Conductor Web UI

Web user interface for Conductor 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 Conductor 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

cd webui
python run.py

Method 2: Start with Shell script

cd webui
chmod +x start.sh
./start.sh

Method 3: Start Flask application directly

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 Conductor 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

• Conductor-mini: 4.1M parameters, lightweight fast prediction
• Conductor-small: 24.7M parameters, balanced performance and speed
• Conductor-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 Conductor 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