app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
import jieba
import jieba.analyse
from collections import Counter
import numpy as np
from pathlib import Path
import re
from datetime import datetime
import platform
import os
from typing import List, Optional, Tuple
import warnings
from sklearn.cluster import KMeans, DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, mean_squared_error, r2_score
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import stats
from scipy.cluster.hierarchy import dendrogram, linkage
import networkx as nx
from matplotlib.patches import Rectangle
import os
import urllib.request
from pathlib import Path
warnings.filterwarnings('ignore')

# 设置绘图样式
sns.set_style("whitegrid")
plt.style.use('seaborn-v0_8-darkgrid')

# 扩展停用词列表
STOPWORDS = set([
    # 基础停用词
    '的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一个', '也', '很', '到',
    '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这个', '还', '可以', '比较',
    '里', '上', '能', '多', '里面', '感觉', '觉得', '然后', '但是', '如果', '因为', '所以',
    # 程度副词
    '非常', '特别', '十分', '挺', '蛮', '相当', '比较', '更', '最',
    # 时间词
    '时候', '现在', '已经', '刚', '正在', '曾经', '以前', '之前', '以后', '之后',
    # 代词
    '我们', '你们', '他们', '她们', '它们', '这里', '那里', '哪里', '什么', '怎么', '为什么',
    # 助词
    '啊', '呀', '吧', '呢', '哦', '哈', '嗯', '唉',
    # 连词
    '而且', '并且', '或者', '还是', '以及', '及其', '虽然', '但是',
    # 其他常见词
    '这样', '那样', '怎样', '这种', '那种', '如此', '确实', '真的', '实在', '其实', '当然',
    '只是', '就是', '而已', '罢了', '左右', '上下', '之类', '等等', '之类的',
    '一下', '一点', '有点', '一些', '这些', '那些', '哪些', '每个', '各种', '所有',
    '进行', '开始', '结束', '成为', '变成', '得到', '拥有', '出现', '发现', '认为',
    '表示', '通过', '根据', '按照', '由于', '关于', '对于', '至于', '作为'
])


def download_chinese_font():
    """自动下载中文字体到项目目录"""
    font_dir = Path("fonts")
    font_dir.mkdir(exist_ok=True)

    font_path = font_dir / "SourceHanSansSC-Regular.otf"

    if font_path.exists():
        print(f"✅ 字体已存在: {font_path}")
        return str(font_path)

    print("📥 正在下载思源黑体...")

    # 使用 GitHub 镜像源（更稳定）
    font_urls = [
        "https://github.com/adobe-fonts/source-han-sans/raw/release/OTF/SimplifiedChinese/SourceHanSansSC-Regular.otf",
        "https://ghproxy.com/https://github.com/adobe-fonts/source-han-sans/raw/release/OTF/SimplifiedChinese/SourceHanSansSC-Regular.otf",
        "https://cdn.jsdelivr.net/gh/adobe-fonts/source-han-sans@release/OTF/SimplifiedChinese/SourceHanSansSC-Regular.otf"
    ]

    for url in font_urls:
        try:
            print(f"尝试从 {url[:50]}... 下载")
            urllib.request.urlretrieve(url, font_path)
            print(f"✅ 字体下载成功: {font_path}")
            return str(font_path)
        except Exception as e:
            print(f"⚠️ 下载失败: {e}")
            continue

    print("❌ 所有字体源下载失败")
    return None


def get_chinese_font():
    """获取中文字体路径（优先下载）"""
    # 1. 优先使用下载的字体
    downloaded_font = download_chinese_font()
    if downloaded_font and os.path.exists(downloaded_font):
        return downloaded_font

    # 2. 尝试系统字体（Debian Trixie）
    system_fonts = [
        '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc',
        '/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc',
        '/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc',
    ]

    for font in system_fonts:
        if os.path.exists(font):
            print(f"✅ 找到系统字体: {font}")
            return font

    # 3. Windows/Mac 备用
    system = platform.system()
    if system == 'Windows':
        win_fonts = ['C:/Windows/Fonts/msyh.ttc', 'C:/Windows/Fonts/simhei.ttf']
        for font in win_fonts:
            if os.path.exists(font):
                return font
    elif system == 'Darwin':
        mac_fonts = ['/System/Library/Fonts/STHeiti Light.ttc']
        for font in mac_fonts:
            if os.path.exists(font):
                return font

    print("⚠️ 未找到任何中文字体")
    return None


# 初始化字体
CHINESE_FONT = get_chinese_font()

# 配置 Matplotlib
if CHINESE_FONT:
    import matplotlib.pyplot as plt
    from matplotlib import font_manager

    # 注册字体
    font_manager.fontManager.addfont(CHINESE_FONT)
    font_prop = font_manager.FontProperties(fname=CHINESE_FONT)

    plt.rcParams['font.sans-serif'] = [font_prop.get_name()]
    plt.rcParams['font.family'] = 'sans-serif'
    plt.rcParams['axes.unicode_minus'] = False

    print(f"✅ Matplotlib 已配置字体: {font_prop.get_name()}")
else:
    import matplotlib.pyplot as plt

    plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
    plt.rcParams['axes.unicode_minus'] = False
    print("⚠️ 使用默认字体（可能不支持中文）")


class TourismDataAnalyzer:
    def __init__(self, data_folder='data'):
        self.data_folder = Path(data_folder)
        self.all_data = None
        self.load_all_data()

    def get_preview_data(self, n_samples=100):
        """获取预览数据（用户名匿名化）"""
        df_preview = self.all_data.copy()

        # 用户名匿名化
        unique_users = df_preview['用户名'].unique()
        user_mapping = {user: f'用户{i + 1:04d}' for i, user in enumerate(unique_users)}
        df_preview['用户名'] = df_preview['用户名'].map(user_mapping)

        # 选择显示的列
        display_columns = ['用户名', '景点', '评分', '时间', '评论内容', '评论长度', '情感分类']
        df_preview = df_preview[display_columns]

        # 格式化时间
        df_preview['时间'] = df_preview['时间'].dt.strftime('%Y-%m-%d %H:%M')

        # 评论内容截断（避免过长）
        df_preview['评论内容'] = df_preview['评论内容'].apply(
            lambda x: x[:100] + '...' if len(str(x)) > 100 else x
        )

        # 随机采样
        if len(df_preview) > n_samples:
            df_preview = df_preview.sample(n=n_samples, random_state=42)

        # 重置索引
        df_preview = df_preview.reset_index(drop=True)
        df_preview.index = df_preview.index + 1  # 从1开始编号

        return df_preview.sort_values('时间', ascending=False)

    def load_all_data(self):
        """加载所有Excel文件"""
        all_dfs = []
        for file_path in self.data_folder.glob('*.xlsx'):
            try:
                df = pd.read_excel(file_path)
                required_columns = ['用户名', '时间', '评分', '评论内容']
                if not all(col in df.columns for col in required_columns):
                    print(f"⚠️  {file_path.name} 缺少必要列，跳过")
                    continue

                df['景点'] = file_path.stem
                all_dfs.append(df)
                print(f"✓ 成功加载: {file_path.name} ({len(df)} 条)")
            except Exception as e:
                print(f"✗ 加载 {file_path.name} 失败: {e}")

        if not all_dfs:
            raise ValueError("未找到任何有效数据文件！")

        self.all_data = pd.concat(all_dfs, ignore_index=True)

        # 数据清洗和预处理
        self.all_data['时间'] = pd.to_datetime(self.all_data['时间'], errors='coerce')
        self.all_data['评分'] = pd.to_numeric(self.all_data['评分'], errors='coerce')
        self.all_data = self.all_data.dropna(subset=['时间', '评分', '评论内容'])

        # 添加派生字段
        self.all_data['评论长度'] = self.all_data['评论内容'].str.len()
        self.all_data['年份'] = self.all_data['时间'].dt.year
        self.all_data['月份'] = self.all_data['时间'].dt.month
        self.all_data['季度'] = self.all_data['时间'].dt.quarter
        self.all_data['星期'] = self.all_data['时间'].dt.dayofweek
        self.all_data['是否周末'] = self.all_data['星期'].isin([5, 6])
        self.all_data['小时'] = self.all_data['时间'].dt.hour

        # 情感分类
        self.all_data['情感分类'] = self.all_data['评分'].apply(self._classify_sentiment)

        print(f"\n✅ 总计加载 {len(self.all_data)} 条有效评论")
        print(f"📍 涵盖 {self.all_data['景点'].nunique()} 个景点")
        print(f"📅 时间跨度: {self.all_data['时间'].min().date()} 至 {self.all_data['时间'].max().date()}")

        return self.all_data

    def _classify_sentiment(self, score):
        """情感分类"""
        if pd.isna(score):
            return '未知'
        elif score >= 4.5:
            return '非常满意'
        elif score >= 4.0:
            return '满意'
        elif score >= 3.0:
            return '一般'
        elif score >= 2.0:
            return '不满意'
        else:
            return '非常不满意'

    def filter_data(self, selected_places):
        """根据选择的景点过滤数据"""
        if selected_places and len(selected_places) > 0:
            return self.all_data[self.all_data['景点'].isin(selected_places)].copy()
        return self.all_data.copy()

    def plot_advanced_rating_analysis(self, selected_places=None):
        """高级评分分析"""
        df = self.filter_data(selected_places)

        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)

        # 1. 评分分布直方图
        ax1 = fig.add_subplot(gs[0, 0])
        df['评分'].hist(bins=30, ax=ax1, edgecolor='black', alpha=0.7, color='#3498db')
        ax1.axvline(df['评分'].mean(), color='red', linestyle='--', linewidth=2,
                    label=f'均值: {df["评分"].mean():.2f}')
        ax1.axvline(df['评分'].median(), color='green', linestyle='--', linewidth=2,
                    label=f'中位数: {df["评分"].median():.2f}')
        ax1.set_xlabel('评分', fontsize=10)
        ax1.set_ylabel('频数', fontsize=10)
        ax1.set_title('评分分布直方图', fontsize=12, fontweight='bold')
        ax1.legend()
        ax1.grid(True, alpha=0.3)

        # 2. 评分箱线图
        ax2 = fig.add_subplot(gs[0, 1])
        bp = ax2.boxplot(df['评分'].dropna(), vert=True, patch_artist=True,
                         boxprops=dict(facecolor='lightblue', alpha=0.7),
                         medianprops=dict(color='red', linewidth=2))
        ax2.set_ylabel('评分', fontsize=10)
        ax2.set_title('评分箱线图', fontsize=12, fontweight='bold')
        ax2.grid(True, alpha=0.3, axis='y')

        # 3. 评分分段统计
        ax3 = fig.add_subplot(gs[0, 2])
        score_bins = [0, 2, 3, 4, 5]
        score_labels = ['差评\n(<2)', '中差评\n(2-3)', '中评\n(3-4)', '好评\n(4-5)']
        df['评分段'] = pd.cut(df['评分'], bins=score_bins, labels=score_labels, include_lowest=True)
        score_counts = df['评分段'].value_counts().sort_index()
        colors_seg = ['#e74c3c', '#f39c12', '#3498db', '#2ecc71']
        score_counts.plot(kind='bar', ax=ax3, color=colors_seg, alpha=0.8)
        ax3.set_ylabel('评论数', fontsize=10)
        ax3.set_title('评分分段统计', fontsize=12, fontweight='bold')
        ax3.tick_params(axis='x', rotation=0)
        for i, v in enumerate(score_counts.values):
            ax3.text(i, v + max(score_counts.values) * 0.01, str(v), ha='center', fontsize=9)
        ax3.grid(True, alpha=0.3, axis='y')

