Update app.py

app.py

@@ -1,5 +1,4 @@
 # HUGING_FACE_V3.1.1.py (多頁面版本)
-
 # 系統套件
 import os
 from datetime import datetime, timedelta
@@ -14,36 +13,23 @@ from plotly.subplots import make_subplots
 import re
 from bs4 import BeautifulSoup
 import requests
-
-# 引用您組員的預測器程式 (假設路徑正確)
-# from Bert_predict import BertPredictor
-# 為了讓程式碼可以獨立執行，這裡暫時移除對 BertPredictor 的引用
-# 如果您有 BertPredictor.py，請取消上面這行的註解，並確保它在同一個目錄下
-# 並且為 predictor 變數提供一個模擬值，以便程式能順利執行
-class BertPredictor:
-    def __init__(self, max_news_per_keyword=5):
-        print("模擬 BertPredictor 初始化...")
-        self.max_news_per_keyword = max_news_per_keyword
-        self.mock_sentiment_score = np.random.uniform(-1, 1) # 模擬一個隨機情緒分數
-        self.mock_news = [
-            "模擬新聞標題 1：市場樂觀情緒高漲。",
-            "模擬新聞標題 2：某公司財報亮眼，股價預期上漲。",
-            "模擬新聞標題 3：經濟數據顯示復甦跡象。"
-        ]
-
-    def get_news_index(self):
-        print(f"模擬獲取新聞情緒分數: {self.mock_sentiment_score:.2f}")
-        return self.mock_sentiment_score
-
-    def get_news(self):
-        print(f"模擬獲取新聞列表 (最多 {self.max_news_per_keyword} 則)")
-        return self.mock_news[:self.max_news_per_keyword]
-
-predictor = BertPredictor(max_news_per_keyword=5)
-print("模擬新聞情緒分析模型初始化完成。")
-
-
-# 台股代號對應表 (移除台指期，因為它現在是獨立區塊)
+import warnings
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+from tensorflow.keras.models import load_model
+
+# 引用您組員的預測器程式
+try:
+    from Bert_predict import BertPredictor
+except ImportError:
+    print("找不到 'Bert_predict.py' 模組，新聞情緒分析功能將無法使用。")
+    BertPredictor = None
+
+# 忽略所有 UserWarning
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# --- 資料準備與輔助函式 ---
+# 台股代號對應表
 TAIWAN_STOCKS = {
     '元大台灣50': '0050.TW', # 新增
     '台積電': '2330.TW',
@@ -60,205 +46,202 @@ TAIWAN_STOCKS = {
     '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'
+    '中信金': '2891.TW',
+    '中鋼': '2002.TW',
+    '聯電': '2303.TW',
+    '國泰金': '2882.TW',
+    '華碩': '2357.TW',
+    '友達': '2409.TW',
+    '緯創': '3231.TW',
+    '廣達': '2382.TW',
+    '技嘉': '2376.TW',
+    '英業達': '2356.TW',
+    '光寶科': '2301.TW',
 }
 
-# 產業分類
+# 產業分類 (簡化範例)
 INDUSTRY_MAPPING = {
-    '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', # 已存在，移除重複
-    '4966.TWO': '高速傳輸',
-    '3665.TW': '連接器',
-    '6870.TWO': '軟體整合',
-    '3105.TWO': 'PA功率'
+    '電子': ['2330.TW', '2454.TW', '2317.TW', '2308.TW', '3711.TW', '2357.TW', '2409.TW', '3231.TW', '2382.TW', '2376.TW', '2356.TW', '2301.TW'],
+    '金融': ['2881.TW', '2882.TW', '2891.TW'],
+    '塑化': ['1301.TW'],
+    '水泥': ['1101.TW'],
+    '傳產': ['2002.TW', '1216.TW', '2603.TW', '2637.TW', '2049.TW'],
+    '通訊': ['2412.TW'],
+    'ETF': ['0050.TW']
 }
 
-def get_stock_data(symbol, period='1y'):
-    """獲取股票資料"""
+def get_stock_data(ticker, period='1y'):
+    """從 yfinance 獲取股票資料"""
     try:
-        stock = yf.Ticker(symbol)
+        stock = yf.Ticker(ticker)
         data = stock.history(period=period)
-        # 處理台指期特殊情況
-        if data.empty and symbol == '^TWII':
-            print("嘗試獲取 ^TWII 資料失敗，嘗試獲取 0050.TW...")
-            stock = yf.Ticker('0050.TW')
-            data = stock.history(period=period)
-            if data.empty:
-                print("嘗試獲取 0050.TW 資料失敗，嘗試獲取 ^TWII...")
-                stock = yf.Ticker('^TWII') # 再次嘗試 ^TWII，以防萬一
-                data = stock.history(period=period)
         return data
     except Exception as e:
-        print(f"獲取股票資料時發生錯誤 ({symbol}): {e}")
+        print(f"無法獲取 {ticker} 的資料: {e}")
         return pd.DataFrame()
 
-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 get_economic_data(ticker, period='2y'):
+    """獲取總經指標資料，例如PMI"""
+    data = yf.download(ticker, period=period)
+    return data['Close']
 
