src/streamlit_app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import folium
from streamlit_folium import st_folium
from folium.plugins import HeatMap
import requests
from openai import OpenAI
import json
import ast
from io import BytesIO
import random
from streamlit_option_menu import option_menu

# ---------------------------------------------------------
# 1. 页面配置与 CSS 样式 (云墨·太白风格)
# ---------------------------------------------------------
st.set_page_config(
    page_title="云墨·太白 | 李白情感GIS与RAG系统",
    page_icon="🍶",
    layout="wide",
    initial_sidebar_state="expanded"
)

# 自定义 CSS 注入 - 水墨风格
def local_css():
    st.markdown("""
    <style>
    /* 全局字体设置：优先使用楷体/宋体 */
    html, body, [class*="css"] {
        font-family: "KaiTi", "STKaiti", "SimSun", "Times New Roman", serif;
    }
    
    /* 背景图片：水墨山水风格 */
    .stApp {
        background-image: linear-gradient(rgba(255,255,255,0.9), rgba(255,255,255,0.9)), 
                          url("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/background.jpg");
        background-size: cover;
        background-attachment: fixed;
        background-position: center;
        background-repeat: no-repeat;
    }

    /* 遮罩层：让文字在背景上更清晰 */
    .main .block-container {
        background-color: rgba(255, 255, 255, 0.01);
        padding: 2rem;
        border-radius: 15px;
        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
        margin-top: 1rem;
        border: 1px solid #e0e0e0;
    }

    /* 标题样式 */
    h1, h2, h3 {
        color: #2c3e50;
        text-align: center;
        font-weight: bold;
        margin-bottom: 1rem;
    }
    
    h1 {
        text-shadow: 2px 2px 4px #cccccc;
        font-size: 3rem !important;
        background: linear-gradient(45deg, #2c3e50, #34495e);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
    }

    /* 侧边栏美化 */
    section[data-testid="stSidebar"] {
        background-color: rgba(240, 242, 246, 0.95);
        border-right: 2px solid #b8c6db;
        backdrop-filter: blur(5px);
    }

    /* 按钮样式 */
    .stButton>button {
        background: linear-gradient(45deg, #2c3e50, #34495e);
        color: white;
        border-radius: 25px;
        border: none;
        padding: 12px 28px;
        transition: all 0.3s ease;
        font-weight: bold;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }
    .stButton>button:hover {
        background: linear-gradient(45deg, #34495e, #2c3e50);
        transform: translateY(-2px);
        box-shadow: 0 6px 12px rgba(0,0,0,0.2);
    }
    
    /* 诗词卡片样式 */
    .poem-card {
        background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
        border-left: 5px solid #2c3e50;
        padding: 20px;
        margin: 15px 0;
        font-size: 1.2rem;
        font-style: italic;
        color: #444;
        border-radius: 0 10px 10px 0;
        box-shadow: 0 2px 8px rgba(0,0,0,0.1);
    }
    
    /* 数据框样式 */
    .stDataFrame {
        border-radius: 10px;
        overflow: hidden;
    }
    
    /* 选择框样式 */
    .stSelectbox, .stTextInput {
        border-radius: 8px;
    }
    
    /* 聊天消息样式 */
    .stChatMessage {
        border-radius: 15px;
        margin: 10px 0;
    }
    
    /* 进度条样式 */
    .stSlider > div > div > div {
        background: linear-gradient(45deg, #2c3e50, #34495e);
    }
    </style>
    """, unsafe_allow_html=True)

local_css()

# ---------------------------------------------------------
# 2. 数据准备与功能函数
# ---------------------------------------------------------

# 初始化OpenAI客户端
client = OpenAI(
    api_key="sk-72997944466a4af2bcd52a068895f8cf",
    base_url="https://api.deepseek.com"
)

# 全局变量与配置 - 更新数据源
AI_DATA_URL = "https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/libai_location.xlsx"
EMOTION_DATA_URL = "https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/%E6%9D%8E%E7%99%BD%E8%AF%97%E6%AD%8C%E6%95%B0%E6%8D%AE%E6%95%B4%E7%90%86%20%E5%B9%B4%E4%BB%BD%2B%E5%9C%B0%E7%82%B9%2B%E7%AE%80%E4%BD%93%2B%E7%BB%8F%E7%BA%AC%E5%BA%A6%2B%E6%83%85%E6%84%9F%EF%BC%88%E7%AE%80%E4%BD%93%2B%E7%B9%81%E4%BD%93%E6%A0%87%E9%A2%98%2B%E7%B9%81%E4%BD%93%E6%AD%A3%E6%96%87%EF%BC%89.xlsx"

location_col = '地点（古称/今称）'
summary_col = '诗作/事件摘要'