        # 4. 各景点平均评分
        ax4 = fig.add_subplot(gs[1, :])
        place_scores = df.groupby('景点').agg({
            '评分': ['mean', 'count']
        }).round(2)
        place_scores.columns = ['平均评分', '评论数']
        place_scores = place_scores.sort_values('平均评分', ascending=True)

        colors = plt.cm.RdYlGn(np.linspace(0.3, 0.9, len(place_scores)))
        bars = ax4.barh(range(len(place_scores)), place_scores['平均评分'], color=colors)
        ax4.set_yticks(range(len(place_scores)))
        ax4.set_yticklabels(place_scores.index, fontsize=9)
        ax4.set_xlabel('平均评分', fontsize=10)
        ax4.set_title('各景点平均评分对比', fontsize=12, fontweight='bold')
        ax4.axvline(df['评分'].mean(), color='red', linestyle='--', alpha=0.5,
                    label=f'总体均值: {df["评分"].mean():.2f}')
        ax4.legend()
        ax4.grid(True, alpha=0.3, axis='x')

        for i, (idx, row) in enumerate(place_scores.iterrows()):
            ax4.text(row['平均评分'] + 0.05, i,
                     f"{row['平均评分']:.2f} ({int(row['评论数'])}条)",
                     va='center', fontsize=8)

        # 5. 评分与评论长度关系
        ax5 = fig.add_subplot(gs[2, 0])
        ax5.scatter(df['评分'], df['评论长度'], alpha=0.3, s=20, c='#3498db')
        ax5.set_xlabel('评分', fontsize=10)
        ax5.set_ylabel('评论长度', fontsize=10)
        ax5.set_title('评分与评论长度关系', fontsize=12, fontweight='bold')
        ax5.grid(True, alpha=0.3)

        z = np.polyfit(df['评分'].dropna(), df['评论长度'].dropna(), 1)
        p = np.poly1d(z)
        ax5.plot(df['评分'].sort_values(), p(df['评分'].sort_values()),
                 "r--", alpha=0.8, linewidth=2, label='趋势线')
        ax5.legend()

        # 6. 评分密度图
        ax6 = fig.add_subplot(gs[2, 1])
        from scipy import stats

        density = stats.gaussian_kde(df['评分'].dropna())
        xs = np.linspace(df['评分'].min(), df['评分'].max(), 200)
        ys = density(xs)

        ax6.plot(xs, ys, linewidth=2, color='#9b59b6')
        ax6.fill_between(xs, 0, ys, alpha=0.3, color='#9b59b6')
        ax6.set_xlabel('评分', fontsize=10)
        ax6.set_ylabel('密度', fontsize=10)
        ax6.set_title('评分密度分布', fontsize=12, fontweight='bold')
        ax6.grid(True, alpha=0.3)

        # 7. 情感分类饼图 - 修复标签重叠
        ax7 = fig.add_subplot(gs[2, 2])
        sentiment_counts = df['情感分类'].value_counts()
        colors_pie = ['#2ecc71', '#3498db', '#f39c12', '#e67e22', '#e74c3c']

        # 使用explode参数分离切片，避免标签重叠
        explode = [0.05] * len(sentiment_counts)

        wedges, texts, autotexts = ax7.pie(
            sentiment_counts.values,
            autopct='%1.1f%%',
            colors=colors_pie[:len(sentiment_counts)],
            startangle=90,
            explode=explode,
            textprops={'fontsize': 10},
            pctdistance=0.85
        )
        ax7.legend(wedges, sentiment_counts.index,
                   loc='center left', bbox_to_anchor=(1, 0.5), fontsize=9)

        # 调整百分比文字样式
        for autotext in autotexts:
            autotext.set_color('white')
            autotext.set_fontweight('bold')
            autotext.set_fontsize(8)

        ax7.set_title('情感分类分布', fontsize=12, fontweight='bold')

        plt.suptitle('评分深度分析', fontsize=16, fontweight='bold', y=0.995)

        return fig

    def plot_time_trend_analysis(self, selected_places=None):
        """时间趋势深度分析"""
        df = self.filter_data(selected_places)

        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(3, 2, hspace=0.3, wspace=0.25)

        # 1. 月度评论数量趋势
        ax1 = fig.add_subplot(gs[0, :])
        df_monthly = df.set_index('时间').resample('ME').agg({
            '评分': 'count',
            '评论长度': 'mean'
        })
        df_monthly.columns = ['评论数', '平均评论长度']

        ax1_twin = ax1.twinx()
        line1 = ax1.plot(df_monthly.index, df_monthly['评论数'],
                         marker='o', linewidth=2, markersize=6, color='#3498db', label='评论数')
        ax1.fill_between(df_monthly.index, df_monthly['评论数'], alpha=0.3, color='#3498db')
        ax1.set_ylabel('评论数', fontsize=11, color='#3498db')
        ax1.tick_params(axis='y', labelcolor='#3498db')

        line2 = ax1_twin.plot(df_monthly.index, df_monthly['平均评论长度'],
                              marker='s', linewidth=2, markersize=6, color='#e74c3c', label='平均长度')
        ax1_twin.set_ylabel('平均评论长度', fontsize=11, color='#e74c3c')
        ax1_twin.tick_params(axis='y', labelcolor='#e74c3c')

        lines = line1 + line2
        labels = [l.get_label() for l in lines]
        ax1.legend(lines, labels, loc='upper left')
        ax1.set_title('月度评论数量与长度趋势', fontsize=12, fontweight='bold')
        ax1.grid(True, alpha=0.3)

        # 2. 月度平均评分趋势
        ax2 = fig.add_subplot(gs[1, 0])
        df_score_monthly = df.set_index('时间').resample('ME')['评分'].agg(['mean', 'std'])
        ax2.plot(df_score_monthly.index, df_score_monthly['mean'],
                 marker='o', linewidth=2, color='#2ecc71', label='平均评分')
        ax2.fill_between(df_score_monthly.index,
                         df_score_monthly['mean'] - df_score_monthly['std'],
                         df_score_monthly['mean'] + df_score_monthly['std'],
                         alpha=0.2, color='#2ecc71', label='±1标准差')
        ax2.axhline(df['评分'].mean(), color='red', linestyle='--', alpha=0.5, label='总体均值')
        ax2.set_ylabel('评分', fontsize=11)
        ax2.set_title('月度评分趋势（含标准差）', fontsize=12, fontweight='bold')
        ax2.legend()
        ax2.grid(True, alpha=0.3)

        # 3. 季度对比
        ax3 = fig.add_subplot(gs[1, 1])
        quarter_data = df.groupby('季度').agg({
            '评分': ['mean', 'count']
        }).round(2)
        quarter_data.columns = ['平均评分', '评论数']

        x = np.arange(len(quarter_data))
        width = 0.35
        bars1 = ax3.bar(x - width / 2, quarter_data['平均评分'], width,
                        label='平均评分', color='#3498db', alpha=0.8)
        ax3_twin = ax3.twinx()
        bars2 = ax3_twin.bar(x + width / 2, quarter_data['评论数'], width,
                             label='评论数', color='#e74c3c', alpha=0.8)

        ax3.set_xlabel('季度', fontsize=11)
        ax3.set_ylabel('平均评分', fontsize=11, color='#3498db')
        ax3_twin.set_ylabel('评论数', fontsize=11, color='#e74c3c')
        ax3.set_xticks(x)
        ax3.set_xticklabels([f'Q{i}' for i in quarter_data.index])
        ax3.set_title('季度对比分析', fontsize=12, fontweight='bold')
        ax3.tick_params(axis='y', labelcolor='#3498db')
        ax3_twin.tick_params(axis='y', labelcolor='#e74c3c')
        ax3.grid(True, alpha=0.3, axis='y')

        lines = [bars1, bars2]
        labels = [l.get_label() for l in lines]
        ax3.legend(lines, labels, loc='upper left')

        # 4. 工作日vs周末
        ax4 = fig.add_subplot(gs[2, 0])
        weekend_comparison = df.groupby('是否周末').agg({
            '评分': ['mean', 'count'],
            '评论长度': 'mean'
        }).round(2)
        weekend_comparison.index = ['工作日', '周末']

        x_pos = np.arange(len(weekend_comparison))
        bars = ax4.bar(x_pos, weekend_comparison[('评分', 'mean')],
                       color=['#3498db', '#e74c3c'], alpha=0.8)
        ax4.set_xticks(x_pos)
        ax4.set_xticklabels(weekend_comparison.index)
        ax4.set_ylabel('平均评分', fontsize=11)
        ax4.set_title('工作日 vs 周末评分对比', fontsize=12, fontweight='bold')

        for i, bar in enumerate(bars):
            height = bar.get_height()
            count = weekend_comparison.iloc[i][('评分', 'count')]
            ax4.text(bar.get_x() + bar.get_width() / 2., height,
                     f'{height:.2f}\n({int(count)}条)',
                     ha='center', va='bottom', fontsize=10)
        ax4.grid(True, alpha=0.3, axis='y')

        # 5. 小时分布热力图
        ax5 = fig.add_subplot(gs[2, 1])
        hour_data = df.groupby('小时').size()
        hours = range(24)
        hour_counts = [hour_data.get(h, 0) for h in hours]

        colors_hour = plt.cm.YlOrRd(np.array(hour_counts) / max(hour_counts))
        bars = ax5.bar(hours, hour_counts, color=colors_hour, alpha=0.8)
        ax5.set_xlabel('小时', fontsize=11)
        ax5.set_ylabel('评论数', fontsize=11)
        ax5.set_title('评论发布时段分布', fontsize=12, fontweight='bold')
        ax5.set_xticks(range(0, 24, 3))
        ax5.grid(True, alpha=0.3, axis='y')

        plt.suptitle('时间趋势深度分析', fontsize=16, fontweight='bold', y=0.995)

        return fig

    def generate_advanced_wordcloud(self, selected_places=None, rating_filter=None,

                                    word_count=100):
        """高级词云分析"""
        df = self.filter_data(selected_places)

        if rating_filter == "高分评论 (>=4)":
            df = df[df['评分'] >= 4]
            title_suffix = "高分评论"
            colormap = 'Greens'
        elif rating_filter == "低分评论 (<3)":
            df = df[df['评分'] < 3]
            title_suffix = "低分评论"
            colormap = 'Reds'
        else:
            title_suffix = "全部评论"
            colormap = 'viridis'

        if len(df) == 0:
            return self._create_empty_plot('没有符合条件的评论数据')

        text = ' '.join(df['评论内容'].astype(str))

        words = jieba.cut(text)
        words_filtered = [w for w in words if len(w) > 1 and w not in STOPWORDS]

        if len(words_filtered) == 0:
            return self._create_empty_plot('没有足够的词汇生成词云')

        word_freq = Counter(words_filtered)
        top_words = word_freq.most_common(word_count)

        fig = plt.figure(figsize=(18, 8))
        gs = fig.add_gridspec(1, 2, width_ratios=[2, 1], wspace=0.15)

        # 词云图
        ax1 = fig.add_subplot(gs[0])
        try:
            wordcloud = WordCloud(
                font_path=CHINESE_FONT,
                width=1200,
                height=600,
                background_color='white',
                max_words=word_count,
                colormap=colormap,
                relative_scaling=0.5,
                min_font_size=10,
                prefer_horizontal=0.7
            ).generate_from_frequencies(dict(top_words))

            ax1.imshow(wordcloud, interpolation='bilinear')
            ax1.axis('off')
            ax1.set_title(f'词云图 - {title_suffix} (Top {word_count})',
                          fontsize=14, fontweight='bold', pad=20)
        except Exception as e:
            ax1.text(0.5, 0.5, f'词云生成失败: {str(e)}',
                     ha='center', va='center', fontsize=14, transform=ax1.transAxes)
            ax1.axis('off')

