Update app.py

app.py

@@ -1,120 +1,131 @@
-# HUGING_FACE_V3.1.3_FIXED.py
+# HUGING_FACE_V2.1.3.py (整合 Bert_predict 版本)
 
-# 系統與資料處理套件
+# 系統套件
 import os
 from datetime import datetime, timedelta
+import google.generativeai as genai # <--- 【新增】引用 Gemini
 import pandas as pd
 import numpy as np
 import yfinance as yf
-import requests
-import uvicorn
-from contextlib import asynccontextmanager # 【新增】用於 FastAPI lifespan
-
-# Gemini AI 套件
-import google.generativeai as genai
-
-# Dash (Web UI) 套件
-from dash import Dash, dcc, html, callback, dependencies
-import dash_bootstrap_components as dbc # 【新增】用於美化介面
+from dash import Dash, dcc, html, callback
+import dash
 import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-
-# FastAPI (API & Webhook) 套件
-from fastapi import FastAPI, Request, Header
-from fastapi.middleware.wsgi import WSGIMiddleware
-from fastapi.exceptions import HTTPException
-
-# LINE Bot 套件
-from linebot import LineBotApi, WebhookHandler
-from linebot.exceptions import InvalidSignatureError
-from linebot.models import MessageEvent, TextMessage, TextSendMessage
+import re
+from bs4 import BeautifulSoup
+import requests
 
 # 引用您組員的預測器程式
-# 假設 Bert_predict 模組存在並可以正常匯入
-# from Bert_predict import BertPredictor
-
-# ==============================================================================
-# 1. 初始化應用程式與 Lifespan 管理
-# ==============================================================================
-
-# 【優化】使用新的 lifespan 寫法來管理啟動事件
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # 服務啟動時執行的程式碼
-    print("服務啟動中...")
-    initialize_line_services()
-    setup_line_message_handler()
-    print("服務初始化完成。")
-    yield
-    # 服務關閉時執行的程式碼 (如果需要的話)
-    print("服務已關閉。")
-
-# 建立 FastAPI 應用程式實例，並掛載 lifespan
-fastapi_app = FastAPI(lifespan=lifespan)
-
-# 建立 Dash 應用程式實例
-# 【修正】使用 dash_bootstrap_components 樣式表
-dash_app = Dash(__name__, suppress_callback_exceptions=True, server=False, url_base_pathname='/', external_stylesheets=[dbc.themes.BOOTSTRAP])
-
-# ==============================================================================
-# 2. 全域變數與共用函式
-# ==============================================================================
-
-# --- LINE Bot 全域變數 ---
-line_bot_api = None
-line_handler = None
-# 【重要！】請務必將此處的 URL 替換成您自己的 Hugging Face Space URL
-TARGET_URL = "https://Your-Space-Name.hf.space"
+from Bert_predict import BertPredictor
 
-# --- 台股代號與產業對應表 ---
+# 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
 TAIWAN_STOCKS = {
-    '元大台灣50': '0050.TW', '台積電': '2330.TW', '聯發科': '2454.TW',
-    '鴻海': '2317.TW', '台塑': '1301.TW', '中華電': '2412.TW',
-    '富邦金': '2881.TW', '國泰金': '2882.TW', '台達電': '2308.TW',
-    '統一': '1216.TW', '日月光': '3711.TW', '長榮': '2603.TW',
-    '慧洋-KY': '2637.TW', '上銀': '2049.TW', '台泥': '1101.TW',
-    '南亞科': '2408.TW', '旺宏': '2337.TW', '譜瑞-KY': '4966.TWO',
-    '貿聯-KY': '3665.TW', '騰雲': '6870.TWO', '穩懋': '3105.TWO'
+    '元大台灣50': '0050.TW', # 新增
+    '台積電': '2330.TW',
+    '聯發科': '2454.TW',
+    '鴻海': '2317.TW',
+    '台塑': '1301.TW',
+    '中華電': '2412.TW',
+    '富邦金': '2881.TW',
+    '國泰金': '2882.TW',
+    '台達電': '2308.TW',
+    '統一': '1216.TW',
+    '日月光': '3711.TW',
+    '長榮': '2603.TW',
+    '慧洋-KY': '2637.TW',
+    '上銀': '2049.TW',
+    '台泥': '1101.TW',
+    '南亞科': '2408.TW',
+    '旺宏': '2337.TW',
+    '譜瑞-KY': '4966.TWO',
+    '貿聯-KY': '3665.TW',
+    '騰雲': '6870.TWO',
+    '穩懋': '3105.TWO'
 }
 
+# 產業分類
 INDUSTRY_MAPPING = {
-    '0050.TW': 'ETF', '2330.TW': '半導體', '2454.TW': '半導體',
-    '2317.TW': '電子組件', '1301.TW': '塑膠', '2412.TW': '電信',
-    '2881.TW': '金融', '2882.TW': '金融', '2308.TW': '電子',
-    '1216.TW': '食品', '3711.TW': '半導體', '2603.TW': '航運',
-    '2637.TW': '散裝航運', '2049.TW': '工具機', '1101.TW': '營建',
-    '2408.TW': 'DRAM', '2337.TW': 'NFLSH', '4966.TWO': '高速傳輸',
-    '3665.TW': '連接器', '6870.TWO': '軟體整合', '3105.TWO': 'PA功率'
+    '0050.TW': 'ETF', # 新增
+    '2330.TW': '半導體',
+    '2454.TW': '半導體',
+    '2317.TW': '電子組件',
+    '1301.TW': '塑膠',
+    '2412.TW': '電信',
+    '2881.TW': '金融',
+    '2882.TW': '金融',
+    '2308.TW': '電子',
+    '1216.TW': '食品',
+    '3711.TW': '半導體',
+    '2603.TW': '航運',
+    '2637.TW': '散裝航運',
+    '2049.TW': '工具機',
+    '1101.TW': '營建',
+    '2408.TW': 'DRAM',
+    '2337.TW': 'NFLSH',
+    '1101.TW': '營建',
+    '4966.TWO': '高速傳輸',
+    '3665.TW': '連接器',
+    '6870.TWO': '軟體整合',
+    '3105.TWO': 'PA功率'
 }
 
-# --- 共用資料獲取與分析函式 ---
 def get_stock_data(symbol, period='1y'):
+    """獲取股票資料"""
     try:
         stock = yf.Ticker(symbol)
         data = stock.history(period=period)
-        if data.empty:
-            stock = yf.Ticker('^TWII')
+        if data.empty and symbol == 'TXF=F':
+            stock = yf.Ticker('0050.TW')
             data = stock.history(period=period)
+            if data.empty:
+                stock = yf.Ticker('^TWII')
+                data = stock.history(period=period)
         return data
-    except Exception as e:
-        print(f"獲取 {symbol} 資料時出錯: {e}")
+    except:
         return pd.DataFrame()
 
