Finishing up NLU module

.gitignore

@@ -0,0 +1,4 @@
+__pycache__
+slot-model*
+frame-model*
+nlu_cache

README.md

@@ -35,3 +35,12 @@ Agent powinien wykazywać elastyczność, adaptując się do potrzeb klienta, np
 | offer                       | rekomendacja (restauracji)                              |
 | request                     | pytanie użytkownika o wartość slotu                     |
 | select                      | prośba o dokonanie wyboru spośród przedstawionych opcji |
+
+# Obsługa projektu
+
+- Python 3.10.12
+- Instalacja dependencies `pip3 install -r requirements.txt`
+- Centralna część systemu - uruchamiamy `python3 src/main.py`
+- NLU:
+    - uczenie modeli od zera `python3 nlu_train.py`
+    - Ewaluacja `python3 evaluate.py`

evaluate.py

@@ -2,11 +2,10 @@ import re
 import os
 import pandas as pd
 import numpy as np
-from nlu_utils import predict_multiple
 from flair.models import SequenceTagger
 from conllu import parse_incr
 from flair.data import Corpus
-from nlu_utils import conllu2flair, nolabel2o
+from src.utils.nlu_utils import conllu2flair, nolabel2o, predict_multiple
 
 # Frame model evaluation
 frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')

nlu_train.py

@@ -6,7 +6,7 @@ from flair.embeddings import CharacterEmbeddings
 from flair.embeddings import FlairEmbeddings
 from flair.models import SequenceTagger
 from flair.trainers import ModelTrainer
-from nlu_utils import conllu2flair, nolabel2o
+from src.utils.nlu_utils import conllu2flair, nolabel2o
 
 import torch
 if torch.cuda.is_available():

requirements.txt

@@ -0,0 +1,5 @@
+flair==0.13.1
+conllu==4.5.3
+pandas==1.5.3
+numpy==1.26.4
+torch==2.3.0

src/main.py

@@ -1,26 +1,38 @@
-from model.frame import Frame
 from service.dialog_state_monitor import DialogStateMonitor
 from service.dialog_policy import DialogPolicy
 from service.natural_languag_understanding import NaturalLanguageUnderstanding
 from service.natural_language_generation import NaturalLanguageGeneration
 
-print("Natural language understanding, example:")
+# initialize classes
-naturalLanguageUnderstanding = NaturalLanguageUnderstanding()
+nlu = NaturalLanguageUnderstanding() # NLU
-print(naturalLanguageUnderstanding.convert_text_to_frame("Cześć, jak masz na imię?"))
+monitor = DialogStateMonitor() # DSM
+dialog_policy = DialogPolicy() # DP
+language_generation = NaturalLanguageGeneration() # NLG
+
+# Main loop
+user_input = input("Możesz zacząć pisać.\n")
+while True:
+    # NLU
+    frame = nlu.process_input(user_input)
+    print(frame)
+
+    # DSM
+    monitor.append(frame)
+
+    # DP
+    print(dialog_policy.next_dialogue_act(monitor.get_all()).act)
+
+    # NLG
+    response = language_generation.respond_to_name_query("Jak masz na imię?")
+    print(response)
+
+    if frame.act == "bye":
+        break
+    
+    user_input = input(">\n")
+
+
+
 
-# Example
-print("Dialog state monitor, examples:")
-monitor = DialogStateMonitor()
-monitor.append(Frame('system', 'hello', []))
-monitor.append(Frame('user', 'some_text', []))
-print(monitor.get_all()[0].act)
-print(monitor.get_last().act)
 
-print("Dialog policy, next dialogue act:")
-dialog_policy = DialogPolicy(monitor.get_all())
-print(dialog_policy.next_dialogue_act().act)
 
-print("Natural Language Generation example:")
-agent = NaturalLanguageGeneration()
-response = agent.respond_to_name_query("Jak masz na imię?")
-print(response)

src/model/frame.py

@@ -1,7 +1,14 @@
-from model.slot import Slot
+from .slot import Slot
 
 class Frame:
     def __init__(self, source: str, act: str, slots: list[Slot]):
         self.source = source
         self.slots = slots
         self.act = act
+
+    def __str__(self):
+        msg = f"Act: {self.act}, Slots: ["
+        for slot in self.slots:
+            msg += f"({slot}), "
+        msg += "]"
+        return msg

src/model/slot.py

@@ -2,3 +2,6 @@ class Slot:
     def __init__(self, name, value=None):
         self.name = name
         self.value = value
+
+    def __str__(self) -> str:
+        return f"Name: {self.name}, Value: {self.value}"

src/nlu_example.py

@@ -1,8 +1,8 @@
 from flair.models import SequenceTagger
-from nlu_utils import predict_single, predict_multiple, predict_and_annotate
+from utils.nlu_utils import predict_single, predict_multiple, predict_and_annotate
 
