telegram-bot-systemy-dialogowe/Modules.py

from convlab2.dst.dst import DST
from convlab2.dst.rule.multiwoz.dst_util import normalize_value
from collections import defaultdict
from convlab2.policy.policy import Policy
from convlab2.util.multiwoz.dbquery import Database
import copy
from copy import deepcopy
import json
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, rule):
        slots = []
        self.get_slots(rule.expansion, slots)
        return {"act": rule.grammar.name, "slots": slots}

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

        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 match(self, utterance):
        list_of_illegal_character = [",", ".", "'", "?", "!", ":", "-", "/"]
        for illegal_character in list_of_illegal_character[:-2]:
            utterance = utterance.replace(f"{illegal_character}", "")
        for illegal_character in list_of_illegal_character[-2:]:
            utterance = utterance.replace(f"{illegal_character}", " ")

        for grammar in self.grammars:
            matched = grammar.find_matching_rules(utterance)
            if matched:
                return self.get_dialog_act(matched[0])
        return {"act": "null", "slots": []}


class DP(Policy):
    def __init__(self):
        Policy.__init__(self)
        self.db = Database()

    def predict(self, state):
        self.results = []
        system_action = defaultdict(list)
        user_action = defaultdict(list)

        for intent, domain, slot, value in state["user_action"]:
            user_action[(domain, intent)].append((slot, value))

        for user_act in user_action:
            self.update_system_action(user_act, user_action, state, system_action)

        system_acts = [
            [intent, domain, slot, value]
            for (domain, intent), slots in system_action.items()
            for slot, value in slots
        ]
        state["system_action"] = system_acts
        return system_acts

    def update_system_action(self, user_act, user_action, state, system_action):
        domain, intent = user_act
        constraints = [
            (slot, value)
            for slot, value in state["belief_state"][domain.lower()]["semi"].items()
            if value != ""
        ]
        self.db.dbs = {
            "books": [
                {
                    "author": "autor",
                    "title": "krew",
                    "edition": "2020",
                    "lang": "polski",
                },
                {
                    "author": "Marcin Bruczkowski",
                    "title": "Bezsenność w Tokio",
                    "genre": "reportaż",
                    "publisher": "Społeczny Instytut Wydawniczy Znak",
                    "edition": "2004",
                    "lang": "polski",
                },
                {
                    "author": "Harari Yuval Noah",
                    "title": "Sapiens Od zwierząt do bogów",
                    "edition": "2011",
                    "lang": "polski",
                },
                {
                    "author": "Haruki Murakami",
                    "title": "1Q84",
                    "edition": "2009",
                    "lang": "polski",
                },
                {
                    "author": "Fiodor Dostojewski",
                    "title": "Zbrodnia i Kara",
                    "publisher": "Wydawnictwo Mg",
                    "edition": "2015",
                    "lang": "polski",
                },
            ]
        }
        self.results = deepcopy(self.db.query(domain.lower(), constraints))

        # Reguła 1
        if intent == "Request":
            if len(self.results) == 0:
                system_action[(domain, "NoOffer")] = []
            else:
                for slot in user_action[user_act]:
                    kb_slot_name = ref[domain].get(slot[0], slot[0])

                    if kb_slot_name in self.results[0]:
                        system_action[(domain, "Inform")].append(
                            [slot[0], self.results[0].get(kb_slot_name, "unknown")]
                        )

        # Reguła 2
        elif intent == "Inform":
            if len(self.results) == 0:
                system_action[(domain, "NoOffer")] = []
            else:
                system_action[(domain, "Inform")].append(
                    ["Choice", str(len(self.results))]
                )
                choice = self.results[0]

                if domain in ["Book"]:
                    system_action[(domain, "Recommend")].append(
                        ["Title", choice["title"]]
                    )