-def calculate_technical_indicators_full(df):
-    """計算所有技術指標 (供 Dash 使用)"""
+def simple_lstm_predict(data, predict_days=5):
+    """簡化的LSTM預測模型 (使用統計方法模擬)"""
+    if len(data) < 60:
+        return None
+    prices = data['Close'].values
+    ma_short = np.mean(prices[-5:])
+    ma_medium = np.mean(prices[-20:])
+    ma_long = np.mean(prices[-60:])
+    recent_trend = np.polyfit(range(20), prices[-20:], 1)[0]
+    volatility = np.std(prices[-20:]) / np.mean(prices[-20:])
+    base_change = recent_trend * predict_days
+    trend_factor = 1.0
+    if ma_short > ma_medium > ma_long:
+        trend_factor = 1.02
+    elif ma_short < ma_medium < ma_long:
+        trend_factor = 0.98
+    else:
+        trend_factor = 1.0
+    noise_factor = np.random.normal(1, volatility * 0.1)
+    predicted_price = prices[-1] * trend_factor + base_change + (prices[-1] * noise_factor * 0.01)
+    change_pct = ((predicted_price - prices[-1]) / prices[-1]) * 100
+    return {
+        'predicted_price': predicted_price,
+        'change_pct': change_pct,
+        'confidence': max(0.6, 1 - volatility * 2)
+    }
+
+def calculate_technical_indicators(df):
+    """計算技術指標"""
     if df.empty: return df
     df['MA5'] = df['Close'].rolling(window=5).mean()
     df['MA20'] = df['Close'].rolling(window=20).mean()
     delta = df['Close'].diff()
     gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
     loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-    rs = gain / loss if loss.any() != 0 else pd.Series(0, index=loss.index)
+    rs = gain / loss
     df['RSI'] = 100 - (100 / (1 + rs))
-    exp1 = df['Close'].ewm(span=12, adjust=False).mean()
-    exp2 = df['Close'].ewm(span=26, adjust=False).mean()
+    exp1 = df['Close'].ewm(span=12).mean()
+    exp2 = df['Close'].ewm(span=26).mean()
     df['MACD'] = exp1 - exp2
-    df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    df['MACD_Signal'] = df['MACD'].ewm(span=9).mean()
     df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']
     df['BB_Middle'] = df['Close'].rolling(window=20).mean()
     bb_std = df['Close'].rolling(window=20).std()
@@ -123,187 +134,558 @@ def calculate_technical_indicators_full(df):
     low_min = df['Low'].rolling(window=9).min()
     high_max = df['High'].rolling(window=9).max()
     rsv = (df['Close'] - low_min) / (high_max - low_min) * 100
-    df['K'] = rsv.ewm(com=2, adjust=False).mean()
-    df['D'] = df['K'].ewm(com=2, adjust=False).mean()
+    df['K'] = rsv.ewm(com=2).mean()
+    df['D'] = df['K'].ewm(com=2).mean()
+    low_min_14 = df['Low'].rolling(window=14).min()
+    high_max_14 = df['High'].rolling(window=14).max()
+    df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)
+    df['up_move'] = df['High'] - df['High'].shift(1)
+    df['down_move'] = df['Low'].shift(1) - df['Low']
+    df['+DM'] = np.where((df['up_move'] > df['down_move']) & (df['up_move'] > 0), df['up_move'], 0)
+    df['-DM'] = np.where((df['down_move'] > df['up_move']) & (df['down_move'] > 0), df['down_move'], 0)
+    df['TR'] = np.max([df['High'] - df['Low'], abs(df['High'] - df['Close'].shift(1)), abs(df['Low'] - df['Close'].shift(1))], axis=0)
+    df['+DI'] = (df['+DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100
+    df['-DI'] = (df['-DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean()) * 100
+    df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']) * 100
+    df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean()
     return df
 
