110 lines
2.8 KiB
Python
110 lines
2.8 KiB
Python
import os
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import jsgf
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#Natural Language Understanding
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class NLU:
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def __init__(self):
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self.grammars = [jsgf.parse_grammar_file(f'JSGFs/{file_name}') for file_name in os.listdir('JSGFs')]
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def get_dialog_act(self, sentence):
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for grammar in self.grammars:
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match = grammar.find_matching_rules(sentence)
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if match:
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return grammar.name
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def match_slots(self, expansion, slots):
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if expansion.tag != '':
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slots.append((expansion.tag, expansion.current_match))
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return slots
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for child in expansion.children:
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self.get_slots(child, slots)
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if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):
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self.get_slots(expansion.referenced_rule.expansion, slots)
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def get_slots(self, utterance):
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slots = []
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for grammar in self.grammars:
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matched = grammar.find_matching_rules(utterance)
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if matched:
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return self.match_slots(matched[0], slots)
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return []
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#Dialogue policy
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class DP:
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#Module decide what act takes next
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def __init__(self, acts, arguments):
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self.acts = acts
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self.arguments = arguments
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def tacticChoice(self, frame_list):
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actVector = [0, 0]
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return actVector
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#Dialogue State Tracker
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class DST:
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#Contain informations about state of the dialogue and data taken from user
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def __init__(self, acts, arguments):
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self.acts = acts
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self.arguments = arguments
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self.frameList= []
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#store new act into frame
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def store(self, frame):
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self.frameList.append(frame)
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def transfer(self):
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return self.frameList
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#Natural Language Generator
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class NLG:
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def __init__(self, acts, arguments):
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self.acts = acts
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self.arguments = arguments
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def vectorToText(self, actVector):
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if(actVector == [0, 0]):
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return "Witaj, nazywam się Mateusz."
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else:
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return "Przykro mi, nie zrozumiałem Cię"
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class Run:
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def __init__(self):
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self.acts={
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0: "hello",
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1: "request",
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}
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self.arguments={
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0: "name"
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}
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self.nlu = NLU()
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self.dp = DP(self.acts, self.arguments)
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self.nlg = NLG(self.acts, self.arguments)
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self.dst = DST(self.acts, self.arguments)
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def inputProcessing(self, command):
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act = self.nlu.analyze(command)
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self.dst.store(act)
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basic_act = self.dp.tacticChoice(self.dst.transfer())
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return self.nlg.vectorToText(basic_act)
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# run = Run()
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# while(1):
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# message = input("Napisz coś: ")
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# print(run.inputProcessing(message))
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