{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ASR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def asr(inputText: str) -> str:\n",
    "    # Do something\n",
    "    inputText\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NaturalLanguageUnderstanding:\n",
    "    acts: dict[list[str], str] = {\n",
    "        ( \"potwierdzam\", \"dobrze\", \"ok\" ): \"ack\",\n",
    "        (\"do widziena\", \"czesc\", \"koniec\", \"do zobaczenia\"): \"bye\",\n",
    "        (\"cześć\", \"dzień dobry\", \"hello\", \"hej\"): \"hello\",\n",
    "        (\"pomóc\", \"pomocy\", \"pomoc\"): \"help\",\n",
    "        (\"zaprzeczam\", \"odrzucam\"): \"negate\",\n",
    "        (\"alternatywny\", \"inne\", \"alternatywa\", \"inna\"): \"requalts\",\n",
    "        (\"szczegółów\", \"informacji\", \"info\", \"informacje\"): \"reqmore\",\n",
    "        (\"restart\"): \"restart\",\n",
    "        (\"dziękuję\", \"dzięki\"): \"thankyou\",\n",
    "        (\"tak\", \"chcę\"): \"confirm\",\n",
    "        (\"nie chce\"): \"deny\",\n",
    "        (\"basen\", \"parking\", \"śniadania\", \"osoby\"): \"inform\",\n",
    "        (\"jaki\",\"?\", \"czy\", \"jak\", \"ile\", \"co\", \"gdzie\"): \"request\"\n",
    "    }\n",
    "    def __init__(self, text: str):\n",
    "        self.text = text\n",
    "        self.act = \"\"\n",
    "        \n",
    "        \n",
    "    def get_dialog_act(self): \n",
    "        for word in self.text.lower().split():\n",
    "            for key in NaturalLanguageUnderstanding.acts:\n",
    "                if word in key:\n",
    "                    self.act = NaturalLanguageUnderstanding.acts[key]\n",
    "                    return\n",
    "        self.act = \"null\"\n",
    "            \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'request'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu = NaturalLanguageUnderstanding(\"Jaki pokój proponujesz w tym hotelu?\")\n",
    "nlu.get_dialog_act()\n",
    "nlu.act"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DialogueStateTracker:\n",
    "    \n",
    "    slots_dict: dict[tuple[str], str] = {\n",
    "        (\"osoby\", \"ludzie\", \"osób\", \"osobowy\"): \"people\",\n",
    "        (\"miasto\", \"miasta\", \"miejsowość\", \"poznań\", \"warszawa\", \"warszawie\", \"poznaniu\", \"kraków\", \"krakowie\"): \"city\",\n",
    "        (\"basen\", \"parking\", \"śniadania\"): \"facilities\",\n",
    "        (\"data\", \"datę\"): \"date\",\n",
    "        (\"pokój\", \"pokoje\"): \"room\"\n",
    "    }\n",
    "    \n",
    "    def __init__(self, nlu: NaturalLanguageUnderstanding):\n",
    "        self.slots = []\n",
    "        self.act = nlu.act\n",
    "        self.text = nlu.text\n",
    "    \n",
    "    def get_dialog_slots(self):\n",
    "        for word in self.text.lower().split():\n",
    "            for key in DialogueStateTracker.slots_dict:\n",
    "                if word in key:\n",
    "                    self.slots.append(DialogueStateTracker.slots_dict[key])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['room']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst: DialogueStateTracker = DialogueStateTracker(nlu)\n",
    "dst.get_dialog_slots()\n",
    "dst.slots\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dialogue Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DialoguePolicy:\n",
    "    user_act_to_system_act_dict: dict[str, str] = {\n",
    "        \"ack\": \"reqmore\",\n",
    "        \"bye\": \"bye\",\n",
    "        \"hello\": \"welcomemsg\",\n",
    "        \"help\": \"inform\",\n",
    "        \"negate\": \"offer\",\n",
    "        \"requalts\": \"offer\",\n",
    "        \"reqmore\": \"inform\",\n",
    "        \"restart\": \"welcomemsg\",\n",
    "        \"thankyou\": \"reqmore\",\n",
    "        \"confirm\": \"reqmore\",\n",
    "        \"deny\": \"offer\",\n",
    "        \"inform\": \"offer\",\n",
    "        \"request\": \"inform\",\n",
    "        \"null\": \"null\"\n",
    "    }\n",
    "    \n",
    "    def __init__(self, dst: DialogueStateTracker):\n",
    "        self.user_text = dst.text\n",
    "        self.user_act = dst.act\n",
    "        self.user_slots = dst.slots\n",
    "        self.system_act = \"\"\n",
    "    \n",
    "    def get_system_act(self):\n",
    "        self.system_act = DialoguePolicy.user_act_to_system_act_dict[self.user_act]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'inform'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp: DialoguePolicy = DialoguePolicy(dst)\n",
    "dp.get_system_act()\n",
    "dp.system_act"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NaturalLanguageGeneration:\n",
    "    system_act_to_text = {\n",
    "        \"reqmore\": \"Informuje więcej o \",\n",
    "        \"bye\": \"Do widzenia\",\n",
    "        \"welcomemsg\": \"Witaj w systemie rezerwacji hotelowych. W czym mogę pomóc?\",\n",
    "        \"inform\": \"Informuje cię o \",\n",
    "        \"offer\": \"Co myślisz o hotlu z \",\n",
    "        \"reqmore\": \"Czy mogę jeszcze jakoś Ci pomóc?\",\n",
    "        \"null\": \"\"\n",
    "    }\n",
    "    user_slots_to_text = {\n",
    "        \"people\": \"pojemności pokoju\",\n",
    "        \"city\": \"mieście\",\n",
    "        \"facilities\": \"udogodnieniach\",\n",
    "        \"date\": \"dacie\",\n",
    "        \"room\": \"pokoju\"\n",
    "    }\n",
    "    \n",
    "    def __init__(self, dp: DialoguePolicy):\n",
    "        self.user_text = dp.user_text\n",
    "        self.user_act = dp.user_act\n",
    "        self.user_slots = dp.user_slots\n",
    "        self.system_act = dp.system_act\n",
    "        self.system_text = \"\"\n",
    "    \n",
    "    def generate_system_text(self):\n",
    "        text: str = NaturalLanguageGeneration.system_act_to_text[self.system_act]\n",
    "        slots_transformed = [NaturalLanguageGeneration.user_slots_to_text[slot] for slot in self.user_slots]\n",
    "        self.system_text = text + \" i \".join(slots_transformed)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Informuje cię o pokoju'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlg: NaturalLanguageGeneration = NaturalLanguageGeneration(dp)\n",
    "nlg.generate_system_text()\n",
    "nlg.system_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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