#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Stage6/7/8整合可视化工具
在Stage6评估结果基础上,为完全匹配帖子增加Stage7解构和Stage8相似度展示
"""

import json
import os
from datetime import datetime
from typing import List, Dict, Any


def load_data(json_path: str) -> List[Dict[str, Any]]:
    """加载JSON数据"""
    with open(json_path, 'r', encoding='utf-8') as f:
        return json.load(f)


def load_stage7_data(json_path: str) -> Dict[str, Any]:
    """加载Stage7解构数据"""
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    # 创建note_id到解构数据的映射
    mapping = {}
    for result in data.get('results', []):
        note_id = result.get('note_id')
        if note_id:
            mapping[note_id] = result

    return mapping


def load_stage8_data(json_path: str) -> Dict[str, Any]:
    """加载Stage8相似度数据"""
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    # 创建note_id到相似度数据的映射
    mapping = {}
    for result in data.get('results', []):
        note_id = result.get('note_id')
        if note_id:
            mapping[note_id] = result

    return mapping


def load_persona_library(json_path: str) -> Dict[str, Any]:
    """加载人设特征库"""
    with open(json_path, 'r', encoding='utf-8') as f:
        return json.load(f)


def extract_all_features_from_how(how_json_path: str) -> Dict[str, Dict[str, Any]]:
    """
    从how解构结果中提取所有特征的完整信息

    Returns:
        Dict[特征名称, {
            'similarity': float,    # 最高相似度
            'weight': float,        # 权重
            'dimension': str,       # 所属维度
            'category': str         # 分类：'已搜索', '待搜索', '低相似度'
        }]
    """
    with open(how_json_path, 'r', encoding='utf-8') as f:
        how_data = json.load(f)

    features = {}

    for dimension_key in ['灵感点列表', '目的点列表', '关键点列表']:
        dimension_list = how_data.get('how解构结果', {}).get(dimension_key, [])
        dimension_name = dimension_key.replace('列表', '')

        for point in dimension_list:
            feature_list = point.get('特征列表', [])

            for feature in feature_list:
                feature_name = feature.get('特征名称', '')
                weight = feature.get('权重', 0)

                # 查找最高相似度
                max_similarity = 0
                how_steps = point.get('how步骤列表', [])

                for step in how_steps:
                    step_features = step.get('特征列表', [])
                    for step_feature in step_features:
                        if step_feature.get('特征名称') == feature_name:
                            matches = step_feature.get('匹配结果', [])
                            for match in matches:
                                similarity = match.get('匹配结果', {}).get('相似度', 0)
                                max_similarity = max(max_similarity, similarity)

                # 确定分类
                if max_similarity < 0.4:
                    category = '低相似度'
                elif 0.4 <= max_similarity <= 0.8:
                    category = '待搜索'  # 后续会根据Stage6数据更新为'已搜索'
                else:
                    category = '高相似度'  # >0.8，孤立点

                features[feature_name] = {
                    'similarity': max_similarity,
                    'weight': weight,
                    'dimension': dimension_name,
                    'category': category
                }

    return features


def is_category_or_feature(persona_name: str, persona_data: Dict[str, Any]) -> str:
    """
    递归判断人设项是特征还是分类

    Args:
        persona_name: 人设项名称
        persona_data: 人设库数据

    Returns:
        'feature' 或 'category'
    """
    def search_in_dict(data, name, path=""):
        """递归搜索字典"""
        if isinstance(data, dict):
            # 检查是否有特征列表
            if '特征列表' in data:
                for feature in data['特征列表']:
                    if feature.get('特征名称') == name:
                        return 'feature'

            # 递归搜索子节点
            for key, value in data.items():
                if key == '_meta':
                    continue

                # 如果键名匹配，且值是字典（说明是分类节点）
                if key == name and isinstance(value, dict):
                    return 'category'

                # 递归搜索
                result = search_in_dict(value, name, f"{path}/{key}")
                if result:
                    return result

        return None

    # 在三个主要维度中搜索
    for dimension in ['灵感点列表', '目的点列表', '关键点列表']:
        if dimension in persona_data:
            result = search_in_dict(persona_data[dimension], persona_name)
            if result:
                return result

    # 默认返回特征
    return 'feature'


def extract_relationship_data(how_json_path: str, persona_data: Dict[str, Any]) -> tuple:
    """
    从how解构结果中提取关系数据

    Returns:
        Tuple of (relationships, all_high_matches):
        - relationships: 最高相似度匹配的关系列表
        - all_high_matches: 所有相似度>0.8的匹配列表

        Data format:
        {
            'post_feature': str,  # 帖子特征名称
            'dimension': str,     # 所属维度（灵感点/目的点/关键点）
            'weight': float,      # 权重
            'persona_item': str,  # 人设特征/分类名称
            'similarity': float,  # 相似度
            'item_type': str,     # 'feature' or 'category'
        }
    """
    with open(how_json_path, 'r', encoding='utf-8') as f:
        how_data = json.load(f)

    relationships = []
    all_high_matches = []  # 所有相似度>0.8的匹配

    # 遍历how解构结果
    for dimension_key in ['灵感点列表', '目的点列表', '关键点列表']:
        dimension_list = how_data.get('how解构结果', {}).get(dimension_key, [])
        dimension_name = dimension_key.replace('列表', '')

        for point in dimension_list:
            features = point.get('特征列表', [])

            for feature in features:
                feature_name = feature.get('特征名称', '')
                weight = feature.get('权重', 0)

                # 查找匹配结果中相似度最高的
                how_steps = point.get('how步骤列表', [])
                max_match = None
                max_similarity = -1

                for step in how_steps:
                    step_features = step.get('特征列表', [])
                    for step_feature in step_features:
                        if step_feature.get('特征名称') == feature_name:
                            matches = step_feature.get('匹配结果', [])
                            for match in matches:
                                similarity = match.get('匹配结果', {}).get('相似度', 0)
                                persona_name = match.get('人设特征名称', '')

                                # 收集所有相似度>0.8的匹配
                                if similarity > 0.8:
                                    item_type = is_category_or_feature(persona_name, persona_data)
                                    all_high_matches.append({
                                        'post_feature': feature_name,
                                        'dimension': dimension_name,
                                        'weight': weight,
                                        'persona_item': persona_name,
                                        'similarity': similarity,
                                        'item_type': item_type
                                    })

                                # 追踪最高相似度
                                if similarity > max_similarity:
                                    max_similarity = similarity
                                    max_match = match

                # 如果找到匹配
                if max_match:
                    persona_name = max_match.get('人设特征名称', '')
                    item_type = is_category_or_feature(persona_name, persona_data)

                    relationships.append({
                        'post_feature': feature_name,
                        'dimension': dimension_name,
                        'weight': weight,
                        'persona_item': persona_name,
                        'similarity': max_similarity,
                        'item_type': item_type
                    })

    return relationships, all_high_matches


def generate_relationship_graph_html(relationships: List[Dict[str, Any]],
                                    all_high_matches: List[Dict[str, Any]]) -> str:
    """
    生成关系图的SVG HTML代码

    Args:
        relationships: 最高相似度匹配的关系数据列表
        all_high_matches: 所有相似度>0.8的匹配列表

    Returns:
        HTML字符串（包含SVG）
    """
    if not relationships:
        return '<div style="padding: 40px; text-align: center; color: #6b7280;">暂无关系数据</div>'

    # 合并所有需要显示的人设项（去重）
    all_personas = set()

    # 添加relationships中的人设项
    for r in relationships:
        all_personas.add((r['persona_item'], r['item_type'], r['similarity']))

    # 添加所有>0.8的人设项
    for match in all_high_matches:
        all_personas.add((match['persona_item'], match['item_type'], match['similarity']))

    persona_items = list(all_personas)

    # 为每个帖子特征关联最高相似度
    post_feature_map = {}
    for r in relationships:
        feature = r['post_feature']
        if feature not in post_feature_map or r['similarity'] > post_feature_map[feature][3]:
            post_feature_map[feature] = (feature, r['dimension'], r['weight'], r['similarity'])

    post_features = list(post_feature_map.values())

    # 排序：都按相似度降序
    persona_items.sort(key=lambda x: x[2], reverse=True)  # 按相似度降序
    post_features.sort(key=lambda x: x[3], reverse=True)  # 按相似度降序

    # 布局参数
    node_spacing = 60  # 节点间距
    node_radius = 20   # 圆形半径
    node_size = 40     # 方形大小
    left_margin = 250  # 左边距
    right_margin = 250 # 右边距
    middle_space = 400 # 中间空间
    top_margin = 50    # 顶部边距

    # 计算SVG尺寸
    max_nodes = max(len(persona_items), len(post_features))
    svg_height = max_nodes * node_spacing + top_margin * 2
    svg_width = left_margin + middle_space + right_margin

    # 辅助函数：获取颜色
    def get_color(similarity):
        if similarity > 0.8:
            return '#10b981'  # 绿色
        elif similarity > 0.4:
            return '#f59e0b'  # 黄色
        else:
            return '#ef4444'  # 红色

    # 辅助函数：获取线条样式
    def get_stroke_dasharray(similarity):
        if similarity > 0.8:
            return 'none'  # 实线
        else:
            return '5,5'  # 虚线

    # 创建节点位置映射
    persona_positions = {}
    post_positions = {}

    for idx, (item, item_type, similarity) in enumerate(persona_items):
        y = top_margin + idx * node_spacing
        persona_positions[item] = {
            'x': left_margin,
            'y': y,
            'type': item_type,
            'similarity': similarity,
            'idx': idx  # 添加索引用于生成ID
        }

    for idx, (feature, dimension, weight, similarity) in enumerate(post_features):
        y = top_margin + idx * node_spacing
        post_positions[feature] = {
            'x': left_margin + middle_space,
            'y': y,
            'dimension': dimension,
            'weight': weight,
            'similarity': similarity,
            'idx': idx  # 添加索引用于生成ID
        }

    # 开始生成SVG
    svg_parts = [f'''
    <div class="relationship-graph-container">
        <svg width="{svg_width}" height="{svg_height}" xmlns="http://www.w3.org/2000/svg">
            <!-- 定义箭头标记 -->
            <defs>
                <marker id="arrowhead-green" markerWidth="10" markerHeight="10" refX="9" refY="3" orient="auto">
                    <polygon points="0 0, 10 3, 0 6" fill="#10b981" />
                </marker>
                <marker id="arrowhead-yellow" markerWidth="10" markerHeight="10" refX="9" refY="3" orient="auto">
                    <polygon points="0 0, 10 3, 0 6" fill="#f59e0b" />
                </marker>
                <marker id="arrowhead-red" markerWidth="10" markerHeight="10" refX="9" refY="3" orient="auto">
                    <polygon points="0 0, 10 3, 0 6" fill="#ef4444" />
                </marker>
            </defs>

            <!-- 左右标记 -->
            <g class="labels">
                <!-- 人设标记 -->
                <text x="{left_margin}" y="30"
                      font-size="18" font-weight="600" fill="#6b7280"
                      text-anchor="middle">人设</text>

                <!-- 帖子标记 -->
                <text x="{left_margin + middle_space}" y="30"
                      font-size="18" font-weight="600" fill="#6b7280"
                      text-anchor="middle">帖子</text>
            </g>

            <!-- 连接线 -->
            <g class="connections">
    ''']

    # 合并所有需要绘制的连接（去重）
    all_connections = []
    connection_keys = set()

    # 添加主要关系（最高相似度匹配）
    for rel in relationships:
        key = (rel['persona_item'], rel['post_feature'])
        if key not in connection_keys:
            all_connections.append(rel)
            connection_keys.add(key)

    # 添加额外的>0.8关系
    for match in all_high_matches:
        key = (match['persona_item'], match['post_feature'])
        if key not in connection_keys:
            all_connections.append(match)
            connection_keys.add(key)

    # 绘制连接线
    line_idx = 0  # 计数器用于标签错开
    for rel in all_connections:
        persona_pos = persona_positions.get(rel['persona_item'])
        post_pos = post_positions.get(rel['post_feature'])

        if persona_pos and post_pos:
            x1 = persona_pos['x'] + (node_size if persona_pos['type'] == 'category' else node_radius)
            y1 = persona_pos['y']
            x2 = post_pos['x'] - node_radius
            y2 = post_pos['y']

            # 贝塞尔曲线控制点
            cx1 = x1 + (x2 - x1) * 0.3
            cx2 = x1 + (x2 - x1) * 0.7

            color = get_color(rel['similarity'])
            dasharray = get_stroke_dasharray(rel['similarity'])

            # 生成节点ID
            persona_id = f"persona-{persona_pos['idx']}"
            post_id = f"post-{post_pos['idx']}"

            # 标签位置：左右错开
            if line_idx % 2 == 0:
                label_x = x1 + (x2 - x1) * 0.35  # 左侧位置
            else:
                label_x = x1 + (x2 - x1) * 0.65  # 右侧位置
            line_idx += 1

            svg_parts.append(f'''
                <path class="connection-line"
                      data-from="{persona_id}"
                      data-to="{post_id}"
                      d="M {x1},{y1} C {cx1},{y1} {cx2},{y2} {x2},{y2}"
                      stroke="{color}"
                      stroke-width="2"
                      stroke-dasharray="{dasharray}"
                      fill="none"
                      opacity="0.7" />

                <!-- 相似度标签 -->
                <text x="{label_x}" y="{(y1 + y2) / 2 - 5}"
                      font-size="12"
                      fill="{color}"
                      text-anchor="middle"
                      font-weight="600"
                      pointer-events="none">
                    {rel['similarity']:.2f}
                </text>
            ''')

    svg_parts.append('</g>\n\n<!-- 左侧：人设特征/分类节点 -->\n<g class="persona-nodes">')

