{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IFN Tutorial for Natural Language Processing\n",
    "\n",
    "by [*Kardi Teknomo*](https://people.revoledu.com/kardi/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IdealFlow.Text as ift     # import package.module as alias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence.\\nIt is concerned with the interactions between computers and human language. It can't be true human language, but it is closed enough, right?\\n\\nIn particular, NLP is used to program computers to process and analyze large amounts of natural language data.\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"\"\"Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence.\n",
    "It is concerned with the interactions between computers and human language. It can't be true human language, but it is closed enough, right?\n",
    "\n",
    "In particular, NLP is used to program computers to process and analyze large amounts of natural language data.\n",
    "\"\"\"\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:\n",
      " [['Natural', 'language', 'processing', '(', 'NLP', ')', 'is', 'a', 'subfield', 'of', 'linguistics', ',', 'computer', 'science', ',', 'and', 'artificial', 'intelligence', '.'], ['It', 'is', 'concerned', 'with', 'the', 'interactions', 'between', 'computers', 'and', 'human', 'language', '.'], ['It', \"can't\", 'be', 'true', 'human', 'language', ',', 'but', 'it', 'is', 'closed', 'enough', ',', 'right', '?'], ['In', 'particular', ',', 'NLP', 'is', 'used', 'to', 'program', 'computers', 'to', 'process', 'and', 'analyze', 'large', 'amounts', 'of', 'natural', 'language', 'data', '.']]\n",
      "y:\n",
      " ['my_text', 'my_text', 'my_text', 'my_text']\n",
      "accuracy =  1.0 \n",
      "\n",
      "\n",
      "lut= {'k1': 'Natural', 'az2': 'language', 'ci7': 'processing', 'aw9': '(', 'bd6': 'NLP', 'ca9': ')', 'ce3': 'is', 'ba3': 'a', 'cu9': 'subfield', 'bq9': 'of', 'cf4': 'linguistics', 'ck9': ',', 'r8': 'computer', 'ab8': 'science', 'aih18': 'and', 'p6': 'artificial', 'wr16': 'intelligence', 'av8': '.', 'zc79': 'It', 'x4': 'concerned', 'f': 'with', 'ads99': 'the', 'be7': 'interactions', 'ad0': 'between', 'i': 'computers', 'bx6': 'human', 'aaz28': \"can't\", 'br0': 'be', 'hwv028': 'true', 's9': 'but', 'g': 'it', 'bz8': 'closed', 'y5': 'enough', 'm3': 'right', 'b': '?', 'ah4': 'In', 'bc5': 'particular', 'glm057': 'used', 'aa7': 'to', 'pl28': 'program', 'agt78': 'process', 'ct8': 'analyze', 'c': 'large', 'oz16': 'amounts', 'bs1': 'natural', 'cp4': 'data'}\n",
      "network:\n",
      " {'my_text': my_text}\n",
      "  It is used to process and analyze large amounts of linguistics, right?  It can't be true human language data. Natural language data.  In particular, but it is used to program computers and artificial intelligence. Natural language. It can't be true human language data.   In particular, and artificial intelligence. It can't be true human language. In particular, NLP) is concerned with the interactions between computers and artificial intelligence. Natural language processing (NLP is used to process and artificial intelligence. \n",
