#Natural Language Understanding
class NLU:
    #Text analysys
    def __init__(self, acts, arguments):
        self.acts = acts
        self.arguments = arguments

    def analyze(self, text):
        #Turn text into frame
        #return vector for further use

        act = "(hello()&request(name))"
        vector = [[0],[1,0]]
        return vector

#Dialogue policy
class DP:
    #Module decide what act takes next
    def __init__(self, acts, arguments):
        self.acts = acts
        self.arguments = arguments

    def tacticChoice(self, frame_list):
        actVector = [0, 0]
        return actVector

#Dialogue State Tracker
class DST:
    #Contain informations about state of the dialogue and data taken from user
    def __init__(self, acts, arguments):
        self.acts = acts
        self.arguments = arguments
        self.frameList= []

    #store new act into frame
    def store(self, frame):
        self.frameList.append(frame)

    def transfer(self):
        return self.frameList
#Natural Language Generator
class NLG:
    def __init__(self, acts, arguments):
        self.acts = acts
        self.arguments = arguments

    def vectorToText(self, actVector):
        if(actVector == [0, 0]):
            return "Witaj, nazywam się Mateusz."
        else:
            return "Przykro mi, nie zrozumiałem Cię"


class Run:
    def __init__(self):
        self.acts={
            0: "hello",
            1: "request",
        }
        self.arguments={
            0: "name"
        }

        self.nlu = NLU(self.acts, self.arguments)
        self.dp = DP(self.acts, self.arguments)
        self.nlg = NLG(self.acts, self.arguments)
        self.dst = DST(self.acts, self.arguments)

    def inputProcessing(self, command):
        act = self.nlu.analyze(command)

        self.dst.store(act)

        basic_act = self.dp.tacticChoice(self.dst.transfer())

        return self.nlg.vectorToText(basic_act)

run = Run()
while(1):
    message = input("Napisz coś: ")
    print(run.inputProcessing(message))