Graphical User Interface (GUI)

Graphical User Interface (GUI)#

Welcome to the user documentation for our Graphical User Interface (GUI). The GUI provides a more intuitive and accessible way to use our platform and clearly displays results.

Main window#

../_images/gui_image.png

The main window includes the following:

  1. Menu bar :

The Menu bar contains four buttons, which, when clicked, will redirect to the document, GitHub repository, website, and QQ group, respectively.

  1. Configuration panel:

Users configure the settings of the large language model and set up the method and task to be executed in the configuration panel.

  1. Results dashboard:

The Results dashboard shows the best algorithm and objective value obtained in real-time.

  1. “Run” button:

Click the “Run” button to execute the LLM4AD platform according to the setups in the Configuration panel.

  1. “Stop” button:

Click the “Stop” button to terminate the execution process.

  1. “Log files” button:

Click the “Log files” button to open the folder containing the log files.

Execution#

Step 1, clone the repository from GitHub and install all requirements (Please refer to Installation).

Step 2, execute the corresponding python script.

$ cd GUI
$ python run_gui.py

Step 3, set the parameters of the large language model.

  • host, the ip of your API provider, no “https://”, such as “api.bltcy.top”.

  • key, your API key which may start with “sk-……”.

  • model, the name of the large language model.

Step 4, select the Method to design the algorithm and set the parameters of the selected method.

Step 5, select which task you want to design an algorithm for. All tasks are divided into three types: machine_learning, optimization, and science_discovery. You can select the problem type in the Combobox.

../_images/gui_combobox.png

Step 6, click the Run button. Results will be displayed in the Results dashboard.

../_images/gui_gif.gif

Adding new methods and tasks#

Step 1, ensure that the code for new methods and tasks is placed in the correct folders:

  • The code for methods should be stored in the llm4ad/method folder.

  • Depending on the type of the problem, code for tasks should be stored in llm4ad/task/machine_learning, llm4ad/task/optimization, or llm4ad/task/science_discovery.

Step 2, configure through a YAML file named paras.yaml in the corresponding method/task folder to enable user-configurable parameters in the GUI. This file should contain all user-configurable parameters and their default values, following the specific format shown below:

name: [class name]
[parameter name]: [default value]

For example, in the case of the eoh method, the content of llm4ad/method/eoh/paras.yaml includes:

name: EoH
max_generations: 10
max_sample_nums: 20
pop_size: 4
num_evaluators: 4

The content indicates that users can configure the four parameters in the eoh method: max_generations, max_sample_nums, pop_size, and num_evaluators. These parameters default to 10, 20, 4, and 4, respectively.