chatbot/main.py

@@ -1,7 +1,8 @@
 from pathlib import Path
-from modules.nlu import NLU, Slot, UserAct
+from modules.nlu import NLU, Slot, Act
 from modules.state_monitor import DialogStateMonitor
 from modules.generator import ResponseGenerator
+from modules.strategy import DialoguePolicy
 from modules.config import Config
 import colorama
 from colorama import Fore, Style
@@ -10,13 +11,15 @@ colorama.init(autoreset=True)
 
 
 def main():
+    print(Fore.CYAN + "Starting chatbot. Please wait...")
     base_path = Path(__file__).resolve().parent
     config_path = base_path / 'config' / 'config.json'
     config = Config.load_config(config_path)
 
     nlu = NLU()
     dst = DialogStateMonitor()
-    generator = ResponseGenerator(config)
+    dp = DialoguePolicy()
+    generator = ResponseGenerator()
 
     print(Fore.CYAN + "Witaj w chatbocie! Rozpocznij rozmowę.")
     print(Fore.YELLOW + "Wpisz 'quit' aby zakończyć program.\n")
@@ -28,13 +31,12 @@ def main():
             break
 
         user_act = nlu.analyze(user_input)
-        # user_act = UserAct(intent='inform',
-        #                    slots=[Slot(name='item', value='laptop'), Slot(name='item', value='kot'),Slot(name='address', value='123 Main St')])
-        dst.update(user_act)
-        print(dst.state)
-        # response = generator.generate(intent)
 
-        # print(Fore.CYAN + "Bot: " + response)
+        dst.update(user_act)
+        system_action = dp.next_action(dst)
+        response = generator.nlg(system_action)
+
+        print(Fore.CYAN + "Bot: " + response)
 
 
 if __name__ == "__main__":

chatbot/models/nlu_train2.py

@@ -70,6 +70,8 @@ def conllu2flair_slot(sentences, label=None):
 
 
 def predict_frame(model, sentence, label_type):
+    if not sentence:
+        return 'unknown'
     csentence = [{'form': word, 'slot': 'O'} for word in sentence]
     fsentence = conllu2flair([csentence])[0]
     model.predict(fsentence)
@@ -84,6 +86,8 @@ def predict_frame(model, sentence, label_type):
 
 
 def predict_slot(model, sentence, label_type):
+    if not sentence:
+        return {'form': '', 'slot': 'unknown'},
     csentence = [{'form': word, 'slot': 'O'} for word in sentence]
     fsentence = conllu2flair([csentence])[0]
     model.predict(fsentence)
@@ -112,10 +116,9 @@ class Model:
             trainset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
         with open(self.test_dataset, encoding='utf-8') as f:
             testset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
-        print('TRAINSET:', trainset)
+
         corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(testset, label_type))
         label_dictionary = corpus.make_label_dictionary(label_type=label_type)
-        print('LABEL:' ,label_dictionary)
         embedding_types = [
             WordEmbeddings('pl'),
             FlairEmbeddings('pl-forward'),

chatbot/modules/generator.py

@@ -8,11 +8,41 @@ class ResponseGenerator:
     def __init__(self, config: Config):
         with config.responses_path.open('r', encoding='utf-8') as file:
             self.responses: Dict[str, list] = json.load(file)
-        self.intent_to_response_key = {
-            "ask_name": "name_response",
-            "unknown": "unknown"
+
+    def nlg(self, system_act):
+        intent = system_act.intent
+        slot = system_act.slots[0].name if system_act.slots else None
+
+        responses = {
+            "inform": {
+                "item": "Nasz sklep oferuje szeroki wybór artykułów, takich jak\n artykuły spożywcze,\n ogrodowe\n oraz kosmetyki. Proszę podaj produkt.",
+                "address": "Nasz sklep nie ma fizycznego adresu. To sklep internetowy.",
+                "delivery_method": "Dostępne formy dostawy: INPOST, DPD, DHL.",
+                "payment_method": "Dostępne formy płatności: Karta, przy odbiorze",
+                "email": "Obsługa klienta: cs2137@gmail.com",
+                "card_nr": random.choice([
+                    "Dzięki karcie rabatowej zbierasz punkty, które poźniej przekładają się na rabat.",
+                    "Dzisiaj z kartą rabatową meble ogrodowe 200zł taniej!",
+                    "Dzisiaj z kartą rabatową szampony 2 w cenie 1!"
+                ])
+            },
+            "canthelp": {
+                "unknown": random.choice([
+                    "Przepraszam, nie rozumiem polecenia...",
+                    "Możesz powtórzyć?",
+                    "Powiedz proszę jeszcze raz.."
+                ])
+            },
+            "request": {
+                "card_nr": "Podaj proszę numer karty rabatowej",
+                "address": "Podaj proszę adres do wysyłki",
+                "item": "Jaki produkt chcesz kupić?",
+                "email": "Podaj proszę email.",
+                "delivery_method": "Jaką formą dostawy jesteś zainteresowany?",
+                "payment_method": "Jaką formą płatności jesteś zainteresowany?"
+            },
+            "bye": "Miłego dnia!",
+            "confirmation": "Zamówienie zostało złożone!"
         }
 