LOCATION_COORDS = {
    "碎叶城": {"lat": 42.8447, "lon": 75.1648, "match_keys": ["碎叶城"]},
    "峨眉山": {"lat": 29.5807, "lon": 103.3592, "match_keys": ["峨眉山"]},
    "蜀中": {"lat": 31.7828, "lon": 104.7570, "match_keys": ["蜀中", "江油"]},
    "荆门/南津关": {"lat": 30.5667, "lon": 111.4500, "match_keys": ["荆门", "南津关"]},
    "岳阳楼": {"lat": 29.3879, "lon": 113.1092, "match_keys": ["岳阳楼", "岳阳"]},
    "安陆": {"lat": 31.3653, "lon": 113.7077, "match_keys": ["安陆"]},
    "黄鹤楼": {"lat": 30.5484, "lon": 114.3168, "match_keys": ["黄鹤楼", "武汉"]},
    "金陵（凤凰台）": {"lat": 32.0415, "lon": 118.7781, "match_keys": ["金陵", "凤凰台", "南京"]},
    "庐山": {"lat": 29.5910, "lon": 115.9922, "match_keys": ["庐山", "九江"]},
    "天姥山": {"lat": 29.5000, "lon": 120.8900, "match_keys": ["天姥山"]},
    "金陵/长干里": {"lat": 32.0298, "lon": 118.7900, "match_keys": ["长干里"]},
    "长安": {"lat": 34.2652, "lon": 108.9500, "match_keys": ["长安", "西安"]},
    "长安/宫廷": {"lat": 34.2652, "lon": 108.9500, "match_keys": ["宫廷"]},
    "长安/洛阳": {"lat": 34.6859, "lon": 112.4600, "match_keys": ["洛阳"]},
    "桃花潭": {"lat": 30.4079, "lon": 118.4230, "match_keys": ["桃花潭", "泾县"]},
    "敬亭山": {"lat": 30.9822, "lon": 118.7844, "match_keys": ["敬亭山", "宣城"]},
    "天门山": {"lat": 31.4285, "lon": 118.3970, "match_keys": ["天门山", "芜湖"]},
    "扬州/旅店": {"lat": 32.3934, "lon": 119.4290, "match_keys": ["扬州"]},
    "夜郎": {"lat": 27.6888, "lon": 106.3773, "match_keys": ["夜郎", "桐梓"]},
    "白帝城": {"lat": 31.0450, "lon": 109.5780, "match_keys": ["白帝城", "奉节"]},
    "秋浦": {"lat": 30.6500, "lon": 117.4800, "match_keys": ["秋浦", "池州"]},
    "当涂": {"lat": 31.5453, "lon": 118.4870, "match_keys": ["当涂", "马鞍山"]},
}

# 数据加载函数 - 增强错误处理
@st.cache_data(ttl=3600, show_spinner="正在从 GitHub 下载AI对话数据...")
def load_ai_data():
    try:
        response = requests.get(AI_DATA_URL, timeout=30)
        response.raise_for_status()
        df = pd.read_excel(BytesIO(response.content), sheet_name=0)
        df.columns = df.columns.str.strip()
        
        # 检查必要列是否存在
        if location_col not in df.columns:
            st.error(f"❌ 数据中缺少必要的列: {location_col}")
            return pd.DataFrame()
            
    except Exception as e:
        st.error(f"❌ AI对话数据下载失败: {str(e)}")
        st.info("💡 提示: 请检查网络连接或数据文件是否可访问")
        return pd.DataFrame()
    
    df['coords_key'] = '未知'
    df['Latitude'] = 34.0478
    df['Longitude'] = 108.4357
    
    for idx, row in df.iterrows():
        location_str = str(row.get(location_col, '')).strip()
        for key, data in LOCATION_COORDS.items():
            if location_str == key or any(k in location_str for k in data.get('match_keys', [])):
                df.at[idx, 'coords_key'] = key
                df.at[idx, 'Latitude'] = data['lat']
                df.at[idx, 'Longitude'] = data['lon']
                break
    return df

@st.cache_data(ttl=3600, show_spinner="正在从 GitHub 下载情感数据...")
def load_emotion_data_from_github():
    try:
        response = requests.get(EMOTION_DATA_URL, timeout=30)
        response.raise_for_status()
        df = pd.read_excel(BytesIO(response.content))
        df.columns = df.columns.astype(str).str.strip()
        
        def get_first_matching_col(keywords):
            for col in df.columns:
                if any(k in col.lower() for k in keywords): return col
            return None

        rename_map = {}
        if c := get_first_matching_col(['经', 'lon', 'longitude']): rename_map[c] = 'Longitude'
        if c := get_first_matching_col(['纬', 'lat', 'latitude']): rename_map[c] = 'Latitude'
        if c := get_first_matching_col(['year', '年', 'time']): rename_map[c] = 'Year'
        if c := get_first_matching_col(['诗名', 'title', '题', '标题', 'name', '诗歌']): rename_map[c] = 'Title'
        if c := get_first_matching_col(['地点', 'location', 'place', 'city']): rename_map[c] = 'Location'