    # 绘制左侧人设节点
    for item, pos in persona_positions.items():
        color = get_color(pos['similarity'])
        node_id = f"persona-{pos['idx']}"

        if pos['type'] == 'category':
            # 方形
            half_size = node_size / 2
            svg_parts.append(f'''
                <g class="clickable" data-node-id="{node_id}" onclick="handleNodeClick('{node_id}', 'persona')">
                    <rect x="{pos['x'] - half_size}" y="{pos['y'] - half_size}"
                          width="{node_size}" height="{node_size}"
                          fill="{color}"
                          stroke="white"
                          stroke-width="2"
                          rx="4" />
                    <text x="{pos['x'] - half_size - 10}" y="{pos['y'] + 5}"
                          font-size="14"
                          fill="#374151"
                          text-anchor="end"
                          font-weight="500"
                          pointer-events="none">⬜ {item}</text>
                </g>
            ''')
        else:
            # 圆形
            svg_parts.append(f'''
                <g class="clickable" data-node-id="{node_id}" onclick="handleNodeClick('{node_id}', 'persona')">
                    <circle cx="{pos['x']}" cy="{pos['y']}"
                            r="{node_radius}"
                            fill="{color}"
                            stroke="white"
                            stroke-width="2" />
                    <text x="{pos['x'] - node_radius - 10}" y="{pos['y'] + 5}"
                          font-size="14"
                          fill="#374151"
                          text-anchor="end"
                          font-weight="500"
                          pointer-events="none">⚪ {item}</text>
                </g>
            ''')

    svg_parts.append('</g>\n\n<!-- 右侧：帖子特征节点 -->\n<g class="post-nodes">')

    # 绘制右侧帖子节点
    for feature, pos in post_positions.items():
        # 帖子特征都是圆形，蓝色
        node_id = f"post-{pos['idx']}"
        svg_parts.append(f'''
            <g class="clickable" data-node-id="{node_id}" onclick="handleNodeClick('{node_id}', 'post')">
                <circle cx="{pos['x']}" cy="{pos['y']}"
                        r="{node_radius}"
                        fill="#3b82f6"
                        stroke="white"
                        stroke-width="2" />
                <text x="{pos['x'] + node_radius + 10}" y="{pos['y'] + 5}"
                      font-size="14"
                      fill="#374151"
                      text-anchor="start"
                      font-weight="500"
                      pointer-events="none">📝 {feature}</text>
                <text x="{pos['x'] + node_radius + 10}" y="{pos['y'] + 20}"
                      font-size="11"
                      fill="#6b7280"
                      text-anchor="start"
                      pointer-events="none">({pos['dimension']} · 权重{pos['weight']:.1f})</text>
            </g>
        ''')

    svg_parts.append('''
            </g>
        </svg>
    </div>
    ''')

    return ''.join(svg_parts)


def calculate_statistics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """计算统计数据(包括评估结果)"""
    total_features = len(data)
    total_search_words = 0
    searched_count = 0
    not_searched_count = 0
    total_notes = 0
    video_count = 0
    normal_count = 0

    # 评估统计
    total_evaluated_notes = 0
    total_filtered = 0
    match_complete = 0
    match_similar = 0
    match_weak = 0
    match_none = 0

    for feature in data:
        grouped_results = feature.get('组合评估结果_分组', [])

        for group in grouped_results:
            search_items = group.get('top10_searches', [])
            total_search_words += len(search_items)

            for search_item in search_items:
                search_result = search_item.get('search_result', {})

                if search_result:
                    searched_count += 1
                    notes = search_result.get('data', {}).get('data', [])
                    total_notes += len(notes)

                    for note in notes:
                        note_type = note.get('note_card', {}).get('type', '')
                        if note_type == 'video':
                            video_count += 1
                        else:
                            normal_count += 1

                    evaluation = search_item.get('evaluation_with_filter')
                    if evaluation:
                        total_evaluated_notes += evaluation.get('total_notes', 0)
                        total_filtered += evaluation.get('filtered_count', 0)

                        stats = evaluation.get('statistics', {})
                        match_complete += stats.get('完全匹配(0.8-1.0)', 0)
                        match_similar += stats.get('相似匹配(0.6-0.79)', 0)
                        match_weak += stats.get('弱相似(0.5-0.59)', 0)
                        match_none += stats.get('无匹配(≤0.4)', 0)
                else:
                    not_searched_count += 1

    total_remaining = total_evaluated_notes - total_filtered if total_evaluated_notes > 0 else 0

    return {
        'total_features': total_features,
        'total_search_words': total_search_words,
        'searched_count': searched_count,
        'not_searched_count': not_searched_count,
        'searched_percentage': round(searched_count / total_search_words * 100, 1) if total_search_words > 0 else 0,
        'total_notes': total_notes,
        'video_count': video_count,
        'normal_count': normal_count,
        'video_percentage': round(video_count / total_notes * 100, 1) if total_notes > 0 else 0,
        'normal_percentage': round(normal_count / total_notes * 100, 1) if total_notes > 0 else 0,
        'total_evaluated': total_evaluated_notes,
        'total_filtered': total_filtered,
        'total_remaining': total_remaining,
        'filter_rate': round(total_filtered / total_evaluated_notes * 100, 1) if total_evaluated_notes > 0 else 0,
        'match_complete': match_complete,
        'match_similar': match_similar,
        'match_weak': match_weak,
        'match_none': match_none,
        'complete_rate': round(match_complete / total_remaining * 100, 1) if total_remaining > 0 else 0,
        'similar_rate': round(match_similar / total_remaining * 100, 1) if total_remaining > 0 else 0,
    }


def generate_html(data: List[Dict[str, Any]], stats: Dict[str, Any],
                  stage7_mapping: Dict[str, Any], stage8_mapping: Dict[str, Any],
                  relationship_graph_html: str,
                  all_features: Dict[str, Dict[str, Any]],
                  low_similarity_features: List[Dict[str, Any]],
                  output_path: str):
    """生成HTML可视化页面"""

    # 准备数据JSON
    data_json = json.dumps(data, ensure_ascii=False, indent=2)
    stage7_json = json.dumps(stage7_mapping, ensure_ascii=False, indent=2)
    stage8_json = json.dumps(stage8_mapping, ensure_ascii=False, indent=2)
    all_features_json = json.dumps(all_features, ensure_ascii=False, indent=2)
    low_similarity_json = json.dumps(low_similarity_features, ensure_ascii=False, indent=2)

    html_content = f'''<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Stage6/7/8 整合可视化</title>
    <style>
        * {{
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }}

        body {{
            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
            background: #f5f7fa;
            color: #333;
            overflow-x: hidden;
        }}

        /* 顶部统计面板 */
        .stats-panel {{
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            padding: 20px;
            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
        }}

        .stats-container {{
            max-width: 1400px;
            margin: 0 auto;
        }}

        .stats-row {{
            display: flex;
            justify-content: space-around;
            align-items: center;
            flex-wrap: wrap;
            gap: 15px;
            margin-bottom: 15px;
        }}

        .stats-row:last-child {{
            margin-bottom: 0;
            padding-top: 15px;
            border-top: 1px solid rgba(255,255,255,0.2);
        }}

        .stat-item {{
            text-align: center;
        }}

        .stat-value {{
            font-size: 28px;
            font-weight: bold;
            margin-bottom: 5px;
        }}

        .stat-label {{
            font-size: 12px;
            opacity: 0.9;
        }}

        .stat-item.small .stat-value {{
            font-size: 22px;
        }}

        /* 过滤控制面板 */
        .filter-panel {{
            background: white;
            max-width: 1400px;
            margin: 20px auto;
            padding: 15px 20px;
            border-radius: 8px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
            display: flex;
            align-items: center;
            gap: 20px;
            flex-wrap: wrap;
        }}

        .filter-label {{
            font-weight: 600;
            color: #374151;
        }}

        .filter-buttons {{
            display: flex;
            gap: 10px;
            flex-wrap: wrap;
        }}

        .filter-btn {{
            padding: 6px 12px;
            border: 2px solid #e5e7eb;
            background: white;
            border-radius: 6px;
            cursor: pointer;
            font-size: 13px;
            font-weight: 500;
            transition: all 0.2s;
        }}

        .filter-btn:hover {{
            border-color: #667eea;
            background: #f9fafb;
        }}

        .filter-btn.active {{
            border-color: #667eea;
            background: #667eea;
            color: white;
        }}

        .filter-btn.complete {{
            border-color: #10b981;
        }}
        .filter-btn.complete.active {{
            background: #10b981;
            border-color: #10b981;
        }}

        .filter-btn.similar {{
            border-color: #f59e0b;
        }}
        .filter-btn.similar.active {{
            background: #f59e0b;
            border-color: #f59e0b;
        }}

        .filter-btn.weak {{
            border-color: #f97316;
        }}
        .filter-btn.weak.active {{
            background: #f97316;
            border-color: #f97316;
        }}

        .filter-btn.none {{
            border-color: #ef4444;
        }}
        .filter-btn.none.active {{
            background: #ef4444;
            border-color: #ef4444;
        }}

        .filter-btn.filtered {{
            border-color: #6b7280;
        }}
        .filter-btn.filtered.active {{
            background: #6b7280;
            border-color: #6b7280;
        }}

        /* 主容器 */
        .main-container {{
            display: flex;
            max-width: 1400px;
            margin: 0 auto 20px;
            gap: 20px;
            padding: 0 20px;
            height: calc(100vh - 260px);
        }}

        /* 左侧导航 */
        .left-sidebar {{
            width: 30%;
            background: white;
            border-radius: 8px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
            overflow-y: auto;
            position: sticky;
            top: 20px;
            height: fit-content;
            max-height: calc(100vh - 280px);
        }}

        .feature-group {{
            border-bottom: 1px solid #e5e7eb;
        }}

        .feature-header {{
            padding: 15px 20px;
            background: #f9fafb;
            cursor: pointer;
            user-select: none;
            transition: background 0.2s;
        }}

        .feature-header:hover {{
            background: #f3f4f6;
        }}

        .feature-header.active {{
            background: #667eea;
            color: white;
        }}

        .feature-title {{
            font-size: 16px;
            font-weight: 600;
            margin-bottom: 5px;
        }}

        .post-target-word {{
            color: #2563eb;
            background: linear-gradient(90deg, #dbeafe 0%, #f0f9ff 100%);
            padding: 8px 12px;
            border-radius: 6px;
            border-left: 4px solid #2563eb;
        }}

        .feature-meta {{
            font-size: 12px;
            color: #6b7280;
        }}

        .feature-header.active .feature-meta {{
            color: rgba(255,255,255,0.8);
        }}

        .search-words-list {{
            display: none;
            padding: 0;
        }}

        .search-words-list.expanded {{
            display: block;
        }}

        .base-word-group {{
            border-bottom: 1px solid #f3f4f6;
        }}

        .base-word-header {{
            padding: 12px 20px 12px 30px;
            background: #fafbfc;
            cursor: pointer;
            user-select: none;
            transition: all 0.2s;
            border-left: 3px solid transparent;
        }}

        .base-word-header:hover {{
            background: #f3f4f6;
            border-left-color: #a78bfa;
        }}

        .base-word-header.active {{
            background: #f3f4f6;
            border-left-color: #7c3aed;
        }}

        .base-word-title {{
            font-size: 15px;
            font-weight: 600;
            color: #7c3aed;
            margin-bottom: 4px;
        }}

        .persona-feature {{
            color: #059669;
            background: linear-gradient(90deg, #d1fae5 0%, #ecfdf5 100%);
            padding: 6px 10px;
            border-radius: 5px;
            border-left: 3px solid #059669;
        }}

        .base-word-meta {{
            font-size: 11px;
            color: #6b7280;
        }}

        .base-word-desc {{
            padding: 8px 20px 8px 30px;
            background: #fefce8;
            font-size: 12px;
            color: #854d0e;
            line-height: 1.5;
            border-left: 3px solid #fbbf24;
            display: none;
        }}

        .base-word-desc.expanded {{
            display: block;
        }}

        .search-words-sublist {{
            display: none;
        }}

        .search-words-sublist.expanded {{
            display: block;
        }}

        .search-word-item {{
            padding: 12px 20px 12px 50px;
            cursor: pointer;
            border-left: 3px solid transparent;
            transition: all 0.2s;
        }}

        .search-word-item:hover {{
            background: #f9fafb;
            border-left-color: #667eea;
        }}

        .search-word-item.active {{
            background: #ede9fe;
            border-left-color: #7c3aed;
        }}

        .search-word-text {{
            font-size: 14px;
            font-weight: 500;
            color: #374151;
            margin-bottom: 4px;
        }}

        .search-word-score {{
            display: inline-block;
            padding: 2px 8px;
            border-radius: 12px;
            font-size: 11px;
            font-weight: 600;
            margin-left: 8px;
        }}

        .score-high {{
            background: #d1fae5;
            color: #065f46;
        }}

        .score-medium {{
            background: #fef3c7;
            color: #92400e;
        }}

        .score-low {{
            background: #fee2e2;
            color: #991b1b;
        }}

        .eval-badge {{
            display: inline-block;
            padding: 2px 6px;
            border-radius: 10px;
            font-size: 11px;
            font-weight: 600;
            margin-left: 6px;
        }}

        .eval-complete {{
            background: #d1fae5;
            color: #065f46;
            border: 1px solid #10b981;
        }}

        .eval-similar {{
            background: #fef3c7;
            color: #92400e;
            border: 1px solid #f59e0b;
        }}

        .eval-weak {{
            background: #fed7aa;
            color: #9a3412;
            border: 1px solid #f97316;
        }}

        .eval-none {{
            background: #fee2e2;
            color: #991b1b;
            border: 1px solid #ef4444;
        }}

        .eval-filtered {{
            background: #e5e7eb;
            color: #4b5563;
            border: 1px solid #6b7280;
        }}

        .search-word-eval {{
            font-size: 11px;
            color: #6b7280;
            margin-top: 4px;
        }}