      "\n",
      "query result:\n",
      " ('Natural language, NLP is used to process and artificial intelligence.', 0.016483516483516484)\n",
      "query result:\n",
      " ('In particular, right?', 0.014285714285714285)\n",
      "predict result ('my_text', 1.0)\n"
     ]
    }
   ],
   "source": [
    "tp=ift.NLP(\"my_text\")\n",
    "tp.text=text\n",
    "X,y=tp.prepareTextInput()\n",
    "print('X:\\n',X)\n",
    "print('y:\\n',y)\n",
    "print('accuracy = ',tp.fit(X, y),'\\n')\n",
    "lut=tp.lut\n",
    "print('\\nlut=',lut)\n",
    "tp.save()\n",
    "print('network:\\n',tp.IFNs)\n",
    "\n",
    "sentences=\"\"\n",
    "for i in range(15):\n",
    "    tr=tp.generate(\"my_text\")\n",
    "    sentence=tp.detokenize(tr)\n",
    "    sentences=sentences+\" \"+ sentence\n",
    "print(sentences,\"\\n\")\n",
    "search_text = \"NLP is used\"\n",
    "print(\"query result:\\n\",tp.query(search_text))\n",
    "print(\"query result:\\n\",tp.query(\"In particular\"))\n",
    "X=\"In particular, NLP is used to program\"\n",
    "print(\"predict result\",tp.predict_text_category(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paragraphs: [\"Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence.\\nIt is concerned with the interactions between computers and human language. It can't be true human language, but it is closed enough, right?\", 'In particular, NLP is used to program computers to process and analyze large amounts of natural language data.']\n",
      "Sentences: ['Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence.', 'It is concerned with the interactions between computers and human language.', \"It can't be true human language, but it is closed enough, right?\"]\n",
      "Tokens: ['Natural', 'language', 'processing', '(', 'NLP', ')', 'is', 'a', 'subfield', 'of', 'linguistics', ',', 'computer', 'science', ',', 'and', 'artificial', 'intelligence', '.']\n",
      "Filtered Tokens: ['Natural', 'language', 'processing', '(', 'NLP', ')', 'subfield', 'linguistics', ',', 'computer', 'science', ',', 'artificial', 'intelligence', '.']\n",
      "Bag of Words: {'Natural': 1, 'language': 1, 'processing': 1, '(': 1, 'NLP': 1, ')': 1, 'subfield': 1, 'linguistics': 1, ',': 2, 'computer': 1, 'science': 1, 'artificial': 1, 'intelligence': 1, '.': 1}\n",
      "Bigrams: [('Natural', 'language'), ('language', 'processing'), ('processing', '('), ('(', 'NLP'), ('NLP', ')'), (')', 'subfield'), ('subfield', 'linguistics'), ('linguistics', ','), (',', 'computer'), ('computer', 'science'), ('science', ','), (',', 'artificial'), ('artificial', 'intelligence'), ('intelligence', '.')]\n",
      "Average Sentence Length: 16.5\n",
      "Vocabulary Size: 0\n"
     ]
    }
   ],
   "source": [
    "tp=ift.NLP(\"new_text\")\n",
    "tp.text=text\n",
    "\n",
    "# Get paragraphs\n",
    "paragraphs = tp.get_paragraphs()\n",
    "print(\"Paragraphs:\", paragraphs)\n",
    "\n",
    "# Get sentences from the first paragraph\n",
    "sentences = tp.get_sentences(paragraphs[0])\n",
    "print(\"Sentences:\", sentences)\n",
    "\n",
    "# Tokenize the first sentence\n",
    "tokens = tp.tokenize(sentences[0])\n",