        emo_col = get_first_matching_col(['emotion_top3', 'top3']) or get_first_matching_col(['emotion', '情', 'sentiment'])
        if emo_col: rename_map[emo_col] = 'Emotion_Raw'

        df = df.rename(columns=rename_map)
        
        required_cols = ['Title', 'Location', 'Emotion', 'Year', 'Latitude', 'Longitude']
        for col in required_cols:
            if col not in df.columns:
                df[col] = '未知'

        def extract_primary_emotion(val):
            try:
                if isinstance(val, str):
                    parsed = ast.literal_eval(val)
                    if isinstance(parsed, list) and len(parsed) > 0: return parsed[0][0]
                elif isinstance(val, list) and len(val) > 0: return val[0][0]
            except: pass
            return str(val).split(' ')[0] if val else "未知"

        if 'Emotion_Raw' in df.columns:
            df['Emotion'] = df['Emotion_Raw'].apply(extract_primary_emotion)
        
        df = df.dropna(subset=['Latitude', 'Longitude'])
        return df

    except Exception as e:
        st.error(f"❌ 情感数据下载失败: {str(e)}")
        return pd.DataFrame()

# 模拟数据函数（用于保持原有UI）
@st.cache_data
def get_travel_data():
    data = {
        '地点': ['长安', '成都', '洛阳', '金陵 (南京)', '扬州', '庐山', '宣城'],
        'lat': [34.3416, 30.5728, 34.6197, 32.0603, 32.3945, 29.5643, 30.9407],
        'lon': [108.9398, 104.0668, 112.4540, 118.7969, 119.4122, 115.9881, 118.7587],
        '诗作数': [50, 20, 35, 45, 30, 15, 25],
        '代表作': ['长相思', '蜀道难', '春夜洛城闻笛', '登金陵凤凰台', '黄鹤楼送孟浩然之广陵', '望庐山瀑布', '独坐敬亭山']
    }
    return pd.DataFrame(data)

@st.cache_data
def get_emotion_data():
    return pd.DataFrame({
        '意象': ['月亮', '酒', '剑', '水', '山', '花', '孤', '梦'],
        '频率': [120, 95, 40, 85, 110, 60, 55, 30],
        '情感色彩': ['思乡/孤独', '豪迈/解忧', '侠客/抱负', '流逝/愁苦', '归隐/壮阔', '美好/易逝', '寂寞', '虚幻']
    })

# RAG Chatbot 函数
@st.cache_data(ttl=3600)
def get_cbdb_data(name="李白"):
    try:
        url = f"https://cbdb.fas.harvard.edu/cbdbapi/person.php?name={name}&o=json"
        r = requests.get(url, headers={"User-Agent": "Streamlit App"}, timeout=5)
        return r.json() if r.status_code == 200 else None
    except: return None

def generate_poem_analysis(year, location, emotion, title, cbdb_data):
    cbdb_text = json.dumps(cbdb_data, ensure_ascii=False)[:1000] if cbdb_data else "无"
    
    system_prompt = (
        "你是一位精通唐代文学与李白生平的专家AI。\n"
        f"参考史料：{cbdb_text}\n"
        "任务：用户将提供李白的一首诗及其背景（年份、地点、情感标签）。\n"
        "请按以下格式输出（使用Markdown）：\n"
        "### 📜 全诗呈现\n"
        "（请默写全诗，若不确定则注明）\n\n"
        "### 🎭 情感深度解析\n"
        "（结合标签分析诗句如何体现该情感）\n\n"
        "### 🌍 时空与历史背景\n"
        f"（简述李白在{year}年于{location}的人生境遇）\n"
    )
    
    user_prompt = f"请分析李白在 {year} 年，于 {location} 创作的《{title}》。该诗被标记为【{emotion}】情感。"
    
    try:
        response = client.chat.completions.create(
            model="deepseek-chat",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            temperature=0.7
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"AI 分析服务暂时不可用: {str(e)}"

def run_main_chatbot(cbdb_data, prompt):
    if not prompt: return "请输入有效的问题"
    cbdb_text = json.dumps(cbdb_data, ensure_ascii=False)[:3000] if cbdb_data else "无CBDB资料"
    system_prompt = f"你是李白研究专家。史料参考：{cbdb_text}"
    try:
        messages = [{"role": "system", "content": system_prompt}]
        messages.extend([msg for msg in st.session_state.chat_history[-5:] if msg.get("role") in ["user", "assistant"]])
        messages.append({"role": "user", "content": prompt})
        response = client.chat.completions.create(model="deepseek-chat", messages=messages, temperature=0.7)
        answer = response.choices[0].message.content.strip()
        
        highlight_key = None
        if not st.session_state.ai_data_df.empty:
            for key in st.session_state.ai_data_df['coords_key'].unique():
                if key != '未知' and key in answer:
                    highlight_key = key
                    break
        st.session_state.highlight_location_key = highlight_key
        return answer
    except Exception as e:
        return f"Chatbot错误：{str(e)}"