        /* P值显示样式 */
        .p-score-container {{
            display: flex;
            align-items: center;
            gap: 8px;
            margin-top: 6px;
            padding: 6px 8px;
            background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%);
            border-radius: 6px;
            border-left: 3px solid #0ea5e9;
        }}

        .p-score-label {{
            font-size: 11px;
            font-weight: 600;
            color: #0369a1;
        }}

        .p-score-value {{
            font-size: 13px;
            font-weight: 700;
            color: #0284c7;
        }}

        .p-score-info-icon {{
            cursor: pointer;
            font-size: 14px;
            color: #0ea5e9;
            transition: all 0.2s;
            padding: 2px 6px;
            border-radius: 50%;
            background: white;
        }}

        .p-score-info-icon:hover {{
            background: #0ea5e9;
            color: white;
            transform: scale(1.1);
        }}

        .p-score-arrow {{
            display: flex;
            align-items: center;
            gap: 6px;
            font-size: 11px;
            color: #64748b;
            margin-top: 4px;
        }}

        .p-score-arrow-symbol {{
            color: #0ea5e9;
            font-weight: bold;
        }}

        /* P值详情模态窗口 */
        .p-detail-modal {{
            display: none;
            position: fixed;
            top: 0;
            left: 0;
            right: 0;
            bottom: 0;
            background: rgba(0, 0, 0, 0.7);
            z-index: 10000;
            animation: fadeIn 0.3s ease;
        }}

        .p-detail-modal.active {{
            display: flex;
            align-items: center;
            justify-content: center;
        }}

        .p-detail-window {{
            background: white;
            border-radius: 12px;
            width: 90%;
            max-width: 800px;
            max-height: 85vh;
            overflow: hidden;
            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
            animation: slideUp 0.3s ease;
            display: flex;
            flex-direction: column;
        }}

        .p-detail-header {{
            padding: 20px 25px;
            background: linear-gradient(135deg, #0ea5e9 0%, #0284c7 100%);
            color: white;
            display: flex;
            align-items: center;
            justify-content: space-between;
            flex-shrink: 0;
        }}

        .p-detail-title {{
            font-size: 18px;
            font-weight: 600;
        }}

        .p-detail-close-btn {{
            background: rgba(255, 255, 255, 0.2);
            border: none;
            color: white;
            width: 36px;
            height: 36px;
            border-radius: 50%;
            cursor: pointer;
            font-size: 20px;
            display: flex;
            align-items: center;
            justify-content: center;
            transition: all 0.2s;
        }}

        .p-detail-close-btn:hover {{
            background: rgba(255, 255, 255, 0.3);
            transform: scale(1.1);
        }}

        .p-detail-body {{
            flex: 1;
            overflow-y: auto;
            padding: 25px;
        }}

        .p-formula-section {{
            background: #f8fafc;
            border: 2px solid #e2e8f0;
            border-radius: 8px;
            padding: 16px;
            margin-bottom: 20px;
        }}

        .p-formula-title {{
            font-size: 14px;
            font-weight: 600;
            color: #0f172a;
            margin-bottom: 12px;
        }}

        .p-formula {{
            font-family: 'Courier New', monospace;
            font-size: 16px;
            color: #0284c7;
            background: white;
            padding: 12px;
            border-radius: 6px;
            text-align: center;
            margin-bottom: 8px;
        }}

        .p-summary {{
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
            gap: 12px;
            margin-bottom: 20px;
        }}

        .p-summary-item {{
            background: white;
            border: 2px solid #e2e8f0;
            border-radius: 8px;
            padding: 12px;
            text-align: center;
        }}

        .p-summary-label {{
            font-size: 12px;
            color: #64748b;
            margin-bottom: 4px;
        }}

        .p-summary-value {{
            font-size: 20px;
            font-weight: 700;
            color: #0f172a;
        }}

        .p-summary-value.highlight {{
            color: #0284c7;
        }}

        .p-matches-section {{
            margin-top: 20px;
        }}

        .p-matches-title {{
            font-size: 15px;
            font-weight: 600;
            color: #0f172a;
            margin-bottom: 12px;
        }}

        .p-match-item {{
            background: white;
            border: 2px solid #e2e8f0;
            border-radius: 8px;
            padding: 12px;
            margin-bottom: 10px;
            transition: all 0.2s;
        }}

        .p-match-item:hover {{
            border-color: #0ea5e9;
            box-shadow: 0 4px 12px rgba(14, 165, 233, 0.1);
        }}

        .p-match-header {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            margin-bottom: 8px;
        }}

        .p-match-title {{
            font-size: 14px;
            font-weight: 600;
            color: #0f172a;
            flex: 1;
        }}

        .p-match-contribution {{
            font-size: 16px;
            font-weight: 700;
            color: #0284c7;
        }}

        .p-match-details {{
            display: flex;
            gap: 16px;
            font-size: 12px;
            color: #64748b;
        }}

        .p-match-detail-item {{
            display: flex;
            gap: 4px;
        }}

        .p-match-detail-label {{
            font-weight: 600;
        }}

        /* ========== 新增: 顶部统计面板折叠样式 ========== */
        .stats-panel {{
            position: relative;
            transition: all 0.3s ease;
            cursor: pointer;
        }}

        .stats-panel.collapsed {{
            max-height: 90px;
            overflow: hidden;
        }}

        .stats-panel.collapsed .stats-row:not(:first-child) {{
            display: none;
        }}

        .stats-toggle-icon {{
            position: absolute;
            right: 30px;
            top: 50%;
            transform: translateY(-50%);
            font-size: 24px;
            color: rgba(255, 255, 255, 0.9);
            transition: transform 0.3s ease;
            cursor: pointer;
            z-index: 100;
        }}

        .stats-panel.collapsed .stats-toggle-icon {{
            transform: translateY(-50%) rotate(180deg);
        }}

        /* ========== 新增: 帖子浮层模态窗口样式 ========== */
        .notes-modal {{
            display: none;
            position: fixed;
            top: 0;
            left: 0;
            right: 0;
            bottom: 0;
            background: rgba(0, 0, 0, 0.8);
            z-index: 10001;
            animation: fadeIn 0.3s ease;
        }}

        .notes-modal.active {{
            display: flex;
            align-items: center;
            justify-content: center;
        }}

        .notes-modal-window {{
            background: white;
            border-radius: 12px;
            width: 95%;
            max-width: 1400px;
            height: 90vh;
            overflow: hidden;
            display: flex;
            flex-direction: column;
            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
            animation: slideUp 0.3s ease;
        }}

        .notes-modal-header {{
            padding: 20px 25px;
            background: linear-gradient(135deg, #10b981 0%, #059669 100%);
            color: white;
            display: flex;
            align-items: center;
            justify-content: space-between;
            flex-shrink: 0;
        }}

        .notes-modal-title {{
            font-size: 18px;
            font-weight: 600;
        }}

        .notes-modal-subtitle {{
            font-size: 14px;
            opacity: 0.9;
            margin-top: 5px;
        }}

        .notes-modal-close-btn {{
            background: rgba(255, 255, 255, 0.2);
            border: none;
            color: white;
            width: 36px;
            height: 36px;
            border-radius: 50%;
            cursor: pointer;
            font-size: 20px;
            display: flex;
            align-items: center;
            justify-content: center;
            transition: all 0.2s;
        }}

        .notes-modal-close-btn:hover {{
            background: rgba(255, 255, 255, 0.3);
            transform: scale(1.1);
        }}

        .notes-modal-body {{
            flex: 1;
            overflow-y: auto;
            padding: 25px;
            background: #fafbfc;
        }}

        .notes-grid-modal {{
            display: grid;
            grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
            gap: 20px;
        }}

        .note-card-modal {{
            background: white;
            border-radius: 12px;
            overflow: hidden;
            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
            cursor: pointer;
            transition: all 0.3s ease;
            display: flex;
            flex-direction: column;
        }}

        .note-card-modal:hover {{
            transform: translateY(-4px);
            box-shadow: 0 8px 20px rgba(0, 0, 0, 0.15);
        }}

        .note-image-wrapper {{
            position: relative;
            width: 100%;
            height: 200px;
            background: #f3f4f6;
            overflow: hidden;
        }}

        .note-image {{
            width: 100%;
            height: 100%;
            object-fit: cover;
        }}

        .note-image-placeholder {{
            width: 100%;
            height: 100%;
            display: flex;
            align-items: center;
            justify-content: center;
            font-size: 48px;
            background: linear-gradient(135deg, #e5e7eb 0%, #d1d5db 100%);
        }}

        .note-content {{
            padding: 15px;
            flex: 1;
            display: flex;
            flex-direction: column;
            gap: 10px;
        }}

        .note-title {{
            font-size: 14px;
            font-weight: 600;
            color: #111827;
            line-height: 1.4;
            overflow: hidden;
            text-overflow: ellipsis;
            display: -webkit-box;
            -webkit-line-clamp: 2;
            -webkit-box-orient: vertical;
        }}

        .note-score {{
            display: flex;
            align-items: center;
            gap: 8px;
            font-size: 13px;
        }}

        .score-label {{
            color: #6b7280;
        }}

        .score-value {{
            font-weight: 700;
            padding: 2px 8px;
            border-radius: 4px;
        }}

        .score-value.high {{
            color: #10b981;
            background: #d1fae5;
        }}

        .score-value.medium {{
            color: #f59e0b;
            background: #fef3c7;
        }}

        .score-value.low {{
            color: #6b7280;
            background: #e5e7eb;
        }}

        @keyframes fadeIn {{
            from {{ opacity: 0; }}
            to {{ opacity: 1; }}
        }}

        @keyframes slideUp {{
            from {{
                opacity: 0;
                transform: translateY(50px);
            }}
            to {{
                opacity: 1;
                transform: translateY(0);
            }}
        }}

        /* 右侧结果区 */
        .right-content {{
            flex: 1;
            overflow-y: auto;
            padding-bottom: 40px;
        }}

        .result-block {{
            background: white;
            border-radius: 8px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
            margin-bottom: 30px;
            padding: 20px;
            scroll-margin-top: 20px;
        }}

        .result-header {{
            margin-bottom: 20px;
            padding-bottom: 15px;
            border-bottom: 2px solid #e5e7eb;
        }}

        .result-title {{
            font-size: 20px;
            font-weight: 600;
            color: #111827;
            margin-bottom: 10px;
        }}

        .result-stats {{
            display: flex;
            gap: 10px;
            font-size: 12px;
            color: #6b7280;
            flex-wrap: wrap;
        }}

        .stat-badge {{
            background: #f3f4f6;
            padding: 4px 10px;
            border-radius: 4px;
        }}

        .stat-badge.eval {{
            font-weight: 600;
        }}

        .stat-badge.eval.complete {{
            background: #d1fae5;
            color: #065f46;
        }}

        .stat-badge.eval.similar {{
            background: #fef3c7;
            color: #92400e;
        }}

        .stat-badge.eval.weak {{
            background: #fed7aa;
            color: #9a3412;
        }}

        .stat-badge.eval.none {{
            background: #fee2e2;
            color: #991b1b;
        }}

        .stat-badge.eval.filtered {{
            background: #e5e7eb;
            color: #4b5563;
        }}

        .notes-grid {{
            display: grid;
            grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
            gap: 20px;
        }}

        .empty-state {{
            text-align: center;
            padding: 60px 40px;
            color: #6b7280;
        }}

        .empty-icon {{
            font-size: 48px;
            margin-bottom: 16px;
        }}

        .empty-title {{
            font-size: 16px;
            font-weight: 600;
            color: #374151;
            margin-bottom: 8px;
        }}

        .empty-desc {{
            font-size: 14px;
            line-height: 1.6;
            color: #9ca3af;
            max-width: 400px;
            margin: 0 auto;
        }}

        .note-card {{
            border: 3px solid #e5e7eb;
            border-radius: 8px;
            overflow: hidden;
            cursor: pointer;
            transition: all 0.3s;
            background: white;
        }}

        .note-card:hover {{
            transform: translateY(-4px);
            box-shadow: 0 10px 25px rgba(0,0,0,0.15);
        }}

        .note-card.eval-complete {{
            border-color: #10b981;
        }}

        .note-card.eval-similar {{
            border-color: #f59e0b;
        }}

        .note-card.eval-weak {{
            border-color: #f97316;
        }}

        .note-card.eval-none {{
            border-color: #ef4444;
        }}

        .note-card.eval-filtered {{
            border-color: #6b7280;
            opacity: 0.6;
        }}

        /* 图片轮播 */
        .image-carousel {{
            position: relative;
            width: 100%;
            height: 280px;
            background: #f3f4f6;
            overflow: hidden;
        }}

        .carousel-images {{
            display: flex;
            height: 100%;
            transition: transform 0.3s ease;
        }}

        .carousel-image {{
            min-width: 100%;
            height: 100%;
            object-fit: cover;
        }}

        .carousel-btn {{
            position: absolute;
            top: 50%;
            transform: translateY(-50%);
            background: rgba(0,0,0,0.5);
            color: white;
            border: none;
            width: 32px;
            height: 32px;
            border-radius: 50%;
            cursor: pointer;
            font-size: 16px;
            display: none;
            align-items: center;
            justify-content: center;
            transition: background 0.2s;
            z-index: 10;
        }}

        .carousel-btn:hover {{
            background: rgba(0,0,0,0.7);
        }}

        .carousel-btn.prev {{
            left: 8px;
        }}

        .carousel-btn.next {{
            right: 8px;
        }}

        .note-card:hover .carousel-btn {{
            display: flex;
        }}

        .carousel-indicators {{
            position: absolute;
            bottom: 10px;
            left: 50%;
            transform: translateX(-50%);
            display: flex;
            gap: 6px;
            z-index: 10;
        }}

        .dot {{
            width: 8px;
            height: 8px;
            border-radius: 50%;
            background: rgba(255,255,255,0.5);
            cursor: pointer;
            transition: all 0.2s;
        }}

        .dot.active {{
            background: white;
            width: 24px;
            border-radius: 4px;
        }}

