package index

import (
	"math"
	"regexp"
	"sort"
	"strings"

	"github.com/david/knox/internal/db"
)

type TopicCluster struct {
	Name     string
	Keywords []string
	Score    float64
	Entries  []db.Entry
	BySource map[string]int
}

var tokenRE = regexp.MustCompile(`[a-z]{4,}`)
var hexTokenRE = regexp.MustCompile(`^[a-f0-9]{8,}$`)

func Tokenize(text string) []string { return tokenize(text) }
func tokenize(text string) []string {
	text = strings.ToLower(text)
	text = strings.ReplaceAll(text, "-", " ")
	text = strings.ReplaceAll(text, "_", " ")
	return tokenRE.FindAllString(text, -1)
}

type document struct {
	id     int
	title  string
	text   string
	source string
	entry  db.Entry
}

// TF-IDF index built from all observations
type TFIDFIndex struct {
	docCount  int
	docFreq   map[string]int       // term → how many docs contain it
	termDocs  []map[string]float64 // per-doc: term → TF score
	documents []document
}

func BuildTFIDF(entries []db.Entry) *TFIDFIndex {
	idx := &TFIDFIndex{
		docFreq:  make(map[string]int),
		termDocs: make([]map[string]float64, 0, len(entries)),
	}

	for _, e := range entries {
		text := e.Title + " " + e.Summary + " " + e.SourceID + " " + e.Project
		tokens := tokenize(text)
		if len(tokens) == 0 {
			continue
		}

		doc := document{
			id:     idx.docCount,
			title:  e.Title,
			text:   text,
			source: e.SourceID,
			entry:  e,
		}

		// Term frequency in this doc
		tf := make(map[string]float64)
		seen := make(map[string]bool)
		for _, t := range tokens {
			if hexTokenRE.MatchString(t) || len(t) > 25 {
				continue
			}
			tf[t]++
			if !seen[t] {
				idx.docFreq[t]++
				seen[t] = true
			}
		}
		// Normalize TF by doc length
		docLen := float64(len(tokens))
		for t, c := range tf {
			tf[t] = c / docLen
		}

		idx.termDocs = append(idx.termDocs, tf)
		idx.documents = append(idx.documents, doc)
		idx.docCount++
	}

	return idx
}

func (idx *TFIDFIndex) TFIDF(term string, docIdx int) float64 {
	tf, ok := idx.termDocs[docIdx][term]
	if !ok {
		return 0
	}
	df := idx.docFreq[term]
	if df == 0 {
		return 0
	}
	idf := math.Log(float64(idx.docCount) / float64(df))
	return tf * idf
}

// TopTerms returns the N highest TF-IDF scoring terms for a document
func (idx *TFIDFIndex) TopTerms(docIdx int, n int) []struct {
	Term  string
	Score float64
} {
	var scored []struct {
		Term  string
		Score float64
	}
	for t := range idx.termDocs[docIdx] {
		s := idx.TFIDF(t, docIdx)
		if s > 0 {
			scored = append(scored, struct {
				Term  string
				Score float64
			}{t, s})
		}
	}
	sort.Slice(scored, func(i, j int) bool { return scored[i].Score > scored[j].Score })
	if len(scored) > n {
		scored = scored[:n]
	}
	return scored
}

// Cluster groups observations into topics by shared top TF-IDF terms
func (idx *TFIDFIndex) Cluster(minSharedTerms int, maxTopics int) []TopicCluster {
	if maxTopics <= 0 {
		maxTopics = 20
	}
	if minSharedTerms <= 0 {
		minSharedTerms = 2
	}

	// Get top 5 terms per doc
	type docTerms struct {
		docIdx int
		terms  []string
	}
	var docTermList []docTerms
	for i := 0; i < idx.docCount; i++ {
		tt := idx.TopTerms(i, 5)
		if len(tt) >= minSharedTerms {
			terms := make([]string, len(tt))
			for j, t := range tt {
				terms[j] = t.Term
			}
			docTermList = append(docTermList, docTerms{i, terms})
		}
	}

	// Greedy clustering: docs sharing >= minSharedTerms terms become a topic
	var clusters []TopicCluster
	assigned := make(map[int]bool)

	for _, dt := range docTermList {
		if assigned[dt.docIdx] {
			continue
		}

		cluster := TopicCluster{
			Keywords: dt.terms,
			Score:    0,
			BySource: make(map[string]int),
		}

		// The seed doc belongs to this cluster too.
		if seed := idx.documents[dt.docIdx]; seed.source != "" {
			cluster.BySource[seed.source]++
			cluster.Entries = append(cluster.Entries, seed.entry)
		}

		// Find all docs sharing terms with this seed
		for _, other := range docTermList {
			if assigned[other.docIdx] {
				continue
			}
			shared := 0
			for _, t1 := range dt.terms {
				for _, t2 := range other.terms {
					if t1 == t2 {
						shared++
						break
					}
				}
			}
			if shared >= minSharedTerms {
				assigned[other.docIdx] = true
				e := idx.documents[other.docIdx]
				cluster.BySource[e.source]++
				cluster.Entries = append(cluster.Entries, e.entry)

				// Score is sum of TF-IDF of shared terms
				for _, t := range dt.terms {
					cluster.Score += idx.TFIDF(t, other.docIdx)
				}
			}
		}

		if len(cluster.BySource) > 0 {
			// Name the topic by top scoring TF-IDF terms across all docs
			allTerms := make(map[string]float64)
			for _, t := range dt.terms {
				for i := 0; i < idx.docCount; i++ {
					allTerms[t] += idx.TFIDF(t, i)
				}
			}
			type kv struct {
				k string
				v float64
			}
			var sorted []kv
			for k, v := range allTerms {
				sorted = append(sorted, kv{k, v})
			}
			sort.Slice(sorted, func(i, j int) bool { return sorted[i].v > sorted[j].v })

			var nameParts []string
			for _, kv := range sorted {
				if len(nameParts) >= 3 {
					break
				}
				nameParts = append(nameParts, kv.k)
			}
			cluster.Name = strings.Join(nameParts, " / ")

			clusters = append(clusters, cluster)
			if len(clusters) >= maxTopics {
				break
			}
		}
	}

	return clusters
}

// Gaps finds topics with high browser activity but no skill coverage
func (idx *TFIDFIndex) Gaps(clusters []TopicCluster, skillKeywords map[string]bool) []TopicCluster {
	var gaps []TopicCluster
	for _, c := range clusters {
		browserScore := c.BySource["browser-history"]
		skillScore := c.BySource["skills-catalog"]
		if browserScore > 0 && skillScore == 0 {
			gaps = append(gaps, c)
		}
	}
	sort.Slice(gaps, func(i, j int) bool {
		return gaps[i].BySource["browser-history"] > gaps[j].BySource["browser-history"]
	})
	return gaps
}