge && !positionConflict && gap <= ratingBridgeStrongGapSec {
			curSet := ratingSignalSetFromLabels(cur.labels)
			nextSet := ratingSignalSetFromLabels(nextLabels)

			curBridgeStrength := ratingBridgeStrengthFromSet(curSet)
			nextBridgeStrength := ratingBridgeStrengthFromSet(nextSet)

			curDur := cur.end - cur.start
			nextDur := n.EndSeconds - n.StartSeconds

			shouldBridge =
				ratingLabelsBridgeCompatible(cur.labels, nextLabels) &&
					curBridgeStrength >= 0.65 &&
					nextBridgeStrength >= 0.65 &&
					(curDur >= 8 || nextDur >= 8)
		}

		if !shouldBridge {
			if finished, ok := finishBlock(cur); ok {
				out = append(out, finished)
			}

			cur = newBlock(n)
			continue
		}

		if n.StartSeconds < cur.start {
			cur.start = n.StartSeconds
		}
		if n.EndSeconds > cur.end {
			cur.end = n.EndSeconds
		}

		dur := n.EndSeconds - n.StartSeconds
		if dur > 0 {
			conf := ratingClamp01(n.Score)
			if conf <= 0 {
				conf = 0.50
			}

			cur.scoreSum += conf * dur
			cur.scoreWeight += dur

			addRatingPositionSignal(
				cur.positionWeights,
				cur.positionBounds,
				cur.positionRanges,
				n.Label,
				conf*math.Max(1, dur),
				n.StartSeconds,
				n.EndSeconds,
			)
		}

		nextMarkerWeight := segmentMarkerWeight(n)
		if nextMarkerWeight > cur.markerWeight {
			cur.markerWeight = nextMarkerWeight
			cur.marker = segmentMarker(n)
		}

		addRatingLabelsFromSegment(cur.labels, n.Label)
	}

	if finished, ok := finishBlock(cur); ok {
		out = append(out, finished)
	}

	return out
}

func prepareAIRatingSegments(segments []aiSegmentMeta) []aiSegmentMeta {
	return mergeRatingActivitySegments(
		segments,
		ratingMaxSilentGapSec,
		ratingMinSegmentDurationSec,
	)
}

func ratingConfidenceWeight(conf float64) float64 {
	conf = ratingClamp01(conf)

	// Confidence soll relevant sein, aber schlechte Scores nicht komplett töten.
	n := ratingSmoothStep((conf - 0.25) / 0.70)

	return 0.58 + 0.42*n
}

func ratingLinearGate(value float64, start float64, full float64) float64 {
	if full <= start {
		if value >= full {
			return 1
		}
		return 0
	}

	return ratingClamp01((value - start) / (full - start))
}

func ratingExplicitContextBonus(
	bodyEffectiveWeighted float64,
	clothingEffectiveWeighted float64,
	objectEffectiveWeighted float64,
	videoMinutes float64,
) float64 {
	if videoMinutes <= 0 {
		return 0
	}

	bodyDensity := bodyEffectiveWeighted / videoMinutes
	clothingDensity := clothingEffectiveWeighted / videoMinutes
	objectDensity := objectEffectiveWeighted / videoMinutes

	bonus :=
		0.060*ratingSaturatingEvidence(bodyDensity, 1.8) +
			0.035*ratingSaturatingEvidence(clothingDensity, 1.5) +
			0.015*ratingSaturatingEvidence(objectDensity, 2.2)

	return math.Min(bonus, 0.10)
}

func ratingBlendWithAction(base, action, actionWeight float64) float64 {
	actionWeight = ratingClamp01(actionWeight)
	return ratingClamp01((1-actionWeight)*base + actionWeight*action)
}

func ratingPositionActionScore(
	durationNorm float64,
	longestNorm float64,
	densityNorm float64,
	coverageNorm float64,
	peakNorm float64,
) float64 {
	return ratingClamp01(
		0.38*durationNorm +
			0.24*longestNorm +
			0.18*densityNorm +
			0.12*coverageNorm +
			0.08*peakNorm,
	)
}

func ratingContextActionScore(
	durationNorm float64,
	longestNorm float64,
	coverageNorm float64,
	densityNorm float64,
	peakNorm float64,
) float64 {
	return ratingClamp01(
		0.40*durationNorm +
			0.25*longestNorm +
			0.20*coverageNorm +
			0.10*densityNorm +
			0.05*peakNorm,
	)
}

