{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\macty\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow_addons\\utils\\tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow_addons as tfa\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\macty\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "c:\\Users\\macty\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\generation_utils.py:24: FutureWarning: Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\macty\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\generation_tf_utils.py:24: FutureWarning: Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.\n",
      "  warnings.warn(\n",
      "loading file vocab.txt from cache at C:\\Users\\macty/.cache\\huggingface\\hub\\models--dkleczek--bert-base-polish-uncased-v1\\snapshots\\62be9821055981deafb23f217b68cc41f38cdb76\\vocab.txt\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at C:\\Users\\macty/.cache\\huggingface\\hub\\models--dkleczek--bert-base-polish-uncased-v1\\snapshots\\62be9821055981deafb23f217b68cc41f38cdb76\\special_tokens_map.json\n",
      "loading file tokenizer_config.json from cache at C:\\Users\\macty/.cache\\huggingface\\hub\\models--dkleczek--bert-base-polish-uncased-v1\\snapshots\\62be9821055981deafb23f217b68cc41f38cdb76\\tokenizer_config.json\n",
      "loading configuration file config.json from cache at C:\\Users\\macty/.cache\\huggingface\\hub\\models--dkleczek--bert-base-polish-uncased-v1\\snapshots\\62be9821055981deafb23f217b68cc41f38cdb76\\config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"dkleczek/bert-base-polish-uncased-v1\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\",\n",
      "    \"BertForPreTraining\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.28.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 60000\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loaded_model = tf.keras.models.load_model('model')\n",
    "from transformers import *\n",
    "tokenizer = BertTokenizer.from_pretrained(\"dkleczek/bert-base-polish-uncased-v1\")"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NLU:\n",
    "    def __init__(self, text: str):\n",
    "        self.text = text\n",
    "        self.act = \"\"\n",
    "\n",
    "    def get_dialog_act(self): \n",
    "        predicted_classes_names=[]\n",
    "        input = [self.text]\n",
    "        encoded_input = tokenizer.batch_encode_plus(input, padding=True, truncation=True, return_tensors='tf')\n",
    "        dataset = tf.data.Dataset.from_tensor_slices({\n",
    "        'input_ids': encoded_input['input_ids'],\n",
    "        'attention_mask': encoded_input['attention_mask'],\n",
    "        'token_type_ids': encoded_input['token_type_ids']\n",
    "        }).batch(2)\n",
    "        predictions = loaded_model.predict(dataset)\n",
    "        classes = [\"ack\",\"affirm\",\"bye\",\"hello\",\"help\",\"negate\",\"null\",\"repeat\",\"reqalts\",\"reqmore\",\"restart\",\"silence\",\"thankyou\",\"confirm\",\"deny\",\"inform\",\"request\"]\n",
    "        for prediction in predictions: #trying to get predictions, if none it take maximum\n",
    "            predicted_classes = (predictions[prediction]> 0.5).astype(\"int32\")\n",
    "            if predicted_classes.sum()==0:\n",
    "                predicted_classes=max(predictions[prediction])\n",
    "        predicted_classes_indexes= np.where(predicted_classes==1)[1]\n",
    "        for p_classes in predicted_classes_indexes:\n",
    "            predicted_classes_names.append(classes[p_classes])\n",
    "            self.act=predicted_classes_names\n",
    "            return self.act\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 58ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['request']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu = NLU(\"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": 18,
   "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: NLU):\n",
    "        self.slots = []\n",
    "        self.act = nlu\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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['room']"
      ]
     },
     "execution_count": 19,
     "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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