363 lines
10 KiB
Python
363 lines
10 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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import argparse
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import html
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import json
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import os
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import re
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import sys
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from dataclasses import dataclass, asdict
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from typing import Dict, List, Any
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import requests
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request_url = "http://127.0.0.1:19633"
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request_timeout = 10
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from fugashi import Tagger
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#import unidic
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# Ensure UTF-8 output
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if hasattr(sys.stdout, "reconfigure"):
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sys.stdout.reconfigure(encoding="utf-8", errors="backslashreplace")
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KATAKANA_START = ord("ァ")
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KATAKANA_END = ord("ヶ")
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KATAKANA_TO_HIRAGANA_OFFSET = ord("ぁ") - ord("ァ")
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CONTENT_POS1 = {"名詞", "動詞", "形容詞", "形状詞", "副詞", "代名詞"}
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def is_vocab_token(pos, keep_pronouns=False, keep_numbers=False):
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pos1, pos2, pos3, pos4 = pos
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if pos1 not in CONTENT_POS1:
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return False
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if pos1 == "代名詞" and not keep_pronouns:
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return False
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if pos1 == "名詞" and pos2 == "数詞" and not keep_numbers:
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return False
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return True
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def katakana_to_hiragana(text: str) -> str:
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return "".join(
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chr(ord(ch) + KATAKANA_TO_HIRAGANA_OFFSET) if KATAKANA_START <= ord(ch) <= KATAKANA_END else ch
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for ch in text
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)
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KANJI_RE = re.compile(r"[\u4E00-\u9FFF]")
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def has_kanji(text: str) -> bool:
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return bool(KANJI_RE.search(text))
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# def has_kanji(text):
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# return any(
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# 0x3400 <= ord(ch) <= 0x4DBF or
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# 0x4E00 <= ord(ch) <= 0x9FFF or
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# 0xF900 <= ord(ch) <= 0xFAFF
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# for ch in text
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# )
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def has_japanese(text: str) -> bool:
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return any(
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0x3040 <= ord(ch) <= 0x30FF or
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0x3400 <= ord(ch) <= 0x9FFF
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for ch in text
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)
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def anki_fields_term(term : str) -> dict:
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params = {
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"text": term,
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"type": "term",
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#"markers": ["audio", "cloze-body-kana", "conjugation", "expression", "furigana", "furigana-plain", "glossary", "glossary-brief", "glossary-no-dictionary", "glossary-first", "glossary-first-brief", "glossary-first-no-dictionary", "part-of-speech", "phonetic-transcriptions", "pitch-accents", "pitch-accent-graphs", "pitch-accent-graphs-jj", "pitch-accent-positions", "pitch-accent-categories", "reading", "tags", "clipboard-image", "clipboard-text", "cloze-body", "cloze-prefix", "cloze-suffix", "dictionary", "dictionary-alias", "document-title", "frequencies", "frequency-harmonic-rank", "frequency-harmonic-occurrence", "frequency-average-rank", "frequency-average-occurrence", "screenshot", "search-query", "popup-selection-text", "sentence", "sentence-furigana", "sentence-furigana-plain", "url"],
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"markers": ["audio", "expression", "cloze-body" , "furigana","furigana-plain", "part-of-speech", "reading", "glossary-first", "glossary-plain-no-dictionary" , "glossary-first-no-dictionary" , "dictionary", "frequencies", "frequency-average-rank", "frequency-average-occurrence", "screenshot", "sentence", "sentence-furigana", "sentence-furigana-plain" ],
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"maxEntries": 2,
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"includeMedia": False,
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}
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response = requests.post(request_url + "/ankiFields", json = params, timeout = request_timeout)
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return json.loads(response.text)
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def term_entries(term :str) -> dict:
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#print("Requesting termEntries:")
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params = {
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"term": term,
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"maxEntries": 1,
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"includeMedia": False,
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}
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response = requests.post(request_url + "/termEntries", json = params, timeout = request_timeout)
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return json.loads(response.text)
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def is_content_word(pos1: str) -> bool:
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return pos1 in {"名詞", "動詞", "形容詞", "副詞"}
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def should_skip(surface: str, pos1: str) -> bool:
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return not surface.strip() or pos1 == "補助記号"
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def clean_lemma(lemma: str) -> str:
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return re.split(r"-", lemma, maxsplit=1)[0] if lemma else lemma
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def get_feature(feature, name: str, default=""):
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return getattr(feature, name, default) or default
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def should_ruby(surface, reading_hiragana):
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if not reading_hiragana:
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return False
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if surface == reading_hiragana:
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return False
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if not has_kanji(surface):
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return False
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return True
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def build_ruby(surface: str, reading: str) -> str:
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if not reading or surface == reading or not has_japanese(surface):
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return html.escape(surface)
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return f"<ruby>{html.escape(surface)}<rt>{html.escape(reading)}</rt></ruby>"
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def build_kana(surface: str, reading: str) -> str:
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if not reading or surface == reading or not has_japanese(surface):
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return surface
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return f"「{reading}」"
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def build_vocab_link(surface: str, reading: str, pos) -> str:
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if not is_vocab_token(pos, keep_pronouns=True,keep_numbers=False):
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return surface
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if surface == reading:
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return f"[[{surface}]]"
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return f"[[{reading}|{surface}]]"
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## Predicate filtering Verbs
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def is_predicate_start(pos):
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pos1, pos2, pos3, pos4 = pos
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return (
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pos1 == "動詞" and pos2 == "一般"
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) or (
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pos1 == "形容詞"
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) or (
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pos1 == "形状詞"
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)
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def attaches_to_predicate(pos):
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pos1, pos2, pos3, pos4 = pos
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# ます, た, ない, れる, られる, たい, etc.
