ocr_verify/streamlit_ocr_validator.py

#!/usr/bin/env python3
"""
基于Streamlit的OCR可视化校验工具（重构版）
提供丰富的交互组件和更好的用户体验
"""

import streamlit as st
from pathlib import Path
from PIL import Image
from typing import Dict, List, Optional
import plotly.graph_objects as go
from io import BytesIO
import pandas as pd
import numpy as np
import plotly.express as px
import json

# 导入工具模块
from ocr_validator_utils import (
    load_config, load_css_styles, load_ocr_data_file, process_ocr_data,
    draw_bbox_on_image, get_ocr_statistics, convert_html_table_to_markdown,
    parse_html_tables, find_available_ocr_files, create_dynamic_css,
    export_tables_to_excel, get_table_statistics, group_texts_by_category
)
from ocr_validator_layout import OCRLayoutManager
from ocr_by_vlm import ocr_with_vlm
from compare_ocr_results import compare_ocr_results


class StreamlitOCRValidator:
    def __init__(self):
        self.config = load_config()
        self.ocr_data = []
        self.md_content = ""
        self.image_path = ""
        self.text_bbox_mapping = {}
        self.selected_text = None
        self.marked_errors = set()
        self.file_info = []
        self.selected_file_index = -1 # 初始化不指向有效文件index
        self.display_options = []
        self.file_paths = []

        # 初始化布局管理器
        self.layout_manager = OCRLayoutManager(self)

        # 加载文件信息
        self.load_file_info()
        
    def load_file_info(self):
        # 查找可用的OCR文件
        self.file_info = find_available_ocr_files(self.config['paths']['ocr_out_dir'])
        # 初始化session_state中的选择索引
        if self.file_info:
            # 创建显示选项列表
            self.display_options = [f"{info['display_name']}" for info in self.file_info]
            self.file_paths = [info['path'] for info in self.file_info]

    def setup_page_config(self):
        """设置页面配置"""
        ui_config = self.config['ui']
        st.set_page_config(
            page_title=ui_config['page_title'],
            page_icon=ui_config['page_icon'],
            layout=ui_config['layout'],
            initial_sidebar_state=ui_config['sidebar_state']
        )
        
        # 加载CSS样式
        css_content = load_css_styles()
        st.markdown(f"<style>{css_content}</style>", unsafe_allow_html=True)
    
    def load_ocr_data(self, json_path: str, md_path: Optional[str] = None, image_path: Optional[str] = None):
        """加载OCR相关数据"""
        try:
            self.ocr_data, self.md_content, self.image_path = load_ocr_data_file(json_path, self.config)
            self.process_data()
        except Exception as e:
            st.error(f"❌ 加载失败: {e}")
    
    def process_data(self):
        """处理OCR数据"""
        self.text_bbox_mapping = process_ocr_data(self.ocr_data, self.config)
    
    def get_statistics(self) -> Dict:
        """获取统计信息"""
        return get_ocr_statistics(self.ocr_data, self.text_bbox_mapping, self.marked_errors)
    
    def display_html_table_as_dataframe(self, html_content: str, enable_editing: bool = False):
        """将HTML表格解析为DataFrame显示 - 增强版本支持横向滚动"""
        tables = parse_html_tables(html_content)
        
        if not tables:
            st.warning("未找到可解析的表格")
            # 对于无法解析的HTML表格，使用自定义CSS显示
            st.markdown("""
            <style>
            .scrollable-table {
                overflow-x: auto;
                white-space: nowrap;
                border: 1px solid #ddd;
                border-radius: 5px;
                margin: 10px 0;
            }
            .scrollable-table table {
                width: 100%;
                border-collapse: collapse;
            }
            .scrollable-table th, .scrollable-table td {
                border: 1px solid #ddd;
                padding: 8px;
                text-align: left;
                min-width: 100px;
            }
            .scrollable-table th {
                background-color: #f5f5f5;
                font-weight: bold;
            }
            </style>
            """, unsafe_allow_html=True)
            
            st.markdown(f'<div class="scrollable-table">{html_content}</div>', unsafe_allow_html=True)
            return
            
        for i, table in enumerate(tables):
            st.subheader(f"📊 表格 {i+1}")
            
            # 表格信息显示
            col_info1, col_info2, col_info3, col_info4 = st.columns(4)
            with col_info1:
                st.metric("行数", len(table))
            with col_info2:
                st.metric("列数", len(table.columns))
            with col_info3:
                # 检查是否有超宽表格
                is_wide_table = len(table.columns) > 8
                st.metric("表格类型", "超宽表格" if is_wide_table else "普通表格")
            with col_info4:
                # 表格操作模式选择
                display_mode = st.selectbox(
                    f"显示模式 (表格{i+1})",
                    ["完整显示", "分页显示", "筛选列显示"],
                    key=f"display_mode_{i}"
                )
            
            # 创建表格操作按钮
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                show_info = st.checkbox(f"显示详细信息", key=f"info_{i}")
            with col2:
                show_stats = st.checkbox(f"显示统计信息", key=f"stats_{i}")
            with col3:
                enable_filter = st.checkbox(f"启用过滤", key=f"filter_{i}")
            with col4:
                enable_sort = st.checkbox(f"启用排序", key=f"sort_{i}")
            
            # 根据显示模式处理表格
            display_table = self._process_table_display_mode(table, i, display_mode)
            
            # 数据过滤和排序逻辑
            filtered_table = self._apply_table_filters_and_sorts(display_table, i, enable_filter, enable_sort)
            
            # 显示表格 - 使用自定义CSS支持横向滚动
            st.markdown("""
            <style>
            .dataframe-container {
                overflow-x: auto;
                border: 1px solid #ddd;
                border-radius: 5px;
                margin: 10px 0;
            }
            
            /* 为超宽表格特殊样式 */
            .wide-table-container {
                overflow-x: auto;
                max-height: 500px;
                overflow-y: auto;
                border: 2px solid #0288d1;
                border-radius: 8px;
                background: linear-gradient(90deg, #f8f9fa 0%, #ffffff 100%);
            }
            
            .dataframe thead th {
                position: sticky;
                top: 0;
                background-color: #f5f5f5 !important;
                z-index: 10;
                border-bottom: 2px solid #0288d1;
            }
            
            .dataframe tbody td {
                white-space: nowrap;
                min-width: 100px;
                max-width: 300px;
                overflow: hidden;
                text-overflow: ellipsis;
            }
            </style>
            """, unsafe_allow_html=True)
            
            # 根据表格宽度选择显示容器
            container_class = "wide-table-container" if len(table.columns) > 8 else "dataframe-container"
            
            if enable_editing:
                st.markdown(f'<div class="{container_class}">', unsafe_allow_html=True)
                edited_table = st.data_editor(
                    filtered_table, 
                    use_container_width=True, 
                    key=f"editor_{i}",
                    height=400 if len(table.columns) > 8 else None
                )
                st.markdown('</div>', unsafe_allow_html=True)
                
                if not edited_table.equals(filtered_table):
                    st.success("✏️ 表格已编辑，可以导出修改后的数据")
            else:
                st.markdown(f'<div class="{container_class}">', unsafe_allow_html=True)
                st.dataframe(
                    filtered_table, 
                    # use_container_width=True,
                    width =400 if len(table.columns) > 8 else "stretch"
                )
                st.markdown('</div>', unsafe_allow_html=True)
            
            # 显示表格信息和统计
            self._display_table_info_and_stats(table, filtered_table, show_info, show_stats, i)
            
            st.markdown("---")
    
    def _apply_table_filters_and_sorts(self, table: pd.DataFrame, table_index: int, enable_filter: bool, enable_sort: bool) -> pd.DataFrame:
        """应用表格过滤和排序"""
        filtered_table = table.copy()
        
        # 数据过滤
        if enable_filter and not table.empty:
            filter_col = st.selectbox(
                f"选择过滤列 (表格 {table_index+1})", 
                options=['无'] + list(table.columns),
                key=f"filter_col_{table_index}"
            )
            
            if filter_col != '无':
                filter_value = st.text_input(f"过滤值 (表格 {table_index+1})", key=f"filter_value_{table_index}")
                if filter_value:
                    filtered_table = table[table[filter_col].astype(str).str.contains(filter_value, na=False)]
        
        # 数据排序
        if enable_sort and not filtered_table.empty:
            sort_col = st.selectbox(
                f"选择排序列 (表格 {table_index+1})", 
                options=['无'] + list(filtered_table.columns),
                key=f"sort_col_{table_index}"
            )
            
            if sort_col != '无':
                sort_order = st.radio(
                    f"排序方式 (表格 {table_index+1})",
                    options=['升序', '降序'],
                    horizontal=True,
                    key=f"sort_order_{table_index}"
                )
                ascending = (sort_order == '升序')
                filtered_table = filtered_table.sort_values(sort_col, ascending=ascending)
        
        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['paths'].get('pre_validation_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)
                
                # 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):
        """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")
                
                # 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
        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['paths'].get('pre_validation_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'])
        # st.markdown("---")
    else:
        # 主内容区域
        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()
    
    with st.container(height=75, horizontal=True, horizontal_alignment='left', gap="medium"):
        # st.subheader("📁 文件选择")
        # 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:
            # 创建显示选项列表
            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,
                width=100,
                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]