func ratingExcellenceBonus(
	peakQuality float64,
	positionEffectiveWeighted float64,
	positionDensity float64,
	weightedCoverageRatio float64,
	totalFlagged float64,
	longest float64,
	avgConfidence float64,
	segmentsPerMinute float64,
	n int,
) float64 {
	// Kein pauschaler Score-Shift:
	// Der Bonus darf nur greifen, wenn wirklich mehrere starke Signale vorhanden sind.
	if n < 2 {
		return 0
	}
	if positionEffectiveWeighted <= 0 {
		return 0
	}
	if totalFlagged < 12.0 {
		return 0
	}
	if peakQuality < 0.68 {
		return 0
	}
	if avgConfidence < 0.42 {
		return 0
	}

	peakGate := ratingLinearGate(peakQuality, 0.68, 0.90)
	positionGate := ratingLinearGate(positionDensity, 2.20, 7.00)
	coverageGate := ratingLinearGate(weightedCoverageRatio, 0.045, 0.160)
	durationGate := ratingLinearGate(totalFlagged, 12.0, 90.0)
	longestGate := ratingLinearGate(longest, 8.0, 45.0)
	confGate := ratingLinearGate(avgConfidence, 0.42, 0.78)
	frequencyGate := ratingLinearGate(segmentsPerMinute, 0.25, 1.00)

	bonus :=
		0.030*peakGate +
			0.022*positionGate +
			0.016*coverageGate +
			0.012*durationGate +
			0.012*longestGate +
			0.010*confGate +
			0.006*frequencyGate

	// Maximal +8.5 Scorepunkte.
	// Dadurch werden starke 72-79 Scores 5-Sterne-fähig,
	// aber mittelmäßige Scores springen nicht einfach pauschal hoch.
	if bonus > 0.085 {
		bonus = 0.085
	}

	return bonus
}

func starsFromHighlightScore(score float64) int {
	switch {
	case score < 22:
		return 1
	case score < 45:
		return 2
	case score < 68:
		return 3
	case score < 85:
		return 4
	default:
		return 5
	}
}

func ratingUsernameFromVideoPath(videoPath string) string {
	id := strings.TrimSpace(assetIDFromVideoPath(videoPath))
	if id == "" {
		return ""
	}

	user := strings.ToLower(strings.TrimSpace(modelNameFromFilename(id)))
	if user != "" && user != "unknown" {
		return user
	}

	// Fallback nur, wenn die ID nicht wie ein kompletter Dateiname mit Timestamp aussieht.
	if !strings.Contains(id, "__") {
		return strings.ToLower(strings.TrimSpace(id))
	}

	return ""
}

func ratingLogSubject(username string) string {
	username = strings.ToLower(strings.TrimSpace(username))
	if username == "" || username == "unknown" {
		return "[rating]"
	}

	return "[" + username + "]"
}

func computeHighlightRating(segments []aiSegmentMeta, durationSec float64) *aiRatingMeta {
	return computeHighlightRatingWithUsername(segments, durationSec, "")
}

func computeHighlightRatingForVideo(segments []aiSegmentMeta, durationSec float64, videoPath string) *aiRatingMeta {
	return computeHighlightRatingWithUsername(segments, durationSec, ratingUsernameFromVideoPath(videoPath))
}

func computeHighlightRatingWithUsername(segments []aiSegmentMeta, durationSec float64, username string) *aiRatingMeta {
	r := &aiRatingMeta{
		Score: 0,
		Stars: 1,
	}

	if durationSec <= 0 || len(segments) == 0 {
		return r
	}

	videoMinutes := math.Max(durationSec/60.0, 0.25)

	var totalFlagged float64
	var totalWeighted float64

	var positionEffectiveWeighted float64
	var positionFlagged float64
	var peakQuality float64
	var qualityDurationSum float64
	var qualityDurationWeight float64

	var contextFlagged float64
	var contextWeighted float64
	var contextEffectiveWeighted float64
	var contextPeakQuality float64
	var contextQualityDurationSum float64
	var contextQualityDurationWeight float64
	var contextLongest float64
	var contextConfSum float64
	var contextConfWeightSum float64
	var contextN int
	var bodyEffectiveWeighted float64
	var clothingEffectiveWeighted float64
	var objectEffectiveWeighted float64

	var longest float64
	var confSum float64
	var confWeightSum float64
	var n int
	uniquePositions := map[string]bool{}

	for _, s := range segments {
		segDur := s.DurationSeconds
		if segDur <= 0 {
			segDur = s.EndSeconds - s.StartSeconds
		}
		if segDur <= 0 {
			continue
		}

