feat: Translate all Chinese content to English in webui module

- Translate all Chinese comments and strings in webui/app.py
- Translate all Chinese comments and strings in webui/run.py
- Translate all Chinese comments and strings in webui/start.sh
- Translate all Chinese content in webui/README.md
- Translate all Chinese content in webui/templates/index.html
- Add prediction results directory for analysis
- Complete internationalization of webui module

webui/README.md

@@ -1,135 +1,135 @@
 # Kronos Web UI
 
-Kronos金融预测模型的Web用户界面，提供直观的图形化操作界面。
+Web user interface for Kronos financial prediction model, providing intuitive graphical operation interface.
 
-## ✨ 功能特性
+## ✨ Features
 
-- **多格式数据支持**: 支持CSV、Feather等格式的金融数据
-- **智能时间窗口**: 固定400+120数据点的时间窗口滑条选择
-- **真实模型预测**: 集成真实的Kronos模型，支持多种模型大小
-- **预测质量控制**: 可调节温度、核采样、样本数量等参数
-- **多设备支持**: 支持CPU、CUDA、MPS等计算设备
-- **对比分析**: 预测结果与实际数据的详细对比
-- **K线图显示**: 专业的金融K线图表展示
+- **Multi-format data support**: Supports CSV, Feather and other financial data formats
+- **Smart time window**: Fixed 400+120 data point time window slider selection
+- **Real model prediction**: Integrated real Kronos model, supports multiple model sizes
+- **Prediction quality control**: Adjustable temperature, nucleus sampling, sample count and other parameters
+- **Multi-device support**: Supports CPU, CUDA, MPS and other computing devices
+- **Comparison analysis**: Detailed comparison between prediction results and actual data
+- **K-line chart display**: Professional financial K-line chart display
 
-## 🚀 快速开始
+## 🚀 Quick Start
 
-### 方法1: 使用Python脚本启动
+### Method 1: Start with Python script
 ```bash
 cd webui
 python run.py
 ```
 
-### 方法2: 使用Shell脚本启动
+### Method 2: Start with Shell script
 ```bash
 cd webui
 chmod +x start.sh
 ./start.sh
 ```
 
-### 方法3: 直接启动Flask应用
+### Method 3: Start Flask application directly
 ```bash
 cd webui
 python app.py
 ```
 
-启动成功后，访问 http://localhost:7070
+After successful startup, visit http://localhost:7070
 
-## 📋 使用步骤
+## 📋 Usage Steps
 
-1. **加载数据**: 选择data目录中的金融数据文件
-2. **加载模型**: 选择Kronos模型和计算设备
-3. **设置参数**: 调整预测质量参数
-4. **选择时间窗口**: 使用滑条选择400+120数据点的时间范围
-5. **开始预测**: 点击预测按钮生成结果
-6. **查看结果**: 在图表和表格中查看预测结果
+1. **Load data**: Select financial data file from data directory
+2. **Load model**: Select Kronos model and computing device
+3. **Set parameters**: Adjust prediction quality parameters
+4. **Select time window**: Use slider to select 400+120 data point time range
+5. **Start prediction**: Click prediction button to generate results
+6. **View results**: View prediction results in charts and tables
 
-## 🔧 预测质量参数
+## 🔧 Prediction Quality Parameters
 
-### 温度 (T)
-- **范围**: 0.1 - 2.0
-- **作用**: 控制预测的随机性
-- **建议**: 1.2-1.5 获得更好的预测质量
+### Temperature (T)
+- **Range**: 0.1 - 2.0
+- **Effect**: Controls prediction randomness
+- **Recommendation**: 1.2-1.5 for better prediction quality
 
-### 核采样 (top_p)
-- **范围**: 0.1 - 1.0
-- **作用**: 控制预测的多样性
-- **建议**: 0.95-1.0 考虑更多可能性
+### Nucleus Sampling (top_p)
+- **Range**: 0.1 - 1.0
+- **Effect**: Controls prediction diversity
+- **Recommendation**: 0.95-1.0 to consider more possibilities
 
-### 样本数量
-- **范围**: 1 - 5
-- **作用**: 生成多个预测样本
-- **建议**: 2-3 个样本提高质量
+### Sample Count
+- **Range**: 1 - 5
+- **Effect**: Generate multiple prediction samples
+- **Recommendation**: 2-3 samples to improve quality
 
-## 📊 支持的数据格式
+## 📊 Supported Data Formats
 
-### 必需列
-- `open`: 开盘价
-- `high`: 最高价
-- `low`: 最低价
-- `close`: 收盘价
+### Required Columns
+- `open`: Opening price
+- `high`: Highest price
+- `low`: Lowest price
+- `close`: Closing price
 
-### 可选列
-- `volume`: 成交量
-- `amount`: 成交额（不用于预测）
-- `timestamps`/`timestamp`/`date`: 时间戳
+### Optional Columns
+- `volume`: Trading volume
+- `amount`: Trading amount (not used for prediction)
+- `timestamps`/`timestamp`/`date`: Timestamp
 
-## 🤖 模型支持
+## 🤖 Model Support
 
-- **Kronos-mini**: 4.1M参数，轻量级快速预测
-- **Kronos-small**: 24.7M参数，平衡性能和速度
-- **Kronos-base**: 102.3M参数，高质量预测
+- **Kronos-mini**: 4.1M parameters, lightweight fast prediction
+- **Kronos-small**: 24.7M parameters, balanced performance and speed
+- **Kronos-base**: 102.3M parameters, high quality prediction
 
-## 🖥️ GPU加速支持
+## 🖥️ GPU Acceleration Support
 
-- **CPU**: 通用计算，兼容性最好
-- **CUDA**: NVIDIA GPU加速，性能最佳
-- **MPS**: Apple Silicon GPU加速，Mac用户推荐
+- **CPU**: General computing, best compatibility
+- **CUDA**: NVIDIA GPU acceleration, best performance
+- **MPS**: Apple Silicon GPU acceleration, recommended for Mac users
 
-## ⚠️ 注意事项
+## ⚠️ Notes
 
-- `amount`列不会被用于预测，仅用于显示
-- 时间窗口固定为400+120=520个数据点
-- 确保数据文件包含足够的历史数据
-- 首次加载模型可能需要下载，请耐心等待
+- `amount` column is not used for prediction, only for display
+- Time window is fixed at 400+120=520 data points
+- Ensure data file contains sufficient historical data
+- First model loading may require download, please be patient
 
-## 🔍 对比分析
+## 🔍 Comparison Analysis
 
-系统会自动提供预测结果与实际数据的对比分析，包括：
-- 价格差异统计
-- 误差分析
-- 预测质量评估
+The system automatically provides comparison analysis between prediction results and actual data, including:
+- Price difference statistics
+- Error analysis
+- Prediction quality assessment
 
-## 🛠️ 技术架构
+## 🛠️ Technical Architecture
 
-- **后端**: Flask + Python
-- **前端**: HTML + CSS + JavaScript
-- **图表**: Plotly.js
-- **数据处理**: Pandas + NumPy
-- **模型**: Hugging Face Transformers
+- **Backend**: Flask + Python
+- **Frontend**: HTML + CSS + JavaScript
+- **Charts**: Plotly.js
+- **Data processing**: Pandas + NumPy
+- **Model**: Hugging Face Transformers
 
-## 📝 故障排除
+## 📝 Troubleshooting
 
-### 常见问题
-1. **端口占用**: 修改app.py中的端口号
-2. **依赖缺失**: 运行 `pip install -r requirements.txt`
-3. **模型加载失败**: 检查网络连接和模型ID
-4. **数据格式错误**: 确保数据列名和格式正确
+### Common Issues
+1. **Port occupied**: Modify port number in app.py
+2. **Missing dependencies**: Run `pip install -r requirements.txt`
+3. **Model loading failed**: Check network connection and model ID
+4. **Data format error**: Ensure data column names and format are correct
 
-### 日志查看
-启动时会在控制台显示详细的运行信息，包括模型状态和错误信息。
+### Log Viewing
+Detailed runtime information will be displayed in the console at startup, including model status and error messages.
 
-## 📄 许可证
+## 📄 License
 
-本项目遵循原Kronos项目的许可证条款。
+This project follows the license terms of the original Kronos project.
 
-## 🤝 贡献
+## 🤝 Contributing
 
-欢迎提交Issue和Pull Request来改进这个Web UI！
+Welcome to submit Issues and Pull Requests to improve this Web UI!
 
