Delete app.py

app.py

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-# HUGING_FACE_V3.1.3.py (整合 Bert_predict 和 XGBoost 版本)
-
-# 系統套件
-import os
-from datetime import datetime, timedelta
-import google.generativeai as genai
-import pandas as pd
-import numpy as np
-import yfinance as yf
-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
-import re
-from bs4 import BeautifulSoup
-import requests
-import time # 引用 time 模組以處理時間戳
-
-# ========================= 引用外部模組 START =========================
-# 引用您組員的預測器程式
-from Bert_predict import BertPredictor
-
-# 引用新的模型預測器
-from model_predictor import XGBoostModel
-# ========================== 引用外部模組 END ==========================
-
-# ========================= 全域設定 START =========================
-# 【【【模型切換開關】】】
-# False: 使用簡易統計模型 (預設)
-# True:  使用 model_predictor.py 中的進階 XGBoost 模型
-# *** 注意：請務必設定為 True 才能啟用您的 XGBoost 模型 ***
-USE_ADVANCED_MODEL = True
-
-# ========================= CACHE 設定 START =========================
-# 分析結果的快取字典
-ANALYSIS_CACHE = {}
-# 快取有效時間（秒），例如：8 小時 = 8 * 60 * 60 = 28800 秒
-CACHE_DURATION_SECONDS = 8 * 60 * 60
-# ========================== CACHE 設定 END ==========================
-# ========================== 全域設定 END ==========================
-
-# 台股代號對應表 (此處省略，與原檔案相同)
-TAIWAN_STOCKS = {
-    '元大台灣50': '0050.TW', '台積電': '2330.TW', '聯發科': '2454.TW', '鴻海': '2317.TW',
-    '台達電': '2308.TW', '廣達': '2382.TW', '富邦金': '2881.TW', '中信金': '2891.TW',
-    '國泰金': '2882.TW', '聯電': '2303.TW', '中華電': '2412.TW', '玉山金': '2884.TW',
-    '兆豐金': '2886.TW', '日月光投控': '3711.TW', '華碩': '2357.TW', '統一': '1216.TW',
-    '元大金': '2885.TW', '智邦': '2345.TW', '緯創': '3231.TW', '聯詠': '3034.TW',
-    '第一金': '2892.TW', '瑞昱': '2379.TW', '緯穎': '6669.TWO', '永豐金': '2890.TW',
-    '合庫金': '5880.TW', '華南金': '2880.TW', '台光電': '2383.TW', '世芯-KY': '3661.TWO',
-    '奇鋐': '3017.TW', '凱基金': '2883.TW', '大立光': '3008.TW', '長榮': '2603.TW',
-    '光寶科': '2301.TW', '中鋼': '2002.TW', '中租-KY': '5871.TW', '國巨': '2327.TW',
-    '台新金': '2887.TW', '上海商銀': '5876.TW', '台泥': '1101.TW', '台灣大': '3045.TW',
-    '和碩': '4938.TW', '遠傳': '4904.TW', '和泰車': '2207.TW', '研華': '2395.TW',
-    '台塑': '1301.TW', '統一超': '2912.TW', '藥華藥': '6446.TWO', '南亞': '1303.TW',
-    '陽明': '2609.TW', '萬海': '2615.TW', '台塑化': '6505.TW', '慧洋-KY': '2637.TW',
-    '上銀': '2049.TW', '南亞科': '2408.TW', '旺宏': '2337.TW', '譜瑞-KY': '4966.TWO',
-    '貿聯-KY': '3665.TW', '驊訊': '6870.TWO', '穩懋': '3105.TWO'
-}
-
-# 產業分類 (此處省略，與原檔案相同)
-INDUSTRY_MAPPING = {
-    '0050.TW': 'ETF', '2330.TW': '半導體', '2454.TW': '半導體', '2317.TW': '電子組件',
-    '2308.TW': '電子', '2382.TW': '電子', '2881.TW': '金融', '2891.TW': '金融', '2882.TW': '金融',
-    '2303.TW': '半導體', '2412.TW': '電信', '2884.TW': '金融', '2886.TW': '金融', '3711.TW': '半導體',
-    '2357.TW': '電子', '1216.TW': '食品', '2885.TW': '金融', '2345.TW': '網通設備', '3231.TW': '電子',
-    '3034.TW': '半導體', '2892.TW': '金融', '2379.TW': '半導體', '6669.TWO': '電子', '2890.TW': '金融',
-    '5880.TW': '金融', '2880.TW': '金融', '2383.TW': '電子', '3661.TWO': '半導體', '3017.TW': '電子',
-    '2883.TW': '金融', '3008.TW': '光學', '2603.TW': '航運', '2301.TW': '電子', '2002.TW': '鋼鐵',
-    '5871.TW': '金融', '2327.TW': '電子被動元件', '2887.TW': '金融', '5876.TW': '金融', '1101.TW': '營建',
-    '3045.TW': '電信', '4938.TW': '電子', '4904.TW': '電信', '2207.TW': '汽車', '2395.TW': '電腦周邊',
-    '1301.TW': '塑膠', '2912.TW': '百貨', '6446.TWO': '生技', '1303.TW': '塑膠', '2609.TW': '航運',
-    '2615.TW': '航運', '6505.TW': '塑膠', '2637.TW': '散裝航運', '2049.TW': '工具機', '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'
-}
-
-# 模型的特徵欄位順序 (與訓練腳本完全一致)
-MODEL_FEATURE_COLUMNS = [
-    'close', 'return_t-1', 'return_t-5', 'MA5_close', 'volatility_5d',
-    'volume_ratio_5d', 'MACD_diff', 'dji_return_t-1', 'sox_return_t-1', 'NEWS',
-    'MACDvol', 'RSI_14', 'ADX', 'volume_weighted_return'
-]
-
-def get_stock_data(symbol, period='2y'):
-    """獲取股票資料"""
-    try:
-        # 確保下載足夠的數據來計算所有指標
-        start_date = (datetime.now() - timedelta(days=730)).strftime('%Y-%m-%d')
-        data = yf.download(symbol, start=start_date, progress=False)
-        if data.empty:
-            print(f"警告: {symbol} 數據為空。")
-            return pd.DataFrame()
-        # 欄位名稱統一為大寫開頭，以利後續處理
-        data.columns = [col.capitalize() for col in data.columns]
-        return data
-    except Exception as e:
-        print(f"獲取 {symbol} 數據時發生錯誤: {e}")
-        return pd.DataFrame()
-
-
-def create_new_features(df, dji_df, sox_df):
-    """
-    【【核心修正】】
-    創建與訓練腳本完全一致的新技術指標特徵。
-    """
-    # 確保索引是 datetime 格式
-    df.index = pd.to_datetime(df.index)
-    dji_df.index = pd.to_datetime(dji_df.index)
-    sox_df.index = pd.to_datetime(sox_df.index)
-
-    # 重新命名欄位以符合訓練腳本
-    df = df.rename(columns={'Close': 'close', 'Volume': 'volume'})
-
-    # 1. return_t-1 — 前一日報酬率
-    df['return_t-1'] = df['close'].pct_change()
-
-    # 2. return_t-5 — 過去 5 日累積報酬率
-    df['return_t-5'] = (df['close'] / df['close'].shift(5) - 1)
-
-    # 3. MA5_close — 5 日移動平均價
-    df['MA5_close'] = df['close'].rolling(window=5).mean()
-
-    # 4. volatility_5d — 5 日報酬標準差（短期波動）
-    df['volatility_5d'] = df['return-t-1'].rolling(window=5).std()
-
-    # 5. volume_ratio_5d — 今日成交量 ÷ 5 日均量
-    df['volume_5d_avg'] = df['volume'].rolling(window=5).mean()
-    df['volume_ratio_5d'] = df['volume'] / df['volume_5d_avg']
-
