a68d6f7ab6
- Replaced print statements with logging for better traceability in gimp.py. - Added handling for transparent images in structure checks and size evaluations. - Updated JSON examples to include delays in pyautogui commands for improved execution reliability. - Changed image URL in example to a more accessible source.
Evaluation examples
Here we put the data examples to benchmark the ability of agents when interacting with GUI.
The examples are stored in ./examples where each data item formatted as:
{
"id": "uid", # unique id
"snapshot": "snapshot_id", # the snapshot id of the environment, with some data already there and apps already opened, or just desktop
"instruction": "natural_language_instruction", # the natural language instruction of the task, what we want the agent to do
"source": "website_url", # where we know this example, some forum, or some website, or some paper
"config": {xxx}, # the scripts to setup the donwload and open files actions, as the initial state of a task
# (coming in next project) "trajectory": "trajectory_directory", # the trajectory directory, which contains the action sequence file, the screenshots and the recording video
"related_apps": ["app1", "app2", ...], # the related apps, which are opened during the task
"evaluator": "evaluation_dir", # the directory of the evaluator, which contains the evaluation script for this example
…
}
The ./trajectories file contains the annotated trajectories for each data item in ./examples for finishing the task.
For now, it is under construction, and only tested on Windows 10. Please:
- Modify the path accordingly to run the evaluation;
- Remind us if some parts are overfit to our environment.