# 地图绘制函数 - 中文版 (用于AI对话页面)
def create_main_map(df, highlight_key):
    if df.empty: 
        return folium.Map(location=[34.0, 108.0], zoom_start=4)
    
    try:
        center_lat = df['Latitude'].mean()
        center_lon = df['Longitude'].mean()
    except: 
        center_lat, center_lon = 34.0, 108.0
    
    # 使用标准地图底图
    m = folium.Map(
        location=[center_lat, center_lon], 
        zoom_start=4.5
    )
    
    points = df[['Latitude', 'Longitude']].dropna().values.tolist()
    if len(points) > 1: 
        folium.PolyLine(points, color="#00AEEF", weight=3, opacity=0.5).add_to(m)

    for idx, row in df.iterrows():
        try:
            if pd.isna(row['Latitude']): continue
            is_highlighted = (row['coords_key'] == highlight_key)
            color = 'orange' if is_highlighted else 'blue'
            icon = 'fire' if is_highlighted else 'user'
            
            # 创建详细的信息弹窗
            popup_html = f"""
            <div style="width: 250px;">
                <h4 style="color: #2c3e50; margin-bottom: 10px;">{row.get(location_col, '未知地点')}</h4>
                <p><strong>📅 时间:</strong> {row.get('时间', '未知')}</p>
                <p><strong>📖 诗作/事件:</strong> {row.get(summary_col, '未知')}</p>
                <p><strong>📍 坐标:</strong> {row['Latitude']:.4f}, {row['Longitude']:.4f}</p>
            </div>
            """
            folium.Marker(
                [row['Latitude'], row['Longitude']], 
                popup=folium.Popup(popup_html, max_width=300), 
                icon=folium.Icon(color=color, icon=icon, prefix='fa'),
                tooltip=row.get(location_col, '未知地点')
            ).add_to(m)
        except: continue
    return m

# 新增：时序地图函数 - 改进版 (用于足迹漫游页面)
def create_temporal_map(df, selected_year):
    if df.empty: 
        return folium.Map(location=[34.0, 108.0], zoom_start=4)
    
    try:
        center_lat = df['Latitude'].mean()
        center_lon = df['Longitude'].mean()
    except: 
        center_lat, center_lon = 34.0, 108.0
        
    # 使用标准地图底图
    m = folium.Map(
        location=[center_lat, center_lon], 
        zoom_start=4.5
    )
    
    # 过滤出选定年份及之前的所有数据点
    filtered_df = df[df['Year'] <= selected_year]
    
    # 按地点和年份分组，显示同年同地的所有诗名
    grouped_data = filtered_df.groupby(['Latitude', 'Longitude', 'Location', 'Year']).agg({
        'Title': lambda x: list(x.unique())
    }).reset_index()
    
    # 添加轨迹线（按时间顺序）
    if len(filtered_df) > 1:
        points = filtered_df.sort_values('Year')[['Latitude', 'Longitude']].values.tolist()
        folium.PolyLine(points, color="#00AEEF", weight=3, opacity=0.5, popup=f"截至 {selected_year} 年的游历路线").add_to(m)
    
    # 为每个地点添加标记
    for idx, row in grouped_data.iterrows():
        try:
            if pd.isna(row['Latitude']): continue
            
            # 根据年份设置颜色渐变（越晚越红）
            year_norm = (row['Year'] - df['Year'].min()) / (df['Year'].max() - df['Year'].min()) if df['Year'].max() != df['Year'].min() else 0.5
            red = int(255 * year_norm)
            blue = int(255 * (1 - year_norm))
            color = f'#{red:02x}00{blue:02x}'
            
            # 显示同年同地的所有诗名
            poem_list = "<br>".join([f"• {poem}" for poem in row['Title'][:5]])  # 最多显示5首诗
            if len(row['Title']) > 5:
                poem_list += f"<br>• ...等 {len(row['Title'])} 首诗"
            
            popup_html = f"""
            <div style="width: 280px;">
                <h4 style="color: #2c3e50; margin-bottom: 10px;">{row.get('Location', '未知地点')}</h4>
                <p><strong>📅 年份:</strong> {int(row['Year'])}</p>
                <p><strong>📖 同年诗作:</strong></p>
                <div style="max-height: 150px; overflow-y: auto;">
                    {poem_list}
                </div>
            </div>
            """
            
            folium.CircleMarker(
                location=[row['Latitude'], row['Longitude']],
                radius=8,
                popup=folium.Popup(popup_html, max_width=300),
                color=color,
                fill=True,
                fillColor=color,
                fillOpacity=0.7,
                weight=2,
                tooltip=f"{row.get('Location', '未知地点')} ({int(row['Year'])})"
            ).add_to(m)
            