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if pos1 == "助動詞":
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return True
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# helper verbs like いる, ある, しまう in constructions
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if pos1 == "動詞" and pos2 == "非自立可能":
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return True
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# te-form connector in 読んでいる, 食べている
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if pos1 == "助詞" and pos2 == "接続助詞":
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return True
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# suffixes attached to predicates
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if pos1 == "接尾辞":
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return True
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return False
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def chunk_predicates(tokens):
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"""
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tokens should be a list of TokenReading objects:
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token.surface
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token.lemma
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token.pos
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"""
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chunks = []
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i = 0
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while i < len(tokens):
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token = tokens[i]
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if not is_predicate_start(token.pos):
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i += 1
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continue
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chunk = [token]
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j = i + 1
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while j < len(tokens) and attaches_to_predicate(tokens[j].pos):
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chunk.append(tokens[j])
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j += 1
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chunks.append({
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"surface": "".join(t.surface for t in chunk),
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"head_lemma": token.lemma,
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"head_reading_hiragana": token.lemma_reading_hiragana,
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"pos": token.pos,
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"parts": [
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{
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"surface": t.surface,
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"lemma": t.lemma,
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"reading_hiragana": t.reading_hiragana,
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"pos": t.pos,
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}
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for t in chunk
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],
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})
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i = j
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return chunks
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## Vocab Filtering
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def is_vocab_token(pos, keep_pronouns=False, keep_numbers=False):
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pos1, pos2, pos3, pos4 = pos
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if pos1 not in {"名詞", "動詞", "形容詞", "形状詞", "副詞", "代名詞"}:
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return False
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if pos1 == "代名詞" and not keep_pronouns:
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return False
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if pos1 == "名詞" and pos2 == "数詞" and not keep_numbers:
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return False
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return True
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@dataclass
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class TokenReading:
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surface: str
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reading_hiragana: str
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lemma: str
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lemma_reading_hiragana: str
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pos: List[str]
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# ---------- Persistent vocab handling ----------
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def load_vocab(path: str) -> Dict[str, Dict]:
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if not os.path.exists(path):
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return {}
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with open(path, "r", encoding="utf-8") as f:
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raw = json.load(f)
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# normalize + convert surfaces_seen → set
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out = {}
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for lemma, entry in raw.items():
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out[lemma] = {
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"lemma": entry.get("lemma", lemma),
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"lemma_reading_hiragana": entry.get("lemma_reading_hiragana", ""),
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"pos": entry.get("pos", []),
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"mention_count": int(entry.get("mention_count", 0)),
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"surfaces_seen": set(entry.get("surfaces_seen", [])), # ← key change
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}
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return out
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def save_vocab(path: str, vocab: Dict[str, Dict]):
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os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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# convert sets → sorted lists
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serializable = {
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lemma: {
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**entry,
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"surfaces_seen": sorted(entry.get("surfaces_seen", []))
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}
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for lemma, entry in vocab.items()
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}
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with open(path, "w", encoding="utf-8") as f:
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json.dump(serializable, f, ensure_ascii=False, indent=2)
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def update_vocab(vocab: Dict[str, Dict], tokens: List[TokenReading]):
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for t in tokens:
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pos1 = t.pos[0] if t.pos else ""
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# if should_skip(t.surface, pos1) or not is_content_word(pos1):
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# continue
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if not is_vocab_token(t.pos, keep_pronouns=True,keep_numbers=False):
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continue
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entry = vocab.get(t.lemma)
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if entry is None:
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vocab[t.lemma] = {
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"lemma": t.lemma,
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"lemma_reading_hiragana": t.lemma_reading_hiragana,
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"pos": t.pos,
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"mention_count": 1,
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"surfaces_seen": {t.surface}, # ← set
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}
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else:
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entry["mention_count"] += 1
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entry.setdefault("surfaces_seen", set()).add(t.surface)
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# ---------- Main analysis ----------
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def analyze(text: str, vocab: Dict[str, Dict]):
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tagger = Tagger()
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tokens: List[TokenReading] = []
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ruby_parts = []
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kana_parts = []
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vocab_link_parts = []
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for word in tagger(text):
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f = word.feature
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surface = word.surface
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pos = [
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get_feature(f, "pos1"),
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get_feature(f, "pos2"),
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get_feature(f, "pos3"),
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get_feature(f, "pos4"),
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]
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kana = get_feature(f, "kana")
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#reading = katakana_to_hiragana(kana) if kana else ""
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if kana:
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if kana == surface:
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reading = ""
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else:
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reading = katakana_to_hiragana(kana)
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else:
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reading = ""
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lemma = clean_lemma(get_feature(f, "lemma") or surface)
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vocab_link_parts.append(build_vocab_link(surface, lemma, pos))
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lform = get_feature(f, "lForm")
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kana_base = get_feature(f, "kanaBase")
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lemma_reading = katakana_to_hiragana(lform or kana_base or kana or lemma)
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token = TokenReading(surface, reading, lemma, lemma_reading, pos)
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tokens.append(token)
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ruby_parts.append(build_ruby(surface, reading))
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kana_parts.append(build_kana(surface, reading))
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predicate_chunks = chunk_predicates(tokens)
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update_vocab(vocab, tokens)
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return {
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"original_text": text,
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"kana_reading": "".join(kana_parts),
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"ruby_html": "".join(ruby_parts),
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"notes_link":"".join(vocab_link_parts),
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"token_readings": [asdict(t) for t in tokens],
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"predicate_chunks" : predicate_chunks,
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}
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