def extract_age_group_anywhere_in_conversation(dialog):
curr_uttr_text = dialog["human_utterances"][-1]["text"]
if re.search(KIDS_WORDS_RE, curr_uttr_text):
age_group = "kid"
elif re.search(ADULTS_WORDS_RE, curr_uttr_text) or is_sensitive_situation(
dialog["human_utterances"][-1]):
age_group = "adult"
else:
age_group = "unknown"
return age_group
def extract_age_group_using_activities(dialog):
curr_uttr_text = dialog["human_utterances"][-1]["text"]
if re.search(KIDS_ACTIVITIES_RE, curr_uttr_text):
age_group = "kid"
elif re.search(ADULTS_ACTIVITIES_RE,
curr_uttr_text) or is_sensitive_situation(
dialog["human_utterances"][-1]):
age_group = "adult"
else:
age_group = "unknown"
return age_group
def rule_based_prioritization(cand_uttr, dialog):
flag = False
if (len(dialog["human_utterances"]) == 1
and cand_uttr["skill_name"] == "dff_friendship_skill"
and greeting_spec in cand_uttr["text"] and GREETING_FIRST):
# prioritize greeting phrase in the beginning of the dialog
flag = True
if (cand_uttr["skill_name"] == "small_talk_skill"
and is_sensitive_situation(dialog["human_utterances"][-1])
and RESTRICTION_FOR_SENSITIVE_CASE):
# small talk skill (if hypothesis is available) priority for sensitive situations when required
flag = True
if cand_uttr["skill_name"] == "misheard_asr" and any([
x in cand_uttr["text"]
for x in [misheard_with_spec1, misheard_with_spec2]
]):
# prioritize misheard_asr response when low ASR conf
flag = True
if cand_uttr["confidence"] >= 1.0:
flag = True
return flag
def tag_based_response_selection(dialog,
candidates,
scores,
confidences,
bot_utterances,
all_prev_active_skills=None):
all_prev_active_skills = all_prev_active_skills if all_prev_active_skills is not None else []
all_prev_active_skills = Counter(all_prev_active_skills)
annotated_uttr = dialog["human_utterances"][-1]
all_user_intents, all_user_topics, all_user_named_entities, all_user_nounphrases = get_main_info_annotations(
annotated_uttr)
_is_switch_topic_request = is_switch_topic(annotated_uttr)
_is_force_intent = any(
[_intent in all_user_intents for _intent in FORCE_INTENTS_IC.keys()])
# if user utterance contains any question (REGEXP & punctuation check!)
_is_require_action_intent = is_any_question_sentence_in_utterance({
"text":
annotated_uttr.get("annotations",
{}).get("sentseg", {}).get("punct_sent",
annotated_uttr["text"])
})
# if user utterance contains any question AND requires some intent by socialbot
_is_require_action_intent = _is_require_action_intent and any([
_intent in all_user_intents
for _intent in REQUIRE_ACTION_INTENTS.keys()
])
_force_intents_detected = [
_intent for _intent in FORCE_INTENTS_IC.keys()
if _intent in all_user_intents
]
# list of user intents which require some action by socialbot
_require_action_intents_detected = [
_intent for _intent in REQUIRE_ACTION_INTENTS.keys()
if _intent in all_user_intents
]
_force_intents_skills = sum([
FORCE_INTENTS_IC.get(_intent, [])
for _intent in _force_intents_detected
], [])
# list of intents required by the socialbot
_required_actions = sum([
REQUIRE_ACTION_INTENTS.get(_intent, [])
for _intent in _require_action_intents_detected
], [])
_contains_entities = len(
get_entities(annotated_uttr, only_named=False, with_labels=False)) > 0
_is_active_skill_can_not_continue = False
_prev_bot_uttr = dialog["bot_utterances"][-1] if len(
dialog["bot_utterances"]) > 0 else {}
_prev_active_skill = dialog["bot_utterances"][-1]["active_skill"] if len(
dialog["bot_utterances"]) > 0 else ""
_prev_prev_active_skill = dialog["bot_utterances"][-2][
"active_skill"] if len(dialog["bot_utterances"]) > 1 else ""
_no_script_two_times_in_a_row = False
if _prev_active_skill and _prev_prev_active_skill:
