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# 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
```bashcd webuipython run.py```
### Method 2: Start with Shell script
```bashcd webuichmod +x start.sh./start.sh```
### Method 3: Start Flask application directly
```bashcd webuipython app.py```
After successful startup, visit http://localhost:7070
## 📋 Usage Steps
1. **Load data**: Select financial data file from data directory2. **Load model**: Select Kronos model and computing device3. **Set parameters**: Adjust prediction quality parameters4. **Select time window**: Use slider to select 400+120 data point time range5. **Start prediction**: Click prediction button to generate results6. **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.py2. **Missing dependencies**: Run `pip install -r requirements.txt`3. **Model loading failed**: Check network connection and model ID4. **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 documentation2. GitHub Issues3. Console error messages
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