-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()
+def get_business_climate_data():
+    """模擬獲取台灣景氣對策信號分數"""
+    df = pd.DataFrame({
+        'Date': pd.to_datetime(['2024-01', '2024-02', '2024-03', '2024-04', '2024-05', '2024-06', '2024-07']),
+        'Score': [22, 24, 25, 27, 29, 30, 31],
+        'Signal': ['黃藍', '黃藍', '綠', '綠', '綠', '綠', '綠']
+    })
+    return df
+
+def add_technical_indicators(df):
+    """計算並新增技術指標"""
+    # RSI
     delta = df['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-    # 避免除以零
-    rs = gain / loss.replace(0, 1e-9)
+    gain = delta.where(delta > 0, 0)
+    loss = -delta.where(delta < 0, 0)
+    avg_gain = gain.ewm(com=13, min_periods=14).mean()
+    avg_loss = loss.ewm(com=13, min_periods=14).mean()
+    rs = avg_gain / avg_loss
     df['RSI'] = 100 - (100 / (1 + rs))
-    exp1 = df['Close'].ewm(span=12).mean()
-    exp2 = df['Close'].ewm(span=26).mean()
+
+    # MACD
+    exp1 = df['Close'].ewm(span=12, adjust=False).mean()
+    exp2 = df['Close'].ewm(span=26, adjust=False).mean()
     df['MACD'] = exp1 - exp2
-    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()
-    df['BB_Upper'] = df['BB_Middle'] + (bb_std * 2)
-    df['BB_Lower'] = df['BB_Middle'] - (bb_std * 2)
-    low_min = df['Low'].rolling(window=9).min()
-    high_max = df['High'].rolling(window=9).max()
-    # 避免除以零
-    rsv = (df['Close'] - low_min) / (high_max - low_min).replace(0, 1e-9) * 100
-    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()
-    # 避免除以零
-    df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14).replace(0, 1e-9)
-    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)
-    # TR 計算
-    df['TR'] = np.maximum.reduce([
-        df['High'] - df['Low'],
-        abs(df['High'] - df['Close'].shift(1)),
-        abs(df['Low'] - df['Close'].shift(1))
-    ])
-    # 避免除以零
-    df['+DI'] = (df['+DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean().replace(0, 1e-9)) * 100
-    df['-DI'] = (df['-DM'].ewm(com=13, adjust=False).mean() / df['TR'].ewm(com=13, adjust=False).mean().replace(0, 1e-9)) * 100
-    df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']).replace(0, 1e-9) * 100
-    df['ADX'] = df['DX'].ewm(com=13, adjust=False).mean()
+    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    df['MACD_Hist'] = df['MACD'] - df['Signal_Line']
+
+    # Bollinger Bands
+    df['MA20'] = df['Close'].rolling(window=20).mean()
+    df['StdDev'] = df['Close'].rolling(window=20).std()
+    df['Upper_BB'] = df['MA20'] + (df['StdDev'] * 2)
+    df['Lower_BB'] = df['MA20'] - (df['StdDev'] * 2)
+
+    # KD (Stochastic Oscillator)
+    low_14 = df['Low'].rolling(window=14).min()
+    high_14 = df['High'].rolling(window=14).max()
+    df['%K'] = 100 * ((df['Close'] - low_14) / (high_14 - low_14))
+    df['%D'] = df['%K'].rolling(window=3).mean()
+
+    # %R (Williams %R)
+    df['%R'] = -100 * (high_14 - df['Close']) / (high_14 - low_14)
+
+    # DMI
+    df['DMplus'] = df['High'].diff().clip(lower=0)
+    df['DMminus'] = (-df['Low'].diff()).clip(lower=0)
+    df['TR'] = df[['High', 'Low', 'Close']].apply(lambda x: max(x['High'] - x['Low'], abs(x['High'] - x['Close']), abs(x['Low'] - x['Close'])), axis=1)
+    df['ADX'] = df['TR'].ewm(alpha=1/14, adjust=False).mean()
+    df['DIplus'] = df['DMplus'].ewm(alpha=1/14, adjust=False).mean() / df['ADX']
+    df['DIminus'] = df['DMminus'].ewm(alpha=1/14, adjust=False).mean() / df['ADX']
+    df['ADX'] = abs(df['DIplus'] - df['DIminus']) / (df['DIplus'] + df['DIminus']) * 100
+
     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
-    # 確保 range 符合實際數據範圍
-    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 generate_analysis_text(df):
+    """生成股票分析文字"""
+    if df.empty:
+        return {
+            'technical': "找不到技術資料。",
+            'fundamental': "找不到基本面資料。",
+            'outlook': "無法提供市場展望。"
+        }
+    
+    latest = df.iloc[-1]
+    
+    # 技術面分析
+    tech_text = "找不到技術分析資料。"
+    if 'RSI' in df.columns:
+        rsi = latest['RSI']
+        rsi_signal = "超買" if rsi > 70 else "超賣" if rsi < 30 else "中性"
+        tech_text = f"目前RSI為 {rsi:.2f}，顯示市場處於**{rsi_signal}**。近期走勢強勁，但需留意過熱風險。"
+    
+    # 基本面分析（簡化）
+    fundamental_text = "找不到基本面分析資料。"
+    fundamental_text = f"基本面表現穩健，產業前景看好。公司財務狀況良好，建議持續關注。"
+
+    # 市場展望
+    outlook_text = "市場展望樂觀，但仍需留意全球經濟不確定性。建議投資人審慎評估，並隨時關注最新市場動態。"
+    
+    return {
+        'technical': tech_text,
+        'fundamental': fundamental_text,
+        'outlook': outlook_text
+    }
 
-def get_business_climate_data():
-    """嘗試從 CSV 讀取景氣燈號資料"""
-    try:
-        if not os.path.exists('business_climate.csv'):
-            print("business_climate.csv 檔案不存在，返回空 DataFrame。")
-            return pd.DataFrame()
-        df = pd.read_csv('business_climate.csv')
-        # 嘗試自動識別欄位名稱
-        if 'Date' not in df.columns and '日期' not in df.columns:
-            if len(df.columns) == 2:
-                df.columns = ['Date', 'Index']
-                print("自動設定景氣燈號欄位為 'Date', 'Index'")
-            else:
-                print("景氣燈號 CSV 格式不正確，無法識別 'Date' 或 'Index' 欄位。")
-                return pd.DataFrame()
-        else:
-            if '日期' in df.columns: df = df.rename(columns={'日期': 'Date'})
-            if '燈號分數' in df.columns: df = df.rename(columns={'燈號分數': 'Index'})
-
-        if 'Date' in df.columns:
-            try: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-            except Exception as e:
-                print(f"轉換景氣燈號日期時出錯: {e}")
-                df['Date'] = pd.to_datetime(df['Date'].str.replace('-01', ''), errors='coerce') # 嘗試移除 -01
-
-        df = df.dropna(subset=['Date', 'Index']) # 確保都有日期和分數
-        return df
-    except Exception as e:
-        print(f"無法獲取景氣燈號資料: {str(e)}")
-        return pd.DataFrame()
+# --- LSTM 預測模型 ---
+def simple_lstm_predict(ticker, n_days=5):
+    """使用簡單 LSTM 預測未來 n 天的收盤價"""
+    model_path = 'lstm_model_taiex.h5'
+    scaler_path = 'scaler_taiex.pkl'
+    
+    # 檢查模型和 scaler 是否存在
+    if not os.path.exists(model_path) or not os.path.exists(scaler_path):
+        return None, "模型或縮放器檔案不存在，無法進行預測。"
 
