next_word_prediction_using_cnn(GPT).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7uOCNWBm1sd2"
   },
   "source": [
    "ML Phases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Eqe7dl21yeM"
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9ZzoeHf3izj"
   },
   "source": [
    "### Importing library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
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      "Requirement already satisfied: packaging in c:\\users\\chinnu\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (24.0)\n",
      "Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow-intel==2.17.0->tensorflow)\n",
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      "Installing collected packages: namex, libclang, flatbuffers, wrapt, termcolor, tensorboard-data-server, protobuf, optree, opt-einsum, ml-dtypes, mdurl, h5py, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, markdown-it-py, rich, keras, tensorflow-intel, tensorflow\n",
      "Successfully installed absl-py-2.1.0 astunparse-1.6.3 flatbuffers-24.3.25 gast-0.6.0 google-pasta-0.2.0 grpcio-1.66.1 h5py-3.11.0 keras-3.5.0 libclang-18.1.1 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.4.0 namex-0.0.8 opt-einsum-3.3.0 optree-0.12.1 protobuf-4.25.4 rich-13.8.0 tensorboard-2.17.1 tensorboard-data-server-0.7.2 tensorflow-2.17.0 tensorflow-intel-2.17.0 termcolor-2.4.0 wrapt-1.16.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 6399,
     "status": "ok",
     "timestamp": 1724612400895,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "EBF6RHYJ-Fjs"
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Traceback (most recent call last):\n  File \"c:\\Users\\Chinnu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 70, in <module>\n    from tensorflow.python._pywrap_tensorflow_internal import *\nImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.\n\n\nFailed to load the native TensorFlow runtime.\nSee https://www.tensorflow.org/install/errors for some common causes and solutions.\nIf you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Chinnu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py:70\u001b[0m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 70\u001b[0m   \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pywrap_tensorflow_internal\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;66;03m# This try catch logic is because there is no bazel equivalent for py_extension.\u001b[39;00m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;66;03m# Externally in opensource we must enable exceptions to load the shared object\u001b[39;00m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;66;03m# by exposing the PyInit symbols with pybind. This error will only be\u001b[39;00m\n\u001b[0;32m     74\u001b[0m \u001b[38;5;66;03m# caught internally or if someone changes the name of the target _pywrap_tensorflow_internal.\u001b[39;00m\n\u001b[0;32m     75\u001b[0m \n\u001b[0;32m     76\u001b[0m \u001b[38;5;66;03m# This logic is used in other internal projects using py_extension.\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tokenizer\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Embedding, LSTM, Dense\n",
      "File \u001b[1;32mc:\\Users\\Chinnu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\__init__.py:38\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m     37\u001b[0m \u001b[38;5;66;03m# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596\u001b[39;00m\n\u001b[1;32m---> 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pywrap_tensorflow \u001b[38;5;28;01mas\u001b[39;00m _pywrap_tensorflow  \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m module_util \u001b[38;5;28;01mas\u001b[39;00m _module_util\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlazy_loader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasLazyLoader \u001b[38;5;28;01mas\u001b[39;00m _KerasLazyLoader\n",
      "File \u001b[1;32mc:\\Users\\Chinnu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py:85\u001b[0m\n\u001b[0;32m     83\u001b[0m     sys\u001b[38;5;241m.\u001b[39msetdlopenflags(_default_dlopen_flags)\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m---> 85\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m     86\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtraceback\u001b[38;5;241m.\u001b[39mformat_exc()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     87\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mFailed to load the native TensorFlow runtime.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     88\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee https://www.tensorflow.org/install/errors \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     89\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor some common causes and solutions.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     90\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIf you need help, create an issue \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     91\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mat https://github.com/tensorflow/tensorflow/issues \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     92\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand include the entire stack trace above this error message.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: Traceback (most recent call last):\n  File \"c:\\Users\\Chinnu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 70, in <module>\n    from tensorflow.python._pywrap_tensorflow_internal import *\nImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.\n\n\nFailed to load the native TensorFlow runtime.\nSee https://www.tensorflow.org/install/errors for some common causes and solutions.\nIf you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message."