-    # 6. MACD_diff — MACD - signal
-    exp1 = df['close'].ewm(span=12, adjust=False).mean()
-    exp2 = df['close'].ewm(span=26, adjust=False).mean()
-    macd_line = exp1 - exp2
-    signal_line = macd_line.ewm(span=9, adjust=False).mean()
-    df['MACD_diff'] = macd_line - signal_line
-    df['MACDvol'] = (macd_line - signal_line) # 訓練腳本中使用 MACD Histogram 作為 MACDvol
-
-    # 7. dji_return_t-1 & 8. sox_return_t-1
-    dji_df['dji_return_t-1'] = dji_df['Close'].pct_change()
-    sox_df['sox_return_t-1'] = sox_df['Close'].pct_change()
-    # 合併美股數據
-    df = df.merge(dji_df[['dji_return_t-1']], left_index=True, right_index=True, how='left')
-    df = df.merge(sox_df[['sox_return_t-1']], left_index=True, right_index=True, how='left')
-
-    # 9. NEWS (由外部傳入，此處先設為0)
-    df['NEWS'] = 0
-
-    # 10. RSI_14
-    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
-    df['RSI_14'] = 100 - (100 / (1 + rs))
-
-    # 11. ADX
-    high_minus_low = df['High'] - df['Low']
-    high_minus_close_prev = abs(df['High'] - df['close'].shift(1))
-    low_minus_close_prev = abs(df['Low'] - df['close'].shift(1))
-    tr = pd.concat([high_minus_low, high_minus_close_prev, low_minus_close_prev], axis=1).max(axis=1)
-    atr = tr.rolling(window=14).mean()
-    up_move = df['High'] - df['High'].shift(1)
-    down_move = df['Low'].shift(1) - df['Low']
-    plus_dm = ((up_move > down_move) & (up_move > 0)) * up_move
-    minus_dm = ((down_move > up_move) & (down_move > 0)) * down_move
-    plus_di = 100 * (plus_dm.ewm(alpha=1/14, min_periods=0, adjust=False).mean() / atr)
-    minus_di = 100 * (minus_dm.ewm(alpha=1/14, min_periods=0, adjust=False).mean() / atr)
-    dx = 100 * (abs(plus_di - minus_di) / (plus_di + minus_di))
-    df['ADX'] = dx.ewm(alpha=1/14, min_periods=0, adjust=False).mean()
-
-    # 12. volume_weighted_return
-    df['volume_weighted_return'] = abs(df['return_t-1']) * df['volume']
-
-    # 處理 NaN 值
-    df.fillna(method='ffill', inplace=True)
-    df.fillna(0, inplace=True)
-
-    return df
-
-def simple_statistical_predict(data, predict_days=5):
-    """【備用模型】簡化的統計預測模型。"""
-    if len(data) < 60:
-        return {'predicted_price': data['Close'].iloc[-1], 'change_pct': 0, 'confidence': 0.5}
-    prices = data['Close'].values
-    # ... (其餘邏輯與原檔案相同)
-    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 + (0.02 if ma_short > ma_medium > ma_long else -0.02 if ma_short < ma_medium < ma_long else 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 advanced_xgboost_predict(predict_days=5):
-    """
-    【進階模型】使用 XGBoost 模型進行預測
-    """
-    try:
-        print(f"開始使用 XGBoost 模型進行 {predict_days} 天預測...")
-
-        # 初始化 XGBoost 模型
-        xgb_model = XGBoostModel()
-
-        # 獲取主要標的、道瓊、費半的歷史數據
-        taiex_data = get_stock_data('^TWII')
-        dji_data = get_stock_data('^DJI')
-        sox_data = get_stock_data('^SOX')
-
-        if taiex_data.empty or dji_data.empty or sox_data.empty or len(taiex_data) < 60:
-            print("主要或美股指數數據不足，無法進行XGBoost預測")
-            return None
-
-        # 創建特徵
-        processed_data = create_new_features(taiex_data, dji_data, sox_data)
-
-        # 獲取新聞情緒分數
-        news_score = 0
-        if predictor is not None:
-            try:
-                news_score = predictor.get_news_index()
-                if news_score is None:
-                    news_score = 0
-            except Exception as e:
-                print(f"獲取新聞分數失敗: {e}")
-                news_score = 0
-        
-        # 將最新的新聞分數更新到最後一筆數據
-        processed_data['NEWS'].iloc[-1] = news_score
-
-        # 準備特徵 DataFrame (只取最後一筆，並確保欄位順序正確)
-        latest_features = processed_data.iloc[-1:][MODEL_FEATURE_COLUMNS]
-
-        print("準備送入模型的特徵數據 (最後一筆):")
-        print(latest_features.to_string())
-        
-        # 進行預測
-        predictions = xgb_model.predict('xgboost_model', latest_features)
-        
-        # 根據預測天數選擇對應的預測值
-        pred_mapping = {
-            1: 'Change_pct_t1_pred',
-            5: 'Change_pct_t5_pred',
-            10: 'Change_pct_t10_pred',
-            20: 'Change_pct_t20_pred'
-        }
-        
-        # 找到最接近的預測天數
-        available_days = [1, 5, 10, 20]
-        closest_day = min(available_days, key=lambda x: abs(x - predict_days))
-        pred_key = pred_mapping[closest_day]
-        
-        change_pct = predictions[pred_key]
-        current_price = taiex_data['Close'].iloc[-1]
-        predicted_price = current_price * (1 + change_pct / 100)
-        
-        print(f"XGBoost 預測完成:")
-        print(f"- 預測天期: {predict_days} 天 (使用 {closest_day} 天模型)")
-        print(f"- 當前指數: {current_price:.2f}")
-        print(f"- 預測漲跌幅: {change_pct:+.2f}%")
-        print(f"- 預測指數: {predicted_price:.2f}")
-        
-        return {
-            'predicted_price': predicted_price,
-            'change_pct': change_pct,
-            'confidence': 0.85  # XGBoost 模型的信心度 (可調整)
-        }
-        
-    except Exception as e:
-        print(f"XGBoost 預測時發生嚴重錯誤: {e}")
-        import traceback
-        traceback.print_exc()
-        return None
-
-def get_prediction(predict_days=5):
-    """
-    【【模型預測控制器】】
-    根據 USE_ADVANCED_MODEL 的設定，呼叫對應的預測模型。
-    """
-    if USE_ADVANCED_MODEL:
-        print(f"模式: 進階XGBoost模型 | 預測天期: {predict_days}天")
-        prediction = advanced_xgboost_predict(predict_days)
-        # 如果進階模型預測失敗，則自動降級使用簡易模型
-        if prediction is not None:
-            return prediction
-        else:
-            print("進階模型預測失敗，自動降級為簡易統計模型。")
-    
-    # 預設或降級時執行簡易模型
-    print(f"模式: 簡易統計模型 | 預測天期: {predict_days}天")
-    data = get_stock_data('^TWII', '2y')
-    return simple_statistical_predict(data, predict_days)
-
-def calculate_technical_indicators(df):
-    """計算用於繪圖的技術指標"""
-    if df.empty: return df
-    # 移動平均線
-    df['MA5'] = df['Close'].rolling(window=5).mean()
-    df['MA20'] = df['Close'].rolling(window=20).mean()
-    # 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()
-    rs = gain / loss
-    df['RSI'] = 100 - (100 / (1 + rs))
-    # 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, adjust=False).mean()
-    df['MACD_Histogram'] = df['MACD'] - df['MACD_Signal']
-    # 布林通道
-    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)
-    # KD 指標
-    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()
-    # 威廉指標
-    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)
-    # DMI 指標
-    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
-
-# 其餘輔助函式 (get_business_climate_data, get_pmi_data, generate_gemini_analysis, etc.)
