AgentOccam/eval_webarena.py

import os
import time
import re
import argparse
import os
import shutil

from AgentOccam.env import WebArenaEnvironmentWrapper

from AgentOccam.AgentOccam import AgentOccam
from webagents_step.utils.data_prep import *
from webagents_step.agents.step_agent import StepAgent

from AgentOccam.prompts import AgentOccam_prompt
from webagents_step.prompts.webarena import step_fewshot_template_adapted, step_fewshot_template

from AgentOccam.utils import EVALUATOR_DIR

def run():
    parser = argparse.ArgumentParser(
        description="Only the config file argument should be passed"
    )
    parser.add_argument(
        "--config", type=str, required=True, help="yaml config file location"
    )
    args = parser.parse_args()
    with open(args.config, "r") as file:
        config = DotDict(yaml.safe_load(file))
    
    if config.logging:
        if config.logname:
            dstdir = f"{config.logdir}/{config.logname}"
        else:
            dstdir = f"{config.logdir}/{time.strftime('%Y%m%d-%H%M%S')}"
        os.makedirs(dstdir, exist_ok=True)
        shutil.copyfile(args.config, os.path.join(dstdir, args.config.split("/")[-1]))
    random.seed(42)
    
    config_file_list = []
    
    task_ids = config.env.task_ids
    if hasattr(config.env, "relative_task_dir"):
        relative_task_dir = config.env.relative_task_dir
    else:
        relative_task_dir = "tasks"
    if task_ids == "all" or task_ids == ["all"]:
        task_ids = [filename[:-len(".json")] for filename in os.listdir(f"config_files/{relative_task_dir}") if filename.endswith(".json")]
    for task_id in task_ids:
        config_file_list.append(f"config_files/{relative_task_dir}/{task_id}.json")

    fullpage = config.env.fullpage if hasattr(config.env, "fullpage") else True
    current_viewport_only = not fullpage

    if config.agent.type == "AgentOccam":
        agent_init = lambda: AgentOccam(
            prompt_dict = {k: v for k, v in AgentOccam_prompt.__dict__.items() if isinstance(v, dict)},
            config = config.agent,
        )
    elif config.agent.type == "AgentOccam-SteP":
            agent_init = lambda: StepAgent(
            root_action = config.agent.root_action,
            action_to_prompt_dict = {k: v for k, v in step_fewshot_template_adapted.__dict__.items() if isinstance(v, dict)},
            low_level_action_list = config.agent.low_level_action_list,
            max_actions=config.env.max_env_steps,
            verbose=config.verbose,
            logging=config.logging,
            debug=config.debug,
            model=config.agent.model_name,
            prompt_mode=config.agent.prompt_mode,
            )    
    elif config.agent.type == "SteP-replication":
        agent_init = lambda: StepAgent(
            root_action = config.agent.root_action,
            action_to_prompt_dict = {k: v for k, v in step_fewshot_template.__dict__.items() if isinstance(v, dict)},
            low_level_action_list = config.agent.low_level_action_list,
            max_actions=config.env.max_env_steps,
            verbose=config.verbose,
            logging=config.logging,
            debug=config.debug,
            model=config.agent.model_name,
            prompt_mode=config.agent.prompt_mode,
        )
    else:
        raise NotImplementedError(f"{config.agent.type} not implemented")

    
    for config_file in config_file_list:
        with open(config_file, "r") as f:
            task_config = json.load(f)
            print(f"Task {task_config['task_id']}.")
        if os.path.exists(os.path.join(dstdir, f"{task_config['task_id']}.json")):
            print(f"Skip {task_config['task_id']}.")
            continue
        if task_config['task_id'] in list(range(600, 650))+list(range(681, 689)):
            print("Reddit post task. Sleep 30 mins.")
            time.sleep(1800)
        env = WebArenaEnvironmentWrapper(config_file=config_file, 
                                        max_browser_rows=config.env.max_browser_rows, 
                                        max_steps=config.max_steps, 
                                        slow_mo=1, 
                                        observation_type="accessibility_tree", 
                                        current_viewport_only=current_viewport_only, 
                                        viewport_size={"width": 1920, "height": 1080}, 
                                        headless=config.env.headless,
                                        global_config=config,
                                        proxy_url=config.env.proxy_url)
        
        agent = agent_init()
        objective = env.get_objective()
        status = agent.act(objective=objective, env=env)
        env.close()

        if config.logging:
            with open(config_file, "r") as f:
                task_config = json.load(f)
            log_file = os.path.join(dstdir, f"{task_config['task_id']}.json")
            log_data = {
                "task": config_file,
                "id": task_config['task_id'],
                "model": config.agent.actor.model if hasattr(config.agent, "actor") else config.agent.model_name,
                "type": config.agent.type,
                "trajectory": agent.get_trajectory(),
            }
            summary_file = os.path.join(dstdir, "summary.csv")
            summary_data = {
                "task": config_file,
                "task_id": task_config['task_id'],
                "model": config.agent.actor.model if hasattr(config.agent, "actor") else config.agent.model_name,
                "type": config.agent.type,
                "logfile": re.search(r"/([^/]+/[^/]+\.json)$", log_file).group(1),
            }
            if status:
                summary_data.update(status)
            log_run(
                log_file=log_file,
                log_data=log_data,
                summary_file=summary_file,
                summary_data=summary_data,
            )
    
if __name__ == "__main__":
    run()