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@@ -10,6 +10,8 @@ from PIL import Image
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from typing import Dict, List, Optional
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import plotly.graph_objects as go
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from typing import Tuple
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+import re
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+import html
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from ocr_validator_utils import (
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rotate_image_and_coordinates,
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@@ -64,6 +66,69 @@ class OCRLayoutManager:
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st.session_state[search_key] = ""
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st.session_state[quick_select_key] = 0
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+ def _highlight_text_safely(self, content: str, text_to_highlight: str,
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+ highlight_class: str, title: Optional[str] = None) -> str:
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+ """
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+ 安全地高亮文本，避免替换base64编码中的内容
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+
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+ Args:
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+ content: 要处理的HTML内容
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+ text_to_highlight: 要高亮的文本
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+ highlight_class: 高亮样式类名
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+ title: 鼠标悬停提示文本，默认为text_to_highlight
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+
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+ Returns:
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+ 处理后的HTML内容
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+ """
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+ if not text_to_highlight or text_to_highlight not in content:
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+ return content
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+
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+ if title is None:
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+ title = text_to_highlight
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+
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+ # 转义特殊字符用于正则表达式
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+ escaped_text = re.escape(text_to_highlight)
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+
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+ # 找出所有base64编码区域的位置
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+ # 匹配两种格式：
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+ # 1. HTML: src="data:image/...;base64,..." 或 src='data:image/...;base64,...'
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+ # 2. Markdown: 
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+ # 使用更通用的模式来匹配base64数据
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+ base64_pattern = r'data:image/[^;]+;base64,[A-Za-z0-9+/=]+'
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+ base64_regions = []
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+
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+ for match in re.finditer(base64_pattern, content):
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+ base64_regions.append((match.start(), match.end()))
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+
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+ # 找出所有要高亮文本的位置
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+ text_pattern = re.compile(escaped_text)
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+ matches = []
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+
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+ for match in text_pattern.finditer(content):
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+ start, end = match.start(), match.end()
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+
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+ # 检查该位置是否在base64区域内
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+ in_base64 = False
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+ for base64_start, base64_end in base64_regions:
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+ if base64_start <= start < base64_end:
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+ in_base64 = True
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+ break
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+
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+ # 只保留不在base64区域内的匹配
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+ if not in_base64:
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+ matches.append((start, end))
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+
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+ # 从后向前替换，避免位置偏移
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+ for start, end in reversed(matches):
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+ original_text = content[start:end]
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+ # 转义HTML特殊字符
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+ escaped_original = html.escape(original_text)
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+ escaped_title = html.escape(title)
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+ highlighted = f'<span class="{highlight_class}" title="{escaped_title}">{escaped_original}</span>'
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+ content = content[:start] + highlighted + content[end:]
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+
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+ return content
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+
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def clear_image_cache(self):
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"""清理所有图像缓存"""
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self._rotated_image_cache.clear()
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@@ -529,7 +594,7 @@ class OCRLayoutManager:
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match_type = "no_bbox"
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# 🎯 应用高亮
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- if len(selected_text) > 2:
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+ if len(selected_text) >= self.config.get('ocr', {}).get('min_text_length', 2):
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# 1. 高亮原始文本
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if selected_text in highlighted_content:
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if match_type == "exact":
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@@ -539,16 +604,21 @@ class OCRLayoutManager:
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else:
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highlight_class = "highlight-text default"
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- highlighted_content = highlighted_content.replace(
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+ # 使用正则表达式避免替换base64编码中的内容
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+ highlighted_content = self._highlight_text_safely(
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+ highlighted_content,
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selected_text,
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- f'<span class="{highlight_class}" title="{selected_text}">{selected_text}</span>'
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+ highlight_class
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)
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# 2. 如果有 matched_text 且不同，也高亮
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if matched_text and matched_text != selected_text and matched_text in highlighted_content:
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- highlighted_content = highlighted_content.replace(
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+ # 使用正则表达式避免替换base64编码中的内容
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+ highlighted_content = self._highlight_text_safely(
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+ highlighted_content,
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matched_text,
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- f'<span class="highlight-text ocr-match" title="OCR: {matched_text}">{matched_text}</span>'
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+ "highlight-text ocr-match",
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+ f"OCR: {matched_text}"
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)
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# 🎯 调用渲染方法（样式已内置）
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zhch158_admin