优化epub附加译文效果v1.0
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xunbu
@@ -20,14 +20,16 @@ from docutranslate.translator.ai_translator.base import AiTranslatorConfig, AiTr
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@dataclass
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class EpubTranslatorConfig(AiTranslatorConfig):
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insert_mode: Literal["replace", "append", "prepend"] = "replace"
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# 建议使用 <br />，它在 XHTML (EPUB标准) 中更规范
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separator: str = "<br />"
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# 建议使用 \n，代码会将其转换为 <br />，更灵活
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separator: str = "\n"
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class EpubTranslator(AiTranslator):
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"""
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一个用于翻译 EPUB 文件中内容的翻译器。
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【高级版】此版本直接翻译HTML内容，以保留内联格式，并支持表格翻译。
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【结构化修改版 v2】借鉴 DocxTranslator 的实现，在 append/prepend 模式下，
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对常规块级元素创建新标签存放译文，对表格单元格则在内部追加内容，以保证文档结构的正确性。
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"""
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def __init__(self, config: EpubTranslatorConfig):
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@@ -90,10 +92,7 @@ class EpubTranslator(AiTranslator):
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full_href = os.path.join(opf_dir, href).replace('\\', '/')
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manifest_items[item_id] = {'href': full_href, 'media_type': item.get('media-type')}
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# ==================== 代码修改 1: 添加表格相关的标签 ====================
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TAGS_TO_TRANSLATE = ['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'li', 'div', 'td', 'th']
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# 定义一个正则表达式，用于按 <br> 和 <img> 标签分割内容
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split_pattern = re.compile(r'(<br\s*/?>|<img[^>]*>)', re.IGNORECASE)
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for item_id, item_data in manifest_items.items():
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media_type = item_data['media_type']
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@@ -114,60 +113,20 @@ class EpubTranslator(AiTranslator):
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tags_to_process = []
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for tag in all_potential_tags:
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# 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。
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contains_other_block = tag.find(
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lambda child_tag: child_tag in all_potential_tags_set and child_tag is not tag
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)
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if not contains_other_block:
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tags_to_process.append(tag)
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# ==================== 修改结束 ====================
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for tag in tags_to_process:
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inner_html = tag.decode_contents()
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if not inner_html or inner_html.isspace():
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continue
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# ==================== 代码修改 2: 增加对表格标签的特殊处理 ====================
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# 对于表格单元格（td, th），我们希望直接翻译其内容，并替换。
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# 这样做可以避免在单元格内部错误地插入 <br> 导致表格布局破坏。
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# 其他标签（如 p, div）则可以继续使用分割逻辑，以支持段内换行。
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is_table_cell = tag.name in ['td', 'th']
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# ==================== 修改结束 ====================
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html_parts = split_pattern.split(inner_html)
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# 如果不是表格单元格，且存在 <br> 或 <img>，则按片段处理
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is_split = len(html_parts) > 1 and not is_table_cell
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if is_split:
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# 逻辑保持不变：处理被 <br> 或 <img> 分割的段落
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for part in html_parts:
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part_stripped = part.strip()
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if not part_stripped:
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continue
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is_separator_tag = split_pattern.fullmatch(part_stripped)
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plain_text = BeautifulSoup(part, 'html.parser').get_text(strip=True)
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if not is_separator_tag and plain_text:
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item_info = {
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"file_path": file_path,
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"tag": tag,
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"original_html": part,
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"original_full_html": inner_html,
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"is_split": True
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}
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items_to_translate.append(item_info)
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original_texts.append(part)
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else:
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# 对于完整的标签内容（或表格单元格），我们整体处理
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plain_text = tag.get_text(strip=True)
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if plain_text:
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item_info = {
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"file_path": file_path,
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"tag": tag,
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"original_html": inner_html,
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"is_split": False
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}
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items_to_translate.append(item_info)
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original_texts.append(inner_html)
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@@ -183,84 +142,76 @@ class EpubTranslator(AiTranslator):
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original_texts: List[str],
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) -> bytes:
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# ==================== 代码修改 3: 重构 _after_translate 逻辑 ====================
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# 使用一个更清晰的 defaultdict 来处理内容的重构
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# key 是每个独立 tag 对象的 id，value 是待处理的信息
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tag_reconstruction_map = defaultdict(
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lambda: {'chunks': [], 'is_split': False, 'original_full_html': None, 'tag_obj': None})
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for i, item_info in enumerate(items_to_translate):
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tag = item_info["tag"]
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tag_id = id(tag)
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original_tag = item_info["tag"]
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soup = soups[item_info["file_path"]]
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original_html = original_texts[i]
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translated_html = translated_texts[i]
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tag_reconstruction_map[tag_id]['is_split'] = item_info['is_split']
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tag_reconstruction_map[tag_id]['tag_obj'] = tag
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if item_info['is_split']:
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tag_reconstruction_map[tag_id]['original_full_html'] = item_info['original_full_html']
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tag_reconstruction_map[tag_id]['chunks'].append({
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'original': original_texts[i],
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'translated': translated_texts[i]
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})
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for tag_id, data in tag_reconstruction_map.items():
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tag: Tag = data['tag_obj']
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final_html = ""
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if data['is_split']:
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# 如果是分割的段落，我们需要重组它
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reconstructed_html = data['original_full_html']
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for chunk in data['chunks']:
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original_chunk = chunk['original']
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translated_chunk = chunk['translated']
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# --- 关键逻辑：根据标签类型选择不同的处理策略 ---
