package engine

import (
	"context"
	"fmt"
	"strings"

	"github.com/local/glpi-neural-brain/internal/model"
)

func clusterPendingArticleCandidates(values []*pendingArticleCandidate) [][]*pendingArticleCandidate {
	clusters := make([][]*pendingArticleCandidate, 0)
	for _, value := range values {
		if value == nil || len(value.Seeds) == 0 {
			continue
		}
		placed := false
		for i := range clusters {
			for _, existing := range clusters[i] {
				if articleCandidatesBelongTogether(existing, value) {
					clusters[i] = append(clusters[i], value)
					placed = true
					break
				}
			}
			if placed {
				break
			}
		}
		if !placed {
			clusters = append(clusters, []*pendingArticleCandidate{value})
		}
	}
	return clusters
}

func articleCandidatesBelongTogether(a, b *pendingArticleCandidate) bool {
	if a == nil || b == nil {
		return false
	}
	ids := map[string]bool{}
	for _, seed := range a.Seeds {
		ids[seed.ID] = true
	}
	for _, seed := range b.Seeds {
		if ids[seed.ID] {
			return true
		}
	}
	textA := articleCandidateTerms(a)
	textB := articleCandidateTerms(b)
	termScore := boolSetJaccard(textA, textB)
	if termScore >= .42 {
		return true
	}
	catA := map[string]bool{}
	catB := map[string]bool{}
	for _, seed := range a.Seeds {
		for _, cat := range seed.Categories {
			catA[strings.ToLower(strings.TrimSpace(cat))] = true
		}
	}
	for _, seed := range b.Seeds {
		for _, cat := range seed.Categories {
			catB[strings.ToLower(strings.TrimSpace(cat))] = true
		}
	}
	return termScore >= .18 && boolSetJaccard(catA, catB) >= .50
}

func articleCandidateTerms(value *pendingArticleCandidate) map[string]bool {
	parts := []string{value.Relation.TopicLabel}
	parts = append(parts, value.Relation.Keywords...)
	for _, seed := range value.Seeds {
		parts = append(parts, seed.Label)
	}
	return researchTerms(strings.Join(parts, " "))
}

func boolSetJaccard(a, b map[string]bool) float64 {
	if len(a) == 0 || len(b) == 0 {
		return 0
	}
	intersection := 0
	union := map[string]bool{}
	for key := range a {
		union[key] = true
		if b[key] {
			intersection++
		}
	}
	for key := range b {
		union[key] = true
	}
	if len(union) == 0 {
		return 0
	}
	return float64(intersection) / float64(len(union))
}

func combinePendingArticleCluster(cluster []*pendingArticleCandidate) ([]model.Node, model.RelationDecision, []model.ResearchResult) {
	seedByID := map[string]model.Node{}
	keywords := []string{}
	topics := []string{}
	relationTypes := map[string]int{}
	confidenceSum := 0.0
	count := 0
	researchResults := []model.ResearchResult{}
	for _, item := range cluster {
		if item == nil {
			continue
		}
		for _, seed := range item.Seeds {
			seedByID[seed.ID] = seed
		}
		keywords = append(keywords, item.Relation.Keywords...)
		if topic := strings.TrimSpace(item.Relation.TopicLabel); topic != "" {
			topics = append(topics, topic)
		}
		if rel := safeRelation(item.Relation.RelationType); rel != "" {
			relationTypes[rel]++
		}
		confidenceSum += item.Relation.Confidence
		count++
		researchResults = append(researchResults, item.Research...)
	}
	seeds := make([]model.Node, 0, len(seedByID))
	for _, seed := range seedByID {
		seeds = append(seeds, seed)
	}
	topic := ""
	if len(topics) > 0 {
		topic = topics[0]
	}
	relationType := "same_topic"
	maxCount := 0
	for rel, n := range relationTypes {
		if n > maxCount {
			relationType, maxCount = rel, n
		}
	}
	confidence := .8
	if count > 0 {
		confidence = confidenceSum / float64(count)
	}
	decision := model.RelationDecision{
		Related:      true,
		RelationType: relationType,
		Confidence:   confidence,
		TopicLabel:   topic,
		Keywords:     unique(keywords),
		Explanation:  fmt.Sprintf("%d thematisch kompatible neue Relationen wurden für einen gemeinsamen Artikelauftrag gebündelt.", count),
	}
	return seeds, decision, uniqueResearchEvidence(researchResults)
}

func (e *Engine) synthesizePendingArticleClusters(ctx context.Context, trigger string, pending []*pendingArticleCandidate) (created, skipped int) {
	clusters := clusterPendingArticleCandidates(pending)
	for index, cluster := range clusters {
		seeds, relation, researchResults := combinePendingArticleCluster(cluster)
		if len(seeds) == 0 {
			continue
		}
		e.Broker.Publish(model.Activity{Type: "article.cluster.started", Source: "brain", Phase: "knowledge-planning", NodeIDs: nodeIDsFromNodes(seeds), Message: fmt.Sprintf("%d Relation(en) werden als gemeinsamer Artikelauftrag verarbeitet", len(cluster)), Strength: .62, Metadata: map[string]any{"trigger": trigger, "cluster_index": index + 1, "relation_count": len(cluster), "seed_count": len(seeds), "processing_mode": "clustered"}})
		outcome, err := e.synthesizeKnowledgeArticle(ctx, trigger, seeds, relation, researchResults)
		if err != nil {
			skipped++
			e.Broker.Publish(model.Activity{Type: "article.failed", Source: "brain", Phase: "knowledge-synthesis", NodeIDs: nodeIDsFromNodes(seeds), Message: "Gebündelte Artikelsynthese ist fehlgeschlagen; die bereits erzeugten Relationen bleiben erhalten", Strength: .4, Metadata: map[string]any{"trigger": trigger, "cluster_index": index + 1, "relation_count": len(cluster), "error": err.Error()}})
			continue
		}
		if outcome.Created {
			created++
		}
		if outcome.Skipped {
			skipped++
		}
	}
	return created, skipped
}