-def calculate_technical_indicators_simple(df):
-    """計算簡易技術指標 (供 LINE Bot 使用)"""
-    if df.empty: return df
-    df['MA5'] = df['Close'].rolling(window=5).mean()
-    df['MA20'] = df['Close'].rolling(window=20).mean()
-    delta = df['Close'].diff()
-    gain = delta.clip(lower=0).rolling(window=14).mean()
-    loss = -delta.clip(upper=0).rolling(window=14).mean()
-    rs = gain / loss if loss.any() != 0 else pd.Series(0, index=loss.index)
-    df['RSI'] = 100 - (100 / (1 + rs))
-    return df
+def calculate_volume_profile(df, num_bins=50):
+    if df.empty or 'High' not in df.columns or 'Low' not in df.columns or 'Volume' not in df.columns: return None, None, None
+    all_prices = np.concatenate([df['High'].values, df['Low'].values])
+    min_price, max_price = all_prices.min(), all_prices.max()
+    price_for_volume = (df['High'] + df['Low'] + df['Close']) / 3
+    df_vol_profile = df.copy()
+    df_vol_profile['Price_Indicator'] = price_for_volume
+    hist, bin_edges = np.histogram(df_vol_profile['Price_Indicator'], bins=num_bins, range=(min_price, max_price), weights=df_vol_profile['Volume'])
+    price_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    return bin_edges, hist, price_centers
+
+def get_business_climate_data():
+    try:
+        if not os.path.exists('business_climate.csv'): return pd.DataFrame()
+        df = pd.read_csv('business_climate.csv')
+        if 'Date' not in df.columns: df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
+        if 'Date' in df.columns:
+            try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
+            except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+        df = df.dropna(subset=['Date'])
+        return df
+    except Exception as e:
+        print(f"無法獲取景氣燈號資料: {str(e)}")
+        return pd.DataFrame()
+
+def get_pmi_data():
+    try:
+        if not os.path.exists('taiwan_pmi.csv'): return pd.DataFrame()
+        df = pd.read_csv('taiwan_pmi.csv')
+        if 'DATE' in df.columns: df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
+        elif len(df.columns) == 2: df.columns = ['Date', 'Index']
+        if 'Date' in df.columns:
+            try: df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
+            except: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+        df = df.dropna(subset=['Date'])
+        return df
+    except Exception as e:
+        print(f"無法獲取 PMI 資料: {str(e)}")
+        return pd.DataFrame()
+
+# 建立 Dash 應用程式
+app = dash.Dash(__name__, suppress_callback_exceptions=True)
+
+# --- 【新增】在程式啟動時，初始化 BERT 新聞預測器 ---
+try:
+    print("正在初始化新聞情緒分析模型...")
+    predictor = BertPredictor(max_news_per_keyword=5)
+    print("新聞情緒分析模型初始化成功。")
+except Exception as e:
+    print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}")
+    predictor = None
+
+# 應用程式佈局
+app.layout = html.Div([
+    html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '30px'}),
+    html.Div([
+        html.H2("🤖 AI深度學習預測 - 台指期指數", style={'text-align': 'center','color': '#FFCC22','margin-bottom': '25px'}),
+        html.Div([
+            html.Div([
+                html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
+                dcc.Dropdown(id='taiex-prediction-period',
+                    options=[
+                        {'label': '1日後預測', 'value': 1},{'label': '5日後預測', 'value': 5},
+                        {'label': '10日後預測', 'value': 10},{'label': '20日後預測', 'value': 20},
+                        {'label': '60日後預測', 'value': 60}], value=5,
+                    style={'margin-bottom': '10px', 'color': '#272727'})
+            ], style={'width': '30%', 'display': 'inline-block'}),
+            html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
+        ]),
+        html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
+    ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
+    
+    # 新聞情感分析區域
+    html.Div([
+        html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
+        html.Div([
+            html.Div([
+                html.H4("市場情緒指標", style={'color': '#8E44AD'}),
+                html.Div(id='sentiment-gauge')
+            ], style={'width': '48%', 'display': 'inline-block'}),
+            html.Div([
+                html.H4("關鍵新聞摘要", style={'color': '#27AE60'}),
+                html.Div(id='news-summary', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','max-height': '200px','overflow-y': 'auto'})
+            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
+        ])
+    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+
+    html.Div([
+        html.H3("景氣燈號與 PMI 分析"),
+        html.Div([
+            html.Div([dcc.Graph(id='business-climate-chart')], style={'width': '48%', 'display': 'inline-block'}),
+            html.Div([dcc.Graph(id='pmi-chart')], style={'width': '48%', 'display': 'inline-block', 'margin-left': '2%'})
+        ])
+    ], style={'margin-top': '30px'}),
+    
+    html.Div([
+        html.Div([
+            html.Label("選擇股票:"),
+            dcc.Dropdown(id='stock-dropdown', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value='2330.TW', style={'margin-bottom': '10px'})
+        ], style={'width': '30%', 'display': 'inline-block', 'vertical-align': 'top'}),
+        html.Div([
+            html.Label("時間範圍:"),
+            dcc.Dropdown(id='period-dropdown',
+                options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'},{'label': '2年', 'value': '2y'}],
+                value='6mo', style={'margin-bottom': '10px'})
+        ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'}),
+        html.Div([
+            html.Label("圖表類型:"),
+            dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'},{'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick', style={'margin-bottom': '10px'})
+        ], style={'width': '30%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
+    ], style={'margin-bottom': '30px'}),
+
+    html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),
+    html.Div([html.Div([dcc.Graph(id='price-chart')], style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'})]),
+    html.Div([
+        html.H3("📊 進階技術指標分析", style={'margin-bottom': '20px'}),
+        html.Div([
+            html.Label("選擇技術指標:", style={'font-weight': 'bold', 'margin-right': '10px'}),
+            dcc.Dropdown(id='technical-indicator-selector',
+                options=[{'label': 'RSI 相對強弱指標', 'value': 'RSI'},{'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},{'label': '布林通道 Bollinger Bands', 'value': 'BB'},
+                         {'label': 'KD 隨機指標', 'value': 'KD'},{'label': '威廉指標 %R', 'value': 'WR'},{'label': 'DMI 動向指標', 'value': 'DMI'}],
+                value='RSI', style={'width': '100%'})
+        ], style={'margin-bottom': '20px'}),
+        html.Div([dcc.Graph(id='advanced-technical-chart')])
+    ], style={'margin-top': '20px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+    html.Div([dcc.Graph(id='volume-chart')], style={'margin-top': '20px'}),
+    html.Div([html.H3("產業表現分析"), dcc.Graph(id='industry-analysis')], style={'margin-top': '30px'}),
+    html.Div([
+        html.H3("📊 分析師觀點與市場解讀", style={'color': '#2E86AB', 'margin-bottom': '20px'}),
+        html.Div([
+            html.Div([
+                html.H4("🔍 技術面分析", style={'color': '#A23B72', 'margin-bottom': '15px'}),
+                html.Div(id='technical-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #A23B72','min-height': '150px','font-size': '14px','line-height': '1.6'})
+            ], style={'width': '48%', 'display': 'inline-block', 'vertical-align': 'top'}),
+            html.Div([
+                html.H4("📈 基本面分析", style={'color': '#F18F01', 'margin-bottom': '15px'}),
+                html.Div(id='fundamental-analysis-text', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','border-left': '4px solid #F18F01','min-height': '150px','font-size': '14px','line-height': '1.6'})
+            ], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%', 'vertical-align': 'top'})
+        ]),
+        html.Div([
+            html.H4("🎯 市場展望與投資建議", style={'color': '#C73E1D', 'margin-bottom': '15px', 'margin-top': '25px'}),
+            html.Div(id='market-outlook-text', style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','color': 'white','padding': '20px','border-radius': '10px','min-height': '100px','font-size': '15px','line-height': '1.7','box-shadow': '0 4px 15px rgba(0,0,0,0.1)'})
+        ])
+    ], style={'margin-top': '30px','padding': '25px','background': 'white','border-radius': '12px','box-shadow': '0 4px 20px rgba(0,0,0,0.08)','border': '1px solid #e9ecef'}),
+    html.Div([
+        html.H3("📊 多檔股票比較分析", style={'margin-bottom': '20px'}),
+        html.Div([
+            html.Div([
+                html.Label("選擇比較股票（最多5檔）:", style={'font-weight': 'bold'}),
+                dcc.Dropdown(id='comparison-stocks', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value=['0050.TW', '2330.TW', '2454.TW'], multi=True, style={'margin-bottom': '5px'}),
+                html.Small('（元大台灣50 (0050.TW) 為固定比較基準，不可移除）', style={'display': 'block', 'font-style': 'italic', 'color': 'gray'})
+            ], style={'width': '60%', 'display': 'inline-block'}),
+            html.Div([
+                html.Label("比較期間:", style={'font-weight': 'bold'}),
+                dcc.Dropdown(id='comparison-period', options=[{'label': '1個月', 'value': '1mo'},{'label': '3個月', 'value': '3mo'},{'label': '6個月', 'value': '6mo'},{'label': '1年', 'value': '1y'}], value='3mo')
+            ], style={'width': '35%', 'display': 'inline-block', 'margin-left': '5%', 'vertical-align': 'top'})
+        ]),
+        html.Div([
+            html.Div([dcc.Graph(id='comparison-chart')], style={'width': '65%', 'display': 'inline-block'}),
+            html.Div([html.H4("比較結果", style={'color': '#2E86AB'}), html.Div(id='comparison-table')], style={'width': '33%', 'display': 'inline-block', 'margin-left': '2%', 'vertical-align': 'top'})
+        ])
+    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
+])
+
+# 台指期獨立預測回調函數
+@app.callback(
+    [dash.dependencies.Output('taiex-prediction-results', 'children'),
+     dash.dependencies.Output('taiex-prediction-chart', 'figure')],
+    [dash.dependencies.Input('taiex-prediction-period', 'value')]
+)
+def update_taiex_prediction(predict_days):
+    data = get_stock_data('^TWII', '2y')
+    if data.empty: return html.Div("無法獲取台指期資料"), {}
+    final_prediction = simple_lstm_predict(data, predict_days)
+    if final_prediction is None: return html.Div("資料不足，無法進行預測"), {}
+    current_price, last_date = data['Close'].iloc[-1], data.index[-1]
+    predicted_price, change_pct, confidence = final_prediction['predicted_price'], final_prediction['change_pct'], final_prediction['confidence']
+    prediction_paths = {1: [1], 5: [1, 5], 10: [1, 5, 10], 20: [1, 10, 20], 60: [1, 10, 20, 60]}
+    intervals_to_predict = prediction_paths.get(predict_days, [predict_days])
+    prediction_dates, prediction_prices = [last_date], [current_price]
+    for days in intervals_to_predict:
+        interim_prediction = simple_lstm_predict(data, days)
+        if interim_prediction:
+            prediction_dates.append(last_date + timedelta(days=days))
+            prediction_prices.append(interim_prediction['predicted_price'])
+    color, arrow = ('red', '📈') if change_pct >= 0 else ('green', '📉')
+    result_card = html.Div([
+        html.H4(f"{predict_days}日後預測結果", style={'margin': '0 0 15px 0', 'color': 'white'}),
+        html.Div([html.Span(f"{arrow} ", style={'font-size': '24px'}), html.Span(f"{change_pct:+.2f}%", style={'font-size': '28px','font-weight': 'bold','color': color})], style={'margin': '10px 0'}),
+        html.P(f"目前價格: {current_price:.2f}", style={'margin': '5px 0'}), html.P(f"預測價格: {predicted_price:.2f}", style={'margin': '5px 0'}),
+        html.P(f"信心度: {confidence:.1%}", style={'margin': '5px 0', 'font-size': '14px'})
+    ], style={'background': 'rgba(255,255,255,0.1)','padding': '20px','border-radius': '10px','border': '1px solid rgba(255,255,255,0.2)'})
+    fig = go.Figure()
+    recent_data = data.tail(30)
+    fig.add_trace(go.Scatter(x=recent_data.index, y=recent_data['Close'], mode='lines', name='歷史價格', line=dict(color='#FFA726', width=2)))
+    fig.add_trace(go.Scatter(x=prediction_dates, y=prediction_prices, mode='lines+markers', name=f'{predict_days}日預測路徑', line=dict(color=color, width=3, dash='dash'), marker=dict(size=8)))
+    fig.update_layout(title=f'台指期 {predict_days}日預測走勢', xaxis_title='日期', yaxis_title='指數點位', height=350, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font=dict(color='white'))
+    return result_card, fig
+
+# 更新股價資訊卡片
+@app.callback(
+    dash.dependencies.Output('stock-info-cards', 'children'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_stock_info(selected_stock):
+    data = get_stock_data(selected_stock, '5d')
+    if data.empty: return html.Div("無法獲取股票資料")
+    current_price = data['Close'].iloc[-1]
+    prev_price = data['Close'].iloc[-2] if len(data) > 1 else current_price
+    change = current_price - prev_price
+    change_pct = (change / prev_price) * 100
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    color, arrow = ('red', '▲') if change >= 0 else ('green', '▼')