 # Exploratory tests
-frame_model = SequenceTagger.load('frame-model/best-model.pt')
+frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')
 tests = [
     'chciałbym zamówić pizzę',
     'na godzinę 12',
@@ -16,7 +16,7 @@ tests = [
     'pizzę barcelona bez cebuli',
 ]
 
-# print("=== Exploratory tests - frame model ===")
+print("=== Exploratory tests - frame model ===")
 for test in tests:
     print(f"Sentence: {test}")
     print(f"Single prediction: {predict_single(frame_model, test.split(), 'frame')}")
@@ -24,7 +24,7 @@ for test in tests:
     print(f"Annotated sentence: {predict_and_annotate(frame_model, test.split(), 'frame')}")
 
 print("=== Exploratory tests - slot model ===")
-slot_model = SequenceTagger.load('slot-model/final-model.pt')
+slot_model = SequenceTagger.load('slot-model-prod/best-model.pt')
 for test in tests:
     print(f"Sentence: {test}")
     print(f"Prediction: {predict_and_annotate(slot_model, test.split(), 'slot')}")

src/service/dialog_policy.py

@@ -1,11 +1,8 @@
 from model.frame import Frame
 
 class DialogPolicy:
-    def __init__(self, frames: list[Frame]) -> None:
+    def next_dialogue_act(self, frames: list[Frame]) -> Frame:
-        self.frames = frames
+        if frames[-1].act == "welcomemsg":
-
-    def next_dialogue_act(self) -> Frame:
-        if self.frames[-1].act == "welcomemsg":
             return Frame("system", "welcomemsg", [])
         else:
             return Frame("system", "canthelp", [])

src/service/natural_languag_understanding.py

@@ -1,14 +1,92 @@
+from flair.models import SequenceTagger
+from utils.nlu_utils import predict_single, predict_and_annotate
+from model.frame import Frame, Slot
+
+"""
+ACTS:
+    inform/order
+    request/menu
+    inform/address
+    request/price
+    request/ingredients
+    request/sauce
+    inform/phone
+    inform/order-complete
+    request/time
+    request/size
+    welcomemsg
+    affirm
+    inform/delivery
+    inform/payment
+    request/delivery-price
+    bye
+    inform/time
+    request/drinks
+    inform/name
+    negate
+
+SLOTS:
+    food
+    pizza
+    size
+    address
+    quantity
+    ingredient
+    payment-method
+    delivery
+    drink
+    ingredient/neg
+    name
+    phone
+    sauce
+"""
+
 class NaturalLanguageUnderstanding:
+    def __init__(self):
+        print("\n========================================================")
+        print("Models are loading, it may take a moment, please wait...")
+        print("========================================================\n")
 
-    dictionary = {
+        self.frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')
-        "Cześć," : "welcomemsg()",
+        self.slot_model = SequenceTagger.load('slot-model-prod/best-model.pt')
-        "imię?" : "request(name)"
-    }
 
-    def convert_text_to_frame(self, text: str):
+        print("\n========================================================")
-        frame = ""
+        print("Models loaded. NLU system is ready.")
-        text = text.split(" ")
+        print("========================================================\n")
-        for word in text:
+    
-            if(word in self.dictionary):
+    def __predict_intention(self, text: str):
-                    frame+=self.dictionary[word]+"&"
+        return predict_single(self.frame_model, text.split(), 'frame')
-        return frame[0:-1]
+
+    def __predict_slot(self, text: str):
+        anootations = predict_and_annotate(self.slot_model, text.split(), 'slot')
+        current_slot = None
+        current_slot_value = ""
+        slots = []
+
+        for annotation in anootations:
+            form = annotation["form"]
+            slot = annotation["slot"]
+
+            if slot[0:2] == "B-":
+                if current_slot != None:
+                    slots.append(Slot(name=current_slot, value=current_slot_value))
+                current_slot = slot[2:]
+                current_slot_value = form
+            elif slot[0:2] == "I-":
+                current_slot_value = current_slot_value + " " + form
+            elif slot == "O":
+                if current_slot != None:
+                    slots.append(Slot(name=current_slot, value=current_slot_value))
+                    current_slot = None
+                    current_slot_value = ""
+
+        if current_slot != None:
+            slots.append(Slot(name=current_slot, value=current_slot_value))
+
+        return slots
+
+    def process_input(self, text: str):
+        act = self.__predict_intention(text)
+        slots = self.__predict_slot(text)
+        frame = Frame(source = 'user', act = act, slots = slots)
+        return frame

src/service/natural_language_generation.py

@@ -1,5 +1,5 @@
 class NaturalLanguageGeneration:
-    def __init__(self, name):
+    def __init__(self):
         self.name = ["Michał"]
 
     def respond_to_name_query(self, question):

src/utils/nlu_utils.py

@@ -69,7 +69,6 @@ def predict_single(model, sentence, label_type):
     csentence = __csentence(sentence, label_type)
     fsentence = __predict(model, csentence)
     intent = {}
-
     for span in fsentence.get_spans(label_type):
         tag = span.get_label(label_type).value
         if tag in intent: