'''
基于向量聚类方式的可视化模块
'''
# cluster_insight.py


import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.manifold import TSNE
import plotly.graph_objects as go
import matplotlib.cm as cm
import matplotlib.colors as mcolors
# from wordcloud import WordCloud
import matplotlib.pyplot as plt
import base64
from io import BytesIO
from sentence_transformers import SentenceTransformer, util
import os
import pickle
# cluster_insight.py → 新增函数
from sklearn.feature_extraction.text import TfidfVectorizer
import jieba

# ========== 核心配置（和之前一致） ==========
import plotly.io as pio
pio.kaleido.scope.default_font = "Noto Sans CJK SC"

FONT_FILE_PATH = "./SourceHanSansCN-Light.otf"  # 字体文件在根目录
# CHINESE_FONT = "Source Han Sans CN Light"
CHINESE_FONT = "Noto Sans CJK SC"
    
# CHINESE_FONT = "Noto Sans SC" # 思源黑体（跨平台兼容，Plotly 自带）

def extract_cluster_keywords_auto(sentences, labels, cluster_id, top_n=3):
    """
    自动提取聚类关键词
    :param sentences: 所有句子
    :param labels: 聚类标签
    :param cluster_id: 当前聚类
    :param top_n: 提取前 n 个词
    :return: 关键词字符串
    """
    # 1. 提取该聚类所有句子
    cluster_texts = [sentences[i] for i in range(len(sentences)) if labels[i] == cluster_id]
    if not cluster_texts:
        return "无数据"

    # 2. 分词（保护领域词）
    DOMAIN_SET = {
    # 中文通用领域词（去重 + 合并）
    "空间连接", "字段计算器", "建筑面积", "城市规划", "叠加分析", "空间连接功能",
    "数据表", "建筑层数", "地理处理", "相交功能", "现状地块", "相交叠加",
    "地块属性", "分地块", "容积率统计", "计算方法", "参数设置", "软件设置",
    "核密度分析", "热点分析", "带宽", "密度场", "焦点", "焦点统计",
    "地图代数", "条件分析", "差运算", "最大值", "交通", "像元大小",
    "参数", "凸包", "餐饮", "住宿", "搜索半径", "栅格计算器", "重分类", "Con函数",

    # 英文通用领域词（去重）
    "ArcGIS", "spatial join", "ArcMap", "Map algebra", "Kernel Density",
    "Con", "Getis - Ord Gi*", "NDVI", "Raster Calculator", "dwg", "catalog",
    "spatial join", "data manager", "POI",
    }
    for word in DOMAIN_SET:
        jieba.add_word(word, freq=10000)
        # 1. 基础停用词（通用功能词）
    STOPWORDS = {"的", "了", "在", "是", "是否", "我", "有", "和", "就", "不", "人", "都", "一", "一个", "上", "也", "很", "到", "说", "要", "去", "你", "会", "着", "没有", "看", "好", "自己", "这", "那", "这个", "那个", "什么",
                 "怎么", "哪里", "时候", "然后", "可能", "应该", "可以", "就是", "还是", "但是", "不过", "如果", "因为", "所以", "而且", "或者", "其实", "觉得", "认为", "希望", "能够", "需要", "知道", "表示", "这样", "那样", "这些", "那些", "有点",
                 "一点", "一些", "进一步", "具体", "问题", "疑惑", "讲解", "需求", "难点", "操作", "应用", "场景", "对于", "进行", "实际", "情况", "结合",
                 "学生", "老师", "实验", "报告", "作业", "课程", "课堂", "学习", "理解", "掌握", "明白", "清楚",
                 "建议", "希望", "请问", "想问", "不懂", "不会", "不知道", "不太会", "不太懂", "不太清楚",
                 }
    def tokenize(text):
        words = jieba.lcut(text)
        return [
            w for w in words
            if len(w) > 1 and w not in STOPWORDS and not w.isdigit()
        ]

    tokenized = [" ".join(tokenize(text)) for text in cluster_texts]

    # 3. TF-IDF 提取关键词
    vectorizer = TfidfVectorizer(max_features=100, ngram_range=(1, 2))
    try:
        tfidf_matrix = vectorizer.fit_transform(tokenized)
        feature_names = vectorizer.get_feature_names_out()

        # 取平均 TF-IDF 最高的词
        mean_tfidf = tfidf_matrix.mean(axis=0).A1
        top_indices = mean_tfidf.argsort()[-top_n:][::-1]
        keywords = [feature_names[i] for i in top_indices]
        return " | ".join(keywords)
    except:
        return "关键词提取失败"


# model_path = r'.\sbert\models--shibing624--text2vec-base-chinese\snapshots\183bb99aa7af74355fb58d16edf8c13ae7c5433e'
# 从本地加载模型
model_dir = os.path.join("sbert", "models--shibing624--text2vec-base-chinese", "snapshots", "183bb99aa7af74355fb58d16edf8c13ae7c5433e")
# model = SentenceTransformer(model_dir)
MODEL = SentenceTransformer(model_dir)

def encode_sentences_with_cache(sentences, model): #, cache_path='sentence_vectors.pkl'
    """
    使用 BERT 编码句子，并支持本地缓存
    """
    # if os.path.exists(cache_path):
    #     print(f"已找到缓存文件：{cache_path}，正在加载向量...")
    #     with open(cache_path, 'rb') as f:
    #         sentence_vectors = pickle.load(f)
    # else:
    #     print("未找到缓存，开始编码...")
    sentence_vectors = model.encode(
        sentences,
        batch_size=32,
        show_progress_bar=False,
        convert_to_tensor=False,
        normalize_embeddings=True,       # 直接归一化
        device="cpu"                     # 明确指定 cpu
    )
        # print(f"编码完成，保存到：{cache_path}")
        # with open(cache_path, 'wb') as f:
        #     pickle.dump(sentence_vectors, f)

    return sentence_vectors


def auto_select_k(embeddings, max_k=10):
    """自动选择最佳聚类数（轮廓系数最高）"""
    sil_scores = []
    k_range = range(2, min(max_k + 1, len(embeddings) // 2))
    for k in k_range:
        kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
        labels = kmeans.fit_predict(embeddings)
        sil_scores.append(silhouette_score(embeddings, labels))

    best_k = k_range[np.argmax(sil_scores)]
    print(f"自动选择最佳聚类数: k = {best_k} (轮廓系数: {max(sil_scores):.3f})")
    return best_k


def fig_to_base64(fig):
    # 关键修复：转图片前，强制给 fig 中所有文本设置注册字体（覆盖所有场景）
    fig.update_layout(
        # 全局字体（标题、坐标轴、图例等）
        font=dict(family=CHINESE_FONT, size=18),
        # 标题字体（单独强化，避免被覆盖）
        titlefont=dict(family=CHINESE_FONT, size=22),
        # 坐标轴标签字体（如果有坐标轴，必须显式指定）
        xaxis=dict(titlefont=dict(family=CHINESE_FONT), tickfont=dict(family=CHINESE_FONT)),
        yaxis=dict(titlefont=dict(family=CHINESE_FONT), tickfont=dict(family=CHINESE_FONT)),
        # 图例字体（如果有图例，补充）
        legend=dict(font=dict(family=CHINESE_FONT))
    )
    
    # 处理图表中的所有 trace（比如桑基图节点、散点图文本等）
    for trace in fig.data:
        # 桑基图节点文本
        if hasattr(trace, "node") and hasattr(trace.node, "font"):
            trace.node.font.family = CHINESE_FONT
        # 散点图/折线图的文本标签（比如聚类中心的关键词）
        if hasattr(trace, "textfont"):
            trace.textfont.family = CHINESE_FONT
        # Hover 提示文本（强化，避免遗漏）
        if hasattr(trace, "hoverlabel") and hasattr(trace.hoverlabel, "font"):
            trace.hoverlabel.font.family = CHINESE_FONT
    
