diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..8266c2d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,76 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# PyCharm
+.idea/
+
+# VS Code
+.vscode/
+
+# macOS
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+
+# Linux
+*~
+
+# Data files (large files)
+*.feather
+*.csv
+*.parquet
+*.h5
+*.hdf5
+
+# Model files (large files)
+*.pth
+*.pt
+*.ckpt
+*.bin
+
+# Logs
+*.log
+logs/
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Temporary files
+*.tmp
+*.temp
+temp/
+tmp/
diff --git a/webui/README.md b/webui/README.md
new file mode 100644
index 0000000..571d93e
--- /dev/null
+++ b/webui/README.md
@@ -0,0 +1,135 @@
+# Kronos Web UI
+
+Web user interface for Kronos financial prediction model, providing intuitive graphical operation interface.
+
+## ✨ Features
+
+- **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
+
+## 🚀 Quick Start
+
+### Method 1: Start with Python script
+```bash
+cd webui
+python run.py
+```
+
+### Method 2: Start with Shell script
+```bash
+cd webui
+chmod +x start.sh
+./start.sh
+```
+
+### Method 3: Start Flask application directly
+```bash
+cd webui
+python app.py
+```
+
+After successful startup, visit http://localhost:7070
+
+## 📋 Usage Steps
+
+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
+
+## 🔧 Prediction Quality Parameters
+
+### Temperature (T)
+- **Range**: 0.1 - 2.0
+- **Effect**: Controls prediction randomness
+- **Recommendation**: 1.2-1.5 for better prediction quality
+
+### Nucleus Sampling (top_p)
+- **Range**: 0.1 - 1.0
+- **Effect**: Controls prediction diversity
+- **Recommendation**: 0.95-1.0 to consider more possibilities
+
+### Sample Count
+- **Range**: 1 - 5
+- **Effect**: Generate multiple prediction samples
+- **Recommendation**: 2-3 samples to improve quality
+
+## 📊 Supported Data Formats
+
+### Required Columns
+- `open`: Opening price
+- `high`: Highest price
+- `low`: Lowest price
+- `close`: Closing price
+
+### Optional Columns
+- `volume`: Trading volume
+- `amount`: Trading amount (not used for prediction)
+- `timestamps`/`timestamp`/`date`: Timestamp
+
+## 🤖 Model Support
+
+- **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 Acceleration Support
+
+- **CPU**: General computing, best compatibility
+- **CUDA**: NVIDIA GPU acceleration, best performance
+- **MPS**: Apple Silicon GPU acceleration, recommended for Mac users
+
+## ⚠️ Notes
+
+- `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
+
+## 🔍 Comparison Analysis
+
+The system automatically provides comparison analysis between prediction results and actual data, including:
+- Price difference statistics
+- Error analysis
+- Prediction quality assessment
+
+## 🛠️ Technical Architecture
+
+- **Backend**: Flask + Python
+- **Frontend**: HTML + CSS + JavaScript
+- **Charts**: Plotly.js
+- **Data processing**: Pandas + NumPy
+- **Model**: Hugging Face Transformers
+
+## 📝 Troubleshooting
+
+### 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
+
+### Log Viewing
+Detailed runtime information will be displayed in the console at startup, including model status and error messages.
+
+## 📄 License
+
+This project follows the license terms of the original Kronos project.
+
+## 🤝 Contributing
+
+Welcome to submit Issues and Pull Requests to improve this Web UI!
+
+## 📞 Support
+
+If you have questions, please check:
+1. Project documentation
+2. GitHub Issues
+3. Console error messages
diff --git a/webui/app.py b/webui/app.py
new file mode 100644
index 0000000..d240a37
--- /dev/null
+++ b/webui/app.py
@@ -0,0 +1,708 @@
+import os
+import pandas as pd
+import numpy as np
+import json
+import plotly.graph_objects as go
+import plotly.utils
+from flask import Flask, render_template, request, jsonify
+from flask_cors import CORS
+import sys
+import warnings
+import datetime
+warnings.filterwarnings('ignore')
+
+# Add project root directory to path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+try:
+    from model import Kronos, KronosTokenizer, KronosPredictor
+    MODEL_AVAILABLE = True
+except ImportError:
+    MODEL_AVAILABLE = False
+    print("Warning: Kronos model cannot be imported, will use simulated data for demonstration")
+
+app = Flask(__name__)
+CORS(app)
+
+# Global variables to store models
+tokenizer = None
+model = None
+predictor = None
+
+# Available model configurations
+AVAILABLE_MODELS = {
+    'kronos-mini': {
+        'name': 'Kronos-mini',
+        'model_id': 'NeoQuasar/Kronos-mini',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
+        'context_length': 2048,
+        'params': '4.1M',
+        'description': 'Lightweight model, suitable for fast prediction'
+    },
+    'kronos-small': {
+        'name': 'Kronos-small',
+        'model_id': 'NeoQuasar/Kronos-small',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '24.7M',
+        'description': 'Small model, balanced performance and speed'
+    },
+    'kronos-base': {
+        'name': 'Kronos-base',
+        'model_id': 'NeoQuasar/Kronos-base',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '102.3M',
+        'description': 'Base model, provides better prediction quality'
+    }
+}
+
+def load_data_files():
+    """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 = []
+    
+    if os.path.exists(data_dir):
+        for file in os.listdir(data_dir):
+            if file.endswith(('.csv', '.feather')):
+                file_path = os.path.join(data_dir, file)
+                file_size = os.path.getsize(file_path)
+                data_files.append({
+                    'name': file,
+                    'path': file_path,
+                    'size': f"{file_size / 1024:.1f} KB" if file_size < 1024*1024 else f"{file_size / (1024*1024):.1f} MB"
+                })
+    
+    return data_files
+
+def load_data_file(file_path):
+    """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, "Unsupported file format"
+        
+        # Check required columns
+        required_cols = ['open', 'high', 'low', 'close']
+        if not all(col in df.columns for col in required_cols):
+            return None, f"Missing required columns: {required_cols}"
+        
+        # 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:
+            # If column name is 'date', rename it to 'timestamps'
+            df['timestamps'] = pd.to_datetime(df['date'])
+        else:
+            # If no timestamp column exists, create one
+            df['timestamps'] = pd.date_range(start='2024-01-01', periods=len(df), freq='1H')
+        
+        # Ensure numeric columns are numeric type
+        for col in ['open', 'high', 'low', 'close']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        # Process volume column (optional)
+        if 'volume' in df.columns:
+            df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
+        
+        # Process amount column (optional, but not used for prediction)
+        if 'amount' in df.columns:
+            df['amount'] = pd.to_numeric(df['amount'], errors='coerce')
+        
+        # Remove rows containing NaN values
+        df = df.dropna()
+        
+        return df, None
+        
+    except Exception as e:
+        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):
+    """Save prediction results to file"""
+    try:
+        # 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)
+        
+        # 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)
+        
+        # Prepare data for saving
+        save_data = {
+            'timestamp': datetime.datetime.now().isoformat(),
+            'file_path': file_path,
+            'prediction_type': prediction_type,
+            'prediction_params': prediction_params,
+            'input_data_summary': {
+                'rows': len(input_data),
+                'columns': list(input_data.columns),
+                'price_range': {
+                    'open': {'min': float(input_data['open'].min()), 'max': float(input_data['open'].max())},
+                    'high': {'min': float(input_data['high'].min()), 'max': float(input_data['high'].max())},
+                    'low': {'min': float(input_data['low'].min()), 'max': float(input_data['low'].max())},
+                    'close': {'min': float(input_data['close'].min()), 'max': float(input_data['close'].max())}
+                },
+                'last_values': {
+                    'open': float(input_data['open'].iloc[-1]),
+                    'high': float(input_data['high'].iloc[-1]),
+                    'low': float(input_data['low'].iloc[-1]),
+                    'close': float(input_data['close'].iloc[-1])
+                }
+            },
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'analysis': {}
+        }
+        
+        # If actual data exists, perform comparison analysis
+        if actual_data and len(actual_data) > 0:
+            # Calculate continuity analysis
+            if len(prediction_results) > 0 and len(actual_data) > 0:
+                last_pred = prediction_results[0]  # First prediction point
+            first_actual = actual_data[0]      # First actual point
+                
+            save_data['analysis']['continuity'] = {
+                    'last_prediction': {
+                        'open': last_pred['open'],
+                        'high': last_pred['high'],
+                        'low': last_pred['low'],
+                        'close': last_pred['close']
+                    },
+                    'first_actual': {
+                        'open': first_actual['open'],
+                        'high': first_actual['high'],
+                        'low': first_actual['low'],
+                        'close': first_actual['close']
+                    },
+                    'gaps': {
+                        'open_gap': abs(last_pred['open'] - first_actual['open']),
+                        'high_gap': abs(last_pred['high'] - first_actual['high']),
+                        'low_gap': abs(last_pred['low'] - first_actual['low']),
+                        'close_gap': abs(last_pred['close'] - first_actual['close'])
+                    },
+                    'gap_percentages': {
+                        'open_gap_pct': (abs(last_pred['open'] - first_actual['open']) / first_actual['open']) * 100,