if all([
skill not in ACTIVE_SKILLS + ALMOST_ACTIVE_SKILLS
for skill in [_prev_active_skill, _prev_prev_active_skill]
]):
_no_script_two_times_in_a_row = True
disliked_skills = get_updated_disliked_skills(
dialog, can_not_be_disliked_skills=CAN_NOT_BE_DISLIKED_SKILLS)
_is_dummy_linkto_available = any([
cand_uttr["skill_name"] == "dummy_skill"
and cand_uttr.get("type", "") == "link_to_for_response_selector"
for cand_uttr in candidates
])
categorized_hyps = {}
categorized_prompts = {}
for dasuffix in ["reqda", ""]:
for actsuffix in ["active", "continued", "finished"]:
for suffix in [
"same_topic_entity_no_db",
"same_topic_entity_db",
"othr_topic_entity_no_db",
"othr_topic_entity_db",
]:
categorized_hyps[f"{actsuffix}_{suffix}_{dasuffix}"] = []
categorized_prompts[f"{actsuffix}_{suffix}_{dasuffix}"] = []
CASE = ""
acknowledgement_hypothesis = {}
for cand_id, cand_uttr in enumerate(candidates):
if confidences[cand_id] == 0.0 and cand_uttr[
"skill_name"] not in ACTIVE_SKILLS:
logger.info(
f"Dropping cand_id: {cand_id} due to toxicity/badlists")
continue
all_cand_intents, all_cand_topics, all_cand_named_entities, all_cand_nounphrases = get_main_info_annotations(
cand_uttr)
skill_name = cand_uttr["skill_name"]
_is_dialog_abandon = get_dialog_breakdown_annotations(
cand_uttr) and PRIORITIZE_NO_DIALOG_BREAKDOWN
_is_just_prompt = (cand_uttr["skill_name"] == "dummy_skill" and any([
question_type in cand_uttr.get("type", "") for question_type in
["normal_question", "link_to_for_response_selector"]
])) or cand_uttr.get("response_parts", []) == ["prompt"]
if cand_uttr["confidence"] == 1.0:
# for those hypotheses where developer forgot to set tag to MUST_CONTINUE
cand_uttr["can_continue"] = MUST_CONTINUE
_can_continue = cand_uttr.get("can_continue", CAN_NOT_CONTINUE)
_user_wants_to_chat_about_topic = (
if_chat_about_particular_topic(annotated_uttr)
and "about it" not in annotated_uttr["text"].lower())
_user_does_not_want_to_chat_about_topic = if_not_want_to_chat_about_particular_topic(
annotated_uttr)
_user_wants_bot_to_choose_topic = if_choose_topic(
annotated_uttr, _prev_bot_uttr)
if any([
phrase.lower() in cand_uttr["text"].lower()
for phrase in LINK_TO_PHRASES
]):
# add `prompt` to response_parts if any linkto phrase in hypothesis
cand_uttr["response_parts"] = cand_uttr.get("response_parts",
[]) + ["prompt"]
# identifies if candidate contains named entities from last human utterance
_same_named_entities = (len(
get_common_tokens_in_lists_of_strings(
all_cand_named_entities, all_user_named_entities)) > 0)
# identifies if candidate contains all (not only named) entities from last human utterance
_same_nounphrases = len(
get_common_tokens_in_lists_of_strings(all_cand_nounphrases,
all_user_nounphrases)) > 0
_same_topic_entity = (_same_named_entities or _same_nounphrases
) and PRIORITIZE_WITH_SAME_TOPIC_ENTITY
_is_active_skill = (_prev_active_skill == cand_uttr["skill_name"] or
cand_uttr.get("can_continue", "") == MUST_CONTINUE)
_is_active_skill = _is_active_skill and skill_name in ACTIVE_SKILLS
_is_active_skill = _is_active_skill and (_can_continue in [
MUST_CONTINUE, CAN_CONTINUE_SCENARIO, CAN_NOT_CONTINUE
] or (_can_continue == CAN_CONTINUE_PROMPT
and all_prev_active_skills.get(skill_name, []) < 10))
_is_active_skill = _is_active_skill and PRIORITIZE_SCRIPTED_SKILLS
if _is_active_skill:
# we will forcibly add prompt if current scripted skill finishes scenario,
# and has no opportunity to continue at all.
_is_active_skill_can_not_continue = _is_active_skill and _can_continue in [
CAN_NOT_CONTINUE
]
if _is_force_intent:
# =====force intents, choose as best_on_topic hypotheses from skills responding this request=====
CASE = "Force intent."