		sev := ratingSegmentSeverity(s.Label)
		if sev <= 0 {
			appLogf("🧪 [rating] skip zero severity label=%q normalized=%q", s.Label, normalizeRatingSignalLabel(s.Label))
			continue
		}

		conf := ratingClamp01(s.Score)
		if conf <= 0 {
			conf = 0.50
		}

		confWeight := ratingConfidenceWeight(conf)
		quality := sev * confWeight

		if quality <= 0 {
			continue
		}

		effectiveDur := ratingEffectiveDurationSeconds(segDur)
		weightedDur := segDur * quality
		effectiveWeightedDur := effectiveDur * quality

		set := ratingSignalSetFromLabel(s.Label)
		bodyEffectiveWeighted += effectiveDur * set.Body * confWeight
		clothingEffectiveWeighted += effectiveDur * set.Clothing * confWeight
		objectEffectiveWeighted += effectiveDur * set.Object * confWeight

		if !set.HasPosition {
			contextFlagged += segDur
			contextWeighted += weightedDur
			contextEffectiveWeighted += effectiveWeightedDur
			contextQualityDurationSum += quality * effectiveDur
			contextQualityDurationWeight += effectiveDur
			contextConfSum += conf * effectiveDur
			contextConfWeightSum += effectiveDur
			contextN++

			if quality > contextPeakQuality {
				contextPeakQuality = quality
			}
			if segDur > contextLongest {
				contextLongest = segDur
			}

			continue
		}

		totalFlagged += segDur
		totalWeighted += weightedDur

		positionEffectiveWeighted += effectiveWeightedDur
		positionFlagged += segDur
		qualityDurationSum += quality * effectiveDur
		qualityDurationWeight += effectiveDur

		if quality > peakQuality {
			peakQuality = quality
		}

		confSum += conf * effectiveDur
		confWeightSum += effectiveDur
		n++

		if segDur > longest {
			longest = segDur
		}

		labels := map[string]bool{}
		addRatingLabelsFromSegment(labels, s.Label)
		for position := range ratingPositionSetFromLabels(labels) {
			uniquePositions[position] = true
		}
	}

	if n == 0 {
		if contextN == 0 {
			appLogf("⚠️ %s rating result is zero because all segments were skipped", ratingLogSubject(username))
			return r
		}

		contextCoverageRatio := ratingClamp01(contextFlagged / durationSec)
		contextWeightedCoverageRatio := ratingClamp01(contextWeighted / durationSec)
		contextSegmentsPerMinute := float64(contextN) / videoMinutes
		contextAvgConfidence := contextConfSum / contextConfWeightSum
		contextAvgQuality := contextQualityDurationSum / contextQualityDurationWeight
		contextDensity := contextEffectiveWeighted / videoMinutes

		qualityNorm := ratingSmoothStep(
			ratingLinearGate(contextAvgQuality, 0.08, 0.55),
		)
		peakNorm := ratingSmoothStep(
			ratingLinearGate(contextPeakQuality, 0.12, 0.62),
		)
		durationNorm := ratingSaturatingEvidence(contextFlagged, 150.0)
		longestNorm := ratingSaturatingEvidence(contextLongest, 60.0)
		densityNorm := ratingSaturatingEvidence(contextDensity, 2.5)
		coverageNorm := ratingSaturatingEvidence(contextWeightedCoverageRatio, 0.08)

		raw :=
			0.28*qualityNorm +
				0.12*peakNorm +
				0.25*durationNorm +
				0.15*longestNorm +
				0.15*coverageNorm +
				0.05*densityNorm

		actionRaw := ratingContextActionScore(
			durationNorm,
			longestNorm,
			coverageNorm,
			densityNorm,
			peakNorm,
		)
		raw = ratingBlendWithAction(raw, actionRaw, 0.35)

		// Context can produce useful 1-3 star ratings, but never the 4-5 star
		// range reserved for sustained position detections.
		if contextFlagged < 20.0 {
			raw = math.Min(raw, 0.44)
		}
		raw = math.Min(raw, 0.64)

		score := ratingRound(ratingClamp01(raw)*100, 1)

		r.Score = score
		r.ActionScore = ratingRound(actionRaw*100, 1)
		r.Stars = starsFromHighlightScore(score)
		r.Segments = contextN
		r.SegmentsPerMinute = ratingRound(contextSegmentsPerMinute, 2)
		r.FlaggedSeconds = ratingRound(contextFlagged, 2)
		r.WeightedFlaggedSeconds = ratingRound(contextWeighted, 2)
		r.CoverageRatio = ratingRound(contextCoverageRatio, 4)
		r.WeightedCoverageRatio = ratingRound(contextWeightedCoverageRatio, 4)
		r.LongestSegmentSeconds = ratingRound(contextLongest, 2)
		r.AvgConfidence = ratingRound(contextAvgConfidence, 3)