+                        'high_gap_pct': (abs(last_pred['high'] - first_actual['high']) / first_actual['high']) * 100,
+                        'low_gap_pct': (abs(last_pred['low'] - first_actual['low']) / first_actual['low']) * 100,
+                        'close_gap_pct': (abs(last_pred['close'] - first_actual['close']) / first_actual['close']) * 100
+                    }
+                }
+        
+        # Save to file
+        with open(filepath, 'w', encoding='utf-8') as f:
+            json.dump(save_data, f, indent=2, ensure_ascii=False)
+        
+        print(f"Prediction results saved to: {filepath}")
+        return filepath
+        
+    except Exception as 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):
+    """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):
+        # 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:
+        # 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)
+    
+    # Create chart
+    fig = go.Figure()
+    
+    # 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='Historical Data (400 data points)',
+        increasing_line_color='#26A69A',
+        decreasing_line_color='#EF5350'
+    ))
+    
+    # Add prediction data (candlestick chart)
+    if pred_df is not None and len(pred_df) > 0:
+        # Calculate prediction data timestamps - ensure continuity with historical data
+        if 'timestamps' in df.columns and len(historical_df) > 0:
+            # 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)
+            
+            pred_timestamps = pd.date_range(
+                start=last_timestamp + time_diff,
+                periods=len(pred_df),
+                freq=time_diff
+            )
+        else:
+            # If no timestamps, use index
+            pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df))
+        
+        fig.add_trace(go.Candlestick(
+            x=pred_timestamps,
+            open=pred_df['open'],
+            high=pred_df['high'],
+            low=pred_df['low'],
+            close=pred_df['close'],
+            name='Prediction Data (120 data points)',
+            increasing_line_color='#66BB6A',
+            decreasing_line_color='#FF7043'
+        ))
+    
+    # Add actual data for comparison (if exists)
+    if actual_df is not None and len(actual_df) > 0:
+        # Actual data should be in the same time period as prediction data
+        if 'timestamps' in df.columns:
+            # Actual data should use the same timestamps as prediction data to ensure time alignment
+            if 'pred_timestamps' in locals():
+                actual_timestamps = pred_timestamps
+            else:
+                # 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)
+                    actual_timestamps = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=len(actual_df),
+                        freq=time_diff
+                    )
+                else:
+                    actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+        else:
+            actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+        
+        fig.add_trace(go.Candlestick(
+            x=actual_timestamps,
+            open=actual_df['open'],
+            high=actual_df['high'],
+            low=actual_df['low'],
+            close=actual_df['close'],
+            name='Actual Data (120 data points)',
+            increasing_line_color='#FF9800',
+            decreasing_line_color='#F44336'
+        ))
+    
+    # Update layout
+    fig.update_layout(
+        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
+    )
+    
+    # Ensure x-axis time continuity
+    if 'timestamps' in historical_df.columns:
+        # Get all timestamps and sort them
+        all_timestamps = []
+        if len(historical_df) > 0:
+            all_timestamps.extend(historical_df['timestamps'])
+        if 'pred_timestamps' in locals():
+            all_timestamps.extend(pred_timestamps)
+        if 'actual_timestamps' in locals():
+            all_timestamps.extend(actual_timestamps)
+        
+        if all_timestamps:
+            all_timestamps = sorted(all_timestamps)
+            fig.update_xaxes(
+                range=[all_timestamps[0], all_timestamps[-1]],
+                rangeslider_visible=False,
+                type='date'
+            )
+    
+    return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
+
+@app.route('/')
+def index():
+    """Home page"""
+    return render_template('index.html')
+
+@app.route('/api/data-files')
+def get_data_files():
+    """Get available data file list"""
+    data_files = load_data_files()
+    return jsonify(data_files)
+
+@app.route('/api/load-data', methods=['POST'])
+def load_data():
+    """Load data file"""
+    try:
+        data = request.get_json()
+        file_path = data.get('file_path')
+        
+        if not file_path:
+            return jsonify({'error': 'File path cannot be empty'}), 400
+        
+        df, error = load_data_file(file_path)
+        if error:
+            return jsonify({'error': error}), 400
+        
+        # Detect data time frequency
+        def detect_timeframe(df):
+            if len(df) < 2:
+                return "Unknown"
+            
+            time_diffs = []
+            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 "Unknown"
+            
+            # Calculate average time difference
+            avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs)
+            
+            # Convert to readable format
+            if avg_diff < pd.Timedelta(minutes=1):
+                return f"{avg_diff.total_seconds():.0f} seconds"
+            elif avg_diff < pd.Timedelta(hours=1):
+                return f"{avg_diff.total_seconds() / 60:.0f} minutes"
+            elif avg_diff < pd.Timedelta(days=1):
+                return f"{avg_diff.total_seconds() / 3600:.0f} hours"
+            else:
+                return f"{avg_diff.days} days"
+        
+        # Return data information
+        data_info = {
+            'rows': len(df),
+            'columns': list(df.columns),
+            'start_date': df['timestamps'].min().isoformat() if 'timestamps' in df.columns else 'N/A',
+            'end_date': df['timestamps'].max().isoformat() if 'timestamps' in df.columns else 'N/A',
+            'price_range': {
+                'min': float(df[['open', 'high', 'low', 'close']].min().min()),
+                'max': float(df[['open', 'high', 'low', 'close']].max().max())
+            },
+            'prediction_columns': ['open', 'high', 'low', 'close'] + (['volume'] if 'volume' in df.columns else []),
+            'timeframe': detect_timeframe(df)
+        }
+        
+        return jsonify({
+            'success': True,
+            'data_info': data_info,
+            'message': f'Successfully loaded data, total {len(df)} rows'
+        })
+        
+    except Exception as e:
+        return jsonify({'error': f'Failed to load data: {str(e)}'}), 500
+
+@app.route('/api/predict', methods=['POST'])
+def predict():
+    """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))
+        
+        # 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': 'File path cannot be empty'}), 400
+        
+        # Load data
+        df, error = load_data_file(file_path)
+        if error:
+            return jsonify({'error': error}), 400
+        
+        if len(df) < lookback:
+            return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400
+        
+        # Perform prediction
+        if MODEL_AVAILABLE and predictor is not None:
+            try:
+                # 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')
+                
+                # Process time period selection
+                start_date = data.get('start_date')
+                
+                if start_date:
+                    # Custom time period - fix logic: use data within selected window
+                    start_dt = pd.to_datetime(start_date)
+                    
+                    # Find data after start time
+                    mask = df['timestamps'] >= start_dt
+                    time_range_df = df[mask]
+                    
+                    # Ensure sufficient data: lookback + pred_len
+                    if len(time_range_df) < lookback + pred_len:
+                        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
+                    
+                    # 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']
+                    
+                    # Use last pred_len data points within selected window as actual values
+                    y_timestamp = time_range_df.iloc[lookback:lookback+pred_len]['timestamps']
+                    
+                    # 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 model prediction (within selected window: first {lookback} data points for prediction, last {pred_len} data points for comparison, time span: {time_span})"
+                else:
+                    # 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 model prediction (latest data)"
+                
+                # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model
+                if isinstance(x_timestamp, pd.DatetimeIndex):
+                    x_timestamp = pd.Series(x_timestamp, name='timestamps')
+                if isinstance(y_timestamp, pd.DatetimeIndex):
+                    y_timestamp = pd.Series(y_timestamp, name='timestamps')
+                
+                pred_df = predictor.predict(
+                    df=x_df,
+                    x_timestamp=x_timestamp,
+                    y_timestamp=y_timestamp,
+                    pred_len=pred_len,
+                    T=temperature,
+                    top_p=top_p,
+                    sample_count=sample_count
+                )
+                
+            except Exception as e:
+                return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500
+        else:
+            return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400
+        
+        # Prepare actual data for comparison (if exists)
+        actual_data = []
+        actual_df = None
+        
+        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)
+            
+            # Find data starting from start_date