-def get_pmi_data():
-    """嘗試從 CSV 讀取 PMI 資料"""
     try:
-        if not os.path.exists('taiwan_pmi.csv'):
-            print("taiwan_pmi.csv 檔案不存在，返回空 DataFrame。")
-            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']
+        # 載入模型和 scaler
+        model = load_model(model_path)
+        scaler = joblib.load(scaler_path)
+
+        # 獲取歷史資料
+        data = yf.download(ticker, period='60d', interval='1d')
+        if data.empty:
+            return None, "無法獲取歷史數據。"
+
+        # 使用最新的 60 ���收盤價作為輸入
+        last_60_days = data['Close'].values[-60:].reshape(-1, 1)
+        last_60_days_scaled = scaler.transform(last_60_days)
+        X_test = last_60_days_scaled.reshape(1, 60, 1)
+
+        # 進行預測
+        future_predictions = []
+        current_input = X_test
+        for _ in range(n_days):
+            predicted_scaled_price = model.predict(current_input, verbose=0)
+            future_predictions.append(predicted_scaled_price[0, 0])
+            current_input = np.append(current_input[:, 1:, :], predicted_scaled_price.reshape(1, 1, 1), axis=1)
+
+        # 反向轉換回原始價格
+        predicted_prices = scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1)).flatten()
+
+        # 建立預測結果 DataFrame
+        last_date = data.index[-1]
+        future_dates = [last_date + timedelta(days=i) for i in range(1, n_days + 1)]
+        pred_df = pd.DataFrame({'Date': future_dates, 'Predicted_Price': predicted_prices})
         
-        if 'Date' in df.columns:
-            try: df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-            except Exception as e:
-                print(f"轉換 PMI 日期時出錯: {e}")
-                df['Date'] = pd.to_datetime(df['Date'].str.replace('-01', ''), errors='coerce') # 嘗試移除 -01
+        # 建立歷史價格 DataFrame
+        history_df = data.reset_index()
+        history_df = history_df[['Date', 'Close']]
+        history_df.rename(columns={'Close': 'Price'}, inplace=True)
         
-        df = df.dropna(subset=['Date', 'Index']) # 確保都有日期和分數
-        return df
-    except Exception as e:
-        print(f"無法獲取 PMI 資料: {str(e)}")
-        return pd.DataFrame()
-
-# --- 頁面佈局定義 ---
+        return history_df, pred_df
 
+    except Exception as e:
+        print(f"預測過程中發生錯誤: {e}")
+        return None, f"預測過程中發生錯誤: {e}"
+
+# --- 主要應用程式 ---
+# 建立 Dash 應用程式
+app = dash.Dash(__name__, suppress_callback_exceptions=True)
+
+# 新聞預測器初始化
+predictor = None
+try:
+    if BertPredictor:
+        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
+
+
+# --- 頁面內容定義 ---
 # 首頁：預測與總經
 homepage_layout = html.Div([
-    html.H1("🤖 AI 預測與總體經濟分析", style={'text-align': 'center','color': '#FFCC22','margin-bottom': '25px'}),
     html.Div([
-        html.H2("📈 台指期指數預測", style={'text-align': 'center','color': 'white','margin-bottom': '25px'}),
+        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'}),
@@ -289,7 +272,7 @@ homepage_layout = html.Div([
     ], 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("📊 總體經濟指標", style={'color': '#2C3E50', 'margin-bottom': '20px'}),
+        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%'})
@@ -300,27 +283,24 @@ homepage_layout = html.Div([
 
 # 個股分析頁面
 stock_page_layout = html.Div([
-    html.H1("📈 個股深度分析", style={'text-align': 'center', 'margin-bottom': '30px'}),
     html.Div([
         html.Div([
-            html.Label("選擇股票:", style={'font-weight': 'bold', 'margin-right': '10px'}),
-            dcc.Dropdown(id='stock-dropdown', options=[{'label': name, 'value': symbol} for name, symbol in TAIWAN_STOCKS.items()], value='2330.TW', style={'width': '70%', 'display': 'inline-block'})
+            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:", style={'font-weight': 'bold', 'margin-right': '10px'}),
+            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={'width': '70%', 'display': 'inline-block'})
+                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:", style={'font-weight': 'bold', 'margin-right': '10px'}),
-            dcc.Dropdown(id='chart-type', options=[{'label': '線圖', 'value': 'line'},{'label': '蠟燭圖', 'value': 'candlestick'}], value='candlestick', style={'width': '70%', 'display': 'inline-block'})
+            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', 'padding': '20px', 'background': '#f8f9fa', 'border-radius': '10px'}),
-
+    ], style={'margin-bottom': '30px'}),
     html.Div(id='stock-info-cards', style={'margin-bottom': '30px'}),
-    html.Div([dcc.Graph(id='price-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([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([
@@ -331,14 +311,11 @@ stock_page_layout = html.Div([
                 value='RSI', style={'width': '100%'})
         ], style={'margin-bottom': '20px'}),
         html.Div([dcc.Graph(id='advanced-technical-chart')])
-    ], style={'margin-top': '30px','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': '30px', 'padding': '20px', 'background': 'white', 'border-radius': '10px', 'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
-
-    html.Div([html.H3("📊 產業表現分析"), dcc.Graph(id='industry-analysis')], style={'margin-top': '30px', 'padding': '20px', 'background': 'white', 'border-radius': '10px', 'box-shadow': '0 2px 10px rgba(0,0,0,0.1)'}),
-
+    ], 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.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'}),
@@ -354,7 +331,6 @@ stock_page_layout = html.Div([
             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([
@@ -377,15 +353,14 @@ stock_page_layout = html.Div([
 
 # --- 主要應用程式佈局 ---
 app.layout = html.Div([
-    dcc.Location(id='url', refresh=False), # 用於追蹤 URL
-    html.H1("台股趨勢分析儀表板", style={'text-align': 'center', 'margin-bottom': '10px', 'color': '#2c3e50'}),
-    # 導航列
+    dcc.Location(id='url', refresh=False),
+    html.H1("台股分析儀表板", style={'text-align': 'center', 'margin-bottom': '10px'}),
     html.Div([
-        dcc.Link('市場總覽 📈', href='/', style={'margin-right': '20px', 'font-size': '18px', 'text-decoration': 'none', 'color': '#3498db'}),
-        dcc.Link('個股分析 🔍', href='/stock-analysis', style={'font-size': '18px', 'text-decoration': 'none', 'color': '#e67e22'}),
-    ], style={'text-align': 'center', 'margin-bottom': '30px', 'padding': '10px', 'background-color': '#f8f9fa', 'border-radius': '8px'}),
-    html.Hr(style={'border-top': '1px solid #e0e0e0'}),
-    html.Div(id='page-content') # 這裡將動態載入頁面內容
+        dcc.Link('市場總覽', href='/', style={'margin-right': '20px', 'font-size': '18px'}),
+        dcc.Link('個股分析', href='/stock-analysis', style={'font-size': '18px'}),
+    ], style={'text-align': 'center', 'margin-bottom': '30px'}),
+    html.Hr(),
+    html.Div(id='page-content')
 ])
 
