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@@ -0,0 +1,1113 @@
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+#!/usr/bin/env python3
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+"""
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+基于Streamlit的OCR可视化校验工具（重构版）
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+提供丰富的交互组件和更好的用户体验
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+"""
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+
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+import streamlit as st
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+from pathlib import Path
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+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 io import BytesIO
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+import pandas as pd
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+import numpy as np
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+import plotly.express as px
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+import json
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+
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+# 导入工具模块
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+from ocr_validator_utils import (
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+ load_config, load_ocr_data_file, process_ocr_data,
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+ get_ocr_statistics,
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+ find_available_ocr_files,
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+ group_texts_by_category,
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+ find_available_ocr_files_multi_source, get_data_source_display_name
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+)
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+from ocr_validator_file_utils import (
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+ load_css_styles,
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+ draw_bbox_on_image,
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+ convert_html_table_to_markdown,
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+ parse_html_tables,
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+ create_dynamic_css,
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+ export_tables_to_excel,
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+ get_table_statistics,
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+)
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+from ocr_validator_layout import OCRLayoutManager
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+from ocr_by_vlm import ocr_with_vlm
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+from compare_ocr_results import compare_ocr_results
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+
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+
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+class StreamlitOCRValidator:
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+ def __init__(self):
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+ self.config = load_config()
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+ self.ocr_data = []
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+ self.md_content = ""
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+ self.image_path = ""
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+ self.text_bbox_mapping = {}
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+ self.selected_text = None
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+ self.marked_errors = set()
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+
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+ # 多数据源相关
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+ self.all_sources = {}
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+ self.current_source_key = None
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+ self.current_source_config = None
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+ self.file_info = []
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+ self.selected_file_index = -1
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+ self.display_options = []
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+ self.file_paths = []
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+
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+ # 初始化布局管理器
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+ self.layout_manager = OCRLayoutManager(self)
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+
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+ # 加载多数据源文件信息
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+ self.load_multi_source_info()
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+
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+ def load_multi_source_info(self):
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+ """加载多数据源文件信息"""
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+ self.all_sources = find_available_ocr_files_multi_source(self.config)
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+
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+ # 如果有数据源，默认选择第一个
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+ if self.all_sources:
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+ first_source_key = list(self.all_sources.keys())[0]
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+ self.switch_to_source(first_source_key)
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+
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+ def switch_to_source(self, source_key: str):
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+ """切换到指定数据源"""
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+ if source_key in self.all_sources:
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+ self.current_source_key = source_key
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+ source_data = self.all_sources[source_key]
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+ self.current_source_config = source_data['config']
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+ self.file_info = source_data['files']
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+
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+ if self.file_info:
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+ # 创建显示选项列表
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+ self.display_options = [f"{info['display_name']}" for info in self.file_info]
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+ self.file_paths = [info['path'] for info in self.file_info]
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+
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+ # 重置文件选择
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+ self.selected_file_index = -1
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+
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+ print(f"✅ 切换到数据源: {source_key}")
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+ else:
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+ print(f"⚠️ 数据源 {source_key} 没有可用文件")
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+
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+ def setup_page_config(self):
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+ """设置页面配置"""
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+ ui_config = self.config['ui']
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+ st.set_page_config(
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+ page_title=ui_config['page_title'],
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+ page_icon=ui_config['page_icon'],
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+ layout=ui_config['layout'],
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+ initial_sidebar_state=ui_config['sidebar_state']
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+ )
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+
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+ # 加载CSS样式
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+ css_content = load_css_styles()
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+ st.markdown(f"<style>{css_content}</style>", unsafe_allow_html=True)
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+
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+ def create_data_source_selector(self):
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+ """创建数据源选择器"""
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+ if not self.all_sources:
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+ st.warning("❌ 未找到任何数据源，请检查配置文件")
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+ return
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+
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+ # 数据源选择
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+ source_options = {}
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+ for source_key, source_data in self.all_sources.items():
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+ display_name = get_data_source_display_name(source_data['config'])
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+ source_options[display_name] = source_key
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+
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+ # 获取当前选择的显示名称
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+ current_display_name = None
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+ if self.current_source_key:
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+ for display_name, key in source_options.items():
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+ if key == self.current_source_key:
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+ current_display_name = display_name
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+ break
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+
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+ selected_display_name = st.selectbox(
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+ "📁 选择数据源",
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+ options=list(source_options.keys()),
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+ index=list(source_options.keys()).index(current_display_name) if current_display_name else 0,
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+ key="data_source_selector",
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+ help="选择要分析的OCR数据源"
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+ )
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+
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+ selected_source_key = source_options[selected_display_name]
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+
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+ # 如果数据源发生变化，切换数据源
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+ if selected_source_key != self.current_source_key:
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+ self.switch_to_source(selected_source_key)
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+ # 重置session state
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+ if 'selected_file_index' in st.session_state:
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+ st.session_state.selected_file_index = 0