+            mask = df['timestamps'] >= start_dt
+            time_range_df = df[mask]
+            
+            if len(time_range_df) >= lookback + pred_len:
+                # 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()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+        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()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+        
+        # Create chart - pass historical data start position
+        if start_date:
+            # 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:
+            # 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)
+        
+        # Prepare prediction result data - fix timestamp calculation logic
+        if 'timestamps' in df.columns:
+            if start_date:
+                # 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:
+                    # 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(
+                        start=last_timestamp + time_diff,
+                        periods=pred_len,
+                        freq=time_diff
+                    )
+                else:
+                    future_timestamps = []
+            else:
+                # 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(
+                    start=last_timestamp + time_diff,
+                    periods=pred_len,
+                    freq=time_diff
+                )
+        else:
+            future_timestamps = range(len(df), len(df) + pred_len)
+        
+        prediction_results = []
+        for i, (_, row) in enumerate(pred_df.iterrows()):
+            prediction_results.append({
+                'timestamp': future_timestamps[i].isoformat() if i < len(future_timestamps) else f"T{i}",
+                'open': float(row['open']),
+                'high': float(row['high']),
+                'low': float(row['low']),
+                'close': float(row['close']),
+                'volume': float(row['volume']) if 'volume' in row else 0,
+                'amount': float(row['amount']) if 'amount' in row else 0
+            })
+        
+        # Save prediction results to file
+        try:
+            save_prediction_results(
+                file_path=file_path,
+                prediction_type=prediction_type,
+                prediction_results=prediction_results,
+                actual_data=actual_data,
+                input_data=x_df,
+                prediction_params={
+                    'lookback': lookback,
+                    'pred_len': pred_len,
+                    'temperature': temperature,
+                    'top_p': top_p,
+                    'sample_count': sample_count,
+                    'start_date': start_date if start_date else 'latest'
+                }
+            )
+        except Exception as e:
+            print(f"Failed to save prediction results: {e}")
+        
+        return jsonify({
+            'success': True,
+            'prediction_type': prediction_type,
+            'chart': chart_json,
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'has_comparison': len(actual_data) > 0,
+            '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'Prediction failed: {str(e)}'}), 500
+
+@app.route('/api/load-model', methods=['POST'])
+def load_model():
+    """Load Kronos model"""
+    global tokenizer, model, predictor
+    
+    try:
+        if not MODEL_AVAILABLE:
+            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'Unsupported model: {model_key}'}), 400
+        
+        model_config = AVAILABLE_MODELS[model_key]
+        
+        # Load tokenizer and model
+        tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id'])
+        model = Kronos.from_pretrained(model_config['model_id'])
+        
+        # Create predictor
+        predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length'])
+        
+        return jsonify({
+            'success': True,
+            'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}',
+            'model_info': {
+                'name': model_config['name'],
+                'params': model_config['params'],
+                'context_length': model_config['context_length'],
+                'description': model_config['description']
+            }
+        })
+        
+    except Exception as e:
+        return jsonify({'error': f'Model loading failed: {str(e)}'}), 500
+
+@app.route('/api/available-models')
+def get_available_models():
+    """Get available model list"""
+    return jsonify({
+        'models': AVAILABLE_MODELS,
+        'model_available': MODEL_AVAILABLE
+    })
+
+@app.route('/api/model-status')
+def get_model_status():
+    """Get model status"""
+    if MODEL_AVAILABLE:
+        if predictor is not None:
+            return jsonify({
+                'available': True,
+                'loaded': True,
+                'message': 'Kronos model loaded and available',
+                'current_model': {
+                    'name': predictor.model.__class__.__name__,
+                    'device': str(next(predictor.model.parameters()).device)
+                }
+            })
+        else:
+            return jsonify({
+                'available': True,
+                'loaded': False,
+                'message': 'Kronos model available but not loaded'
+            })
+    else:
+        return jsonify({
+            'available': False,
+            'loaded': False,
+            'message': 'Kronos model library not available, please install related dependencies'
+        })
+
+if __name__ == '__main__':
+    print("Starting Kronos Web UI...")
+    print(f"Model availability: {MODEL_AVAILABLE}")
+    if MODEL_AVAILABLE:
+        print("Tip: You can load Kronos model through /api/load-model endpoint")
+    else:
+        print("Tip: Will use simulated data for demonstration")
+    
+    app.run(debug=True, host='0.0.0.0', port=7070)
diff --git a/webui/prediction_results/prediction_20250826_163800.json b/webui/prediction_results/prediction_20250826_163800.json
new file mode 100644
index 0000000..5c3f7ba
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_163800.json
@@ -0,0 +1,2239 @@
+{
+  "timestamp": "2025-08-26T16:38:00.302387",
+  "file_path": "/Users/charles/Kronos/data/BTC_USDT_USDT-5m-futures.feather",
+  "prediction_type": "Kronos模型预测 (选择的窗口内：前400个数据点预测，后120个数据点对比，时间跨度: 1 days 19:15:00)",
+  "prediction_params": {
+    "lookback": 400,
+    "pred_len": 120,
+    "temperature": 1.0,
+    "top_p": 0.9,
+    "sample_count": 1,
+    "start_date": "2025-08-02T13:24"
+  },
+  "input_data_summary": {
+    "rows": 400,
+    "columns": [
+      "open",
+      "high",
+      "low",
+      "close",
+      "volume"
+    ],
+    "price_range": {
+      "open": {
+        "min": 112046.9,
+        "max": 114231.95
+      },
+      "high": {
+        "min": 112200.0,
+        "max": 114260.31
+      },
+      "low": {
+        "min": 111920.0,
+        "max": 114085.61
+      },
+      "close": {
+        "min": 112046.9,
+        "max": 114231.95
+      }
+    },
+    "last_values": {
+      "open": 113769.56,
+      "high": 113852.6,
+      "low": 113731.29,
+      "close": 113818.87
+    }
+  },
+  "prediction_results": [
+    {
+      "timestamp": "2025-08-03T22:45:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
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+    },
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+      "open": 113381.140625,
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+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
+      "timestamp": "2025-08-03T22:55:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
+    },
+    {
+      "timestamp": "2025-08-03T23:00:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
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+    {
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+      "close": 113379.7890625,
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+    },
+    {
+      "timestamp": "2025-08-03T23:30:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
+    },
+    {
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+      "open": 113381.140625,
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+      "close": 113379.7890625,
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+      "amount": 4109972.0
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+    {
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+      "open": 113381.140625,
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+    {
+      "timestamp": "2025-08-04T00:20:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
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+    {
+      "timestamp": "2025-08-04T00:25:00",
+      "open": 113381.140625,
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+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
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+    {
+      "timestamp": "2025-08-04T00:30:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
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+    {
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+      "close": 113379.7890625,
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+    },
+    {
+      "timestamp": "2025-08-04T00:40:00",
+      "open": 113381.140625,
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+      "low": 113326.859375,
+      "close": 113379.7890625,
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+      "amount": 4109972.0
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+    {
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+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
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+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
+      "timestamp": "2025-08-04T00:55:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
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+      "volume": 36.73231506347656,
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+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
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+    {
+      "timestamp": "2025-08-04T01:10:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
+      "timestamp": "2025-08-04T01:15:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
