Finish chatbot

DialoguePolicy.py

@@ -7,6 +7,7 @@ db_path = './hotels_data.json'
 
 
 class DialoguePolicy(Policy):
+    info_dict = None
     def __init__(self):
         Policy.__init__(self)
         self.db = self.load_database(db_path)

DialogueStateTracker.py

@@ -32,6 +32,7 @@ def default_state():
 
 
 class DialogueStateTracker(DST):
+    info_dict = None
     def __init__(self):
         DST.__init__(self)
         self.state = default_state()

Main.py

@@ -1,31 +1,22 @@
-import requests
-
 from NaturalLanguageAnalyzer import NaturalLanguageAnalyzer
 from DialoguePolicy import DialoguePolicy
 from DialogueStateTracker import DialogueStateTracker
+from NaturalLanguageGeneration import NaturalLanguageGeneration
 from convlab.dialog_agent import PipelineAgent
-from MachineLearningNLG import MachineLearningNLG  # Importujemy nowy komponent NLG
+import warnings
-
+warnings.filterwarnings("ignore")
-
-def translate_text(text, target_language='pl'):
-    url = 'https://translate.googleapis.com/translate_a/single?client=gtx&sl=auto&tl={}&dt=t&q={}'.format(
-        target_language, text)
-    response = requests.get(url)
-    if response.status_code == 200:
-        translated_text = response.json()[0]
-        translated_text_joined = ''.join([sentence[0] for sentence in translated_text])
-        return translated_text_joined
-    else:
-        return None
 
 
 if __name__ == "__main__":
-    text = "chciałbym zarezerwować drogi hotel z parkingiem 1 stycznia w Warszawie w centrum"
     nlu = NaturalLanguageAnalyzer()
     dst = DialogueStateTracker()
     policy = DialoguePolicy()
-    nlg = MachineLearningNLG()
+    nlg = NaturalLanguageGeneration()
 
     agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')
-    response = agent.response(text)
+
-    print(translate_text(response))
+    print("Witam, jestem systemem do rezerwowania pokoi hotelowych. W czym mogę Ci pomóc?")
+    while True:
+        text = input()
+        response = agent.response(text)
+        print(response)

NaturalLanguageAnalyzer.py

@@ -14,6 +14,7 @@ def translate_text(text, target_language='en'):
 
 
 class NaturalLanguageAnalyzer:
+    info_dict = None
     def predict(self, text, context=None):
         # Inicjalizacja modelu NLU
         model_name = "ConvLab/t5-small-nlu-multiwoz21"

NaturalLanguageGeneration.py

@@ -1,12 +1,28 @@
-import re
+import requests
+
+from convlab.nlg.template.multiwoz import TemplateNLG
+
+
+def translate_text(text, target_language='pl'):
+    url = 'https://translate.googleapis.com/translate_a/single?client=gtx&sl=auto&tl={}&dt=t&q={}'.format(
+        target_language, text)
+    response = requests.get(url)
+    if response.status_code == 200:
+        translated_text = response.json()[0]
+        translated_text_joined = ''.join([sentence[0] for sentence in translated_text])
+        return translated_text_joined
+    else:
+        return None
 
 class NaturalLanguageGeneration:
+    info_dict = None
+    def generate(self, system_act):
+        if len(system_act) == 0:
+            return "Nie rozumiem."
+        tnlg = TemplateNLG(is_user=False)
+        response_en = tnlg.generate(system_act)
+        return translate_text(response_en)
 
-    def nlg(self, system_act):
+
-        response = None
+    def init_session(self):
-        pattern = r'inform\(name=([^\)]+)\)'
+        pass  # Dodanie pustej metody init_session
-        matching = re.search(pattern, system_act)
-        if matching:
-            name = matching.group(1)
-            response = f"Witaj, nazywam się {name}"
-        return response