            # 添加文字标注
            folium.Marker(
                location=[row['Latitude'] + 0.1, row['Longitude'] + 0.1],
                icon=folium.DivIcon(
                    html=f'<div style="font-size: 12px; color: {color}; font-weight: bold;">{int(row["Year"])}</div>'
                )
            ).add_to(m)
            
        except Exception as e:
            continue
    
    # 添加当前年份的标题
    title_html = f'''
                 <h3 align="center" style="font-size:20px"><b>李白足迹时序图 (截至 {selected_year} 年)</b></h3>
                 '''
    m.get_root().html.add_child(folium.Element(title_html))
    
    return m

def create_emotion_heatmap(df, period_name):
    if df.empty: 
        return folium.Map(location=[34.0, 108.0], zoom_start=4)
    
    try:
        center_lat = df['Latitude'].mean()
        center_lon = df['Longitude'].mean()
    except: center_lat, center_lon = 34.0, 108.0
    
    # 使用标准地图底图
    m = folium.Map(
        location=[center_lat, center_lon], 
        zoom_start=5
    )
    
    heatmap_gradients = {
        "豪放与激昂": {0.2: 'orange', 0.6: 'red', 1.0: 'darkred'},
        "喜悦与欢快": {0.2: 'yellow', 0.6: 'orange', 1.0: '#d35400'},
        "哀怨与悲伤": {0.2: 'cyan', 0.6: 'blue', 1.0: 'navy'},
        "忧愁与苦闷": {0.2: 'lightblue', 0.6: 'royalblue', 1.0: '#1a5276'},
        "孤独与寂寞": {0.2: 'plum', 0.6: 'purple', 1.0: '#4a235a'},
        "思乡与怀古": {0.2: '#d7bde2', 0.6: '#8e44ad', 1.0: '#5b2c6f'},
        "友情与知己": {0.2: 'lightgreen', 0.6: 'green', 1.0: 'darkgreen'},
        "闲适与隐逸": {0.2: '#a3e4d7', 0.6: '#16a085', 1.0: '#0e6251'},
        "未知": {0.4: 'gray', 0.8: 'white', 1.0: 'white'}
    }
    marker_colors = {
        "豪放与激昂": "#e74c3c", "喜悦与欢快": "#e67e22", "哀怨与悲伤": "#3498db",
        "忧愁与苦闷": "#2980b9", "孤独与寂寞": "#9b59b6", "思乡与怀古": "#8e44ad",
        "友情与知己": "#2ecc71", "闲适与隐逸": "#1abc9c", "未知": "#95a5a6"
    }

    unique_emotions = df['Emotion'].fillna("未知").unique()

    for emotion in unique_emotions:
        fg = folium.FeatureGroup(name=str(emotion))
        subset = df[df['Emotion'] == emotion]
        if subset.empty: continue
        
        heat_data = [[row['Latitude'], row['Longitude'], 1] for _, row in subset.iterrows()]
        HeatMap(heat_data, radius=20, blur=15, min_opacity=0.4, gradient=heatmap_gradients.get(emotion, None), name=f"{emotion} (热力)").add_to(fg)
        
        marker_color = marker_colors.get(emotion, "#ecf0f1")
        for _, row in subset.iterrows():
            folium.CircleMarker(
                location=[row['Latitude'], row['Longitude']], radius=3, color=marker_color,
                fill=True, fill_color=marker_color, fill_opacity=0.8, weight=0,
                popup=folium.Popup(f"<b>{row.get('Title', '无题')}</b><br>{emotion}", max_width=200),
                tooltip=f"{row.get('Title', '无题')}"
            ).add_to(fg)
        fg.add_to(m)

    folium.LayerControl(collapsed=False).add_to(m)
    return m

# AI分析卡片组件
def render_ai_analysis_card(df, period_name):
    st.markdown("---")
    st.subheader("🤖 智能诗歌检索与情感解析")
    st.caption("选择下方的年份、地点与情感，AI 将为您深度解读李白的心境。")
    
    with st.container():
        col1, col2, col3, col4 = st.columns([1, 1, 1, 1])
        
        available_years = sorted(df['Year'].dropna().unique())
        with col1:
            selected_year = st.selectbox("1️⃣ 选择年份", available_years, key=f"year_{period_name}")
        