+      "timestamp": "2025-08-04T01:20:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
+    },
+    {
+      "timestamp": "2025-08-04T01:25:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
+    },
+    {
+      "timestamp": "2025-08-04T01:30:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.0
+    },
+    {
+      "timestamp": "2025-08-04T01:35:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
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diff --git a/webui/prediction_results/prediction_20250826_164030.json b/webui/prediction_results/prediction_20250826_164030.json
new file mode 100644
index 0000000..1f54336
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_164030.json
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diff --git a/webui/prediction_results/prediction_20250826_164422.json b/webui/prediction_results/prediction_20250826_164422.json
new file mode 100644
index 0000000..cc55ba7
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_164422.json
@@ -0,0 +1,2239 @@
+{
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diff --git a/webui/prediction_results/prediction_20250826_170831.json b/webui/prediction_results/prediction_20250826_170831.json
new file mode 100644
index 0000000..72f74f2
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_170831.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_171720.json b/webui/prediction_results/prediction_20250826_171720.json
new file mode 100644
index 0000000..c4e1bfb
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_171720.json
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+}
\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_171913.json b/webui/prediction_results/prediction_20250826_171913.json
new file mode 100644
index 0000000..6c7873a
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_171913.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_172031.json b/webui/prediction_results/prediction_20250826_172031.json
new file mode 100644
index 0000000..671b155
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_172031.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_172153.json b/webui/prediction_results/prediction_20250826_172153.json
new file mode 100644
index 0000000..e94ba88
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_172153.json
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+}
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diff --git a/webui/prediction_results/prediction_20250826_172740.json b/webui/prediction_results/prediction_20250826_172740.json
new file mode 100644
index 0000000..4be2fef
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_172740.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_173322.json b/webui/prediction_results/prediction_20250826_173322.json
new file mode 100644
index 0000000..3b4e319
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_173322.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_173455.json b/webui/prediction_results/prediction_20250826_173455.json
new file mode 100644
index 0000000..1ae7835
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_173455.json
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\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_174410.json b/webui/prediction_results/prediction_20250826_174410.json
new file mode 100644
index 0000000..1c53b1e
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_174410.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_174809.json b/webui/prediction_results/prediction_20250826_174809.json
new file mode 100644
index 0000000..88c7bcc
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_174809.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_175057.json b/webui/prediction_results/prediction_20250826_175057.json
new file mode 100644
index 0000000..db15fdc
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175057.json
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diff --git a/webui/prediction_results/prediction_20250826_175135.json b/webui/prediction_results/prediction_20250826_175135.json
new file mode 100644
index 0000000..d2097d1
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175135.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_175909.json b/webui/prediction_results/prediction_20250826_175909.json
new file mode 100644
index 0000000..bfdb791
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175909.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_180308.json b/webui/prediction_results/prediction_20250826_180308.json
new file mode 100644
index 0000000..1ef1dc3
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180308.json
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\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_180632.json b/webui/prediction_results/prediction_20250826_180632.json
new file mode 100644
index 0000000..71095bf
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180632.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_180745.json b/webui/prediction_results/prediction_20250826_180745.json
new file mode 100644
index 0000000..0025d1e
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180745.json
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diff --git a/webui/prediction_results/prediction_20250826_180806.json b/webui/prediction_results/prediction_20250826_180806.json
new file mode 100644
index 0000000..1e42b13
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180806.json
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diff --git a/webui/prediction_results/prediction_20250826_181012.json b/webui/prediction_results/prediction_20250826_181012.json
new file mode 100644
index 0000000..48dacc2
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181012.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181139.json b/webui/prediction_results/prediction_20250826_181139.json
new file mode 100644
index 0000000..590c834
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181139.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181240.json b/webui/prediction_results/prediction_20250826_181240.json
new file mode 100644
index 0000000..337c03b
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181240.json
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+}
\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_181434.json b/webui/prediction_results/prediction_20250826_181434.json
new file mode 100644
index 0000000..0b370d8
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181434.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181513.json b/webui/prediction_results/prediction_20250826_181513.json
new file mode 100644
index 0000000..e2b098d
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181513.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181612.json b/webui/prediction_results/prediction_20250826_181612.json
new file mode 100644
index 0000000..5e13056
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181612.json
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diff --git a/webui/prediction_results/prediction_20250826_181648.json b/webui/prediction_results/prediction_20250826_181648.json
new file mode 100644
index 0000000..e3eea8d
--- /dev/null
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diff --git a/webui/prediction_results/prediction_20250826_181800.json b/webui/prediction_results/prediction_20250826_181800.json
new file mode 100644
index 0000000..27569fd
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181800.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181932.json b/webui/prediction_results/prediction_20250826_181932.json
new file mode 100644
index 0000000..84d0d1d
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181932.json
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+  "analysis": {
+    "continuity": {
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+      "gaps": {
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+        "low_gap": 789.8743749999994,
+        "close_gap": 753.2103124999994
+      },
+      "gap_percentages": {
+        "open_gap_pct": 0.9088582774486117,
+        "high_gap_pct": 0.8843787547611065,
+        "low_gap_pct": 0.6928244903465423,
+        "close_gap_pct": 0.6606652493082489
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/webui/requirements.txt b/webui/requirements.txt
new file mode 100644
index 0000000..cfce804
--- /dev/null
+++ b/webui/requirements.txt
@@ -0,0 +1,7 @@
+flask==2.3.3
+flask-cors==4.0.0
+pandas==2.2.2
+numpy==1.24.3
+plotly==5.17.0
+torch>=2.1.0
+huggingface_hub==0.33.1
diff --git a/webui/run.py b/webui/run.py
new file mode 100644
index 0000000..64a7b16
--- /dev/null
+++ b/webui/run.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""
+Kronos Web UI startup script
+"""
+
+import os
+import sys
+import subprocess
+import webbrowser
+import time
+
+def check_dependencies():
+    """Check if dependencies are installed"""
+    try:
+        import flask
+        import flask_cors
+        import pandas
+        import numpy
+        import plotly
+        print("✅ All dependencies installed")
+        return True
+    except ImportError as e:
+        print(f"❌ Missing dependency: {e}")
+        print("Please run: pip install -r requirements.txt")
+        return False
+
+def install_dependencies():
+    """Install dependencies"""
+    print("Installing dependencies...")
+    try:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
+        print("✅ Dependencies installation completed")
+        return True
+    except subprocess.CalledProcessError:
+        print("❌ Dependencies installation failed")
+        return False
+
+def main():
+    """Main function"""
+    print("🚀 Starting Kronos Web UI...")
+    print("=" * 50)
+    
+    # Check dependencies
+    if not check_dependencies():
+        print("\nAuto-install dependencies? (y/n): ", end="")
+        if input().lower() == 'y':
+            if not install_dependencies():
+                return
+        else:
+            print("Please manually install dependencies and retry")
+            return
+    
+    # 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 model library available")
+        model_available = True
+    except ImportError:
+        print("⚠️  Kronos model library not available, will use simulated prediction")
+        model_available = False
+    
+    # Start Flask application
+    print("\n🌐 Starting Web server...")