    "print(\"Tokens:\", tokens)\n",
    "\n",
    "# Remove stop words\n",
    "filtered_tokens = tp.remove_stopwords(tokens)\n",
    "print(\"Filtered Tokens:\", filtered_tokens)\n",
    "\n",
    "# Get bag of words\n",
    "bow = tp.bag_of_words(filtered_tokens)\n",
    "print(\"Bag of Words:\", bow)\n",
    "\n",
    "# Generate bigrams\n",
    "bigrams = tp.ngrams(filtered_tokens, n=2)\n",
    "print(\"Bigrams:\", bigrams)\n",
    "\n",
    "# Additional methods\n",
    "avg_sentence_length = tp.average_sentence_length()\n",
    "print(\"Average Sentence Length:\", avg_sentence_length)\n",
    "\n",
    "vocab_size = tp.vocabulary_size\n",
    "print(\"Vocabulary Size:\", vocab_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'entity': '@', 'type': 'EmotionEmoji'}, {'entity': 'D', 'type': 'EmotionEmoji'}, {'entity': 'jane.doe', 'type': 'DockerImageTag'}, {'entity': '5th', 'type': 'DockerImageTag'}, {'entity': 'o', 'type': 'EmotionEmoji'}, {'entity': 'm', 'type': 'EmotionEmoji'}, {'entity': 'c', 'type': 'EmotionEmoji'}, {'entity': 'p', 'type': 'EmotionEmoji'}, {'entity': 'h', 'type': 'EmotionEmoji'}, {'entity': 'e', 'type': 'EmotionEmoji'}, {'entity': 'on', 'type': 'DockerImageTag'}, {'entity': 'John', 'type': 'FirstName'}, {'entity': 'J', 'type': 'EmotionEmoji'}, {'entity': 'l', 'type': 'EmotionEmoji'}, {'entity': 'y', 'type': 'EmotionEmoji'}, {'entity': 'Doe sent', 'type': 'ScientificName'}, {'entity': 'an', 'type': 'DockerImageTag'}, {'entity': 'John Doe', 'type': 'Person'}, {'entity': 'd', 'type': 'EmotionEmoji'}, {'entity': 'John Doe', 'type': 'Location'}, {'entity': 'January', 'type': 'Person'}, {'entity': '5th', 'type': 'OrdinalNumber'}, {'entity': 'u', 'type': 'EmotionEmoji'}, {'entity': '5', 'type': 'EmotionEmoji'}, {'entity': 'January', 'type': 'Month'}, {'entity': 'example.com', 'type': 'URL'}, {'entity': 'i', 'type': 'EmotionEmoji'}, {'entity': 't', 'type': 'EmotionEmoji'}, {'entity': 'r', 'type': 'EmotionEmoji'}, {'entity': 'x', 'type': 'EmotionEmoji'}, {'entity': '@example.com', 'type': 'EmailDomain'}, {'entity': 'example.com', 'type': 'DockerImageTag'}, {'entity': 'email', 'type': 'DockerImageTag'}, {'entity': 'January', 'type': 'Location'}, {'entity': 'a', 'type': 'EmotionEmoji'}, {'entity': 'sent', 'type': 'DockerImageTag'}, {'entity': 'j', 'type': 'EmotionEmoji'}, {'entity': 'jane.doe@example.com', 'type': 'Email'}, {'entity': 'to', 'type': 'DockerImageTag'}, {'entity': 'n', 'type': 'EmotionEmoji'}, {'entity': 's', 'type': 'EmotionEmoji'}, {'entity': 'jane.doe', 'type': 'URL'}]\n",
      "Input Text: Book a flight to New York on September 21st at 5:00 PM\n",
      "Predicted Intent: schedule_meeting\n",
      "Identified Entities: [{'entity': 'at 5:00 PM', 'type': 'TimeExpressionSpecific'}, {'entity': 'September', 'type': 'Month'}, {'entity': 'flight', 'type': 'DockerImageTag'}, {'entity': 'September', 'type': 'Location'}, {'entity': 'o', 'type': 'EmotionEmoji'}, {'entity': 'Y', 'type': 'EmotionEmoji'}, {'entity': 'm', 'type': 'EmotionEmoji'}, {'entity': 's', 'type': 'EmotionEmoji'}, {'entity': '2', 'type': 'EmotionEmoji'}, {'entity': 'a', 'type': 'DockerImageTag'}, {'entity': '5:00 