+    return html.Div([
+        html.Div([
+            html.H3(f"{stock_name} ({selected_stock})", style={'margin': '0'}),
+            html.H2(f"${current_price:.2f}", style={'margin': '5px 0', 'color': color}),
+            html.P(f"{arrow} {change:+.2f} ({change_pct:+.2f}%)", style={'margin': '0', 'color': color, 'font-weight': 'bold'})
+        ], style={'background': 'white','padding': '20px','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)','display': 'inline-block','margin-right': '20px'}),
+        html.Div([
+            html.H4("今日統計", style={'margin': '0 0 10px 0'}),
+            html.P(f"最高: ${data['High'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
+            html.P(f"最低: ${data['Low'].iloc[-1]:.2f}", style={'margin': '5px 0'}),
+            html.P(f"成交量: {data['Volume'].iloc[-1]:,.0f}", style={'margin': '5px 0'})
+        ], style={'background': 'white','padding': '20px','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)','display': 'inline-block'})
+    ])
+
+# 更新主要圖表 (股價與成交量分佈)
+@app.callback(
+    dash.dependencies.Output('price-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value'),
+     dash.dependencies.Input('chart-type', 'value')]
+)
+def update_price_chart(selected_stock, period, chart_type):
+    data = get_stock_data(selected_stock, period)
+    if data.empty: return {}
+    data = calculate_technical_indicators(data)
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    fig = make_subplots(rows=1, cols=2, shared_yaxes=True, column_widths=[0.8, 0.2], horizontal_spacing=0.01)
+    if chart_type == 'candlestick':
+        fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name=stock_name, increasing_line_color='red', decreasing_line_color='green'), row=1, col=1)
+    else:
+        fig.add_trace(px.line(data, y='Close').data[0], row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue')), row=1, col=1)
+    bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
+    if volume_per_bin is not None:
+        fig.add_trace(go.Bar(orientation='h', y=price_centers, x=volume_per_bin, name='Volume Profile', text=[f'{vol/1000:.0f}k' for vol in volume_per_bin], textposition='auto', marker=dict(color='rgba(173, 216, 230, 0.6)', line=dict(color='rgba(30, 144, 255, 0.8)', width=1))), row=1, col=2)
+    fig.update_layout(title_text=f'{stock_name} 股價走勢與成交量分佈', height=500, showlegend=True, xaxis1=dict(title='日期', type='date', rangeslider_visible=False), yaxis1=dict(title='價格 (TWD)'), xaxis2=dict(title='成交量', showticklabels=True), yaxis2=dict(showticklabels=False), bargap=0.05)
+    return fig
+
+# 更新進階技術指標圖表
+@app.callback(
+    dash.dependencies.Output('advanced-technical-chart', 'figure'),
+    [dash.dependencies.Input('technical-indicator-selector', 'value'),
+     dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
+)
+def update_advanced_technical_chart(indicator, selected_stock, period):
+    data = get_stock_data(selected_stock, period)
+    if data.empty: return {}
+    data = calculate_technical_indicators(data)
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    fig = go.Figure() # Fallback
+    if indicator == 'RSI':
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI', line=dict(color='purple', width=2)))
+        fig.add_hline(y=70, line_dash="dash", line_color="green", annotation_text="超買線(70)")
+        fig.add_hline(y=30, line_dash="dash", line_color="red", annotation_text="超賣線(30)")
+        fig.add_hline(y=50, line_dash="dot", line_color="gray", annotation_text="中線(50)")
+        fig.update_layout(title=f'{stock_name} - RSI 相對強弱指標', xaxis_title='日期', yaxis_title='RSI', height=450, yaxis=dict(range=[0, 100]))
+    elif indicator == 'MACD':
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.7, 0.3], subplot_titles=('價格走勢', 'MACD 指標'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1.5)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD (快線)', line=dict(color='blue', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='Signal (慢線)', line=dict(color='red', width=2)), row=2, col=1)
+        colors = ['red' if x >= 0 else 'green' for x in data['MACD_Histogram']]
+        fig.add_trace(go.Bar(x=data.index, y=data['MACD_Histogram'], name='MACD柱狀圖', marker_color=colors), row=2, col=1)
+        fig.update_layout(title_text=f'{stock_name} - MACD 指數平滑異同移動平均線', height=550)
+    elif indicator == 'BB':
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=2)))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], mode='lines', name='上軌', line=dict(color='red', width=1, dash='dash')))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Middle'], mode='lines', name='中軌(MA20)', line=dict(color='blue', width=1)))
+        fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], mode='lines', name='下軌', line=dict(color='green', width=1, dash='dash')))
+        fig.update_layout(title=f'{stock_name} - 布林通道 (20日, 2σ)', xaxis_title='日期', yaxis_title='價格 (TWD)', height=450)
+    elif indicator == 'KD':
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', 'KD指標'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['K'], mode='lines', name='K線', line=dict(color='blue', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['D'], mode='lines', name='D線', line=dict(color='red', width=2)), row=2, col=1)
+        fig.add_hline(y=80, line_dash="dash", line_color="green", annotation_text="超買線(80)", row=2, col=1)
+        fig.add_hline(y=20, line_dash="dash", line_color="red", annotation_text="超賣線(20)", row=2, col=1)
+        fig.update_layout(title=f'{stock_name} - KD 隨機指標 (9,3,3)', height=500, yaxis2_range=[0, 100])
+    elif indicator == 'WR':
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', '威廉指標 %R'))
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['Williams_R'], mode='lines', name='威廉%R', line=dict(color='purple', width=2)), row=2, col=1)
+        fig.add_hline(y=-20, line_dash="dash", line_color="green", annotation_text="超買線(-20)", row=2, col=1)
+        fig.add_hline(y=-80, line_dash="dash", line_color="red", annotation_text="超賣線(-80)", row=2, col=1)
+        fig.update_layout(title=f'{stock_name} - 威廉指標 %R (14日)', height=500, yaxis2_range=[-100, 0])
+    elif indicator == 'DMI':
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.6, 0.4], subplot_titles=('價格走勢', 'DMI 指標'))
+        data_filtered = data.iloc[14:]
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['Close'], mode='lines', name='收盤價', line=dict(color='black', width=1)), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['+DI'], mode='lines', name='+DI', line=dict(color='red', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['-DI'], mode='lines', name='-DI', line=dict(color='green', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data_filtered.index, y=data_filtered['ADX'], mode='lines', name='ADX', line=dict(color='blue', width=2, dash='dot')), row=2, col=1)
+        fig.update_layout(title=f'{stock_name} - DMI 動向指標 (14日)', height=500, showlegend=True, yaxis2_range=[0, 100])
+    return fig
+
+# 更新成交量圖表
+@app.callback(
+    dash.dependencies.Output('volume-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
+)
+def update_volume_chart(selected_stock, period):
+    data = get_stock_data(selected_stock, period)
+    if data.empty: return {}
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    colors = ['red' if data['Close'].iloc[i] > data['Open'].iloc[i] else 'green' for i in range(len(data))]
+    fig = go.Figure(go.Bar(x=data.index, y=data['Volume'], marker_color=colors, name='成交量'))
+    fig.update_layout(title=f'{stock_name} 成交量', xaxis_title='日期', yaxis_title='成交量', height=300)
+    return fig
+
+# 更新產業分析圖表
+@app.callback(
+    dash.dependencies.Output('industry-analysis', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_industry_analysis(selected_stock):
+    industry_data = []
+    for symbol in list(TAIWAN_STOCKS.values())[:10]:
+        data = get_stock_data(symbol, '1mo')
+        if not data.empty:
+            stock_name = [name for name, symbol_code in TAIWAN_STOCKS.items() if symbol_code == symbol][0]
+            return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+            industry_data.append({'股票': stock_name, '代碼': symbol, '月報酬率(%)': return_pct, '產業': INDUSTRY_MAPPING.get(symbol, '其他')})
+    if not industry_data: return {}
+    df_industry = pd.DataFrame(industry_data)
+    fig = px.pie(df_industry, values='月報酬率(%)', names='股票', title='各股票月報酬率比較', color_discrete_sequence=px.colors.qualitative.Set3)
+    fig.update_layout(height=400)
+    return fig
+
+# 更新景氣燈號圖表
+@app.callback(
+    dash.dependencies.Output('business-climate-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_business_climate_chart(selected_stock):
+    df = get_business_climate_data()
+    if df.empty:
+        fig = go.Figure().add_annotation(text="無法���入景氣燈號資料", showarrow=False)
+        fig.update_layout(title="台灣景氣燈號", height=300)
+        return fig
+    def get_light_color(score):
+        if score >= 32: return 'red'
+        elif score >= 24: return 'orange'
+        elif score >= 17: return 'yellow'
+        elif score >= 10: return 'lightgreen'
+        else: return 'blue'
+    colors = [get_light_color(score) for score in df['Index']]
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Index'], mode='lines+markers', name='景氣燈號', line=dict(color='darkblue', width=2), marker=dict(size=8, color=colors, line=dict(width=2, color='darkblue'))))
+    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈(32)")
+    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃燈(17)")
+    fig.update_layout(title="台灣景氣燈號走勢", xaxis_title='日期', yaxis_title='燈號分數', height=300, yaxis=dict(range=[0, 40]))
+    return fig
+
+# 更新分析師觀點
+@app.callback(
+    [dash.dependencies.Output('technical-analysis-text', 'children'),
+     dash.dependencies.Output('fundamental-analysis-text', 'children'),
+     dash.dependencies.Output('market-outlook-text', 'children')],
+    [dash.dependencies.Input('stock-dropdown', 'value'),
+     dash.dependencies.Input('period-dropdown', 'value')]
+)
+def update_analysis_text(selected_stock, period):
+    data = get_stock_data(selected_stock, period)
+    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
+    if data.empty: return "無法獲取資料", "無法獲取資料", "無法獲取資料"
+    data = calculate_technical_indicators(data)
+    current_price = data['Close'].iloc[-1]
+    price_change = ((current_price - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
+    rsi_current = data['RSI'].iloc[-1] if not pd.isna(data['RSI'].iloc[-1]) else 50
+    macd_current = data['MACD'].iloc[-1] if not pd.isna(data['MACD'].iloc[-1]) else 0
+    macd_signal_current = data['MACD_Signal'].iloc[-1] if not pd.isna(data['MACD_Signal'].iloc[-1]) else 0
+    technical_text = html.Div([
+        html.P([html.Strong("價格趨勢："), f"近期{period}期間內，{stock_name}呈現", html.Span(f"{'上漲' if price_change > 5 else '下跌' if price_change < -5 else '盤整'}", style={'color': 'red' if price_change > 5 else 'green' if price_change < -5 else 'orange', 'font-weight': 'bold'}), f"走勢，累計變動{price_change:+.1f}%。"]),
+        html.P([html.Strong("RSI指標："), f"目前為{rsi_current:.1f}，", html.Span("處於超買區間" if rsi_current > 70 else "處於超賣區間" if rsi_current < 30 else "在正常範圍內", style={'color': 'green' if rsi_current > 70 else 'red' if rsi_current < 30 else 'blue', 'font-weight': 'bold'}), "。"]),
+        html.P([html.Strong("MACD指標："), f"MACD線({macd_current:.3f})", html.Span("高於" if macd_current > macd_signal_current else "低於", style={'color': 'red' if macd_current > macd_signal_current else 'green', 'font-weight': 'bold'}), f"信號線({macd_signal_current:.3f})，", f"顯示{'多頭' if macd_current > macd_signal_current else '空頭'}格局。"]),
+    ])
+    industry = INDUSTRY_MAPPING.get(selected_stock, '綜合')
+    fundamental_text = html.Div([
+        html.P([html.Strong("產業地位："), f"{stock_name}屬於{industry}產業，在產業鏈中具有", html.Span("重要地位" if selected_stock in ['2330.TW', '2454.TW', '2317.TW'] else "一定影響力", style={'font-weight': 'bold'}), "。"]),
+        html.P([html.Strong("營運展望："), f"建議持續關注季報表現及未來指引。"]),
+    ])
+    outlook_tone = "謹慎樂觀" if price_change > 10 else "保守觀望" if price_change < -10 else "中性持平"
+    market_outlook = html.Div([
+        html.P([html.Strong("整體評估："), f"基於技術面及基本面分析，對{stock_name}採取", html.Span(f"{outlook_tone}", style={'font-weight': 'bold'}), "態度。"]),
+        html.P([html.Strong("投資建議："), "短線操作注意技術指標，長線投資關注基本面變化。"]),
+    ])
+    return technical_text, fundamental_text, market_outlook
+
+# 更新PMI圖表
+@app.callback(
+    dash.dependencies.Output('pmi-chart', 'figure'),
+    [dash.dependencies.Input('stock-dropdown', 'value')]
+)
+def update_pmi_chart(selected_stock):
+    df = get_pmi_data()
+    if df.empty:
+        fig = go.Figure().add_annotation(text="無法載入PMI資料", showarrow=False)
+        fig.update_layout(title="台灣PMI指數", height=300)
+        return fig
+    colors = ['red' if value >= 50 else 'green' for value in df['Index']]
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Index'], mode='lines+markers', name='PMI指數', line=dict(color='darkblue', width=2), marker=dict(size=8, color=colors, line=dict(width=2, color='darkblue'))))
+    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")
+    fig.update_layout(title="台灣PMI指數走勢", xaxis_title='日期', yaxis_title='PMI指數', height=300, yaxis=dict(range=[35, 60]))
+    return fig
 