+    
+    # Set environment variables
+    os.environ['FLASK_APP'] = 'app.py'
+    os.environ['FLASK_ENV'] = 'development'
+    
+    # Start server
+    try:
+        from app import app
+        print("✅ Web server started successfully!")
+        print(f"🌐 Access URL: http://localhost:7070")
+        print("💡 Tip: Press Ctrl+C to stop server")
+        
+        # Auto-open browser
+        time.sleep(2)
+        webbrowser.open('http://localhost:7070/')
+        
+        # Start Flask application
+        app.run(debug=True, host='0.0.0.0', port=7070)
+        
+    except Exception as e:
+        print(f"❌ Startup failed: {e}")
+        print("Please check if port 7070 is occupied")
+
+if __name__ == "__main__":
+    main()
diff --git a/webui/start.sh b/webui/start.sh
new file mode 100755
index 0000000..0d832b3
--- /dev/null
+++ b/webui/start.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+
+# Kronos Web UI startup script
+
+echo "🚀 Starting Kronos Web UI..."
+echo "================================"
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null; then
+    echo "❌ Python3 not installed, please install Python3 first"
+    exit 1
+fi
+
+# Check if in correct directory
+if [ ! -f "app.py" ]; then
+    echo "❌ Please run this script in the webui directory"
+    exit 1
+fi
+
+# Check dependencies
+echo "📦 Checking dependencies..."
+if ! python3 -c "import flask, flask_cors, pandas, numpy, plotly" &> /dev/null; then
+    echo "⚠️  Missing dependencies, installing..."
+    pip3 install -r requirements.txt
+    if [ $? -ne 0 ]; then
+        echo "❌ Dependencies installation failed"
+        exit 1
+    fi
+    echo "✅ Dependencies installation completed"
+else
+    echo "✅ All dependencies installed"
+fi
+
+# Start application
+echo "🌐 Starting Web server..."
+echo "Access URL: http://localhost:7070"
+echo "Press Ctrl+C to stop server"
+echo ""
+
+python3 app.py
diff --git a/webui/templates/index.html b/webui/templates/index.html
new file mode 100644
index 0000000..dd24a49
--- /dev/null
+++ b/webui/templates/index.html
@@ -0,0 +1,1238 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <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>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            color: #333;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            color: white;
+        }
+
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .header p {
+            font-size: 1.1rem;
+            opacity: 0.9;
+        }
+
+        .main-content {
+            display: grid;
+            grid-template-columns: 1fr 2fr;
+            gap: 30px;
+            margin-bottom: 30px;
+        }
+
+        .control-panel {
+            background: white;
+            border-radius: 15px;
+            padding: 25px;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+            height: fit-content;
+        }
+
+        .control-panel h2 {
+            color: #4a5568;
+            margin-bottom: 20px;
+            font-size: 1.5rem;
+            border-bottom: 2px solid #e2e8f0;
+            padding-bottom: 10px;
+        }
+
+        .form-group {
+            margin-bottom: 20px;
+        }
+
+        .form-group label {
+            display: block;
+            margin-bottom: 8px;
+            font-weight: 600;
+            color: #4a5568;
+        }
+
+        .form-group select,
+        .form-group input {
+            width: 100%;
+            padding: 12px;
+            border: 2px solid #e2e8f0;
+            border-radius: 8px;
+            font-size: 14px;
+            transition: border-color 0.3s ease;
+        }
+
+        .form-group select:focus,
+        .form-group input:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+
+        /* Prediction quality parameter styles */
+        .form-group input[type="range"] {
+            width: 70%;
+            margin-right: 10px;
+        }
+
+        .form-group input[type="number"] {
+            width: 100%;
+        }
+
+        .form-group span {
+            display: inline-block;
+            min-width: 40px;
+            font-weight: 600;
+            color: #667eea;
+        }
+
+        .form-text {
+            font-size: 12px;
+            color: #718096;
+            margin-top: 5px;
+        }
+
+        .btn {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            padding: 12px 24px;
+            border-radius: 8px;
+            font-size: 16px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: transform 0.2s ease, box-shadow 0.2s ease;
+            width: 100%;
+            margin-bottom: 10px;
+        }
+
+        .btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .btn:disabled {
+            opacity: 0.6;
+            cursor: not-allowed;
+            transform: none;
+        }
+
+        .btn-secondary {
+            background: linear-gradient(135deg, #718096 0%, #4a5568 100%);
+        }
+
+        .btn-success {
+            background: linear-gradient(135deg, #48bb78 0%, #38a169 100%);
+        }
+
+        .btn-warning {
+            background: linear-gradient(135deg, #ed8936 0%, #dd6b20 100%);
+        }
+
+        .status {
+            padding: 15px;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            font-weight: 500;
+        }
+
+        .status.success {
+            background: #c6f6d5;
+            color: #22543d;
+            border: 1px solid #9ae6b4;
+        }
+
+        .status.error {
+            background: #fed7d7;
+            color: #742a2a;
+            border: 1px solid #feb2b2;
+        }
+
+        .status.info {
+            background: #bee3f8;
+            color: #2a4365;
+            border: 1px solid #90cdf4;
+        }
+
+        .status.warning {
+            background: #fef5e7;
+            color: #744210;
+            border: 1px solid #fbd38d;
+        }
+
+        .chart-container {
+            background: white;
+            border-radius: 15px;
+            padding: 25px;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+        }
+
+        .chart-container h2 {
+            color: #4a5568;
+            margin-bottom: 20px;
+            font-size: 1.5rem;
+            border-bottom: 2px solid #e2e8f0;
+            padding-bottom: 10px;
+        }
+
+        #chart {
+            width: 100%;
+            height: 600px;
+        }
+
+        .data-info {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 15px;
+            margin-bottom: 20px;
+        }
+
+        .data-info h3 {
+            color: #4a5568;
+            margin-bottom: 10px;
+            font-size: 1.1rem;
+        }
+
+        .data-info p {
+            margin-bottom: 5px;
+            color: #4a5568;
+        }
+
+        .data-info strong {
+            color: #2d3748;
+        }
+
+        /* Time window selector styles */
+        .time-window-container {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 20px;
+            margin-bottom: 20px;
+        }
+
+        .time-window-container h3 {
+            color: #4a5568;
+            margin-bottom: 15px;
+            font-size: 1.1rem;
+        }
+
+        .time-window-info {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 15px;
+            font-size: 12px;
+            color: #666;
+        }
+
+        .time-window-slider {
+            position: relative;
+            margin-bottom: 10px;
+        }
+
+        .slider-track {
+            position: relative;
+            height: 6px;
+            background: #e2e8f0;
+            border-radius: 3px;
+            cursor: pointer;
+        }
+
+        .slider-handle {
+            position: absolute;
+            top: -7px;
+            width: 20px;
+            height: 20px;
+            background: #667eea;
+            border-radius: 50%;
+            cursor: grab;
+            border: 2px solid white;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.2);
+            z-index: 10;
+        }
+
+        .slider-handle:hover {
+            background: #5a67d8;
+            transform: scale(1.1);
+        }
+
+        .slider-handle:active {
+            cursor: grabbing;
+        }
+
+        .slider-selection {
+            position: absolute;
+            height: 6px;
+            background: #48bb78;
+            border-radius: 3px;
+            top: 0;
+        }
+
+        .slider-labels {
+            display: flex;
+            justify-content: space-between;
+            font-size: 11px;
+            color: #999;
+            margin-top: 5px;
+        }
+
+        /* Comparison analysis styles */
+        .comparison-section {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 20px;
+            margin-top: 20px;
+        }
+
+        .comparison-section h3 {
+            color: #4a5568;
+            margin-bottom: 15px;
+            font-size: 1.1rem;
+        }
+
+        .comparison-info {
+            background: white;
+            border: 1px solid #e2e8f0;
+            border-radius: 6px;
+            padding: 15px;
+            margin-bottom: 15px;
+        }
+
+        .comparison-table {
+            width: 100%;
+            border-collapse: collapse;
+            margin-top: 15px;
+        }
+
+        .comparison-table th,
+        .comparison-table td {
+            border: 1px solid #e2e8f0;
+            padding: 8px;
+            text-align: center;
+            font-size: 12px;
+        }
+
+        .comparison-table th {
+            background: #f7fafc;
+            font-weight: 600;
+        }
+
+        .error-stats {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+            gap: 15px;
+            margin-top: 15px;
+        }
+
+        .error-stat {
+            background: white;
+            border: 1px solid #e2e8f0;
+            border-radius: 6px;
+            padding: 15px;
+            text-align: center;
+        }
+
+        .error-stat h4 {
+            color: #4a5568;
+            margin-bottom: 5px;
+            font-size: 0.9rem;
+        }
+
+        .error-stat .value {
+            font-size: 1.5rem;
+            font-weight: 600;
+            color: #667eea;
+        }
+
+        .error-stat .unit {
+            font-size: 0.8rem;
+            color: #718096;
+        }
+
+        .loading {
+            display: none;
+            text-align: center;