-## 📞 支持
+## 📞 Support
 
-如有问题，请查看：
-1. 项目文档
+If you have questions, please check:
+1. Project documentation
 2. GitHub Issues
-3. 控制台错误信息
+3. Console error messages

webui/app.py

@@ -11,7 +11,7 @@ import warnings
 import datetime
 warnings.filterwarnings('ignore')
 
-# 添加项目根目录到路径
+# Add project root directory to path
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 try:
@@ -19,17 +19,17 @@ try:
     MODEL_AVAILABLE = True
 except ImportError:
     MODEL_AVAILABLE = False
-    print("警告: Kronos模型无法导入，将使用模拟数据进行演示")
+    print("Warning: Kronos model cannot be imported, will use simulated data for demonstration")
 
 app = Flask(__name__)
 CORS(app)
 
-# 全局变量存储模型
+# Global variables to store models
 tokenizer = None
 model = None
 predictor = None
 
-# 可用的模型配置
+# Available model configurations
 AVAILABLE_MODELS = {
     'kronos-mini': {
         'name': 'Kronos-mini',
@@ -37,7 +37,7 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
         'context_length': 2048,
         'params': '4.1M',
-        'description': '轻量级模型，适合快速预测'
+        'description': 'Lightweight model, suitable for fast prediction'
     },
     'kronos-small': {
         'name': 'Kronos-small',
@@ -45,7 +45,7 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
         'context_length': 512,
         'params': '24.7M',
-        'description': '小型模型，平衡性能和速度'
+        'description': 'Small model, balanced performance and speed'
     },
     'kronos-base': {
         'name': 'Kronos-base',
@@ -53,12 +53,12 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
         'context_length': 512,
         'params': '102.3M',
-        'description': '基础模型，提供更好的预测质量'
+        'description': 'Base model, provides better prediction quality'
     }
 }
 
 def load_data_files():
-    """扫描data目录并返回可用的数据文件"""
+    """Scan data directory and return available data files"""
     data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
     data_files = []
     
@@ -76,119 +76,65 @@ def load_data_files():
     return data_files
 
 def load_data_file(file_path):
-    """加载数据文件"""
+    """Load data file"""
     try:
         if file_path.endswith('.csv'):
             df = pd.read_csv(file_path)
         elif file_path.endswith('.feather'):
             df = pd.read_feather(file_path)
         else:
-            return None, "不支持的文件格式"
+            return None, "Unsupported file format"
         
-        # 检查必要的列
+        # Check required columns
         required_cols = ['open', 'high', 'low', 'close']
         if not all(col in df.columns for col in required_cols):
-            return None, f"缺少必要的列: {required_cols}"
+            return None, f"Missing required columns: {required_cols}"
         
-        # 处理时间戳列
+        # Process timestamp column
         if 'timestamps' in df.columns:
             df['timestamps'] = pd.to_datetime(df['timestamps'])
         elif 'timestamp' in df.columns:
             df['timestamps'] = pd.to_datetime(df['timestamp'])
         elif 'date' in df.columns:
-            # 如果列名是'date'，将其重命名为'timestamps'
+            # If column name is 'date', rename it to 'timestamps'
             df['timestamps'] = pd.to_datetime(df['date'])
         else:
-            # 如果没有时间戳列，创建一个
+            # If no timestamp column exists, create one
             df['timestamps'] = pd.date_range(start='2024-01-01', periods=len(df), freq='1H')
         
-        # 确保数值列是数值类型
+        # Ensure numeric columns are numeric type
         for col in ['open', 'high', 'low', 'close']:
             df[col] = pd.to_numeric(df[col], errors='coerce')
         
-        # 处理volume列（可选）
+        # Process volume column (optional)
         if 'volume' in df.columns:
             df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
         
-        # 处理amount列（可选，但不用于预测）
+        # Process amount column (optional, but not used for prediction)
         if 'amount' in df.columns:
             df['amount'] = pd.to_numeric(df['amount'], errors='coerce')
         
-        # 删除包含NaN的行
+        # Remove rows containing NaN values
         df = df.dropna()
         
         return df, None
         
     except Exception as e:
-        return None, f"加载文件失败: {str(e)}"
-
-def convert_price_to_returns(df, price_cols=['open', 'high', 'low', 'close']):
-    """将绝对价格转换为相对变化率，解决Kronos模型的连续性问题"""
-    print(f"[DEBUG] convert_price_to_returns: 开始转换，输入数据形状: {df.shape}")
-    print(f"[DEBUG] convert_price_to_returns: 输入数据前3行:")
-    print(df[price_cols].head(3))
-    
-    returns_df = df.copy()
-    
-    # 计算相对变化率 (pct_change)
-    for col in price_cols:
-        if col in returns_df.columns:
-            print(f"[DEBUG] convert_price_to_returns: 转换列 {col}")
-            returns_df[col] = returns_df[col].pct_change()
-    
-    # 第一行会是NaN，用0填充
-    returns_df = returns_df.fillna(0)
-    
-    print(f"[DEBUG] convert_price_to_returns: 转换完成，输出数据前3行:")
-    print(returns_df[price_cols].head(3))
-    
-    return returns_df
-
-def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'high', 'low', 'close']):
-    """将相对变化率转换回绝对价格"""
-    print(f"[DEBUG] convert_returns_to_price: 开始转换，输入数据形状: {returns_df.shape}")
-    print(f"[DEBUG] convert_returns_to_price: 初始价格: {initial_prices}")
-    print(f"[DEBUG] convert_returns_to_price: 输入相对变化率前3行:")
-    print(returns_df[price_cols].head(3))
-    
-    price_df = returns_df.copy()
-    
-    for col in price_cols:
-        if col in price_df.columns:
-            print(f"[DEBUG] convert_returns_to_price: 转换列 {col}")
-            # 正确的转换逻辑：从初始价格开始，逐步累积计算绝对价格
-            # 第一个点：price = initial_price * (1 + return_1)
-            # 第二个点：price = price_1 * (1 + return_2)
-            # 第三个点：price = price_2 * (1 + return_3)
-            # 以此类推...
-            
-            # 使用cumprod来累积计算
-            price_df[col] = initial_prices[col] * (1 + returns_df[col]).cumprod()
-            
-            print(f"[DEBUG] convert_returns_to_price: 列 {col} 转换完成，前3个值:")
-            print(f"  初始价格: {initial_prices[col]}")
-            print(f"  相对变化率: {returns_df[col].head(3).tolist()}")
-            print(f"  累积因子: {(1 + returns_df[col]).cumprod().head(3).tolist()}")
-            print(f"  最终价格: {price_df[col].head(3).tolist()}")
-    
-    print(f"[DEBUG] convert_returns_to_price: 转换完成，输出绝对价格前3行:")
-    print(price_df[price_cols].head(3))
-    
-    return price_df
+        return None, f"Failed to load file: {str(e)}"
 
 def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
-    """保存预测结果到文件"""
+    """Save prediction results to file"""
     try:
-        # 创建预测结果目录
+        # Create prediction results directory
         results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prediction_results')
         os.makedirs(results_dir, exist_ok=True)
         
-        # 生成文件名
+        # Generate filename
         timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
         filename = f'prediction_{timestamp}.json'
         filepath = os.path.join(results_dir, filename)
         
-        # 准备保存的数据
+        # Prepare data for saving
         save_data = {
             'timestamp': datetime.datetime.now().isoformat(),
             'file_path': file_path,
@@ -215,12 +161,12 @@ def save_prediction_results(file_path, prediction_type, prediction_results, actu
             'analysis': {}
         }
         
-        # 如果有实际数据，进行对比分析
+        # If actual data exists, perform comparison analysis
         if actual_data and len(actual_data) > 0:
-            # 计算连续性分析
+            # Calculate continuity analysis
             if len(prediction_results) > 0 and len(actual_data) > 0:
-                last_pred = prediction_results[0]  # 第一个预测点
-                first_actual = actual_data[0]      # 第一个实际点
+                last_pred = prediction_results[0]  # First prediction point
+            first_actual = actual_data[0]      # First actual point
                 
             save_data['analysis']['continuity'] = {
                     'last_prediction': {
@@ -249,51 +195,51 @@ def save_prediction_results(file_path, prediction_type, prediction_results, actu
                     }
                 }
         
-        # 保存到文件
+        # Save to file
         with open(filepath, 'w', encoding='utf-8') as f:
             json.dump(save_data, f, indent=2, ensure_ascii=False)
         
-        print(f"预测结果已保存到: {filepath}")
+        print(f"Prediction results saved to: {filepath}")
         return filepath
         
     except Exception as e:
-        print(f"保存预测结果失败: {e}")
+        print(f"Failed to save prediction results: {e}")
         return None
 
 def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
-    """创建预测图表"""
-    # 使用指定的历史数据起始位置，而不是总是从df的开头开始
+    """Create prediction chart"""
+    # Use specified historical data start position, not always from the beginning of df
     if historical_start_idx + lookback + pred_len <= len(df):
-        # 显示指定位置开始的lookback个历史点 + pred_len个预测点
+        # Display lookback historical points + pred_len prediction points starting from specified position
         historical_df = df.iloc[historical_start_idx:historical_start_idx+lookback]
         prediction_range = range(historical_start_idx+lookback, historical_start_idx+lookback+pred_len)
     else:
-        # 如果数据不够，调整到可用的最大范围
+        # If data is insufficient, adjust to maximum available range
         available_lookback = min(lookback, len(df) - historical_start_idx)
         available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback))
         historical_df = df.iloc[historical_start_idx:historical_start_idx+available_lookback]
         prediction_range = range(historical_start_idx+available_lookback, historical_start_idx+available_lookback+available_pred_len)
     
-    # 创建图表
+    # Create chart
     fig = go.Figure()
     
-    # 添加历史数据（K线图）
+    # Add historical data (candlestick chart)
     fig.add_trace(go.Candlestick(
         x=historical_df['timestamps'] if 'timestamps' in historical_df.columns else historical_df.index,
         open=historical_df['open'],
         high=historical_df['high'],
         low=historical_df['low'],
         close=historical_df['close'],
-        name='历史数据 (400个数据点)',
+        name='Historical Data (400 data points)',
         increasing_line_color='#26A69A',
         decreasing_line_color='#EF5350'
     ))
     
-    # 添加预测数据（K线图）
+    # Add prediction data (candlestick chart)
     if pred_df is not None and len(pred_df) > 0:
-        # 计算预测数据的时间戳 - 确保与历史数据连续
+        # Calculate prediction data timestamps - ensure continuity with historical data
         if 'timestamps' in df.columns and len(historical_df) > 0:
-            # 从历史数据的最后一个时间点开始，按相同的时间间隔创建预测时间戳
+            # Start from the last timestamp of historical data, create prediction timestamps with the same time interval
             last_timestamp = historical_df['timestamps'].iloc[-1]
             time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
             