		appLogf(
			"✅ %s context rating score=%.1f stars=%d segs=%d flagged=%.1f density=%.2f coverage=%.4f longest=%.1f avgConf=%.3f",
			ratingLogSubject(username),
			r.Score,
			r.Stars,
			r.Segments,
			r.FlaggedSeconds,
			contextDensity,
			r.WeightedCoverageRatio,
			r.LongestSegmentSeconds,
			r.AvgConfidence,
		)

		return r
	}

	coverageRatio := ratingClamp01(totalFlagged / durationSec)
	weightedCoverageRatio := ratingClamp01(totalWeighted / durationSec)
	segmentsPerMinute := float64(n) / videoMinutes
	avgConfidence := confSum / confWeightSum
	avgPositionQuality := qualityDurationSum / qualityDurationWeight

	positionDensity := positionEffectiveWeighted / videoMinutes

	avgQualityNorm := ratingSmoothStep(
		ratingLinearGate(avgPositionQuality, 0.45, 0.95),
	)
	peakNorm := ratingSmoothStep(
		ratingLinearGate(peakQuality, 0.42, 0.95),
	)
	positionDurationNorm := ratingSaturatingEvidence(positionFlagged, 65.0)
	longestNorm := ratingSaturatingEvidence(longest, 35.0)
	positionDensityNorm := ratingSaturatingEvidence(positionDensity, 3.2)
	coverageNorm := ratingSaturatingEvidence(weightedCoverageRatio, 0.075)
	positionVarietyNorm := ratingLinearGate(float64(len(uniquePositions)), 1, 4)

	// Position quality is the primary signal. Actual position duration and the
	// longest continuous segment decide whether a detection is brief or strong.
	raw :=
		0.30*avgQualityNorm +
			0.10*peakNorm +
			0.26*positionDurationNorm +
			0.16*longestNorm +
			0.10*positionDensityNorm +
			0.06*coverageNorm +
			0.02*positionVarietyNorm

	actionRaw := ratingPositionActionScore(
		positionDurationNorm,
		longestNorm,
		positionDensityNorm,
		coverageNorm,
		peakNorm,
	)
	raw = ratingBlendWithAction(raw, actionRaw, 0.30)

	explicitContextBonus := ratingExplicitContextBonus(
		bodyEffectiveWeighted,
		clothingEffectiveWeighted,
		objectEffectiveWeighted,
		videoMinutes,
	)

	// Sehr wenig Material nicht überbewerten.
	if totalFlagged < 5.0 && n <= 1 {
		raw = math.Min(raw, 0.44)
	}

	excellenceBonus := ratingExcellenceBonus(
		peakQuality,
		positionEffectiveWeighted,
		positionDensity,
		weightedCoverageRatio,
		totalFlagged,
		longest,
		avgConfidence,
		segmentsPerMinute,
		n,
	)

	raw = ratingClamp01(raw + explicitContextBonus + excellenceBonus)

	score := ratingRound(raw*100, 1)

	r.Score = score
	r.ActionScore = ratingRound(actionRaw*100, 1)
	r.Stars = starsFromHighlightScore(score)
	r.Segments = n
	r.SegmentsPerMinute = ratingRound(segmentsPerMinute, 2)
	r.FlaggedSeconds = ratingRound(totalFlagged, 2)
	r.WeightedFlaggedSeconds = ratingRound(totalWeighted, 2)
	r.CoverageRatio = ratingRound(coverageRatio, 4)
	r.WeightedCoverageRatio = ratingRound(weightedCoverageRatio, 4)
	r.LongestSegmentSeconds = ratingRound(longest, 2)
	r.AvgConfidence = ratingRound(avgConfidence, 3)

	appLogf(
		"✅ %s rating score=%.1f stars=%d contextBonus=%.1f excellenceBonus=%.1f segs=%d flagged=%.1f posFlagged=%.1f posDensity=%.2f coverage=%.4f longest=%.1f avgConf=%.3f",
		ratingLogSubject(username),
		r.Score,
		r.Stars,
		ratingRound(explicitContextBonus*100, 1),
		ratingRound(excellenceBonus*100, 1),
		r.Segments,
		r.FlaggedSeconds,
		ratingRound(positionFlagged, 1),
		positionDensity,
		r.WeightedCoverageRatio,
		r.LongestSegmentSeconds,
		r.AvgConfidence,
	)

	return r
}