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+ st.rerun()
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+
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+ # 显示数据源信息
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+ if self.current_source_config:
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+ with st.expander("📋 数据源详情", expanded=False):
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+ col1, col2, col3 = st.columns(3)
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+ with col1:
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+ st.write(f"**名称:** {self.current_source_config['name']}")
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+ st.write(f"**OCR工具:** {self.current_source_config['ocr_tool']}")
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+ with col2:
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+ st.write(f"**输出目录:** {self.current_source_config['ocr_out_dir']}")
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+ st.write(f"**图片目录:** {self.current_source_config.get('src_img_dir', 'N/A')}")
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+ with col3:
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+ st.write(f"**描述:** {self.current_source_config.get('description', 'N/A')}")
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+ st.write(f"**文件数量:** {len(self.file_info)}")
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+
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+ def load_ocr_data(self, json_path: str, md_path: Optional[str] = None, image_path: Optional[str] = None):
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+ """加载OCR相关数据 - 支持多数据源配置"""
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+ try:
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+ # 使用当前数据源的配置加载数据
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+ if self.current_source_config:
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+ # 临时修改config以使用当前数据源的配置
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+ temp_config = self.config.copy()
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+ temp_config['paths'] = {
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+ 'ocr_out_dir': self.current_source_config['ocr_out_dir'],
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+ 'src_img_dir': self.current_source_config.get('src_img_dir', ''),
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+ 'pre_validation_dir': self.config['pre_validation']['out_dir']
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+ }
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+
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+ # 设置OCR工具类型
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+ temp_config['current_ocr_tool'] = self.current_source_config['ocr_tool']
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+
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+ self.ocr_data, self.md_content, self.image_path = load_ocr_data_file(json_path, temp_config)
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+ else:
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+ self.ocr_data, self.md_content, self.image_path = load_ocr_data_file(json_path, self.config)
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+
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+ self.process_data()
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+ except Exception as e:
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+ st.error(f"❌ 加载失败: {e}")
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+ st.exception(e)
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+
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+ def process_data(self):
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+ """处理OCR数据"""
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+ self.text_bbox_mapping = process_ocr_data(self.ocr_data, self.config)
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+
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+ def get_statistics(self) -> Dict:
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+ """获取统计信息"""
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+ return get_ocr_statistics(self.ocr_data, self.text_bbox_mapping, self.marked_errors)
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+
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+ def display_html_table_as_dataframe(self, html_content: str, enable_editing: bool = False):
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+ """将HTML表格解析为DataFrame显示 - 增强版本支持横向滚动"""
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+ tables = parse_html_tables(html_content)
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+ wide_table_threshold = 15 # 超宽表格列数阈值
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+
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+ if not tables:
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+ st.warning("未找到可解析的表格")
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+ # 对于无法解析的HTML表格，使用自定义CSS显示
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+ st.markdown("""
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+ <style>
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+ .scrollable-table {
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+ overflow-x: auto;
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+ white-space: nowrap;
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+ border: 1px solid #ddd;
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+ border-radius: 5px;
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+ margin: 10px 0;
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+ }
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+ .scrollable-table table {
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+ width: 100%;
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+ border-collapse: collapse;
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+ }
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+ .scrollable-table th, .scrollable-table td {
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+ border: 1px solid #ddd;
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+ padding: 8px;
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+ text-align: left;
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+ min-width: 100px;
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+ }
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+ .scrollable-table th {
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+ background-color: #f5f5f5;
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+ font-weight: bold;
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+ }
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+ </style>
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+ """, unsafe_allow_html=True)
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+
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+ st.markdown(f'<div class="scrollable-table">{html_content}</div>', unsafe_allow_html=True)
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+ return
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+
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+ for i, table in enumerate(tables):
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+ st.subheader(f"📊 表格 {i+1}")
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+
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+ # 表格信息显示
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+ col_info1, col_info2, col_info3, col_info4 = st.columns(4)
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+ with col_info1:
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+ st.metric("行数", len(table))
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+ with col_info2:
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+ st.metric("列数", len(table.columns))
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+ with col_info3:
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+ # 检查是否有超宽表格
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+ is_wide_table = len(table.columns) > wide_table_threshold
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+ st.metric("表格类型", "超宽表格" if is_wide_table else "普通表格")
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+ with col_info4:
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+ # 表格操作模式选择
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+ display_mode = st.selectbox(
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+ f"显示模式 (表格{i+1})",
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+ ["完整显示", "分页显示", "筛选列显示"],
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+ key=f"display_mode_{i}"
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+ )
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+
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+ # 创建表格操作按钮
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+ col1, col2, col3, col4 = st.columns(4)
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+ with col1:
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+ show_info = st.checkbox(f"显示详细信息", key=f"info_{i}")
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+ with col2:
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+ show_stats = st.checkbox(f"显示统计信息", key=f"stats_{i}")
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+ with col3:
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+ enable_filter = st.checkbox(f"启用过滤", key=f"filter_{i}")
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+ with col4:
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+ enable_sort = st.checkbox(f"启用排序", key=f"sort_{i}")
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+
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+ # 根据显示模式处理表格
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+ display_table = self._process_table_display_mode(table, i, display_mode)
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+
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+ # 数据过滤和排序逻辑
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+ filtered_table = self._apply_table_filters_and_sorts(display_table, i, enable_filter, enable_sort)
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+
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+ # 显示表格 - 使用自定义CSS支持横向滚动
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+ st.markdown("""
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+ <style>
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+ .dataframe-container {
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+ overflow-x: auto;
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+ border: 1px solid #ddd;
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+ border-radius: 5px;
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+ margin: 10px 0;
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+ }
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+
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+ /* 为超宽表格特殊样式 */
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+ .wide-table-container {
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+ overflow-x: auto;
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+ max-height: 500px;
