diff --git a/docutranslate/__init__.py b/docutranslate/__init__.py index df5eb7f..760e2e1 100644 --- a/docutranslate/__init__.py +++ b/docutranslate/__init__.py @@ -1,3 +1,3 @@ # SPDX-FileCopyrightText: 2025 QinHan # SPDX-License-Identifier: MPL-2.0 -__version__="1.4.16" \ No newline at end of file +__version__="1.4.16.post1" \ No newline at end of file diff --git a/docutranslate/agents/segments_agent.py b/docutranslate/agents/segments_agent.py index 3c6921b..351b3d0 100644 --- a/docutranslate/agents/segments_agent.py +++ b/docutranslate/agents/segments_agent.py @@ -108,6 +108,7 @@ class SegmentsTranslateAgent(Agent): result = get_target_segments(result) if result == "": if original_segments.strip() != "": + # print(f"【测试】origin_prompt:\n{origin_prompt}\nresult:\n{result}") raise AgentResultError("result为空值但原文不为空") return {} try: diff --git a/docutranslate/translator/ai_translator/epub_translator.py b/docutranslate/translator/ai_translator/epub_translator.py index ad6c0c6..1deb0a1 100644 --- a/docutranslate/translator/ai_translator/epub_translator.py +++ b/docutranslate/translator/ai_translator/epub_translator.py @@ -61,7 +61,7 @@ class EpubTranslator(AiTranslator): List[str] # original_texts: 原始HTML片段 ]: all_files = {} - soups = {} # << [关键修改] 存储解析后的BS对象 + soups = {} items_to_translate = [] original_texts = [] @@ -90,9 +90,11 @@ class EpubTranslator(AiTranslator): full_href = os.path.join(opf_dir, href).replace('\\', '/') manifest_items[item_id] = {'href': full_href, 'media_type': item.get('media-type')} - # TAGS_TO_TRANSLATE 定义了哪些块级标签的内容需要被翻译 TAGS_TO_TRANSLATE = ['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'li', 'div'] + # 定义一个正则表达式，用于按
和 标签分割内容 + split_pattern = re.compile(r'(|]*>)', re.IGNORECASE) + for item_id, item_data in manifest_items.items(): media_type = item_data['media_type'] if media_type in ['application/xhtml+xml', 'text/html']: @@ -106,57 +108,80 @@ class EpubTranslator(AiTranslator): soups[file_path] = BeautifulSoup(content_bytes, "html.parser") soup = soups[file_path] - for tag in soup.find_all(TAGS_TO_TRANSLATE): + + # ==================== 关键代码修改 ==================== + # 采用“Bottom-Up”逻辑：只选择不包含其他可翻译块级标签的“叶子”标签。 + # 这种方法能准确地选取段落，并自动忽略像
这样的父容器。 + + # 1. 找到所有可能的翻译标签 + all_potential_tags = soup.find_all(TAGS_TO_TRANSLATE) + all_potential_tags_set = set(all_potential_tags) # 用于快速查找 + + tags_to_process = [] + for tag in all_potential_tags: + # 2. 检查当前标签内部是否还包含其他需要翻译的标签 + # 如果没有，说明它是一个“叶子”节点，是我们要找的翻译单元。 + contains_other_block = tag.find( + lambda child_tag: child_tag in all_potential_tags_set and child_tag is not tag + ) + if not contains_other_block: + tags_to_process.append(tag) + # ==================== 修改结束 ==================== + + for tag in tags_to_process: inner_html = tag.decode_contents() if not inner_html or inner_html.isspace(): continue - # 使用正则表达式按
标签分割内容，同时保留
标签本身 - html_parts = re.split(r'()', inner_html, flags=re.IGNORECASE) + # 使用正则表达式分割内容，同时保留
和 标签 + html_parts = split_pattern.split(inner_html) is_split = len(html_parts) > 1 for part in html_parts: part_stripped = part.strip() - # 判断当前部分是否是
标签 - is_br_tag = re.fullmatch(r'', part_stripped, flags=re.IGNORECASE) + if not part_stripped: + continue - # 我们只翻译那些不是
标签且有实际内容的片段 - if not is_br_tag and part_stripped: + # 判断当前部分是否是
或 分隔符标签 + is_separator_tag = split_pattern.fullmatch(part_stripped) + + # ==================== 关键代码修改 ==================== + # 检查片段是否包含实际可翻译的文本内容，而不仅仅是空白、 或空的HTML标签 + plain_text = BeautifulSoup(part, 'html.parser').get_text(strip=True) + + # 我们只翻译那些不是分隔符标签（如
, ）且含有实际文本内容的片段 + if not is_separator_tag and plain_text: item_info = { "file_path": file_path, - "tag": tag, # 父标签的引用 - "original_html": part, # 这部分是需要翻译的原文 - "original_full_html": inner_html if is_split else None # 仅在分割时保存完整原文 + "tag": tag, + "original_html": part, + "original_full_html": inner_html if is_split else None } items_to_translate.append(item_info) original_texts.append(part) + # ==================== 修改结束 ==================== return all_files, soups, items_to_translate, original_texts def _after_translate( self, all_files: Dict[str, bytes], - soups: Dict[str, BeautifulSoup], # << [关键修改] 接收解析好的BS对象 + soups: Dict[str, BeautifulSoup], items_to_translate: List[Dict[str, Any]], translated_texts: List[str], original_texts: List[str], ) -> bytes: - # 由于一个父标签可能被
分割成多个翻译块，我们需要重构替换逻辑 - # 按父标签（通过其对象id）对所有翻译块进行分组 tag_reconstruction_map = defaultdict(lambda: {'new_html': None, 'chunks': []}) - # 1. 初始化每个父标签的重建信息 for i, item_info in enumerate(items_to_translate): tag = item_info["tag"] tag_id = id(tag) if tag_reconstruction_map[tag_id]['new_html'] is None: - # 如果有分割，使用保存的完整原文；否则，使用当前块的原文（因为就这一个块） original_full_html = item_info.get("original_full_html") or item_info["original_html"] tag_reconstruction_map[tag_id]['new_html'] = original_full_html tag_reconstruction_map[tag_id]['tag_obj'] = tag - # 2. 为每个父标签准备好所有原始块和翻译块的对应关系 for i, item_info in enumerate(items_to_translate): tag = item_info["tag"] tag_id = id(tag) @@ -174,17 +199,13 @@ class EpubTranslator(AiTranslator): tag_reconstruction_map[tag_id]['chunks'].append({'original': original_chunk, 'final': final_chunk}) - # 3. 对每个父标签，用其所有的翻译块重建完整内容 for tag_id, data in tag_reconstruction_map.items(): tag: Tag = data['tag_obj'] reconstructed_html = data['new_html'] for chunk_info in data['chunks']: - # 使用replace函数进行替换。为避免错误替换，处理原始文本中的特殊字符 - # 这个方法在原始文本块在父标签中重复出现时可能出错，但对于大多数情况是有效的 reconstructed_html = reconstructed_html.replace(chunk_info['original'], chunk_info['final'], 1) - # 4. 更新父标签的内容 tag.clear() new_content_soup = BeautifulSoup(reconstructed_html, 'html.parser') @@ -196,7 +217,6 @@ class EpubTranslator(AiTranslator): for node in nodes_to_insert: tag.append(node.extract()) - # 从修改后的soups对象生成新的文件内容 for file_path, soup in soups.items(): all_files[file_path] = str(soup).encode('utf-8')