 def summarize_news_with_gemini(news_list: list) -> str:
+    """
+    使用 Gemini API 將英文新聞標題列表摘要成一段繁體中文。
+
+    Args:
+        news_list (list): 包含英文新聞標題字串的列表。
+
+    Returns:
+        str: Gemini 生成的繁體中文摘要，或在發生錯誤時回傳錯誤訊息。
+    """
+    # 從 Hugging Face Secrets 安全地讀取 API 金鑰
     api_key = os.getenv("GEMINI_API_KEY")
     if not api_key:
         return "錯誤：找不到 GEMINI_API_KEY。請在 Hugging Face Secrets 中設定。"
+
     try:
         genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-1.5-flash')
+        model = genai.GenerativeModel('gemini-1.5-flash') # 使用速度較快的 Flash 模型
+
+        # 將新聞列表格式化，方便 AI 閱讀
         formatted_news = "\n".join([f"- {news}" for news in news_list])
+
+        # 這就是您對 AI 下的指令 (Prompt)
         prompt = f"""
         請扮演一位專業的金融市場分析師。
-        以下是幾則最新的英文財經新聞標題，請將它們整合成一段簡潔、流暢、約 200 字的繁體中文市場動態摘要，並點出可能利多與利空的產業。
-        請提供3段重點，並專注於可能影響市場情緒和股價的關鍵資訊，直接提供摘要內容，不要包含任何額外的問候語或說明。
+        以下是幾則最新的英文財經新聞標題，請將它們整合成一段簡潔、流暢、約 200 字的繁體中文市場動態摘要，與利多哪些產業，利空哪些產業。
+        提供3段重點，
+        請專注於可能影響市場情緒和股價的關鍵資訊，並直接提供摘要內容，不要包含任何額外的問候語或說明。
 