-# 與原檔案相同，此處省略以保持簡潔
-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()
-def generate_gemini_analysis(stock_name, stock_symbol, period, data):
-    api_key = os.getenv("GEMINI_API_KEY")
-    if not api_key:
-        return "無法讀取 GEMINI API 金鑰", "請在系統環境變數中設定您的金鑰"
-    try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-1.5-flash')
-        price_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-        rsi_current = data['RSI'].iloc[-1]
-        macd_current = data['MACD'].iloc[-1]
-        macd_signal_current = data['MACD_Signal'].iloc[-1]
-        industry = INDUSTRY_MAPPING.get(stock_symbol, '綜合')
-        prompt = f"""
-        請扮演一位專業、資深的台灣股市金融分析師。
-        我將提供一檔台股的即時技術指標數據，請你基於這些數據，結合你對這家公司、其所在產業以及當前市場趨勢的理解，為我生成一段專業的「基本面分析」和一段「市場展望與投資建議」。
-
-        **股票資訊:**
-        - **公司名稱:** {stock_name} ({stock_symbol})
-        - **分析期間:** 最近 {period}
-        - **所屬產業:** {industry}
-        - **期間價格變動:** {price_change:+.2f}%
-        - **目前 RSI 指標:** {rsi_current:.2f}
-        - **目前 MACD 指標:** MACD線為 {macd_current:.3f}, 信號線為 {macd_signal_current:.3f}
-
-        **你的任務:**
-        1.  **基本面分析 (約 150 字):**
-            -   評論這家公司的產業地位、近期營運亮點或挑戰。
-            -   提及任何可能影響其基本面的關鍵因素 (例如：財報、法說會、政策、供應鏈變化等)。
-            -   請用專業、客觀的語氣撰寫。
-
-        2.  **市場展望與投資建議 (約 150 字):**
-            -   基於上述所有資訊，提供對該股票的短期和中期市場展望。
-            -   提出具體的投資建議，例如：適合何種類型的投資人、潛在的風險點。
-            -   請直接提供分析內容，不要包含任何問候語。
-
-        **輸出格式:**
-        請嚴格按照以下格式回傳，使用"$$"作為兩個段落之間的分隔符：
-        [基本面分析內容]$$[市場展望與投資建議內容]
-        """
-        response = model.generate_content(prompt)
-        parts = response.text.split('$$')
-        if len(parts) == 2:
-            fundamental_analysis = parts[0].strip()
-            market_outlook = parts[1].strip()
-            return dcc.Markdown(fundamental_analysis), dcc.Markdown(market_outlook)
-        else:
-            return dcc.Markdown("無法解析 Gemini 回應，請稍後再試。"), dcc.Markdown(response.text)
-    except Exception as e:
-        error_message = f"呼叫 Gemini API 時發生錯誤: {str(e)}"
-        print(error_message)
-        return dcc.Markdown(error_message), dcc.Markdown("請檢查後台日誌或 API 金鑰設定")
-def summarize_news_with_gemini(news_list: list) -> str:
-    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')
-        formatted_news = "\n".join([f"- {news}" for news in news_list])
-        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，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}"
-
-# 建立 Dash 應用程式
-app = dash.Dash(__name__, suppress_callback_exceptions=True)
-
-# 初始化模型
-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 (與原檔案相同，此處省略)
-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'}),
-    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='1mo', 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 (AI 生成)", 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 (AI 生成)", 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)'}),
-])
-# 回調函數區域
-@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):
-    # 獲取最新資料用於顯示
-    data = get_stock_data('^TWII', '2y')
-    if data.empty: return html.Div("無法獲取台指期資料"), {}
-    
-    # === 修改點：統一呼叫 get_prediction 控制器 ===
-    # 注意：get_prediction 不再需要傳入 data，它會自己獲取所需數據
-    final_prediction = get_prediction(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']
-    
-    # 預測路徑的邏輯
-    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 = get_prediction(days)
-        if interim_prediction:
-            prediction_dates.append(last_date + timedelta(days=days))
-            prediction_prices.append(interim_prediction['predicted_price'])
-            
-    # 後續繪圖邏輯不變
-    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"目前指數: {current_price:.2f}", style={'margin': '5px 0'}),
-        html.P(f"預測指數: {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(60) # 顯示最近60天歷史數據
-    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
-
-# 其餘回調函數 (update_stock_info, update_price_chart, etc.)
