Change requirements

requirements.txt

@@ -1,5 +1,6 @@
 flair==0.13.1
 conllu==4.5.3
 pandas==1.5.3
-numpy==1.26.4
+numpy==1.24.4
 torch==2.3.0
+fuzzywuzzy==0.18.0

src/service/convlab/policy/policy.py

@@ -0,0 +1,24 @@
+"""Policy Interface"""
+from convlab.util.module import Module
+
+
+class Policy(Module):
+    """Policy module interface."""
+
+    def predict(self, state):
+        """Predict the next agent action given dialog state.
+
+        Args:
+            state (dict or list of list):
+                when the policy takes dialogue state as input, the type is dict.
+                else when the policy takes dialogue act as input, the type is list of list.
+        Returns:
+            action (list of list or str):
+                when the policy outputs dialogue act, the type is list of list.
+                else when the policy outputs utterance directly, the type is str.
+        """
+        return []
+
+    def update_memory(self, utterance_list, state_list, action_list, reward_list):
+        pass
+

src/service/convlab/util/module.py

@@ -0,0 +1,25 @@
+"""module interface."""
+from abc import ABC
+
+
+class Module(ABC):
+
+    def train(self, *args, **kwargs):
+        """Model training entry point"""
+        pass
+
+    def test(self, *args, **kwargs):
+        """Model testing entry point"""
+        pass
+
+    def from_cache(self, *args, **kwargs):
+        """restore internal state for multi-turn dialog"""
+        return None
+
+    def to_cache(self, *args, **kwargs):
+        """save internal state for multi-turn dialog"""
+        return None
+
+    def init_session(self):
+        """Init the class variables for a new session."""
+        pass

src/service/convlab/util/restaurant/dbquery.py

@@ -0,0 +1,38 @@
+"""
+"""
+import json
+import os
+import random
+from fuzzywuzzy import fuzz
+from itertools import chain
+from copy import deepcopy
+
+
+class Database(object):
+    def __init__(self):
+        super(Database, self).__init__()
+        # loading databases
+        domains = ['menu', 'pizza', 'drink', 'size']
+        self.dbs = {}
+        for domain in domains:
+            with open(os.path.join(os.path.dirname(
+                    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))),
+                    'data/restaurant/db/{}_db.json'.format(domain))) as f:
+                self.dbs[domain] = json.load(f)
+
+    def query(self, domain):
+        """Returns the list of entities for a given domain
+        based on the annotation of the belief state"""
+        # query the db
+        if domain == 'pizza':
+            return [{'Name': random.choice(self.dbs[domain]['name'])}]
+        if domain == 'menu':
+            return deepcopy(self.dbs[domain])
+        if domain == 'drink':
+            return [{'Name': random.choice(self.dbs[domain]['name'])}]
+        if domain == 'size':
+            return [{'Size': random.choice(self.dbs[domain]['size'])}]
+
+
+if __name__ == '__main__':
+    db = Database()

src/service/data/restaurant/db/confirm_db.json

@@ -0,0 +1,4 @@
+[
+  "true",
+  "false"
+]

src/service/data/restaurant/db/dough_db.json

@@ -0,0 +1,5 @@
+[
+    "pepsi", 
+    "cola", 
+    "water"
+]

src/service/data/restaurant/db/drink_db.json

@@ -0,0 +1,11 @@
+[
+    {
+        "name":"pepsi"
+    },
+    {
+        "name":"cola"
+    },
+    {
+        "name":"water"
+    }
+]

src/service/data/restaurant/db/food_db.json

@@ -0,0 +1,3 @@
+[
+    "pizza"
+]

src/service/data/restaurant/db/meat_db.json

@@ -0,0 +1,5 @@
+[
+    "chicken", 
+    "ham", 
+    "tuna"
+]

src/service/data/restaurant/db/menu_db.json

@@ -0,0 +1,7 @@
+[
+    "capri", 
+    "margarita",
+    "hawajska",
+    "barcelona",
+    "tuna"
+]

src/service/data/restaurant/db/pizza_db.json

@@ -0,0 +1,51 @@
+[
+    {
+        "name": "capri",
+        "ingredient": [
+            "tomato",
+            "ham",
+            "mushrooms",
+            "cheese"
+        ],
+        "price": 25
+    },
+    {
+        "name": "margarita",
+        "ingredient": [
+            "tomato",
+            "cheese"
+        ],
+        "price": 20
+    },
+    {
+        "name": "hawajska",
+        "ingredient": [
+            "tomato",
+            "pineapple",
+            "chicken",
+            "cheese"
+        ],
+        "price": 30
+    },
+    {
+        "name": "barcelona",
+        "ingredient": [
+            "tomato",
+            "onion",
+            "ham",
+            "pepper",
+            "cheese"
+        ],
+        "price": 40
+    },
+    {
+        "name": "tuna",
+        "ingredient": [
+            "tomato",
+            "tuna",
+            "onion",
+            "cheese"
+        ],
+        "price": 40
+    }
+]

src/service/data/restaurant/db/sauce_db.json

@@ -0,0 +1,4 @@
+[
+    "garlic", 
+    "1000w"
+]

src/service/data/restaurant/db/size_db.json

@@ -0,0 +1,14 @@
+[
+    {
+        "size": "m",
+        "price_multiplier": 1
+    },
+    {
+        "size": "l",
+        "price_multiplier": 1.2
+    },
+    {
+        "size": "xl",
+        "price_multiplier": 1.4
+    }
+]

src/service/dialog_policy.py

@@ -1,8 +1,61 @@
-from model.frame import Frame
+from collections import defaultdict
+import copy
+import json
+from copy import deepcopy
 
-class DialogPolicy:
-    def next_dialogue_act(self, frames: list[Frame]) -> Frame:
-        if frames[-1].act == "welcomemsg":
-            return Frame("system", "welcomemsg", [])
+from convlab.policy.policy import Policy
+from convlab.util.restaurant.dbquery import Database
+
+class SimpleRulePolicy(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.lower(), intent.lower())].append((slot.lower(), value))
+
+        for user_act in user_action:
+            self.update_system_action(user_act, user_action, state, system_action)
+
+        # Reguła 3
+        if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['pizza', 'size', 'drink']):
+            if self.results:
+                system_action = {('Ordering', 'Order'): [["Ref", self.results[0].get('Ref', 'N/A')]]}
+
+        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()].items() if value != '']
+        self.results = deepcopy(self.db.query(domain.lower(), constraints))
+
+        # Reguła 1
+        if intent == 'request':
+            if len(self.results) == 0:
+                system_action[(domain, 'NoOffer')] = []
             else:
-            return Frame("system", "canthelp", [])
+                for slot in user_action[user_act]:                  
+                    if slot[0] in self.results[0]:
+                        system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], '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 ["pizza", "drink"]:
+                    system_action[(domain, 'Recommend')].append(['Name', choice['name']])
+                if domain in ["size"]:
+                    system_action[(domain, 'Recommend')].append(['Size', choice['size']])
+
+dialogPolicy = SimpleRulePolicy()