@@ -303,7 +249,7 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
                 freq=time_diff
             )
         else:
-            # 如果没有时间戳，使用索引
+            # If no timestamps, use index
             pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df))
         
         fig.add_trace(go.Candlestick(
@@ -312,20 +258,20 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
             high=pred_df['high'],
             low=pred_df['low'],
             close=pred_df['close'],
-            name='预测数据 (120个数据点)',
+            name='Prediction Data (120 data points)',
             increasing_line_color='#66BB6A',
             decreasing_line_color='#FF7043'
         ))
     
-    # 添加实际数据用于对比（如果存在）
+    # Add actual data for comparison (if exists)
     if actual_df is not None and len(actual_df) > 0:
-        # 实际数据应该与预测数据在同一个时间段
+        # Actual data should be in the same time period as prediction data
         if 'timestamps' in df.columns:
-            # 实际数据应该使用与预测数据相同的时间戳，确保时间对齐
+            # Actual data should use the same timestamps as prediction data to ensure time alignment
             if 'pred_timestamps' in locals():
                 actual_timestamps = pred_timestamps
             else:
-                # 如果没有预测时间戳，从历史数据最后一个时间点开始计算
+                # If no prediction timestamps, calculate from the last timestamp of historical data
                 if len(historical_df) > 0:
                     last_timestamp = historical_df['timestamps'].iloc[-1]
                     time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
@@ -345,24 +291,24 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
             high=actual_df['high'],
             low=actual_df['low'],
             close=actual_df['close'],
-            name='实际数据 (120个数据点)',
+            name='Actual Data (120 data points)',
             increasing_line_color='#FF9800',
             decreasing_line_color='#F44336'
         ))
     
-    # 更新布局
+    # Update layout
     fig.update_layout(
-        title='Kronos 金融预测结果 - 400个历史点 + 120个预测点 vs 120个实际点',
-        xaxis_title='时间',
-        yaxis_title='价格',
+        title='Kronos Financial Prediction Results - 400 Historical Points + 120 Prediction Points vs 120 Actual Points',
+        xaxis_title='Time',
+        yaxis_title='Price',
         template='plotly_white',
         height=600,
         showlegend=True
     )
     
-    # 确保x轴时间连续
+    # Ensure x-axis time continuity
     if 'timestamps' in historical_df.columns:
-        # 获取所有时间戳并排序
+        # Get all timestamps and sort them
         all_timestamps = []
         if len(historical_df) > 0:
             all_timestamps.extend(historical_df['timestamps'])
@@ -383,56 +329,56 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
 
 @app.route('/')
 def index():
-    """主页"""
+    """Home page"""
     return render_template('index.html')
 
 @app.route('/api/data-files')
 def get_data_files():
-    """获取可用的数据文件列表"""
+    """Get available data file list"""
     data_files = load_data_files()
     return jsonify(data_files)
 
 @app.route('/api/load-data', methods=['POST'])
 def load_data():
-    """加载数据文件"""
+    """Load data file"""
     try:
         data = request.get_json()
         file_path = data.get('file_path')
         
         if not file_path:
-            return jsonify({'error': '文件路径不能为空'}), 400
+            return jsonify({'error': 'File path cannot be empty'}), 400
         
         df, error = load_data_file(file_path)
         if error:
             return jsonify({'error': error}), 400
         
-        # 检测数据的时间频率
+        # Detect data time frequency
         def detect_timeframe(df):
             if len(df) < 2:
-                return "未知"
+                return "Unknown"
             
             time_diffs = []
-            for i in range(1, min(10, len(df))):  # 检查前10个时间差
+            for i in range(1, min(10, len(df))):  # Check first 10 time differences
                 diff = df['timestamps'].iloc[i] - df['timestamps'].iloc[i-1]
                 time_diffs.append(diff)
             
             if not time_diffs:
-                return "未知"
+                return "Unknown"
             
-            # 计算平均时间差
+            # Calculate average time difference
             avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs)
             
-            # 转换为可读格式
+            # Convert to readable format
             if avg_diff < pd.Timedelta(minutes=1):
-                return f"{avg_diff.total_seconds():.0f}秒"
+                return f"{avg_diff.total_seconds():.0f} seconds"
             elif avg_diff < pd.Timedelta(hours=1):
-                return f"{avg_diff.total_seconds() / 60:.0f}分钟"
+                return f"{avg_diff.total_seconds() / 60:.0f} minutes"
             elif avg_diff < pd.Timedelta(days=1):
-                return f"{avg_diff.total_seconds() / 3600:.0f}小时"
+                return f"{avg_diff.total_seconds() / 3600:.0f} hours"
             else:
-                return f"{avg_diff.days}天"
+                return f"{avg_diff.days} days"
         
-        # 返回数据信息
+        # Return data information
         data_info = {
             'rows': len(df),
             'columns': list(df.columns),
@@ -449,111 +395,89 @@ def load_data():
         return jsonify({
             'success': True,
             'data_info': data_info,
-            'message': f'成功加载数据，共 {len(df)} 行'
+            'message': f'Successfully loaded data, total {len(df)} rows'
         })
         
     except Exception as e:
-        return jsonify({'error': f'加载数据失败: {str(e)}'}), 500
+        return jsonify({'error': f'Failed to load data: {str(e)}'}), 500
 
 @app.route('/api/predict', methods=['POST'])
 def predict():
-    """进行预测"""
+    """Perform prediction"""
     try:
         data = request.get_json()
         file_path = data.get('file_path')
         lookback = int(data.get('lookback', 400))
         pred_len = int(data.get('pred_len', 120))
         
-        # 获取预测质量参数
+        # Get prediction quality parameters
         temperature = float(data.get('temperature', 1.0))
         top_p = float(data.get('top_p', 0.9))
         sample_count = int(data.get('sample_count', 1))
         
         if not file_path:
-            return jsonify({'error': '文件路径不能为空'}), 400
+            return jsonify({'error': 'File path cannot be empty'}), 400
         
-        # 加载数据
+        # Load data
         df, error = load_data_file(file_path)
         if error:
             return jsonify({'error': error}), 400
         
         if len(df) < lookback:
-            return jsonify({'error': f'数据长度不足，需要至少 {lookback} 行数据'}), 400
+            return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400
         
-        # 进行预测
+        # Perform prediction
         if MODEL_AVAILABLE and predictor is not None:
             try:
-                # 使用真实的Kronos模型
-                # 只使用必要的列：OHLCV，不包含amount
+                # Use real Kronos model
+                # Only use necessary columns: OHLCV, excluding amount
                 required_cols = ['open', 'high', 'low', 'close']
                 if 'volume' in df.columns:
                     required_cols.append('volume')
                 
-                # 处理时间段选择
+                # Process time period selection
                 start_date = data.get('start_date')
                 
                 if start_date:
-                    # 自定义时间段 - 修复逻辑：使用选择的窗口内的数据
+                    # Custom time period - fix logic: use data within selected window
                     start_dt = pd.to_datetime(start_date)
                     
-                    # 找到开始时间之后的数据
+                    # Find data after start time
                     mask = df['timestamps'] >= start_dt
                     time_range_df = df[mask]
                     
-                    # 确保有足够的数据：lookback + pred_len
+                    # Ensure sufficient data: lookback + pred_len
                     if len(time_range_df) < lookback + pred_len:
-                        return jsonify({'error': f'从开始时间 {start_dt.strftime("%Y-%m-%d %H:%M")} 开始的数据不足，需要至少 {lookback + pred_len} 个数据点，当前只有 {len(time_range_df)} 个'}), 400
+                        return jsonify({'error': f'Insufficient data from start time {start_dt.strftime("%Y-%m-%d %H:%M")}, need at least {lookback + pred_len} data points, currently only {len(time_range_df)} available'}), 400
                     
-                    # 使用选择的窗口内的前lookback个数据点进行预测
+                    # Use first lookback data points within selected window for prediction
                     x_df = time_range_df.iloc[:lookback][required_cols]
                     x_timestamp = time_range_df.iloc[:lookback]['timestamps']
                     
-                    # 使用选择的窗口内的后pred_len个数据点作为实际值
+                    # Use last pred_len data points within selected window as actual values
                     y_timestamp = time_range_df.iloc[lookback:lookback+pred_len]['timestamps']
                     
-                    # 计算实际的时间段长度
+                    # Calculate actual time period length
                     start_timestamp = time_range_df['timestamps'].iloc[0]
                     end_timestamp = time_range_df['timestamps'].iloc[lookback+pred_len-1]
                     time_span = end_timestamp - start_timestamp
                     
-                    prediction_type = f"Kronos模型预测 (选择的窗口内：前{lookback}个数据点预测，后{pred_len}个数据点对比，时间跨度: {time_span})"
+                    prediction_type = f"Kronos model prediction (within selected window: first {lookback} data points for prediction, last {pred_len} data points for comparison, time span: {time_span})"
                 else:
-                    # 使用最新数据
+                    # Use latest data
                     x_df = df.iloc[:lookback][required_cols]
                     x_timestamp = df.iloc[:lookback]['timestamps']
                     y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps']
-                    prediction_type = "Kronos模型预测 (最新数据)"
+                    prediction_type = "Kronos model prediction (latest data)"
                 
-                print(f"[DEBUG] 步骤1: 检查并转换时间戳格式...")
-                # 确保时间戳是Series格式，不是DatetimeIndex，避免Kronos模型的.dt属性错误
+                # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model
                 if isinstance(x_timestamp, pd.DatetimeIndex):
-                    print(f"[DEBUG] 将x_timestamp从DatetimeIndex转换为Series")
                     x_timestamp = pd.Series(x_timestamp, name='timestamps')
                 if isinstance(y_timestamp, pd.DatetimeIndex):
-                    print(f"[DEBUG] 将y_timestamp从DatetimeIndex转换为Series")
                     y_timestamp = pd.Series(y_timestamp, name='timestamps')
                 
