import json
import os
from pathlib import Path
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
import matplotlib as mpl
import sqlite3

# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 使用微软雅黑
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
plt.rcParams['font.family'] = 'sans-serif'

# 获取脚本所在目录
SCRIPT_DIR = Path(__file__).parent

def get_group_name(stream_id):
    """从数据库中获取群组名称"""
    conn = sqlite3.connect('data/maibot.db')
    cursor = conn.cursor()
    
    cursor.execute('''
        SELECT group_name, user_nickname, platform 
        FROM chat_streams 
        WHERE stream_id = ?
    ''', (stream_id,))
    
    result = cursor.fetchone()
    conn.close()
    
    if result:
        group_name, user_nickname, platform = result
        if group_name:
            return group_name
        if user_nickname:
            return user_nickname
        if platform:
            return f"{platform}-{stream_id[:8]}"
    return stream_id

def load_group_data(group_dir):
    """加载单个群组的数据"""
    json_path = Path(group_dir) / "expressions.json"
    if not json_path.exists():
        return [], [], []
    
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
    
    situations = []
    styles = []
    combined = []
    
    for item in data:
        count = item['count']
        situations.extend([item['situation']] * count)
        styles.extend([item['style']] * count)
        combined.extend([f"{item['situation']} {item['style']}"] * count)
    
    return situations, styles, combined

def analyze_group_similarity():
    # 获取所有群组目录
    base_dir = Path("data/expression/learnt_style")
    group_dirs = [d for d in base_dir.iterdir() if d.is_dir()]
    group_ids = [d.name for d in group_dirs]
    
    # 获取群组名称
    group_names = [get_group_name(group_id) for group_id in group_ids]
    
    # 加载所有群组的数据
    group_situations = []
    group_styles = []
    group_combined = []
    
    for d in group_dirs:
        situations, styles, combined = load_group_data(d)
        group_situations.append(' '.join(situations))
        group_styles.append(' '.join(styles))
        group_combined.append(' '.join(combined))
    
    # 创建TF-IDF向量化器
    vectorizer = TfidfVectorizer()
    
    # 计算三种相似度矩阵
    situation_matrix = cosine_similarity(vectorizer.fit_transform(group_situations))
    style_matrix = cosine_similarity(vectorizer.fit_transform(group_styles))
    combined_matrix = cosine_similarity(vectorizer.fit_transform(group_combined))
    
    # 对相似度矩阵进行对数变换
    log_situation_matrix = np.log1p(situation_matrix)
    log_style_matrix = np.log1p(style_matrix)
    log_combined_matrix = np.log1p(combined_matrix)
    
    # 创建一个大图，包含三个子图
    plt.figure(figsize=(45, 12))
    
    # 场景相似度热力图
    plt.subplot(1, 3, 1)
    sns.heatmap(log_situation_matrix, 
                xticklabels=group_names,
                yticklabels=group_names,
                cmap='YlOrRd',
                annot=True,
                fmt='.2f',
                vmin=0,
                vmax=np.log1p(0.2))
    plt.title('群组场景相似度热力图 (对数变换)')
    plt.xticks(rotation=45, ha='right')
    
    # 表达方式相似度热力图
    plt.subplot(1, 3, 2)
    sns.heatmap(log_style_matrix, 
                xticklabels=group_names,
                yticklabels=group_names,
                cmap='YlOrRd',
                annot=True,
                fmt='.2f',
                vmin=0,
                vmax=np.log1p(0.2))
    plt.title('群组表达方式相似度热力图 (对数变换)')
    plt.xticks(rotation=45, ha='right')
    
    # 组合相似度热力图
    plt.subplot(1, 3, 3)
    sns.heatmap(log_combined_matrix, 
                xticklabels=group_names,
                yticklabels=group_names,
                cmap='YlOrRd',
                annot=True,
                fmt='.2f',
                vmin=0,
                vmax=np.log1p(0.2))
    plt.title('群组场景+表达方式相似度热力图 (对数变换)')
    plt.xticks(rotation=45, ha='right')
    
    plt.tight_layout()
    plt.savefig(SCRIPT_DIR / 'group_similarity_heatmaps.png', dpi=300, bbox_inches='tight')
    plt.close()
    
    # 保存匹配详情到文本文件
    with open(SCRIPT_DIR / 'group_similarity_details.txt', 'w', encoding='utf-8') as f:
        f.write('群组相似度详情\n')
        f.write('=' * 50 + '\n\n')
        
        for i in range(len(group_ids)):
            for j in range(i+1, len(group_ids)):
                if log_combined_matrix[i][j] > np.log1p(0.05):
                    f.write(f'群组1: {group_names[i]}\n')
                    f.write(f'群组2: {group_names[j]}\n')
                    f.write(f'场景相似度: {situation_matrix[i][j]:.4f}\n')
                    f.write(f'表达方式相似度: {style_matrix[i][j]:.4f}\n')
                    f.write(f'组合相似度: {combined_matrix[i][j]:.4f}\n')
                    
                    # 获取两个群组的数据
                    situations1, styles1, _ = load_group_data(group_dirs[i])
                    situations2, styles2, _ = load_group_data(group_dirs[j])
                    
                    # 找出共同的场景
                    common_situations = set(situations1) & set(situations2)
                    if common_situations:
                        f.write('\n共同场景:\n')
                        for situation in common_situations:
                            f.write(f'- {situation}\n')
                    
                    # 找出共同的表达方式
                    common_styles = set(styles1) & set(styles2)
                    if common_styles:
                        f.write('\n共同表达方式:\n')
                        for style in common_styles:
                            f.write(f'- {style}\n')
                    
                    f.write('\n' + '-' * 50 + '\n\n')

if __name__ == "__main__":
    analyze_group_similarity()