        # 词频统计图
        ax2 = fig.add_subplot(gs[1])
        top_20 = top_words[:20]
        words_list = [w[0] for w in top_20]
        freqs_list = [w[1] for w in top_20]

        colors = plt.cm.viridis(np.linspace(0, 1, len(words_list)))
        ax2.barh(range(len(words_list)), freqs_list, color=colors, alpha=0.8)
        ax2.set_yticks(range(len(words_list)))
        ax2.set_yticklabels(words_list, fontsize=9)
        ax2.invert_yaxis()
        ax2.set_xlabel('频次', fontsize=10)
        ax2.set_title('Top 20 高频词', fontsize=12, fontweight='bold')
        ax2.grid(True, alpha=0.3, axis='x')

        for i, v in enumerate(freqs_list):
            ax2.text(v, i, f' {v}', va='center', fontsize=8)

        return fig

    def extract_advanced_keywords(self, selected_places=None, top_n=30):
        """高级关键词提取（TF-IDF + TextRank）"""
        df = self.filter_data(selected_places)

        if len(df) == 0:
            return self._create_empty_plot('没有数据'), pd.DataFrame()

        text = ' '.join(df['评论内容'].astype(str))

        tfidf_keywords = jieba.analyse.extract_tags(text, topK=top_n, withWeight=True)
        textrank_keywords = jieba.analyse.textrank(text, topK=top_n, withWeight=True)

        positive_text = ' '.join(df[df['评分'] >= 4]['评论内容'].astype(str))
        negative_text = ' '.join(df[df['评分'] < 3]['评论内容'].astype(str))

        positive_keywords = jieba.analyse.extract_tags(positive_text, topK=15, withWeight=True) if positive_text else []
        negative_keywords = jieba.analyse.extract_tags(negative_text, topK=15, withWeight=True) if negative_text else []

        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.25)

        # 1. TF-IDF关键词
        ax1 = fig.add_subplot(gs[0, 0])
        if tfidf_keywords:
            words = [w[0] for w in tfidf_keywords[:20]]
            weights = [w[1] for w in tfidf_keywords[:20]]
            colors = plt.cm.plasma(np.linspace(0, 1, len(words)))
            ax1.barh(range(len(words)), weights, color=colors, alpha=0.8)
            ax1.set_yticks(range(len(words)))
            ax1.set_yticklabels(words, fontsize=9)
            ax1.invert_yaxis()
            ax1.set_xlabel('TF-IDF权重', fontsize=10)
            ax1.set_title('TF-IDF Top 20 关键词', fontsize=12, fontweight='bold')
            ax1.grid(True, alpha=0.3, axis='x')

        # 2. TextRank关键词
        ax2 = fig.add_subplot(gs[0, 1])
        if textrank_keywords:
            words = [w[0] for w in textrank_keywords[:20]]
            weights = [w[1] for w in textrank_keywords[:20]]
            colors = plt.cm.viridis(np.linspace(0, 1, len(words)))
            ax2.barh(range(len(words)), weights, color=colors, alpha=0.8)
            ax2.set_yticks(range(len(words)))
            ax2.set_yticklabels(words, fontsize=9)
            ax2.invert_yaxis()
            ax2.set_xlabel('TextRank权重', fontsize=10)
            ax2.set_title('TextRank Top 20 关键词', fontsize=12, fontweight='bold')
            ax2.grid(True, alpha=0.3, axis='x')

        # 3. 正面关键词
        ax3 = fig.add_subplot(gs[1, 0])
        if positive_keywords:
            words = [w[0] for w in positive_keywords]
            weights = [w[1] for w in positive_keywords]
            colors = plt.cm.Greens(np.linspace(0.4, 1, len(words)))
            ax3.barh(range(len(words)), weights, color=colors, alpha=0.8)
            ax3.set_yticks(range(len(words)))
            ax3.set_yticklabels(words, fontsize=9)
            ax3.invert_yaxis()
            ax3.set_xlabel('权重', fontsize=10)
            ax3.set_title('正面评论关键词 (评分≥4)', fontsize=12, fontweight='bold')
            ax3.grid(True, alpha=0.3, axis='x')
        else:
            ax3.text(0.5, 0.5, '无正面评论数据', ha='center', va='center',
                     transform=ax3.transAxes, fontsize=12)
            ax3.axis('off')

        # 4. 负面关键词
        ax4 = fig.add_subplot(gs[1, 1])
        if negative_keywords:
            words = [w[0] for w in negative_keywords]
            weights = [w[1] for w in negative_keywords]
            colors = plt.cm.Reds(np.linspace(0.4, 1, len(words)))
            ax4.barh(range(len(words)), weights, color=colors, alpha=0.8)
            ax4.set_yticks(range(len(words)))
            ax4.set_yticklabels(words, fontsize=9)
            ax4.invert_yaxis()
            ax4.set_xlabel('权重', fontsize=10)
            ax4.set_title('负面评论关键词 (评分<3)', fontsize=12, fontweight='bold')
            ax4.grid(True, alpha=0.3, axis='x')
        else:
            ax4.text(0.5, 0.5, '无负面评论数据', ha='center', va='center',
                     transform=ax4.transAxes, fontsize=12)
            ax4.axis('off')

        plt.suptitle('多维度关键词分析', fontsize=16, fontweight='bold', y=0.995)

        df_keywords = pd.DataFrame({
            'TF-IDF关键词': [w[0] for w in tfidf_keywords[:top_n]],
            'TF-IDF权重': [round(w[1], 4) for w in tfidf_keywords[:top_n]],
            'TextRank关键词': [w[0] for w in textrank_keywords[:top_n]],
            'TextRank权重': [round(w[1], 4) for w in textrank_keywords[:top_n]],
        })

        return fig, df_keywords

    def advanced_sentiment_analysis(self, selected_places=None):
        """高级情感分析 - 修复饼图重叠"""
        df = self.filter_data(selected_places)

        fig = plt.figure(figsize=(18, 10))
        gs = fig.add_gridspec(2, 3, hspace=0.3, wspace=0.3)

        # 1. 整体情感分布 - 修复标签重叠
        ax1 = fig.add_subplot(gs[0, 0])
        sentiment_counts = df['情感分类'].value_counts()
        colors_pie = ['#2ecc71', '#3498db', '#f39c12', '#e67e22', '#e74c3c']

        # 使用explode分离切片
        explode = [0.05] * len(sentiment_counts)

        wedges, texts, autotexts = ax1.pie(
            sentiment_counts.values,
            autopct='%1.1f%%',
            colors=colors_pie[:len(sentiment_counts)],
            startangle=90,
            explode=explode,
            textprops={'fontsize': 10},
            pctdistance=0.85
        )
        ax1.legend(wedges, sentiment_counts.index,
                   loc='center left', bbox_to_anchor=(1, 0.5), fontsize=9)

        # 调整百分比文字样式
        for autotext in autotexts:
            autotext.set_color('white')
            autotext.set_fontweight('bold')
            autotext.set_fontsize(8)

        ax1.set_title('整体情感分布', fontsize=12, fontweight='bold')

        # 2. 各景点情感对比
        ax2 = fig.add_subplot(gs[0, 1:])
        sentiment_by_place = pd.crosstab(df['景点'], df['情感分类'], normalize='index') * 100
        sentiment_by_place = sentiment_by_place.reindex(columns=['非常满意', '满意', '一般', '不满意', '非常不满意'],
                                                        fill_value=0)
        sentiment_by_place.plot(kind='barh', stacked=True, ax=ax2,
                                color=colors_pie[:len(sentiment_by_place.columns)],
                                alpha=0.8)
        ax2.set_xlabel('百分比 (%)', fontsize=10)
        ax2.set_ylabel('')
        ax2.set_title('各景点情感分布对比', fontsize=12, fontweight='bold')
        ax2.legend(title='情感', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)
        ax2.grid(True, alpha=0.3, axis='x')

        # 3. 情感随时间变化
        ax3 = fig.add_subplot(gs[1, :])
        df_monthly_sentiment = df.set_index('时间').groupby([pd.Grouper(freq='ME'), '情感分类']).size().unstack(
            fill_value=0)

        for col in ['非常满意', '满意', '一般', '不满意', '非常不满意']:
            if col in df_monthly_sentiment.columns:
                idx = ['非常满意', '满意', '一般', '不满意', '非常不满意'].index(col)
                ax3.plot(df_monthly_sentiment.index, df_monthly_sentiment[col],
                         marker='o', label=col, linewidth=2, color=colors_pie[idx])

        ax3.set_ylabel('评论数', fontsize=10)
        ax3.set_xlabel('时间', fontsize=10)
        ax3.set_title('情感趋势变化', fontsize=12, fontweight='bold')
        ax3.legend(fontsize=9)
        ax3.grid(True, alpha=0.3)

        plt.suptitle('情感深度分析', fontsize=16, fontweight='bold', y=0.995)

        stats_table = df.groupby('情感分类').agg({
            '评分': ['count', 'mean'],
            '评论长度': 'mean'
        }).round(2)
        stats_table.columns = ['数量', '平均评分', '平均评论长度']
        stats_table['占比(%)'] = (stats_table['数量'] / len(df) * 100).round(1)

        return fig, stats_table

    def comprehensive_place_comparison(self):
        """综合景点对比 - 修复雷达图标签重叠"""
        df = self.all_data

        comparison = df.groupby('景点').agg({
            '评分': ['mean', 'std', 'median', 'count'],
            '评论长度': ['mean', 'median'],
            '用户名': 'nunique'
        }).round(2)

        comparison.columns = ['平均评分', '评分标准差', '评分中位数', '评论总数',
                              '平均评论长度', '评论长度中位数', '独立用户数']

        good_rate = df[df['评分'] >= 4].groupby('景点').size() / df.groupby('景点').size() * 100
        comparison['好评率(%)'] = good_rate.round(1)

        comparison['用户活跃度'] = (comparison['评论总数'] / comparison['独立用户数']).round(2)

        comparison['综合得分'] = (
                comparison['平均评分'] * 0.4 +
                (comparison['好评率(%)'] / 20) +
                (comparison['评论总数'] / comparison['评论总数'].max() * 5) * 0.2 +
                (comparison['用户活跃度'] / comparison['用户活跃度'].max() * 5) * 0.1
        ).round(2)

        comparison = comparison.sort_values('综合得分', ascending=False)

        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(3, 2, hspace=0.35, wspace=0.25)

        # 1. 雷达图 - 修复标签重叠
        ax1 = fig.add_subplot(gs[0, :], projection='polar')
        top_places = comparison.head(6)

        categories = ['平均评分', '好评率', '评论数', '用户活跃度', '评论长度']
        N = len(categories)
        angles = [n / float(N) * 2 * np.pi for n in range(N)]
        angles += angles[:1]

        ax1.set_theta_offset(np.pi / 2)
        ax1.set_theta_direction(-1)
        ax1.set_xticks(angles[:-1])
        ax1.set_xticklabels(categories, fontsize=10)  # 增大字体

        for idx, (place, row) in enumerate(top_places.iterrows()):
            values = [
                row['平均评分'],
                row['好评率(%)'] / 20,
                row['评论总数'] / comparison['评论总数'].max() * 5,
                row['用户活跃度'] / comparison['用户活跃度'].max() * 5,
                row['平均评论长度'] / comparison['平均评论长度'].max() * 5
            ]
            values += values[:1]
            ax1.plot(angles, values, 'o-', linewidth=2, label=place, alpha=0.7)
            ax1.fill(angles, values, alpha=0.15)

        ax1.set_ylim(0, 5)
        # 图例放在更合适的位置
        ax1.legend(loc='upper left', bbox_to_anchor=(1.15, 1.05), fontsize=9, framealpha=0.9)
        ax1.set_title('Top 6 景点雷达图对比', fontsize=13, fontweight='bold', pad=25)
        ax1.grid(True)