# Dialogue State Tracker
class SDST(DST):
    def __init__(self):
        DST.__init__(self)
        self.state = {
            "user_action": [],
            "system_action": [],
            "belief_state": {
                "books": {
                    "reserve": {"reservation": []},
                    "semi": {
                        "title": "",
                        "author": "",
                        "genre": "",
                        "publisher": "",
                        "edition": "",
                        "lang": "",
                    },
                },
                "library": {
                    "semi": {
                        "location": "",
                        "status": "",
                        "events": "",
                        "days": "",
                        "phone number": "",
                    }
                },
                "card": {"semi": {"lost": "", "destroyed": "", "new": ""}},
                "date": {"semi": {"day": "", "month": "", "year": ""}},
            },
            "request_state": {
                "reserve": {"reservation": []},
            },
            "reqmore_state": {
                "books": {
                    "reserve": {"reservation": []},
                    "semi": {
                        "title": "",
                        "author": "",
                        "genre": "",
                        "publisher": "",
                        "edition": "",
                        "lang": "",
                    },
                },
                "library": {
                    "semi": {
                        "location": "",
                        "status": "",
                        "events": "",
                        "days": "",
                        "phone number": "",
                    }
                },
                "card": {"semi": {"lost": "", "destroyed": "", "new": ""}},
                "date": {"semi": {"day": "", "month": "", "year": ""}},
            },
            "terminated": False,
            "history": [],
        }

        self.ref = {
            "Books": {
                "Title": "title",
                "Author": "author",
                "Genre": "genre",
                "Publisher": "publisher",
                "Edition": "edition",
                "Lang": "lang",
                "None": "none",
            },
            "Library": {
                "Location": "location",
                "Status": "status",
                "Events": "events",
                "Days": "days",
                "Phone number": "phone number",
                "None": "none",
            },
            "Card": {
                "Lost": "lost",
                "Destroyed": "destroyed",
                "New": "new",
                "None": "none",
            },
            "Date": {"Day": "day", "Month": "month", "Year": "year", "None": "none"},
        }
        self.value_dict = json.load(open("value_dict.json"))

    def update(self, user_act=None):
        for intent, domain, slot, value in user_act:
            domain = domain.lower()
            intent = intent.lower()

            if domain in ["bye", "thankyou", "hello"]:
                continue

            if intent == "inform":
                k = self.ref[domain.capitalize()].get(slot, slot)

                if k is None:
                    continue

                domain_dic = self.state["belief_state"][domain]

                if k in domain_dic["semi"]:
                    nvalue = normalize_value(self.value_dict, domain, k, value)
                    self.state["belief_state"][domain]["semi"][k] = value
                elif k in domain_dic["books"]:
                    self.state["belief_state"][domain]["books"][k] = value
                elif k.lower() in domain_dic["books"]:
                    self.state["belief_state"][domain]["books"][k.lower()] = value

            elif intent == "request":
                k = self.ref[domain.capitalize()].get(slot, slot)

                if k is None:
                    continue

                if domain not in self.state["request_state"]:
                    self.state["request_state"][domain] = {}
                    self.state["request_state"]["reserve"]["reservation"].append(value)
                else:
                    if (
                        value
                        not in self.state["request_state"]["reserve"]["reservation"]
                    ):
                        self.state["request_state"]["reserve"]["reservation"].append(
                            value
                        )

                if k not in self.state["request_state"][domain]:
                    self.state["request_state"][domain][k] = value
                else:
                    self.state["request_state"][domain][k] = value

                self.state["history"].append(
                    self.state["request_state"]["reserve"]["reservation"][-1]
                )

            elif intent == "reqmore":
                k = self.ref[domain.capitalize()].get(slot, slot)

                if k is None:
                    continue

                domain_dic = self.state["reqmore_state"][domain]

                if k in domain_dic["semi"]:
                    nvalue = normalize_value(self.value_dict, domain, k, value)
                    self.state["reqmore_state"][domain]["semi"][k] = value
                elif k in domain_dic["books"]:
                    self.state["reqmore_state"][domain]["books"][k] = value
                elif k.lower() in domain_dic["books"]:
                    self.state["reqmore_state"][domain]["books"][k.lower()] = value

            elif intent == "negate":
                try:
                    self.state["request_state"]["reserve"]["reservation"].remove(
                        self.state["history"][-1]
                    )
                except Exception:
                    pass

            elif intent == "affirm":
                self.state["belief_state"]["books"]["reserve"][
                    "reservation"
                ] = self.state["request_state"]["reserve"]["reservation"]

            else:
                continue

        return self.state

    def init_session(self):
        self.state = self.state


# 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))