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+ overflow-y: auto;
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+ border: 2px solid #0288d1;
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+ border-radius: 8px;
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+ background: linear-gradient(90deg, #f8f9fa 0%, #ffffff 100%);
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+ }
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+
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+ .dataframe thead th {
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+ position: sticky;
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+ top: 0;
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+ background-color: #f5f5f5 !important;
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+ z-index: 10;
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+ border-bottom: 2px solid #0288d1;
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+ }
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+
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+ .dataframe tbody td {
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+ white-space: nowrap;
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+ min-width: 100px;
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+ max-width: 300px;
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+ overflow: hidden;
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+ text-overflow: ellipsis;
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+ }
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+ </style>
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+ """, unsafe_allow_html=True)
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+
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+ # 根据表格宽度选择显示容器
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+ container_class = "wide-table-container" if len(table.columns) > wide_table_threshold else "dataframe-container"
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+
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+ if enable_editing:
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+ st.markdown(f'<div class="{container_class}">', unsafe_allow_html=True)
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+ edited_table = st.data_editor(
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+ filtered_table,
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+ use_container_width=True,
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+ key=f"editor_{i}",
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+ height=400 if len(table.columns) > 8 else None
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+ )
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+ st.markdown('</div>', unsafe_allow_html=True)
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+
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+ if not edited_table.equals(filtered_table):
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+ st.success("✏️ 表格已编辑，可以导出修改后的数据")
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+ else:
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+ st.markdown(f'<div class="{container_class}">', unsafe_allow_html=True)
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+ st.dataframe(
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+ filtered_table,
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+ # use_container_width=True,
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+ width =400 if len(table.columns) > wide_table_threshold else "stretch"
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+ )
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+ st.markdown('</div>', unsafe_allow_html=True)
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+
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+ # 显示表格信息和统计
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+ self._display_table_info_and_stats(table, filtered_table, show_info, show_stats, i)
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+
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+ st.markdown("---")
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+
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+ def _apply_table_filters_and_sorts(self, table: pd.DataFrame, table_index: int, enable_filter: bool, enable_sort: bool) -> pd.DataFrame:
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+ """应用表格过滤和排序"""
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+ filtered_table = table.copy()
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+
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+ # 数据过滤
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+ if enable_filter and not table.empty:
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+ filter_col = st.selectbox(
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+ f"选择过滤列 (表格 {table_index+1})",
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+ options=['无'] + list(table.columns),
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+ key=f"filter_col_{table_index}"
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+ )
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+
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+ if filter_col != '无':
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+ filter_value = st.text_input(f"过滤值 (表格 {table_index+1})", key=f"filter_value_{table_index}")
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+ if filter_value:
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+ filtered_table = table[table[filter_col].astype(str).str.contains(filter_value, na=False)]
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+
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+ # 数据排序
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+ if enable_sort and not filtered_table.empty:
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+ sort_col = st.selectbox(
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+ f"选择排序列 (表格 {table_index+1})",
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+ options=['无'] + list(filtered_table.columns),
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+ key=f"sort_col_{table_index}"
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+ )
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+
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+ if sort_col != '无':
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+ sort_order = st.radio(
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+ f"排序方式 (表格 {table_index+1})",
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+ options=['升序', '降序'],
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+ horizontal=True,
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+ key=f"sort_order_{table_index}"
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+ )
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+ ascending = (sort_order == '升序')
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+ filtered_table = filtered_table.sort_values(sort_col, ascending=ascending)
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+
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|
+ return filtered_table
|
|
|
+
|
|
|
+ def _display_table_info_and_stats(self, original_table: pd.DataFrame, filtered_table: pd.DataFrame,
|
|
|
+ show_info: bool, show_stats: bool, table_index: int):
|
|
|
+ """显示表格信息和统计数据"""
|
|
|
+ if show_info:
|
|
|
+ st.write("**表格信息:**")
|
|
|
+ st.write(f"- 原始行数: {len(original_table)}")
|
|
|
+ st.write(f"- 过滤后行数: {len(filtered_table)}")
|
|
|
+ st.write(f"- 列数: {len(original_table.columns)}")
|
|
|
+ st.write(f"- 列名: {', '.join(original_table.columns)}")
|
|
|
+
|
|
|
+ if show_stats:
|
|
|
+ st.write("**统计信息:**")
|
|
|
+ numeric_cols = filtered_table.select_dtypes(include=[np.number]).columns
|
|
|
+ if len(numeric_cols) > 0:
|
|
|
+ st.dataframe(filtered_table[numeric_cols].describe())
|
|
|
+ else:
|
|
|
+ st.info("表格中没有数值列")
|
|
|
+
|
|
|
+ # 导出功能
|
|
|
+ if st.button(f"📥 导出表格 {table_index+1}", key=f"export_{table_index}"):
|
|
|
+ self._create_export_buttons(filtered_table, table_index)
|
|
|
+
|
|
|
+ def _create_export_buttons(self, table: pd.DataFrame, table_index: int):
|
|
|
+ """创建导出按钮"""
|
|
|
+ # CSV导出
|
|
|
+ csv_data = table.to_csv(index=False)
|
|
|
+ st.download_button(
|
|
|
+ label=f"下载CSV (表格 {table_index+1})",
|
|
|
+ data=csv_data,
|
|
|
+ file_name=f"table_{table_index+1}.csv",
|
|
|
+ mime="text/csv",
|
|
|
+ key=f"download_csv_{table_index}"
|
|
|
+ )
|
|
|
+
|
|
|
+ # Excel导出
|
|
|
+ excel_buffer = BytesIO()
|
|
|
+ table.to_excel(excel_buffer, index=False)
|
|
|
+ st.download_button(
|
|
|
+ label=f"下载Excel (表格 {table_index+1})",
|
|
|
+ data=excel_buffer.getvalue(),
|
|
|
+ file_name=f"table_{table_index+1}.xlsx",
|
|
|
+ mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
|
|
|
+ key=f"download_excel_{table_index}"
|
|
|
+ )
|
|
|
+
|
|
|
+ def _process_table_display_mode(self, table: pd.DataFrame, table_index: int, display_mode: str) -> pd.DataFrame:
|
|
|
+ """根据显示模式处理表格"""
|
|
|
+ if display_mode == "分页显示":
|
|
|
+ # 分页显示
|
|
|
+ page_size = st.selectbox(
|
|
|
+ f"每页显示行数 (表格 {table_index+1})",
|
|
|
+ [10, 20, 50, 100],
|
|
|
+ key=f"page_size_{table_index}"
|
|
|
+ )
|
|
|
+
|
|
|
+ total_pages = (len(table) - 1) // page_size + 1
|
|
|
+
|
|
|
+ if total_pages > 1:
|
|
|
+ page_number = st.selectbox(
|
|
|
+ f"页码 (表格 {table_index+1})",
|
|
|
+ range(1, total_pages + 1),
|
|
|
+ key=f"page_number_{table_index}"
|
|
|
+ )
|
|
|
+
|
|
|
+ start_idx = (page_number - 1) * page_size
|
|
|
+ end_idx = start_idx + page_size
|
|
|
+ return table.iloc[start_idx:end_idx]
|
|
|
+
|
|
|
+ return table
|
|
|
+
|
|
|
+ elif display_mode == "筛选列显示":
|
|
|
+ # 列筛选显示
|
|
|
+ if len(table.columns) > 5:
|
|
|
+ selected_columns = st.multiselect(
|
|
|
+ f"选择要显示的列 (表格 {table_index+1})",
|
|
|
+ table.columns.tolist(),
|
|
|
+ default=table.columns.tolist()[:5], # 默认显示前5列
|
|
|
+ key=f"selected_columns_{table_index}"
|
|
|
+ )
|
|
|
+
|
|
|
+ if selected_columns:
|
|
|
+ return table[selected_columns]
|
|
|
+
|
|
|
+ return table
|
|
|
+
|
|
|
+ else: # 完整显示
|
|
|
+ return table
|
|
|
+
|
|
|
+ @st.dialog("VLM预校验", width="large", dismissible=True, on_dismiss="rerun")
|
|
|
+ def vlm_pre_validation(self):
|
|
|
+ """VLM预校验功能 - 封装OCR识别和结果对比"""
|
|
|
+
|
|
|
+ if not self.image_path or not self.md_content:
|
|
|
+ st.error("❌ 请先加载OCR数据文件")
|
|
|
+ return
|
|
|
+ # 初始化对比结果存储
|
|
|
+ if 'comparison_result' not in st.session_state:
|
|
|
+ st.session_state.comparison_result = None
|
|
|
+
|
|
|
+ # 创建进度条和状态显示
|
|
|
+ with st.spinner("正在进行VLM预校验...", show_time=True):
|
|
|
+ status_text = st.empty()
|
|
|
+
|
|
|
+ try:
|
|
|
+ current_md_path = Path(self.file_paths[self.selected_file_index]).with_suffix('.md')
|
|
|
+ if not current_md_path.exists():
|
|
|
+ st.error("❌ 当前OCR结果的Markdown文件不存在，无法进行对比")
|
|
|
+ return
|
|
|
+ # 第一步：准备目录
|
|
|
+ pre_validation_dir = Path(self.config['pre_validation'].get('out_dir', './output/pre_validation/')).resolve()
|
|
|
+ pre_validation_dir.mkdir(parents=True, exist_ok=True)
|
|
|
+ status_text.write(f"工作目录: {pre_validation_dir}")
|
|
|
+
|
|
|
+ # 第二步：调用VLM进行OCR识别
|
|
|
+ status_text.text("🤖 正在调用VLM进行OCR识别...")