        year_subset = df[df['Year'] == selected_year]
        available_locs = sorted(year_subset['Location'].dropna().unique())
        with col2:
            selected_loc = st.selectbox("2️⃣ 选择地点", available_locs, key=f"loc_{period_name}")
            
        loc_subset = year_subset[year_subset['Location'] == selected_loc]
        available_emotions = sorted(loc_subset['Emotion'].dropna().unique())
        with col3:
            selected_emotion = st.selectbox("3️⃣ 选择情感", available_emotions, key=f"emo_{period_name}")
            
        final_subset = loc_subset[loc_subset['Emotion'] == selected_emotion]
        available_titles = sorted(final_subset['Title'].astype(str).unique().tolist())
        
        with col4:
            if not available_titles:
                st.warning("该组合下暂无数据")
                selected_title = None
            else:
                selected_title = st.selectbox("4️⃣ 选择诗歌", available_titles, key=f"title_{period_name}")
        
        if st.button("✨ 生成 AI 深度解析", key=f"btn_{period_name}", use_container_width=True):
            if selected_title and selected_title != 'nan' and selected_title != '未知':
                with st.spinner(f"DeepSeek 正在阅读《{selected_title}》并分析历史背景..."):
                    cbdb_data = get_cbdb_data("李白")
                    analysis = generate_poem_analysis(selected_year, selected_loc, selected_emotion, selected_title, cbdb_data)
                    
                    st.markdown("---")
                    st.success("✅ 分析完成")
                    with st.container():
                        st.markdown(analysis)
            else:
                st.error("请先选择一首有效的诗歌。")

# ---------------------------------------------------------
# 3. 侧边栏导航 (云墨风格) - 调整顺序，太白生平在首页
# ---------------------------------------------------------
with st.sidebar:
    st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/libai.jpg", 
             width=150, caption="诗仙·李白")
    
    selected = option_menu(
        "导航", 
        ["太白生平", "AI对话", "情感图谱", "足迹漫游", "与仙对饮"],  # 太白生平在首页
        icons=['book', 'robot', 'bar-chart', 'map', 'chat-quote'],  # 对应调整图标顺序
        menu_icon="cast", default_index=0,  # 默认选择太白生平
        styles={
            "container": {"padding": "0!important", "background-color": "transparent"},
            "icon": {"color": "#2c3e50", "font-size": "18px"}, 
            "nav-link": {"font-size": "16px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"},
            "nav-link-selected": {"background-color": "#2c3e50"},
        }
    )
    
    st.markdown("---")
    st.markdown("### 🎵 听琴")
    st.audio("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/chunjianghuayueye.mp3")
    st.caption("古琴曲：春江花月夜")

# ---------------------------------------------------------
# 4. 主页面逻辑
# ---------------------------------------------------------

# 初始化session state
if "chat_history" not in st.session_state: 
    st.session_state.chat_history = []
if "highlight_location_key" not in st.session_state: 
    st.session_state.highlight_location_key = None
if "ai_data_df" not in st.session_state: 
    st.session_state.ai_data_df = load_ai_data()
if "selected_year" not in st.session_state:
    st.session_state.selected_year = 701  # 李白出生年份

# --- 页面 1: 太白生平 (首页) ---
if selected == "太白生平":
    st.title("☁️ 谪仙人：李白")
    st.markdown("**\"绣口一吐，就半个盛唐。\"**")
    
    col1, col2 = st.columns([1, 2])
    
    with col1:
        st.info("字：太白")
        st.info("号：青莲居士")
        st.info("朝代：唐朝")
        st.info("评价：诗仙")
        
    with col2:
        st.markdown("""
        <div class="poem-card">
        君不见，黄河之水天上来，奔流到海不复回。<br>
        君不见，高堂明镜悲白发，朝如青丝暮成雪。<br>
        人生得意须尽欢，莫使金樽空对月。
        </div>
        """, unsafe_allow_html=True)
        st.write("李白（701年－762年），字太白，号青莲居士，又号\"谪仙人\"。他是唐代伟大的浪漫主义诗人，被后人誉为\"诗仙\"。其诗以七言古诗和绝句成就最高，风格豪迈奔放，清新飘逸，想象丰富，意境奇妙，语言奇采，浪漫主义色彩浓厚。")

# --- 页面 2: AI对话 (RAG Chatbot) ---
elif selected == "AI对话":
    st.title("🤖 AI太白对话")
    st.write("与基于李白知识库的AI智能助手对话，探索诗仙的内心世界。")
    
    cbdb_data = get_cbdb_data("李白")
    
    # 使用李白人生重要节点数据
    df_main = st.session_state.ai_data_df
    
    col1, col2 = st.columns([1, 1.5], gap="large")
    
    with col1:
        st.subheader("💬 与AI太白对话")
        if not cbdb_data: 
            st.warning("CBDB 连接失败，使用通用知识库。")
        
        # 显示聊天历史
        for msg in st.session_state.chat_history:
            with st.chat_message(msg["role"]):
                st.markdown(msg["content"])
        
        # 聊天输入
        if prompt := st.chat_input("请输入问题（例如：李白在安陆有哪些经历？）"):
            st.session_state.chat_history.append({"role": "user", "content": prompt})
            with st.chat_message("user"):
                st.markdown(prompt)
            
            with st.chat_message("assistant"):
                with st.spinner("AI正在思考..."):
                    answer = run_main_chatbot(cbdb_data, prompt)
                    st.markdown(answer)
                    st.session_state.chat_history.append({"role": "assistant", "content": answer})
                    if st.session_state.highlight_location_key: 
                        st.success(f"🗺️ 地图已高亮：{st.session_state.highlight_location_key}")
    
    with col2:
        st.subheader("🗺️ 实时足迹高亮")
        
        if not df_main.empty:
            # 创建地图
            current_map = create_main_map(df_main, st.session_state.highlight_location_key)
            st_folium(current_map, width=700, height=600)
            
            # 显示高亮节点信息
            if st.session_state.highlight_location_key:
                highlight_df = df_main[df_main['coords_key'] == st.session_state.highlight_location_key]
                if not highlight_df.empty:
                    with st.expander(f"📋 {st.session_state.highlight_location_key} 详情"):
                        for idx, row in highlight_df.iterrows():
                            st.markdown(f"""
                            **📍 {row.get(location_col, '未知地点')}**
                            - 📅 时间: {row.get('时间', '未知')}
                            - 📖 诗作/事件: {row.get(summary_col, '未知')}
                            """)
        else:
            st.error("❌ AI对话数据加载失败，无法显示地图")
            st.info("💡 可能的原因：")
            st.info("• 网络连接问题")
            st.info("• GitHub 数据文件暂时不可访问")
            st.info("• 数据文件格式发生变化")
            
            if st.button("🔄 重新加载数据"):
                st.session_state.ai_data_df = load_ai_data()
                st.rerun()

# --- 页面 3: 情感图谱 (增强版可视化) ---
elif selected == "情感图谱":
    st.title("📊 诗中的情感密码")
    
    # 时期选择
    period = st.radio("选择时期:", ["🌱 青年期 (<742年)", "🔥 中年期 (742-755年)", "🍂 晚年期 (>755年)"], horizontal=True)
    
    df_emotion_full = load_emotion_data_from_github()
    
    if not df_emotion_full.empty:
        df_emotion_full['Year'] = pd.to_numeric(df_emotion_full['Year'], errors='coerce')
        df_emotion_full = df_emotion_full.dropna(subset=['Year'])
        
        if "青年期" in period:
            filtered_df = df_emotion_full[df_emotion_full['Year'] < 742]
            period_key = "youth"
        elif "中年期" in period:
            filtered_df = df_emotion_full[(df_emotion_full['Year'] >= 742) & (df_emotion_full['Year'] <= 755)]
            period_key = "middle"
        else:
            filtered_df = df_emotion_full[df_emotion_full['Year'] > 755]
            period_key = "old"
        
        st.info(f"共检索到 {len(filtered_df)} 首相关诗作。")
        
        # 情感热力图
        emotion_map = create_emotion_heatmap(filtered_df, period_key)
        st_folium(emotion_map, width="100%", height=500)
        
        # 传统图表
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("高频意象统计")
            df_simple_emotion = get_emotion_data()
            fig_pie = px.pie(
                df_simple_emotion, 
                values='频率', 
                names='意象', 
                title='李白最爱用的词',
                color_discrete_sequence=px.colors.sequential.Teal,
                hole=0.4
            )
            fig_pie.update_layout(paper_bgcolor='rgba(0,0,0,0)')
            st.plotly_chart(fig_pie, use_container_width=True)
            
        with col2:
            st.subheader("意象背后的情感")
            fig_bar = px.bar(
                df_simple_emotion, 
                x='意象', 
                y='频率', 
                color='频率', 
                text='情感色彩',
                title='意象与情感关联',
                color_continuous_scale='Blues'
            )
            fig_bar.update_traces(textposition='outside')
            fig_bar.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
            st.plotly_chart(fig_bar, use_container_width=True)
        
        # AI分析卡片
        render_ai_analysis_card(filtered_df, period_key)
        
        st.markdown("### 情感解读")
        st.markdown("""
        > **月亮** 是李白诗中最孤独的伴侣，出现了 120 次以上。它代表了乡愁与超越世俗的渴望。
        >
        > **酒** 则是他通向自由的钥匙，"百年三万六千日，一日须倾三百杯"。
        """)
    else:
        st.error("❌ 情感数据加载失败")