        # 2. 综合得分排名
        ax2 = fig.add_subplot(gs[1, 0])
        colors = plt.cm.RdYlGn(np.linspace(0.3, 0.9, len(comparison)))
        bars = ax2.barh(range(len(comparison)), comparison['综合得分'], color=colors, alpha=0.8)
        ax2.set_yticks(range(len(comparison)))
        ax2.set_yticklabels(comparison.index, fontsize=9)
        ax2.invert_yaxis()
        ax2.set_xlabel('综合得分', fontsize=10)
        ax2.set_title('景点综合得分排名', fontsize=12, fontweight='bold')
        ax2.grid(True, alpha=0.3, axis='x')

        for i, v in enumerate(comparison['综合得分'].values):
            ax2.text(v, i, f' {v:.2f}', va='center', fontsize=8)

        # 3. 评分vs评论数散点图
        ax3 = fig.add_subplot(gs[1, 1])
        scatter = ax3.scatter(comparison['评论总数'], comparison['平均评分'],
                              s=comparison['独立用户数'] * 10, alpha=0.6,
                              c=comparison['好评率(%)'], cmap='RdYlGn',
                              edgecolors='black', linewidth=1)

        for idx, place in enumerate(comparison.index):
            ax3.annotate(place,
                         (comparison.iloc[idx]['评论总数'], comparison.iloc[idx]['平均评分']),
                         fontsize=8, alpha=0.7)

        ax3.set_xlabel('评论总数', fontsize=10)
        ax3.set_ylabel('平均评分', fontsize=10)
        ax3.set_title('评分-评论数-用户数关系图\n(气泡大小=用户数, 颜色=好评率)',
                      fontsize=11, fontweight='bold')
        ax3.grid(True, alpha=0.3)

        cbar = plt.colorbar(scatter, ax=ax3)
        cbar.set_label('好评率(%)', fontsize=9)

        # 4. 热力图
        ax4 = fig.add_subplot(gs[2, :])
        heatmap_data = comparison[['平均评分', '评分标准差', '好评率(%)',
                                   '评论总数', '用户活跃度', '综合得分']].T

        heatmap_normalized = (heatmap_data - heatmap_data.min(axis=1).values.reshape(-1, 1)) / \
                             (heatmap_data.max(axis=1).values.reshape(-1, 1) -
                              heatmap_data.min(axis=1).values.reshape(-1, 1))

        sns.heatmap(heatmap_normalized, annot=heatmap_data.round(1), fmt='g',
                    cmap='RdYlGn', ax=ax4, cbar_kws={'label': '标准化值'},
                    linewidths=0.5, linecolor='gray')
        ax4.set_title('景点多维度指标热力图', fontsize=12, fontweight='bold', pad=15)
        ax4.set_xlabel('')
        ax4.set_ylabel('指标', fontsize=10)

        plt.suptitle('景点综合对比分析', fontsize=16, fontweight='bold', y=0.995)

        return fig, comparison

    def user_profile_and_clustering(self, selected_places=None):
        """用户画像与聚类分析 - 修复饼图重叠"""
        df = self.filter_data(selected_places)

        # 构建用户特征
        user_features = df.groupby('用户名').agg({
            '评分': ['mean', 'std', 'count'],
            '评论长度': ['mean', 'std'],
            '景点': 'nunique',
            '时间': lambda x: (x.max() - x.min()).days
        }).reset_index()

        user_features.columns = ['用户ID', '平均评分', '评分标准差', '评论次数',
                                 '平均评论长度', '评论长度标准差', '访问景点数', '活跃天数']

        # 填充缺失值
        user_features = user_features.fillna(0)

        # 添加派生特征
        user_features['评分稳定性'] = 1 / (1 + user_features['评分标准差'])
        user_features['是否活跃用户'] = (user_features['评论次数'] >= 3).astype(int)
        user_features['是否忠诚用户'] = (user_features['访问景点数'] > 1).astype(int)

        # 用户类型识别
        def classify_user_type(row):
            if row['评论次数'] >= 5 and row['访问景点数'] > 1:
                return '资深探索型'
            elif row['评论次数'] >= 3 and row['平均评分'] >= 4:
                return '满意常客型'
            elif row['平均评分'] < 3:
                return '挑剔批评型'
            elif row['评论次数'] == 1:
                return '偶然访客型'
            else:
                return '普通游客型'

        user_features['用户类型'] = user_features.apply(classify_user_type, axis=1)

        # K-means聚类
        features_for_clustering = user_features[['平均评分', '评论次数', '平均评论长度',
                                                 '访问景点数', '评分稳定性']].copy()

        scaler = StandardScaler()
        features_scaled = scaler.fit_transform(features_for_clustering)

        n_clusters = min(4, len(user_features))
        kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
        user_features['聚类标签'] = kmeans.fit_predict(features_scaled)

        # PCA降维可视化
        pca = PCA(n_components=2)
        features_pca = pca.fit_transform(features_scaled)
        user_features['PCA1'] = features_pca[:, 0]
        user_features['PCA2'] = features_pca[:, 1]

        # 可视化
        fig = plt.figure(figsize=(18, 14))
        gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.3)

        # 1. 用户类型分布 - 修复标签重叠
        ax1 = fig.add_subplot(gs[0, 0])
        type_counts = user_features['用户类型'].value_counts()
        colors_type = ['#3498db', '#2ecc71', '#e74c3c', '#f39c12', '#9b59b6']

        # 使用explode分离切片
        explode = [0.05] * len(type_counts)

        wedges, texts, autotexts = ax1.pie(
            type_counts.values,
            autopct='%1.1f%%',
            colors=colors_type[:len(type_counts)],
            startangle=90,
            explode=explode,
            textprops={'fontsize': 10},
            pctdistance=0.85
        )
        ax1.legend(wedges, type_counts.index,
                   loc='center left', bbox_to_anchor=(1, 0.5), fontsize=9)

        # 调整百分比文字样式
        for autotext in autotexts:
            autotext.set_color('white')
            autotext.set_fontweight('bold')
            autotext.set_fontsize(7)

        ax1.set_title('用户类型分布', fontsize=12, fontweight='bold')

        # 2. 聚类结果（PCA可视化）
        ax2 = fig.add_subplot(gs[0, 1:])
        scatter = ax2.scatter(user_features['PCA1'], user_features['PCA2'],
                              c=user_features['聚类标签'], cmap='viridis',
                              s=100, alpha=0.6, edgecolors='black', linewidth=0.5)

        # 绘制聚类中心
        centers_pca = pca.transform(scaler.transform(
            user_features.groupby('聚类标签')[['平均评分', '评论次数', '平均评论长度',
                                               '访问景点数', '评分稳定性']].mean()
        ))
        ax2.scatter(centers_pca[:, 0], centers_pca[:, 1],
                    c='red', s=300, alpha=0.8, marker='*',
                    edgecolors='black', linewidth=2, label='聚类中心')

        ax2.set_xlabel(f'主成分1 ({pca.explained_variance_ratio_[0]:.1%} 方差)', fontsize=10)
        ax2.set_ylabel(f'主成分2 ({pca.explained_variance_ratio_[1]:.1%} 方差)', fontsize=10)
        ax2.set_title('用户聚类分析（PCA降维）', fontsize=12, fontweight='bold')
        ax2.legend()
        ax2.grid(True, alpha=0.3)

        cbar = plt.colorbar(scatter, ax=ax2)
        cbar.set_label('聚类标签', fontsize=9)

        # 3. 评分分布（按用户类型）
        ax3 = fig.add_subplot(gs[1, 0])
        type_scores = user_features.groupby('用户类型')['平均评分'].mean().sort_values()
        colors_bar = plt.cm.RdYlGn(np.linspace(0.3, 0.9, len(type_scores)))
        type_scores.plot(kind='barh', ax=ax3, color=colors_bar, alpha=0.8)
        ax3.set_xlabel('平均评分', fontsize=10)
        ax3.set_title('各类型用户平均评分', fontsize=12, fontweight='bold')
        ax3.grid(True, alpha=0.3, axis='x')

        for i, v in enumerate(type_scores.values):
            ax3.text(v, i, f' {v:.2f}', va='center', fontsize=9)

        # 4. 活跃度分布
        ax4 = fig.add_subplot(gs[1, 1])
        bins = [1, 2, 3, 5, 10, float('inf')]
        labels = ['1次', '2次', '3-4次', '5-9次', '10次+']
        user_features['活跃度分组'] = pd.cut(user_features['评论次数'], bins=bins,
                                             labels=labels, right=False)
        activity_counts = user_features['活跃度分组'].value_counts().sort_index()

        colors_activity = ['#e74c3c', '#f39c12', '#3498db', '#2ecc71', '#9b59b6']
        activity_counts.plot(kind='bar', ax=ax4, color=colors_activity, alpha=0.8)
        ax4.set_xlabel('评论次数', fontsize=10)
        ax4.set_ylabel('用户数', fontsize=10)
        ax4.set_title('用户活跃度分布', fontsize=12, fontweight='bold')
        ax4.tick_params(axis='x', rotation=45)
        ax4.grid(True, alpha=0.3, axis='y')

        for i, v in enumerate(activity_counts.values):
            ax4.text(i, v, str(v), ha='center', va='bottom', fontsize=9)

        # 5. 访问景点数分布
        ax5 = fig.add_subplot(gs[1, 2])
        place_counts = user_features['访问景点数'].value_counts().sort_index()
        ax5.bar(range(len(place_counts)), place_counts.values,
                color=plt.cm.plasma(np.linspace(0, 1, len(place_counts))), alpha=0.8)
        ax5.set_xticks(range(len(place_counts)))
        ax5.set_xticklabels([f'{i}个' for i in place_counts.index], fontsize=9)
        ax5.set_xlabel('访问景点数', fontsize=10)
        ax5.set_ylabel('用户数', fontsize=10)
        ax5.set_title('用户忠诚度分布', fontsize=12, fontweight='bold')
        ax5.grid(True, alpha=0.3, axis='y')

        # 6. 聚类特征雷达图 - 修复标签重叠
        ax6 = fig.add_subplot(gs[2, :], projection='polar')

        cluster_profiles = user_features.groupby('聚类标签')[
            ['平均评分', '评论次数', '平均评论长度', '访问景点数', '评分稳定性']
        ].mean()

        categories = ['平均评分', '评论次数', '评论长度', '访问景点数', '评分稳定性']
        N = len(categories)
        angles = [n / float(N) * 2 * np.pi for n in range(N)]
        angles += angles[:1]

        ax6.set_theta_offset(np.pi / 2)
        ax6.set_theta_direction(-1)
        ax6.set_xticks(angles[:-1])
        ax6.set_xticklabels(categories, fontsize=10)  # 增大字体

        for idx, (cluster_id, row) in enumerate(cluster_profiles.iterrows()):
            # 标准化到0-5范围
            values = [
                row['平均评分'],
                row['评论次数'] / user_features['评论次数'].max() * 5,
                row['平均评论长度'] / user_features['平均评论长度'].max() * 5,
                row['访问景点数'] / user_features['访问景点数'].max() * 5,
                row['评分稳定性'] / user_features['评分稳定性'].max() * 5
            ]
            values += values[:1]

            ax6.plot(angles, values, 'o-', linewidth=2,
                     label=f'聚类{cluster_id}', alpha=0.7)
            ax6.fill(angles, values, alpha=0.15)

        ax6.set_ylim(0, 5)
        ax6.legend(loc='upper left', bbox_to_anchor=(1.15, 1.05), fontsize=10, framealpha=0.9)
        ax6.set_title('各聚类用户特征画像', fontsize=13, fontweight='bold', pad=25)
        ax6.grid(True)

        plt.suptitle('用户画像与聚类分析', fontsize=16, fontweight='bold', y=0.995)