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import pickle\n",
    "import re\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1guTtKui3X-y"
   },
   "source": [
    "### Data Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 706,
     "status": "ok",
     "timestamp": 1724612427128,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "sqjx8QAo7M17"
   },
   "outputs": [],
   "source": [
    "data  = open('/content/drive/MyDrive/ML Docs/ML Projects/next-word-prediction/data/nextWordPrediction.txt', 'r', encoding = \"utf8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u9f5f5yt2GZm"
   },
   "source": [
    "### EDA (evaluation Data Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O5_Ap5mA2w9O"
   },
   "source": [
    "### missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QJwcqvgr22Nv"
   },
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 755,
     "status": "ok",
     "timestamp": 1724612433601,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "ufbolqS2IIK1"
   },
   "outputs": [],
   "source": [
    "lines=[]\n",
    "for i in data:\n",
    "    lines.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 8949,
     "status": "ok",
     "timestamp": 1724612443208,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "FyVY4vQ9I7wP"
   },
   "outputs": [],
   "source": [
    "data=\"\"\n",
    "for i in lines:\n",
    "    data = ' '.join(lines)\n",
    "data = data.replace('\\n', '').replace('\\r', '').replace('\\ufeff', '').replace('“','').replace('”','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1724612443887,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "JfsTQ-CPMglJ"
   },
   "outputs": [],
   "source": [
    "# Remove punctuation and special characters\n",
    "# data = re.sub(r'[^\\w\\s]', '', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1724612443888,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "Itm36_ZSLY6m"
   },
   "outputs": [],
   "source": [
    "data = data.split()\n",
    "data=' '.join(data)\n",
    "\n",
    "# Convert text to lowercase\n",
    "data = data.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1724612443889,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "AqTvORmKLd_0",
    "outputId": "ec84b900-ad9f-4fca-8cdc-37ef84db64b2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"project gutenberg's the adventures of sherlock holmes, by arthur conan doyle this ebook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. you may copy it, give it away or re-use it under the terms of the project gutenberg license included with this ebook or online at www.gutenberg.net title: the adventures of sherlock holmes author: arthur conan doyle release date: november 29, 2002 [ebook #1661] last updated: may 20, 2019 language: english character set encoding: utf-8 *** start of this project gutenberg ebook the adventures of sherlock holmes *** produced by an anonymous project gutenberg volunteer and jose menendez cover the adventures of sherlock holmes by arthur conan doyle contents i. a scandal in bohemia ii. the red-headed league iii. a case of identity iv. the boscombe valley mystery v. the five orange pips vi. the man with the twisted lip vii. the adventure of the blue carbuncle viii. the adventure of the speckled band ix. the adventure o\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ia2z17oQaCY"
   },
   "source": [
    "### Tokenizer Initialization Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1724612445127,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "wSsn3vijQi4g"
   },
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts([data])\n",
    "\n",
    "# Save the tokenizer to a file\n",
    "pickle.dump(tokenizer, open('token.pkl', 'wb'))\n",
    "\n",
    "sequences = tokenizer.texts_to_sequences([data])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1724612445133,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "Bi2rBZFuZZ2_"
   },
   "outputs": [],
   "source": [
    "# len(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1724612445134,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "-9wVJYK5ZgJE",
    "outputId": "0584c699-ce40-4a70-bc9b-0e3455fb88f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8624"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_size = len(tokenizer.word_index)+1\n",
    "vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 657,
     "status": "ok",
     "timestamp": 1724612482512,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "DMELc8kacMbU"
   },
   "outputs": [],
   "source": [
    "sequence_length = 5  # Set the length of your input sequence\n",
    "input_sequences = []\n",
    "output_words = []\n",
    "\n",
    "for i in range(sequence_length, len(sequences)):\n",
    "    input_sequences.append(sequences[i-sequence_length:i])\n",
    "    output_words.append(sequences[i])\n",
    "\n",
    "input_sequences = np.array(input_sequences)\n",
    "output_words = np.array(output_words)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1724612483816,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "R-aPZ6ioyHrX"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "max_len = max([len(seq) for seq in input_sequences])\n",
    "input_sequences = pad_sequences(input_sequences, maxlen=max_len, padding='pre')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EttS03nU29ih"
   },
   "source": [
    "### Handling outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MHG80vuT3D6a"
   },
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JGiLeziw3MBz"
   },
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HW_T4jbz3Rs2"
   },
   "source": [
    "### Train and split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 1271,