if cand_uttr["skill_name"] in _force_intents_skills:
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
elif _is_switch_topic_request or _user_does_not_want_to_chat_about_topic or _user_wants_bot_to_choose_topic:
# =====direct request by user to switch the topic of current conversation=====
# give priority to dummy linkto hypothesis if available, else other prompts if available.
_is_active_skill = (
cand_uttr.get("type", "") == "link_to_for_response_selector"
if _is_dummy_linkto_available else _is_just_prompt)
# no priority to must_continue to skip incorrect continuation of script
_can_continue = CAN_CONTINUE_SCENARIO if _can_continue == MUST_CONTINUE else _can_continue
CASE = "Switch topic intent."
if len(all_user_named_entities) > 0 or len(
all_user_nounphrases) > 0:
# -----user defines new topic/entity-----
# _same_topic_entity does not depend on hyperparameter in these case
_same_topic_entity = _same_named_entities or _same_nounphrases
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
else:
# -----user want socialbot to define new topic/entity-----
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
elif _user_wants_to_chat_about_topic:
# user wants to chat about particular topic
CASE = "User wants to talk about topic."
# in this case we do not give priority to previously active skill (but give to must continue skill!)
# because now user wants to talk about something particular
_is_active_skill = cand_uttr.get("can_continue",
"") == MUST_CONTINUE
# _same_topic_entity does not depend on hyperparameter in these case
_same_topic_entity = _same_named_entities or _same_nounphrases
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
elif _is_require_action_intent and PRIORITIZE_WITH_REQUIRED_ACT:
# =====user intent requires particular action=====
CASE = "User intent requires action. USER UTTERANCE CONTAINS QUESTION."
_is_grounding_reqda = (skill_name == "dff_grounding_skill"
and cand_uttr.get(
"type", "") == "universal_response")
_is_active_skill = cand_uttr.get(
"can_continue",
"") == MUST_CONTINUE # no priority to prev active skill
_can_continue = CAN_NOT_CONTINUE # no priority to scripted skills
if set(all_cand_intents).intersection(
set(_required_actions
)) or _is_grounding_reqda or _is_active_skill:
# -----one of the can intent is in intents required by user-----
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=True,
)
else:
# -----NO required dialog acts-----
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
else:
# =====user intent does NOT require particular action=====
CASE = "General case."
categorized_hyps, categorized_prompts = categorize_candidate(
cand_id,
skill_name,
categorized_hyps,
categorized_prompts,
_is_just_prompt,
_is_active_skill,
_can_continue,
_same_topic_entity,
_is_dialog_abandon,
_is_required_da=False,
)
# a bit of rule based help
if (len(dialog["human_utterances"]) == 1
and cand_uttr["skill_name"] == "dff_friendship_skill"
and greeting_spec in cand_uttr["text"]):
categorized_hyps = add_to_top1_category(cand_id, categorized_hyps,
_is_require_action_intent)
elif (cand_uttr["skill_name"] == "dff_friendship_skill"
and (how_are_you_spec in cand_uttr["text"]
or what_i_can_do_spec in cand_uttr["text"])
and len(dialog["utterances"]) < 16):
categorized_hyps = add_to_top1_category(cand_id, categorized_hyps,
_is_require_action_intent)
# elif cand_uttr["skill_name"] == 'program_y_dangerous' and cand_uttr['confidence'] == 0.98:
# categorized_hyps = add_to_top1_category(cand_id, categorized_hyps, _is_require_action_intent)
elif cand_uttr[
"skill_name"] == "small_talk_skill" and is_sensitive_situation(
dialog["human_utterances"][-1]):