        # 生成统计表格
        stats_table = pd.DataFrame({
            '用户类型': type_counts.index,
            '用户数': type_counts.values,
            '占比(%)': (type_counts.values / len(user_features) * 100).round(1),
            '平均评分': [user_features[user_features['用户类型'] == t]['平均评分'].mean().round(2)
                         for t in type_counts.index],
            '平均评论次数': [user_features[user_features['用户类型'] == t]['评论次数'].mean().round(1)
                             for t in type_counts.index],
        })

        return fig, stats_table

    def ml_satisfaction_predictor(self, selected_places=None):
        """🤖 机器学习满意度预测模型"""
        df = self.filter_data(selected_places)

        if len(df) < 50:
            return self._create_empty_plot('数据量不足（需要至少50条）'), pd.DataFrame()

        # 特征工程
        df['是否高分'] = (df['评分'] >= 4).astype(int)
        df['评论长度段'] = pd.cut(df['评论长度'], bins=[0, 50, 150, 500, float('inf')],
                                  labels=[1, 2, 3, 4])
        df['评论长度段'] = df['评论长度段'].astype(int)

        # 时间特征
        df['是周末'] = df['是否周末'].astype(int)
        df['月份编码'] = df['月份']
        df['季度编码'] = df['季度']

        # 景点编码
        place_encoding = {place: idx for idx, place in enumerate(df['景点'].unique())}
        df['景点编码'] = df['景点'].map(place_encoding)

        # 准备训练数据
        feature_cols = ['评论长度', '评论长度段', '是周末', '月份编码', '季度编码', '景点编码', '小时']
        X = df[feature_cols].fillna(0)
        y = df['是否高分']

        # 分割数据
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

        # 训练随机森林分类器
        rf_model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
        rf_model.fit(X_train, y_train)

        # 预测
        y_pred = rf_model.predict(X_test)
        y_pred_proba = rf_model.predict_proba(X_test)[:, 1]

        # 特征重要性
        feature_importance = pd.DataFrame({
            '特征': feature_cols,
            '重要性': rf_model.feature_importances_
        }).sort_values('重要性', ascending=False)

        # 评估指标
        from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, \
            confusion_matrix

        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        auc = roc_auc_score(y_test, y_pred_proba)
        cm = confusion_matrix(y_test, y_pred)

        # 可视化
        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.3)

        # 1. 特征重要性
        ax1 = fig.add_subplot(gs[0, :])
        colors = plt.cm.viridis(np.linspace(0, 1, len(feature_importance)))
        ax1.barh(range(len(feature_importance)), feature_importance['重要性'],
                 color=colors, alpha=0.8)
        ax1.set_yticks(range(len(feature_importance)))
        ax1.set_yticklabels(feature_importance['特征'], fontsize=10)
        ax1.invert_yaxis()
        ax1.set_xlabel('重要性得分', fontsize=11)
        ax1.set_title('特征重要性排名（随机森林）', fontsize=13, fontweight='bold')
        ax1.grid(True, alpha=0.3, axis='x')

        for i, v in enumerate(feature_importance['重要性'].values):
            ax1.text(v, i, f' {v:.3f}', va='center', fontsize=9)

        # 2. 混淆矩阵
        ax2 = fig.add_subplot(gs[1, 0])
        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax2,
                    xticklabels=['低分', '高分'], yticklabels=['低分', '高分'])
        ax2.set_xlabel('预测标签', fontsize=10)
        ax2.set_ylabel('真实标签', fontsize=10)
        ax2.set_title('混淆矩阵', fontsize=12, fontweight='bold')

        # 3. ROC曲线
        ax3 = fig.add_subplot(gs[1, 1])
        from sklearn.metrics import roc_curve
        fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
        ax3.plot(fpr, tpr, linewidth=2, label=f'ROC (AUC={auc:.3f})', color='#2ecc71')
        ax3.plot([0, 1], [0, 1], 'k--', linewidth=1, label='随机猜测')
        ax3.fill_between(fpr, tpr, alpha=0.3, color='#2ecc71')
        ax3.set_xlabel('假阳性率', fontsize=10)
        ax3.set_ylabel('真阳性率', fontsize=10)
        ax3.set_title('ROC曲线', fontsize=12, fontweight='bold')
        ax3.legend()
        ax3.grid(True, alpha=0.3)

        # 4. 预测概率分布
        ax4 = fig.add_subplot(gs[1, 2])
        ax4.hist(y_pred_proba[y_test == 0], bins=30, alpha=0.6, color='#e74c3c', label='实际低分')
        ax4.hist(y_pred_proba[y_test == 1], bins=30, alpha=0.6, color='#2ecc71', label='实际高分')
        ax4.axvline(0.5, color='black', linestyle='--', linewidth=2, label='决策阈值')
        ax4.set_xlabel('预测为高分的概率', fontsize=10)
        ax4.set_ylabel('样本数', fontsize=10)
        ax4.set_title('预测概率分布', fontsize=12, fontweight='bold')
        ax4.legend()
        ax4.grid(True, alpha=0.3, axis='y')

        # 5. 评估指标雷达图 - 修复标签
        ax5 = fig.add_subplot(gs[2, 0], projection='polar')
        metrics_names = ['准确率', '精确率', '召回率', 'F1分数', 'AUC']
        metrics_values = [accuracy, precision, recall, f1, auc]

        N = len(metrics_names)
        angles = [n / float(N) * 2 * np.pi for n in range(N)]
        metrics_values += metrics_values[:1]
        angles += angles[:1]

        ax5.plot(angles, metrics_values, 'o-', linewidth=2, color='#3498db', label='模型表现')
        ax5.fill(angles, metrics_values, alpha=0.25, color='#3498db')
        ax5.set_xticks(angles[:-1])
        ax5.set_xticklabels(metrics_names, fontsize=9)
        ax5.set_ylim(0, 1)
        ax5.set_title('模型评估指标', fontsize=12, fontweight='bold', pad=20)
        ax5.legend(loc='upper right')
        ax5.grid(True)

        # 6. 各景点预测满意度
        ax6 = fig.add_subplot(gs[2, 1:])
        df_with_pred = df.copy()
        df_with_pred['预测高分概率'] = rf_model.predict_proba(X)[:, 1]

        place_pred = df_with_pred.groupby('景点').agg({
            '预测高分概率': 'mean',
            '是否高分': 'mean'
        }).sort_values('预测高分概率', ascending=True)

        x = np.arange(len(place_pred))
        width = 0.35

        bars1 = ax6.barh(x - width / 2, place_pred['预测高分概率'] * 100, width,
                         label='预测满意度', color='#3498db', alpha=0.8)
        bars2 = ax6.barh(x + width / 2, place_pred['是否高分'] * 100, width,
                         label='实际满意度', color='#2ecc71', alpha=0.8)

        ax6.set_yticks(x)
        ax6.set_yticklabels(place_pred.index, fontsize=9)
        ax6.set_xlabel('满意度 (%)', fontsize=10)
        ax6.set_title('各景点预测 vs 实际满意度', fontsize=12, fontweight='bold')
        ax6.legend()
        ax6.grid(True, alpha=0.3, axis='x')

        plt.suptitle('🤖 机器学习满意度预测模型', fontsize=16, fontweight='bold', y=0.995)

        # 生成报告表格
        report_table = pd.DataFrame({
            '评估指标': ['准确率', '精确率', '召回率', 'F1分数', 'AUC-ROC'],
            '得分': [f'{accuracy:.3f}', f'{precision:.3f}', f'{recall:.3f}',
                     f'{f1:.3f}', f'{auc:.3f}'],
            '说明': [
                '预测正确的比例',
                '预测为高分中真正高分的比例',
                '实际高分中被预测出的比例',
                '精确率和召回率的调和平均',
                '模型区分能力（越接近1越好）'
            ]
        })

        return fig, report_table

    def intelligent_tour_recommendation(self, user_preferences=None):
        """🎯 智能旅游路线推荐系统 - 修复雷达图标签"""
        df = self.all_data

        # 计算景点综合评分
        place_stats = df.groupby('景点').agg({
            '评分': ['mean', 'std', 'count'],
            '评论长度': 'mean',
            '用户名': 'nunique'
        }).reset_index()

        place_stats.columns = ['景点', '平均评分', '评分标准差', '评论数', '平均评论长度', '独立用户数']

        # 好评率
        good_rate = df[df['评分'] >= 4].groupby('景点').size() / df.groupby('景点').size()
        place_stats['好评率'] = place_stats['景点'].map(good_rate).fillna(0)

        # 热度得分（基于评论数和用户数）
        place_stats['热度得分'] = (
                (place_stats['评论数'] / place_stats['评论数'].max()) * 0.6 +
                (place_stats['独立用户数'] / place_stats['独立用户数'].max()) * 0.4
        )

        # 质量得分（基于评分和好评率）
        place_stats['质量得分'] = (
                (place_stats['平均评分'] / 5) * 0.7 +
                place_stats['好评率'] * 0.3
        )

        # 综合推荐得分
        place_stats['推荐得分'] = (
                place_stats['质量得分'] * 0.6 +
                place_stats['热度得分'] * 0.3 +
                (1 - place_stats['评分标准差'] / place_stats['评分标准差'].max()) * 0.1
        )

        place_stats = place_stats.sort_values('推荐得分', ascending=False)

        # 使用层次聚类进行景点分组
        from scipy.cluster.hierarchy import dendrogram, linkage, fcluster

        clustering_features = place_stats[['平均评分', '好评率', '热度得分']].values
        scaler = StandardScaler()
        clustering_features_scaled = scaler.fit_transform(clustering_features)

        linkage_matrix = linkage(clustering_features_scaled, method='ward')
        place_stats['景点分组'] = fcluster(linkage_matrix, t=3, criterion='maxclust')

        # 可视化
        fig = plt.figure(figsize=(20, 14))
        gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.3)

        # 1. 推荐得分排名
        ax1 = fig.add_subplot(gs[0, :])
        colors = plt.cm.RdYlGn(np.linspace(0.3, 1, len(place_stats)))
        bars = ax1.barh(range(len(place_stats)), place_stats['推荐得分'],
                        color=colors, alpha=0.8)
        ax1.set_yticks(range(len(place_stats)))
        ax1.set_yticklabels(place_stats['景点'], fontsize=10)
        ax1.invert_yaxis()
        ax1.set_xlabel('综合推荐得分', fontsize=11)
        ax1.set_title('🏆 景点智能推荐排名', fontsize=13, fontweight='bold')
        ax1.grid(True, alpha=0.3, axis='x')

        for i, (idx, row) in enumerate(place_stats.iterrows()):
            ax1.text(row['推荐得分'], i, f" {row['推荐得分']:.3f}",
                     va='center', fontsize=9)

        # 2. 三维散点图（质量 vs 热度 vs 评分）
        ax2 = fig.add_subplot(gs[1, 0], projection='3d')
        scatter = ax2.scatter(place_stats['质量得分'],
                              place_stats['热度得分'],
                              place_stats['平均评分'],
                              c=place_stats['景点分组'],
                              cmap='Set2', s=200, alpha=0.7,
                              edgecolors='black', linewidth=1)

        ax2.set_xlabel('质量得分', fontsize=9)
        ax2.set_ylabel('热度得分', fontsize=9)
        ax2.set_zlabel('平均评分', fontsize=9)
        ax2.set_title('景点三维特征空间', fontsize=11, fontweight='bold')

        for idx, row in place_stats.iterrows():
            ax2.text(row['质量得分'], row['热度得分'], row['平均评分'],
                     row['景点'], fontsize=7, alpha=0.8)

        # 3. 层次聚类树状图
        ax3 = fig.add_subplot(gs[1, 1:])
        dendrogram(linkage_matrix, labels=place_stats['景点'].values, ax=ax3,
                   leaf_font_size=9, color_threshold=0)
        ax3.set_xlabel('景点', fontsize=10)
        ax3.set_ylabel('距离', fontsize=10)
        ax3.set_title('景点层次聚类树状图', fontsize=12, fontweight='bold')
        ax3.tick_params(axis='x', rotation=90)
        ax3.grid(True, alpha=0.3, axis='y')

        # 4. 景点分组雷达图 - 修复标签重叠
        ax4 = fig.add_subplot(gs[2, 0], projection='polar')

        group_profiles = place_stats.groupby('景点分组')[
            ['平均评分', '好评率', '热度得分', '质量得分', '推荐得分']
        ].mean()

        categories = ['平均评分', '好评率', '热度', '质量', '推荐度']
        N = len(categories)
        angles = [n / float(N) * 2 * np.pi for n in range(N)]
        angles += angles[:1]

        ax4.set_theta_offset(np.pi / 2)
        ax4.set_theta_direction(-1)
        ax4.set_xticks(angles[:-1])
        ax4.set_xticklabels(categories, fontsize=10)  # 增大字体

        colors_group = ['#e74c3c', '#3498db', '#2ecc71']
        for idx, (group_id, row) in enumerate(group_profiles.iterrows()):
            values = [
                row['平均评分'] / 5,
                row['好评率'],
                row['热度得分'],
                row['质量得分'],
                row['推荐得分']
            ]
            values += values[:1]

            ax4.plot(angles, values, 'o-', linewidth=2,
                     label=f'分组{group_id}', color=colors_group[idx], alpha=0.7)
            ax4.fill(angles, values, alpha=0.15, color=colors_group[idx])

        ax4.set_ylim(0, 1)
        ax4.legend(loc='upper left', bbox_to_anchor=(1.15, 1.05), fontsize=10, framealpha=0.9)
        ax4.set_title('景点分组特征对比', fontsize=11, fontweight='bold', pad=20)
        ax4.grid(True)

        # 5. 质量-热度矩阵
        ax5 = fig.add_subplot(gs[2, 1])

        # 定义四象限
        quality_median = place_stats['质量得分'].median()
        heat_median = place_stats['热度得分'].median()

        # 绘制散点
        for idx, row in place_stats.iterrows():
            color = colors_group[row['景点分组'] - 1]
            ax5.scatter(row['质量得分'], row['热度得分'],
                        s=200, alpha=0.7, color=color, edgecolors='black', linewidth=1)
            ax5.annotate(row['景点'], (row['质量得分'], row['热度得分']),
                         fontsize=8, alpha=0.8, ha='center')

        # 绘制四象限线
        ax5.axhline(heat_median, color='gray', linestyle='--', alpha=0.5)
        ax5.axvline(quality_median, color='gray', linestyle='--', alpha=0.5)

        # 标注象限
        ax5.text(0.05, 0.95, '高热度\n低质量', transform=ax5.transAxes,
                 fontsize=9, alpha=0.6, va='top')
        ax5.text(0.95, 0.95, '高热度\n高质量\n⭐推荐', transform=ax5.transAxes,
                 fontsize=9, alpha=0.6, va='top', ha='right',
                 bbox=dict(boxstyle='round', facecolor='yellow', alpha=0.3))
        ax5.text(0.05, 0.05, '低热度\n低质量', transform=ax5.transAxes,
                 fontsize=9, alpha=0.6)
        ax5.text(0.95, 0.05, '低热度\n高质量\n💎潜力', transform=ax5.transAxes,
                 fontsize=9, alpha=0.6, ha='right',
                 bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.3))

        ax5.set_xlabel('质量得分', fontsize=10)
        ax5.set_ylabel('热度得分', fontsize=10)
        ax5.set_title('质量-热度矩阵分析', fontsize=12, fontweight='bold')
        ax5.grid(True, alpha=0.3)

        # 6. 推荐路线（Top 5）
        ax6 = fig.add_subplot(gs[2, 2])
        ax6.axis('off')

        top_5 = place_stats.head(5)

        # 创建推荐卡片
        y_pos = 0.95
        for i, (idx, row) in enumerate(top_5.iterrows()):
            # 背景框
            rect = Rectangle((0.05, y_pos - 0.18), 0.9, 0.16,
                             facecolor=colors[i], alpha=0.2,
                             edgecolor='black', linewidth=1,
                             transform=ax6.transAxes)
            ax6.add_patch(rect)

            # 文字信息
            text = f"🏅 {i + 1}. {row['景点']}\n"
            text += f"   推荐度: {row['推荐得分']:.3f} | "
            text += f"评分: {row['平均评分']:.2f} | "
            text += f"好评率: {row['好评率']:.1%}\n"
            text += f"   热度: {'🔥' * int(row['热度得分'] * 5)}"

            ax6.text(0.5, y_pos - 0.1, text,
                     transform=ax6.transAxes, fontsize=9,
                     ha='center', va='center',
                     bbox=dict(boxstyle='round', facecolor='white',
                               alpha=0.8, pad=0.3))

            y_pos -= 0.19

        ax6.set_title('🎯 Top 5 智能推荐路线', fontsize=12,
                      fontweight='bold', pad=10)

        plt.suptitle('🎯 AI智能旅游路线推荐系统', fontsize=16, fontweight='bold', y=0.995)

        # 生成推荐表格
        recommendation_table = place_stats[['景点', '推荐得分', '平均评分', '好评率',
                                            '热度得分', '质量得分', '景点分组']].copy()
        recommendation_table['推荐等级'] = pd.cut(recommendation_table['推荐得分'],
                                                  bins=[0, 0.5, 0.7, 0.85, 1],
                                                  labels=['C级', 'B级', 'A级', 'S级'])
        recommendation_table = recommendation_table.round(3)

        return fig, recommendation_table

    def topic_modeling_analysis(self, selected_places=None, n_topics=5):
        """📚 主题建模分析（LDA）"""
        df = self.filter_data(selected_places)

        if len(df) < 20:
            return self._create_empty_plot('数据量不足（需要至少20条）'), pd.DataFrame()

        # 文本预处理
        texts = df['评论内容'].astype(str).tolist()

        # 分词和停用词过滤
        processed_texts = []
        for text in texts:
            words = jieba.cut(text)
            filtered_words = [w for w in words if len(w) > 1 and w not in STOPWORDS]
            processed_texts.append(' '.join(filtered_words))

        # TF-IDF向量化
        vectorizer = TfidfVectorizer(max_features=100, max_df=0.8, min_df=2)
        try:
            tfidf_matrix = vectorizer.fit_transform(processed_texts)
        except:
            return self._create_empty_plot('文本数据不足以进行主题分析'), pd.DataFrame()

        # LDA主题建模
        from sklearn.decomposition import LatentDirichletAllocation

        lda_model = LatentDirichletAllocation(
            n_components=n_topics,
            random_state=42,
            max_iter=50,
            learning_method='batch'
        )

        lda_output = lda_model.fit_transform(tfidf_matrix)

        # 获取主题词
        feature_names = vectorizer.get_feature_names_out()
        topics_words = []

        for topic_idx, topic in enumerate(lda_model.components_):
            top_indices = topic.argsort()[-10:][::-1]
            top_words = [feature_names[i] for i in top_indices]
            top_weights = [topic[i] for i in top_indices]
            topics_words.append((top_words, top_weights))

        # 为每条评论分配主题
        df['主题编号'] = lda_output.argmax(axis=1)
        df['主题概率'] = lda_output.max(axis=1)

        # 可视化
        fig = plt.figure(figsize=(20, 12))
        gs = fig.add_gridspec(3, n_topics, hspace=0.4, wspace=0.3)

        # 1. 各主题的Top词汇（第一行）
        colors_topic = plt.cm.Set3(np.linspace(0, 1, n_topics))

        for topic_idx in range(n_topics):
            ax = fig.add_subplot(gs[0, topic_idx])
            words, weights = topics_words[topic_idx]

            ax.barh(range(len(words)), weights,
                    color=colors_topic[topic_idx], alpha=0.8)
            ax.set_yticks(range(len(words)))
            ax.set_yticklabels(words, fontsize=8)
            ax.invert_yaxis()
            ax.set_xlabel('权重', fontsize=9)
            ax.set_title(f'主题 {topic_idx + 1}', fontsize=11, fontweight='bold')
            ax.grid(True, alpha=0.3, axis='x')

        # 2. 主题分布（第二行，跨列）- 修复饼图标签
        ax_dist = fig.add_subplot(gs[1, :])
        topic_counts = df['主题编号'].value_counts().sort_index()

        bars = ax_dist.bar(range(n_topics),
                           [topic_counts.get(i, 0) for i in range(n_topics)],
                           color=colors_topic, alpha=0.8, edgecolor='black', linewidth=1)
        ax_dist.set_xticks(range(n_topics))
        ax_dist.set_xticklabels([f'主题{i + 1}' for i in range(n_topics)], fontsize=10)
        ax_dist.set_ylabel('评论数量', fontsize=11)
        ax_dist.set_title('各主题评论分布', fontsize=13, fontweight='bold')
        ax_dist.grid(True, alpha=0.3, axis='y')

        for i, bar in enumerate(bars):
            height = bar.get_height()
            ax_dist.text(bar.get_x() + bar.get_width() / 2., height,
                         f'{int(height)}\n({height / len(df) * 100:.1f}%)',
                         ha='center', va='bottom', fontsize=9)

        # 3. 主题-评分关系（第三行）
        ax_score = fig.add_subplot(gs[2, :2])
        topic_scores = df.groupby('主题编号')['评分'].agg(['mean', 'std']).reset_index()

        x = range(n_topics)
        means = [topic_scores[topic_scores['主题编号'] == i]['mean'].values[0]
                 if i in topic_scores['主题编号'].values else 0
                 for i in range(n_topics)]
        stds = [topic_scores[topic_scores['主题编号'] == i]['std'].values[0]
                if i in topic_scores['主题编号'].values else 0
                for i in range(n_topics)]

        ax_score.bar(x, means, yerr=stds, color=colors_topic, alpha=0.8,
                     capsize=5, edgecolor='black', linewidth=1)
        ax_score.set_xticks(x)
        ax_score.set_xticklabels([f'主题{i + 1}' for i in range(n_topics)], fontsize=10)
        ax_score.set_ylabel('平均评分', fontsize=11)
        ax_score.set_title('各主题平均评分（含标准差）', fontsize=12, fontweight='bold')
        ax_score.axhline(df['评分'].mean(), color='red', linestyle='--',
                         alpha=0.5, label='总体均值')
        ax_score.legend()
        ax_score.grid(True, alpha=0.3, axis='y')

        # 4. 主题-景点热力图
        if df['景点'].nunique() > 1:
            ax_heatmap = fig.add_subplot(gs[2, 2:])
            topic_place = pd.crosstab(df['景点'], df['主题编号'], normalize='index') * 100

            sns.heatmap(topic_place, annot=True, fmt='.1f', cmap='YlOrRd',
                        ax=ax_heatmap, cbar_kws={'label': '百分比(%)'},
                        linewidths=0.5, linecolor='gray')
            ax_heatmap.set_xlabel('主题编号', fontsize=10)
            ax_heatmap.set_ylabel('景点', fontsize=10)
            ax_heatmap.set_title('景点-主题分布热力图', fontsize=12, fontweight='bold')

        plt.suptitle(f'📚 主题建模分析（LDA，{n_topics}个主题）',
                     fontsize=16, fontweight='bold', y=0.995)