     "status": "ok",
     "timestamp": 1724612535463,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "3YMXB0dA6tRH"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(input_sequences, output_words, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nK7U73kO6kA7"
   },
   "source": [
    "### Model initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 453,
     "status": "ok",
     "timestamp": 1724612571701,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "QPwAIN1Mz8DR"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, Conv1D, MaxPooling1D, Flatten, Dense\n",
    "from keras.optimizers import Adam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 448,
     "status": "ok",
     "timestamp": 1724612590202,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "Q2i24LWm0BMS"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 761,
     "status": "ok",
     "timestamp": 1724612603379,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "s9SupiBW686o",
    "outputId": "8d310721-9b0e-4da1-a65c-cc470df018dd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "vocab_size = len(tokenizer.word_index) + 1  # Vocabulary size from the tokenizer\n",
    "embedding_dim = 100  # You can adjust this value based on your preference\n",
    "\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_len))\n",
    "model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(vocab_size, activation='softmax'))  # Softmax activation for multi-class classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 589,
     "status": "ok",
     "timestamp": 1724612615208,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "WWEkjW_U7AyS"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "afmxPtDY6qpw"
   },
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 821594,
     "status": "ok",
     "timestamp": 1724613453080,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "4Hhsg0Gv7E3q",
    "outputId": "f35ca75b-7140-4deb-fd17-15c1c4f52cca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 48ms/step - accuracy: 0.0532 - loss: 6.9604 - val_accuracy: 0.0584 - val_loss: 6.5705\n",
      "Epoch 2/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 54ms/step - accuracy: 0.0638 - loss: 6.3638 - val_accuracy: 0.0634 - val_loss: 6.5827\n",
      "Epoch 3/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 50ms/step - accuracy: 0.0740 - loss: 6.1081 - val_accuracy: 0.0699 - val_loss: 6.6176\n",
      "Epoch 4/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 49ms/step - accuracy: 0.0904 - loss: 5.7695 - val_accuracy: 0.0720 - val_loss: 6.7875\n",
      "Epoch 5/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 49ms/step - accuracy: 0.1083 - loss: 5.4411 - val_accuracy: 0.0706 - val_loss: 7.0881\n",
      "Epoch 6/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 50ms/step - accuracy: 0.1317 - loss: 5.1118 - val_accuracy: 0.0648 - val_loss: 7.4190\n",
      "Epoch 7/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 48ms/step - accuracy: 0.1616 - loss: 4.7513 - val_accuracy: 0.0616 - val_loss: 7.9185\n",
      "Epoch 8/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 49ms/step - accuracy: 0.1984 - loss: 4.3553 - val_accuracy: 0.0541 - val_loss: 8.6172\n",
      "Epoch 9/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 49ms/step - accuracy: 0.2386 - loss: 3.9258 - val_accuracy: 0.0492 - val_loss: 9.5089\n",
      "Epoch 10/10\n",
      "\u001b[1m1362/1362\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 52ms/step - accuracy: 0.3003 - loss: 3.4616 - val_accuracy: 0.0491 - val_loss: 10.9259\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7964c14d6410>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=64)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qE0OuHgd6wXh"
   },
   "source": [
    "### Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qv3Un6kR68wq"
   },
   "source": [
    "## Evaluation and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9916,
     "status": "ok",
     "timestamp": 1724613462969,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "tIVVYEym7MEy",
    "outputId": "65c525f6-0a6c-44f1-cce7-143907125d83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m681/681\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 13ms/step - accuracy: 0.0502 - loss: 10.9124\n",
      "Validation Accuracy: 4.91%\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_val, y_val)\n",
    "print(f\"Validation Accuracy: {accuracy * 100:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UffaLX5p7VXT"
   },
   "source": [
    "## Using the Model to Predict the Next Word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 580,
     "status": "ok",
     "timestamp": 1724613790585,
     "user": {
      "displayName": "Chinmai Sagar",
      "userId": "11326853931051596406"
     },
     "user_tz": -330
    },
    "id": "re1i73jt7cgP",
    "outputId": "bee4f452-3d2e-42db-b5b4-b61810acb6a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step\n",
      "Next word prediction: sherlock\n"
     ]
    }
   ],
   "source": [
    "def predict_next_word(model, tokenizer, text_seq, max_len):\n",
    "    sequence = tokenizer.texts_to_sequences([text_seq])[0]\n",
    "    sequence = pad_sequences([sequence], maxlen=max_len, padding='pre')\n",
    "    predicted = model.predict(sequence)\n",
    "    predicted_word = tokenizer.index_word[np.argmax(predicted)]\n",
    "    return predicted_word\n",
    "\n",
    "text_seq = \"The Adventures of\"\n",
    "next_word = predict_next_word(model, tokenizer, text_seq, max_len)\n",
    "print(f\"Next word prediction: {next_word}\")\n"
   ]
  }
 ],
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