PM', 'type': 'Time'}, {'entity': 'h', 'type': 'EmotionEmoji'}, {'entity': 'e', 'type': 'EmotionEmoji'}, {'entity': 'on', 'type': 'DockerImageTag'}, {'entity': 'w', 'type': 'EmotionEmoji'}, {'entity': 'York on', 'type': 'ScientificName'}, {'entity': 'l', 'type': 'EmotionEmoji'}, {'entity': '21st', 'type': 'OrdinalNumber'}, {'entity': '5', 'type': 'ComputerPortNumber'}, {'entity': 'PM', 'type': 'ChemicalFormula'}, {'entity': '00', 'type': 'DockerImageTag'}, {'entity': 'Book', 'type': 'Person'}, {'entity': 'Book a', 'type': 'ScientificName'}, {'entity': 'New York', 'type': 'Person'}, {'entity': 'b', 'type': 'EmotionEmoji'}, {'entity': 'Book', 'type': 'Location'}, {'entity': '5', 'type': 'DockerImageTag'}, {'entity': 'P', 'type': 'EmotionEmoji'}, {'entity': '5', 'type': 'EmotionEmoji'}, {'entity': 'New York', 'type': 'Location'}, {'entity': ':', 'type': 'EmotionEmoji'}, {'entity': 'S', 'type': 'EmotionEmoji'}, {'entity': 'i', 'type': 'EmotionEmoji'}, {'entity': 'N', 'type': 'EmotionEmoji'}, {'entity': '5:00 PM', 'type': 'Time12Hour'}, {'entity': 't', 'type': 'EmotionEmoji'}, {'entity': 'r', 'type': 'EmotionEmoji'}, {'entity': '21st', 'type': 'DockerImageTag'}, {'entity': 'at', 'type': 'DockerImageTag'}, {'entity': 'k', 'type': 'EmotionEmoji'}, {'entity': 'a', 'type': 'EmotionEmoji'}, {'entity': 'g', 'type': 'EmotionEmoji'}, {'entity': '1', 'type': 'EmotionEmoji'}, {'entity': '00', 'type': 'ComputerPortNumber'}, {'entity': 'f', 'type': 'EmotionEmoji'}, {'entity': 'M', 'type': 'EmotionEmoji'}, {'entity': '0', 'type': 'EmotionEmoji'}, {'entity': 'to', 'type': 'DockerImageTag'}, {'entity': 'September', 'type': 'Person'}, {'entity': 'n', 'type': 'EmotionEmoji'}, {'entity': 'p', 'type': 'EmotionEmoji'}, {'entity': 'PM', 'type': 'StockTicker'}, {'entity': 'B', 'type': 'EmotionEmoji'}]\n",
      "Evaluation Metrics: {'accuracy': 1.0}\n",
      "Parsed Sentence: {'subject': ['John', 'reads', 'books', '.']}\n",
      "Intents saved successfully.\n",
      "Entity patterns saved successfully.\n",
      "Intents saved successfully.\n",
      "Intents saved successfully.\n",
      "Input Text: Schedule a meeting with Bob on October 10th\n",
      "Predicted Intent: schedule_meeting\n",
      "Identified Entities: [{'entity': 'with', 'type': 'DockerImageTag'}, {'entity': 'Schedule a', 'type': 'ScientificName'}, {'entity': 'O', 'type': 'EmotionEmoji'}, {'entity': 'o', 'type': 'EmotionEmoji'}, {'entity': 'm', 'type': 'EmotionEmoji'}, {'entity': 'c', 'type': 'EmotionEmoji'}, {'entity': 'a', 'type': 'DockerImageTag'}, {'entity': 'h', 'type': 'EmotionEmoji'}, {'entity': 'e', 'type': 'EmotionEmoji'}, {'entity': '10th', 'type': 'OrdinalNumber'}, {'entity': 'on', 'type': 'DockerImageTag'}, {'entity': 'w', 'type': 'EmotionEmoji'}, {'entity': 'l', 'type': 'EmotionEmoji'}, {'entity': 'meeting', 'type': 'DockerImageTag'}, {'entity': 'Bob', 'type': 'Person'}, {'entity': 'b', 'type': 'EmotionEmoji'}, {'entity': 'd', 'type': 'EmotionEmoji'}, {'entity': 'Schedule', 'type': 'Person'}, {'entity': 'Bob', 'type': 'Location'}, {'entity': '10th', 'type': 'DockerImageTag'}, {'entity': 'u', 'type': 'EmotionEmoji'}, {'entity': 'S', 