         英文新聞標題如下：
         {formatted_news}
         """
+
         response = model.generate_content(prompt)
         return response.text
+
     except Exception as e:
         print(f"呼叫 Gemini API 時發生錯誤: {e}")
         return f"無法生成新聞摘要，請稍後再試。錯誤訊息：{e}"
 
-# ==============================================================================
-# 3. LINE Bot 專用函式
-# ==============================================================================
-def initialize_line_services():
-    global line_bot_api, line_handler
-    # 【修正】在 getenv 後面加上 .strip()，移除潛在的頭尾空白或換行符
-    channel_access_token = os.getenv("CHANNEL_ACCESS_TOKEN", "").strip()
-    channel_secret = os.getenv("CHANNEL_SECRET", "").strip()
-    if channel_access_token and channel_secret:
-        try:
-            from linebot.v3.messaging import LineBotApi
-            from linebot.v3.webhooks import WebhookHandler
-            line_bot_api = LineBotApi(channel_access_token)
-            line_handler = WebhookHandler(channel_secret)
-            print("Line Bot API (v3) 初始化成功。")
-        except ImportError:
-            # 兼容舊版 linebot 程式庫
-            print("警告：找不到 linebot.v3 模組。將嘗試使用舊版 API。")
-            from linebot import LineBotApi, WebhookHandler
-            line_bot_api = LineBotApi(channel_access_token)
-            line_handler = WebhookHandler(channel_secret)
-            print("Line Bot API (舊版) 初始化成功。")
-    else:
-        print("警告: 找不到 LINE Bot 的 CHANNEL_ACCESS_TOKEN 或 CHANNEL_SECRET 環境變數。")
-
-def generate_analysis_report(data, stock_name):
-    if data.empty or data.iloc[-1].isnull().any():
-        return f"抱歉，{stock_name} 的資料不足，無法產生報告。"
-    last_row = data.iloc[-1]
-    rsi_val = last_row['RSI']
-    if pd.isna(rsi_val):
-        rsi_comment = "RSI 指標數據不足，無法評估。"
-    elif rsi_val > 70:
-        rsi_comment = "RSI指標顯示處於超買區，可能有回檔風險。"
-    elif rsi_val < 30:
-        rsi_comment = "RSI指標顯示處於超賣區，可能有反彈機會。"
-    else:
-        rsi_comment = "RSI指標處於中性區間。"
-    report = f"""📈 {stock_name} 分析報告
-🗓️ 最新收盤價: ${last_row['Close']:.2f}
-🚀 RSI: {rsi_val:.2f}
-💬 趨勢評估:
-{rsi_comment}"""
-    return report
-
-def generate_market_outlook(data, stock_name):
-    if data.empty or data.iloc[-1].isnull().any(): return ""
-    last_row = data.iloc[-1]
-    outlook_text = f"\n---\n📊 **市場展望與投資建議**"
-    rsi_val = last_row['RSI']
-    if pd.isna(rsi_val):
-        outlook_text += "\n🔴 技術面觀察：數據不足，無法生成 RSI 建議。"
-    elif rsi_val > 70:
-        outlook_text += "\n🔴 技術面觀察：RSI 指標顯示股價處於超買區，短期內可能有回檔壓力，建議密切關注。"
-    elif rsi_val < 30:
-        outlook_text += "\n🟢 技術面觀察：RSI 指標顯示股價處於超賣區，短期可能出現反彈，但仍需留意下方支撐力道。"
-    else:
-        outlook_text += "\n🟡 技術面觀察：RSI 指標處於中性區間，顯示目前市場趨勢不明顯，可能進入盤整階段。"
-    if pd.isna(last_row['MA5']) or pd.isna(last_row['MA20']):
-        outlook_text += "\n📈 趨勢分析：均線數據不足，無法生成趨勢建議。"
-    elif last_row['MA5'] > last_row['MA20']:
-        outlook_text += "\n📈 趨勢分析：短期均線（MA5）位於中期均線（MA20）上方，顯示短期趨勢偏多。"
-    else:
-        outlook_text += "\n📉 趨勢分析：短期均線（MA5）位於中期均線（MA20）下方，顯示短期趨勢偏空。"
-    outlook_text += "\n\n💡 **綜合建議**：投資決策應搭配其他分析工具與個人風險承受能力。以上僅供參考，不構成任何投資建議。"
-    return outlook_text
-
-def setup_line_message_handler():
-    if not line_handler: return
-    @line_handler.add(MessageEvent, message=TextMessage)
-    def handle_message(event):
-        if event.message.type != "text": return
-        user_message = event.message.text.strip()
-        response_text = f"感謝您的訊息！\n若想查看完整的互動式圖表，請點擊以下連結：\n{TARGET_URL}"
-        stock_symbol, stock_name = None, None
-        for name, symbol in TAIWAN_STOCKS.items():
-            if name in user_message or symbol.split('.')[0] in user_message:
-                stock_symbol, stock_name = symbol, name
-                break
-        if stock_symbol:
-            stock_data = get_stock_data(stock_symbol, period='6mo')
-            data_with_indicators = calculate_technical_indicators_simple(stock_data)
-            report_part = generate_analysis_report(data_with_indicators, stock_name)
-            outlook_part = generate_market_outlook(data_with_indicators, stock_name)
-            response_text = report_part + outlook_part
-        try:
-            line_bot_api.reply_message(event.reply_token, TextSendMessage(text=response_text))
-        except Exception as e:
-            print(f"LINE Bot 回覆訊息時出錯: {e}")
 