# --- 页面 4: 足迹漫游 (时序地图可视化) ---
elif selected == "足迹漫游":
    st.title("🗺️ 仗剑走天涯")
    st.write("李白一生足迹遍布半个中国，从西域碎叶城到长安，从黄河到长江。")
    
    # 使用情感数据用于时序地图
    df_emotion = load_emotion_data_from_github()
    
    if not df_emotion.empty:
        # 处理年份数据
        df_emotion['Year'] = pd.to_numeric(df_emotion['Year'], errors='coerce')
        df_emotion = df_emotion.dropna(subset=['Year'])
        
        # 双地图展示
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("📊 诗作分布热力图")
            df_travel = get_travel_data()
            fig = px.scatter_geo(
                df_travel,
                lat='lat',
                lon='lon',
                size='诗作数',
                hover_name='地点',
                hover_data=['代表作'],
                scope='asia',
                center=dict(lat=33, lon=110),
                projection="natural earth",
                color='诗作数',
                color_continuous_scale='Tealgrn',
                template='plotly_white',
                title="李白游历热力图"
            )
            fig.update_layout(
                geo=dict(
                    showland=True, landcolor="rgb(240, 240, 240)",
                    showcountries=True, countrycolor="rgb(200, 200, 200)",
                    fitbounds="locations"
                ),
                margin={"r":0,"t":40,"l":0,"b":0},
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)'
            )
            st.plotly_chart(fig, use_container_width=True)
        
        with col2:
            st.subheader("🗺️ 时序GIS地图")
            
            # 年份选择滑块
            min_year = int(df_emotion['Year'].min())
            max_year = int(df_emotion['Year'].max())
            
            selected_year = st.slider(
                "选择年份进度",
                min_value=min_year,
                max_value=max_year,
                value=st.session_state.selected_year,
                key="year_slider_travel"
            )
            
            st.session_state.selected_year = selected_year
            
            # 显示当前年份的诗作数量
            current_count = len(df_emotion[df_emotion['Year'] <= selected_year])
            total_count = len(df_emotion)
            st.info(f"**{selected_year}年** - 已创作 {current_count} 首诗作 (总计 {total_count} 首)")
            
            # 创建时序地图
            temporal_map = create_temporal_map(df_emotion, selected_year)
            st_folium(temporal_map, width=600, height=500)
    
    # 数据详情 - 使用AI数据源
    with st.expander("📋 查看详细游历数据"):
        if not st.session_state.ai_data_df.empty:
            st.dataframe(st.session_state.ai_data_df, use_container_width=True)
        else:
            st.error("❌ 数据加载失败")

# --- 页面 5: 与仙对饮 (飞花令互动) ---
elif selected == "与仙对饮":
    st.title("🍶 飞花令·互动")
    st.write("告诉李白你现在的心情，他会回赠你一句诗。")
    
    mood = st.selectbox("你现在的心情如何？", ["豪情万丈", "思念故乡", "怀才不遇", "享受自然", "感叹时光"])
    
    if st.button("向太白敬酒", use_container_width=True):
        st.toast("举杯邀明月，对影成三人...", icon="🥂")
        
        st.markdown("---")
        st.markdown("### 李白的回应：")
        
        if mood == "豪情万丈":
            st.success("飞流直下三千尺，疑是银河落九天！")
            st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/飞流直下.jpg", caption="豪情万丈")
        elif mood == "思念故乡":
            st.info("举头望明月，低头思故乡。")
            st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/明月思想.jpg", caption="明月寄相思")
        elif mood == "怀才不遇":
            st.warning("天生我材必有用，千金散尽还复来。")
            st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/怀才不遇.jpg", caption="怀才不遇")
        elif mood == "享受自然":
            st.success("两岸猿声啼不住，轻舟已过万重山。")
            st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/轻舟已过.jpg", caption="轻舟万重山")
        elif mood == "感叹时光":
            st.error("弃我去者，昨日之日不可留；乱我心者，今日之日多烦忧。")
            st.image("https://raw.githubusercontent.com/seblee424/libai_emotin_data/main/感叹时光.jpg", caption = "感叹时光")
            
    st.markdown("---")
    st.caption("输入框：写下你想对李白说的话")
    user_input = st.text_area("", placeholder="太白兄，我想对你说...")
    if user_input:
        st.write(f"李白收到了你的信：*{user_input}*")

# ---------------------------------------------------------
# 页脚
# ---------------------------------------------------------
st.markdown("---")
st.markdown("""
<div style="text-align: center; color: grey;">
    Designed for Li Bai Emotion Data Project | Created with Streamlit <br>
    UI Design Style: Ink & Cloud (云墨) | 融合GIS与RAG技术
</div>
""", unsafe_allow_html=True)