def run_hit(hit_index, assignment_index, opt, mturk_manager):
conversation_id = str(hit_index) + '_' + str(assignment_index)
# Create mturk agents
mturk_agent_1 = MTurkAgent(id=mturk_agent_1_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
mturk_agent_2 = MTurkAgent(id=mturk_agent_2_id, manager=mturk_manager, conversation_id=conversation_id, opt=opt)
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
human_agent_2 = LocalHumanAgent(opt=None)
human_agent_2.id = human_agent_2_id
world = MultiAgentDialogWorld(opt=opt, agents=[human_agent_1, human_agent_2, mturk_agent_1, mturk_agent_2])
while not world.episode_done():
world.parley()
world.shutdown()
def run_conversation(mturk_manager, opt, workers):
# Create mturk agents
mturk_agent_1 = workers[0]
mturk_agent_2 = workers[1]
# Create the local human agents
human_agent_1 = LocalHumanAgent(opt=None)
human_agent_1.id = human_agent_1_id
world = MTurkMultiAgentDialogWorld(
opt=opt,
agents=[human_agent_1, mturk_agent_1, mturk_agent_2]
)
while not world.episode_done():
world.parley()
world.shutdown()
def run_conversation(mturk_manager, opt, workers):
agents = workers[:]
# Create a local agent
if not opt['two_mturk_agents']:
if 'model' in opt:
local_agent = create_agent(opt)
else:
local_agent = LocalHumanAgent(opt=None)
local_agent.id = local_agent_1_id
agents.append(local_agent)
opt["batchindex"] = mturk_manager.started_conversations
world = MTurkDealNoDealDialogWorld(
opt=opt,
agents=agents
)
while not world.episode_done():
world.parley()
world.shutdown()
def setup_args(parser=None):
if parser is None:
parser = ParlaiParser(True, True, 'Interactive chat with a model')
parser.add_argument('-d', '--display-examples', type='bool', default=False)
parser.add_argument(
'--display-prettify',
type='bool',
default=False,
help='Set to use a prettytable when displaying '
'examples with text candidates',
)
parser.add_argument(
'--display-ignore-fields',
type=str,
default='label_candidates,text_candidates',
help='Do not display these fields',
)
parser.add_argument(
'-it',
'--interactive-task',
type='bool',
default=True,
help='Create interactive version of task',
)
parser.add_argument(
'-sc',
'--script-chateval',
type='bool',
default=False,
dest='chat_script',
help='Chateval script read file'
'True: chateval evaluation, False: single-turn conversation with agent(original model)',
)
parser.add_argument(
'-scip',
'--chateval-input-path',
type=str,
default=None,
dest='script_input_path',
help='Chateval script input path',
)
parser.add_argument(
'-scop',
'--chateval-output-path',
type=str,
default=None,
dest='script_output_path',
help='Chateval result output path',
)
parser.add_argument(
'--chateval-multi-num',
type=int,
default=0,
dest='chateval_multi_num',
help='True is chateval multiturn setting, turn coverage count.',
)
parser.add_argument(
'--chateval-multi',
type='bool',
default=False,
hidden=True,
dest='chateval_multi',
help='True is chateval multiturn setting, False just single turn.',
)
parser.set_defaults(interactive_mode=True, task='interactive')
LocalHumanAgent.add_cmdline_args(parser)
return parser