import re from typing import List, Tuple, Dict, Literal, Type from fastapi import HTTPException from opencc import OpenCC from tortoise import Tortoise, Model from tortoise.expressions import Q from app.api.search_dict.search_schemas import SearchRequest, ProverbSearchResponse, ProverbSearchRequest from app.models import WordlistFr, WordlistJp from app.models.fr import ProverbFr from app.utils.all_kana import all_in_kana from app.utils.textnorm import normalize_text from settings import TORTOISE_ORM def detect_language(text: str) -> Tuple[str, Literal["fr", "zh", "jp", "other"]]: """ 自动检测输入语言: - zh: 简体中文 - jp: 日语（含假名或繁体/旧体字） - fr: 拉丁字母（法语等） - other: 其他 """ cc_s2t = OpenCC('s2t') # 简体 → 繁体 cc_t2s = OpenCC('t2s') # 繁体 → 简体 JAPANESE_HIRAGANA = r"[\u3040-\u309F]" JAPANESE_KATAKANA = r"[\u30A0-\u30FF\u31F0-\u31FF]" text = text.strip() if not text: return "", "other" # ✅ Step 1: 假名检测 if re.search(JAPANESE_HIRAGANA, text) or re.search(JAPANESE_KATAKANA, text): return text, "jp" # ✅ Step 2: 汉字检测 if re.search(r"[\u4e00-\u9fff]", text): # 简繁互转对比 to_trad = cc_s2t.convert(text) to_simp = cc_t2s.convert(text) # 如果输入等于繁体转换结果 → 繁体或日文汉字 if text == to_trad and text != to_simp: return text, "jp" # 如果输入等于简体转换结果 → 简体中文 elif text == to_simp and text != to_trad: return to_trad, "zh" # 注意返回的是繁体形式用于补充搜索 # 否则混合（既有简体又有繁体） else: # 混合时可优先认定为繁体（日语） return to_trad, "jp" # ✅ Step 3: 拉丁字母检测 if re.search(r"[a-zA-ZÀ-ÿ]", text): return text, "fr" return text, "other" async def accurate_proverb(proverb_id: int) -> ProverbSearchResponse: """对于查询法语谚语的精准查询，返回详细信息""" proverb = await ProverbFr.get_or_none(id=proverb_id) if not proverb: raise HTTPException(status_code=404, detail="Proverb not found") proverb.freq = proverb.freq + 1 await proverb.save() return ProverbSearchResponse( proverb_text=proverb.text, chi_exp=proverb.chi_exp, ) async def accurate_idiom(idiom_id: int): proverb = await ProverbFr.get_or_none(id=idiom_id) if not proverb: raise HTTPException(status_code=404, detail="Proverb not found") proverb.freq = proverb.freq + 1 await proverb.save() return proverb async def suggest_proverb( query: str, lang: Literal["fr", "zh", "jp"], model: Type[Model], search_field: str = "search_text", target_field: str = "text", chi_exp_field: str = "chi_exp", limit: int = 10, ) -> List[Dict[str, str]]: """ 通用搜索建议函数，用于多语言谚语表。 参数: query: 搜索关键词 lang: 'fr' 或 'zh' model: Tortoise ORM 模型类，例如 ProverbFr proverb_field: 外语谚语字段名 chi_exp_field: 中文释义字段名 limit: 每类匹配的最大返回数量 搜索逻辑: 1. 根据语言选择搜索字段； 2. 优先匹配以输入开头的结果； 3. 其次匹配包含输入但非开头的结果； 4. 合并去重后返回。 """ keyword = query.strip() if not keyword: return [] # ✅ 根据语言选择搜索字段 if lang == "zh": startswith_field = f"{chi_exp_field}__istartswith" contains_field = f"{chi_exp_field}__icontains" else: startswith_field = f"{search_field}__istartswith" contains_field = f"{search_field}__icontains" # ✅ 1. 开头匹配 start_matches = await ( model.filter(**{startswith_field: keyword}) .order_by("-freq") .limit(limit) .values("id", target_field, search_field, chi_exp_field) ) # ✅ 2. 包含匹配（非开头） contain_matches = await ( model.filter( Q(**{contains_field: keyword}) & ~Q(**{startswith_field: keyword}) ) .order_by("-freq") .limit(limit) .values("id", target_field, search_field, chi_exp_field) ) # ✅ 3. 合并去重并保持顺序 results: List[Dict[str, str]] = [] seen_ids = set() for row in start_matches + contain_matches: if row["id"] not in seen_ids: seen_ids.add(row["id"]) results.append({ "id": row["id"], "proverb": row[target_field], "search_text": row[search_field], "chi_exp": row[chi_exp_field] }) return results async def suggest_autocomplete(query: SearchRequest, limit: int = 10): """ :param query: 当前用户输入的内容 :param limit: 返回列表限制长度 :return: 联想的单词列表（非完整信息，单纯单词） """ if query.language == 'fr': query_word = normalize_text(query.query) exact = await ( WordlistFr .get_or_none(search_text=query.query) .values("text", "freq") ) if exact: exact_word = [(exact.get("text"), exact.get("freq"))] else: exact_word = [] qs_prefix = ( WordlistFr .filter(Q(search_text__startswith=query_word) | Q(text__startswith=query.query)) .exclude(search_text=query.query) .only("text", "freq") ) prefix_objs = await qs_prefix[:limit] prefix: List[Tuple[str, int]] = [(o.text, o.freq) for o in prefix_objs] need = max(0, limit - len(prefix)) contains: List[Tuple[str, int]] = [] if need > 0: qs_contain = ( WordlistFr .filter(Q(search_text__icontains=query_word) | Q(text__icontains=query.query)) .exclude(Q(search_text__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query)) .only("text", "freq") .only("text", "freq") ) contains_objs = await qs_contain[: need * 2] contains = [(o.text, o.freq) for o in contains_objs] seen_text, out = set(), [] for text, freq in list(exact_word) + list(prefix) + list(contains): key = text if key not in seen_text: seen_text.add(key) out.append((text, freq)) if len(out) >= limit: break out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0])) return [text for text, _ in out] else: query_word = all_in_kana(query.query) exact = await ( WordlistJp .get_or_none( text=query.query ) .only("text", "hiragana", "freq") ) if exact: exact_word = [(exact.text, exact.hiragana, exact.freq)] else: exact_word = [] qs_prefix = ( WordlistJp .filter(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query)) .exclude(text=query.query) .only("text", "hiragana", "freq") ) prefix_objs = await qs_prefix[:limit] prefix: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in prefix_objs] need = max(0, limit - len(prefix)) contains: List[Tuple[str, str, int]] = [] if need > 0: qs_contain = await ( WordlistJp .filter(Q(hiragana__icontains=query_word) | Q(text__icontains=query.query)) .exclude(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query)) .only("text", "hiragana", "freq") ) contains_objs = qs_contain[:need * 2] contains: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in contains_objs] seen_text, out = set(), [] for text, hiragana, freq in list(exact_word) + list(prefix) + list(contains): key = (text, hiragana) if key not in seen_text: seen_text.add(key) out.append((text, hiragana, freq)) if len(out) >= limit: break out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0])) return [(text, hiragana) for text, hiragana, _ in out] async def __test(): query_word: str = '棋逢' return await ( suggest_proverb( query=ProverbSearchRequest(query=query_word), lang='zh' ) ) async def __main(): await Tortoise.init(config=TORTOISE_ORM) print(await __test()) if __name__ == '__main__': # asyncio.run(__main()) print(detect_language(text="ahsjdasd"))