-# ==============================================================================
-# 4. FastAPI 端點 (Endpoints)
-# ==============================================================================
-@fastapi_app.post("/webhook")
-async def webhook(request: Request, x_line_signature: str = Header(None)):
-    if not line_handler:
-        raise HTTPException(status_code=500, detail="Line Bot 服務未設定")
-    body = await request.body()
-    try:
-        line_handler.handle(body.decode("utf-8"), x_line_signature)
-    except InvalidSignatureError:
-        raise HTTPException(status_code=400, detail="簽名錯誤")
-    return "ok"
 
-# ==============================================================================
-# 5. Dash 儀表板的佈局 (Layout) 與回呼 (Callbacks)
-# ==============================================================================
+# 更新多檔股票比較
+@app.callback(
+    [dash.dependencies.Output('comparison-chart', 'figure'),
+     dash.dependencies.Output('comparison-table', 'children')],
+    [dash.dependencies.Input('comparison-stocks', 'value'),
+     dash.dependencies.Input('comparison-period', 'value')]
+)
+def update_comparison_analysis(selected_stocks, period):
+    fixed_stock = '0050.TW'
+    if not selected_stocks: selected_stocks = [fixed_stock]
+    elif fixed_stock not in selected_stocks: selected_stocks.insert(0, fixed_stock)
+    selected_stocks = selected_stocks[:5]
+    fig = go.Figure()
+    comparison_data = []
+    for stock in selected_stocks:
+        data = get_stock_data(stock, period)
+        if not data.empty:
+            stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == stock), stock)
+            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100
+            fig.add_trace(go.Scatter(x=data.index, y=normalized_prices, mode='lines', name=stock_name, line=dict(width=2)))
+            total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100
+            volatility = data['Close'].pct_change().std() * np.sqrt(252) * 100
+            comparison_data.append({'name': stock_name, 'return': total_return, 'volatility': volatility, 'current_price': data['Close'].iloc[-1]})
+    fig.update_layout(title=f'股票績效比較 - {period}', xaxis_title='日期', yaxis_title='相對績效 (基期=100)', height=400, hovermode='x unified')
+    if comparison_data:
+        table_rows = []
+        for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True):
+            color = 'red' if item['return'] > 0 else 'green'
+            table_rows.append(html.Tr([html.Td(item['name'], style={'font-weight': 'bold'}), html.Td(f"{item['return']:+.1f}%", style={'color': color, 'font-weight': 'bold'}), html.Td(f"{item['volatility']:.1f}%"), html.Td(f"${item['current_price']:.2f}")]))
+        table = html.Table([html.Thead(html.Tr([html.Th("股票"), html.Th("報酬率"), html.Th("波動率"), html.Th("現價")])), html.Tbody(table_rows)], style={'width': '100%'})
+        return fig, table
+    return fig, html.Div("無可比較資料")
 
-# --- BERT 新聞預測器初始化 ---
-try:
-    print("正在初始化新聞情緒分析模型...")
-    # 假設 BertPredictor 類別已正確定義
-    # predictor = BertPredictor(max_news_per_keyword=5)
-    # 為了讓程式碼可執行，這裡使用一個 Mock 物件
-    class MockPredictor:
-        def get_news_index(self): return 0.5
-        def get_news(self): return ["Tech stocks rally on strong earnings reports.", "Global economic outlook improves.", "Supply chain disruptions ease for key industries."]
-    predictor = MockPredictor()
-    print("新聞情緒分析模型初始化成功。")
-except Exception as e:
-    print(f"錯誤：新聞情緒分析模型初始化失敗 - {e}")
-    predictor = None
 