+            padding: 20px;
+        }
+
+        .loading.show {
+            display: block;
+        }
+
+        .spinner {
+            border: 4px solid #f3f3f3;
+            border-top: 4px solid #667eea;
+            border-radius: 50%;
+            width: 40px;
+            height: 40px;
+            animation: spin 1s linear infinite;
+            margin: 0 auto 10px;
+        }
+
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+
+        .model-info {
+            background: #e6fffa;
+            border: 1px solid #81e6d9;
+            border-radius: 8px;
+            padding: 15px;
+            margin-bottom: 20px;
+        }
+
+        .model-info h3 {
+            color: #234e52;
+            margin-bottom: 10px;
+            font-size: 1.1rem;
+        }
+
+        .model-info p {
+            margin-bottom: 5px;
+            color: #234e52;
+        }
+
+        .model-info strong {
+            color: #0f2027;
+        }
+
+        @media (max-width: 768px) {
+            .main-content {
+                grid-template-columns: 1fr;
+            }
+            
+            .container {
+                padding: 10px;
+            }
+            
+            .header h1 {
+                font-size: 2rem;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <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>🎯 Control Panel</h2>
+
+                <!-- Model Selection -->
+                <div class="form-group">
+                    <label for="model-select">Select Model:</label>
+                    <select id="model-select">
+                        <option value="">Please load available models first</option>
+                    </select>
+                    <small class="form-text">Select the Kronos model to use</small>
+                </div>
+
+                <!-- Device Selection -->
+                <div class="form-group">
+                    <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">Select the device to run the model on</small>
+                </div>
+
+                <!-- Model Status -->
+                <div id="model-status" class="status info" style="display: none;">
+                    Model status information
+                </div>
+
+                <!-- Load Model Button -->
+                <button id="load-model-btn" class="btn btn-secondary">
+                    🔄 Load Model
+                </button>
+
+                <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
+
+                <!-- Data File Selection -->
+                <div class="form-group">
+                    <label for="data-file-select">Select Data File:</label>
+                    <select id="data-file-select">
+                        <option value="">Please load data file list first</option>
+                    </select>
+                    <small class="form-text">Select K-line data file from data directory</small>
+                </div>
+
+                <button id="load-data-btn" class="btn btn-secondary">
+                    📁 Load Data
+                </button>
+
+                <!-- Data Information Display -->
+                <div id="data-info" class="data-info" style="display: none;">
+                    <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;">
+
+                <!-- Time Window Selector -->
+                <div class="time-window-container">
+                    <h3>⏰ Time Window Selection</h3>
+                    <div class="time-window-info">
+                        <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">
+                        <div class="slider-track">
+                            <div class="slider-handle start-handle" id="start-handle"></div>
+                            <div class="slider-selection" id="slider-selection"></div>
+                            <div class="slider-handle end-handle" id="end-handle"></div>
+                        </div>
+                        <div class="slider-labels">
+                            <span id="min-label">Earliest</span>
+                            <span id="max-label">Latest</span>
+                        </div>
+                    </div>
+                    
+                    <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>
+
+                <!-- Prediction Parameters -->
+                <div class="form-group">
+                    <label for="lookback">Lookback Window Size:</label>
+                    <input type="number" id="lookback" value="400" readonly>
+                    <small class="form-text">Fixed at 400 data points</small>
+                </div>
+
+                <div class="form-group">
+                    <label for="pred-len">Prediction Length:</label>
+                    <input type="number" id="pred-len" value="120" readonly>
+                    <small class="form-text">Fixed at 120 data points</small>
+                </div>
+
+                <!-- Prediction Quality Parameters -->
+                <div class="form-group">
+                    <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">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">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">Controls prediction diversity, higher values consider broader probability distributions</small>
+                </div>
+
+                <div class="form-group">
+                    <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">Generate multiple prediction samples to improve quality (recommended 1-3)</small>
+                </div>
+
+                <button id="predict-btn" class="btn btn-success" disabled>
+                    🔮 Start Prediction
+                </button>
+
+                <!-- Loading Status -->
+                <div id="loading" class="loading">
+                    <div class="spinner"></div>
+                    <p>Processing, please wait...</p>
+                </div>
+            </div>
+
+            <div class="chart-container">
+                <h2>📈 Prediction Results Chart</h2>
+                <div id="chart"></div>
+                
+                <!-- Comparison Analysis -->
+                <div id="comparison-section" class="comparison-section" style="display: none;">
+                    <h3>📊 Prediction vs Actual Data Comparison</h3>
+                    <div id="comparison-info" class="comparison-info">
+                        <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>Mean Absolute Error</h4>
+                            <div class="value" id="mae">-</div>
+                            <div class="unit">Price Units</div>
+                        </div>
+                        <div class="error-stat">
+                            <h4>Root Mean Square Error</h4>
+                            <div class="value" id="rmse">-</div>
+                            <div class="unit">Price Units</div>
+                        </div>
+                        <div class="error-stat">
+                            <h4>Mean Absolute Percentage Error</h4>
+                            <div class="value" id="mape">-</div>
+                            <div class="unit">%</div>
+                        </div>
+                    </div>
+                    
+                    <div class="error-details">
+                        <h4>Detailed Comparison Data:</h4>
+                        <div style="max-height: 300px; overflow-y: auto;">
+                            <table class="comparison-table">
+                                <thead>
+                                    <tr>
+                                        <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">
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Global variables
+        let currentDataFile = null;
+        let currentDataInfo = null;
+        let availableModels = [];
+        let modelLoaded = false;
+
+        // Initialize after page loads
+        document.addEventListener('DOMContentLoaded', function() {
+            initializeApp();
+        });
+
+        // Initialize application
+        async function initializeApp() {
+            console.log('🚀 Initializing Kronos Web UI...');
+            
+            // Load available models
+            await loadAvailableModels();
+            
+            // Load data file list
+            await loadDataFiles();
+            
+            // Set up event listeners
+            setupEventListeners();
+            
+            // Initialize time slider
+            initializeTimeSlider();
+            
+            console.log('✅ Application initialization completed');
+        }
+
+        // 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('✅ Available models loaded successfully:', availableModels);
+                } else {
+                    console.warn('⚠️ Kronos model library not available');
+                    showStatus('warning', 'Kronos model library not available, will use simulated prediction');
+                }
+            } catch (error) {
+                console.error('❌ Failed to load available models:', error);
+                showStatus('error', 'Failed to load available models');
+            }
+        }
+
+        // Populate model selection dropdown
+        function populateModelSelect() {
+            const modelSelect = document.getElementById('model-select');
+            modelSelect.innerHTML = '<option value="">Please select model</option>';
+            
+            Object.entries(availableModels).forEach(([key, model]) => {
+                const option = document.createElement('option');
+                option.value = key;
+                option.textContent = `${model.name} (${model.params}) - ${model.description}`;
+                modelSelect.appendChild(option);
+            });
+        }
+
+        // Load model
+        async function loadModel() {
+            const modelKey = document.getElementById('model-select').value;
+            const device = document.getElementById('device-select').value;
+            
+            if (!modelKey) {
+                showStatus('error', 'Please select a model to load');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('load-model-btn').disabled = true;
+                