    # 原有转 Base64 逻辑不变
    buffer = BytesIO()
    fig.write_image(
        buffer,
        format="png",
        engine="kaleido",
        width=900,
        height=600,
        scale=2,  # 提高清晰度，同时确保字体渲染更稳定
        validate=False
    )
    buffer.seek(0)
    return base64.b64encode(buffer.read()).decode("ascii")

def cluster_and_visualize(
        excel_path: str,
        questions=['s1', 's2', 's3', 's4'],
        max_k=15
    ):
    """
    输入：Excel + 句向量 pkl
    输出：(聚类图 base64, 统计信息 dict)
    """
    # 1. 加载数据
    df = pd.read_excel(excel_path)
    sentences = []
    meta = []
    for idx, row in df.iterrows():
        for q in questions:
            text = str(row[q]).strip()
            if text:
                sentences.append(text)
                meta.append((row['no'], q))
    emb = encode_sentences_with_cache(sentences, MODEL)
    # with open(pkl_path, 'rb') as f:
    #     emb = pickle.load(f)

    # 2. 自动选 k
    n_clusters = auto_select_k(emb, max_k=max_k)
    # n_clusters = 8

    # 3. 聚类
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10).fit(emb)
    labels = kmeans.labels_
    closest = np.argmin(np.linalg.norm(emb - kmeans.cluster_centers_[:, np.newaxis], axis=2), axis=1)

    # 在聚类后，替换人工关键词
    cluster_keywords_auto = []
    for i in range(n_clusters):
        kw = extract_cluster_keywords_auto(sentences, labels, i, top_n=5)
        cluster_keywords_auto.append(kw)

    # 4. 统计
    stats = []
    total = len(sentences)
    for i in range(n_clusters):
        cluster_sents = [s for s, l in zip(sentences, labels) if l == i]
        size = len(cluster_sents)
        rep_sent = sentences[closest[i]]
        stats.append({
            'cluster_id': i,
            'size': size,
            'ratio': size / total,
            'rep_sentence': rep_sent,
            'keyword': cluster_keywords_auto[i] if cluster_keywords_auto else f"聚类 {i}"
        })

    # 5. 可视化
    tsne = TSNE(n_components=2, random_state=42)
    emb_2d = tsne.fit_transform(emb)

    cmap = cm.get_cmap('rainbow', n_clusters)
    cluster_colors = [mcolors.rgb2hex(cmap(i)[:3]) for i in range(n_clusters)]
    point_colors = [cluster_colors[l] for l in labels]

    fig = go.Figure()
    # 数据点
    fig.add_trace(go.Scatter(
        x=emb_2d[:, 0], y=emb_2d[:, 1],
        mode='markers',
        marker=dict(size=10, color=point_colors, opacity=0.7),
        text=[f"聚类 {l}" for l in labels],
        hoverinfo='text',
        hoverlabel=dict(
            font=dict(family="Noto Sans CJK SC", size=16),  # hover 字体适配中文
            bgcolor='white'
        ),
        textfont=dict(family="Noto Sans CJK SC"),
        showlegend=False
    ))
    # 聚类中心
    center_x = emb_2d[closest, 0]
    center_y = emb_2d[closest, 1]
    for i, (x, y) in enumerate(zip(center_x, center_y)):
        keyword = cluster_keywords_auto[i] if cluster_keywords_auto else f"聚类 {i}"
        fig.add_trace(go.Scatter(
            x=[x], y=[y],
            mode='markers+text',
            marker=dict(size=30, color=cluster_colors[i], line=dict(width=2, color='black')),
            text=[keyword],
            textposition="top center",
            textfont=dict(family="Noto Sans CJK SC", size=20, color='black'),
            showlegend=False
        ))

    fig.update_layout(
        title="EGISInsight：学生反馈聚类洞察",
        titlefont=dict(
            family="Noto Sans CJK SC",
            size=22
        ),
        font=dict(family="Noto Sans CJK SC", size=18),
        width=900, height=600,
        plot_bgcolor='#F5F5F5',
        autosize=False,   # 关闭自动缩放
        margin=dict(l=50, r=50, t=80, b=50) # 让 Plotly 内部也居中
    )

    img_bytes = fig.to_image(format="png", width=900, height=600, scale=2,
                            engine='kaleido' )  # 新增这一行！
    b64 = base64.b64encode(img_bytes).decode('utf-8')
    # 新代码：
    # img_bytes = pio.to_image(
    #     fig,
    #     format="png",
    #     width=900,
    #     height=600,
    #     scale=2
    #     )
    # b64 = base64.b64encode(img_bytes).decode('utf-8')
    # b64 = fig_to_base64(fig)
    # print(f"{b64}解析成功！")
    # return b64, stats
    return fig, b64, stats