# let small talk to talk about sex ^_^
categorized_hyps = add_to_top1_category(cand_id, categorized_hyps,
_is_require_action_intent)
elif cand_uttr["confidence"] >= 1.0:
# -------------------- SUPER CONFIDENCE CASE HERE! --------------------
categorized_hyps = add_to_top1_category(cand_id, categorized_hyps,
_is_require_action_intent)
if cand_uttr["skill_name"] == "dff_grounding_skill" and [
"acknowledgement"
] == cand_uttr.get("response_parts", []):
acknowledgement_hypothesis = deepcopy(cand_uttr)
logger.info(f"Current CASE: {CASE}")
# now compute current scores as one float value
curr_single_scores = compute_curr_single_scores(candidates, scores,
confidences)
# remove disliked skills from hypotheses
if IGNORE_DISLIKED_SKILLS:
for category in categorized_hyps:
new_ids = []
for cand_id in categorized_hyps[category]:
if (candidates[cand_id]["skill_name"] in disliked_skills
and candidates[cand_id].get("can_continue",
CAN_NOT_CONTINUE)
== MUST_CONTINUE):
disliked_skills.remove(candidates[cand_id]["skill_name"])
if candidates[cand_id]["skill_name"] not in disliked_skills:
new_ids.append(cand_id)
categorized_hyps[category] = deepcopy(new_ids)
for category in categorized_prompts:
new_ids = []
for cand_id in categorized_prompts[category]:
if (candidates[cand_id]["skill_name"] in disliked_skills
and candidates[cand_id].get("can_continue",
CAN_NOT_CONTINUE)
== MUST_CONTINUE):
disliked_skills.remove(candidates[cand_id]["skill_name"])
if candidates[cand_id]["skill_name"] not in disliked_skills:
new_ids.append(cand_id)
categorized_prompts[category] = deepcopy(new_ids)
best_cand_id = pickup_best_id(categorized_hyps, candidates,
curr_single_scores, bot_utterances)
best_candidate = candidates[best_cand_id]
best_candidate["human_attributes"] = best_candidate.get(
"human_attributes", {})
# save updated disliked skills to human attributes of the best candidate
best_candidate["human_attributes"]["disliked_skills"] = disliked_skills
logger.info(f"Best candidate: {best_candidate}")
n_sents_without_prompt = len(sent_tokenize(best_candidate["text"]))
_is_best_not_script = best_candidate[
"skill_name"] not in ACTIVE_SKILLS + ALMOST_ACTIVE_SKILLS
no_question_by_user = "******" not in dialog["human_utterances"][-1][
"annotations"].get("sentseg",
{}).get("punct_sent",
dialog["human_utterances"][-1]["text"])
# if `no` to 1st in a row linkto question, and chosen response is not from scripted skill
_no_to_first_linkto = is_no(dialog["human_utterances"][-1]) and any([
phrase.lower() in _prev_bot_uttr.get("text", "").lower()
for phrase in LINK_TO_PHRASES
])
# if chosen short response or question by not-scripted skill
_is_short_or_question_by_not_script = _is_best_not_script and (
"?" in best_candidate["text"]
or len(best_candidate["text"].split()) < 4)
_no_questions_for_3_steps = not any([
is_any_question_sentence_in_utterance(uttr)
for uttr in dialog["bot_utterances"][-3:]
])
if PRIORITIZE_PROMTS_WHEN_NO_SCRIPTS:
if (_no_script_two_times_in_a_row
and _is_short_or_question_by_not_script
and no_question_by_user) or (_no_to_first_linkto
and _is_best_not_script):
# if no scripted skills 2 time sin a row before, current chosen best cand is not scripted, contains `?`,
# and user utterance does not contain "?", replace utterance with dummy!
best_prompt_id = pickup_best_id(categorized_prompts, candidates,
curr_single_scores, bot_utterances)
best_candidate = deepcopy(candidates[best_prompt_id])
best_cand_id = best_prompt_id
if does_not_require_prompt(candidates, best_cand_id):
# the candidate already contains a prompt or a question or of a length more than 200 symbols
logger.info(
"Best candidate contains prompt, question, request or length of > 200 symbols. Do NOT add prompt."