+                const response = await axios.post('/api/load-model', {
+                    model_key: modelKey,
+                    device: device
+                });
+                
+                if (response.data.success) {
+                    modelLoaded = true;
+                    showStatus('success', response.data.message);
+                    updateModelStatus();
+                    document.getElementById('predict-btn').disabled = false;
+                    console.log('✅ Model loaded successfully:', response.data.model_info);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                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;
+            }
+        }
+
+        // Update model status
+        async function updateModelStatus() {
+            try {
+                const response = await axios.get('/api/model-status');
+                const status = response.data;
+                
+                if (status.loaded) {
+                    showStatus('success', `Model loaded: ${status.current_model.name} on ${status.current_model.device}`);
+                } else if (status.available) {
+                    showStatus('info', 'Model available but not loaded');
+                } else {
+                    showStatus('warning', 'Model library not available');
+                }
+            } catch (error) {
+                console.error('❌ Failed to get model status:', error);
+            }
+        }
+
+        // Load data file list
+        async function loadDataFiles() {
+            try {
+                const response = await axios.get('/api/data-files');
+                const dataFiles = response.data;
+                
+                const dataFileSelect = document.getElementById('data-file-select');
+                dataFileSelect.innerHTML = '<option value="">Please select data file</option>';
+                
+                dataFiles.forEach(file => {
+                    const option = document.createElement('option');
+                    option.value = file.path;
+                    option.textContent = `${file.name} (${file.size})`;
+                    dataFileSelect.appendChild(option);
+                });
+                
+                console.log('✅ Data file list loaded successfully:', dataFiles);
+            } catch (error) {
+                console.error('❌ Failed to load data file list:', error);
+                showStatus('error', 'Failed to load data file list');
+            }
+        }
+
+        // Load data file
+        async function loadData() {
+            const filePath = document.getElementById('data-file-select').value;
+            
+            if (!filePath) {
+                showStatus('error', 'Please select a data file to load');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('load-data-btn').disabled = true;
+                
+                const response = await axios.post('/api/load-data', {
+                    file_path: filePath
+                });
+                
+                if (response.data.success) {
+                    currentDataFile = filePath;
+                    currentDataInfo = response.data.data_info;
+                    showDataInfo(response.data.data_info);
+                    showStatus('success', response.data.message);
+                    
+                    // Update prediction button status
+                    if (modelLoaded) {
+                        document.getElementById('predict-btn').disabled = false;
+                    }
+                    
+                    console.log('✅ Data loaded successfully:', response.data.data_info);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                console.error('❌ Data loading failed:', error);
+                showStatus('error', `Data loading failed: ${error.response?.data?.error || error.message}`);
+            } finally {
+                showLoading(false);
+                document.getElementById('load-data-btn').disabled = false;
+            }
+        }
+
+        // Display data information
+        function showDataInfo(dataInfo) {
+            document.getElementById('data-info').style.display = 'block';
+            document.getElementById('data-rows').textContent = dataInfo.rows;
+            document.getElementById('data-cols').textContent = dataInfo.columns.length;
+            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} 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(', ');
+            
+            // Initialize time window slider
+            initializeTimeWindowSlider(dataInfo);
+        }
+
+                    // Time window slider related variables
+        let sliderData = null;
+        let isDragging = false;
+        let currentHandle = null;
+
+        // Initialize time window slider
+        function initializeTimeSlider() {
+            // Set up slider event listeners
+            setupSliderEventListeners();
+        }
+
+        // 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');
+            
+            // Start dragging
+            startHandle.addEventListener('mousedown', (e) => {
+                isDragging = true;
+                currentHandle = 'start';
+                e.preventDefault();
+            });
+            
+            endHandle.addEventListener('mousedown', (e) => {
+                isDragging = true;
+                currentHandle = 'end';
+                e.preventDefault();
+            });
+            
+            // Dragging
+            document.addEventListener('mousemove', (e) => {
+                if (!isDragging) return;
+                
+                const rect = track.getBoundingClientRect();
+                const x = e.clientX - rect.left;
+                const percentage = Math.max(0, Math.min(1, x / rect.width));
+                
+                if (currentHandle === 'start') {
+                    updateStartHandle(percentage);
+                } else if (currentHandle === 'end') {
+                    updateEndHandle(percentage);
+                }
+                
+                updateSliderFromHandles();
+            });
+            
+            // End dragging
+            document.addEventListener('mouseup', () => {
+                isDragging = false;
+                currentHandle = null;
+            });
+            
+            // 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));
+                
+                // 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();
+                const endRect = endHandle.getBoundingClientRect();
+                
+                if (Math.abs(x - (startRect.left - rect.left)) < Math.abs(x - (endRect.left - rect.left))) {
+                    updateStartHandle(percentage);
+                } else {
+                    updateEndHandle(percentage);
+                }
+                
+                updateSliderFromHandles();
+            });
+        }
+
+        // Update start handle position
+        function updateStartHandle(percentage) {
+            const startHandle = document.getElementById('start-handle');
+            const selection = document.getElementById('slider-selection');
+            
+            // Fixed window size of 520 data points
+            const windowSize = 520;
+            const totalRows = sliderData ? sliderData.totalRows : 1000;
+            const windowPercentage = windowSize / totalRows;
+            
+            // Ensure start handle doesn't cause window to exceed data range
+            if (percentage + windowPercentage > 1) {
+                percentage = 1 - windowPercentage;
+            }
+            
+            startHandle.style.left = (percentage * 100) + '%';
+            selection.style.left = (percentage * 100) + '%';
+            selection.style.width = (windowPercentage * 100) + '%';
+            
+            // Automatically adjust end handle position to maintain fixed window size
+            const endHandle = document.getElementById('end-handle');
+            endHandle.style.left = ((percentage + windowPercentage) * 100) + '%';
+        }
+
+        // Update end handle position
+        function updateEndHandle(percentage) {
+            const endHandle = document.getElementById('end-handle');
+            const selection = document.getElementById('slider-selection');
+            
+            // Fixed window size of 520 data points
+            const windowSize = 520;
+            const totalRows = sliderData ? sliderData.totalRows : 1000;
+            const windowPercentage = windowSize / totalRows;
+            
+            // Ensure end handle doesn't cause window to exceed data range
+            if (percentage - windowPercentage < 0) {
+                percentage = windowPercentage;
+            }
+            
+            endHandle.style.left = (percentage * 100) + '%';
+            selection.style.left = ((percentage - windowPercentage) * 100) + '%';
+            selection.style.width = (windowPercentage * 100) + '%';
+            
+            // Automatically adjust start handle position to maintain fixed window size
+            const startHandle = document.getElementById('start-handle');
+            startHandle.style.left = ((percentage - windowPercentage) * 100) + '%';
+        }
+
+        // Update slider display based on handle positions
+        function updateSliderFromHandles() {
+            const startHandle = document.getElementById('start-handle');
+            const endHandle = document.getElementById('end-handle');
+            
+            const startPercentage = parseFloat(startHandle.style.left) / 100;
+            const endPercentage = parseFloat(endHandle.style.left) / 100;
+            
+            if (!sliderData) return;
+            
+            // 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);
+            
+            const startDate = new Date(startTime);
+            const endDate = new Date(endTime);
+            
+            // Update display information
+            document.getElementById('window-start').textContent = `Start: ${startDate.toLocaleDateString()}`;
+            document.getElementById('window-end').textContent = `End: ${endDate.toLocaleDateString()}`;
+            
