90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
#Natural Language Understanding
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class NLU:
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#Text analysys
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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 analyze(self, text):
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#Turn text into frame
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#return vector for further use
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act = "(hello()&request(name))"
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vector = [[0],[1,0]]
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return vector
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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(self.acts, self.arguments)
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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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