120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
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import importlib
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from adapt.engine import DomainIntentDeterminationEngine
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adapt_engine = DomainIntentDeterminationEngine()
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intents_handlers_adapt = dict()
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intents_handlers_padatious = dict()
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def register_entity_adapt(entity_value, entity_type, domain):
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adapt_engine.register_entity(entity_value=entity_value, entity_type=entity_type, domain=domain)
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# print("[Adapt]: Added entity with type " + entity_type + " for " + domain)
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def register_regex_adapt(regex, domain):
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adapt_engine.register_regex_entity(regex, domain)
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# print("[Adapt]: Added new regex for " + domain)
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def register_intent_adapt(intent, domain):
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adapt_engine.register_intent_parser(intent, domain=domain)
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print("[Adapt]: Registered new intent " + intent.name + " for skill " + domain + ".")
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def load_all_skills():
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for handler in intents_handlers_adapt:
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function_handler = intents_handlers_adapt.get(handler)
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intent_builder = getattr(function_handler[0], "_data", [])[0]
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skill_name = function_handler[1]
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register_intent_adapt(intent_builder.build(), domain=skill_name)
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def handle(intent_name, data):
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module_path_str = None
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handler_method_name = None
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if intent_name in intents_handlers_adapt:
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handler_method_name = intents_handlers_adapt.get(intent_name)[2]
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module_path_str = intents_handlers_adapt.get(intent_name)[3]
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if module_path_str is not None and handler_method_name is not None:
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# import the create_skill method from the skill using the skill module path
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create_skill_method = import_method_from_string(module_path_str, "create_skill")
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skill_init_data = {'client_ip': data['client_ip'], 'client_port': data['client_port']}
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# create a new object of the right skill for the utterance
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skill = create_skill_method(skill_init_data)
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# import and call the handler method from the skill
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getattr(skill, handler_method_name)(data=data)
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def import_method_from_string(file, method_name):
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"""
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Add the possibility to import method dynamically using a string like "skills.daily.date_and_time.intent" as file and
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"what_time_is_it" as method_name
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"""
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mod = importlib.import_module(file)
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met = getattr(mod, method_name)
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return met
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def recognise(sentence, client_ip=None, client_port=None):
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sentence = sentence.lower()
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print(sentence)
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data = dict()
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data['client_ip'] = client_ip
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data['client_port'] = client_port
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data['utterance'] = sentence
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best_intent_adapt = get_best_intent_adapt(sentence)
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confidence_adapt = get_confidence(best_intent_adapt)
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if confidence_adapt < 0.2:
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# TODO: implement fallback to something like wolfram alpha or smart assistant
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# Nothing found at all
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return "I didn't understand..."
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else:
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return handle_adapt_intent(data, best_intent_adapt)
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def get_confidence(intent):
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if intent is None:
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return 0
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if 'confidence' in intent:
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return intent['confidence']
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elif hasattr(intent, 'conf'):
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return intent.conf
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else:
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return 0
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def get_best_intent_adapt(sentence):
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if len(intents_handlers_adapt) > 0:
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try:
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best_intents = adapt_engine.determine_intent(sentence, 100)
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best_intent = next(best_intents)
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return best_intent
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except StopIteration:
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pass
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return None # No match (Adapt)
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def handle_adapt_intent(data, intent):
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for key, val in intent.items():
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if key != 'intent_type' and key != 'target' and key != 'confidence':
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data[key] = val
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handle(intent['intent_type'], data=data)
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return intent
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