        # 生成主题摘要表
        topic_summary = []
        for topic_idx in range(n_topics):
            words, _ = topics_words[topic_idx]
            topic_df = df[df['主题编号'] == topic_idx]

            topic_summary.append({
                '主题编号': f'主题{topic_idx + 1}',
                '关键词': '、'.join(words[:5]),
                '评论数': len(topic_df),
                '占比(%)': round(len(topic_df) / len(df) * 100, 1),
                '平均评分': round(topic_df['评分'].mean(), 2) if len(topic_df) > 0 else 0,
                '主要景点': topic_df['景点'].mode()[0] if len(topic_df) > 0 else 'N/A'
            })

        summary_table = pd.DataFrame(topic_summary)

        return fig, summary_table

    def anomaly_detection_analysis(self, selected_places=None):
        """🚨 异常评论检测（Isolation Forest）"""
        df = self.filter_data(selected_places)

        if len(df) < 30:
            return self._create_empty_plot('数据量不足（需要至少30条）'), pd.DataFrame()

        # 特征工程
        features = pd.DataFrame({
            '评分': df['评分'],
            '评论长度': df['评论长度'],
            '评分-均值差': df['评分'] - df['评分'].mean(),
            '长度-均值差': df['评论长度'] - df['评论长度'].mean(),
            '评分标准化': (df['评分'] - df['评分'].mean()) / df['评分'].std(),
            '长度标准化': (df['评论长度'] - df['评论长度'].mean()) / df['评论长度'].std(),
        })

        # 使用Isolation Forest检测异常
        from sklearn.ensemble import IsolationForest

        iso_forest = IsolationForest(contamination=0.1, random_state=42)
        anomaly_labels = iso_forest.fit_predict(features)
        anomaly_scores = iso_forest.score_samples(features)

        df['是否异常'] = (anomaly_labels == -1).astype(int)
        df['异常得分'] = -anomaly_scores  # 转换为正值，越大越异常

        # 可视化
        fig = plt.figure(figsize=(18, 12))
        gs = fig.add_gridspec(3, 3, hspace=0.35, wspace=0.3)

        # 1. 评分-长度散点图（标注异常点）
        ax1 = fig.add_subplot(gs[0, :2])

        normal_data = df[df['是否异常'] == 0]
        anomaly_data = df[df['是否异常'] == 1]

        ax1.scatter(normal_data['评分'], normal_data['评论长度'],
                    alpha=0.5, s=50, c='#3498db', label='正常评论', edgecolors='none')
        ax1.scatter(anomaly_data['评分'], anomaly_data['评论长度'],
                    alpha=0.8, s=100, c='#e74c3c', label='异常评论',
                    marker='X', edgecolors='black', linewidth=1)

        ax1.set_xlabel('评分', fontsize=10)
        ax1.set_ylabel('评论长度', fontsize=10)
        ax1.set_title('异常评论检测（评分-长度空间）', fontsize=12, fontweight='bold')
        ax1.legend(fontsize=10)
        ax1.grid(True, alpha=0.3)

        # 2. 异常得分分布
        ax2 = fig.add_subplot(gs[0, 2])
        ax2.hist(df['异常得分'], bins=30, color='#9b59b6', alpha=0.7, edgecolor='black')
        threshold = df['异常得分'].quantile(0.9)
        ax2.axvline(threshold, color='red', linestyle='--', linewidth=2,
                    label=f'90%分位数: {threshold:.3f}')
        ax2.set_xlabel('异常得分', fontsize=10)
        ax2.set_ylabel('频数', fontsize=10)
        ax2.set_title('异常得分分布', fontsize=12, fontweight='bold')
        ax2.legend()
        ax2.grid(True, alpha=0.3, axis='y')

        # 3. 各景点异常率
        if df['景点'].nunique() > 1:
            ax3 = fig.add_subplot(gs[1, :])
            place_anomaly = df.groupby('景点').agg({
                '是否异常': ['sum', 'mean'],
                '评分': 'count'
            })
            place_anomaly.columns = ['异常数', '异常率', '总数']
            place_anomaly['异常率'] *= 100
            place_anomaly = place_anomaly.sort_values('异常率', ascending=True)

            x = np.arange(len(place_anomaly))
            width = 0.35

            ax3_twin = ax3.twinx()
            bars1 = ax3.barh(x - width / 2, place_anomaly['异常率'], width,
                             label='异常率(%)', color='#e74c3c', alpha=0.8)
            bars2 = ax3_twin.barh(x + width / 2, place_anomaly['异常数'], width,
                                  label='异常数', color='#3498db', alpha=0.8)

            ax3.set_yticks(x)
            ax3.set_yticklabels(place_anomaly.index, fontsize=9)
            ax3.set_xlabel('异常率 (%)', fontsize=10, color='#e74c3c')
            ax3_twin.set_xlabel('异常评论数', fontsize=10, color='#3498db')
            ax3.set_title('各景点异常评论分析', fontsize=12, fontweight='bold')
            ax3.tick_params(axis='x', labelcolor='#e74c3c')
            ax3_twin.tick_params(axis='x', labelcolor='#3498db')

            lines = [bars1, bars2]
            labels = [l.get_label() for l in lines]
            ax3.legend(lines, labels, loc='upper right')
            ax3.grid(True, alpha=0.3, axis='x')

        # 4. 异常评论特征对比
        ax4 = fig.add_subplot(gs[2, 0])
        comparison_data = pd.DataFrame({
            '正常评论': [
                normal_data['评分'].mean(),
                normal_data['评论长度'].mean(),
                normal_data['评分'].std(),
                normal_data['评论长度'].std()
            ],
            '异常评论': [
                anomaly_data['评分'].mean(),
                anomaly_data['评论长度'].mean(),
                anomaly_data['评分'].std(),
                anomaly_data['评论长度'].std()
            ]
        }, index=['平均评分', '平均长度', '评分标准差', '长度标准差'])

        comparison_data.plot(kind='bar', ax=ax4, color=['#3498db', '#e74c3c'], alpha=0.8)
        ax4.set_ylabel('数值', fontsize=10)
        ax4.set_title('正常 vs 异常评论特征对比', fontsize=11, fontweight='bold')
        ax4.tick_params(axis='x', rotation=45)
        ax4.legend(fontsize=9)
        ax4.grid(True, alpha=0.3, axis='y')

        # 5. 箱线图对比
        ax5 = fig.add_subplot(gs[2, 1])
        box_data = [normal_data['评分'].dropna(), anomaly_data['评分'].dropna()]
        bp = ax5.boxplot(box_data, labels=['正常评论', '异常评论'],
                         patch_artist=True)
        bp['boxes'][0].set_facecolor('#3498db')
        bp['boxes'][1].set_facecolor('#e74c3c')
        for element in ['whiskers', 'fliers', 'means', 'medians', 'caps']:
            plt.setp(bp[element], color='black', linewidth=1.5)

        ax5.set_ylabel('评分', fontsize=10)
        ax5.set_title('评分分布对比（箱线图）', fontsize=11, fontweight='bold')
        ax5.grid(True, alpha=0.3, axis='y')

        # 6. 异常类型分析 - 修复饼图标签
        ax6 = fig.add_subplot(gs[2, 2])

        # 定义异常类型
        def categorize_anomaly(row):
            if row['是否异常'] == 0:
                return '正常'
            elif row['评分'] < 2 and row['评论长度'] < 20:
                return '低分短评'
            elif row['评分'] >= 4.5 and row['评论长度'] < 20:
                return '高分短评'
            elif row['评论长度'] > df['评论长度'].quantile(0.95):
                return '超长评论'
            else:
                return '其他异常'

        df['异常类型'] = df.apply(categorize_anomaly, axis=1)
        anomaly_type_counts = df[df['是否异常'] == 1]['异常类型'].value_counts()

        colors_anomaly = ['#e74c3c', '#f39c12', '#9b59b6', '#e67e22']

        # 使用explode分离切片
        explode = [0.05] * len(anomaly_type_counts)

        wedges, texts, autotexts = ax6.pie(
            anomaly_type_counts.values,
            autopct='%1.1f%%',
            colors=colors_anomaly,
            startangle=90,
            explode=explode,
            textprops={'fontsize': 10},
            pctdistance=0.85
        )
        ax6.legend(wedges, anomaly_type_counts.index,
                   loc='center left', bbox_to_anchor=(1, 0.5), fontsize=9)

        # 调整百分比文字样式
        for autotext in autotexts:
            autotext.set_color('white')
            autotext.set_fontweight('bold')
            autotext.set_fontsize(8)

        ax6.set_title('异常类型分布', fontsize=11, fontweight='bold')

        plt.suptitle('🚨 异常评论智能检测分析', fontsize=16, fontweight='bold', y=0.995)

        # 生成异常报告表
        anomaly_report = pd.DataFrame({
            '指标': ['总评论数', '异常评论数', '异常率(%)', '---',
                     '正常评论平均分', '异常评论平均分', '---',
                     '正常评论平均长度', '异常评论平均长度'],
            '数值': [
                len(df),
                len(anomaly_data),
                round(len(anomaly_data) / len(df) * 100, 2),
                '---',
                round(normal_data['评分'].mean(), 2),
                round(anomaly_data['评分'].mean(), 2) if len(anomaly_data) > 0 else 0,
                '---',
                round(normal_data['评论长度'].mean(), 1),
                round(anomaly_data['评论长度'].mean(), 1) if len(anomaly_data) > 0 else 0
            ]
        })

        return fig, anomaly_report

    def _create_empty_plot(self, message):
        """创建空图表"""
        fig, ax = plt.subplots(figsize=(14, 7))
        ax.text(0.5, 0.5, message, ha='center', va='center',
                fontsize=16, transform=ax.transAxes)
        ax.axis('off')
        return fig


def create_interface():
    print("\n正在初始化分析器...")
    analyzer = TourismDataAnalyzer('data')

    all_places = sorted(analyzer.all_data['景点'].unique().tolist())

    print(f"✓ 界面初始化完成！发现 {len(all_places)} 个景点")
    print(f"✓ 使用字体: {CHINESE_FONT or '系统默认'}\n")

    with gr.Blocks(title="AI旅游智能分析系统", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""

        # 🤖 AI驱动的旅游计划评估与智能决策系统

        ### 基于机器学习的深度数据分析与智能推荐平台

        """)

        gr.Markdown(f"""

        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 

                    padding: 25px; border-radius: 15px; color: white; margin: 20px 0; 

                    box-shadow: 0 4px 15px rgba(0,0,0,0.2);">

            <h3 style="margin: 0;">📊 数据概览</h3>

            <p style="margin: 15px 0; font-size: 18px;">

                总评论数: <b>{len(analyzer.all_data)}</b> 条 | 

                景点数: <b>{len(all_places)}</b> 个 | 

                用户数: <b>{analyzer.all_data['用户名'].nunique()}</b> 人

            </p>

            <p style="margin: 5px 0;">

                时间范围: {analyzer.all_data['时间'].min().date()} 至 {analyzer.all_data['时间'].max().date()}

            </p>

            <p style="margin-top: 15px; font-size: 14px; opacity: 0.9;">

                🚀 集成机器学习算法：随机森林、层次聚类、主题建模(LDA)、异常检测(Isolation Forest)

            </p>

        </div>

        """)

        with gr.Row():
            place_selector = gr.CheckboxGroup(
                choices=all_places,
                label="🎯 选择要分析的景点（不选则分析全部数据）",
                value=[],
                elem_id="place_selector"
            )

        with gr.Tabs():
            # 🆕 Tab 0: 数据集预览（添加在最前面）
            with gr.Tab("📋 数据集预览"):
                gr.Markdown("""

