{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "706dd5e1-57ee-416b-a77c-5d15df8dbdc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from convlab.base_models.t5.nlu import T5NLU\n",
    "import requests\n",
    "\n",
    "\n",
    "def translate_text(text, target_language='en'):\n",
    "    url = 'https://translate.googleapis.com/translate_a/single?client=gtx&sl=auto&tl={}&dt=t&q={}'.format(\n",
    "        target_language, text)\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        translated_text = response.json()[0][0][0]\n",
    "        return translated_text\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "class NaturalLanguageAnalyzer: \n",
    "    def predict(self, text, context=None):\n",
    "        # Inicjalizacja modelu NLU\n",
    "        model_name = \"ConvLab/t5-small-nlu-multiwoz21\"\n",
    "        nlu_model = T5NLU(speaker='user', context_window_size=0, model_name_or_path=model_name)\n",
    "\n",
    "        # Automatyczne tłumaczenie na język angielski\n",
    "        translated_input = translate_text(text)\n",
    "\n",
    "        # Wygenerowanie odpowiedzi z modelu NLU\n",
    "        nlu_output = nlu_model.predict(translated_input)\n",
    "\n",
    "        return nlu_output\n",
    "\n",
    "    def init_session(self):\n",
    "        # Inicjalizacja sesji (jeśli konieczne)\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "423f0821-000a-4aaa-b400-2e7554866175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_action': [],\n",
       " 'system_action': [],\n",
       " 'belief_state': {'attraction': {'type': '', 'name': '', 'area': ''},\n",
       "  'hotel': {'name': '',\n",
       "   'area': '',\n",
       "   'parking': '',\n",
       "   'price range': '',\n",
       "   'stars': '4',\n",
       "   'internet': 'yes',\n",
       "   'type': 'hotel',\n",
       "   'book stay': '',\n",
       "   'book day': '',\n",
       "   'book people': ''},\n",
       "  'restaurant': {'food': '',\n",
       "   'price range': '',\n",
       "   'name': '',\n",
       "   'area': '',\n",
       "   'book time': '',\n",
       "   'book day': '',\n",
       "   'book people': ''},\n",
       "  'taxi': {'leave at': '',\n",
       "   'destination': '',\n",
       "   'departure': '',\n",
       "   'arrive by': ''},\n",
       "  'train': {'leave at': '',\n",
       "   'destination': '',\n",
       "   'day': '',\n",
       "   'arrive by': '',\n",
       "   'departure': '',\n",
       "   'book people': ''},\n",
       "  'hospital': {'department': ''}},\n",
       " 'booked': {},\n",
       " 'request_state': {},\n",
       " 'terminated': False,\n",
       " 'history': []}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from convlab.util.multiwoz.state import default_state\n",
    "default_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06926543-cab1-48e7-8e82-0560fc0fa16a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from convlab.dst.dst import DST\n",
    "from convlab.dst.rule.multiwoz.dst_util import normalize_value\n",
    "\n",
    "class SimpleRuleDST(DST):\n",
    "    def __init__(self):\n",
    "        DST.__init__(self)\n",
    "        self.state = default_state()\n",
    "        self.value_dict = json.load(open('value_dict.json'))\n",
    "\n",
    "    def update(self, user_act=None):\n",
    "        for intent, domain, slot, value in user_act:\n",
    "            domain = domain.lower()\n",
    "            intent = intent.lower()\n",
    "            slot = slot.lower()\n",
    "            \n",
    "            if domain not in self.state['belief_state']:\n",
    "                continue\n",
    "\n",
    "            if intent == 'inform':\n",
    "                if slot == 'none' or slot == '':\n",
    "                    continue\n",
    "\n",
    "                domain_dic = self.state['belief_state'][domain]\n",
    "\n",
    "                if slot in domain_dic:\n",
    "                    nvalue = normalize_value(self.value_dict, domain, slot, value)\n",
    "                    self.state['belief_state'][domain][slot] = nvalue\n",
    "\n",
    "            elif intent == 'request':\n",
    "                if domain not in self.state['request_state']:\n",
    "                    self.state['request_state'][domain] = {}\n",
    "                if slot not in self.state['request_state'][domain]:\n",
    "                    self.state['request_state'][domain][slot] = 0\n",
    "\n",
    "        return self.state\n",
    "\n",
    "    def init_session(self):\n",
    "        self.state = default_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1d42d5f-e923-4c46-a930-48da9b72d77b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dst = SimpleRuleDST()\n",
    "dst.state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "749e3a90-17c3-4a3e-acd7-856560445eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': '',\n",
       " 'area': '',\n",
       " 'parking': 'yes',\n",
       " 'price range': 'cheap',\n",
       " 'stars': '4',\n",
       " 'internet': 'yes',\n",
       " 'type': 'hotel',\n",
       " 'book stay': '',\n",
       " 'book day': '',\n",
