LWATS/run_demo_baseline.py at main · PathOnAIOrg/LWATS · GitHub

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import argparse
from dotenv import load_dotenv
load_dotenv()
from lwats.core_async.agent_factory import setup_prompting_web_agent
from lwats.core_async.config import PromptingAgentConfig
import asyncio


async def main(headless, browser_mode, storage_state, starting_url, agent_type, goal,
         action_generation_model, images, plan, evaluator_type, eval_url, eval_criteria):
    # Create config
    config = PromptingAgentConfig(
        headless=headless,
        browser_mode=browser_mode,
        storage_state=storage_state,
        action_generation_model=action_generation_model,
        features=['axtree'],
        branching_factor=5,
        log_folder="log",
        fullpage=True
    )

    # Split the comma-separated string into a list of images
    images_list = [img.strip() for img in images.split(',')] if images else []

    agent, playwright_manager = await setup_prompting_web_agent(
        starting_url=starting_url,
        goal=goal,
        images=images_list,
        agent_type=agent_type,
        config=config
    )

    # Ensure the agent is of the specified type
    expected_agent_class = globals().get(agent_type)
    if expected_agent_class and not isinstance(agent, expected_agent_class):
        raise TypeError(f"Agent is not an instance of {agent_type}")

    # Run the search
    trajectory, result = await agent.send_prompt(plan if plan is not None else goal)
    print(trajectory)
    print(result)

    # Close the playwright_manager when done
    await playwright_manager.close()
    return trajectory, result


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Run web agent with specified configuration")
    parser.add_argument("--headless", type=bool, default=False,
                        help="Specify if the browser should run in headless mode (default: False)")
    parser.add_argument("--browser-mode", type=str, default="chromium",
                        help="Specify the browser mode (default: chromium)")
    parser.add_argument("--storage-state", type=str, default=None,
                        help="Storage state json file")
    parser.add_argument("--action_generation_model", type=str, default="gpt-4o",
                        help="action grounding model, right now only supports openai models")
    parser.add_argument("--starting-url", type=str, default="http://xwebarena.pathonai.org:7770/",
                        help="Starting URL for the web agent")
    parser.add_argument("--agent-type", type=str, default="LATSAgent",
                        help="Type of agent to use (default: LATSAgent)")
    parser.add_argument("--goal", type=str, default="search running shoes, click on the first result",
                        help="Goal for the web agent to accomplish")
    parser.add_argument("--images", type=str, default="",
                        help="Comma-separated paths to image files (e.g., 'path1.jpg,path2.jpg')")
    parser.add_argument("--plan", type=str, default=None,
                        help="Optional plan for the web agent to follow (default: None)")
    # Added new arguments for evaluation with None as default
    parser.add_argument("--evaluator-type", type=str, default=None,
                        help="Type of evaluator to use (default: None, no evaluation)")
    parser.add_argument("--eval-url", type=str, default=None,
                        help="URL for evaluation purposes")
    parser.add_argument("--eval-criteria", type=str, default=None,
                        help="Criteria for evaluation")

    args = parser.parse_args()

    # Run the async main function with asyncio
    trajectory, result = asyncio.run(main(args.headless,
        args.browser_mode,
        args.storage_state,
        args.starting_url,
        args.agent_type,
        args.goal,
        args.action_generation_model,
        args.images,
        args.plan,
        args.evaluator_type,
        args.eval_url,
        args.eval_criteria))
    print(trajectory)
    print(result)