 
@@ -395,601 +370,368 @@ app.layout = html.Div([
     [dash.dependencies.Input('url', 'pathname')]
 )
 def display_page(pathname):
-    """根據 URL 路徑顯示對應的頁面���容"""
     if pathname == '/stock-analysis':
         return stock_page_layout
-    else: # 預設顯示首頁
+    else:
         return homepage_layout
 
-# --- 以下是所有回調函數 ---
-# 這些回調函數保持與您原程式碼相同的功能，但現在它們由 display_page 根據 URL 決定是否被觸發。
+# --- 回調函數 (所有原始的 callback 都放在這裡) ---
 
-# 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.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", style={'color': 'red'}), {}
-    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", style={'color': 'orange'}), {}
-    
-    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']
-    
-    # 為了讓圖表更平滑，我們預測幾個點
-    prediction_intervals = [1, 5, 10, 20, 60] # 預測的間隔天數
-    prediction_dates, prediction_prices = [last_date], [current_price]
-    
-    # 根據使用者選擇的 predict_days，決定要顯示哪些預測點
-    intervals_to_show = sorted([d for d in prediction_intervals if d <= predict_days] + [predict_days])
-    
-    for days in intervals_to_show:
-        # 這裡假設 simple_lstm_predict 可以處理任意間隔
-        # 如果是真實的 LSTM 模型，可能需要更複雜的邏輯來生成多步預測
-        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 = ('#4CAF50', '📈') if change_pct >= 0 else ('#F44336', '📉') # 綠色和紅色
-    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)'})
+def update_taiex_prediction(n_days):
+    """更新台指期預測結果"""
+    history_df, pred_df = simple_lstm_predict('^TWII', n_days)
     
-    fig = go.Figure()
-    # 顯示最近 60 天的歷史數據
-    recent_data = data.tail(60)
-    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)))
+    if history_df is None:
+        return html.P(pred_df, style={'color': 'red'}), go.Figure()
+
+    current_price = history_df.iloc[-1]['Price']
+    predicted_price = pred_df.iloc[-1]['Predicted_Price']
+    change = predicted_price - current_price
+    change_percent = (change / current_price) * 100
+    
+    direction = "📈 上漲" if change > 0 else "📉 下跌" if change < 0 else "↔ 持平"
+    color = "green" if change > 0 else "red" if change < 0 else "orange"
     
-    # 顯示預測路徑
-    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)))
+    result_text = html.Div([
+        html.P(f"當前價格: {current_price:.2f}", style={'font-size': '1.2em', 'margin': '5px 0'}),
+        html.P(f"預測 {n_days} 天後價格: {predicted_price:.2f}", style={'font-size': '1.2em', 'margin': '5px 0'}),
+        html.P(f"預測變動: {change:.2f} ({change_percent:.2f}%) {direction}", style={'font-size': '1.5em', 'font-weight': 'bold', 'color': color, 'margin': '10px 0'})
+    ])
+    
+    # 繪製圖表
+    fig = go.Figure()
+    # 歷史價格
+    fig.add_trace(go.Scatter(x=history_df['Date'], y=history_df['Price'], mode='lines', name='歷史價格', line=dict(color='#8E44AD')))
+    # 預測價格
+    fig.add_trace(go.Scatter(x=pred_df['Date'], y=pred_df['Predicted_Price'], mode='lines', name='預測價格', line=dict(color='#E74C3C', dash='dash')))
     
     fig.update_layout(
-        title=f'台指期 {predict_days}日預測走勢',
+        title=f'台指期指數歷史與預測 ({n_days}天)',
         xaxis_title='日期',
-        yaxis_title='指數點位',
-        height=350,
-        plot_bgcolor='rgba(0,0,0,0)', # 透明背景
-        paper_bgcolor='rgba(0,0,0,0)', # 透明背景
-        font=dict(color='white')
+        yaxis_title='價格',
+        legend_title='圖例',
+        template='plotly_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') # 獲取最近5天的數據
-    if data.empty: return html.Div("無法獲取股票資料", style={'color': 'red'})
-    
-    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 if prev_price != 0 else 0
-    
-    stock_name = [name for name, symbol in TAIWAN_STOCKS.items() if symbol == selected_stock][0]
-    color, arrow = ('#F44336', '▲') if change >= 0 else ('#4CAF50', '▼') # 紅色上漲, 綠色下跌
     
-    # 確保最高、最低、成交量有值
-    today_high = data['High'].iloc[-1] if not data.empty else 0
-    today_low = data['Low'].iloc[-1] if not data.empty else 0
-    today_volume = data['Volume'].iloc[-1] if not data.empty else 0
+    return result_text, fig
+
 
-    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', 'width': '30%'}),
-        html.Div([
-            html.H4("今日統計", style={'margin': '0 0 10px 0'}),
-            html.P(f"最高: ${today_high:.2f}", style={'margin': '5px 0'}),
-            html.P(f"最低: ${today_low:.2f}", style={'margin': '5px 0'}),
-            html.P(f"成交量: {today_volume:,.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', 'width': '30%'})
-    ], style={'display': 'flex', 'justify-content': 'flex-start', 'gap': '20px'})
-
-# 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')]
+    dash.dependencies.Output('sentiment-gauge', 'children'),
+    dash.dependencies.Output('news-summary', 'children')
 )
-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]
-    
-    # 創建子圖
-    fig = make_subplots(rows=1, cols=2, shared_yaxes=True, column_widths=[0.8, 0.2], horizontal_spacing=0.01)
+def update_sentiment_analysis():
+    """更新新聞情緒分析"""
+    if not predictor:
+        return html.Div("新聞情緒分析模型未初始化。"), html.Div("請檢查 'Bert_predict.py' 檔案是否存在。")
+
+    try:
+        sentiment_score, news_list = predictor.get_sentiment_score()
+        
+    except Exception as e:
+        sentiment_score = None
+        news_list = []
+        print(f"情緒分析獲取失敗: {e}")
     
-    # 添加股價圖 (蠟燭圖或線圖)
-    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: # line chart
-        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name=stock_name, line=dict(color='#3498db')), row=1, col=1)
-    
-    # 添加移動平均線
-    if 'MA5' in data.columns:
-        fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], mode='lines', name='MA5', line=dict(color='orange', width=1)), row=1, col=1)
-    if 'MA20' in data.columns:
-        fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], mode='lines', name='MA20', line=dict(color='blue', width=1)), row=1, col=1)
-
-    # 添加成交量分佈圖 (Volume Profile)
-    bin_edges, volume_per_bin, price_centers = calculate_volume_profile(data, num_bins=50)
-    if volume_per_bin is not None and price_centers is not None and len(price_centers) == len(volume_per_bin):
-        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)
+    # 1. 建立儀表板 (Gauge)
+    if sentiment_score is not None:
+        gauge_fig = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=sentiment_score,
+            title={'text': "市場情緒分數 (0-100)"},
+            domain={'x': [0, 1], 'y': [0, 1]},
+            gauge={
+                'axis': {'range': [0, 100]},
+                'bar': {'color': "#667eea"},
+                '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)
     else:
-        # 如果沒有成交量資料，則不顯示 Volume Profile
-        print("無成交量資料，跳過 Volume Profile 顯示")
-        fig.update_layout(column_widths=[1.0]) # 調整為單欄佈局
+        # 處理無法計算分數的情況
+        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)
+
+
+    # 2. 從 predictor 獲取分數最高的3則新聞
+    top_news_list = predictor.get_news()
+
+    # 3. 建立新聞摘要元件
+    if top_news_list: # 如果列表不為空
+        news_content = html.Div([
+            html.P(f"• {news}", style={
+                'margin': '8px 0',
+                'padding-left': '5px',
+                'font-size': '14px',
+                'border-left': '3px solid #E74C3C'
+            }) for news in top_news_list
+        ])
+    else:
+        news_content = html.Div("今日尚無重大新聞摘要。")
+    
+    return gauge_content, news_content
 
-    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)'),
-        # x-axis for volume profile, y-axis for volume profile is shared with price chart
-        xaxis2=dict(title='成交量', showticklabels=True), 
-        yaxis2=dict(showticklabels=False), # 隱藏 Y 軸標籤，因為它與左邊共享
-        bargap=0.05, # 調整柱狀圖間隔
-        margin=dict(l=50, r=20, t=50, b=50)
-    )
-    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')]
+    dash.dependencies.Output('business-climate-chart', 'figure')
 )
-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() # 預設圖形
-    
-    if indicator == 'RSI':
-        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=[0.6, 0.4], subplot_titles=('收盤價', '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', 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', 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='Histogram', 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.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)
+def update_business_climate_chart():
+    """更新景氣燈號圖表"""
+    df = get_business_climate_data()
     
-    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=('收盤價', '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])
+    color_map = {'藍': '#3498DB', '黃藍': '#F39C12', '綠': '#27AE60', '黃紅': '#E67E22', '紅': '#E74C3C'}
+    df['Color'] = df['Signal'].map(color_map)
     
-    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=('收盤價', '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])
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'], y=df['Score'], mode='lines+markers', marker=dict(color=df['Color'], size=10), line=dict(color='gray')))
     
-    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=('收盤價', 'DMI 指標'))
-        # DMI 通常需要前 14 天的數據，所以從第 14 天開始繪製
-        data_filtered = data.iloc[14:] if len(data) > 14 else data
-        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])
-        
+    fig.update_layout(
+        title='台灣景氣對策信號',
+        xaxis_title='日期',
+        yaxis_title='分數',
+        yaxis=dict(range=[9, 45]),
+        template='plotly_white'
+    )
     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')]
+    dash.dependencies.Output('pmi-chart', 'figure')
 )
-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]
+def update_pmi_chart():
+    """更新PMI圖表"""
+    # 模擬獲取PMI資料，可替換為真實API
+    pmi = get_economic_data('ISM-MAN_PMI') 
     
-    # 根據開盤價與收盤價決定柱狀圖顏色
-    colors = ['#F44336' if data['Close'].iloc[i] >= data['Open'].iloc[i] else '#4CAF50' for i in range(len(data))]
+    fig = px.line(x=pmi.index, y=pmi, title='美國ISM製造業PMI', labels={'x':'日期', 'y':'PMI'})
+    fig.add_hline(y=50, line_dash="dash", line_color="red", annotation_text="50 榮枯線")
     
-    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)
+    fig.update_layout(template='plotly_white')
     return fig
 
-# 更新產業分析圖表 (僅顯示前10檔股票的月報酬率比較)
-@app.callback(
-    dash.dependencies.Output('industry-analysis', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')] # 觸發條件，確保圖表會更新
-)
-def update_industry_analysis(selected_stock):
-    industry_data = []
-    # 僅取列表中的前10支股票進行比較，避免圖表過於擁擠
-    stocks_to_analyze = list(TAIWAN_STOCKS.items())[:10] 
-    
-    for name, symbol in stocks_to_analyze:
-        data = get_stock_data(symbol, '1mo') # 獲取一個月的數據
-        if not data.empty:
-            try:
-                return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-                industry = INDUSTRY_MAPPING.get(symbol, '其他')
-                industry_data.append({'股票': name, '代碼': symbol, '月報酬率(%)': return_pct, '產業': industry})
-            except ZeroDivisionError:
-                print(f"注意：{name} ({symbol}) 的起始股價為 0，無法計算報酬率。")
-                industry_data.append({'股票': name, '代碼': symbol, '月報酬率(%)': 0, '產業': industry})
-
-    if not industry_data: 
-        fig = go.Figure().add_annotation(text="無股票資料可供分析", showarrow=False)
-        fig.update_layout(title="產業表現分析 (月報酬率)", height=400)
-        return fig
-
-    df_industry = pd.DataFrame(industry_data)
-    
-    # 創建圓餅圖
-    fig = px.pie(df_industry, values='月報酬率(%)', names='股票', title='前10檔股票月報酬率比較', 
-                 color_discrete_sequence=px.colors.qualitative.Pastel) # 使用 Pastel 調色盤
-    
-    fig.update_layout(height=400, margin=dict(t=50, b=0, l=0, r=0))
-    return fig
 
-# 更新景氣燈號圖表
 @app.callback(
-    dash.dependencies.Output('business-climate-chart', 'figure'),
-    [dash.dependencies.Input('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
+    [
+        dash.dependencies.Output('stock-info-cards', 'children'),
+        dash.dependencies.Output('price-chart', 'figure'),
+        dash.dependencies.Output('volume-chart', 'figure'),
+        dash.dependencies.Output('industry-analysis', 'figure'),
+        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'),
+        dash.dependencies.Input('chart-type', 'value')
+    ]
 )
-def update_business_climate_chart(pathname):
-    if pathname != '/': return {} # 確保只在首頁觸發
-    df = get_business_climate_data()
-    if df.empty:
-        fig = go.Figure().add_annotation(text="無法載入景氣燈號資料", showarrow=False)
-        fig.update_layout(title="台灣景氣燈號", height=300)
-        return fig
-        
-    # 定義燈號顏色
-    def get_light_color(score):
-        if score >= 32: return 'red' # 紅燈
-        elif score >= 24: return 'orange' # 黃紅燈
-        elif score >= 17: return 'yellow' # 黃藍燈
-        elif score >= 10: return 'lightgreen' # 綠燈
-        else: return 'blue' # 藍燈
-
-    colors = [get_light_color(score) for score in df['Index']]
+def update_all_stock_info(selected_stock, selected_period, chart_type):
+    """根據選取股票更新所有相關圖表與資訊"""
     
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        x=df['Date'], 
-        y=df['Index'], 
-        mode='lines+markers', 
-        name='景氣燈號分數', 
-        line=dict(color='#2E86C1', width=2), # 深藍色線
-        marker=dict(size=8, color=colors, line=dict(width=2, color='#2E86C1')) # 標記點顏色隨燈號變化
-    ))
-    
-    # 添加參考線
-    fig.add_hline(y=32, line_dash="dash", line_color="red", annotation_text="紅燈 (32)", annotation_position="bottom right")
-    fig.add_hline(y=17, line_dash="dash", line_color="yellow", annotation_text="黃藍燈 (17)", annotation_position="bottom right")
-    fig.add_hline(y=10, line_dash="dash", line_color="lightgreen", annotation_text="綠燈 (10)", annotation_position="bottom right")
+    # 獲取資料
+    df = get_stock_data(selected_stock, period=selected_period)
+    df = add_technical_indicators(df)
     
-    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]), # 調整 Y 軸範圍
-        margin=dict(l=50, r=20, t=50, b=50)
-    )
-    return fig
+    if df.empty:
+        return [
+            html.Div("找不到股票資訊。"),
+            go.Figure(),
+            go.Figure(),
+            go.Figure(),
+            "找不到技術分析資料。",
+            "找不到基本面分析資料。",
+            "無法提供市場展望。"
+        ]
 
-# 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]
+    # --- 1. 股票資訊卡片 ---
+    latest_data = df.iloc[-1]
+    last_close = df['Close'].iloc[-2] if len(df) > 1 else latest_data['Close']
+    change = latest_data['Close'] - last_close
+    change_percent = (change / last_close) * 100
+    change_color = 'green' if change >= 0 else 'red'
     
-    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)
-    
-    # 確保有足夠數據計算
-    if len(data) < 2: return "數據不足", "數據不足", "數據不足"
-
-    current_price = data['Close'].iloc[-1]
-    first_price = data['Close'].iloc[0]
-    price_change = ((current_price - first_price) / first_price) * 100 if first_price != 0 else 0
-    
-    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
-
-    # --- 技術面分析 ---
-    trend_desc = "上漲" if price_change > 5 else "下跌" if price_change < -5 else "盤整"
-    trend_color = '#F44336' if price_change > 5 else '#4CAF50' if price_change < -5 else '#FF9800'
-    rsi_desc = "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"
-    rsi_color = '#F44336' if rsi_current > 70 else '#4CAF50' if rsi_current < 30 else '#2196F3'
-    macd_desc = "多頭" if macd_current > macd_signal_current else "空頭"
-    macd_color = '#F44336' if macd_current > macd_signal_current else '#4CAF50'
-
-    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"{trend_desc}", style={'color': trend_color, '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(f"{rsi_desc}", style={'color': rsi_color, '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': macd_color, 'font-weight': 'bold'}), 
-                f"信號線({macd_signal_current:.3f})，顯示", html.Span(f"{macd_desc}", style={'color': macd_color, 'font-weight': 'bold'}), "格局。"]),
-    ])
+    info_cards = html.Div([
+        html.Div([
+            html.H5("收盤價", style={'color': '#8e44ad'}),
+            html.H3(f"{latest_data['Close']:.2f} TWD", style={'color': '#8e44ad'})
+        ], className="card", style={'border-left': '5px solid #8e44ad'}),
+        html.Div([
+            html.H5("漲跌幅", style={'color': '#27ae60'}),
+            html.H3(f"{change:.2f} ({change_percent:.2f}%)", style={'color': change_color})
+        ], className="card", style={'border-left': '5px solid #27ae60'}),
+        html.Div([
+            html.H5("成交量", style={'color': '#d35400'}),
+            html.H3(f"{latest_data['Volume']/10000:,.0f} 萬股", style={'color': '#d35400'})
+        ], className="card", style={'border-left': '5px solid #d35400'}),
+    ], style={'display': 'flex', 'justify-content': 'space-around', 'margin-bottom': '20px'})
 
-    # --- 基本面分析 ---
-    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"建議持續關注公司最新財報、新聞動態及產業趨勢，以掌握其長期發展潛力。"]),
-        html.P([html.Strong("風險提示："), f"基本面分析僅供參考，實際投資決策請獨立判斷��"])
-    ])
+    # --- 2. 股價圖 ---
+    if chart_type == 'candlestick':
+        price_fig = go.Figure(data=[go.Candlestick(x=df.index,
+            open=df['Open'],
+            high=df['High'],
+            low=df['Low'],
+            close=df['Close'])
+        ])
+    else: # line chart
+        price_fig = px.line(df, x=df.index, y='Close', title='股價走勢圖')
+
+    price_fig.update_layout(xaxis_rangeslider_visible=False, title=f'{selected_stock} 股價走勢圖', template='plotly_white')
 
-    # --- 市場展望與投資建議 ---
-    outlook_tone = "謹慎樂觀" if price_change > 10 else "保守觀望" if price_change < -10 else "中性持平"
-    outlook_color = '#4CAF50' if price_change > 10 else '#FF9800' if price_change < -10 else '#757575'
+    # --- 3. 成交量圖 ---
+    volume_fig = px.bar(df, x=df.index, y='Volume', title='成交量', color='Volume', color_continuous_scale='bluered')
+    volume_fig.update_layout(template='plotly_white', coloraxis_showscale=False)
     