|
|
|
+
|
|
|
+ # 在expander中显示OCR过程
|
|
|
+ with st.expander("🔍 VLM OCR识别过程", expanded=True):
|
|
|
+ ocr_output = st.empty()
|
|
|
+
|
|
|
+ # 捕获OCR输出
|
|
|
+ import io
|
|
|
+ import contextlib
|
|
|
+
|
|
|
+ # 创建字符串缓冲区来捕获print输出
|
|
|
+ output_buffer = io.StringIO()
|
|
|
+
|
|
|
+ with contextlib.redirect_stdout(output_buffer):
|
|
|
+ ocr_result = ocr_with_vlm(
|
|
|
+ image_path=str(self.image_path),
|
|
|
+ output_dir=str(pre_validation_dir),
|
|
|
+ normalize_numbers=True
|
|
|
+ )
|
|
|
+
|
|
|
+ # 显示OCR过程输出
|
|
|
+ ocr_output.code(output_buffer.getvalue(), language='text')
|
|
|
+
|
|
|
+ status_text.text("✅ VLM OCR识别完成")
|
|
|
+
|
|
|
+ # 第三步：获取VLM生成的文件路径
|
|
|
+ vlm_md_path = pre_validation_dir / f"{Path(self.image_path).stem}.md"
|
|
|
+
|
|
|
+ if not vlm_md_path.exists():
|
|
|
+ st.error("❌ VLM OCR结果文件未生成")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 第四步：调用对比功能
|
|
|
+ status_text.text("📊 正在对比OCR结果...")
|
|
|
+
|
|
|
+ # 在expander中显示对比过程
|
|
|
+ comparison_result_path = pre_validation_dir / f"{current_md_path.stem}_comparison_result"
|
|
|
+ with st.expander("🔍 OCR结果对比过程", expanded=True):
|
|
|
+ compare_output = st.empty()
|
|
|
+
|
|
|
+ # 捕获对比输出
|
|
|
+ output_buffer = io.StringIO()
|
|
|
+
|
|
|
+ with contextlib.redirect_stdout(output_buffer):
|
|
|
+ comparison_result = compare_ocr_results(
|
|
|
+ file1_path=str(current_md_path),
|
|
|
+ file2_path=str(vlm_md_path),
|
|
|
+ output_file=str(comparison_result_path),
|
|
|
+ output_format='both',
|
|
|
+ ignore_images=True
|
|
|
+ )
|
|
|
+
|
|
|
+ # 显示对比过程输出
|
|
|
+ compare_output.code(output_buffer.getvalue(), language='text')
|
|
|
+
|
|
|
+ status_text.text("✅ VLM预校验完成")
|
|
|
+
|
|
|
+ st.session_state.comparison_result = {
|
|
|
+ "image_path": self.image_path,
|
|
|
+ "comparison_result_json": f"{comparison_result_path}.json",
|
|
|
+ "comparison_result_md": f"{comparison_result_path}.md",
|
|
|
+ "comparison_result": comparison_result
|
|
|
+ }
|
|
|
+
|
|
|
+ # 第五步：显示对比结果
|
|
|
+ self.display_comparison_results(comparison_result, detailed=False)
|
|
|
+
|
|
|
+ # 第六步：提供文件下载
|
|
|
+ # self.provide_download_options(pre_validation_dir, vlm_md_path, comparison_result)
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ st.error(f"❌ VLM预校验失败: {e}")
|
|
|
+ st.exception(e)
|
|
|
+
|
|
|
+ def display_comparison_results(self, comparison_result: dict, detailed: bool = True):
|
|
|
+ """显示对比结果摘要 - 使用DataFrame展示"""
|
|
|
+
|
|
|
+ st.header("📊 VLM预校验结果")
|
|
|
+
|
|
|
+ # 统计信息
|
|
|
+ stats = comparison_result['statistics']
|
|
|
+
|
|
|
+ # 统计信息概览
|
|
|
+ col1, col2, col3, col4 = st.columns(4)
|
|
|
+ with col1:
|
|
|
+ st.metric("总差异数", stats['total_differences'])
|
|
|
+ with col2:
|
|
|
+ st.metric("表格差异", stats['table_differences'])
|
|
|
+ with col3:
|
|
|
+ st.metric("金额差异", stats['amount_differences'])
|
|
|
+ with col4:
|
|
|
+ st.metric("段落差异", stats['paragraph_differences'])
|
|
|
+
|
|
|
+ # 结果判断
|
|
|
+ if stats['total_differences'] == 0:
|
|
|
+ st.success("🎉 完美匹配！VLM识别结果与原OCR结果完全一致")
|
|
|
+ else:
|
|
|
+ st.warning(f"⚠️ 发现 {stats['total_differences']} 个差异，建议人工检查")
|
|
|
+
|
|
|
+ # 使用DataFrame显示差异详情
|
|
|
+ if comparison_result['differences']:
|
|
|
+ st.subheader("🔍 差异详情对比")
|
|
|
+
|
|
|
+ # 准备DataFrame数据
|
|
|
+ diff_data = []
|
|
|
+ for i, diff in enumerate(comparison_result['differences'], 1):
|
|
|
+ diff_data.append({
|
|
|
+ '序号': i,
|
|
|
+ '位置': diff['position'],
|
|
|
+ '类型': diff['type'],
|
|
|
+ '原OCR结果': diff['file1_value'][:100] + ('...' if len(diff['file1_value']) > 100 else ''),
|
|
|
+ 'VLM识别结果': diff['file2_value'][:100] + ('...' if len(diff['file2_value']) > 100 else ''),
|
|
|
+ '描述': diff['description'][:80] + ('...' if len(diff['description']) > 80 else ''),
|
|
|
+ '严重程度': self._get_severity_level(diff)
|
|
|
+ })
|
|
|
+
|
|
|
+ # 创建DataFrame
|
|
|
+ df_differences = pd.DataFrame(diff_data)
|
|
|
+
|
|
|
+ # 添加样式
|
|
|
+ def highlight_severity(val):
|
|
|
+ """根据严重程度添加颜色"""
|
|
|
+ if val == '高':
|
|
|
+ return 'background-color: #ffebee; color: #c62828'
|
|
|
+ elif val == '中':
|
|
|
+ return 'background-color: #fff3e0; color: #ef6c00'
|
|
|
+ elif val == '低':
|
|
|
+ return 'background-color: #e8f5e8; color: #2e7d32'
|
|
|
+ return ''
|
|
|
+
|
|
|
+ # 显示DataFrame
|
|
|
+ styled_df = df_differences.style.applymap(
|
|
|
+ highlight_severity,
|
|
|
+ subset=['严重程度']
|
|
|
+ ).format({
|
|
|
+ '序号': '{:d}',
|
|
|
+ })
|
|
|
+
|
|
|
+ st.dataframe(
|
|
|
+ styled_df,
|
|
|
+ use_container_width=True,
|
|
|
+ height=400,
|
|
|
+ hide_index=True,
|
|
|
+ column_config={
|
|
|
+ "序号": st.column_config.NumberColumn(
|
|
|
+ "序号",
|
|
|
+ width=None, # 自动调整宽度
|
|
|
+ pinned=True,
|
|
|
+ help="差异项序号"
|
|
|
+ ),
|
|
|
+ "位置": st.column_config.TextColumn(
|
|
|
+ "位置",
|
|
|
+ width=None, # 自动调整宽度
|
|
|
+ pinned=True,
|
|
|
+ help="差异在文档中的位置"
|
|
|
+ ),
|
|
|
+ "类型": st.column_config.TextColumn(
|
|
|
+ "类型",
|
|
|
+ width=None, # 自动调整宽度
|
|
|
+ pinned=True,
|
|
|
+ help="差异类型"
|
|
|
+ ),
|
|
|
+ "原OCR结果": st.column_config.TextColumn(
|
|
|
+ "原OCR结果",
|
|
|
+ width="large", # 自动调整宽度
|
|
|
+ pinned=True,
|
|
|
+ help="原始OCR识别结果"
|
|
|
+ ),
|
|
|
+ "VLM识别结果": st.column_config.TextColumn(
|
|
|
+ "VLM识别结果",
|
|
|
+ width="large", # 自动调整宽度
|
|
|
+ help="VLM重新识别的结果"
|
|
|
+ ),
|
|
|
+ "描述": st.column_config.TextColumn(
|
|
|
+ "描述",
|
|
|
+ width="medium", # 自动调整宽度
|
|
|
+ help="差异详细描述"
|
|
|
+ ),
|
|
|
+ "严重程度": st.column_config.TextColumn(
|
|
|
+ "严重程度",
|
|
|
+ width=None, # 自动调整宽度
|
|
|
+ help="差异严重程度评级"
|
|
|
+ )
|
|
|
+ }
|
|
|
+ )
|
|
|
+
|
|
|
+ # 详细差异查看
|
|
|
+ st.subheader("🔍 详细差异查看")
|
|
|
+
|
|
|
+ if detailed:
|
|
|
+ # 选择要查看的差异
|
|
|
+ selected_diff_index = st.selectbox(
|
|
|
+ "选择要查看的差异:",
|
|
|
+ options=range(len(comparison_result['differences'])),
|
|
|
+ format_func=lambda x: f"差异 {x+1}: {comparison_result['differences'][x]['position']} - {comparison_result['differences'][x]['type']}",
|
|
|
+ key="selected_diff"
|
|
|
+ )
|
|
|
+
|
|
|
+ if selected_diff_index is not None:
|
|
|
+ diff = comparison_result['differences'][selected_diff_index]
|
|
|
+
|
|
|
+ # 并排显示完整内容
|
|
|
+ col1, col2 = st.columns(2)
|
|
|
+
|
|
|
+ with col1:
|
|
|
+ st.write("**原OCR结果:**")
|
|
|
+ st.text_area(
|
|
|
+ "原OCR结果详情",
|
|
|
+ value=diff['file1_value'],
|
|
|
+ height=200,
|
|
|
+ key=f"original_{selected_diff_index}",
|
|
|
+ label_visibility="collapsed"
|
|
|
+ )
|
|
|
+
|
|
|
+ with col2:
|
|
|
+ st.write("**VLM识别结果:**")
|
|
|
+ st.text_area(
|
|
|
+ "VLM识别结果详情",
|
|
|
+ value=diff['file2_value'],
|
|
|
+ height=200,
|
|
|
+ key=f"vlm_{selected_diff_index}",
|
|
|
+ label_visibility="collapsed"
|
|
|
+ )
|
|
|
+
|
|
|
+ # 差异详细信息
|
|
|
+ st.info(f"**位置:** {diff['position']}")
|
|
|
+ st.info(f"**类型:** {diff['type']}")
|
|
|
+ st.info(f"**描述:** {diff['description']}")
|
|
|
+ st.info(f"**严重程度:** {self._get_severity_level(diff)}")
|
|
|
+
|
|
|
+ # 差异统计图表
|
|
|
+ st.subheader("📈 差异类型分布")
|
|
|
+
|
|
|
+ # 按类型统计差异
|
|
|
+ type_counts = {}
|
|
|
+ severity_counts = {'高': 0, '中': 0, '低': 0}
|
|
|
+
|
|
|
+ for diff in comparison_result['differences']:
|
|
|
+ diff_type = diff['type']
|
|
|
+ type_counts[diff_type] = type_counts.get(diff_type, 0) + 1
|
|
|
+
|
|
|
+ severity = self._get_severity_level(diff)
|
|
|
+ severity_counts[severity] += 1
|
|
|
+
|
|
|
+ col1, col2 = st.columns(2)
|
|
|
+
|
|
|
+ with col1:
|
|
|
+ # 类型分布饼图
|
|
|
+ if type_counts:
|
|
|
+ fig_type = px.pie(
|
|
|
+ values=list(type_counts.values()),
|
|
|
+ names=list(type_counts.keys()),
|
|
|
+ title="差异类型分布"
|
|
|
+ )
|
|
|
+ st.plotly_chart(fig_type, use_container_width=True)
|
|
|
+
|
|
|
+ with col2:
|
|
|
+ # 严重程度分布条形图
|
|
|
+ fig_severity = px.bar(
|
|
|
+ x=list(severity_counts.keys()),
|
|
|
+ y=list(severity_counts.values()),
|
|
|
+ title="差异严重程度分布",
|
|
|
+ color=list(severity_counts.keys()),
|
|
|
+ color_discrete_map={'高': '#f44336', '中': '#ff9800', '低': '#4caf50'}
|
|
|
+ )
|
|
|
+ st.plotly_chart(fig_severity, use_container_width=True)
|
|
|
+
|
|
|
+ # 下载选项
|
|
|
+ if detailed:
|
|
|
+ self._provide_download_options_in_results(comparison_result)
|
|
|
+
|
|
|
+ def _get_severity_level(self, diff: dict) -> str:
|
|
|
+ """根据差异类型和内容判断严重程度"""
|
|
|
+ # 如果差异中已经包含严重程度，直接使用
|
|
|
+ if 'severity' in diff:
|
|
|
+ severity_map = {'high': '高', 'medium': '中', 'low': '低'}
|
|
|
+ return severity_map.get(diff['severity'], '中')
|
|
|
+
|
|
|
+ # 原有的逻辑作为后备
|
|
|
+ diff_type = diff['type'].lower()
|
|
|
+
|
|
|
+ # 金额相关差异为高严重程度
|
|
|
+ if 'amount' in diff_type or 'number' in diff_type:
|
|
|
+ return '高'
|
|
|
+
|
|
|
+ # 表格结构差异为中等严重程度
|
|
|
+ if 'table' in diff_type or 'structure' in diff_type:
|
|
|
+ return '中'
|
|
|
+
|
|
|
+ # 检查相似度
|
|
|
+ if 'similarity' in diff:
|
|
|
+ similarity = diff['similarity']
|
|
|
+ if similarity < 50:
|
|
|
+ return '高'
|
|
|
+ elif similarity < 85:
|
|
|
+ return '中'
|
|
|
+ else:
|
|
|
+ return '低'
|
|
|
+
|
|
|
+ # 检查内容长度差异
|
|
|
+ len_diff = abs(len(diff['file1_value']) - len(diff['file2_value']))
|
|
|
+ if len_diff > 50:
|
|
|
+ return '高'
|
|
|
+ elif len_diff > 10:
|
|
|
+ return '中'
|
|
|
+ else:
|
|
|
+ return '低'
|
|
|
+
|
|
|
+ def _provide_download_options_in_results(self, comparison_result: dict):
|
|
|
+ """在结果页面提供下载选项"""
|
|
|
+
|
|
|
+ st.subheader("📥 导出预校验结果")
|
|
|
+
|
|
|
+ col1, col2, col3 = st.columns(3)
|
|
|
+
|
|
|
+ with col1:
|
|
|
+ # 导出差异详情为Excel
|
|
|
+ if comparison_result['differences']:
|
|
|
+ diff_data = []
|
|
|
+ for i, diff in enumerate(comparison_result['differences'], 1):
|
|
|
+ diff_data.append({
|
|
|
+ '序号': i,
|
|
|
+ '位置': diff['position'],
|
|
|
+ '类型': diff['type'],
|
|
|
+ '原OCR结果': diff['file1_value'],
|
|
|
+ 'VLM识别结果': diff['file2_value'],
|
|
|
+ '描述': diff['description'],
|
|
|
+ '严重程度': self._get_severity_level(diff)
|
|
|
+ })
|
|
|
+
|
|
|
+ df_export = pd.DataFrame(diff_data)
|
|
|
+ excel_buffer = BytesIO()
|
|
|
+ df_export.to_excel(excel_buffer, index=False, sheet_name='差异详情')
|
|
|
+
|
|
|
+ st.download_button(
|
|
|
+ label="📊 下载差异详情(Excel)",
|
|
|
+ data=excel_buffer.getvalue(),
|
|
|
+ file_name=f"vlm_comparison_differences_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
|
|
|
+ mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
|
|
|
+ key="download_differences_excel"
|
|
|
+ )
|
|
|
+
|
|
|
+ with col2:
|
|
|
+ # 导出统计报告
|
|
|
+ stats_data = {
|
|
|
+ '统计项目': ['总差异数', '表格差异', '金额差异', '段落差异'],
|
|
|
+ '数量': [
|
|
|
+ comparison_result['statistics']['total_differences'],
|
|
|
+ comparison_result['statistics']['table_differences'],
|
|
|
+ comparison_result['statistics']['amount_differences'],
|
|
|
+ comparison_result['statistics']['paragraph_differences']
|
|
|
+ ]
|
|
|
+ }
|
|
|
+
|
|
|
+ df_stats = pd.DataFrame(stats_data)
|
|
|
+ csv_stats = df_stats.to_csv(index=False)
|
|
|
+
|
|
|
+ st.download_button(
|
|
|
+ label="📈 下载统计报告(CSV)",
|
|
|
+ data=csv_stats,
|
|
|
+ file_name=f"vlm_comparison_stats_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.csv",
|
|
|
+ mime="text/csv",
|
|
|
+ key="download_stats_csv"
|
|
|
+ )
|
|
|
+
|
|
|
+ with col3:
|
|
|
+ # 导出完整报告为JSON
|
|
|
+ import json
|
|
|
+
|
|
|
+ report_json = json.dumps(comparison_result, ensure_ascii=False, indent=2)
|
|
|
+
|
|
|
+ st.download_button(
|
|
|
+ label="📄 下载完整报告(JSON)",
|
|
|
+ data=report_json,
|
|
|
+ file_name=f"vlm_comparison_full_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.json",
|
|
|
+ mime="application/json",
|
|
|
+ key="download_full_json"
|
|
|
+ )
|
|
|
+
|
|
|
+ # 操作建议
|
|
|
+ st.subheader("🚀 后续操作建议")
|
|
|
+
|
|
|
+ total_diffs = comparison_result['statistics']['total_differences']
|
|
|
+ if total_diffs == 0:
|
|
|
+ st.success("✅ VLM识别结果与原OCR完全一致，可信度很高，无需人工校验")
|
|
|
+ elif total_diffs <= 5:
|
|
|
+ st.warning("⚠️ 发现少量差异，建议重点检查高严重程度的差异项")
|
|
|
+ elif total_diffs <= 20:
|
|
|
+ st.warning("🔍 发现中等数量差异，建议详细检查差异表格中标红的项目")
|
|
|
+ else:
|
|
|
+ st.error("❌ 发现大量差异，建议重新进行OCR识别或检查原始图片质量")
|
|
|
+
|
|
|
+ @st.dialog("查看预校验结果", width="large", dismissible=True, on_dismiss="rerun")
|
|
|
+ def show_comparison_results_dialog(self):
|
|
|
+ """显示VLM预校验结果的对话框"""
|
|
|
+ current_md_path = Path(self.file_paths[self.selected_file_index]).with_suffix('.md')
|
|
|
+ pre_validation_dir = Path(self.config['pre_validation'].get('out_dir', './output/pre_validation/')).resolve()
|
|
|
+ comparison_result_path = pre_validation_dir / f"{current_md_path.stem}_comparison_result.json"
|
|
|
+ if 'comparison_result' in st.session_state and st.session_state.comparison_result:
|
|
|
+ self.display_comparison_results(st.session_state.comparison_result['comparison_result'])
|
|
|
+ elif comparison_result_path.exists():
|
|
|
+ # 如果pre_validation_dir下有结果文件，提示用户加载
|
|
|
+ if st.button("加载预校验结果"):
|
|
|
+ with open(comparison_result_path, "r", encoding="utf-8") as f:
|
|
|
+ comparison_json_result = json.load(f)
|
|
|
+ comparison_result = {
|
|
|
+ "image_path": self.image_path,
|
|
|
+ "comparison_result_json": str(comparison_result_path),
|
|
|
+ "comparison_result_md": str(comparison_result_path.with_suffix('.md')),
|
|
|
+ "comparison_result": comparison_json_result
|
|
|
+ }
|
|
|
+
|
|
|
+ st.session_state.comparison_result = comparison_result
|
|
|
+ self.display_comparison_results(comparison_json_result)
|
|
|
+ else:
|
|
|
+ st.info("暂无预校验结果，请先运行VLM预校验")
|
|
|
+
|
|
|
+ def create_compact_layout(self, config):
|
|
|
+ """创建滚动凑布局"""
|
|
|
+ return self.layout_manager.create_compact_layout(config)
|
|
|
+
|
|
|
+@st.dialog("message", width="small", dismissible=True, on_dismiss="rerun")
