import json import logging import multiprocessing as mp import os from typing import Callable import pandas as pd from openhands.controller.state.state import State from openhands.core.logger import get_console_handler from openhands.core.logger import openhands_logger as logger from openhands.events.action import Action from openhands.events.action.message import MessageAction def codeact_user_response( state: State, encapsulate_solution: bool = False, try_parse: Callable[[Action | None], str] | None = None, ) -> str: encaps_str = ( ( 'Please encapsulate your final answer (answer ONLY) within and .\n' 'For example: The answer to the question is 42 .\n' ) if encapsulate_solution else '' ) msg = ( 'Please continue working on the task on whatever approach you think is suitable.\n' 'If you think you have solved the task, please first send your answer to user through message and then finish the interaction.\n' f'{encaps_str}' 'IMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP.\n' ) if state.history: # check if the last action has an answer, if so, early exit if try_parse is not None: last_action = next( ( event for event in reversed(state.history) if isinstance(event, Action) ), None, ) ans = try_parse(last_action) if ans is not None: return '/exit' # check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up user_msgs = [ event for event in state.history if isinstance(event, MessageAction) and event.source == 'user' ] if len(user_msgs) >= 2: # let the agent know that it can give up when it has tried 3 times return ( msg + 'If you want to give up, run: exit .\n' ) return msg def cleanup(): print('Cleaning up child processes...') for process in mp.active_children(): print(f'Terminating child process: {process.name}') process.terminate() process.join() def prepare_dataset(dataset: pd.DataFrame, output_file: str, eval_n_limit: int): assert 'instance_id' in dataset.columns, ( "Expected 'instance_id' column in the dataset. You should define your own " "unique identifier for each instance and use it as the 'instance_id' column." ) id_column = 'instance_id' logger.info(f'Writing evaluation output to {output_file}') finished_ids = set() if os.path.exists(output_file): with open(output_file, 'r') as f: for line in f: data = json.loads(line) finished_ids.add(data[id_column]) logger.warning( f'Output file {output_file} already exists. Loaded ' f'{len(finished_ids)} finished instances.' ) if eval_n_limit: dataset = dataset.head(eval_n_limit) logger.info(f'Limiting evaluation to first {eval_n_limit} instances.') new_dataset = [ instance for _, instance in dataset.iterrows() if instance[id_column] not in finished_ids ] logger.info( f'Finished instances: {len(finished_ids)}, ' f'Remaining instances: {len(new_dataset)}' ) return pd.DataFrame(new_dataset) def reset_logger_for_multiprocessing( logger: logging.Logger, instance_id: str, log_dir: str ): """Reset the logger for multiprocessing. Save logs to a separate file for each process, instead of trying to write to the same file/console from multiple processes. """ # Set up logger log_file = os.path.join( log_dir, f'instance_{instance_id}.log', ) # Remove all existing handlers from logger for handler in logger.handlers[:]: logger.removeHandler(handler) # add back the console handler to print ONE line logger.addHandler(get_console_handler()) logger.info( f'Starting resolver for instance {instance_id}.\n' f'Hint: run "tail -f {log_file}" to see live logs in a separate shell' ) # Remove all existing handlers from logger for handler in logger.handlers[:]: logger.removeHandler(handler) os.makedirs(os.path.dirname(log_file), exist_ok=True) file_handler = logging.FileHandler(log_file) file_handler.setFormatter( logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') ) logger.addHandler(file_handler)