petal/internal/lexicon/lexicon.go

package lexicon

import (
	"bytes"
	"compress/gzip"
	"encoding/json"
	"fmt"
	"io"
	"strings"
	"sync"
)

// Meaning is one sense of a word: its part of speech, the gloss, and an optional
// usage example. The frontend renders a few of these in the word popover.
type Meaning struct {
	PartOfSpeech string `json:"part_of_speech"`
	Definition   string `json:"definition"`
	Example      string `json:"example,omitempty"`
}

// Result is the full lookup for one word. Either list may be empty (the word
// isn't a headword in that dataset); the frontend handles a partial or empty
// result gracefully.
type Result struct {
	Word        string    `json:"word"`
	Definitions []Meaning `json:"definitions"`
	Synonyms    []string  `json:"synonyms"`
}

// maxSynonyms caps how many synonyms we hand the popover, even though the dataset
// stores up to ~50 per word — a long flat wall of words overwhelms more than it
// helps, especially for an ESL reader scanning for the right fit.
const maxSynonyms = 16

// maxDefinitions caps the senses shown so the popover stays a glance, not an
// essay.
const maxDefinitions = 4

// Lexicon holds the lazily-loaded datasets. The maps are populated once, on the
// first Lookup, behind a sync.Once so startup stays instant and a load error is
// remembered rather than retried on every request.
type Lexicon struct {
	once     sync.Once
	loadErr  error
	defs     map[string][][]string // word → [[pos, def, example], …]
	synonyms map[string][]string   // word → [synonym, …]
}

// New returns a Lexicon. The datasets aren't read until the first Lookup.
func New() *Lexicon { return &Lexicon{} }

func (l *Lexicon) load() {
	l.once.Do(func() {
		if err := gunzipJSON(definitionsGz, &l.defs); err != nil {
			l.loadErr = fmt.Errorf("load definitions: %w", err)
			return
		}
		if err := gunzipJSON(synonymsGz, &l.synonyms); err != nil {
			l.loadErr = fmt.Errorf("load synonyms: %w", err)
			return
		}
	})
}

// Lookup returns the definition senses and synonyms for word. It first tries the
// word as written (lowercased), then a few simple morphological reductions
// (plurals, -ed/-ing/-ly) so "running" or "happily" still resolve. A word found
// in neither dataset yields a Result with empty lists (not an error).
func (l *Lexicon) Lookup(word string) (Result, error) {
	l.load()
	if l.loadErr != nil {
		return Result{}, l.loadErr
	}

	norm := strings.ToLower(strings.TrimSpace(word))
	res := Result{Word: word, Definitions: []Meaning{}, Synonyms: []string{}}
	if norm == "" {
		return res, nil
	}

	if raw := lookupDefs(l.defs, norm); raw != nil {
		for _, m := range raw {
			res.Definitions = append(res.Definitions, toMeaning(m))
			if len(res.Definitions) >= maxDefinitions {
				break
			}
		}
	}

	if syns := lookupSyns(l.synonyms, norm); syns != nil {
		if len(syns) > maxSynonyms {
			syns = syns[:maxSynonyms]
		}
		res.Synonyms = append(res.Synonyms, syns...)
	}

	return res, nil
}

// lookupDefs / lookupSyns walk the candidate forms of a word and return the
// first dataset hit. They're separate (rather than a generic helper) only
// because the two maps have different value types.
func lookupDefs(m map[string][][]string, word string) [][]string {
	for _, c := range candidates(word) {
		if v, ok := m[c]; ok {
			return v
		}
	}
	return nil
}

func lookupSyns(m map[string][]string, word string) []string {
	for _, c := range candidates(word) {
		if v, ok := m[c]; ok {
			return v
		}
	}
	return nil
}

// candidates returns the lemma forms to try, in priority order: the word itself,
// then conservative de-inflections. This is deliberately lightweight — a full
// stemmer would over-reduce ("business" → "busy") and surface wrong entries; a
// handful of common English suffix rules covers the everyday cases without a
// dependency. Duplicates are fine (map lookup is cheap); order is what matters.
func candidates(word string) []string {
	out := []string{word}
	add := func(s string) {
		if len(s) >= 2 && s != word {
			out = append(out, s)
		}
	}

	switch {
	case strings.HasSuffix(word, "ies"): // studies → study
		add(word[:len(word)-3] + "y")
	case strings.HasSuffix(word, "es"): // boxes → box, wishes → wish
		add(word[:len(word)-2])
		add(word[:len(word)-1])
	case strings.HasSuffix(word, "s"): // cats → cat
		add(word[:len(word)-1])
	}

	if strings.HasSuffix(word, "ing") { // running → run, making → make
		stem := word[:len(word)-3]
		add(stem)
		add(stem + "e")
		add(undouble(stem))
	}
	if strings.HasSuffix(word, "ed") { // hoped → hope, stopped → stop
		stem := word[:len(word)-2]
		add(stem)
		add(word[:len(word)-1])
		add(undouble(stem))
	}
	if strings.HasSuffix(word, "ly") { // happily handled above via ies path? no — quickly → quick
		add(word[:len(word)-2])
	}
	if strings.HasSuffix(word, "ily") { // happily → happy
		add(word[:len(word)-3] + "y")
	}

	return out
}

// undouble collapses a doubled final consonant (stopp → stop, runn → run) so the
// -ed/-ing stems of doubled-consonant verbs resolve to their base form.
func undouble(stem string) string {
	n := len(stem)
	if n >= 2 && stem[n-1] == stem[n-2] {
		return stem[:n-1]
	}
	return stem
}

// toMeaning maps a compact [pos, def, example] triple from the dataset onto the
// JSON-friendly Meaning. The dataset always stores three elements, but we guard
// the length so a malformed row can't panic.
func toMeaning(m []string) Meaning {
	var out Meaning
	if len(m) > 0 {
		out.PartOfSpeech = m[0]
	}
	if len(m) > 1 {
		out.Definition = m[1]
	}
	if len(m) > 2 {
		out.Example = m[2]
	}
	return out
}

// gunzipJSON decompresses gz and decodes the JSON into v.
func gunzipJSON(gz []byte, v any) error {
	r, err := gzip.NewReader(bytes.NewReader(gz))
	if err != nil {
		return err
	}
	defer r.Close()
	data, err := io.ReadAll(r)
	if err != nil {
		return err
	}
	return json.Unmarshal(data, v)
}