        .image-counter {{
            position: absolute;
            top: 10px;
            right: 10px;
            background: rgba(0,0,0,0.6);
            color: white;
            padding: 4px 8px;
            border-radius: 4px;
            font-size: 12px;
            z-index: 10;
        }}

        .note-info {{
            padding: 12px;
        }}

        .note-title {{
            font-size: 14px;
            font-weight: 500;
            color: #111827;
            margin-bottom: 8px;
            display: -webkit-box;
            -webkit-line-clamp: 2;
            -webkit-box-orient: vertical;
            overflow: hidden;
            line-height: 1.4;
        }}

        .note-meta {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            font-size: 12px;
            color: #6b7280;
            margin-bottom: 8px;
        }}

        .note-type {{
            padding: 3px 8px;
            border-radius: 4px;
            font-weight: 500;
        }}

        .type-video {{
            background: #dbeafe;
            color: #1e40af;
        }}

        .type-normal {{
            background: #d1fae5;
            color: #065f46;
        }}

        .note-author {{
            display: flex;
            align-items: center;
            gap: 6px;
        }}

        .author-avatar {{
            width: 24px;
            height: 24px;
            border-radius: 50%;
        }}

        .note-eval {{
            padding: 8px 12px;
            background: #f9fafb;
            border-top: 1px solid #e5e7eb;
            font-size: 12px;
        }}

        .note-eval-header {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            cursor: pointer;
            user-select: none;
        }}

        .note-eval-score {{
            font-weight: 600;
        }}

        .note-eval-toggle {{
            color: #6b7280;
            font-size: 10px;
        }}

        .note-eval-details {{
            margin-top: 8px;
            padding-top: 8px;
            border-top: 1px solid #e5e7eb;
            display: none;
            line-height: 1.5;
        }}

        .note-eval-details.expanded {{
            display: block;
        }}

        .eval-detail-label {{
            font-weight: 600;
            color: #374151;
            margin-top: 6px;
            margin-bottom: 2px;
        }}

        .eval-detail-label:first-child {{
            margin-top: 0;
        }}

        .eval-detail-text {{
            color: #6b7280;
        }}

        /* ========== 新增: 解构面板样式 ========== */

        .deconstruction-toggle-btn {{
            width: 100%;
            padding: 10px;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            border: none;
            border-top: 1px solid #e5e7eb;
            cursor: pointer;
            font-size: 13px;
            font-weight: 600;
            transition: all 0.3s;
            display: flex;
            align-items: center;
            justify-content: center;
            gap: 6px;
            position: relative;
            z-index: 1;
        }}

        .deconstruction-toggle-btn:hover {{
            background: linear-gradient(135deg, #5568d3 0%, #6a3f8f 100%);
            transform: scale(1.02);
        }}

        /* 浮层遮罩 */
        .modal-overlay {{
            display: none;
            position: fixed;
            top: 0;
            left: 0;
            right: 0;
            bottom: 0;
            background: rgba(0, 0, 0, 0.7);
            z-index: 9998;
            animation: fadeIn 0.3s ease;
        }}

        .modal-overlay.active {{
            display: flex;
            align-items: center;
            justify-content: center;
        }}

        /* 浮层窗口 */
        .modal-window {{
            background: white;
            border-radius: 12px;
            width: 90%;
            max-width: 1200px;
            max-height: 90vh;
            overflow: hidden;
            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
            animation: slideUp 0.3s ease;
            display: flex;
            flex-direction: column;
        }}

        @keyframes fadeIn {{
            from {{ opacity: 0; }}
            to {{ opacity: 1; }}
        }}

        @keyframes slideUp {{
            from {{
                opacity: 0;
                transform: translateY(50px);
            }}
            to {{
                opacity: 1;
                transform: translateY(0);
            }}
        }}

        /* 浮层头部 */
        .modal-header {{
            padding: 20px 25px;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            display: flex;
            align-items: center;
            justify-content: space-between;
            flex-shrink: 0;
        }}

        .modal-title {{
            font-size: 18px;
            font-weight: 600;
            display: flex;
            align-items: center;
            gap: 10px;
        }}

        .modal-note-title {{
            font-size: 14px;
            opacity: 0.9;
            margin-top: 5px;
        }}

        .modal-close-btn {{
            background: rgba(255, 255, 255, 0.2);
            border: none;
            color: white;
            width: 36px;
            height: 36px;
            border-radius: 50%;
            cursor: pointer;
            font-size: 20px;
            display: flex;
            align-items: center;
            justify-content: center;
            transition: all 0.2s;
        }}

        .modal-close-btn:hover {{
            background: rgba(255, 255, 255, 0.3);
            transform: scale(1.1);
        }}

        /* 浮层内容区 */
        .modal-body {{
            flex: 1;
            overflow-y: auto;
            padding: 25px;
            background: #fafbfc;
        }}

        .deconstruction-content {{
            max-width: 1000px;
            margin: 0 auto;
        }}

        .deconstruction-header {{
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            padding: 12px 15px;
            border-radius: 6px;
            margin-bottom: 15px;
            font-size: 14px;
            font-weight: 600;
        }}

        .original-feature {{
            font-size: 16px;
            margin-top: 4px;
        }}

        .dimension-card {{
            background: white;
            border: 2px solid #e5e7eb;
            border-radius: 8px;
            margin-bottom: 12px;
            overflow: hidden;
        }}

        .dimension-header {{
            padding: 10px 15px;
            background: #667eea;
            color: white;
            cursor: pointer;
            user-select: none;
            display: flex;
            align-items: center;
            justify-content: space-between;
            font-weight: 600;
            font-size: 14px;
        }}

        .dimension-header:hover {{
            background: #5568d3;
        }}

        .dimension-title {{
            display: flex;
            align-items: center;
            gap: 8px;
        }}

        .dimension-count {{
            font-size: 12px;
            opacity: 0.9;
        }}

        .dimension-toggle {{
            font-size: 12px;
        }}

        .dimension-body {{
            max-height: 0;
            overflow: hidden;
            transition: max-height 0.3s ease-in-out;
        }}

        .dimension-body.expanded {{
            max-height: 1000px;
        }}

        .feature-list {{
            padding: 10px;
        }}

        .feature-item {{
            padding: 10px;
            margin-bottom: 8px;
            background: #f9fafb;
            border-left: 3px solid #e5e7eb;
            border-radius: 4px;
            transition: all 0.2s;
        }}

        .feature-item:hover {{
            background: #f3f4f6;
            border-left-color: #667eea;
        }}

        .feature-item.top-score {{
            background: #fff9e6;
            border-left: 3px solid #FFD700;
            box-shadow: 0 2px 8px rgba(255, 215, 0, 0.2);
        }}

        .feature-item.top-score .feature-name {{
            color: #b8860b;
            font-weight: 700;
        }}

        .feature-name {{
            font-size: 13px;
            font-weight: 600;
            color: #111827;
            margin-bottom: 6px;
            display: flex;
            align-items: center;
            gap: 6px;
        }}

        .top-badge {{
            background: #FFD700;
            color: #000;
            padding: 2px 6px;
            border-radius: 4px;
            font-size: 11px;
            font-weight: 700;
        }}

        .feature-meta-row {{
            display: flex;
            align-items: center;
            gap: 8px;
            margin-bottom: 6px;
            font-size: 11px;
            color: #6b7280;
        }}

        .feature-dimension-detail {{
            background: #e5e7eb;
            padding: 2px 6px;
            border-radius: 3px;
        }}

        .feature-weight {{
            background: #dbeafe;
            padding: 2px 6px;
            border-radius: 3px;
        }}

        .similarity-row {{
            display: flex;
            align-items: center;
            gap: 10px;
        }}

        .similarity-score {{
            font-size: 14px;
            font-weight: 700;
            min-width: 50px;
        }}

        .similarity-score.high {{
            color: #10b981;
        }}

        .similarity-score.medium {{
            color: #f59e0b;
        }}

        .similarity-score.low {{
            color: #6b7280;
        }}

        .similarity-bar-container {{
            flex: 1;
            height: 8px;
            background: #e5e7eb;
            border-radius: 4px;
            overflow: hidden;
        }}

        .similarity-bar {{
            height: 100%;
            border-radius: 4px;
            transition: width 0.3s ease;
        }}

        .similarity-bar.high {{
            background: linear-gradient(90deg, #10b981 0%, #059669 100%);
        }}

        .similarity-bar.medium {{
            background: linear-gradient(90deg, #f59e0b 0%, #d97706 100%);
        }}

        .similarity-bar.low {{
            background: linear-gradient(90deg, #9ca3af 0%, #6b7280 100%);
        }}

        .similarity-explanation {{
            margin-top: 8px;
            padding: 8px;
            background: white;
            border-radius: 4px;
            font-size: 11px;
            color: #6b7280;
            line-height: 1.5;
            display: none;
        }}

        .feature-item:hover .similarity-explanation {{
            display: block;
        }}

        /* 滚动条样式 */
        ::-webkit-scrollbar {{
            width: 8px;
            height: 8px;
        }}

        ::-webkit-scrollbar-track {{
            background: #f1f1f1;
        }}

        ::-webkit-scrollbar-thumb {{
            background: #888;
            border-radius: 4px;
        }}

        ::-webkit-scrollbar-thumb:hover {{
            background: #555;
        }}

        .hidden {{
            display: none !important;
        }}

        /* 关系图样式 */
        .relationship-section {{
            margin: 20px;
            padding: 20px;
            background: white;
            border-radius: 8px;
            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
        }}

        .relationship-section .section-header {{
            font-size: 18px;
            font-weight: 600;
            color: #1f2937;
            margin-bottom: 20px;
            padding-bottom: 10px;
            border-bottom: 2px solid #e5e7eb;
        }}

        .relationship-graph-container {{
            display: flex;
            justify-content: center;
            align-items: center;
            padding: 20px;
            background: #f9fafb;
            border-radius: 8px;
            overflow-x: auto;
        }}

        .relationship-graph-container svg {{
            max-width: 100%;
            height: auto;
        }}

        /* Tab导航样式 */
        .tab-navigation {{
            display: flex;
            gap: 4px;
            background: white;
            padding: 10px 20px 0;
            margin: 0 20px;
            border-bottom: 2px solid #e5e7eb;
        }}

        .tab-button {{
            padding: 12px 24px;
            background: none;
            border: none;
            border-bottom: 3px solid transparent;
            cursor: pointer;
            font-size: 15px;
            font-weight: 500;
            color: #6b7280;
            transition: all 0.2s;
            position: relative;
            top: 2px;
        }}

        .tab-button:hover {{
            color: #374151;
            background: #f9fafb;
            border-radius: 6px 6px 0 0;
        }}

        .tab-button.active {{
            color: #2563eb;
            border-bottom-color: #2563eb;
            font-weight: 600;
        }}

        /* Tab内容区域 */
        .tab-content {{
            min-height: 600px;
        }}

        .tab-pane {{
            display: none;
        }}

        .tab-pane.active {{
            display: block;
        }}

        /* 交互高亮样式 */
        .clickable {{
            cursor: pointer;
            transition: opacity 0.3s ease, filter 0.3s ease;
        }}

        .clickable:hover {{
            filter: brightness(1.15);
        }}

        .highlighted {{
            filter: drop-shadow(0 0 8px currentColor);
        }}

        .highlighted circle,
        .highlighted rect {{
            stroke-width: 3;
        }}

        .dimmed {{
            opacity: 0.15;
        }}

        .connection-line {{
            transition: opacity 0.3s ease, stroke-width 0.3s ease;
        }}

        .connection-line.highlighted {{
            stroke-width: 4;
            opacity: 1 !important;
        }}

        .connection-line.dimmed {{
            opacity: 0.1 !important;
        }}

        /* ========== 低相似度特征样式 ========== */
        .low-similarity-section {{
            margin-top: 20px;
            border-top: 2px solid #fca5a5;
            padding-top: 15px;
        }}

        .low-similarity-header {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            padding: 12px 15px;
            background: linear-gradient(135deg, #fee2e2 0%, #fef2f2 100%);
            border-radius: 8px;
            cursor: pointer;
            border: 2px solid #fca5a5;
            transition: all 0.3s ease;
        }}

        .low-similarity-header:hover {{
            background: linear-gradient(135deg, #fecaca 0%, #fee2e2 100%);
            border-color: #f87171;
        }}

        .low-similarity-title {{
            font-size: 14px;
            font-weight: 600;
            color: #dc2626;
        }}

        .low-similarity-count {{
            font-size: 12px;
            font-weight: 600;
            color: #991b1b;
            background: white;
            padding: 2px 8px;
            border-radius: 12px;
        }}

        .toggle-icon {{
            font-size: 12px;
            color: #dc2626;
            transition: transform 0.3s ease;
        }}

        .toggle-icon.rotated {{
            transform: rotate(180deg);
        }}

        .low-similarity-list {{
            margin-top: 10px;
        }}

        .low-similarity-item {{
            padding: 10px 12px;
            margin: 8px 0;
            background: white;
            border: 2px solid #fecaca;
            border-radius: 6px;
            cursor: pointer;
            transition: all 0.2s ease;
        }}

        .low-similarity-item:hover {{
            border-color: #f87171;
            background: #fef2f2;
        }}

        .low-feature-header {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            margin-bottom: 4px;
        }}

        .low-feature-name {{
            font-size: 13px;
            font-weight: 600;
            color: #991b1b;
            flex: 1;
        }}

        .low-feature-score {{
            font-size: 14px;
            font-weight: 700;
            color: #dc2626;
            background: #fee2e2;
            padding: 2px 8px;
            border-radius: 4px;
        }}

        .low-feature-meta {{
            font-size: 11px;
            color: #9ca3af;
            margin-bottom: 6px;
        }}

        .low-feature-detail {{
            padding: 8px;
            background: #fef2f2;
            border-radius: 4px;
            border-left: 3px solid #f87171;
            margin-top: 8px;
        }}