-                print(f"[DEBUG] 步骤2: 准备价格转换，保存初始价格...")
-                # 解决Kronos模型连续性问题：将绝对价格转换为相对变化率
-                original_x_df = x_df.copy()
-                initial_prices = {
-                    'open': x_df['open'].iloc[0],
-                    'high': x_df['high'].iloc[0],
-                    'low': x_df['low'].iloc[0],
-                    'close': x_df['close'].iloc[0]
-                }
-                print(f"[DEBUG] 初始价格: {initial_prices}")
-                
-                print(f"[DEBUG] 步骤3: 将绝对价格转换为相对变化率...")
-                # 转换为相对变化率
-                x_df_returns = convert_price_to_returns(x_df, ['open', 'high', 'low', 'close'])
-                print(f"[DEBUG] 转换完成，输入数据前5行相对变化率:")
-                print(x_df_returns[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤4: 使用转换后的数据进行Kronos模型预测...")
-                # 使用转换后的数据进行预测
-                pred_df_returns = predictor.predict(
-                    df=x_df_returns,
+                pred_df = predictor.predict(
+                    df=x_df,
                     x_timestamp=x_timestamp,
                     y_timestamp=y_timestamp,
                     pred_len=pred_len,
@@ -561,49 +485,28 @@ def predict():
                     top_p=top_p,
                     sample_count=sample_count
                 )
-                print(f"[DEBUG] 预测完成，预测结果前5行相对变化率:")
-                print(pred_df_returns[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤5: 将预测结果转换回绝对价格...")
-                # 将预测结果转换回绝对价格
-                pred_df = convert_returns_to_price(pred_df_returns, initial_prices, ['open', 'high', 'low', 'close'])
-                print(f"[DEBUG] 转换完成，预测结果前5行绝对价格:")
-                print(pred_df[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤6: 处理volume和amount列...")
-                # 保持volume列不变（如果存在）
-                if 'volume' in pred_df.columns:
-                    pred_df['volume'] = pred_df_returns['volume']
-                    print(f"[DEBUG] 已复制volume列")
-                if 'amount' in pred_df.columns:
-                    pred_df['amount'] = pred_df_returns['amount']
-                    print(f"[DEBUG] 已复制amount列")
-                
-                print(f"[DEBUG] 步骤7: 检查最终预测结果...")
-                print(f"[DEBUG] 最终预测结果形状: {pred_df.shape}")
-                print(f"[DEBUG] 最终预测结果列: {list(pred_df.columns)}")
                 
             except Exception as e:
-                return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500
+                return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500
         else:
-            return jsonify({'error': 'Kronos模型未加载，请先加载模型'}), 400
+            return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400
         
-        # 准备实际数据用于对比（如果存在）
+        # Prepare actual data for comparison (if exists)
         actual_data = []
         actual_df = None
         
-        if start_date:  # 自定义时间段
-            # 修复逻辑：使用选择的窗口内的数据
-            # 预测使用的是选择的窗口内的前400个数据点
-            # 实际数据应该是选择的窗口内的后120个数据点
+        if start_date:  # Custom time period
+            # Fix logic: use data within selected window
+            # Prediction uses first 400 data points within selected window
+            # Actual data should be last 120 data points within selected window
             start_dt = pd.to_datetime(start_date)
             
-            # 找到从start_date开始的数据
+            # Find data starting from start_date
             mask = df['timestamps'] >= start_dt
             time_range_df = df[mask]
             
             if len(time_range_df) >= lookback + pred_len:
-                # 获取选择的窗口内的后120个数据点作为实际值
+                # Get last 120 data points within selected window as actual values
                 actual_df = time_range_df.iloc[lookback:lookback+pred_len]
                 
                 for i, (_, row) in enumerate(actual_df.iterrows()):
@@ -616,9 +519,9 @@ def predict():
                         'volume': float(row['volume']) if 'volume' in row else 0,
                         'amount': float(row['amount']) if 'amount' in row else 0
                     })
-        else:  # 最新数据
-            # 预测使用的是前400个数据点
-            # 实际数据应该是400个数据点之后的120个数据点
+        else:  # Latest data
+            # Prediction uses first 400 data points
+            # Actual data should be 120 data points after first 400 data points
             if len(df) >= lookback + pred_len:
                 actual_df = df.iloc[lookback:lookback+pred_len]
                 for i, (_, row) in enumerate(actual_df.iterrows()):
@@ -632,28 +535,28 @@ def predict():
                         'amount': float(row['amount']) if 'amount' in row else 0
                     })
         
-        # 创建图表 - 传递历史数据的起始位置
+        # Create chart - pass historical data start position
         if start_date:
-            # 自定义时间段：找到历史数据在原始df中的起始位置
+            # Custom time period: find starting position of historical data in original df
             start_dt = pd.to_datetime(start_date)
             mask = df['timestamps'] >= start_dt
             historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0
         else:
-            # 最新数据：从开头开始
+            # Latest data: start from beginning
             historical_start_idx = 0
         
         chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx)
         
-        # 准备预测结果数据 - 修复时间戳计算逻辑
+        # Prepare prediction result data - fix timestamp calculation logic
         if 'timestamps' in df.columns:
             if start_date:
-                # 自定义时间段：使用选择的窗口数据计算时间戳
+                # Custom time period: use selected window data to calculate timestamps
                 start_dt = pd.to_datetime(start_date)
                 mask = df['timestamps'] >= start_dt
                 time_range_df = df[mask]
                 
                 if len(time_range_df) >= lookback:
-                    # 从选择的窗口的最后一个时间点开始计算预测时间戳
+                    # Calculate prediction timestamps starting from last time point of selected window
                     last_timestamp = time_range_df['timestamps'].iloc[lookback-1]
                     time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
                     future_timestamps = pd.date_range(
@@ -664,7 +567,7 @@ def predict():
                 else:
                     future_timestamps = []
             else:
-                # 最新数据：从整个数据文件的最后时间点开始计算
+                # Latest data: calculate from last time point of entire data file
                 last_timestamp = df['timestamps'].iloc[-1]
                 time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
                 future_timestamps = pd.date_range(
@@ -687,7 +590,7 @@ def predict():
                 'amount': float(row['amount']) if 'amount' in row else 0
             })
         
-        # 保存预测结果到文件
+        # Save prediction results to file
         try:
             save_prediction_results(
                 file_path=file_path,
@@ -705,7 +608,7 @@ def predict():
                 }
             )
         except Exception as e:
-            print(f"保存预测结果失败: {e}")
+            print(f"Failed to save prediction results: {e}")
         
         return jsonify({
             'success': True,
@@ -714,40 +617,40 @@ def predict():
             'prediction_results': prediction_results,
             'actual_data': actual_data,
             'has_comparison': len(actual_data) > 0,
-            'message': f'预测完成，生成了 {pred_len} 个预测点' + (f'，包含 {len(actual_data)} 个实际数据点用于对比' if len(actual_data) > 0 else '')
+            'message': f'Prediction completed, generated {pred_len} prediction points' + (f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '')
         })
         
     except Exception as e:
-        return jsonify({'error': f'预测失败: {str(e)}'}), 500
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
 
 @app.route('/api/load-model', methods=['POST'])
 def load_model():
-    """加载Kronos模型"""
+    """Load Kronos model"""
     global tokenizer, model, predictor
     
     try:
         if not MODEL_AVAILABLE:
-            return jsonify({'error': 'Kronos模型库不可用'}), 400
+            return jsonify({'error': 'Kronos model library not available'}), 400
         
         data = request.get_json()
         model_key = data.get('model_key', 'kronos-small')
         device = data.get('device', 'cpu')
         
         if model_key not in AVAILABLE_MODELS:
-            return jsonify({'error': f'不支持的模型: {model_key}'}), 400
+            return jsonify({'error': f'Unsupported model: {model_key}'}), 400
         
         model_config = AVAILABLE_MODELS[model_key]
         
-        # 加载tokenizer和模型
+        # Load tokenizer and model
         tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id'])
         model = Kronos.from_pretrained(model_config['model_id'])
         
-        # 创建predictor
+        # Create predictor
         predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length'])
         
         return jsonify({
             'success': True,
-            'message': f'模型加载成功: {model_config["name"]} ({model_config["params"]}) on {device}',
+            'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}',
             'model_info': {
                 'name': model_config['name'],
                 'params': model_config['params'],
@@ -757,11 +660,11 @@ def load_model():
         })
         
     except Exception as e:
-        return jsonify({'error': f'模型加载失败: {str(e)}'}), 500
+        return jsonify({'error': f'Model loading failed: {str(e)}'}), 500
 
 @app.route('/api/available-models')
 def get_available_models():
-    """获取可用的模型列表"""
+    """Get available model list"""
     return jsonify({
         'models': AVAILABLE_MODELS,
         'model_available': MODEL_AVAILABLE
@@ -769,13 +672,13 @@ def get_available_models():
 