-# --- Dash Callbacks ---
-@dash_app.callback(
-    [dependencies.Output('sentiment-gauge', 'children'),
-     dependencies.Output('news-summary', 'children')],
-    [dependencies.Input('stock-dropdown', 'value')]
+# ==============================================================================
+# ===== 【修改】市場情緒與新聞分析 (使用真實 BERT 模型) =====
+# ==============================================================================
+@app.callback(
+    [dash.dependencies.Output('sentiment-gauge', 'children'),
+     dash.dependencies.Output('news-summary', 'children')],
+    [dash.dependencies.Input('stock-dropdown', 'value')]
 )
 def update_sentiment_analysis(selected_stock):
+    # 檢查 predictor 是否成功初始化 (這部分邏輯不變)
     if predictor is None:
         error_fig = go.Figure().add_annotation(text="情緒指標模型載入失敗", showarrow=False)
         error_fig.update_layout(height=200)
-        return dcc.Graph(figure=error_fig), html.P("新聞分析模型載入失敗。")
+        return dcc.Graph(figure=error_fig), html.P("新聞分析模型載入失敗，請檢查後台日誌。")
+
+    # --- 1. 從 predictor 獲取新聞情緒平均分數 (不變) ---
     sentiment_score_raw = predictor.get_news_index()
+
+    # --- 2. 建立情緒指標儀表板 (不變) ---
     if sentiment_score_raw is not None:
-        sentiment_score_normalized = max(0, min(100, (sentiment_score_raw + 1) * 50))
-        level_text = "樂觀" if sentiment_score_normalized >= 65 else "悲觀" if sentiment_score_normalized < 35 else "中性"
-        bar_color = "#5cb85c" if level_text == "樂觀" else "#d9534f" if level_text == "悲觀" else "#f0ad4e"
+        sentiment_score_normalized = (sentiment_score_raw + 1) * 50
+        sentiment_score_normalized = max(0, min(100, sentiment_score_normalized))
+        if sentiment_score_normalized >= 65:
+            bar_color, level_text = "#5cb85c", "樂觀"
+        elif sentiment_score_normalized >= 35:
+            bar_color, level_text = "#f0ad4e", "中性"
+        else:
+            bar_color, level_text = "#d9534f", "悲觀"
         gauge_fig = go.Figure(go.Indicator(
-            mode="gauge+number", value=sentiment_score_normalized,
-            title={'text': f"昨日市場情緒: {level_text}"},
-            gauge={'axis': {'range': [0, 100]}, 'bar': {'color': bar_color}}
+            mode = "gauge+number", value = sentiment_score_normalized,
+            domain = {'x': [0, 1], 'y': [0, 1]},
+            title = {'text': f"昨日市場情緒: {level_text}", 'font': {'size': 18}},
+            gauge = {'axis': {'range': [0, 100]}, 'bar': {'color': bar_color},
+                     'steps': [{'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"},
+                               {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"},
+                               {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}]}
         ))
         gauge_fig.update_layout(height=200, margin=dict(l=30, r=30, t=50, b=20))
         gauge_content = dcc.Graph(figure=gauge_fig)
@@ -311,90 +693,28 @@ def update_sentiment_analysis(selected_stock):
         error_fig = go.Figure().add_annotation(text="今日尚無情緒分數", showarrow=False)
         error_fig.update_layout(height=200)
         gauge_content = dcc.Graph(figure=error_fig)
-    top_news_list = predictor.get_news()
-    if top_news_list and isinstance(top_news_list, list):
-        summary_text = summarize_news_with_gemini(top_news_list)
-        news_content = dcc.Markdown(summary_text, style={'font-size': '15px', 'line-height': '1.7'})
-    else:
-        news_content = html.P("昨日無重大相關新聞或讀取時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px'})
-    return gauge_content, news_content
-
-@dash_app.callback(
-    dependencies.Output('price-chart', 'figure'),
-    [dependencies.Input('stock-dropdown', 'value'),
-     dependencies.Input('period-dropdown', 'value'),
-     dependencies.Input('chart-type', 'value')]
-)
-def update_price_chart(selected_stock, period, chart_type):
-    data = get_stock_data(selected_stock, period)
-    if data.empty: return {}
-    data = calculate_technical_indicators_full(data)
-    stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock), selected_stock)
-    
-    fig = make_subplots(rows=1, cols=1)
-    
-    if chart_type == 'candlestick':
-        fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name=stock_name, increasing_line_color='red', decreasing_line_color='green'))
-    else:
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價'))
-
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange')))
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue')))
-    
-    fig.update_layout(title_text=f'{stock_name} 股價走勢', height=500, xaxis_rangeslider_visible=False)
-    return fig
 
-# (此處省略了其他所有 callback 函式，它們應放在這裡，內容與您原檔案相同)
-# 由於原程式碼沒有 taiex-prediction-period 和 taiex-prediction-results 的 callback，
-# 為了避免錯誤，先將其移除。您可以依需求自行補上。
-# @dash_app.callback(...)
-# def update_taiex_prediction(...):
-#     ...
 
-# ==============================================================================
-# 6. 【修正關鍵】先定義 Layout，再進行掛載
-# ==============================================================================
-
-# --- Dash 應用程式佈局 ---
-dash_app.layout = html.Div([
-    html.H1("台股分析儀表板 (已整合 LINE Bot)", style={'text-align': 'center', 'margin-bottom': '30px'}),
-    html.Div([
-        html.H2("🤖 AI深度學習預測 - 台指期指數", style={'text-align': 'center','color': '#FFCC22','margin-bottom': '25px'}),
-        html.Div([
-            html.Div([
-                html.Label("預測期間:", style={'font-weight': 'bold', 'color': '#FFCC22'}),
-                dcc.Dropdown(id='taiex-prediction-period',
-                    options=[{'label': f'{d}日後預測', 'value': d} for d in [1, 5, 10, 20, 60]], value=5,
-                    style={'margin-bottom': '10px', 'color': '#272727'})
-            ], style={'width': '30%', 'display': 'inline-block'}),
-            html.Div(id='taiex-prediction-results', style={'width': '65%', 'display': 'inline-block', 'margin-left': '5%'})
-        ]),
-        html.Div([dcc.Graph(id='taiex-prediction-chart')], style={'margin-top': '20px'})
-    ], style={'background': 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)','padding': '25px','border-radius': '15px','box-shadow': '0 8px 25px rgba(0,0,0,0.15)','color': 'white','margin-bottom': '40px'}),
-    html.Div([
-        html.H3("📰 市場情緒與新聞分析", style={'color': '#E74C3C', 'margin-bottom': '20px'}),
-        html.Div([
-            html.Div([html.H4("市場情緒指標", style={'color': '#8E44AD'}), html.Div(id='sentiment-gauge')], style={'width': '48%', 'display': 'inline-block'}),
-            html.Div([html.H4("關鍵新聞摘要", style={'color': '#27AE60'}), html.Div(id='news-summary', style={'background': '#f8f9fa','padding': '15px','border-radius': '8px','max-height': '200px','overflow-y': 'auto'})], style={'width': '48%', 'display': 'inline-block', 'margin-left': '4%'})
-        ])
-    ], style={'margin-top': '30px','padding': '20px','background': 'white','border-radius': '10px','box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
-    html.Div([
-        html.Label("選擇股票:"),
-        dcc.Dropdown(id='stock-dropdown', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value='2330.TW'),
-        html.Label("時間範圍:"),
-        dcc.Dropdown(id='period-dropdown', options=[{'label': '1個月', 'value': '1mo'}, {'label': '6個月', 'value': '6mo'}, {'label': '1年', 'value': '1y'}], value='6mo'),
-        html.Label("圖表類型:"),
-        dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'}, {'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick'),
-    ]),
-    dcc.Graph(id='price-chart'),
-])
+    # --- 3. 【核心修改】獲取新聞並使用 Gemini 進行摘要 ---
+    top_news_list = predictor.get_news()
+    news_content = None # 先初始化
 
-# 從 Dash App 中獲取底層的 Flask server
-dash_server = dash_app.server
+    if top_news_list and isinstance(top_news_list, list): # 確保列表不為空且格式正確
+        # *** 呼叫我們的新函式來生成中文摘要 ***
+        summary_text = summarize_news_with_gemini(top_news_list)
+        # 使用 dcc.Markdown 來顯示，這樣如果摘要包含換行等格式會更好看
+        news_content = dcc.Markdown(summary_text, style={
+            'margin': '8px 0', 'padding-left': '5px',
+            'font-size': '15px', 'line-height': '1.7'
+        })
+    elif top_news_list == []: # 如果是空列表
+         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px'})
+    else: # 如果是 None 或其他錯誤
+        news_content = html.P("讀取新聞時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px'})
 
-# 【修正】使用 WSGIMiddleware 將 Flask server 掛載到 FastAPI 的根路徑 "/"
-fastapi_app.mount("/", WSGIMiddleware(dash_server))
+    return gauge_content, news_content
 
-# 主程式執行點 (用於本機測試)
-if __name__ == "__main__":
-    uvicorn.run(fastapi_app, host="0.0.0.0", port=7860)
+# 主程式執行
+if __name__ == '__main__':
+    # 在 Hugging Face Spaces 中執行
+    app.run(host="0.0.0.0", port=7860, debug=False)