)
pass
elif sum(categorized_prompts.values(), []):
# best cand is 3d times in a row not scripted skill, let's append linkto
# need to add some prompt, and have a prompt
_add_prompt_forcibly = best_candidate[
"skill_name"] == _prev_active_skill and _is_active_skill_can_not_continue
_add_prompt_forcibly = _add_prompt_forcibly and not _contains_entities
# prompts are added:
# - in 1 out of 10 cases, if current human utterance does not contain entities,
# and no prompt for several last bot utterances
# - if PRIORITIZE_PROMTS_WHEN_NO_SCRIPTS and current utterance is from active on prev step scripted skill and
# it has a status can-not-continue
# - if PRIORITIZE_PROMTS_WHEN_NO_SCRIPTS and last 2 bot uttr are not from scripted skill,
# and current best uttr is also from not-scripted skill
if ((prompt_decision() and not _contains_entities
and _no_questions_for_3_steps)
or (_add_prompt_forcibly and PRIORITIZE_PROMTS_WHEN_NO_SCRIPTS)
or
(PRIORITIZE_PROMTS_WHEN_NO_SCRIPTS
and _no_script_two_times_in_a_row and _is_best_not_script)):
logger.info("Decided to add a prompt to the best candidate.")
best_prompt_id = pickup_best_id(categorized_prompts, candidates,
curr_single_scores, bot_utterances)
# as we have only one active skill, let's consider active skill as that one providing prompt
# but we also need to reassign all the attributes
best_prompt = candidates[best_prompt_id]
best_candidate[
"text"] = f'{best_candidate["text"]} {best_prompt["text"]}'
best_candidate["attributes"] = best_candidate.get("attributes", {})
best_candidate["attributes"]["prompt_skill"] = best_prompt
# anyway we must combine used links
best_candidate["human_attributes"] = best_candidate.get(
"human_attributes", {})
best_candidate["human_attributes"] = join_used_links_in_attributes(
best_candidate["human_attributes"],
best_prompt.get("human_attributes", {}))
if len(best_candidate["human_attributes"]["used_links"]) == 0:
best_candidate["human_attributes"].pop("used_links")
was_ackn = if_acknowledgement_in_previous_bot_utterance(dialog)
best_resp_cont_ackn = "acknowledgement" in best_candidate.get(
"response_parts", [])
if (ADD_ACKNOWLEDGMENTS_IF_POSSIBLE and acknowledgement_hypothesis
and acknowledgement_decision(all_user_intents)
and n_sents_without_prompt == 1 and not was_ackn
and not best_resp_cont_ackn):
logger.info(
"Acknowledgement is given, Final hypothesis contains only 1 sentence, no ackn in prev bot uttr,"
"and we decided to add an acknowledgement to the best candidate.")
best_candidate[
"text"] = f'{acknowledgement_hypothesis["text"]} {best_candidate["text"]}'
best_candidate["response_parts"] = ["acknowledgement"
] + best_candidate.get(
"response_parts", [])
return best_candidate, best_cand_id, curr_single_scores
async def send(self, payload: Dict, callback: Callable):
try:
st_time = time.time()
dialog = deepcopy(payload["payload"]["dialogs"][0])
is_sensitive_case = is_sensitive_situation(dialog["human_utterances"][-1])
all_prev_active_skills = payload["payload"]["all_prev_active_skills"][0]
curr_topics = get_topics(dialog["human_utterances"][-1], which="cobot_topics")
curr_nounphrases = get_entities(dialog["human_utterances"][-1], only_named=False, with_labels=False)
if len(curr_topics) == 0:
curr_topics = ["Phatic"]
logger.info(f"Found topics: {curr_topics}")
for i in range(len(curr_nounphrases)):
np = re.sub(np_remove_expr, "", curr_nounphrases[i])
np = re.sub(rm_spaces_expr, " ", np)
if re.search(np_ignore_expr, np):
curr_nounphrases[i] = ""
else:
curr_nounphrases[i] = np.strip()
curr_nounphrases = [np for np in curr_nounphrases if len(np) > 0]
logger.info(f"Found nounphrases: {curr_nounphrases}")
cands = []
confs = []
human_attrs = []
bot_attrs = []
attrs = []
cands += [choice(donotknow_answers)]
confs += [0.5]
attrs += [{"type": "dummy"}]
human_attrs += [{}]
bot_attrs += [{}]
if len(dialog["utterances"]) > 14 and not is_sensitive_case:
questions_same_nps = []
for i, nphrase in enumerate(curr_nounphrases):
for q_id in NP_QUESTIONS.get(nphrase, []):
questions_same_nps += [QUESTIONS_MAP[str(q_id)]]
if len(questions_same_nps) > 0:
logger.info("Found special nounphrases for questions. Return question with the same nounphrase.")
