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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 <solution> and </solution>.\n'
'For example: The answer to the question is <solution> 42 </solution>.\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: <execute_bash> exit </execute_bash>.\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)
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