        .low-feature-reason {{
            font-size: 12px;
            color: #7f1d1d;
            line-height: 1.6;
        }}
    </style>
</head>
<body>
    <!-- 统计面板 -->
    <div class="stats-panel collapsed">
        <div class="stats-toggle-icon">▼</div>
        <div class="stats-container">
            <div class="stats-row">
                <div class="stat-item">
                    <div class="stat-value">📊 {stats['total_features']}</div>
                    <div class="stat-label">原始特征数</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">🔍 {stats['total_search_words']}</div>
                    <div class="stat-label">搜索词总数</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">✅ {stats['searched_count']}</div>
                    <div class="stat-label">已搜索 ({stats['searched_percentage']}%)</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">⏸️ {stats['not_searched_count']}</div>
                    <div class="stat-label">未搜索</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">📝 {stats['total_notes']}</div>
                    <div class="stat-label">帖子总数</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">🎬 {stats['video_count']}</div>
                    <div class="stat-label">视频 ({stats['video_percentage']}%)</div>
                </div>
                <div class="stat-item">
                    <div class="stat-value">📷 {stats['normal_count']}</div>
                    <div class="stat-label">图文 ({stats['normal_percentage']}%)</div>
                </div>
            </div>
            <div class="stats-row">
                <div class="stat-item small">
                    <div class="stat-value">⚡ {stats['total_evaluated']}</div>
                    <div class="stat-label">已评估</div>
                </div>
                <div class="stat-item small">
                    <div class="stat-value">⚫ {stats['total_filtered']}</div>
                    <div class="stat-label">已过滤 ({stats['filter_rate']}%)</div>
                </div>
                <div class="stat-item small">
                    <div class="stat-value">🟢 {stats['match_complete']}</div>
                    <div class="stat-label">完全匹配 ({stats['complete_rate']}%)</div>
                </div>
                <div class="stat-item small">
                    <div class="stat-value">🟡 {stats['match_similar']}</div>
                    <div class="stat-label">相似匹配 ({stats['similar_rate']}%)</div>
                </div>
                <div class="stat-item small">
                    <div class="stat-value">🟠 {stats['match_weak']}</div>
                    <div class="stat-label">弱相似</div>
                </div>
                <div class="stat-item small">
                    <div class="stat-value">🔴 {stats['match_none']}</div>
                    <div class="stat-label">无匹配</div>
                </div>
            </div>
        </div>
    </div>

    <!-- 过滤控制面板 -->
    <div class="filter-panel">
        <span class="filter-label">🔍 筛选显示:</span>
        <div class="filter-buttons">
            <button class="filter-btn active" onclick="filterNotes('all')">全部</button>
            <button class="filter-btn complete" onclick="filterNotes('complete')">🟢 完全匹配</button>
            <button class="filter-btn similar" onclick="filterNotes('similar')">🟡 相似匹配</button>
            <button class="filter-btn weak" onclick="filterNotes('weak')">🟠 弱相似</button>
            <button class="filter-btn none" onclick="filterNotes('none')">🔴 无匹配</button>
            <button class="filter-btn filtered" onclick="filterNotes('filtered')">⚫ 已过滤</button>
        </div>
    </div>

    <!-- Tab导航 -->
    <div class="tab-navigation">
        <button class="tab-button active" onclick="switchTab('search')">📋 搜索结果</button>
        <button class="tab-button" onclick="switchTab('relationship')">📊 人设-帖子匹配</button>
    </div>

    <!-- Tab内容区域 -->
    <div class="tab-content">
        <!-- Tab1: 搜索结果 -->
        <div class="tab-pane active" id="tab-search">
            <!-- 主容器 -->
            <div class="main-container">
                <!-- 左侧导航 -->
                <div class="left-sidebar" id="leftSidebar"></div>

                <!-- 右侧结果区 -->
                <div class="right-content" id="rightContent"></div>
            </div>
        </div>

        <!-- Tab2: 关系图 -->
        <div class="tab-pane" id="tab-relationship">
            <div class="relationship-section">
                <div class="section-header">📊 人设-帖子关系图</div>
                {relationship_graph_html}
            </div>
        </div>
    </div>

    <!-- 解构结果模态窗口 -->
    <div class="modal-overlay" id="deconstructionModal">
        <div class="modal-window">
            <div class="modal-header">
                <div>
                    <div class="modal-title">🎯 解构特征相似度分析</div>
                    <div class="modal-note-title" id="modalNoteTitle"></div>
                </div>
                <button class="modal-close-btn" onclick="closeModal()">×</button>
            </div>
            <div class="modal-body">
                <div class="deconstruction-content" id="modalContent"></div>
            </div>
        </div>
    </div>

    <!-- P值详情模态窗口 -->
    <div class="p-detail-modal" id="pDetailModal">
        <div class="p-detail-window">
            <div class="p-detail-header">
                <div class="p-detail-title">📊 综合得分P 计算详情</div>
                <button class="p-detail-close-btn" onclick="closePDetailModal()">×</button>
            </div>
            <div class="p-detail-body" id="pDetailContent"></div>
        </div>
    </div>

    <!-- 帖子浮层模态窗口 -->
    <div class="notes-modal" id="notesModal">
        <div class="notes-modal-window">
            <div class="notes-modal-header">
                <div class="notes-modal-title">📝 搜索词帖子列表</div>
                <button class="notes-modal-close-btn" onclick="closeNotesModal()">×</button>
            </div>
            <div class="notes-modal-body" id="notesModalContent"></div>
        </div>
    </div>

    <script>
        // 数据
        const data = {data_json};
        const stage7Data = {stage7_json};
        const stage8Data = {stage8_json};
        const allFeatures = {all_features_json};
        const lowSimilarityFeatures = {low_similarity_json};
        let currentFilter = 'all';

        // Tab切换功能
        function switchTab(tabName) {{
            // 更新Tab按钮状态
            document.querySelectorAll('.tab-button').forEach(btn => {{
                btn.classList.remove('active');
            }});
            event.target.classList.add('active');

            // 更新Tab内容
            document.querySelectorAll('.tab-pane').forEach(pane => {{
                pane.classList.remove('active');
            }});
            document.getElementById('tab-' + tabName).classList.add('active');
        }}

        // 关系图节点点击高亮功能
        let selectedNodeId = null;

        function handleNodeClick(nodeId, nodeType) {{
            const svg = document.querySelector('.relationship-graph-container svg');
            if (!svg) return;

            // 如果点击的是已选中节点，取消高亮
            if (selectedNodeId === nodeId) {{
                clearHighlight();
                selectedNodeId = null;
                return;
            }}

            selectedNodeId = nodeId;

            // 找到所有相关的连接线
            const connections = svg.querySelectorAll('.connection-line');
            const relatedNodes = new Set([nodeId]);

            connections.forEach(conn => {{
                const from = conn.getAttribute('data-from');
                const to = conn.getAttribute('data-to');

                if (from === nodeId || to === nodeId) {{
                    // 这是相关的连接线
                    conn.classList.add('highlighted');
                    conn.classList.remove('dimmed');

                    // 收集相关节点
                    relatedNodes.add(from);
                    relatedNodes.add(to);
                }} else {{
                    // 非相关连接线变暗
                    conn.classList.add('dimmed');
                    conn.classList.remove('highlighted');
                }}
            }});

            // 处理所有节点
            const allNodes = svg.querySelectorAll('.clickable');
            allNodes.forEach(node => {{
                const nId = node.getAttribute('data-node-id');
                if (relatedNodes.has(nId)) {{
                    node.classList.add('highlighted');
                    node.classList.remove('dimmed');
                }} else {{
                    node.classList.add('dimmed');
                    node.classList.remove('highlighted');
                }}
            }});
        }}

        function clearHighlight() {{
            const svg = document.querySelector('.relationship-graph-container svg');
            if (!svg) return;

            svg.querySelectorAll('.highlighted, .dimmed').forEach(el => {{
                el.classList.remove('highlighted', 'dimmed');
            }});
        }}

        // 创建评估映射
        const noteEvaluations = {{}};
        data.forEach((feature, fIdx) => {{
            const groups = feature['组合评估结果_分组'] || [];
            groups.forEach((group, gIdx) => {{
                const searches = group['top10_searches'] || [];
                searches.forEach((search, sIdx) => {{
                    const evaluation = search['evaluation_with_filter'];
                    if (evaluation && evaluation.notes_evaluation) {{
                        evaluation.notes_evaluation.forEach(noteEval => {{
                            const key = `${{fIdx}}-${{gIdx}}-${{sIdx}}-${{noteEval.note_index}}`;
                            noteEvaluations[key] = noteEval;
                        }});
                    }}
                }});
            }});
        }});

        // 获取评估类别
        function getEvalCategory(noteEval) {{
            if (!noteEval || noteEval['Query相关性'] !== '相关') {{
                return 'filtered';
            }}
            const score = noteEval['综合得分'];
            if (score >= 0.8) return 'complete';
            if (score >= 0.6) return 'similar';
            if (score >= 0.5) return 'weak';
            return 'none';
        }}

        // 渲染左侧导航
        function renderLeftSidebar() {{
            const sidebar = document.getElementById('leftSidebar');
            let html = '';

            data.forEach((feature, featureIdx) => {{
                const groups = feature['组合评估结果_分组'] || [];
                let totalSearches = 0;
                groups.forEach(group => {{
                    totalSearches += (group['top10_searches'] || []).length;
                }});

                // 从allFeatures获取特征信息
                const featureName = feature['原始特征名称'];
                const featureInfo = allFeatures[featureName] || {{}};
                const similarity = featureInfo.similarity || 0;
                const weight = featureInfo.weight || 0;
                const dimension = featureInfo.dimension || '';

                html += `
                    <div class="feature-group">
                        <div class="feature-header" onclick="toggleFeature(${{featureIdx}})" id="feature-header-${{featureIdx}}">
                            <div class="feature-title post-target-word">📝 ${{featureName}}</div>
                            <div class="feature-meta" style="font-size:11px;color:#9ca3af;margin-top:4px;">
                                相似度: ${{similarity.toFixed(2)}} · ${{dimension}}
                            </div>
                        </div>
                        <div class="search-words-list" id="search-words-${{featureIdx}}">
                `;

                groups.forEach((group, groupIdx) => {{
                    const baseWord = group['base_word'] || '';
                    const baseSimilarity = group['base_word_similarity'] || 0;
                    const searches = group['top10_searches'] || [];

                    const relatedWords = feature['高相似度候选_按base_word']?.[baseWord] || [];
                    const relatedWordNames = relatedWords.map(w => w['人设特征名称']).slice(0, 10).join('、');

                    html += `
                        <div class="base-word-group">
                            <div class="base-word-header" onclick="toggleBaseWord(${{featureIdx}}, ${{groupIdx}})"
                                 id="base-word-header-${{featureIdx}}-${{groupIdx}}">
                                <div class="base-word-title persona-feature">👤 ${{baseWord}}</div>
                                <div class="base-word-meta">相似度: ${{baseSimilarity.toFixed(2)}} · ${{searches.length}}个搜索词</div>
                            </div>
                            <div class="base-word-desc" id="base-word-desc-${{featureIdx}}-${{groupIdx}}">
                                <span style="color:#92400e;font-weight:600;">来源人设候选特征/分类: </span>${{relatedWordNames || '无相关词汇'}}
                            </div>
                            <div class="search-words-sublist" id="search-words-sublist-${{featureIdx}}-${{groupIdx}}">
                    `;

                    searches.forEach((sw, swIdx) => {{
                        const score = sw.score || 0;
                        const blockId = `block-${{featureIdx}}-${{groupIdx}}-${{swIdx}}`;
                        const sourceWord = sw.source_word || '';
                        const searchWord = sw.search_word || '';

                        const evaluation = sw['evaluation_with_filter'];
                        let evalBadges = '';
                        if (evaluation) {{
                            const stats = evaluation.statistics || {{}};
                            const complete = stats['完全匹配(0.8-1.0)'] || 0;
                            const similar = stats['相似匹配(0.6-0.79)'] || 0;
                            const weak = stats['弱相似(0.5-0.59)'] || 0;
                            const none = stats['无匹配(≤0.4)'] || 0;
                            const filtered = evaluation.filtered_count || 0;

                            if (complete > 0) evalBadges += `<span class="eval-badge eval-complete">🟢${{complete}}</span>`;
                            if (similar > 0) evalBadges += `<span class="eval-badge eval-similar">🟡${{similar}}</span>`;
                            if (weak > 0) evalBadges += `<span class="eval-badge eval-weak">🟠${{weak}}</span>`;
                            if (none > 0) evalBadges += `<span class="eval-badge eval-none">🔴${{none}}</span>`;
                            if (filtered > 0) evalBadges += `<span class="eval-badge eval-filtered">⚫${{filtered}}</span>`;
                        }}

                        // P值显示
                        let pScoreHtml = '';
                        const pScore = sw.comprehensive_score;
                        const pDetail = sw.comprehensive_score_detail;
                        if (pScore !== undefined && pScore !== null) {{
                            const originalFeature = feature['原始特征名称'] || '';
                            pScoreHtml = `
                                <div class="p-score-container" onclick="event.stopPropagation();">
                                    <div class="p-score-arrow">
                                        <span>${{sourceWord}}</span>
                                        <span class="p-score-arrow-symbol">→</span>
                                        <span>${{originalFeature}}</span>
                                    </div>
                                </div>
                                <div class="p-score-container" onclick="event.stopPropagation(); openPDetailModal('${{searchWord}}', ${{featureIdx}}, ${{groupIdx}}, ${{swIdx}});">
                                    <span class="p-score-label">综合得分P:</span>
                                    <span class="p-score-value">${{pScore.toFixed(3)}}</span>
                                    <span class="p-score-info-icon" title="点击查看计算详情">ℹ️</span>
                                </div>
                            `;
                        }}