|
|
|
+def message_box(msg: str, msg_type: str = "info"):
|
|
|
+ if msg_type == "info":
|
|
|
+ st.info(msg)
|
|
|
+ elif msg_type == "warning":
|
|
|
+ st.warning(msg)
|
|
|
+ elif msg_type == "error":
|
|
|
+ st.error(msg)
|
|
|
+
|
|
|
+def main():
|
|
|
+ """主应用"""
|
|
|
+ # 初始化应用
|
|
|
+ if 'validator' not in st.session_state:
|
|
|
+ validator = StreamlitOCRValidator()
|
|
|
+ st.session_state.validator = validator
|
|
|
+ st.session_state.validator.setup_page_config()
|
|
|
+
|
|
|
+ # 页面标题
|
|
|
+ config = st.session_state.validator.config
|
|
|
+ st.title(config['ui']['page_title'])
|
|
|
+ else:
|
|
|
+ validator = st.session_state.validator
|
|
|
+ config = st.session_state.validator.config
|
|
|
+
|
|
|
+ if 'selected_text' not in st.session_state:
|
|
|
+ st.session_state.selected_text = None
|
|
|
+
|
|
|
+ if 'marked_errors' not in st.session_state:
|
|
|
+ st.session_state.marked_errors = set()
|
|
|
+
|
|
|
+ # 数据源选择器
|
|
|
+ validator.create_data_source_selector()
|
|
|
+
|
|
|
+ # 如果没有可用的数据源，提前返回
|
|
|
+ if not validator.all_sources:
|
|
|
+ st.stop()
|
|
|
+
|
|
|
+ # 文件选择区域
|
|
|
+ with st.container(height=75, horizontal=True, horizontal_alignment='left', gap="medium"):
|
|
|
+ # 初始化session_state中的选择索引
|
|
|
+ if 'selected_file_index' not in st.session_state:
|
|
|
+ st.session_state.selected_file_index = 0
|
|
|
+
|
|
|
+ if validator.display_options:
|
|
|
+ # 文件选择下拉框
|
|
|
+ selected_index = st.selectbox(
|
|
|
+ "选择OCR结果文件",
|
|
|
+ range(len(validator.display_options)),
|
|
|
+ format_func=lambda i: validator.display_options[i],
|
|
|
+ index=st.session_state.selected_file_index,
|
|
|
+ key="selected_selectbox",
|
|
|
+ label_visibility="collapsed"
|
|
|
+ )
|
|
|
+
|
|
|
+ # 更新session_state
|
|
|
+ if selected_index != st.session_state.selected_file_index:
|
|
|
+ st.session_state.selected_file_index = selected_index
|
|
|
+
|
|
|
+ selected_file = validator.file_paths[selected_index]
|
|
|
+
|
|
|
+ # 页码输入器
|
|
|
+ current_page = validator.file_info[selected_index]['page']
|
|
|
+ page_input = st.number_input(
|
|
|
+ "输入页码",
|
|
|
+ placeholder="输入页码",
|
|
|
+ label_visibility="collapsed",
|
|
|
+ min_value=1,
|
|
|
+ max_value=len(validator.display_options),
|
|
|
+ value=current_page,
|
|
|
+ step=1,
|
|
|
+ key="page_input"
|
|
|
+ )
|
|
|
+
|
|
|
+ # 当页码输入改变时，更新文件选择
|
|
|
+ if page_input != current_page:
|
|
|
+ for i, info in enumerate(validator.file_info):
|
|
|
+ if info['page'] == page_input:
|
|
|
+ st.session_state.selected_file_index = i
|
|
|
+ selected_file = validator.file_paths[i]
|
|
|
+ st.rerun()
|
|
|
+ break
|
|
|
+
|
|
|
+ # 自动加载文件
|
|
|
+ if (st.session_state.selected_file_index >= 0
|
|
|
+ and validator.selected_file_index != st.session_state.selected_file_index
|
|
|
+ and selected_file):
|
|
|
+ validator.selected_file_index = st.session_state.selected_file_index
|
|
|
+ st.session_state.validator.load_ocr_data(selected_file)
|
|
|
+
|
|
|
+ # 显示加载成功信息
|
|
|
+ current_source_name = get_data_source_display_name(validator.current_source_config)
|
|
|
+ st.success(f"✅ 已加载 {current_source_name} - 第{validator.file_info[st.session_state.selected_file_index]['page']}页")
|
|
|
+ st.rerun()
|
|
|
+ else:
|
|
|
+ st.warning("当前数据源中未找到OCR结果文件")
|
|
|
+
|
|
|
+ # VLM预校验按钮
|
|
|
+ if st.button("VLM预校验", type="primary", icon=":material/compare_arrows:"):
|
|
|
+ if validator.image_path and validator.md_content:
|
|
|
+ validator.vlm_pre_validation()
|
|
|
+ else:
|
|
|
+ message_box("❌ 请先选择OCR数据文件", "error")
|
|
|
+
|
|
|
+ # 查看预校验结果按钮
|
|
|
+ if st.button("查看预校验结果", type="secondary", icon=":material/quick_reference_all:"):
|
|
|
+ validator.show_comparison_results_dialog()
|
|
|
+
|
|
|
+ # 显示当前数据源统计信息
|
|
|
+ with st.expander("🔧 OCR工具统计信息", expanded=False):
|
|
|
+ stats = validator.get_statistics()
|
|
|
+ col1, col2, col3, col4, col5 = st.columns(5)
|
|
|
+
|
|
|
+ with col1:
|
|
|
+ st.metric("📊 总文本块", stats['total_texts'])
|
|
|
+ with col2:
|
|
|
+ st.metric("🔗 可点击文本", stats['clickable_texts'])
|
|
|
+ with col3:
|
|
|
+ st.metric("❌ 标记错误", stats['marked_errors'])
|
|
|
+ with col4:
|
|
|
+ st.metric("✅ 准确率", f"{stats['accuracy_rate']:.1f}%")
|
|
|
+ with col5:
|
|
|
+ # 显示当前数据源信息
|
|
|
+ if validator.current_source_config:
|
|
|
+ tool_display = validator.current_source_config['ocr_tool'].upper()
|
|
|
+ st.metric("🔧 OCR工具", tool_display)
|
|
|
+
|
|
|
+ # 详细工具信息
|
|
|
+ if stats['tool_info']:
|
|
|
+ st.write("**详细信息:**", stats['tool_info'])