cands += [choice(questions_same_nps)]
confs += [0.5]
attrs += [{"type": "nounphrase_question"}]
human_attrs += [{}]
bot_attrs += [{}]
link_to_question, human_attr = get_link_to_question(dialog, all_prev_active_skills)
if link_to_question:
_prev_bot_uttr = dialog["bot_utterances"][-2]["text"] if len(dialog["bot_utterances"]) > 1 else ""
_bot_uttr = dialog["bot_utterances"][-1]["text"] if len(dialog["bot_utterances"]) > 0 else ""
_prev_active_skill = (
dialog["bot_utterances"][-1]["active_skill"] if len(dialog["bot_utterances"]) > 0 else ""
)
_no_to_first_linkto = any([phrase in _bot_uttr for phrase in LINK_TO_PHRASES])
_no_to_first_linkto = _no_to_first_linkto and all(
[phrase not in _prev_bot_uttr for phrase in LINK_TO_PHRASES]
)
_no_to_first_linkto = _no_to_first_linkto and is_no(dialog["human_utterances"][-1])
_no_to_first_linkto = _no_to_first_linkto and _prev_active_skill != "dff_friendship_skill"
_if_switch_topic = is_switch_topic(dialog["human_utterances"][-1])
bot_uttr_dict = dialog["bot_utterances"][-1] if len(dialog["bot_utterances"]) > 0 else {}
_if_choose_topic = if_choose_topic(dialog["human_utterances"][-1], bot_uttr_dict)
_is_ask_me_something = ASK_ME_QUESTION_PATTERN.search(dialog["human_utterances"][-1]["text"])
if len(dialog["human_utterances"]) > 1:
_was_cant_do = "cant_do" in get_intents(dialog["human_utterances"][-2]) and (
len(curr_nounphrases) == 0 or is_yes(dialog["human_utterances"][-1])
)
_was_cant_do_stop_it = "cant_do" in get_intents(dialog["human_utterances"][-2]) and is_no(
dialog["human_utterances"][-1]
)
else:
_was_cant_do = False
_was_cant_do_stop_it = False
if _was_cant_do_stop_it:
link_to_question = "Sorry, bye! #+#exit"
confs += [1.0] # finish dialog request
elif _no_to_first_linkto:
confs += [0.99]
elif _is_ask_me_something or _if_switch_topic or _was_cant_do or _if_choose_topic:
confs += [1.0] # Use it only as response selector retrieve skill output modifier
else:
confs += [0.05] # Use it only as response selector retrieve skill output modifier
cands += [link_to_question]
attrs += [{"type": "link_to_for_response_selector"}]
human_attrs += [human_attr]
bot_attrs += [{}]
facts_same_nps = []
for i, nphrase in enumerate(curr_nounphrases):
for fact_id in NP_FACTS.get(nphrase, []):
facts_same_nps += [
f"Well, now that you've mentioned {nphrase}, I've remembered this. {FACTS_MAP[str(fact_id)]}. "
f"{(opinion_request_question() if random.random() < ASK_QUESTION_PROB else '')}"
]
if len(facts_same_nps) > 0 and not is_sensitive_case:
logger.info("Found special nounphrases for facts. Return fact with the same nounphrase.")
cands += [choice(facts_same_nps)]
confs += [0.5]
attrs += [{"type": "nounphrase_fact"}]
human_attrs += [{}]
bot_attrs += [{}]
total_time = time.time() - st_time
logger.info(f"dummy_skill exec time: {total_time:.3f}s")
asyncio.create_task(
callback(task_id=payload["task_id"], response=[cands, confs, human_attrs, bot_attrs, attrs])
)
except Exception as e:
logger.exception(e)
sentry_sdk.capture_exception(e)
asyncio.create_task(callback(task_id=payload["task_id"], response=e))