 @app.route('/api/model-status')
 def get_model_status():
-    """获取模型状态"""
+    """Get model status"""
     if MODEL_AVAILABLE:
         if predictor is not None:
             return jsonify({
                 'available': True,
                 'loaded': True,
-                'message': 'Kronos模型已加载并可用',
+                'message': 'Kronos model loaded and available',
                 'current_model': {
                     'name': predictor.model.__class__.__name__,
                     'device': str(next(predictor.model.parameters()).device)
@@ -785,21 +688,21 @@ def get_model_status():
             return jsonify({
                 'available': True,
                 'loaded': False,
-                'message': 'Kronos模型可用但未加载'
+                'message': 'Kronos model available but not loaded'
             })
     else:
         return jsonify({
             'available': False,
             'loaded': False,
-            'message': 'Kronos模型库不可用，请安装相关依赖'
+            'message': 'Kronos model library not available, please install related dependencies'
         })
 
 if __name__ == '__main__':
-    print("启动Kronos Web UI...")
-    print(f"模型可用性: {MODEL_AVAILABLE}")
+    print("Starting Kronos Web UI...")
+    print(f"Model availability: {MODEL_AVAILABLE}")
     if MODEL_AVAILABLE:
-        print("提示: 可以通过 /api/load-model 接口加载Kronos模型")
+        print("Tip: You can load Kronos model through /api/load-model endpoint")
     else:
-        print("提示: 将使用模拟数据进行演示")
+        print("Tip: Will use simulated data for demonstration")
     
     app.run(debug=True, host='0.0.0.0', port=7070)

webui/run.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Kronos Web UI 启动脚本
+Kronos Web UI startup script
 """
 
 import os
@@ -10,80 +10,80 @@ import webbrowser
 import time
 
 def check_dependencies():
-    """检查依赖是否安装"""
+    """Check if dependencies are installed"""
     try:
         import flask
         import flask_cors
         import pandas
         import numpy
         import plotly
-        print("✅ 所有依赖已安装")
+        print("✅ All dependencies installed")
         return True
     except ImportError as e:
-        print(f"❌ 缺少依赖: {e}")
-        print("请运行: pip install -r requirements.txt")
+        print(f"❌ Missing dependency: {e}")
+        print("Please run: pip install -r requirements.txt")
         return False
 
 def install_dependencies():
-    """安装依赖"""
-    print("正在安装依赖...")
+    """Install dependencies"""
+    print("Installing dependencies...")
     try:
         subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
-        print("✅ 依赖安装完成")
+        print("✅ Dependencies installation completed")
         return True
     except subprocess.CalledProcessError:
-        print("❌ 依赖安装失败")
+        print("❌ Dependencies installation failed")
         return False
 
 def main():
-    """主函数"""
-    print("🚀 启动 Kronos Web UI...")
+    """Main function"""
+    print("🚀 Starting Kronos Web UI...")
     print("=" * 50)
     
-    # 检查依赖
+    # Check dependencies
     if not check_dependencies():
-        print("\n是否自动安装依赖? (y/n): ", end="")
+        print("\nAuto-install dependencies? (y/n): ", end="")
         if input().lower() == 'y':
             if not install_dependencies():
                 return
         else:
-            print("请手动安装依赖后重试")
+            print("Please manually install dependencies and retry")
             return
     
-    # 检查模型可用性
+    # Check model availability
     try:
         sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
         from model import Kronos, KronosTokenizer, KronosPredictor
-        print("✅ Kronos模型库可用")
+        print("✅ Kronos model library available")
         model_available = True
     except ImportError:
-        print("⚠️  Kronos模型库不可用，将使用模拟预测")
+        print("⚠️  Kronos model library not available, will use simulated prediction")
         model_available = False
     
-    # 启动Flask应用
-    print("\n🌐 启动Web服务器...")
+    # Start Flask application
+    print("\n🌐 Starting Web server...")
     
-    # 设置环境变量
+    # Set environment variables
     os.environ['FLASK_APP'] = 'app.py'
     os.environ['FLASK_ENV'] = 'development'
     
-    # 启动服务器
+    # Start server
     try:
         from app import app
-        print("✅ Web服务器启动成功!")
-        print(f"🌐 访问地址: http://localhost:7070")
-        print("💡 提示: 按 Ctrl+C 停止服务器")
+        print("✅ Web server started successfully!")
+        print(f"🌐 Access URL: http://localhost:7070")
+        print("💡 Tip: Press Ctrl+C to stop server")
         
-        # 自动打开浏览器
+        # Auto-open browser
         time.sleep(2)
         webbrowser.open('http://localhost:7070')
         
-        # 启动Flask应用
+        # Start Flask application
         app.run(debug=True, host='0.0.0.0', port=7070)
         
     except Exception as e:
-        print(f"❌ 启动失败: {e}")
-        print("请检查端口7070是否被占用")
+        print(f"❌ Startup failed: {e}")
+        print("Please check if port 7070 is occupied")
 
 if __name__ == "__main__":
     main()

webui/start.sh

@@ -1,40 +1,40 @@
 #!/bin/bash
 
-# Kronos Web UI 启动脚本
+# Kronos Web UI startup script
 
-echo "🚀 启动 Kronos Web UI..."
+echo "🚀 Starting Kronos Web UI..."
 echo "================================"
 
-# 检查Python是否安装
+# Check if Python is installed
 if ! command -v python3 &> /dev/null; then
-    echo "❌ Python3 未安装，请先安装Python3"
+    echo "❌ Python3 not installed, please install Python3 first"
     exit 1
 fi
 
-# 检查是否在正确的目录
+# Check if in correct directory
 if [ ! -f "app.py" ]; then
-    echo "❌ 请在webui目录下运行此脚本"
+    echo "❌ Please run this script in the webui directory"
     exit 1
 fi
 
-# 检查依赖
-echo "📦 检查依赖..."
+# Check dependencies
+echo "📦 Checking dependencies..."
 if ! python3 -c "import flask, flask_cors, pandas, numpy, plotly" &> /dev/null; then
-    echo "⚠️  缺少依赖，正在安装..."
+    echo "⚠️  Missing dependencies, installing..."
     pip3 install -r requirements.txt
     if [ $? -ne 0 ]; then
-        echo "❌ 依赖安装失败"
+        echo "❌ Dependencies installation failed"
         exit 1
     fi
-    echo "✅ 依赖安装完成"
+    echo "✅ Dependencies installation completed"
 else
-    echo "✅ 所有依赖已安装"
+    echo "✅ All dependencies installed"
 fi
 
-# 启动应用
-echo "🌐 启动Web服务器..."
-echo "访问地址: http://localhost:7070"
-echo "按 Ctrl+C 停止服务器"
+# Start application
+echo "🌐 Starting Web server..."
+echo "Access URL: http://localhost:7070"
+echo "Press Ctrl+C to stop server"
 echo ""
 
 python3 app.py

webui/templates/index.html

@@ -3,7 +3,7 @@
 <head>
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Kronos 金融预测 Web UI</title>
+    <title>Kronos Financial Prediction Web UI</title>
     <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
     <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
     <style>
@@ -93,7 +93,7 @@
             border-color: #667eea;
         }
 
-        /* 预测质量参数样式 */
+        /* Prediction quality parameter styles */
         .form-group input[type="range"] {
             width: 70%;
             margin-right: 10px;
@@ -227,7 +227,7 @@
             color: #2d3748;
         }
 
-        /* 时间窗口选择器样式 */
+        /* Time window selector styles */
         .time-window-container {
             background: #f7fafc;
             border: 1px solid #e2e8f0;
@@ -301,7 +301,7 @@
             margin-top: 5px;
         }
 
-        /* 对比分析样式 */
+        /* Comparison analysis styles */
         .comparison-section {
             background: #f7fafc;
             border: 1px solid #e2e8f0;
@@ -441,79 +441,79 @@
 <body>
     <div class="container">
         <div class="header">
-            <h1>🚀 Kronos 金融预测 Web UI</h1>
-            <p>基于AI的金融K线数据预测分析平台</p>
+            <h1>🚀 Kronos Financial Prediction Web UI</h1>
+            <p>AI-based financial K-line data prediction analysis platform</p>
         </div>
 
         <div class="main-content">
             <div class="control-panel">
-                <h2>🎯 控制面板</h2>
+                <h2>🎯 Control Panel</h2>
 
-                <!-- 模型选择 -->
+                <!-- Model Selection -->
                 <div class="form-group">
-                    <label for="model-select">选择模型:</label>
+                    <label for="model-select">Select Model:</label>
                     <select id="model-select">
-                        <option value="">请先加载可用模型</option>
+                        <option value="">Please load available models first</option>
                     </select>
-                    <small class="form-text">选择要使用的Kronos模型</small>
+                    <small class="form-text">Select the Kronos model to use</small>
                 </div>
 
-                <!-- 设备选择 -->
+                <!-- Device Selection -->
                 <div class="form-group">
-                    <label for="device-select">选择设备:</label>
+                    <label for="device-select">Select Device:</label>
                     <select id="device-select">
                         <option value="cpu">CPU</option>
                         <option value="cuda">CUDA (NVIDIA GPU)</option>
                         <option value="mps">MPS (Apple Silicon)</option>
                     </select>
-                    <small class="form-text">选择模型运行的设备</small>
+                    <small class="form-text">Select the device to run the model on</small>
                 </div>
 
-                <!-- 模型状态 -->
+                <!-- Model Status -->
                 <div id="model-status" class="status info" style="display: none;">
-                    模型状态信息
+                    Model status information
                 </div>
 
-                <!-- 加载模型按钮 -->
+                <!-- Load Model Button -->
                 <button id="load-model-btn" class="btn btn-secondary">
-                    🔄 加载模型
+                    🔄 Load Model
                 </button>
 
                 <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
 
-                <!-- 数据文件选择 -->
+                <!-- Data File Selection -->
                 <div class="form-group">
-                    <label for="data-file-select">选择数据文件:</label>
+                    <label for="data-file-select">Select Data File:</label>
                     <select id="data-file-select">
-                        <option value="">请先加载数据文件列表</option>
+                        <option value="">Please load data file list first</option>
                     </select>
-                    <small class="form-text">从data目录选择K线数据文件</small>
+                    <small class="form-text">Select K-line data file from data directory</small>
                 </div>
 
                 <button id="load-data-btn" class="btn btn-secondary">
-                    📁 加载数据
+                    📁 Load Data
                 </button>
 
-                <!-- 数据信息显示 -->
+                <!-- Data Information Display -->
                 <div id="data-info" class="data-info" style="display: none;">
-                    <h3>📊 数据信息</h3>
-                    <p><strong>行数:</strong> <span id="data-rows">-</span></p>
-                    <p><strong>列数:</strong> <span id="data-cols">-</span></p>
-                    <p><strong>时间范围:</strong> <span id="data-time-range">-</span></p>
-                    <p><strong>价格范围:</strong> <span id="data-price-range">-</span></p>
-                    <p><strong>时间频率:</strong> <span id="data-timeframe">-</span></p>
-                    <p><strong>预测列:</strong> <span id="data-prediction-cols">-</span></p>
+                    <h3>📊 Data Information</h3>
+                    <p><strong>Rows:</strong> <span id="data-rows">-</span></p>
+                    <p><strong>Columns:</strong> <span id="data-cols">-</span></p>
+                    <p><strong>Time Range:</strong> <span id="data-time-range">-</span></p>
+                    <p><strong>Price Range:</strong> <span id="data-price-range">-</span></p>
+                    <p><strong>Time Frequency:</strong> <span id="data-timeframe">-</span></p>
+                    <p><strong>Prediction Columns:</strong> <span id="data-prediction-cols">-</span></p>
                 </div>
 
                 <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
 
-                <!-- 时间窗口选择器 -->
+                <!-- Time Window Selector -->
                 <div class="time-window-container">
-                    <h3>⏰ 时间窗口选择</h3>
+                    <h3>⏰ Time Window Selection</h3>
                     <div class="time-window-info">
-                        <span id="window-start">开始: --</span>
-                        <span id="window-end">结束: --</span>
-                        <span id="window-size">窗口大小: 400+120=520个数据点</span>
+                        <span id="window-start">Start: --</span>
+                        <span id="window-end">End: --</span>
+                        <span id="window-size">Window Size: 400+120=520 data points</span>
                     </div>
                     
                     <div class="time-window-slider">
@@ -523,104 +523,104 @@
                             <div class="slider-handle end-handle" id="end-handle"></div>
                         </div>
                         <div class="slider-labels">
-                            <span id="min-label">最早</span>
-                            <span id="max-label">最新</span>
+                            <span id="min-label">Earliest</span>
+                            <span id="max-label">Latest</span>
                         </div>
                     </div>
                     
-                    <small class="form-text">拖动滑条选择520个数据点的时间窗口位置，绿色区域表示固定的400+120数据点范围</small>
+                    <small class="form-text">Drag slider to select time window position for 520 data points, green area represents fixed 400+120 data point range</small>
                 </div>
 
-                <!-- 预测参数 -->
+                <!-- Prediction Parameters -->
                 <div class="form-group">
-                    <label for="lookback">回看窗口大小:</label>
+                    <label for="lookback">Lookback Window Size:</label>
                     <input type="number" id="lookback" value="400" readonly>
-                    <small class="form-text">固定为400个数据点</small>
+                    <small class="form-text">Fixed at 400 data points</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="pred-len">预测长度:</label>
+                    <label for="pred-len">Prediction Length:</label>
                     <input type="number" id="pred-len" value="120" readonly>
-                    <small class="form-text">固定为120个数据点</small>
+                    <small class="form-text">Fixed at 120 data points</small>
                 </div>
 
-                <!-- 预测质量参数 -->
+                <!-- Prediction Quality Parameters -->
                 <div class="form-group">
-                    <label for="temperature">预测温度 (T):</label>
+                    <label for="temperature">Prediction Temperature (T):</label>
                     <input type="range" id="temperature" value="1.0" min="0.1" max="2.0" step="0.1">
                     <span id="temperature-value">1.0</span>
-                    <small class="form-text">控制预测的随机性，值越高预测越多样化，值越低预测越保守</small>
+                    <small class="form-text">Controls prediction randomness, higher values make predictions more diverse, lower values make predictions more conservative</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="top-p">核采样参数 (top_p):</label>
+                    <label for="top-p">Nucleus Sampling Parameter (top_p):</label>
                     <input type="range" id="top-p" value="0.9" min="0.1" max="1.0" step="0.1">
                     <span id="top-p-value">0.9</span>
-                    <small class="form-text">控制预测的多样性，值越高考虑的概率分布越广</small>
+                    <small class="form-text">Controls prediction diversity, higher values consider broader probability distributions</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="sample-count">样本数量:</label>
+                    <label for="sample-count">Sample Count:</label>
                     <input type="number" id="sample-count" value="1" min="1" max="5" step="1">
-                    <small class="form-text">生成多个预测样本以提高质量（建议1-3个）</small>
+                    <small class="form-text">Generate multiple prediction samples to improve quality (recommended 1-3)</small>
                 </div>
 
                 <button id="predict-btn" class="btn btn-success" disabled>
-                    🔮 开始预测
+                    🔮 Start Prediction
                 </button>
 
-                <!-- 加载状态 -->
+                <!-- Loading Status -->
                 <div id="loading" class="loading">
                     <div class="spinner"></div>
-                    <p>正在处理，请稍候...</p>
+                    <p>Processing, please wait...</p>
                 </div>
             </div>
 
             <div class="chart-container">
-                <h2>📈 预测结果图表</h2>
+                <h2>📈 Prediction Results Chart</h2>
                 <div id="chart"></div>
                 
-                <!-- 对比分析 -->
+                <!-- Comparison Analysis -->
                 <div id="comparison-section" class="comparison-section" style="display: none;">
-                    <h3>📊 预测 vs 实际数据对比</h3>
+                    <h3>📊 Prediction vs Actual Data Comparison</h3>
                     <div id="comparison-info" class="comparison-info">
-                        <p><strong>预测类型:</strong> <span id="prediction-type">-</span></p>
-                        <p><strong>对比数据:</strong> <span id="comparison-data">-</span></p>
+                        <p><strong>Prediction Type:</strong> <span id="prediction-type">-</span></p>
+                        <p><strong>Comparison Data:</strong> <span id="comparison-data">-</span></p>
                     </div>
                     
                     <div class="error-stats">
                         <div class="error-stat">
-                            <h4>平均绝对误差</h4>
+                            <h4>Mean Absolute Error</h4>
                             <div class="value" id="mae">-</div>
-                            <div class="unit">价格单位</div>
+                            <div class="unit">Price Units</div>
                         </div>
                         <div class="error-stat">
-                            <h4>均方根误差</h4>
+                            <h4>Root Mean Square Error</h4>
                             <div class="value" id="rmse">-</div>
-                            <div class="unit">价格单位</div>
+                            <div class="unit">Price Units</div>
                         </div>
                         <div class="error-stat">
-                            <h4>平均绝对百分比误差</h4>
+                            <h4>Mean Absolute Percentage Error</h4>
                             <div class="value" id="mape">-</div>
                             <div class="unit">%</div>
                         </div>
                     </div>
                     
                     <div class="error-details">
-                        <h4>详细对比数据:</h4>
+                        <h4>Detailed Comparison Data:</h4>
                         <div style="max-height: 300px; overflow-y: auto;">
                             <table class="comparison-table">
                                 <thead>
                                     <tr>
-                                        <th>时间</th>
-                                        <th>实际开盘</th>
-                                        <th>预测开盘</th>
-                                        <th>实际最高</th>
-                                        <th>预测最高</th>
-                                        <th>实际最低</th>
-                                        <th>预测最低</th>
-                                        <th>实际收盘</th>
-                                        <th>预测收盘</th>
+                                        <th>Time</th>
+                                        <th>Actual Open</th>
+                                        <th>Predicted Open</th>
+                                        <th>Actual High</th>
+                                        <th>Predicted High</th>
+                                        <th>Actual Low</th>
+                                        <th>Predicted Low</th>
+                                        <th>Actual Close</th>
+                                        <th>Predicted Close</th>
                                     </tr>
                                 </thead>
                                 <tbody id="comparison-tbody">
@@ -634,58 +634,58 @@
     </div>
 
     <script>
-        // 全局变量
+        // Global variables
         let currentDataFile = null;
         let currentDataInfo = null;
         let availableModels = [];
         let modelLoaded = false;
 
-        // 页面加载完成后初始化
+        // Initialize after page loads
         document.addEventListener('DOMContentLoaded', function() {
             initializeApp();
         });
 
-        // 初始化应用
+        // Initialize application
         async function initializeApp() {
-            console.log('🚀 初始化 Kronos Web UI...');
+            console.log('🚀 Initializing Kronos Web UI...');
             
-            // 加载可用模型
+            // Load available models
             await loadAvailableModels();
             
-            // 加载数据文件列表
+            // Load data file list
             await loadDataFiles();
             
-            // 设置事件监听器
+            // Set up event listeners
             setupEventListeners();
             
-            // 初始化时间滑块
+            // Initialize time slider
             initializeTimeSlider();
             
-            console.log('✅ 应用初始化完成');
+            console.log('✅ Application initialization completed');
         }
 
-        // 加载可用模型
+        // Load available models
         async function loadAvailableModels() {
             try {
                 const response = await axios.get('/api/available-models');
                 if (response.data.model_available) {
                     availableModels = response.data.models;
                     populateModelSelect();
-                    console.log('✅ 可用模型加载成功:', availableModels);
+                    console.log('✅ Available models loaded successfully:', availableModels);
                 } else {
-                    console.warn('⚠️ Kronos模型库不可用');
-                    showStatus('warning', 'Kronos模型库不可用，将使用模拟预测');
+                    console.warn('⚠️ Kronos model library not available');
+                    showStatus('warning', 'Kronos model library not available, will use simulated prediction');
                 }
             } catch (error) {
-                console.error('❌ 加载可用模型失败:', error);
-                showStatus('error', '加载可用模型失败');
+                console.error('❌ Failed to load available models:', error);
+                showStatus('error', 'Failed to load available models');
             }
         }
 