            # number_input, 范围是文件数量，默认值是1，步长是1
            # 页码输入器
            current_page = validator.file_info[selected_index]['page']
            page_input = st.number_input("输入一个数字", 
                placeholder="输入页码", 
                width=200,
                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:
                # 查找对应页码的文件索引
                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

            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)
                st.success(f"✅ 已加载第{validator.file_info[st.session_state.selected_file_index]['page']}页")
                st.rerun()
            # if st.button("🔄 加载文件", type="secondary") and selected_file:
            #     st.session_state.validator.load_ocr_data(selected_file)
            #     st.success(f"✅ 已加载第{validator.file_info[selected_index]['page']}页")
            #     st.rerun()
        else:
            st.warning("未找到OCR结果文件")
            st.info("请确保output目录下有OCR结果文件")
        
        if st.button("VLM预校验", type="primary", icon=":material/compare_arrows:"):
            if validator.image_path and validator.md_content:
                # 创建新的页面区域来显示VLM预校验结果
                validator.vlm_pre_validation()
            else:
                message_box("❌ 请先加载OCR数据文件", "error")

        if st.button("查看预校验结果", type="secondary", icon=":material/quick_reference_all:"):
            validator.show_comparison_results_dialog()

    with st.expander("🔧 OCR工具统计信息", expanded=False):
        # 显示统计信息
        stats = validator.get_statistics()
        col1, col2, col3, col4, col5 = st.columns(5)  # 增加一列
        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OCR工具信息
            if stats['tool_info']:
                tool_names = list(stats['tool_info'].keys())
                main_tool = tool_names[0] if tool_names else "未知"
                st.metric("🔧 OCR工具", main_tool)
        # 详细工具信息
        if stats['tool_info']:
            st.write(stats['tool_info'])
    
    # st.markdown("---")
    
    # 创建标签页
    tab1, tab2, tab3, tab4 = st.tabs(["📄 内容校验", "📊 表格分析", "📈 数据统计", "🚀 快速导航"])
    
    with tab1:
        validator.create_compact_layout(config)

    with tab2:
        # 表格分析页面
        st.header("📊 表格数据分析")
        
        if validator.md_content and '<table' in validator.md_content.lower():
            col1, col2 = st.columns([2, 1])
            
            with col1:
                st.subheader("🔍 表格数据预览")
                validator.display_html_table_as_dataframe(validator.md_content)
            
            with col2:
                st.subheader("⚙️ 表格操作")
                
                if st.button("📥 导出表格数据", type="primary"):
                    tables = parse_html_tables(validator.md_content)
                    if tables:
                        output = export_tables_to_excel(tables)
                        st.download_button(
                            label="📥 下载Excel文件",
                            data=output.getvalue(),
                            file_name="ocr_tables.xlsx",
                            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                        )
        else:
            st.info("当前OCR结果中没有检测到表格数据")
    
    with tab3:
        # 数据统计页面
        st.header("📈 OCR数据统计")
        
        if stats['categories']:
            st.subheader("📊 类别分布")
            fig_pie = px.pie(
                values=list(stats['categories'].values()),
                names=list(stats['categories'].keys()),
                title="文本类别分布"
            )
            st.plotly_chart(fig_pie, use_container_width=True)
        
        # 错误率分析
        st.subheader("📈 质量分析")
        accuracy_data = {
            '状态': ['正确', '错误'],
            '数量': [stats['clickable_texts'] - stats['marked_errors'], stats['marked_errors']]
        }
        
        fig_bar = px.bar(
            accuracy_data, x='状态', y='数量', title="识别质量分布",
            color='状态', color_discrete_map={'正确': 'green', '错误': 'red'}
        )
        st.plotly_chart(fig_bar, use_container_width=True)
    
    with tab4:
        # 快速导航功能
        st.header("🚀 快速导航")
        
        if not validator.text_bbox_mapping:
            st.info("没有可用的文本项进行导航")
        else:
            # 按类别分组
            categories = group_texts_by_category(validator.text_bbox_mapping)
            
            # 创建导航按钮
            for category, texts in categories.items():
                with st.expander(f"{category} ({len(texts)}项)", expanded=False):
                    cols = st.columns(3)  # 每行3个按钮
                    for i, text in enumerate(texts):
                        col_idx = i % 3
                        with cols[col_idx]:
                            display_text = text[:15] + "..." if len(text) > 15 else text
                            if st.button(display_text, key=f"nav_{category}_{i}"):
                                st.session_state.selected_text = text
                                st.rerun()


if __name__ == "__main__":
    main()