       " 'book people': ''}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst.update([['Inform', 'Hotel', 'Price Range', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n",
    "dst.state['belief_state']['hotel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7f3d067-3a95-4ef5-b216-be5840bc8831",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import copy\n",
    "import json\n",
    "from copy import deepcopy\n",
    "\n",
    "from convlab.policy.policy import Policy\n",
    "from convlab.util.multiwoz.dbquery import Database\n",
    "\n",
    "\n",
    "class SimpleRulePolicy(Policy):\n",
    "    def __init__(self):\n",
    "        Policy.__init__(self)\n",
    "        self.db = Database()\n",
    "\n",
    "    def predict(self, state):\n",
    "        self.results = []\n",
    "        system_action = defaultdict(list)\n",
    "        user_action = defaultdict(list)\n",
    "\n",
    "        for intent, domain, slot, value in state['user_action']:\n",
    "            user_action[(domain.lower(), intent.lower())].append((slot.lower(), value))\n",
    "\n",
    "        for user_act in user_action:\n",
    "            self.update_system_action(user_act, user_action, state, system_action)\n",
    "\n",
    "        # Reguła 3\n",
    "        if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['book stay', 'book day', 'book people']):\n",
    "            if self.results:\n",
    "                system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
    "\n",
    "        system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]\n",
    "        state['system_action'] = system_acts\n",
    "        return system_acts\n",
    "\n",
    "    def update_system_action(self, user_act, user_action, state, system_action):\n",
    "        domain, intent = user_act\n",
    "        constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()].items() if value != '']\n",
    "        self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
    "\n",
    "        # Reguła 1\n",
    "        if intent == 'request':\n",
    "            if len(self.results) == 0:\n",
    "                system_action[(domain, 'NoOffer')] = []\n",
    "            else:\n",
    "                for slot in user_action[user_act]:                  \n",
    "                    if slot[0] in self.results[0]:\n",
    "                        system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(slot[0], 'unknown')])\n",
    "\n",
    "        # Reguła 2\n",
    "        elif intent == 'inform':\n",
    "            if len(self.results) == 0:\n",
    "                system_action[(domain, 'NoOffer')] = []\n",
    "            else:\n",
    "                system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
    "                choice = self.results[0]\n",
    "\n",
    "                if domain in [\"hotel\", \"attraction\", \"police\", \"restaurant\"]:\n",
    "                    system_action[(domain, 'Recommend')].append(['Name', choice['name']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "089dbfa8-d34a-457c-9084-ef335372ea05",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:nlu info_dict is not initialized\n",
      "WARNING:root:dst info_dict is not initialized\n",
      "WARNING:root:policy info_dict is not initialized\n",
      "WARNING:root:nlg info_dict is not initialized\n"
     ]
    }
   ],
   "source": [
    "from convlab.dialog_agent import PipelineAgent\n",
    "dst.init_session()\n",
    "policy = SimpleRulePolicy()\n",
    "agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ac57cc8-6650-4a1b-a87e-2cda67d9b0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Inform', 'hotel', 'Choice', '3'],\n",
       " ['Recommend', 'hotel', 'Name', 'huntingdon marriott hotel']]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.response([['Inform', 'Hotel', 'Price Range', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eaeca7b0-08d5-4db0-9eb3-3aceda24f987",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:nlu info_dict is not initialized\n",
      "WARNING:root:dst info_dict is not initialized\n",
      "WARNING:root:policy info_dict is not initialized\n",
      "WARNING:root:nlg info_dict is not initialized\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLG seed 0\n"
     ]
    }
   ],
   "source": [
    "from convlab.base_models.t5.nlu import T5NLU\n",
    "from convlab.nlg.template.multiwoz import TemplateNLG\n",
    "\n",
    "# nlu =  T5NLU(speaker='user', context_window_size=0, model_name_or_path='ConvLab/t5-small-nlu-multiwoz21')\n",
    "nlu = NaturalLanguageAnalyzer()\n",
    "nlg = TemplateNLG(is_user=False)\n",
    "agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b559fcd3-861b-49d7-ac2b-d3160d4c5a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'We have 3 such places . Would huntingdon marriott hotel work for you ?'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.response(\"I need a cheap hotel with free parking .\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f831f56-10ba-40da-a89c-baeed37df81e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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