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is_table_cell = original_tag.name in ['td', 'th']
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if self.insert_mode == "replace":
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final_chunk = translated_chunk
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elif self.insert_mode == "append":
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final_chunk = original_chunk + self.separator + translated_chunk
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original_tag.clear()
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new_content_soup = BeautifulSoup(translated_html, 'html.parser')
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for node in list(new_content_soup.children):
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original_tag.append(node.extract())
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elif is_table_cell:
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# --- 表格单元格处理：在标签内部组合内容 ---
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original_tag.clear()
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# 解析HTML片段
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original_nodes = BeautifulSoup(original_html, 'html.parser').contents
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translated_nodes = BeautifulSoup(translated_html, 'html.parser').contents
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# 创建分隔符节点
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separator_nodes = []
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if self.separator:
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lines = self.separator.split('\n')
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for j, line in enumerate(lines):
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if line:
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separator_nodes.append(NavigableString(line))
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if j < len(lines) - 1:
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separator_nodes.append(soup.new_tag('br'))
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# 根据模式按顺序重新填充
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if self.insert_mode == "append":
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nodes_order = [original_nodes, separator_nodes, translated_nodes]
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else: # prepend
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final_chunk = translated_chunk + self.separator + original_chunk
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nodes_order = [translated_nodes, separator_nodes, original_nodes]
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# 使用带计数的替换，确保只替换第一个匹配项
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reconstructed_html = reconstructed_html.replace(original_chunk, final_chunk, 1)
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for node_list in nodes_order:
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for node in node_list:
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original_tag.append(node.extract() if isinstance(node, Tag) else node)
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final_html = reconstructed_html
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else:
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# 如果是完整的标签内容（包括表格单元格），则直接处理
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chunk = data['chunks'][0]
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original_chunk = chunk['original']
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translated_chunk = chunk['translated']
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# --- 常规块级元素处理：创建新标签 ---
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translated_tag = soup.new_tag(original_tag.name, attrs=original_tag.attrs)
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new_content_soup = BeautifulSoup(translated_html, 'html.parser')
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for node in list(new_content_soup.children):
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translated_tag.append(node.extract())
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if self.insert_mode == "replace":
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final_html = translated_chunk
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elif self.insert_mode == "append":
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# 对于表格，即使是 append 模式，直接拼接也可能破坏格式。
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# 因此，对于 td/th，我们强制在内部用 separator 分隔，而不是在标签外。
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if tag.name in ['td', 'th']:
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final_html = f"{original_chunk}{self.separator}{translated_chunk}"
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else:
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final_html = original_chunk + self.separator + translated_chunk
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else: # prepend
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if tag.name in ['td', 'th']:
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final_html = f"{translated_chunk}{self.separator}{original_chunk}"
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else:
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final_html = translated_chunk + self.separator + original_chunk
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separator_tag = None
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if self.separator:
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separator_tag = soup.new_tag('p')
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lines = self.separator.split('\n')
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for j, line in enumerate(lines):
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if line:
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separator_tag.append(NavigableString(line))
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if j < len(lines) - 1:
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separator_tag.append(soup.new_tag('br'))
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# 清空旧内容并插入新内容
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tag.clear()
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# 使用 BeautifulSoup 解析最终的 HTML 片段，以正确处理嵌套标签
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new_content_soup = BeautifulSoup(final_html, 'html.parser')
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# new_content_soup.body 可能不存在，如果 final_html 不含 body 标签。
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# 我们需要从 soup 的顶层子节点开始插入。
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nodes_to_insert = list(new_content_soup.children)
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if len(nodes_to_insert) == 1 and nodes_to_insert[0].name == 'html':
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nodes_to_insert = list(nodes_to_insert[0].body.children)
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for node in nodes_to_insert:
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# .extract() 会将节点从原文档树中移除，这样可以直接 append 到新位置
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tag.append(node.extract())
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# ==================== 修改结束 ====================
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if self.insert_mode == "append":
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current_node = original_tag
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if separator_tag:
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current_node.insert_after(separator_tag)
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current_node = separator_tag
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current_node.insert_after(translated_tag)
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elif self.insert_mode == "prepend":
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original_tag.insert_before(translated_tag)
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if separator_tag:
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translated_tag.insert_after(separator_tag)
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for file_path, soup in soups.items():
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all_files[file_path] = str(soup).encode('utf-8')
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@@ -278,7 +229,9 @@ class EpubTranslator(AiTranslator):
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all_files, soups, items_to_translate, original_texts = self._pre_translate(document)
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if not items_to_translate:
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self.logger.info("\n文件中没有找到需要翻译的内容。")
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document.content = self._after_translate(all_files, soups, [], [], [])
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return self
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if self.glossary_agent:
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self.glossary_dict_gen = self.glossary_agent.send_segments(original_texts, self.chunk_size)
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if self.translate_agent:
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@@ -298,6 +251,7 @@ class EpubTranslator(AiTranslator):
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)
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if not items_to_translate:
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self.logger.info("\n文件中没有找到需要翻译的内容。")
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document.content = await asyncio.to_thread(self._after_translate, all_files, soups, [], [], [])
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return self
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if self.glossary_agent:
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