+            // Display fixed window size
+            document.getElementById('window-size').textContent = `Window Size: 400 + 120 = 520 data points (fixed)`;
+            
+            // Input field values remain fixed
+            document.getElementById('lookback').value = 400;
+            document.getElementById('pred-len').value = 120;
+        }
+
+        // Update slider based on input fields
+        function updateSliderFromInputs() {
+            if (!sliderData) return;
+            
+            // Fixed window size: 400 + 120 = 520 data points
+            const lookback = 400;
+            const predLen = 120;
+            const windowSize = lookback + predLen; // Fixed at 520
+            
+            // Calculate slider position
+            const totalRows = sliderData.totalRows;
+            
+            if (windowSize > totalRows) {
+                // If window size exceeds total data amount, show error
+                showStatus('error', `Insufficient data, need at least ${windowSize} data points, currently only ${totalRows} available`);
+                return;
+            }
+            
+            // Calculate slider position (default select first half of data)
+            const startPercentage = 0.1; // Start from 10%
+            const endPercentage = startPercentage + (windowSize / totalRows);
+            
+            // Update handle positions
+            updateStartHandle(startPercentage);
+            updateEndHandle(endPercentage);
+            
+            // Update display information
+            updateSliderFromHandles();
+        }
+
+        // Initialize time window slider
+        function initializeTimeWindowSlider(dataInfo) {
+            sliderData = {
+                startDate: new Date(dataInfo.start_date),
+                endDate: new Date(dataInfo.end_date),
+                totalRows: dataInfo.rows,
+                timeframe: dataInfo.timeframe
+            };
+            
+            // 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];
+            
+            // Initialize slider position
+            updateSliderFromInputs();
+        }
+
+        // Start prediction
+        async function startPrediction() {
+            if (!currentDataFile) {
+                showStatus('error', 'Please load data file first');
+                return;
+            }
+            
+            if (!modelLoaded) {
+                showStatus('error', 'Please load model first');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('predict-btn').disabled = true;
+                
+                const lookback = parseInt(document.getElementById('lookback').value);
+                const predLen = parseInt(document.getElementById('pred-len').value);
+                
+                // 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', 'Time window slider not initialized');
+                    return;
+                }
+                
+                // Calculate selected time range
+                const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
+                const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
+                const startDate = new Date(startTime);
+                
+                // 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);
+                
+                let predictionParams = {
+                    file_path: currentDataFile,
+                    lookback: lookback,
+                    pred_len: predLen,
+                    start_date: startDate.toISOString().slice(0, 16), // Format as YYYY-MM-DDTHH:MM
+                    temperature: temperature,
+                    top_p: topP,
+                    sample_count: sampleCount
+                };
+                
+                console.log('🚀 Starting prediction, parameters:', predictionParams);
+                
+                const response = await axios.post('/api/predict', predictionParams);
+                
+                if (response.data.success) {
+                    // Display prediction results
+                    displayPredictionResult(response.data);
+                    showStatus('success', response.data.message);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                console.error('❌ Prediction failed:', error);
+                showStatus('error', `Prediction failed: ${error.response?.data?.error || error.message}`);
+            } finally {
+                showLoading(false);
+                document.getElementById('predict-btn').disabled = false;
+            }
+        }
+
+        // Display prediction results
+        function displayPredictionResult(result) {
+            // Display chart
+            const chartData = JSON.parse(result.chart);
+            Plotly.newPlot('chart', chartData.data, chartData.layout);
+            
+            // Display comparison analysis (if actual data exists)
+            if (result.has_comparison) {
+                displayComparisonAnalysis(result);
+            } else {
+                document.getElementById('comparison-section').style.display = 'none';
+            }
+        }
+
+        // Display comparison analysis
+        function displayComparisonAnalysis(result) {
+            document.getElementById('comparison-section').style.display = 'block';
+            
+            // Update comparison information
+            document.getElementById('prediction-type').textContent = result.prediction_type;
+            document.getElementById('comparison-data').textContent = `${result.actual_data.length} actual data points`;
+            
+            // Calculate error statistics
+            const errorStats = getPredictionQuality(result.prediction_results, result.actual_data);
+            
+            // 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);
+            
+            // Fill comparison table
+            fillComparisonTable(result.prediction_results, result.actual_data);
+        }
+
+        // Calculate prediction quality metrics
+        function getPredictionQuality(predictions, actuals) {
+            if (!predictions || !actuals || predictions.length === 0 || actuals.length === 0) {
+                return { mae: 0, rmse: 0, mape: 0 };
+            }
+            
+            const minLen = Math.min(predictions.length, actuals.length);
+            let mae = 0, rmse = 0, mape = 0;
+            
+            for (let i = 0; i < minLen; i++) {
+                const pred = predictions[i];
+                const act = actuals[i];
+                
+                // Use closing price to calculate errors
+                const error = Math.abs(pred.close - act.close);
+                const percentError = (error / act.close) * 100;
+                
+                mae += error;
+                rmse += error * error;
+                mape += percentError;
+            }
+            
+            mae /= minLen;
+            rmse = Math.sqrt(rmse / minLen);
+            mape /= minLen;
+            
+            return { mae, rmse, mape };
+        }
+
+                    // Fill comparison table
+        function fillComparisonTable(predictions, actuals) {
+            const tbody = document.getElementById('comparison-tbody');
+            tbody.innerHTML = '';
+            
+            const minLen = Math.min(predictions.length, actuals.length);
+            
+            for (let i = 0; i < minLen; i++) {
+                const pred = predictions[i];
+                const act = actuals[i];
+                
+                const row = document.createElement('tr');
+                row.innerHTML = `
+                    <td>${new Date(pred.timestamp).toLocaleString()}</td>
+                    <td>${act.open.toFixed(4)}</td>
+                    <td>${pred.open.toFixed(4)}</td>
+                    <td>${act.high.toFixed(4)}</td>
+                    <td>${pred.high.toFixed(4)}</td>
+                    <td>${act.low.toFixed(4)}</td>
+                    <td>${pred.low.toFixed(4)}</td>
+                    <td>${act.close.toFixed(4)}</td>
+                    <td>${pred.close.toFixed(4)}</td>
+                `;
+                tbody.appendChild(row);
+            }
+        }
+
+        // Set up event listeners
+        function setupEventListeners() {
+            // Load model button
+            document.getElementById('load-model-btn').addEventListener('click', loadModel);
+            
+            // Load data button
+            document.getElementById('load-data-btn').addEventListener('click', loadData);
+            
+            // Prediction button
+            document.getElementById('predict-btn').addEventListener('click', startPrediction);
+            
+            // Prediction quality parameter sliders
+            document.getElementById('temperature').addEventListener('input', function() {
+                document.getElementById('temperature-value').textContent = this.value;
+            });
+            
+            document.getElementById('top-p').addEventListener('input', function() {
+                document.getElementById('top-p-value').textContent = this.value;
+            });
+            
+            // Update slider when lookback window size changes
+            document.getElementById('lookback').addEventListener('input', updateSliderFromInputs);
+            document.getElementById('pred-len').addEventListener('input', updateSliderFromInputs);
+        }
+
+        // Display status information
+        function showStatus(type, message) {
+            const statusDiv = document.getElementById('model-status');
+            statusDiv.className = `status ${type}`;
+            statusDiv.textContent = message;
+            statusDiv.style.display = 'block';
+            
+            // Auto-hide
+            setTimeout(() => {
+                statusDiv.style.display = 'none';
+            }, 5000);
+        }
+
+        // Show/hide loading status
+        function showLoading(show) {
+            const loadingDiv = document.getElementById('loading');
+            if (show) {
+                loadingDiv.classList.add('show');
+            } else {
+                loadingDiv.classList.remove('show');
+            }
+        }
+    </script>
+</body>
+</html>