-    market_outlook = html.Div([
-        html.P([html.Strong("整體評估："), f"基於技術面與基本面綜合考量，對 {stock_name} 目前採取", 
-                html.Span(f"{outlook_tone}", style={'color': outlook_color, 'font-weight': 'bold'}), "態度。"]),
-        html.P([html.Strong("投資建議："), "短期交易者可關注技術指標訊號，長期投資者應深入研究其基本面與產業前景。請注意，市場波動風險始終存在，務必做好風險控管。"]),
-    ])
+    # --- 4. 產業表現分析 ---
+    industry_analysis_fig = go.Figure()
+    industry_stock = ''
+    for industry, stocks in INDUSTRY_MAPPING.items():
+        if selected_stock in stocks:
+            industry_stock = industry
+            for stock_symbol in stocks:
+                stock_data = get_stock_data(stock_symbol, period='1y')
+                if not stock_data.empty:
+                    industry_analysis_fig.add_trace(go.Scatter(
+                        x=stock_data.index, y=stock_data['Close'], mode='lines', name=stock_symbol,
+                        visible='legendonly' if stock_symbol != selected_stock else True
+                    ))
+            break
+    
+    industry_analysis_fig.update_layout(title=f'{industry_stock} 產業表現', template='plotly_white')
     
-    return technical_text, fundamental_text, market_outlook
+    # --- 5. 分析師觀點文字 ---
+    analysis_texts = generate_analysis_text(df)
+
+    return info_cards, price_fig, volume_fig, industry_analysis_fig, analysis_texts['technical'], analysis_texts['fundamental'], analysis_texts['outlook']
+
 
-# 更新PMI圖表
 @app.callback(
-    dash.dependencies.Output('pmi-chart', 'figure'),
-    [dash.dependencies.Input('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
+    dash.dependencies.Output('advanced-technical-chart', 'figure'),
+    [
+        dash.dependencies.Input('stock-dropdown', 'value'),
+        dash.dependencies.Input('period-dropdown', 'value'),
+        dash.dependencies.Input('technical-indicator-selector', 'value')
+    ]
 )
-def update_pmi_chart(pathname):
-    if pathname != '/': return {} # 確保只在首頁觸發
-    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
-        
-    # 根據 PMI 值決定柱狀圖顏色
-    colors = ['#F44336' if value >= 50 else '#4CAF50' for value in df['Index']]
+def update_technical_indicator_chart(selected_stock, selected_period, indicator):
+    """更新技術指標圖表"""
+    df = get_stock_data(selected_stock, period=selected_period)
+    df = add_technical_indicators(df)
     
+    if df.empty or indicator not in df.columns:
+        return go.Figure()
+
     fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        x=df['Date'], 
-        y=df['Index'], 
-        mode='lines+markers', 
-        name='PMI 指數', 
-        line=dict(color='#34495E', width=2), # 深灰色線
-        marker=dict(size=8, color=colors, line=dict(width=2, color='#34495E'))
-    ))
     
-    fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線 (50)", annotation_position="bottom right")
+    if indicator == 'RSI':
+        fig = px.line(df, x=df.index, y='RSI', title='RSI 相對強弱指標')
+        fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="超買線")
+        fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="超賣線")
+        fig.update_yaxes(range=[0, 100])
     
-    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]), # 調整 Y 軸範圍
-        margin=dict(l=50, r=20, t=50, b=50)
-    )
+    elif indicator == 'MACD':
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], name='MACD', mode='lines', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=df.index, y=df['Signal_Line'], name='Signal', mode='lines', line=dict(color='red')))
+        colors = ['green' if val > 0 else 'red' for val in df['MACD_Hist']]
+        fig.add_trace(go.Bar(x=df.index, y=df['MACD_Hist'], name='Histogram', marker_color=colors))
+        fig.update_layout(title='MACD 指數平滑異同移動平均線')
+        
+    elif indicator == 'BB':
+        fig = px.line(df, x=df.index, y=['Close', 'Upper_BB', 'Lower_BB'], title='布林通道 Bollinger Bands')
+    
+    elif indicator == 'KD':
+        fig = px.line(df, x=df.index, y=['%K', '%D'], title='KD 隨機指標')
+        fig.add_hline(y=80, line_dash="dash", line_color="red", annotation_text="超買線")
+        fig.add_hline(y=20, line_dash="dash", line_color="green", annotation_text="超賣線")
+        fig.update_yaxes(range=[0, 100])
+
+    elif indicator == 'WR':
+        fig = px.line(df, x=df.index, y='%R', title='威廉指標 %R')
+        fig.add_hline(y=-20, line_dash="dash", line_color="red", annotation_text="超買線")
+        fig.add_hline(y=-80, line_dash="dash", line_color="green", annotation_text="超賣線")
+        fig.update_yaxes(range=[-100, 0])
+
+    elif indicator == 'DMI':
+        fig = px.line(df, x=df.index, y=['DIplus', 'DIminus', 'ADX'], title='DMI 動向指標')
+        fig.add_hline(y=20, line_dash="dash", line_color="orange", annotation_text="趨勢強弱參考線")
+
+    fig.update_layout(template='plotly_white')
     return fig
 
-# 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')]
+    [
+        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' # 固定比較基準
-    
-    # 處理使用者選擇的股票
-    if not selected_stocks: 
-        display_stocks = [fixed_stock] # 如果沒選，預設顯示 0050
-    elif fixed_stock not in selected_stocks:
-        display_stocks = [fixed_stock] + selected_stocks # 如果沒選 0050，則加入
-    else:
-        display_stocks = selected_stocks
-    
-    display_stocks = list(set(display_stocks))[:5] # 去重並限制最多 5 檔
+def update_comparison_chart(tickers, period):
+    if not tickers:
+        return go.Figure(), html.P("請至少選擇一檔股票。")
+    
+    df_dict = {}
+    for ticker in tickers:
+        df = yf.download(ticker, period=period)
+        if not df.empty:
+            df['Normalized'] = df['Close'] / df['Close'].iloc[0] * 100
+            df_dict[ticker] = df['Normalized']
+    
+    if not df_dict:
+        return go.Figure(), html.P("找不到任何股票資料。")
+        
+    normalized_df = pd.DataFrame(df_dict)
     
     fig = go.Figure()
-    comparison_data = []
-    
-    for stock in display_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)
-            
-            # 計算相對績效 (以第一天為基準 100)
-            normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100 if data['Close'].iloc[0] != 0 else data['Close'] * 0
-            fig.add_trace(go.Scatter(x=data.index, y=normalized_prices, mode='lines', name=stock_name, line=dict(width=2)))
-            
-            # 計算總報酬率和波動率
-            total_return = ((data['Close'].iloc[-1] / data['Close'].iloc[0]) - 1) * 100 if data['Close'].iloc[0] != 0 else 0
-            # 波動率 (年化標準差)
-            pct_change = data['Close'].pct_change().dropna()
-            volatility = pct_change.std() * np.sqrt(252) * 100 if not pct_change.empty else 0 # 假設一年有 252 個交易日
-            
-            comparison_data.append({
-                'name': stock_name, 
-                'return': total_return, 
-                'volatility': volatility, 
-                'current_price': data['Close'].iloc[-1]
-            })
-
+    for col in normalized_df.columns:
+        stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == col), col)
+        fig.add_trace(go.Scatter(x=normalized_df.index, y=normalized_df[col], name=stock_name, mode='lines'))
+        
     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', # 滑鼠懸停時顯示所有線的資訊
-        margin=dict(l=50, r=20, t=50, b=50)
+        title='股票相對漲跌幅比較',
+        xaxis_title='日期',
+        yaxis_title='相對漲��幅 (%) (基期=100)',
+        template='plotly_white'
     )
     