'type': 'EmotionEmoji'}, {'entity': 'i', 'type': 'EmotionEmoji'}, {'entity': 'Schedule', 'type': 'Location'}, {'entity': 't', 'type': 'EmotionEmoji'}, {'entity': 'r', 'type': 'EmotionEmoji'}, {'entity': 'Bob on', 'type': 'ScientificName'}, {'entity': 'a', 'type': 'EmotionEmoji'}, {'entity': 'g', 'type': 'EmotionEmoji'}, {'entity': '1', 'type': 'EmotionEmoji'}, {'entity': 'October', 'type': 'Person'}, {'entity': '0', 'type': 'EmotionEmoji'}, {'entity': 'October', 'type': 'Month'}, {'entity': 'n', 'type': 'EmotionEmoji'}, {'entity': 'October', 'type': 'Location'}, {'entity': 'B', 'type': 'EmotionEmoji'}]\n",
      "Parsed Sentence: {'subject': ['Alice', 'sends', 'email', '.']}\n"
     ]
    }
   ],
   "source": [
    "# start new object\n",
    "tp=ift.NLP(\"entity and intent recognition\")\n",
    "text = \"John Doe sent an email to jane.doe@example.com on January 5th.\"\n",
    "entities = tp.entity_recognition(text)\n",
    "print(entities)\n",
    "\n",
    "# Predict intent and entities\n",
    "input_text = \"Book a flight to New York on September 21st at 5:00 PM\"\n",
    "recognition = tp.recognize(input_text)\n",
    "print(\"Input Text:\", input_text)\n",
    "print(\"Predicted Intent:\", recognition['intent'])\n",
    "print(\"Identified Entities:\", recognition['entities'])\n",
    "\n",
    "# Test data for evaluation\n",
    "test_data = [\n",
    "{\"text\": \"Book a flight to Paris\", \"intent\": \"book_flight\"},\n",
    "{\"text\": \"What's the weather like today?\", \"intent\": \"weather_query\"},\n",
    "{\"text\": \"Hi there!\", \"intent\": \"greeting\"},\n",
    "{\"text\": \"Order a pizza for delivery\", \"intent\": \"order_food\"}        \n",
    "]\n",
    "\n",
    "# Evaluate the model\n",
    "metrics = tp.evaluate(test_data)\n",
    "print(\"Evaluation Metrics:\", metrics)\n",
    "\n",
    "sentence = \"John reads books.\"\n",
    "parsed = tp.parse_sentence(sentence)\n",
    "print(\"Parsed Sentence:\", parsed)\n",
    "\n",
    "# Add a new intent\n",
    "tp.add_intent('schedule_meeting', ['schedule', 'meeting', 'appointment'])\n",
    "\n",
    "# Add a new entity pattern\n",
    "tp.add_entity_pattern('Email', r'\\b[\\w.-]+@[\\w.-]+\\.\\w{2,4}\\b', \"Matches email addresses\")\n",
    "\n",
    "# Update model with new training data\n",
    "new_data = [\n",
    "{\"text\": \"Schedule a meeting with Alice\", \"intent\": \"schedule_meeting\"},\n",
    "{\"text\": \"I have an appointment at 3 PM\", \"intent\": \"schedule_meeting\"}\n",
    "]\n",
    "tp.update_model(new_data)\n",
    "\n",
    "# Save patterns    \n",
    "tp.save_intent()\n",
    "\n",
    "# Predict intent and entities\n",
    "input_text = \"Schedule a meeting with Bob on October 10th\"\n",
    "recognition = tp.recognize(input_text)\n",
    "print(\"Input Text:\", input_text)\n",
    "print(\"Predicted Intent:\", recognition['intent'])\n",
    "print(\"Identified Entities:\", recognition['entities'])\n",
    "\n",
    "# Parse a sentence\n",
    "parsed = tp.parse_sentence(\"Alice sends an email.\")\n",
    "print(\"Parsed Sentence:\", parsed)"
   ]
  }
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