-# 與原檔案相同，此處省略以保持簡潔
-@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'})
-    ])
-@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
-@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()
-    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
-@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
-@app.callback(
-    dash.dependencies.Output('industry-analysis', 'figure'),
-    [dash.dependencies.Input('stock-dropdown', 'value')]
-)
-def update_industry_analysis(selected_stock):
-    performance_data = []
-    for name, symbol in TAIWAN_STOCKS.items():
-        data = get_stock_data(symbol, '1mo')
-        if not data.empty and len(data) > 1:
-            return_pct = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
-            performance_data.append({
-                '股票': name,
-                '代碼': symbol,
-                '月報酬率(%)': return_pct,
-                '絕對波動': abs(return_pct)
-            })
-    if not performance_data:
-        fig = go.Figure().add_annotation(text="無法計算產業資料", showarrow=False)
-        fig.update_layout(title="近一月市場波動最大標的", height=400)
-        return fig
-    df_performance = pd.DataFrame(performance_data)
-    df_top_movers = df_performance.sort_values(by='絕對波動', ascending=False).head(10)
-    fig = px.pie(
-        df_top_movers,
-        values='絕對波動',
-        names='股票',
-        title='近一月市場波動最大 Top 10 標的',
-        hover_data={'月報酬率(%)': ':.2f'}
-    )
-    fig.update_traces(
-        textposition='inside',
-        textinfo='percent+label',
-        hovertemplate="<b>%{label}</b><br>月報酬率: %{customdata[0]:.2f}%<extra></extra>"
-    )
-    fig.update_layout(height=400, showlegend=False)
-    return fig
-@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="無法載入景氣燈號資料", 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
-@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):
-    cache_key = f"{selected_stock}-{period}"
-    current_time = time.time()
-    if cache_key in ANALYSIS_CACHE:
-        cached_data = ANALYSIS_CACHE[cache_key]
-        if current_time - cached_data['timestamp'] < CACHE_DURATION_SECONDS:
-            print(f"從快取載入分析: {cache_key}")
-            return cached_data['technical'], cached_data['fundamental'], cached_data['outlook']
-    print(f"重新生成分析: {cache_key}")
-    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 or len(data) < 20:
-        return "資料不足，無法分析", "資料不足，無法分析", "資料不足，無法分析"
-    data = calculate_technical_indicators(data)
-    price_change = ((data['Close'].iloc[-1] - 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"在最近 {period} 期間內，{stock_name} 股價呈現", 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"目前的 RSI 值為 {rsi_current:.1f}，", html.Span("處於超買區(>70)" if rsi_current > 70 else "處於超賣區(<30)" if rsi_current < 30 else "在正常範圍内", 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 快線 ({macd_current:.3f}) 目前", 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" Signal 慢線 ({macd_signal_current:.3f})，", f"顯示市場動能偏向{'多頭' if macd_current > macd_signal_current else '空頭'}。"]),
-    ])
-    fundamental_text, market_outlook_text = generate_gemini_analysis(stock_name, selected_stock, period, data)
-    ANALYSIS_CACHE[cache_key] = {
-        'technical': technical_text,
-        'fundamental': fundamental_text,
-        'outlook': market_outlook_text,
-        'timestamp': current_time
-    }
-    return technical_text, fundamental_text, market_outlook_text
-@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
-@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")
-@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):
-    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。")
-    sentiment_score_raw = predictor.get_news_index()
-    if sentiment_score_raw is not None:
-        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,
-            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)
-    else:
-        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()
-    news_content = None
-    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={
-            'margin': '8px 0', 'padding-left': '5px',
-            'font-size': '15px', 'line-height': '1.7'
-        })
-    elif top_news_list == []:
-         news_content = html.P("昨日無重大相關新聞。", style={'text-align': 'center', 'padding-top': '50px'})
-    else:
-        news_content = html.P("讀取新聞時發生錯誤。", style={'text-align': 'center', 'padding-top': '50px'})
-    return gauge_content, news_content
-
-# 主程式執行
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860, debug=False)