                   ### 📊 原始数据预览

                   - **隐私保护**: 用户名已自动匿名化处理

                   - **数据采样**: 随机展示部分评论数据

                   - **字段说明**: 用户ID | 景点 | 评分 | 时间 | 评论内容 | 长度 | 情感分类

                   """)

                with gr.Row():
                    sample_size = gr.Slider(
                        minimum=50,
                        maximum=500,
                        value=100,
                        step=50,
                        label="📊 预览数据量"
                    )
                    refresh_btn = gr.Button("🔄 刷新数据", variant="secondary", size="sm")

                preview_table = gr.DataFrame(
                    value=analyzer.get_preview_data(100),
                    label="数据集预览（用户名已匿名）",
                    wrap=True,
                    max_height=600,
                    interactive=False
                )

                # 统计信息卡片
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown(f"""

                           <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 

                                       padding: 20px; border-radius: 10px; color: white; text-align: center;">

                               <h3 style="margin: 0;">📊 总评论数</h3>

                               <p style="font-size: 32px; font-weight: bold; margin: 10px 0;">

                                   {len(analyzer.all_data):,}

                               </p>

                           </div>

                           """)

                    with gr.Column(scale=1):
                        gr.Markdown(f"""

                           <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); 

                                       padding: 20px; border-radius: 10px; color: white; text-align: center;">

                               <h3 style="margin: 0;">🏞️ 景点数量</h3>

                               <p style="font-size: 32px; font-weight: bold; margin: 10px 0;">

                                   {analyzer.all_data['景点'].nunique()}

                               </p>

                           </div>

                           """)

                    with gr.Column(scale=1):
                        gr.Markdown(f"""

                           <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); 

                                       padding: 20px; border-radius: 10px; color: white; text-align: center;">

                               <h3 style="margin: 0;">👥 用户数量</h3>

                               <p style="font-size: 32px; font-weight: bold; margin: 10px 0;">

                                   {analyzer.all_data['用户名'].nunique():,}

                               </p>

                           </div>

                           """)

                    with gr.Column(scale=1):
                        gr.Markdown(f"""

                           <div style="background: linear-gradient(135deg, #fa709a 0%, #fee140 100%); 

                                       padding: 20px; border-radius: 10px; color: white; text-align: center;">

                               <h3 style="margin: 0;">⭐ 平均评分</h3>

                               <p style="font-size: 32px; font-weight: bold; margin: 10px 0;">

                                   {analyzer.all_data['评分'].mean():.2f}

                               </p>

                           </div>

                           """)

                # 刷新按钮功能
                refresh_btn.click(
                    fn=lambda n: analyzer.get_preview_data(n),
                    inputs=[sample_size],
                    outputs=[preview_table]
                )

                sample_size.change(
                    fn=lambda n: analyzer.get_preview_data(n),
                    inputs=[sample_size],
                    outputs=[preview_table]
                )

            # Tab 1: 评分分析
            with gr.Tab("⭐ 评分深度分析"):
                gr.Markdown("### 多维度评分分析，含分布、箱线图、景点对比等")
                rating_btn = gr.Button("📊 生成评分深度分析", variant="primary", size="lg")
                rating_plot = gr.Plot(label="评分分析图表")

                rating_btn.click(
                    fn=lambda x: analyzer.plot_advanced_rating_analysis(x if x else None),
                    inputs=[place_selector],
                    outputs=[rating_plot]
                )

            # Tab 2: 时间趋势
            with gr.Tab("📅 时间趋势分析"):
                gr.Markdown("### 评论数量、评分、季节性、工作日/周末全面分析")
                time_btn = gr.Button("📈 生成时间趋势分析", variant="primary", size="lg")
                time_plot = gr.Plot(label="时间趋势图表")

                time_btn.click(
                    fn=lambda x: analyzer.plot_time_trend_analysis(x if x else None),
                    inputs=[place_selector],
                    outputs=[time_plot]
                )

            # Tab 3: 词云分析
            with gr.Tab("☁️ 高级词云分析"):
                gr.Markdown("### 可视化高频词汇，支持按评分筛选，含词频统计")
                with gr.Row():
                    rating_filter = gr.Radio(
                        choices=["全部评论", "高分评论 (>=4)", "低分评论 (<3)"],
                        value="全部评论",
                        label="评论筛选"
                    )
                    word_count = gr.Slider(50, 200, value=100, step=10, label="词云词汇数量")

                wordcloud_btn = gr.Button("☁️ 生成高级词云", variant="primary", size="lg")
                wordcloud_plot = gr.Plot(label="词云与词频图")

                wordcloud_btn.click(
                    fn=lambda x, y, z: analyzer.generate_advanced_wordcloud(
                        x if x else None,
                        None if y == "全部评论" else y,
                        z
                    ),
                    inputs=[place_selector, rating_filter, word_count],
                    outputs=[wordcloud_plot]
                )

            # Tab 4: 关键词提取
            with gr.Tab("🔑 多维度关键词提取"):
                gr.Markdown("### TF-IDF + TextRank双算法，正负面关键词对比")
                keyword_num = gr.Slider(10, 50, value=30, step=5, label="关键词数量")
                keyword_btn = gr.Button("🔍 提取多维度关键词", variant="primary", size="lg")

                keyword_plot = gr.Plot(label="关键词分析图表")
                keyword_table = gr.DataFrame(label="关键词详细列表")

                keyword_btn.click(
                    fn=lambda x, n: analyzer.extract_advanced_keywords(x if x else None, n),
                    inputs=[place_selector, keyword_num],
                    outputs=[keyword_plot, keyword_table]
                )

            # Tab 5: 情感分析
            with gr.Tab("😊 情感深度分析"):
                gr.Markdown("### 五级情感分类，景点对比，时间趋势全覆盖")
                sentiment_btn = gr.Button("💭 生成情感深度分析", variant="primary", size="lg")

                sentiment_plot = gr.Plot(label="情感分析图表")
                sentiment_table = gr.DataFrame(label="情感统计数据")

                sentiment_btn.click(
                    fn=lambda x: analyzer.advanced_sentiment_analysis(x if x else None),
                    inputs=[place_selector],
                    outputs=[sentiment_plot, sentiment_table]
                )

            # Tab 6: 景点对比
            with gr.Tab("🏆 景点综合对比"):
                gr.Markdown("### 雷达图、热力图、散点图多角度对比景点表现")
                comparison_btn = gr.Button("📊 生成景点综合对比", variant="primary", size="lg")

                comparison_plot = gr.Plot(label="景点对比图表")
                comparison_table = gr.DataFrame(label="景点详细数据", wrap=True)

                comparison_btn.click(
                    fn=lambda: analyzer.comprehensive_place_comparison(),
                    inputs=[],
                    outputs=[comparison_plot, comparison_table]
                )

            # Tab 7: 用户画像与聚类
            with gr.Tab("👥 用户画像与行为分析"):
                gr.Markdown("### K-means聚类、PCA降维、用户类型识别（隐私保护）")
                user_profile_btn = gr.Button("🔍 生成用户画像分析", variant="primary", size="lg")

                user_profile_plot = gr.Plot(label="用户画像与聚类图表")
                user_profile_table = gr.DataFrame(label="用户类型统计")

                user_profile_btn.click(
                    fn=lambda x: analyzer.user_profile_and_clustering(x if x else None),
                    inputs=[place_selector],
                    outputs=[user_profile_plot, user_profile_table]
                )

            # Tab 8: 🤖 机器学习满意度预测
            with gr.Tab("🤖 AI满意度预测"):
                gr.Markdown("""

                ### 🎯 基于随机森林的智能满意度预测模型

                - **算法**: Random Forest Classifier

                - **特征**: 评论长度、时间特征、景点编码等7个维度

                - **输出**: 特征重要性、ROC曲线、混淆矩阵、预测 vs 实际对比

                """)
                ml_predict_btn = gr.Button("🚀 训练并预测满意度", variant="primary", size="lg")

                ml_predict_plot = gr.Plot(label="机器学习模型分析")
                ml_predict_table = gr.DataFrame(label="模型评估报告")

                ml_predict_btn.click(
                    fn=lambda x: analyzer.ml_satisfaction_predictor(x if x else None),
                    inputs=[place_selector],
                    outputs=[ml_predict_plot, ml_predict_table]
                )

            # Tab 9: 🎯 智能路线推荐
            with gr.Tab("🎯 AI智能推荐"):
                gr.Markdown("""

                ### 🗺️ 基于层次聚类的智能旅游路线推荐

                - **算法**: Hierarchical Clustering + 多维度评分

                - **维度**: 质量得分、热度得分、稳定性等

                - **输出**: Top 5推荐路线、质量-热度矩阵、景点分组

                """)
                recommend_btn = gr.Button("🎁 生成智能推荐路线", variant="primary", size="lg")

                recommend_plot = gr.Plot(label="智能推荐分析")
                recommend_table = gr.DataFrame(label="推荐排名详情")

                recommend_btn.click(
                    fn=lambda: analyzer.intelligent_tour_recommendation(),
                    inputs=[],
                    outputs=[recommend_plot, recommend_table]
                )

            # Tab 10: 📚 主题建模
            with gr.Tab("📚 主题挖掘"):
                gr.Markdown("""

                ### 📖 基于LDA的评论主题建模分析

                - **算法**: Latent Dirichlet Allocation (LDA)

                - **预处理**: TF-IDF向量化 + 中文分词

                - **输出**: 主题关键词、主题分布、主题-评分关系

                """)
                n_topics = gr.Slider(3, 8, value=5, step=1, label="主题数量")
                topic_btn = gr.Button("📚 提取评论主题", variant="primary", size="lg")

                topic_plot = gr.Plot(label="主题建模分析")
                topic_table = gr.DataFrame(label="主题摘要")

                topic_btn.click(
                    fn=lambda x, n: analyzer.topic_modeling_analysis(x if x else None, n),
                    inputs=[place_selector, n_topics],
                    outputs=[topic_plot, topic_table]
                )

            # Tab 11: 🚨 异常检测
            with gr.Tab("🚨 异常检测"):
                gr.Markdown("""

                ### 🔍 基于Isolation Forest的异常评论检测

                - **算法**: Isolation Forest（孤立森林）

                - **检测维度**: 评分异常、长度异常、综合异常

                - **输出**: 异常评论标注、异常类型分类、景点异常率对比

                """)
                anomaly_btn = gr.Button("🔍 检测异常评论", variant="primary", size="lg")

                anomaly_plot = gr.Plot(label="异常检测分析")
                anomaly_table = gr.DataFrame(label="异常检测报告")

                anomaly_btn.click(
                    fn=lambda x: analyzer.anomaly_detection_analysis(x if x else None),
                    inputs=[place_selector],
                    outputs=[anomaly_plot, anomaly_table]
                )

        gr.Markdown("""

        ---

        <div style="text-align: center; color: #666; padding: 20px;">

            <p style="font-size: 16px; margin-bottom: 10px;">

                🎨 <b>技术栈</b>: Gradio + Pandas + Scikit-learn + Matplotlib + Seaborn + Jieba

            </p>

            <p style="font-size: 14px;">

                🤖 <b>ML算法</b>: Random Forest | K-means | LDA | Isolation Forest | Hierarchical Clustering

            </p>

            <p style="font-size: 12px; margin-top: 10px; opacity: 0.7;">

                💾 数据来源: {count} 条真实用户评论 | 🔒 隐私保护: 匿名化处理

            </p>

        </div>

        """.format(count=len(analyzer.all_data)))

    return demo


if __name__ == "__main__":
    print("\n" + "=" * 70)
    print("🚀 启动 AI旅游智能分析系统 v3.0 - 机器学习增强版")
    print("=" * 70 + "\n")

    demo = create_interface()
    demo.launch(
        share=False,
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True
    )