-    def generate(self, response_key: str) -> str:
-        response_key = self.intent_to_response_key.get(response_key, "unknown")
-        return random.choice(self.responses.get(response_key, ["Przepraszam, nie rozumiem. Możesz to powtórzyć?"]))
+        return responses.get(intent, {}).get(slot, "Nieznane zapytanie.")

chatbot/modules/nlu.py

@@ -17,7 +17,7 @@ class Slot:
         return f"Name: {self.name}, Value: {self.value}"
 
 
-class UserAct:
+class Act:
     def __init__(self, intent: str, slots: list[Slot] = []):
         self.slots = slots
         self.intent = intent
@@ -62,6 +62,4 @@ class NLU:
     def analyze(self, text: str):
         intent = self.get_intent(text)
         slots = self.get_slot(text)
-        print({'intent': intent,
-               'slots': slots})
-        return UserAct(intent=intent, slots=slots)
+        return Act(intent=intent, slots=slots)

chatbot/modules/state_monitor.py

@@ -1,5 +1,5 @@
 import copy
-from modules.nlu import UserAct
+from modules.nlu import Act
 import json
 
 
@@ -39,11 +39,10 @@ class DialogStateMonitor:
 
     def find_first_empty_slot(self):
         for slot_name, slot_value in self.state['belief_state'].items():
-            if slot_name != 'order-completed' and self.is_value_empty(slot_value):
+            if slot_name != 'order-completed' and slot_value in [None, '', [], {}]:
                 return slot_name
 
-    def update(self, act: UserAct) -> None:
-        print(act)
+    def update(self, act: Act) -> None:
         if act.intent == 'inform':
             self.update_act(act.intent)
             slots_mapping = {
@@ -55,7 +54,7 @@ class DialogStateMonitor:
                 'email': []
             }
             for slot in act.slots:
-                if slot.name in slots_mapping and self.is_value_empty(self.state, slot.name):
+                if slot.name in slots_mapping and self.is_value_empty(self.state['belief_state'], slot.name):
                     slots_mapping[slot.name].append(slot.value)  # To do: normalization
 
             for slot_name, values in slots_mapping.items():
@@ -67,5 +66,7 @@ class DialogStateMonitor:
             self.update_slot_names(slots_names)
         elif act.intent == 'bye':
             self.update_act(act.intent)
+        elif act.intent == 'unknown':
+            self.update_act(act.intent)
 
         self.check_order_complete()

chatbot/modules/strategy.py

@@ -1,24 +1,24 @@
-from modules.nlu import UserAct, Slot
-from numpy.random.mtrand import random
+from modules.nlu import Act, Slot
+import random
 
 
 class DialoguePolicy:
-    def __init__(self, dst):
-        self.dialogue_state = dst.state
 
-    def next_action(self):
-        if not self.dialogue_state['belief_state']['order-complete']:
-            user_intent = self.dialogue_state['act']
+    def next_action(self, dst):
+        if not dst.state['belief_state']['order-complete']:
+            user_intent = dst.state['act']
             if user_intent == "inform":
                 empty_slot = dst.find_first_empty_slot()
-                return UserAct(intent="request", slots=[Slot(name=empty_slot, value='')])
-            if user_intent == "request":
-                if self.dialogue_state['slot_names']:
-                    slot = random.choice(self.state['slot_names'])
-                    return UserAct(intent="inform", slots=[Slot(name=slot, value='')])
+                return Act(intent="request", slots=[Slot(name=empty_slot, value='')])
+            elif user_intent == "request":
+                if dst.state['slot_names']:
+                    slot = random.choice(dst.state['slot_names'])
+                    return Act(intent="inform", slots=[Slot(name=slot, value='')])
                 else:
-                    return UserAct(intent="inform", slots=[Slot(name='unknown', value='')])
-            if user_intent == "bye":
-                return UserAct(intent="bye", slots=[Slot(name='', value='')])
+                    return Act(intent="canthelp", slots=[Slot(name='unknown', value='')])
+            elif user_intent == "bye":
+                return Act(intent="bye", slots=[Slot(name='', value='')])
             else:
-            return UserAct(intent=  "inform", slots=[Slot(name='confirmation', value='Zamówienie złożono poprawnie.')])
+                return Act(intent="canthelp", slots=[Slot(name='unknown', value='')])
+        else:
+            return Act(intent="inform", slots=[Slot(name='confirmation', value='Zamówienie złożono poprawnie.')])