                        html += `
                            <div class="search-word-item" onclick="scrollToBlock('${{blockId}}')"
                                 id="sw-${{featureIdx}}-${{groupIdx}}-${{swIdx}}"
                                 data-block-id="${{blockId}}">
                                <div class="search-word-text">
                                    🔍 ${{sw.search_word}}
                                </div>
                                <div class="search-word-meta" style="font-size:11px;color:#9ca3af;margin-top:2px">
                                    来源: ${{sourceWord}}
                                </div>
                                <div class="search-word-eval">${{evalBadges}}</div>
                                ${{pScoreHtml}}
                            </div>
                        `;
                    }});

                    html += `
                            </div>
                        </div>
                    `;
                }});

                html += `
                        </div>
                    </div>
                `;
            }});

            // 添加低相似度特征板块
            if (lowSimilarityFeatures && lowSimilarityFeatures.length > 0) {{
                html += `
                    <div class="low-similarity-section">
                        <div class="low-similarity-header" onclick="toggleLowSimilarity()">
                            <div class="low-similarity-title">🔴 低相似度特征（未搜索）</div>
                            <div class="low-similarity-count">${{lowSimilarityFeatures.length}}个</div>
                            <div class="toggle-icon" id="low-similarity-toggle">▼</div>
                        </div>
                        <div class="low-similarity-list" id="low-similarity-list" style="display:none;">
                `;

                lowSimilarityFeatures.forEach((feature, idx) => {{
                    const name = feature.name || '';
                    const similarity = feature.similarity || 0;
                    const weight = feature.weight || 0;
                    const dimension = feature.dimension || '';

                    html += `
                        <div class="low-similarity-item" onclick="toggleLowFeatureDetail(${{idx}})" id="low-feature-${{idx}}">
                            <div class="low-feature-header">
                                <div class="low-feature-name">📝 ${{name}}</div>
                                <div class="low-feature-score">${{similarity.toFixed(2)}}</div>
                            </div>
                            <div class="low-feature-meta">
                                ${{dimension}}
                            </div>
                            <div class="low-feature-detail" id="low-feature-detail-${{idx}}" style="display:none;">
                                <div class="low-feature-reason">
                                    ❌ 相似度 ${{similarity.toFixed(2)}} &lt; 0.4<br>
                                    低于搜索阈值，未执行搜索
                                </div>
                            </div>
                        </div>
                    `;
                }});

                html += `
                        </div>
                    </div>
                `;
            }}

            sidebar.innerHTML = html;
        }}

        // 渲染右侧结果区
        function renderRightContent() {{
            const content = document.getElementById('rightContent');
            let html = '';

            data.forEach((feature, featureIdx) => {{
                const groups = feature['组合评估结果_分组'] || [];

                groups.forEach((group, groupIdx) => {{
                    const searches = group['top10_searches'] || [];

                    searches.forEach((sw, swIdx) => {{
                        const blockId = `block-${{featureIdx}}-${{groupIdx}}-${{swIdx}}`;
                        const hasSearchResult = sw.search_result != null;
                        const searchResult = sw.search_result || {{}};
                        const notes = searchResult.data?.data || [];

                        const videoCount = notes.filter(n => n.note_card?.type === 'video').length;
                        const normalCount = notes.length - videoCount;

                        const evaluation = sw['evaluation_with_filter'];
                        let evalStats = '';
                        if (evaluation) {{
                            const stats = evaluation.statistics || {{}};
                            const complete = stats['完全匹配(0.8-1.0)'] || 0;
                            const similar = stats['相似匹配(0.6-0.79)'] || 0;
                            const weak = stats['弱相似(0.5-0.59)'] || 0;
                            const none = stats['无匹配(≤0.4)'] || 0;
                            const filtered = evaluation.filtered_count || 0;

                            if (complete > 0) evalStats += `<span class="stat-badge eval complete">🟢 完全:${{complete}}</span>`;
                            if (similar > 0) evalStats += `<span class="stat-badge eval similar">🟡 相似:${{similar}}</span>`;
                            if (weak > 0) evalStats += `<span class="stat-badge eval weak">🟠 弱:${{weak}}</span>`;
                            if (none > 0) evalStats += `<span class="stat-badge eval none">🔴 无:${{none}}</span>`;
                            if (filtered > 0) evalStats += `<span class="stat-badge eval filtered">⚫ 过滤:${{filtered}}</span>`;
                        }}

                        html += `
                            <div class="result-block" id="${{blockId}}">
                                <div class="result-header">
                                    <div class="result-title">${{sw.search_word}}</div>
                                    <div class="result-stats">
                        `;

                        if (!hasSearchResult) {{
                            html += `<span class="stat-badge" style="background:#fef3c7;color:#92400e;font-weight:600">⏸️ 未执行搜索</span>`;
                        }} else if (notes.length === 0) {{
                            html += `
                                <span class="stat-badge">📝 0 条帖子</span>
                                <span class="stat-badge" style="background:#fee2e2;color:#991b1b;font-weight:600">❌ 未找到匹配</span>
                            `;
                        }} else {{
                            html += `
                                <span class="stat-badge">📝 ${{notes.length}} 条帖子</span>
                                <span class="stat-badge">🎬 ${{videoCount}} 视频</span>
                                <span class="stat-badge">📷 ${{normalCount}} 图文</span>
                                ${{evalStats}}
                            `;
                        }}

                        html += `</div></div>`;

                        if (!hasSearchResult) {{
                            html += `
                                <div class="empty-state">
                                    <div class="empty-icon">⏸️</div>
                                    <div class="empty-title">该搜索词未执行搜索</div>
                                    <div class="empty-desc">由于搜索次数限制,该搜索词未被执行</div>
                                </div>
                            `;
                        }} else if (notes.length === 0) {{
                            html += `
                                <div class="empty-state">
                                    <div class="empty-icon">❌</div>
                                    <div class="empty-title">搜索完成,但未找到匹配的帖子</div>
                                    <div class="empty-desc">该搜索词已执行,但小红书返回了 0 条结果</div>
                                </div>
                            `;
                        }} else {{
                            html += `
                                <div class="notes-grid">
                                    ${{notes.map((note, noteIdx) => renderNoteCard(note, featureIdx, groupIdx, swIdx, noteIdx)).join('')}}
                                </div>
                            `;
                        }}

                        html += `</div>`;
                    }});
                }});
            }});

            content.innerHTML = html;
        }}

        // 渲染单个帖子卡片
        function renderNoteCard(note, featureIdx, groupIdx, swIdx, noteIdx) {{
            const card = note.note_card || {{}};
            const images = card.image_list || [];
            const title = card.display_title || '无标题';
            const noteType = card.type || 'normal';
            const noteId = note.id || '';
            const user = card.user || {{}};
            const userName = user.nick_name || '未知用户';
            const userAvatar = user.avatar || '';

            const carouselId = `carousel-${{featureIdx}}-${{groupIdx}}-${{swIdx}}-${{noteIdx}}`;

            const evalKey = `${{featureIdx}}-${{groupIdx}}-${{swIdx}}-${{noteIdx}}`;
            const noteEval = noteEvaluations[evalKey];
            const evalCategory = getEvalCategory(noteEval);
            const evalClass = `eval-${{evalCategory}}`;

            let evalSection = '';
            if (noteEval) {{
                const score = noteEval['综合得分'];
                const scoreEmoji = score >= 0.8 ? '🟢' : score >= 0.6 ? '🟡' : score >= 0.5 ? '🟠' : '🔴';
                const scoreText = score >= 0.8 ? '完全匹配' : score >= 0.6 ? '相似匹配' : score >= 0.5 ? '弱相似' : '无匹配';
                const reasoning = noteEval['评分说明'] || '无';
                const matchingPoints = (noteEval['关键匹配点'] || []).join('、') || '无';

                evalSection = `
                    <div class="note-eval">
                        <div class="note-eval-header" onclick="event.stopPropagation(); toggleEvalDetails('${{carouselId}}')">
                            <span class="note-eval-score">${{scoreEmoji}} ${{scoreText}} (${{score}}分)</span>
                            <span class="note-eval-toggle" id="${{carouselId}}-toggle">▼ 详情</span>
                        </div>
                        <div class="note-eval-details" id="${{carouselId}}-details">
                            <div class="eval-detail-label">评估理由:</div>
                            <div class="eval-detail-text">${{reasoning}}</div>
                            <div class="eval-detail-label">匹配要点:</div>
                            <div class="eval-detail-text">${{matchingPoints}}</div>
                        </div>
                    </div>
                `;
            }} else if (evalCategory === 'filtered') {{
                evalSection = `
                    <div class="note-eval">
                        <div class="note-eval-score">⚫ 已过滤(与搜索无关)</div>
                    </div>
                `;
            }}

            // 检查是否有解构数据(仅完全匹配)
            const inStage8 = !!stage8Data[noteId];
            const stage8Features = inStage8 ? (stage8Data[noteId].deconstructed_features || []) : [];
            const hasValidFeatures = stage8Features.length > 0;
            const hasDeconstruction = evalCategory === 'complete' && hasValidFeatures;

            // 调试日志: 记录解构按钮判断过程
            if (noteId === '67e554a300000000090148a7' || hasDeconstruction) {{
                console.log(`[解构按钮] noteId=${{noteId}}, evalCategory=${{evalCategory}}, inStage8=${{inStage8}}, 特征数=${{stage8Features.length}}, 显示按钮=${{hasDeconstruction}}`);
            }}

            let deconstructionSection = '';

            if (hasDeconstruction) {{
                deconstructionSection = `
                    <button class="deconstruction-toggle-btn" data-note-id="${{noteId}}" data-note-title="${{title.replace(/"/g, '&quot;')}}">
                        <span>📊</span>
                        <span>查看解构结果</span>
                    </button>
                `;
            }}

            return `
                <div class="note-card ${{evalClass}}" data-eval-category="${{evalCategory}}" onclick="openNoteImagesModal(${{featureIdx}}, ${{groupIdx}}, ${{swIdx}}, ${{noteIdx}})">
                    <div class="image-carousel" id="${{carouselId}}">
                        <div class="carousel-images">
                            ${{images.map(img => `<img class="carousel-image" src="${{img}}" alt="帖子图片" loading="lazy">`).join('')}}
                        </div>
                        ${{images.length > 1 ? `
                            <button class="carousel-btn prev" onclick="event.stopPropagation(); changeImage('${{carouselId}}', -1)">←</button>
                            <button class="carousel-btn next" onclick="event.stopPropagation(); changeImage('${{carouselId}}', 1)">→</button>
                            <div class="carousel-indicators">
                                ${{images.map((_, i) => `<span class="dot ${{i === 0 ? 'active' : ''}}" onclick="event.stopPropagation(); goToImage('${{carouselId}}', ${{i}})"></span>`).join('')}}
                            </div>
                            <span class="image-counter">1/${{images.length}}</span>
                        ` : ''}}
                    </div>
                    <div class="note-info">
                        <div class="note-title">${{title}}</div>
                        <div class="note-meta">
                            <span class="note-type type-${{noteType}}">
                                ${{noteType === 'video' ? '🎬 视频' : '📷 图文'}}
                            </span>
                            <div class="note-author">
                                ${{userAvatar ? `<img class="author-avatar" src="${{userAvatar}}" alt="${{userName}}">` : ''}}
                                <span>${{userName}}</span>
                            </div>
                        </div>
                    </div>
                    ${{evalSection}}
                    ${{deconstructionSection}}
                </div>
            `;
        }}

        // 打开解构模态窗口
        function openDeconstructionModal(noteId, noteTitle) {{
            console.log('🔧 [调试] openDeconstructionModal被调用, noteId:', noteId);

            const modal = document.getElementById('deconstructionModal');
            const modalContent = document.getElementById('modalContent');
            const modalNoteTitle = document.getElementById('modalNoteTitle');

            if (!modal || !modalContent || !modalNoteTitle) {{
                console.error('❌ [错误] 无法找到模态窗口元素');
                return;
            }}

            // 设置标题
            modalNoteTitle.textContent = noteTitle || '解构分析';

            // 检查是否有数据
            const hasStage8Data = !!stage8Data[noteId];
            console.log('📊 [调试] Stage8数据存在:', hasStage8Data);

            if (!hasStage8Data) {{
                console.warn('⚠️ [警告] 未找到Stage8数据, noteId:', noteId);
                console.log('📋 [调试] 可用的noteId列表:', Object.keys(stage8Data));
                modalContent.innerHTML = '<div style="padding: 30px; text-align: center; color: #6b7280;">暂无解构数据</div>';
            }} else {{
                try {{
                    modalContent.innerHTML = renderDeconstructionContent(noteId);
                    console.log('✅ [调试] 解构内容渲染成功');
                }} catch (error) {{
                    console.error('❌ [错误] 渲染解构内容失败:', error);
                    modalContent.innerHTML = `<div style="padding: 30px; text-align: center; color: red;">渲染错误: ${{error.message}}</div>`;
                }}
            }}

            // 显示模态窗口
            modal.classList.add('active');
            document.body.style.overflow = 'hidden'; // 禁止背景滚动
            console.log('✅ [调试] 模态窗口已显示');
        }}

        // 关闭模态窗口
        function closeModal() {{
            console.log('🔧 [调试] closeModal被调用');
            const modal = document.getElementById('deconstructionModal');
            if (modal) {{
                modal.classList.remove('active');
                document.body.style.overflow = ''; // 恢复滚动
                console.log('✅ [调试] 模态窗口已关闭');
            }}
        }}

        // ESC键关闭模态窗口
        document.addEventListener('keydown', function(e) {{
            if (e.key === 'Escape') {{
                const modal = document.getElementById('deconstructionModal');
                if (modal && modal.classList.contains('active')) {{
                    closeModal();
                }}
            }}
        }});

        // 点击遮罩层关闭模态窗口
        document.addEventListener('click', function(e) {{
            const modal = document.getElementById('deconstructionModal');
            if (e.target === modal) {{
                closeModal();
            }}

            const pModal = document.getElementById('pDetailModal');
            if (e.target === pModal) {{
                closePDetailModal();
            }}
        }});

        // 打开P值详情模态窗口
        function openPDetailModal(searchWord, featureIdx, groupIdx, swIdx) {{
            console.log('🔧 [调试] openPDetailModal被调用, searchWord:', searchWord);

            const modal = document.getElementById('pDetailModal');
            const modalContent = document.getElementById('pDetailContent');

            if (!modal || !modalContent) {{
                console.error('❌ [错误] 无法找到P值详情模态窗口元素');
                return;
            }}

            // 获取对应的搜索词数据
            const feature = data[featureIdx];
            const group = feature['组合评估结果_分组'][groupIdx];
            const sw = group['top10_searches'][swIdx];

            const pScore = sw.comprehensive_score;
            const pDetail = sw.comprehensive_score_detail;

            if (!pDetail) {{
                console.warn('⚠️ [警告] 未找到P值详情数据');
                modalContent.innerHTML = '<div style="padding: 30px; text-align: center; color: #6b7280;">暂无P值详情数据</div>';
            }} else {{
                try {{
                    modalContent.innerHTML = renderPDetailContent(searchWord, feature['原始特征名称'], sw.source_word, pScore, pDetail);
                    console.log('✅ [调试] P值详情内容渲染成功');
                }} catch (error) {{
                    console.error('❌ [错误] 渲染P值详情内容失败:', error);
                    modalContent.innerHTML = `<div style="padding: 30px; text-align: center; color: red;">渲染错误: ${{error.message}}</div>`;
                }}
            }}

            // 显示模态窗口
            modal.classList.add('active');
            document.body.style.overflow = 'hidden';
            console.log('✅ [调试] P值详情模态窗口已显示');
        }}

        // 关闭P值详情模态窗口
        function closePDetailModal() {{
            console.log('🔧 [调试] closePDetailModal被调用');
            const modal = document.getElementById('pDetailModal');
            if (modal) {{
                modal.classList.remove('active');
                document.body.style.overflow = '';
                console.log('✅ [调试] P值详情模态窗口已关闭');
            }}
        }}

        // 渲染P值详情内容
        function renderPDetailContent(searchWord, targetFeature, sourceWord, pScore, pDetail) {{
            const N = pDetail.N || 0;
            const M = pDetail.M || 0;
            const totalContribution = pDetail.total_contribution || 0;
            const matches = pDetail.complete_matches || [];

            let html = `
                <div class="p-formula-section">
                    <div class="p-formula-title">📐 计算公式</div>
                    <div class="p-formula">P = Σ(a × b) / N = ${{totalContribution.toFixed(3)}} / ${{N}} = ${{pScore.toFixed(3)}}</div>
                    <div style="font-size: 12px; color: #64748b; margin-top: 8px;">
                        <div>• a: 评估得分（综合得分）</div>
                        <div>• b: 最高相似度（解构特征与目标特征的最高相似度）</div>
                        <div>• N: 总帖子数</div>
                        <div>• M: 完全匹配数（得分 ≥ 0.8）</div>
                    </div>
                </div>

                <div class="p-summary">
                    <div class="p-summary-item">
                        <div class="p-summary-label">搜索词</div>
                        <div class="p-summary-value" style="font-size: 16px;">${{searchWord}}</div>
                    </div>
                    <div class="p-summary-item">
                        <div class="p-summary-label">来源</div>
                        <div class="p-summary-value" style="font-size: 16px;">${{sourceWord}}</div>
                    </div>
                    <div class="p-summary-item">
                        <div class="p-summary-label">目标特征</div>
                        <div class="p-summary-value" style="font-size: 16px;">${{targetFeature}}</div>
                    </div>
                </div>

                <div class="p-summary">
                    <div class="p-summary-item">
                        <div class="p-summary-label">总帖子数 (N)</div>
                        <div class="p-summary-value">${{N}}</div>
                    </div>
                    <div class="p-summary-item">
                        <div class="p-summary-label">完全匹配数 (M)</div>
                        <div class="p-summary-value highlight">${{M}}</div>
                    </div>
                    <div class="p-summary-item">
                        <div class="p-summary-label">总贡献值</div>
                        <div class="p-summary-value highlight">${{totalContribution.toFixed(3)}}</div>
                    </div>
                    <div class="p-summary-item">
                        <div class="p-summary-label">综合得分 (P)</div>
                        <div class="p-summary-value highlight">${{pScore.toFixed(3)}}</div>
                    </div>
                </div>
            `;

            if (matches.length > 0) {{
                html += `
                    <div class="p-matches-section">
                        <div class="p-matches-title">🎯 完全匹配帖子详情 (${{M}}个)</div>
                `;

                matches.forEach((match, idx) => {{
                    html += `
                        <div class="p-match-item">
                            <div class="p-match-header">
                                <div class="p-match-title">${{idx + 1}}. ${{match.note_title || '未知标题'}}</div>
                                <div class="p-match-contribution">+${{match.contribution.toFixed(3)}}</div>
                            </div>
                            <div class="p-match-details">
                                <div class="p-match-detail-item">
                                    <span class="p-match-detail-label">评估得分 (a):</span>
                                    <span>${{match.evaluation_score.toFixed(3)}}</span>
                                </div>
                                <div class="p-match-detail-item">
                                    <span class="p-match-detail-label">最高相似度 (b):</span>
                                    <span>${{match.max_similarity.toFixed(3)}}</span>
                                </div>
                                <div class="p-match-detail-item">
                                    <span class="p-match-detail-label">贡献值 (a×b):</span>
                                    <span>${{match.contribution.toFixed(3)}}</span>
                                </div>
                            </div>
                        </div>
                    `;
                }});

                html += `</div>`;
            }} else {{
                html += `
                    <div class="p-matches-section">
                        <div style="text-align: center; padding: 30px; color: #6b7280;">
                            没有完全匹配的帖子（得分 ≥ 0.8）
                        </div>
                    </div>
                `;
            }}

            return html;
        }}

        // 渲染解构内容
        function renderDeconstructionContent(noteId) {{
            const stage8Info = stage8Data[noteId];
            if (!stage8Info) {{
                return `<div style="padding: 30px; text-align: center; color: #6b7280;">
                    <div style="font-size: 48px; margin-bottom: 10px;">📭</div>
                    <div style="font-size: 16px; margin-bottom: 8px;">暂无解构数据</div>
                    <div style="font-size: 14px; opacity: 0.7;">该帖子未进行特征解构分析</div>
                </div>`;
            }}

            const originalFeature = stage8Info.original_feature || '未知特征';
            const features = stage8Info.deconstructed_features || [];

            if (features.length === 0) {{
                return `<div style="padding: 30px; text-align: center; color: #6b7280;">
                    <div style="font-size: 48px; margin-bottom: 10px;">🔍</div>
                    <div style="font-size: 16px; margin-bottom: 8px;">未提取到解构特征</div>
                    <div style="font-size: 14px; opacity: 0.7;">原始特征："${{originalFeature}}"</div>
                    <div style="font-size: 13px; opacity: 0.6; margin-top: 10px;">该帖子虽然评分较高，但AI未能从中提取到有效的解构特征</div>
                </div>`;
            }}

            // 按维度分组
            const dimensionGroups = {{}};
            features.forEach(feat => {{
                const dim = feat.dimension || '未分类';
                if (!dimensionGroups[dim]) {{
                    dimensionGroups[dim] = [];
                }}
                dimensionGroups[dim].push(feat);
            }});

            // 为每个维度找出最高分
            Object.keys(dimensionGroups).forEach(dim => {{
                const feats = dimensionGroups[dim];
                if (feats.length > 0) {{
                    const maxScore = Math.max(...feats.map(f => f.similarity_score || 0));
                    feats.forEach(f => {{
                        f.isTopInDimension = (f.similarity_score === maxScore);
                    }});
                }}
            }});

            let html = `
                <div class="deconstruction-header">
                    <div>🎯 解构特征相似度分析</div>
                    <div class="original-feature">目标特征: "${{originalFeature}}"</div>
                </div>
            `;

            // 按维度排序: 灵感点 -> 目的点 -> 关键点
            const dimensionOrder = ['灵感点-全新内容', '灵感点-共性差异', '灵感点-共性内容', '目的点', '关键点'];
            const sortedDimensions = Object.keys(dimensionGroups).sort((a, b) => {{
                const aIndex = dimensionOrder.findIndex(d => a.startsWith(d));
                const bIndex = dimensionOrder.findIndex(d => b.startsWith(d));
                if (aIndex === -1 && bIndex === -1) return a.localeCompare(b);
                if (aIndex === -1) return 1;
                if (bIndex === -1) return -1;
                return aIndex - bIndex;
            }});

            sortedDimensions.forEach((dimension, dimIdx) => {{
                const feats = dimensionGroups[dimension];
                const dimId = `dim-${{noteId}}-${{dimIdx}}`;

                html += `
                    <div class="dimension-card">
                        <div class="dimension-header" onclick="event.stopPropagation(); toggleDimension('${{dimId}}')">
                            <div class="dimension-title">
                                <span>${{getDimensionIcon(dimension)}} ${{dimension}}</span>
                                <span class="dimension-count">(${{feats.length}}个特征)</span>
                            </div>
                            <span class="dimension-toggle" id="${{dimId}}-toggle">▼</span>
                        </div>
                        <div class="dimension-body expanded" id="${{dimId}}">
                            <div class="feature-list">
                `;

                // 按分数降序排列
                feats.sort((a, b) => (b.similarity_score || 0) - (a.similarity_score || 0));

                feats.forEach(feat => {{
                    const score = feat.similarity_score || 0;
                    const scoreClass = score >= 0.7 ? 'high' : score >= 0.5 ? 'medium' : 'low';
                    const barWidth = Math.min(score * 100, 100);
                    const isTop = feat.isTopInDimension;

                    html += `
                        <div class="feature-item ${{isTop ? 'top-score' : ''}}">
                            <div class="feature-name">
                                ${{isTop ? '<span class="top-badge">🏆 最高分</span>' : ''}}
                                ${{feat.feature_name || '未命名特征'}}
                            </div>
                            <div class="feature-meta-row">
                                <span class="feature-dimension-detail">${{feat.dimension_detail || '无分类'}}</span>
                                <span class="feature-weight">权重: ${{(feat.weight || 0).toFixed(1)}}</span>
                            </div>
                            <div class="similarity-row">
                                <span class="similarity-score ${{scoreClass}}">${{score.toFixed(3)}}</span>
                                <div class="similarity-bar-container">
                                    <div class="similarity-bar ${{scoreClass}}" style="width: ${{barWidth}}%"></div>
                                </div>
                            </div>
                            <div class="similarity-explanation">
                                ${{feat.similarity_explanation || '无说明'}}
                            </div>
                        </div>
                    `;
                }});

                html += `
                            </div>
                        </div>
                    </div>
                `;
            }});

            return html;
        }}

        // 获取维度图标
        function getDimensionIcon(dimension) {{
            if (dimension.includes('灵感点')) return '💡';
            if (dimension.includes('目的点')) return '🎯';
            if (dimension.includes('关键点')) return '🔑';
            return '📋';
        }}

        // 切换维度卡片
        function toggleDimension(dimId) {{
            const body = document.getElementById(dimId);
            const toggle = document.getElementById(`${{dimId}}-toggle`);

            if (body.classList.contains('expanded')) {{
                body.classList.remove('expanded');
                toggle.textContent = '▶';
            }} else {{
                body.classList.add('expanded');
                toggle.textContent = '▼';
            }}
        }}

        // 图片轮播逻辑
        const carouselStates = {{}};

        function changeImage(carouselId, direction) {{
            if (!carouselStates[carouselId]) {{
                carouselStates[carouselId] = {{ currentIndex: 0 }};
            }}

            const carousel = document.getElementById(carouselId);
            const imagesContainer = carousel.querySelector('.carousel-images');
            const images = carousel.querySelectorAll('.carousel-image');
            const dots = carousel.querySelectorAll('.dot');
            const counter = carousel.querySelector('.image-counter');

            let newIndex = carouselStates[carouselId].currentIndex + direction;
            if (newIndex < 0) newIndex = images.length - 1;
            if (newIndex >= images.length) newIndex = 0;

            carouselStates[carouselId].currentIndex = newIndex;
            imagesContainer.style.transform = `translateX(-${{newIndex * 100}}%)`;

            dots.forEach((dot, i) => {{
                dot.classList.toggle('active', i === newIndex);
            }});

            if (counter) {{
                counter.textContent = `${{newIndex + 1}}/${{images.length}}`;
            }}
        }}

        function goToImage(carouselId, index) {{
            if (!carouselStates[carouselId]) {{
                carouselStates[carouselId] = {{ currentIndex: 0 }};
            }}

            const carousel = document.getElementById(carouselId);
            const imagesContainer = carousel.querySelector('.carousel-images');
            const dots = carousel.querySelectorAll('.dot');
            const counter = carousel.querySelector('.image-counter');

            carouselStates[carouselId].currentIndex = index;
            imagesContainer.style.transform = `translateX(-${{index * 100}}%)`;

            dots.forEach((dot, i) => {{
                dot.classList.toggle('active', i === index);
            }});

            if (counter) {{
                counter.textContent = `${{index + 1}}/${{dots.length}}`;
            }}
        }}

        function toggleFeature(featureIdx) {{
            const searchWordsList = document.getElementById(`search-words-${{featureIdx}}`);
            const featureHeader = document.getElementById(`feature-header-${{featureIdx}}`);

            searchWordsList.classList.toggle('expanded');
            featureHeader.classList.toggle('active');
        }}

        function toggleBaseWord(featureIdx, groupIdx) {{
            const baseWordHeader = document.getElementById(`base-word-header-${{featureIdx}}-${{groupIdx}}`);
            const baseWordDesc = document.getElementById(`base-word-desc-${{featureIdx}}-${{groupIdx}}`);
            const searchWordsSublist = document.getElementById(`search-words-sublist-${{featureIdx}}-${{groupIdx}}`);

            baseWordHeader.classList.toggle('active');
            baseWordDesc.classList.toggle('expanded');
            searchWordsSublist.classList.toggle('expanded');
        }}

        function scrollToBlock(blockId) {{
            const block = document.getElementById(blockId);
            if (block) {{
                block.scrollIntoView({{ behavior: 'smooth', block: 'start' }});

                document.querySelectorAll('.search-word-item').forEach(item => {{
                    item.classList.remove('active');
                }});

                document.querySelectorAll(`[data-block-id="${{blockId}}"]`).forEach(item => {{
                    item.classList.add('active');
                }});
            }}
        }}

        function toggleEvalDetails(carouselId) {{
            const details = document.getElementById(`${{carouselId}}-details`);
            const toggle = document.getElementById(`${{carouselId}}-toggle`);

            if (details && toggle) {{
                details.classList.toggle('expanded');
                toggle.textContent = details.classList.contains('expanded') ? '▲ 收起' : '▼ 详情';
            }}
        }}

        // 切换低相似度特征板块展开/折叠
        function toggleLowSimilarity() {{
            const list = document.getElementById('low-similarity-list');
            const toggle = document.getElementById('low-similarity-toggle');

            if (list && toggle) {{
                if (list.style.display === 'none') {{
                    list.style.display = 'block';
                    toggle.classList.add('rotated');
                }} else {{
                    list.style.display = 'none';
                    toggle.classList.remove('rotated');
                }}
            }}
        }}

        // 切换单个低相似度特征的详情展开/折叠
        function toggleLowFeatureDetail(idx) {{
            const detail = document.getElementById(`low-feature-detail-${{idx}}`);

            if (detail) {{
                if (detail.style.display === 'none') {{
                    detail.style.display = 'block';
                }} else {{
                    detail.style.display = 'none';
                }}
            }}
        }}

        function filterNotes(category) {{
            currentFilter = category;

            document.querySelectorAll('.filter-btn').forEach(btn => {{
                btn.classList.remove('active');
            }});
            event.target.classList.add('active');

            document.querySelectorAll('.note-card').forEach(card => {{
                const evalCategory = card.getAttribute('data-eval-category');
                if (category === 'all' || evalCategory === category) {{
                    card.classList.remove('hidden');
                }} else {{
                    card.classList.add('hidden');
                }}
            }});

            document.querySelectorAll('.result-block').forEach(block => {{
                const visibleCards = block.querySelectorAll('.note-card:not(.hidden)');
                if (visibleCards.length === 0) {{
                    block.classList.add('hidden');
                }} else {{
                    block.classList.remove('hidden');
                }}
            }});
        }}

        function openNote(noteId) {{
            if (noteId) {{
                window.open(`https://www.xiaohongshu.com/explore/${{noteId}}`, '_blank');
            }}
        }}

        // 页面加载时输出调试信息
        console.log('='.repeat(60));
        console.log('🚀 [系统] 页面脚本加载完成');
        console.log('📊 [数据] Stage6特征数:', data.length);
        console.log('📊 [数据] Stage7解构数:', Object.keys(stage7Data).length);
        console.log('📊 [数据] Stage8相似度数:', Object.keys(stage8Data).length);
        console.log('📋 [数据] Stage8可用noteId:', Object.keys(stage8Data));
        console.log('='.repeat(60));

        // 初始化
        document.addEventListener('DOMContentLoaded', () => {{
            console.log('✅ [系统] DOM加载完成，开始初始化...');

            try {{
                renderLeftSidebar();
                console.log('✅ [系统] 左侧导航渲染完成');

                renderRightContent();
                console.log('✅ [系统] 右侧内容渲染完成');

                if (data.length > 0) {{
                    toggleFeature(0);

                    const firstGroups = data[0]['组合评估结果_分组'];
                    if (firstGroups && firstGroups.length > 0) {{
                        toggleBaseWord(0, 0);
                    }}
                }}

                console.log('✅ [系统] 页面初始化完成');

                // 为所有解构按钮添加事件监听器
                setTimeout(() => {{
                    const buttons = document.querySelectorAll('.deconstruction-toggle-btn');
                    console.log('🔍 [系统] 找到解构按钮数量:', buttons.length);

                    buttons.forEach((btn, index) => {{
                        const noteId = btn.getAttribute('data-note-id');
                        const noteTitle = btn.getAttribute('data-note-title');
                        console.log(`  按钮[${{index}}] noteId:`, noteId, ', title:', noteTitle);

                        // 添加事件监听器打开模态窗口
                        btn.addEventListener('click', function(e) {{
                            console.log('🖱️ [事件] 按钮点击, noteId:', noteId);
                            e.stopPropagation();
                            e.preventDefault();
                            openDeconstructionModal(noteId, noteTitle);
                        }});
                    }});
                }}, 500);

            }} catch (error) {{
                console.error('❌ [错误] 初始化失败:', error);
            }}
        }});

        // ========== 新增功能1: 统计面板折叠/展开 ==========
        const statsPanel = document.querySelector('.stats-panel');
        if (statsPanel) {{
            statsPanel.addEventListener('click', function() {{
                this.classList.toggle('collapsed');
            }});
        }}

        // ========== 新增功能2: 帖子图片浮层模态窗口 ==========
        function openNoteImagesModal(featureIdx, groupIdx, searchIdx, noteIdx) {{
            const feature = data[featureIdx];
            const group = feature['组合评估结果_分组'][groupIdx];
            const search = group['top10_searches'][searchIdx];
            const searchResult = search.search_result || {{}};
            const notes = searchResult.data?.data || [];
            const note = notes[noteIdx];

            if (!note) {{
                console.log('❌ [浮层] 找不到帖子数据');
                return;
            }}

            const card = note.note_card || {{}};
            const images = card.image_list || [];
            const title = card.display_title || '无标题';
            const noteId = note.id || '';

            console.log('🔍 [帖子图片浮层] 帖子ID:', noteId);
            console.log('🔍 [帖子图片浮层] 标题:', title);
            console.log('🔍 [帖子图片浮层] 图片数量:', images.length);

            if (images.length === 0) {{
                console.log('❌ [浮层] 该帖子没有图片');
                return;
            }}

            const modal = document.getElementById('notesModal');
            const modalContent = document.getElementById('notesModalContent');

            // 更新模态窗口标题
            const modalTitle = document.querySelector('.notes-modal-title');
            if (modalTitle) {{
                modalTitle.innerHTML = `
                    <div>
                        <div style="font-size: 18px; font-weight: 600;">📷 ${{title}}</div>
                        <div style="font-size: 14px; opacity: 0.9; margin-top: 5px;">共 ${{images.length}} 张图片</div>
                    </div>
                `;
            }}

            // 构建图片网格HTML
            let imagesHtml = '<div class="notes-grid-modal">';

            images.forEach((imageUrl, idx) => {{
                imagesHtml += `
                    <div class="note-card-modal" onclick="window.open('https://www.xiaohongshu.com/explore/${{noteId}}', '_blank'); event.stopPropagation();">
                        <div class="note-image-wrapper" style="height: 300px;">
                            <img src="${{imageUrl}}" alt="图片 ${{idx + 1}}" class="note-image" style="object-fit: contain; background: #000;"/>
                        </div>
                        <div class="note-content">
                            <div style="text-align: center; color: #6b7280; font-size: 13px;">
                                第 ${{idx + 1}} / ${{images.length}} 张
                            </div>
                        </div>
                    </div>
                `;
            }});

            imagesHtml += '</div>';

            modalContent.innerHTML = imagesHtml;
            modal.classList.add('active');

            // 点击背景关闭
            modal.onclick = function(e) {{
                if (e.target === modal) {{
                    closeNotesModal();
                }}
            }};
        }}

        function closeNotesModal() {{
            const modal = document.getElementById('notesModal');
            modal.classList.remove('active');
        }}

        // ========== 新增功能3: 右侧滚动与左侧联动 ==========
        // 使用Intersection Observer监听右侧内容块的可见性
        const observerOptions = {{
            root: null,
            rootMargin: '-20% 0px -70% 0px',  // 当元素在视口上20%位置时触发
            threshold: 0
        }};

        const observer = new IntersectionObserver((entries) => {{
            entries.forEach(entry => {{
                if (entry.isIntersecting) {{
                    const blockId = entry.target.id;
                    const leftItem = document.getElementById('search-' + blockId.replace('block-', ''));

                    if (leftItem) {{
                        // 移除所有active状态
                        document.querySelectorAll('.search-word-item').forEach(item => {{
                            item.classList.remove('active');
                        }});

                        // 添加active到当前项
                        leftItem.classList.add('active');

                        // 滚动到可见区域
                        leftItem.scrollIntoView({{
                            behavior: 'smooth',
                            block: 'nearest'
                        }});
                    }}
                }}
            }});
        }}, observerOptions);

        // 观察所有result-block
        document.addEventListener('DOMContentLoaded', () => {{
            setTimeout(() => {{
                const blocks = document.querySelectorAll('.result-block');
                blocks.forEach(block => {{
                    observer.observe(block);
                }});
                console.log('✅ [系统] 已设置', blocks.length, '个内容块的滚动监听');
            }}, 1000);
        }});
    </script>
</body>
</html>
'''

    with open(output_path, 'w', encoding='utf-8') as f:
        f.write(html_content)


def main():
    """主函数"""
    script_dir = os.path.dirname(os.path.abspath(__file__))

    # 加载数据
    stage6_path = os.path.join(script_dir, 'output_v2', 'stage6_with_evaluations.json')
    stage7_path = os.path.join(script_dir, 'output_v2', 'stage7_with_deconstruction.json')
    stage8_path = os.path.join(script_dir, 'output_v2', 'stage8_similarity_scores.json')
    persona_library_path = os.path.join(script_dir, 'optimized_clustered_data_gemini-3-pro-preview.json')
    how_json_path = os.path.join(script_dir, '690d977d0000000007036331_how.json')

    output_dir = os.path.join(script_dir, 'visualization')
    os.makedirs(output_dir, exist_ok=True)

    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    output_path = os.path.join(output_dir, f'stage6_with_stage78_{timestamp}.html')

    print(f"📖 加载Stage6数据: {stage6_path}")
    data = load_data(stage6_path)
    print(f"✓ 加载了 {len(data)} 个原始特征")

    print(f"📖 加载Stage7数据: {stage7_path}")
    stage7_mapping = load_stage7_data(stage7_path)
    print(f"✓ 加载了 {len(stage7_mapping)} 个解构结果")

    print(f"📖 加载Stage8数据: {stage8_path}")
    stage8_mapping = load_stage8_data(stage8_path)
    print(f"✓ 加载了 {len(stage8_mapping)} 个相似度评分")

    print("📊 计算统计数据...")
    stats = calculate_statistics(data)
    print(f"✓ 统计完成:")
    print(f"  - 原始特征: {stats['total_features']}")
    print(f"  - 搜索词总数: {stats['total_search_words']}")
    print(f"  - 帖子总数: {stats['total_notes']}")
    print(f"  - 完全匹配: {stats['match_complete']} ({stats['complete_rate']}%)")

    # 生成关系图
    print(f"\n📊 生成人设-帖子关系图...")
    relationship_graph_html = ""
    try:
        print(f"📖 加载人设库: {persona_library_path}")
        persona_data = load_persona_library(persona_library_path)
        print(f"✓ 加载人设库完成")

        print(f"📖 提取关系数据: {how_json_path}")
        relationships, all_high_matches = extract_relationship_data(how_json_path, persona_data)
        print(f"✓ 提取了 {len(relationships)} 个最高匹配关系")
        print(f"✓ 提取了 {len(all_high_matches)} 个高相似度(>0.8)匹配")

        relationship_graph_html = generate_relationship_graph_html(relationships, all_high_matches)
        print(f"✓ 关系图生成完成")
    except FileNotFoundError as e:
        print(f"⚠️ 警告: 无法生成关系图 - {e}")
        relationship_graph_html = '<div style="padding: 40px; text-align: center; color: #ef4444;">关系图数据文件未找到</div>'
    except Exception as e:
        print(f"⚠️ 警告: 生成关系图时出错 - {e}")
        relationship_graph_html = f'<div style="padding: 40px; text-align: center; color: #ef4444;">关系图生成失败: {e}</div>'

    # 提取所有特征信息（包括低相似度特征）
    print(f"\n📊 提取所有特征信息...")
    all_features = {}
    low_similarity_features = []
    try:
        all_features = extract_all_features_from_how(how_json_path)

        # 标记Stage6中已搜索的特征
        stage6_feature_names = set([item.get('原始特征名称') for item in data])
        for feature_name in stage6_feature_names:
            if feature_name in all_features and all_features[feature_name]['category'] == '待搜索':
                all_features[feature_name]['category'] = '已搜索'

        # 提取低相似度特征（<0.4）
        low_similarity_features = [
            {'name': name, **info}
            for name, info in all_features.items()
            if info['category'] == '低相似度'
        ]
        # 按相似度降序排序
        low_similarity_features.sort(key=lambda x: x['similarity'], reverse=True)

        print(f"✓ 提取了 {len(all_features)} 个特征")
        print(f"  - 低相似度特征: {len(low_similarity_features)} 个")
    except Exception as e:
        print(f"⚠️ 警告: 提取特征信息时出错 - {e}")
        low_similarity_features = []

    print(f"\n🎨 生成可视化页面...")
    generate_html(data, stats, stage7_mapping, stage8_mapping, relationship_graph_html, all_features, low_similarity_features, output_path)
    print(f"✓ 生成完成: {output_path}")

    print(f"\n🌐 在浏览器中打开查看:")
    print(f"   file://{output_path}")

    return output_path


if __name__ == '__main__':
    main()