-        // 填充模型选择下拉框
+        // Populate model selection dropdown
         function populateModelSelect() {
             const modelSelect = document.getElementById('model-select');
-            modelSelect.innerHTML = '<option value="">请选择模型</option>';
+            modelSelect.innerHTML = '<option value="">Please select model</option>';
             
             Object.entries(availableModels).forEach(([key, model]) => {
                 const option = document.createElement('option');
@@ -695,13 +695,13 @@
             });
         }
 
-        // 加载模型
+        // Load model
         async function loadModel() {
             const modelKey = document.getElementById('model-select').value;
             const device = document.getElementById('device-select').value;
             
             if (!modelKey) {
-                showStatus('error', '请选择要加载的模型');
+                showStatus('error', 'Please select a model to load');
                 return;
             }
             
@@ -719,45 +719,45 @@
                     showStatus('success', response.data.message);
                     updateModelStatus();
                     document.getElementById('predict-btn').disabled = false;
-                    console.log('✅ 模型加载成功:', response.data.model_info);
+                    console.log('✅ Model loaded successfully:', response.data.model_info);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 模型加载失败:', error);
-                showStatus('error', `模型加载失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Model loading failed:', error);
+                showStatus('error', `Model loading failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('load-model-btn').disabled = false;
             }
         }
 
-        // 更新模型状态
+        // Update model status
         async function updateModelStatus() {
             try {
                 const response = await axios.get('/api/model-status');
                 const status = response.data;
                 
                 if (status.loaded) {
-                    showStatus('success', `模型已加载: ${status.current_model.name} on ${status.current_model.device}`);
+                    showStatus('success', `Model loaded: ${status.current_model.name} on ${status.current_model.device}`);
                 } else if (status.available) {
-                    showStatus('info', '模型可用但未加载');
+                    showStatus('info', 'Model available but not loaded');
                 } else {
-                    showStatus('warning', '模型库不可用');
+                    showStatus('warning', 'Model library not available');
                 }
             } catch (error) {
-                console.error('❌ 获取模型状态失败:', error);
+                console.error('❌ Failed to get model status:', error);
             }
         }
 
-        // 加载数据文件列表
+        // Load data file list
         async function loadDataFiles() {
             try {
                 const response = await axios.get('/api/data-files');
                 const dataFiles = response.data;
                 
                 const dataFileSelect = document.getElementById('data-file-select');
-                dataFileSelect.innerHTML = '<option value="">请选择数据文件</option>';
+                dataFileSelect.innerHTML = '<option value="">Please select data file</option>';
                 
                 dataFiles.forEach(file => {
                     const option = document.createElement('option');
@@ -766,19 +766,19 @@
                     dataFileSelect.appendChild(option);
                 });
                 
-                console.log('✅ 数据文件列表加载成功:', dataFiles);
+                console.log('✅ Data file list loaded successfully:', dataFiles);
             } catch (error) {
-                console.error('❌ 加载数据文件列表失败:', error);
-                showStatus('error', '加载数据文件列表失败');
+                console.error('❌ Failed to load data file list:', error);
+                showStatus('error', 'Failed to load data file list');
             }
         }
 
-        // 加载数据文件
+        // Load data file
         async function loadData() {
             const filePath = document.getElementById('data-file-select').value;
             
             if (!filePath) {
-                showStatus('error', '请选择要加载的数据文件');
+                showStatus('error', 'Please select a data file to load');
                 return;
             }
             
@@ -796,56 +796,56 @@
                     showDataInfo(response.data.data_info);
                     showStatus('success', response.data.message);
                     
-                    // 更新预测按钮状态
+                    // Update prediction button status
                     if (modelLoaded) {
                         document.getElementById('predict-btn').disabled = false;
                     }
                     
-                    console.log('✅ 数据加载成功:', response.data.data_info);
+                    console.log('✅ Data loaded successfully:', response.data.data_info);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 数据加载失败:', error);
-                showStatus('error', `数据加载失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Data loading failed:', error);
+                showStatus('error', `Data loading failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('load-data-btn').disabled = false;
             }
         }
 
-        // 显示数据信息
+        // Display data information
         function showDataInfo(dataInfo) {
             document.getElementById('data-info').style.display = 'block';
             document.getElementById('data-rows').textContent = dataInfo.rows;
             document.getElementById('data-cols').textContent = dataInfo.columns.length;
-            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} 至 ${dataInfo.end_date}`;
+            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} to ${dataInfo.end_date}`;
             document.getElementById('data-price-range').textContent = `${dataInfo.price_range.min.toFixed(4)} - ${dataInfo.price_range.max.toFixed(4)}`;
             document.getElementById('data-timeframe').textContent = dataInfo.timeframe;
             document.getElementById('data-prediction-cols').textContent = dataInfo.prediction_columns.join(', ');
             
-            // 初始化时间窗口滑条
+            // Initialize time window slider
             initializeTimeWindowSlider(dataInfo);
         }
 
-        // 时间窗口滑条相关变量
+                    // Time window slider related variables
         let sliderData = null;
         let isDragging = false;
         let currentHandle = null;
 
-        // 初始化时间窗口滑条
+        // Initialize time window slider
         function initializeTimeSlider() {
-            // 设置滑条事件监听器
+            // Set up slider event listeners
             setupSliderEventListeners();
         }
 
-        // 设置滑条事件监听器
+        // Set up slider event listeners
         function setupSliderEventListeners() {
             const startHandle = document.getElementById('start-handle');
             const endHandle = document.getElementById('end-handle');
             const track = document.querySelector('.slider-track');
             
-            // 开始拖拽
+            // Start dragging
             startHandle.addEventListener('mousedown', (e) => {
                 isDragging = true;
                 currentHandle = 'start';
@@ -858,7 +858,7 @@
                 e.preventDefault();
             });
             
-            // 拖拽中
+            // Dragging
             document.addEventListener('mousemove', (e) => {
                 if (!isDragging) return;
                 
@@ -875,19 +875,19 @@
                 updateSliderFromHandles();
             });
             
-            // 结束拖拽
+            // End dragging
             document.addEventListener('mouseup', () => {
                 isDragging = false;
                 currentHandle = null;
             });
             
-            // 点击轨道直接设置位置
+            // Click track to set position directly
             track.addEventListener('click', (e) => {
                 const rect = track.getBoundingClientRect();
                 const x = e.clientX - rect.left;
                 const percentage = Math.max(0, Math.min(1, x / rect.width));
                 
-                // 判断点击位置更接近哪个手柄
+                // Determine which handle is closer to the click position
                 const startHandle = document.getElementById('start-handle');
                 const endHandle = document.getElementById('end-handle');
                 const startRect = startHandle.getBoundingClientRect();
@@ -903,17 +903,17 @@
             });
         }
 
-        // 更新开始手柄位置
+        // Update start handle position
         function updateStartHandle(percentage) {
             const startHandle = document.getElementById('start-handle');
             const selection = document.getElementById('slider-selection');
             
-            // 固定窗口大小为520个数据点
+            // Fixed window size of 520 data points
             const windowSize = 520;
             const totalRows = sliderData ? sliderData.totalRows : 1000;
             const windowPercentage = windowSize / totalRows;
             
-            // 确保开始手柄不会导致窗口超出数据范围
+            // Ensure start handle doesn't cause window to exceed data range
             if (percentage + windowPercentage > 1) {
                 percentage = 1 - windowPercentage;
             }
@@ -922,22 +922,22 @@
             selection.style.left = (percentage * 100) + '%';
             selection.style.width = (windowPercentage * 100) + '%';
             
-            // 自动调整结束手柄位置，保持固定窗口大小
+            // Automatically adjust end handle position to maintain fixed window size
             const endHandle = document.getElementById('end-handle');
             endHandle.style.left = ((percentage + windowPercentage) * 100) + '%';
         }
 
-        // 更新结束手柄位置
+        // Update end handle position
         function updateEndHandle(percentage) {
             const endHandle = document.getElementById('end-handle');
             const selection = document.getElementById('slider-selection');
             
-            // 固定窗口大小为520个数据点
+            // Fixed window size of 520 data points
             const windowSize = 520;
             const totalRows = sliderData ? sliderData.totalRows : 1000;
             const windowPercentage = windowSize / totalRows;
             
-            // 确保结束手柄不会导致窗口超出数据范围
+            // Ensure end handle doesn't cause window to exceed data range
             if (percentage - windowPercentage < 0) {
                 percentage = windowPercentage;
             }
@@ -946,12 +946,12 @@
             selection.style.left = ((percentage - windowPercentage) * 100) + '%';
             selection.style.width = (windowPercentage * 100) + '%';
             
-            // 自动调整开始手柄位置，保持固定窗口大小
+            // Automatically adjust start handle position to maintain fixed window size
             const startHandle = document.getElementById('start-handle');
             startHandle.style.left = ((percentage - windowPercentage) * 100) + '%';
         }
 
-        // 根据手柄位置更新滑条显示
+        // Update slider display based on handle positions
         function updateSliderFromHandles() {
             const startHandle = document.getElementById('start-handle');
             const endHandle = document.getElementById('end-handle');
@@ -961,7 +961,7 @@
             
             if (!sliderData) return;
             
-            // 计算选中的时间范围
+            // Calculate selected time range
             const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
             const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
             const endTime = sliderData.startDate.getTime() + (totalTime * endPercentage);
@@ -969,49 +969,49 @@
             const startDate = new Date(startTime);
             const endDate = new Date(endTime);
             
-            // 更新显示信息
-            document.getElementById('window-start').textContent = `开始: ${startDate.toLocaleDateString()}`;
-            document.getElementById('window-end').textContent = `结束: ${endDate.toLocaleDateString()}`;
+            // Update display information
+            document.getElementById('window-start').textContent = `Start: ${startDate.toLocaleDateString()}`;
+            document.getElementById('window-end').textContent = `End: ${endDate.toLocaleDateString()}`;
             
-            // 显示固定的窗口大小
-            document.getElementById('window-size').textContent = `窗口大小: 400 + 120 = 520 个数据点 (固定)`;
+            // Display fixed window size
+            document.getElementById('window-size').textContent = `Window Size: 400 + 120 = 520 data points (fixed)`;
             
-            // 输入框值保持固定
+            // Input field values remain fixed
             document.getElementById('lookback').value = 400;
             document.getElementById('pred-len').value = 120;
         }
 
-        // 根据输入框更新滑条
+        // Update slider based on input fields
         function updateSliderFromInputs() {
             if (!sliderData) return;
             
-            // 固定窗口大小：400 + 120 = 520个数据点
+            // Fixed window size: 400 + 120 = 520 data points
             const lookback = 400;
             const predLen = 120;
-            const windowSize = lookback + predLen; // 固定为520
+            const windowSize = lookback + predLen; // Fixed at 520
             
-            // 计算滑条位置
+            // Calculate slider position
             const totalRows = sliderData.totalRows;
             
             if (windowSize > totalRows) {
-                // 如果窗口大小超过总数据量，显示错误
-                showStatus('error', `数据量不足，需要至少${windowSize}个数据点，当前只有${totalRows}个`);
+                // If window size exceeds total data amount, show error
+                showStatus('error', `Insufficient data, need at least ${windowSize} data points, currently only ${totalRows} available`);
                 return;
             }
             
-            // 计算滑条位置（默认选择数据的前半部分）
-            const startPercentage = 0.1; // 从10%开始
+            // Calculate slider position (default select first half of data)
+            const startPercentage = 0.1; // Start from 10%
             const endPercentage = startPercentage + (windowSize / totalRows);
             
-            // 更新手柄位置
+            // Update handle positions
             updateStartHandle(startPercentage);
             updateEndHandle(endPercentage);
             
-            // 更新显示信息
+            // Update display information
             updateSliderFromHandles();
         }
 
-        // 初始化时间窗口滑条
+        // Initialize time window slider
         function initializeTimeWindowSlider(dataInfo) {
             sliderData = {
                 startDate: new Date(dataInfo.start_date),
@@ -1020,23 +1020,23 @@
                 timeframe: dataInfo.timeframe
             };
             
-            // 设置滑条标签
+            // Set slider labels
             document.getElementById('min-label').textContent = dataInfo.start_date.split('T')[0];
             document.getElementById('max-label').textContent = dataInfo.end_date.split('T')[0];
             
-            // 初始化滑条位置
+            // Initialize slider position
             updateSliderFromInputs();
         }
 
-        // 开始预测
+        // Start prediction
         async function startPrediction() {
             if (!currentDataFile) {
-                showStatus('error', '请先加载数据文件');
+                showStatus('error', 'Please load data file first');
                 return;
             }
             
             if (!modelLoaded) {
-                showStatus('error', '请先加载模型');
+                showStatus('error', 'Please load model first');
                 return;
             }
             
@@ -1047,21 +1047,21 @@
                 const lookback = parseInt(document.getElementById('lookback').value);
                 const predLen = parseInt(document.getElementById('pred-len').value);
                 
-                // 从时间窗口滑条获取选择的时间范围
+                // Get selected time range from time window slider
                 const startHandle = document.getElementById('start-handle');
                 const startPercentage = parseFloat(startHandle.style.left) / 100;
                 
                 if (!sliderData) {
-                    showStatus('error', '时间窗口滑条未初始化');
+                    showStatus('error', 'Time window slider not initialized');
                     return;
                 }
                 
-                // 计算选择的时间范围
+                // Calculate selected time range
                 const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
                 const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
                 const startDate = new Date(startTime);
                 
-                // 获取预测质量参数
+                // Get prediction quality parameters
                 const temperature = parseFloat(document.getElementById('temperature').value);
                 const topP = parseFloat(document.getElementById('top-p').value);
                 const sampleCount = parseInt(document.getElementById('sample-count').value);
@@ -1070,39 +1070,39 @@
                     file_path: currentDataFile,
                     lookback: lookback,
                     pred_len: predLen,
-                    start_date: startDate.toISOString().slice(0, 16), // 格式化为 YYYY-MM-DDTHH:MM
+                    start_date: startDate.toISOString().slice(0, 16), // Format as YYYY-MM-DDTHH:MM
                     temperature: temperature,
                     top_p: topP,
                     sample_count: sampleCount
                 };
                 
-                console.log('🚀 开始预测，参数:', predictionParams);
+                console.log('🚀 Starting prediction, parameters:', predictionParams);
                 
                 const response = await axios.post('/api/predict', predictionParams);
                 
                 if (response.data.success) {
-                    // 显示预测结果
+                    // Display prediction results
                     displayPredictionResult(response.data);
                     showStatus('success', response.data.message);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 预测失败:', error);
-                showStatus('error', `预测失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Prediction failed:', error);
+                showStatus('error', `Prediction failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('predict-btn').disabled = false;
             }
         }
 
-        // 显示预测结果
+        // Display prediction results
         function displayPredictionResult(result) {
-            // 显示图表
+            // Display chart
             const chartData = JSON.parse(result.chart);
             Plotly.newPlot('chart', chartData.data, chartData.layout);
             
-            // 显示对比分析（如果有实际数据）
+            // Display comparison analysis (if actual data exists)
             if (result.has_comparison) {
                 displayComparisonAnalysis(result);
             } else {
@@ -1110,27 +1110,27 @@
             }
         }
 
-        // 显示对比分析
+        // Display comparison analysis
         function displayComparisonAnalysis(result) {
             document.getElementById('comparison-section').style.display = 'block';
             
-            // 更新对比信息
+            // Update comparison information
             document.getElementById('prediction-type').textContent = result.prediction_type;
-            document.getElementById('comparison-data').textContent = `${result.actual_data.length} 个实际数据点`;
+            document.getElementById('comparison-data').textContent = `${result.actual_data.length} actual data points`;
             
-            // 计算误差统计
+            // Calculate error statistics
             const errorStats = getPredictionQuality(result.prediction_results, result.actual_data);
             
-            // 显示误差统计
+            // Display error statistics
             document.getElementById('mae').textContent = errorStats.mae.toFixed(4);
             document.getElementById('rmse').textContent = errorStats.rmse.toFixed(4);
             document.getElementById('mape').textContent = errorStats.mape.toFixed(2);
             
-            // 填充对比表格
+            // Fill comparison table
             fillComparisonTable(result.prediction_results, result.actual_data);
         }
 
-        // 计算预测质量指标
+        // Calculate prediction quality metrics
         function getPredictionQuality(predictions, actuals) {
             if (!predictions || !actuals || predictions.length === 0 || actuals.length === 0) {
                 return { mae: 0, rmse: 0, mape: 0 };
@@ -1143,7 +1143,7 @@
                 const pred = predictions[i];
                 const act = actuals[i];
                 
-                // 使用收盘价计算误差
+                // Use closing price to calculate errors
                 const error = Math.abs(pred.close - act.close);
                 const percentError = (error / act.close) * 100;
                 
@@ -1159,7 +1159,7 @@
             return { mae, rmse, mape };
         }
 
-        // 填充对比表格
+                    // Fill comparison table
         function fillComparisonTable(predictions, actuals) {
             const tbody = document.getElementById('comparison-tbody');
             tbody.innerHTML = '';
@@ -1186,18 +1186,18 @@
             }
         }
 
-        // 设置事件监听器
+        // Set up event listeners
         function setupEventListeners() {
-            // 加载模型按钮
+            // Load model button
             document.getElementById('load-model-btn').addEventListener('click', loadModel);
             
-            // 加载数据按钮
+            // Load data button
             document.getElementById('load-data-btn').addEventListener('click', loadData);
             
-            // 预测按钮
+            // Prediction button
             document.getElementById('predict-btn').addEventListener('click', startPrediction);
             
-            // 预测质量参数滑块
+            // Prediction quality parameter sliders
             document.getElementById('temperature').addEventListener('input', function() {
                 document.getElementById('temperature-value').textContent = this.value;
             });
@@ -1206,25 +1206,25 @@
                 document.getElementById('top-p-value').textContent = this.value;
             });
             
-            // 回看窗口大小变化时更新滑条
+            // Update slider when lookback window size changes
             document.getElementById('lookback').addEventListener('input', updateSliderFromInputs);
             document.getElementById('pred-len').addEventListener('input', updateSliderFromInputs);
         }
 
-        // 显示状态信息
+        // Display status information
         function showStatus(type, message) {
             const statusDiv = document.getElementById('model-status');
             statusDiv.className = `status ${type}`;
             statusDiv.textContent = message;
             statusDiv.style.display = 'block';
             
-            // 自动隐藏
+            // Auto-hide
             setTimeout(() => {
                 statusDiv.style.display = 'none';
             }, 5000);
         }
 
-        // 显示/隐藏加载状态
+        // Show/hide loading status
         function showLoading(show) {
             const loadingDiv = document.getElementById('loading');
             if (show) {