telegram-bot-systemy-dialogowe/Modules.py

import os
import jsgf

#Natural Language Understanding
class NLU:

    def __init__(self):
        self.grammars = [jsgf.parse_grammar_file(f'JSGFs/{file_name}') for file_name in os.listdir('JSGFs')]

    def get_dialog_act(self, sentence):
        acts = []
        for sentence in sentence.split(','):
            for grammar in self.grammars:
                match = grammar.find_matching_rules(sentence)
                if match:
                    acts.append(grammar.name)
        return acts

    def match_slots(self, expansion, slots):
        if expansion.tag != '':
            slots.append((expansion.tag, expansion.current_match))
            return slots

        for child in expansion.children:
            self.get_slots(child, slots)

        if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):
            self.get_slots(expansion.referenced_rule.expansion, slots)

    def get_slots(self, utterance):
        slots = []
        for grammar in self.grammars:
            matched = grammar.find_matching_rules(utterance)
            if matched:
                return self.match_slots(matched[0], slots)
        return []


#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.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))