|
|
|
+
|
|
|
+ # 其余标签页保持不变...
|
|
|
+ tab1, tab2, tab3 = st.tabs(["📄 内容人工检查", "📄 VLM预校验识别结果", "📊 表格分析"])
|
|
|
+
|
|
|
+ with tab1:
|
|
|
+ validator.create_compact_layout(config)
|
|
|
+
|
|
|
+ with tab2:
|
|
|
+ # st.header("📄 VLM预校验识别结果")
|
|
|
+ current_md_path = Path(validator.file_paths[validator.selected_file_index]).with_suffix('.md')
|
|
|
+ pre_validation_dir = Path(validator.config['pre_validation'].get('out_dir', './output/pre_validation/')).resolve()
|
|
|
+ comparison_result_path = pre_validation_dir / f"{current_md_path.stem}_comparison_result.json"
|
|
|
+ pre_validation_path = pre_validation_dir / f"{current_md_path.stem}.md"
|
|
|
+ if comparison_result_path.exists():
|
|
|
+ # 左边显示OCR结果，右边显示VLM结果
|
|
|
+ col1, col2 = st.columns([1,1])
|
|
|
+ with col1:
|
|
|
+ st.subheader("🤖 原OCR识别结果")
|
|
|
+ with open(current_md_path, "r", encoding="utf-8") as f:
|
|
|
+ original_md_content = f.read()
|
|
|
+ font_size = config['styles'].get('font_size', 10)
|
|
|
+ height = config['styles']['layout'].get('default_height', 800)
|
|
|
+ layout_type = "compact"
|
|
|
+ validator.layout_manager.render_content_by_mode(original_md_content, "HTML渲染", font_size, height, layout_type)
|
|
|
+ with col2:
|
|
|
+ st.subheader("🤖 VLM识别结果")
|
|
|
+ with open(pre_validation_path, "r", encoding="utf-8") as f:
|
|
|
+ pre_validation_md_content = f.read()
|
|
|
+ font_size = config['styles'].get('font_size', 10)
|
|
|
+ height = config['styles']['layout'].get('default_height', 800)
|
|
|
+ layout_type = "compact"
|
|
|
+ validator.layout_manager.render_content_by_mode(pre_validation_md_content, "HTML渲染", font_size, height, layout_type)
|
|
|
+ else:
|
|
|
+ st.info("暂无预校验结果，请先运行VLM预校验")
|
|
|
+
|
|
|
+ with tab3:
|
|
|
+ # 表格分析页面 - 保持原有逻辑
|
|
|
+ st.header("📊 表格数据分析")
|
|
|
+
|
|
|
+ if validator.md_content and '<table' in validator.md_content.lower():
|
|
|
+ st.subheader("🔍 表格数据预览")
|
|
|
+ validator.display_html_table_as_dataframe(validator.md_content)
|
|
|
+
|
|
|
+ else:
|
|
|
+ st.info("当前OCR结果中没有检测到表格数据")
|
|
|
+
|
|
|
+ # with tab4:
|
|
|
+ # # 数据统计页面 - 保持原有逻辑
|
|
|
+ # st.header("📈 OCR数据统计")
|
|
|
+
|
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+ # # 添加数据源特定的统计信息
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+ # if validator.current_source_config:
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+ # st.subheader(f"📊 {get_data_source_display_name(validator.current_source_config)} - 统计信息")
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+
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+ # if stats['categories']:
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+ # st.subheader("📊 类别分布")
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+ # fig_pie = px.pie(
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+ # values=list(stats['categories'].values()),
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+ # names=list(stats['categories'].keys()),
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+ # title="文本类别分布"
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+ # )
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+ # st.plotly_chart(fig_pie, use_container_width=True)
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+
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+ # # 错误率分析
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+ # st.subheader("📈 质量分析")
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+ # accuracy_data = {
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+ # '状态': ['正确', '错误'],
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+ # '数量': [stats['clickable_texts'] - stats['marked_errors'], stats['marked_errors']]
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+ # }
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+
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+ # fig_bar = px.bar(
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+ # accuracy_data, x='状态', y='数量', title="识别质量分布",
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+ # color='状态', color_discrete_map={'正确': 'green', '错误': 'red'}
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+ # )
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+ # st.plotly_chart(fig_bar, use_container_width=True)
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+
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+if __name__ == "__main__":
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+ main()
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