-    # 創建比較結果表格
-    table_rows = []
-    if comparison_data:
-        # 按報酬率排序 (由高到低)
-        sorted_data = sorted(comparison_data, key=lambda x: x['return'], reverse=True)
-        for item in sorted_data:
-            return_color = '#F44336' if item['return'] > 0 else '#4CAF50' # 紅色代表上漲，綠色代表下跌
-            table_rows.append(html.Tr([
-                html.Td(item['name'], style={'font-weight': 'bold', 'padding': '8px'}), 
-                html.Td(f"{item['return']:+.1f}%", style={'color': return_color, 'font-weight': 'bold', 'padding': '8px'}), 
-                html.Td(f"{item['volatility']:.1f}%", style={'padding': '8px'}), 
-                html.Td(f"${item['current_price']:.2f}", style={'padding': '8px'})
-            ]))
-            
-        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%', 'border-collapse': 'collapse', 'margin-top': '15px'})
-        return fig, table
-    else:
-        return fig, html.Div("無可比較資料", style={'margin-top': '15px'})
-
-
-# ==============================================================================
-# ===== 【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('url', 'pathname')] # 觸發條件：當使用者進入首頁時更新
-)
-def update_sentiment_analysis(pathname):
-    if pathname != '/': return {}, {} # 確保只在首頁觸發
-
-    # 檢查 predictor 是否成功初始化 (在程式碼開頭已處理)
-    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，請檢查後台日誌。", style={'color': 'red'})
-
-    # --- 1. 從 predictor 獲取新聞情緒平均分數 ---
-    sentiment_score_raw = predictor.get_news_index()
-
-    # --- 2. 建立情緒指標儀表板 ---
-    gauge_content = html.Div() # 預設值
-    if sentiment_score_raw is not None:
-        # **重要假設**：假設您模型的輸出範圍在 [-1, 1] 之間
-        # 我們需要將其正規化到儀表板的 [0, 100] 範圍內
-        sentiment_score_normalized = max(0, min(100, (sentiment_score_raw + 1) * 50)) # 正規化並確保在0-100之間
-
-        # 根據分數決定顏色和標籤
-        if sentiment_score_normalized >= 65:
-            bar_color, level_text = "#5cb85c", "樂觀" # 綠色
-        elif sentiment_score_normalized >= 35:
-            bar_color, level_text = "#f0ad4e", "中性" # 黃色
-        else:
-            bar_color, level_text = "#d9534f", "悲觀" # 紅色
-            
-        gauge_fig = go.Figure(go.Indicator(
-            mode = "gauge+number",
-            value = sentiment_score_normalized,
-            domain = {'x': [0, 1], 'y': [0, 1]},
-            title = {'text': f"市場情緒: {level_text}", 'font': {'size': 18}},
-            gauge = {
-                'axis': {'range': [0, 100], 'tickwidth': 1, 'tickcolor': "darkblue"},
-                'bar': {'color': bar_color, 'thickness': 0.8},
-                'steps': [
-                    {'range': [0, 35], 'color': "rgba(217, 83, 79, 0.2)"}, # 悲觀區間背景
-                    {'range': [35, 65], 'color': "rgba(240, 173, 78, 0.2)"}, # 中性區間背景
-                    {'range': [65, 100], 'color': "rgba(92, 184, 92, 0.2)"}  # 樂觀區間背景
-                ],
-                'threshold' : { # 設定觸發線
-                    'line': {'color': "red", 'width': 4},
-                    'thickness': 0.75,
-                    'value': sentiment_score_normalized # 這裡設為當前值，也可以設為固定值
-                }
-            }
-        ))
-        gauge_fig.update_layout(height=220, margin=dict(l=30, r=30, t=50, b=20))
-        gauge_content = dcc.Graph(figure=gauge_fig)
-    else:
-        # 處理無法計算分數的情況
-        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)
-
-    # --- 3. 從 predictor 獲取新聞摘要 ---
-    top_news_list = predictor.get_news()
-
-    # --- 4. 建立新聞摘要元件 ---
-    if top_news_list: # 如果列表不為空
-        news_content = html.Div([
-            html.P(f"• {news}", style={
-                'margin': '8px 0',
-                'padding-left': '5px',
-                'font-size': '14px',
-                'line-height': '1.5',
-                'border-left': '3px solid #3498db', # 添加���側邊框
-                'background-color': '#ecf0f1', # 淺灰色背景
-                'border-radius': '5px',
-                'padding-top': '5px',
-                'padding-bottom': '5px'
-            }) for news in top_news_list
+    # 建立表格
+    summary_data = []
+    for ticker, df in df_dict.items():
+        stock_name = next((name for name, symbol in TAIWAN_STOCKS.items() if symbol == ticker), ticker)
+        start_price = df.iloc[0]
+        end_price = df.iloc[-1]
+        change_percent = (end_price - start_price)
+        summary_data.append({
+            '股票': stock_name,
+            '漲跌幅': f'{change_percent:.2f}%'
+        })
+    
+    summary_df = pd.DataFrame(summary_data)
+    table = html.Table([
+        html.Thead(html.Tr([html.Th(col) for col in summary_df.columns])),
+        html.Tbody([
+            html.Tr([
+                html.Td(summary_df.iloc[i][col]) for col in summary_df.columns
+            ]) for i in range(len(summary_df))
         ])
-    elif top_news_list == []: # 如果是空列表 (無新聞)
-         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px', 'color': '#7f8c8d'})
-    else: # 如果是 None (讀取檔案出錯)
-        news_content = html.P("讀取新聞時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px', 'color': 'red'})
-
-    return gauge_content, news_content
-
+    ])
+    
+    return fig, table
 
 # 主程式執行
 if __name__ == '__main__':