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import copy
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import logging
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import random
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from df_engine.core import Actor, Context
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from common.constants import MUST_CONTINUE
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from common.dff.integration.context import (
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    get_last_human_utterance,
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    get_shared_memory,
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    save_to_shared_memory,
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    set_can_continue,
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    set_confidence,
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)
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from common.fact_random import get_fact
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from common.funfact import FUNFACT_LIST, make_question
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from common.utils import get_topics
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logger = logging.getLogger(__name__)
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CONF_HIGH = 1.0
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CONF_ZERO = 0.0
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def random_funfact_response(ctx: Context, actor: Actor, *args, **kwargs) -> str:
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    response = ""
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    set_confidence(ctx, actor, CONF_HIGH)
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    set_can_continue(ctx, actor, MUST_CONTINUE)
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    funfact_list = copy.deepcopy(FUNFACT_LIST)
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    random.shuffle(funfact_list)
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    shared_memory = get_shared_memory(ctx, actor)
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    given_funfacts = []
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    if shared_memory:
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        given_funfacts = shared_memory.get("given_funfacts", [])
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    for funfact, topic in funfact_list:
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        if funfact not in given_funfacts:
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            given_funfacts.append(funfact)
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            save_to_shared_memory(ctx, actor, given_funfacts=given_funfacts)
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            link_question = make_question(topic)
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            response = f"{funfact} {link_question}"
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            break
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    if not response:
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        set_confidence(ctx, actor, CONF_ZERO)
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    return response
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def thematic_funfact_response(ctx: Context, actor: Actor, *args, **kwargs) -> str:
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    response = ""
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    set_confidence(ctx, actor, CONF_HIGH)
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    set_can_continue(ctx, actor, MUST_CONTINUE)
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    entity = ctx.last_request.split("about")
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    if len(entity) > 1:
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        entity = entity[1]
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        human_utter = get_last_human_utterance(ctx, actor)
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        topic = get_topics(human_utter, which="cobot_topics")[0]
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        funfact = get_fact(entity, f"fact about {entity}")
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        if funfact:
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            link_question = make_question(topic)
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            response = f"{funfact} {link_question}"
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    if not response:
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        set_confidence(ctx, actor, CONF_ZERO)
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    return response
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