{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "25a0cd07",
   "metadata": {},
   "source": [
    "# Text Sentiment Analysis of IMDB Movie reviews using NLP (Word2Vec and RNN) for MYM Intern Assesment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a6ca3e39",
   "metadata": {},
   "source": [
    "## Assesment Objectives: \n",
    "### - Preprocessing the data\n",
    "### - Converting Text(words) to Vectors using word2vec \n",
    "### - Using the word representations given by word2vec to feed a RNN and training the model\n",
    "### - Evaluating the model and plotting the performance graphs\n",
    "### - Improving the model by Transfer Learning\n",
    "### - Comparing Accuracy of Baseline model, The model and Improved model.\n",
    "### - Testing the model (predicting the model with new review)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84781652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gensim==4.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip freeze | grep gensim ##Checking the version of Gensim - Word2Vec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12e2dbaa",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b25eb77",
   "metadata": {},
   "source": [
    "### Starting with 20% of the sentences from TensorFlow Datasets of IMDB reviews to check the RAM compatibility of the PC to train the model faster by splitting the datasets as X_train, y_train, X_test and y_test.\n",
    "### Then preprocessing the textual data to create input features for a natural language processing (NLP) model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2079b965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03ff9b6f48284e698019be61052b6fca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "702a447341f64056b5d9f1670a734acc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Size...: 0 MiB [00:00, ? MiB/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e827a156b5f74ba585d28b3478514fa6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b229ce55f92444b93332f6bacbd9b01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d9bc2219dd44947b60b9bb1e5dcb6d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteFINYQS/imdb_reviews-train.tfrecord*...…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d27e2b2af9642139adc2757c6383b28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d620bcefb75a475c9f02583245e1a741",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteFINYQS/imdb_reviews-test.tfrecord*...:…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e204ca1418343aa870f50a9862a20e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating unsupervised examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba05a0bba6024c00b688491622993a22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteFINYQS/imdb_reviews-unsupervised.tfrec…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset imdb_reviews downloaded and prepared to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-05 07:38:22.564741: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow.keras.preprocessing.text import text_to_word_sequence\n",
    "\n",
    "def load_data(percentage_of_sentences=None):\n",
    "    train_data, test_data = tfds.load(name=\"imdb_reviews\", split=[\"train\", \"test\"], batch_size=-1, as_supervised=True)\n",
    "\n",
    "    train_sentences, y_train = tfds.as_numpy(train_data)\n",
    "    test_sentences, y_test = tfds.as_numpy(test_data)\n",
    "    \n",
    "    # Take only a given percentage of the entire data\n",
    "    if percentage_of_sentences is not None:\n",
    "      assert(percentage_of_sentences> 0 and percentage_of_sentences<=100)\n",
    "        \n",
    "      len_train = int(percentage_of_sentences/100*len(train_sentences))\n",
    "      train_sentences, y_train = train_sentences[:len_train], y_train[:len_train]\n",
    "  \n",
    "      len_test = int(percentage_of_sentences/100*len(test_sentences))\n",
    "      test_sentences, y_test = test_sentences[:len_test], y_test[:len_test]\n",
    "    \n",
    "    X_train = [text_to_word_sequence(_.decode(\"utf-8\")) for _ in train_sentences]\n",
    "    X_test = [text_to_word_sequence(_.decode(\"utf-8\")) for _ in test_sentences]\n",
    "    \n",
    "    return X_train, y_train, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_data(percentage_of_sentences=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4352850a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d707253",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fdffccea",
   "metadata": {},
   "source": [
    "## First, training a word2vec model (with the arguments that we want) on your training sentence. Store it into the `word2vec` variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5c2e1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "word2vec = Word2Vec(sentences=X_train, vector_size=60, min_count=10, window=10)\n",
    "word2vec.save(\"word2vec.model\")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81a82d8e",
   "metadata": {},
   "source": [
    "## Embedding the training and test sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62a835d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "\n",
    "# Function to convert a sentence (list of words) into a matrix representing the words in the embedding space\n",
    "def embed_sentence(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec.wv:\n",
    "            embedded_sentence.append(word2vec.wv[word])\n",
    "        \n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Function that converts a list of sentences into a list of matrices\n",
    "def embedding(word2vec, sentences):\n",
    "    embed = []\n",
    "    \n",
    "    for sentence in sentences:\n",
    "        embedded_sentence = embed_sentence(word2vec, sentence)\n",
    "        embed.append(embedded_sentence)\n",
    "        \n",
    "    return embed\n",
    "\n",
    "# Embed the training and test sentences\n",
    "X_train_embed = embedding(word2vec, X_train)\n",
    "X_test_embed = embedding(word2vec, X_test)\n",
    "\n",
    "\n",
    "# Pad the training and test embedded sentences\n",
    "X_train_pad = pad_sequences(X_train_embed, dtype='float32', padding='post', maxlen=200)\n",
    "X_test_pad = pad_sequences(X_test_embed, dtype='float32', padding='post', maxlen=200)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ea1e76af",
   "metadata": {},
   "source": [
    "### It's a good practice to check check the following for `X_train_pad` and `X_test_pad`:\n",
    "#### - they are numpy arrays\n",
    "#### - they are 3-dimensional\n",
    "#### - the last dimension is of the size of your word2vec embedding space (you can get it with `word2vec.wv.vector_size`\\\\\n",
    "#### - the first dimension is of the size of your `X_train` and `X_test`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4770855",
   "metadata": {},
   "outputs": [],
   "source": [
    "for X in [X_train_pad, X_test_pad]:\n",
    "    assert type(X) == np.ndarray\n",
    "    assert X.shape[-1] == word2vec.wv.vector_size\n",
    "\n",
    "\n",
    "assert X_train_pad.shape[0] == len(X_train)\n",
    "assert X_test_pad.shape[0] == len(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6418b6d1",
   "metadata": {},
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b477d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of labels in train set {0: 2474, 1: 2526}\n",
      "Baseline accuracy:  0.499\n"
     ]
    }
   ],
   "source": [
    "# It is always good to have a very simple model to test your own model against\n",
    "# Baseline accuracy can be to predict the label that is the most present in `y_train`.\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "counts = dict(zip(unique, counts))\n",
    "print('Number of labels in train set', counts)\n",
    "\n",
    "y_pred = 0 if counts[0] > counts[1] else 1\n",
    "\n",
    "print('Baseline accuracy: ', accuracy_score(y_test, [y_pred]*len(y_test)))\n",
    "\n",
    "baseline_acc = accuracy_score(y_test, [y_pred]*len(y_test))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b7a80da2",
   "metadata": {},
   "source": [
    "## The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "523cd8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# writing a RNN model with Masking, LSTM and Dense layers.\n",
    "\n",
    "def init_model():\n",
    "    model = Sequential()\n",
    "    model.add(layers.Masking())\n",
    "    model.add(layers.LSTM(20, activation='tanh'))\n",
    "    model.add(layers.Dense(15, activation='relu'))\n",
    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', #compiling the model with rmsprop optimizer\n",
    "                  optimizer='rmsprop',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = init_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5317ce64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "110/110 [==============================] - 8s 47ms/step - loss: 0.6795 - accuracy: 0.5709 - val_loss: 0.6679 - val_accuracy: 0.5707\n",
      "Epoch 2/100\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.6325 - accuracy: 0.6534 - val_loss: 0.6465 - val_accuracy: 0.6007\n",
      "Epoch 3/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.5779 - accuracy: 0.7037 - val_loss: 0.5886 - val_accuracy: 0.6920\n",
      "Epoch 4/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.5413 - accuracy: 0.7349 - val_loss: 0.5846 - val_accuracy: 0.7033\n",
      "Epoch 5/100\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.5257 - accuracy: 0.7486 - val_loss: 0.5924 - val_accuracy: 0.7167\n",
      "Epoch 6/100\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.5123 - accuracy: 0.7580 - val_loss: 0.5703 - val_accuracy: 0.7167\n",
      "Epoch 7/100\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.4767 - accuracy: 0.7826 - val_loss: 0.5412 - val_accuracy: 0.7360\n",
      "Epoch 8/100\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.4607 - accuracy: 0.7880 - val_loss: 0.5545 - val_accuracy: 0.7427\n",
      "Epoch 9/100\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4473 - accuracy: 0.7960 - val_loss: 0.5799 - val_accuracy: 0.7233\n",
      "Epoch 10/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.4407 - accuracy: 0.8006 - val_loss: 0.5336 - val_accuracy: 0.7493\n",
      "Epoch 11/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.4185 - accuracy: 0.8160 - val_loss: 0.5706 - val_accuracy: 0.7313\n",
      "Epoch 12/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.4051 - accuracy: 0.8214 - val_loss: 0.5340 - val_accuracy: 0.7527\n",
      "Epoch 13/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3901 - accuracy: 0.8291 - val_loss: 0.6257 - val_accuracy: 0.7067\n",
      "Epoch 14/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3861 - accuracy: 0.8286 - val_loss: 0.6041 - val_accuracy: 0.7413\n",
      "Epoch 15/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3764 - accuracy: 0.8420 - val_loss: 0.5992 - val_accuracy: 0.7227\n"
     ]
    }
   ],
   "source": [
    "#  Fiting the model on embedded and padded data with the early stopping criterion.\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "es = EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "history = model.fit(X_train_pad, y_train, \n",
    "          batch_size = 32,\n",
    "          epochs=100,\n",
    "          validation_split=0.3,\n",
    "          callbacks=[es]\n",
    "         )\n",
    "the_model_acc = history.history['accuracy'][-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26e4350b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy evaluated on the test set is of 76.940%\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on the test set.\n",
    "\n",
    "result = model.evaluate(X_test_pad, y_test, verbose=0)\n",
    "\n",
    "print(f'The accuracy evaluated on the test set is of {result[1]*100:.3f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d663411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the accuracy and loss curves\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d9c43269",
   "metadata": {},
   "source": [
    "## Trained Word2Vec - Transfer Learning\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92b6edab",
   "metadata": {},
   "source": [
    "### The accuracy of the above the baseline model, might be quite low. By improving the quality of the embedding we can Improve accuracy of the model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e80388e",
   "metadata": {},
   "source": [
    "### Let's improve the quality of our embedding, instead of just loading a larger corpus, let's benefit from the embedding that others have learned. Because, the quality of an embedding, i.e. the proximity of the words, can be derived from different tasks. This is exactly what transfer learning is."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "97d4ebeb",
   "metadata": {},
   "source": [
    "### Listing all the different models available in the word2vec using gensim api."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b07b77ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n"
     ]
    }
   ],
   "source": [
    "import gensim.downloader as api\n",
    "print(list(api.info()['models'].keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bd25af6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's load one of the pre-trained word2vec embedding spaces. \n",
    "\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "062fa47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400000\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(word2vec_transfer.key_to_index))\n",
    "print(len(word2vec_transfer['dog']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc0a78fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert a sentence (list of words) into a matrix representing the words in the embedding space\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "        \n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Function that converts a list of sentences into a list of matrices\n",
    "def embedding(word2vec, sentences):\n",
    "    embed = []\n",
    "    \n",
    "    for sentence in sentences:\n",
    "        embedded_sentence = embed_sentence_with_TF(word2vec, sentence)\n",
    "        embed.append(embedded_sentence)\n",
    "        \n",
    "    return embed\n",
    "\n",
    "# Embed the training and test sentences\n",
    "X_train_embed_2 = embedding(word2vec_transfer, X_train)\n",
    "X_test_embed_2 = embedding(word2vec_transfer, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9270ed0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad the training and test embedded sentences\n",
    "X_train_pad_2 = pad_sequences(X_train_embed_2, dtype='float32', padding='post', maxlen=200)\n",
    "X_test_pad_2 = pad_sequences(X_test_embed_2, dtype='float32', padding='post', maxlen=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdee2b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "110/110 [==============================] - 10s 51ms/step - loss: 0.6670 - accuracy: 0.5871 - val_loss: 0.6609 - val_accuracy: 0.5913\n",
      "Epoch 2/30\n",
      "110/110 [==============================] - 5s 43ms/step - loss: 0.6081 - accuracy: 0.6703 - val_loss: 0.5912 - val_accuracy: 0.6993\n",
      "Epoch 3/30\n",
      "110/110 [==============================] - 4s 41ms/step - loss: 0.5683 - accuracy: 0.7154 - val_loss: 0.6572 - val_accuracy: 0.6453\n",
      "Epoch 4/30\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.5293 - accuracy: 0.7437 - val_loss: 0.6096 - val_accuracy: 0.6787\n",
      "Epoch 5/30\n",
      "110/110 [==============================] - 5s 43ms/step - loss: 0.5061 - accuracy: 0.7611 - val_loss: 0.5034 - val_accuracy: 0.7593\n",
      "Epoch 6/30\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.4810 - accuracy: 0.7749 - val_loss: 0.5051 - val_accuracy: 0.7607\n",
      "Epoch 7/30\n",
      "110/110 [==============================] - 5s 43ms/step - loss: 0.4605 - accuracy: 0.7886 - val_loss: 0.5320 - val_accuracy: 0.7527\n",
      "Epoch 8/30\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.4320 - accuracy: 0.8046 - val_loss: 0.4712 - val_accuracy: 0.7853\n",
      "Epoch 9/30\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.4204 - accuracy: 0.8040 - val_loss: 0.5763 - val_accuracy: 0.7193\n",
      "Epoch 10/30\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.4013 - accuracy: 0.8180 - val_loss: 0.5636 - val_accuracy: 0.7553\n",
      "Epoch 11/30\n",
      "110/110 [==============================] - 5s 49ms/step - loss: 0.3897 - accuracy: 0.8286 - val_loss: 0.5568 - val_accuracy: 0.7420\n",
      "Epoch 12/30\n",
      "110/110 [==============================] - 6s 55ms/step - loss: 0.3762 - accuracy: 0.8357 - val_loss: 0.5653 - val_accuracy: 0.7493\n",
      "Epoch 13/30\n",
      "110/110 [==============================] - 5s 44ms/step - loss: 0.3592 - accuracy: 0.8486 - val_loss: 0.4731 - val_accuracy: 0.7860\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import tensorflow as tf\n",
    "\n",
    "es = EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "model = init_model()\n",
    "\n",
    "history = model.fit(X_train_pad_2, y_train, \n",
    "          batch_size = 32,\n",
    "          epochs=30,\n",
    "          validation_split=0.3,\n",
    "          callbacks=[es]\n",
    "         )\n",
    "model.save('my_model.h5')\n",
    "\n",
    "improved_model_acc = history.history['accuracy'][-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8297abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy evaluated on the test set is of 78.920%\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(X_test_pad_2, y_test, verbose=0)\n",
    "\n",
    "print(f'The accuracy evaluated on the test set is of {result[1]*100:.3f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "070d098d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy and loss curves\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81d63cb7",
   "metadata": {},
   "source": [
    "### There is a significant improvement in the accuracy after Transfer learning."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "379655e1",
   "metadata": {},
   "source": [
    "## Comparing Accuracy of Baseline model, The model and Improved model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44b32cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAGdCAYAAACSIU5iAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABViElEQVR4nO3deVhU5eM28HtmgGERUERWEdxAURZFJVxaSdDCpVIzC8OlvqZl4r7ilrtkvVmWubUKmFtpaGFqrhgqiiCKG6isogygbDPn/cOfkxOgDIJnZrg/1zWXcuY5Z+5Rhrl5zjISQRAEEBEREZHekIodgIiIiIi0wwJHREREpGdY4IiIiIj0DAscERERkZ5hgSMiIiLSMyxwRERERHqGBY6IiIhIz7DAEREREekZI7ED6CKVSoWbN2/C0tISEolE7DhERERUA4IgoLCwEE5OTpBKDXuOigWuCjdv3oSLi4vYMYiIiKgWMjIy0Lx5c7Fj1CsWuCpYWloCuP8NYGVlJXIaIiIiqgmFQgEXFxf1+7ghY4GrwoPdplZWVixwREREeqYhHP5k2DuIiYiIiAwQCxwRERGRnmGBIyIiItIzLHBEREREeoYFjoiIiEjPsMARERER6RkWOCIiIiI9wwJHREREpGdEL3CrV6+Gm5sbTE1N4e/vj/j4+EeOX7VqFTw8PGBmZgYXFxdMmDABJSUl6vvnzp0LiUSicWvXrl19Pw0iIiJ6DKVKwNFLt7Dj9A0cvXQLSpUgdiS9JeonMURFRSE8PBxr1qyBv78/Vq1ahaCgIKSmpsLOzq7S+J9++gnTpk3D+vXr0b17d1y4cAHvvvsuJBIJIiMj1eM6dOiAP//8U/21kRE/cIKIiEhMsUmZmPdrMjIL/p10cbQ2RUSIJ4I7OoqYTD+JOgMXGRmJ0aNHIywsDJ6enlizZg3Mzc2xfv36KscfOXIEPXr0wFtvvQU3Nzf07t0bQ4cOrTRrZ2RkBAcHB/XN1tb2aTwdIiIiqkJsUibG/HBSo7wBQFZBCcb8cBKxSZkiJdNfohW4srIyJCQkIDAw8N8wUikCAwNx9OjRKtfp3r07EhIS1IXt8uXL2L17N/r27asx7uLFi3ByckKrVq0wbNgwpKenPzJLaWkpFAqFxo2IiIienFIlYN6vyahqZ+mDZfN+TebuVC2Jtm8xLy8PSqUS9vb2Gsvt7e1x/vz5Ktd56623kJeXh549e0IQBFRUVOB///sfZsyYoR7j7++PjRs3wsPDA5mZmZg3bx569eqFpKQkWFpaVrndxYsXY968eXX35IiIiAyISiWgTKlCabkKpUolyipU92//t6xMqVIvK1UvV6JMqcKFrMJKM28PEwBkFpQg/ko+Alo3fXpPSs/p1cFh+/fvx6JFi/Dll1/C398faWlpGD9+PBYsWIDZs2cDAPr06aMe7+3tDX9/f7i6uiI6OhojR46scrvTp09HeHi4+muFQgEXF5f6fTJERETVEARBXYpKK1QahenBstIKZaXlGuOVmuuqx1cxtlS9TFllKat4CrNjOYXVlzyqTLQCZ2trC5lMhuzsbI3l2dnZcHBwqHKd2bNn45133sGoUaMAAF5eXiguLsZ7772HmTNnQiqtvEe4cePGcHd3R1paWrVZ5HI55HL5EzwbIiLSZ4IgoEIlVDGzpKxUoCrNOD0021T2iLGl6vJUeZtVlS9dJZEAJjIpTIykkBtJH/q7DCZG9//+YJmJkRSFJeU4djn/sdu1szR9CukNh2gFzsTEBH5+foiLi8OAAQMAACqVCnFxcRg3blyV69y9e7dSSZPJZADuv/iqUlRUhEuXLuGdd96pu/BEpJeUKgHxV/KRU1gCO0tTdGtpA5lUInasBkv5UGF6eLdcaZXFRlnN8sozU9XNNlVZqB5sV6lCNW8jOuHhQvTw3+X/WSY3eqhMPbzO/42RG//fnxrLZZpf/99YU+P79/13ubHs/iW6akqpEtBz6T5kFZRUeRycBICD9f3XI9WcqLtQw8PDMXz4cHTp0gXdunXDqlWrUFxcjLCwMABAaGgonJ2dsXjxYgBASEgIIiMj0alTJ/Uu1NmzZyMkJERd5CZNmoSQkBC4urri5s2biIiIgEwmw9ChQ0V7nkQkPl7C4N/dchqzP1rONj1YX2P33SNmpsr+O/Yp75arLZlUolF4NEpQNbNN/y1B8sfMTMkf/vOhoiT/T2EykUkh1eNfNGRSCSJCPDHmh5OQABol7sGzigjx5C9TWhK1wA0ZMgS5ubmYM2cOsrKy4Ovri9jYWPWJDenp6RozbrNmzYJEIsGsWbNw48YNNGvWDCEhIfjkk0/UY65fv46hQ4fi1q1baNasGXr27Iljx46hWbNmT/35EZFueHAJg//WhQeXMPjq7c71UuIe3i2nWYyUD80UVbcrTfnQLrcaHgv1iNkm/dotJ6s0s1TVbNODZeqC9N+iZVy5XD1qrPyhEsUyUbeCOzriq7c7V/olyqGB/RJVlyRCdfseGzCFQgFra2sUFBTAyspK7DhE9AQe7L551Flwjc2MMTHIHeUVQqXCU2n3XRW766odq2e75aqcbTKWae5yq2a26VEzUzWZbTKSardbjvRTfR/G0JDev/XqLFQiIm3FX8l/ZHkDgDv3yjF7+7l6z2IklVSz202mscututkmjdmp2s5MPXTgOQsTPW0yqYSXCqkjLHBEZNBqemkCL2cruNk2qjzD9N8Dvv9z1l2l45X+MzvF3XJEVB9Y4IjIoNX00gQz+npyZoCI9Iaon4VKRFTfurW0QbNG1V/nUYL7Z6PyEgZEpE9Y4IjI4FmZVb2zgZcwICJ9xQJHRAbt278v41JuMUyNpGhmqTkT52BtWm+XECEiqk88Bo6IDFZqViFW7r0AAJjXvwPe8HPhJzEQkUFggSMig1RWoUJ49GmUKVV4sZ0dBndxgUTCSxgQkWHgLlQiMkhf7LuIczcVaGxujCWvefGaZ0RkUFjgiMjgJGbcwer9lwAACwd0hJ1VzS4lQkSkL1jgiMiglJQrER59GkqVgH4+TnjV20nsSEREdY4FjogMyrLYVFzKLYadpRzz+3cQOw4RUb1ggSMig3H00i2sP3wFALD0DW80NjcRORERUf1ggSMig1BYUo5JMYkAgKHdWuAFDzuRExER1R8WOCIyCAt/S8GNO/fgYmOGma+0FzsOEVG9YoEjIr2373w2ov7JgEQCrBzki0ZyXuKSiAwbCxwR6bXbxWWY+stZAMConi35ofRE1CCwwBGRXpu1Iwm5haVoa9cIE3t7iB2HiOipYIEjIr21M/Emdp3JhJFUgsjBvjA1lokdiYjoqWCBIyK9lK0oweztSQCAcS+2gVdza5ETERE9PSxwRKR3BEHAlC1nUHCvHF7O1hj7QhuxIxERPVUscESkd36Oz8CBC7kwMZIicrAPjGX8UUZEDQt/6hGRXkm/dRcLdyUDAKYEeaCtvaXIiYiInj4WOCLSG0qVgEkxibhbpkS3ljYY0aOl2JGIiETBAkdEemP9oSuIv5oPCxMZVg7ygVQqETsSEZEoWOCISC9cyC7E8r2pAIDZr3rCxcZc5EREROJhgSMinVeuVCE8+jTKKlR4sZ0dhnR1ETsSEZGoWOCISOd9sS8NSTcUaGxujCWveUEi4a5TImrYWOCISKeduX4HX/yVBgBY0L8j7KxMRU5ERCQ+Fjgi0lkl5UqERydCqRLwqrcjQnycxI5ERKQTWOCISGet2JOKtJwiNLOUY0H/jmLHISLSGSxwRKSTjl2+hXWHrwAAlr3ujSYWJiInIiLSHSxwRKRzikorMCkmEYIAvNnVBS+0sxM7EhGRTmGBIyKd88muZFy/fQ/Nm5hh1queYschItI5LHBEpFP2nc/Gz/EZkEiAFYN80EhuJHYkIiKdwwJHRDrjdnEZpv5yFgAwokdLPNOqqciJiIh0EwscEemM2TuSkFtYijZ2jTA5yEPsOEREOkv0Ard69Wq4ubnB1NQU/v7+iI+Pf+T4VatWwcPDA2ZmZnBxccGECRNQUlLyRNskIvH9mngTv53JhEwqQeRgH5gay8SORESks0QtcFFRUQgPD0dERAROnjwJHx8fBAUFIScnp8rxP/30E6ZNm4aIiAikpKRg3bp1iIqKwowZM2q9TSISX46iBLN3JAEAxr7QBt7NG4sbiIhIx0kEQRDEenB/f3907doVX3zxBQBApVLBxcUFH374IaZNm1Zp/Lhx45CSkoK4uDj1sokTJ+L48eM4dOhQrbZZFYVCAWtraxQUFMDKyupJnyYRPYIgCBix8QT+Ss1FR2crbPugB4xlou8cICI91JDev0X7KVlWVoaEhAQEBgb+G0YqRWBgII4ePVrlOt27d0dCQoJ6l+jly5exe/du9O3bt9bbBIDS0lIoFAqNGxE9HVEnMvBXai5MjKSIHOzL8kZEVAOinZ+fl5cHpVIJe3t7jeX29vY4f/58leu89dZbyMvLQ8+ePSEIAioqKvC///1PvQu1NtsEgMWLF2PevHlP+IyISFsZ+Xex4LdkAMDk3h5wt7cUORERkX7Qq1919+/fj0WLFuHLL7/EyZMnsXXrVuzatQsLFix4ou1Onz4dBQUF6ltGRkYdJSai6qhUAibFJKK4TIlubjYY0bOl2JGIiPSGaDNwtra2kMlkyM7O1lienZ0NBweHKteZPXs23nnnHYwaNQoA4OXlheLiYrz33nuYOXNmrbYJAHK5HHK5/AmfERFpY/3hKzh+JR/mJjKsGOQDmVQidiQiIr0h2gyciYkJ/Pz8NE5IUKlUiIuLQ0BAQJXr3L17F1KpZmSZ7P6lBgRBqNU2iejpS8spxLI9qQCAWa94okVTc5ETERHpF1E/oyY8PBzDhw9Hly5d0K1bN6xatQrFxcUICwsDAISGhsLZ2RmLFy8GAISEhCAyMhKdOnWCv78/0tLSMHv2bISEhKiL3OO2SUTiKleqEB6diLIKFZ73aIah3VzEjkREpHdELXBDhgxBbm4u5syZg6ysLPj6+iI2NlZ9EkJ6errGjNusWbMgkUgwa9Ys3LhxA82aNUNISAg++eSTGm+TiMT15V+XcOZ6AazNjLH0dW9IJNx1SkSkLVGvA6erGtJ1ZIieprPXCzDwy8OoUAn47E1f9Pd1FjsSERmQhvT+rVdnoRKR/iopVyI8+jQqVAJe8XJEPx8nsSMREektFjgieipW7k3FxZwi2DaSY8GAjtx1SkT0BFjgiKjeHb98C98eugIAWPq6F2wsTERORESk31jgiKheFZVWYNKWRAgCMLhLc7zUnicUERE9KRY4IqpXn+xKQUb+PTg3NsPsVz3FjkNEZBBY4Iio3vyVmoOf49MBAMsHecPS1FjkREREhoEFjojqxZ27ZZi65QwAIKyHG7q3thU5ERGR4WCBI6J6MWfHOeQUlqJVMwtMDW4ndhwiIoPCAkdEdW7XmUzsTLwJmVSCyMG+MDWWiR2JiMigsMARUZ3KKSzBrO1nAQBjn28NX5fG4gYiIjJALHBEVGcEQcD0X87i9t1ydHCywrgX24odiYjIILHAEVGdifnnOuLO58BEJkXkYF+YGPFHDBFRfeBPVyKqExn5dzH/t2QAwMTe7vBwsBQ5ERGR4WKBI6InplIJmLwlEUWlFejq1gSjerUSOxIRkUFjgSOiJ7bxyFUcu5wPcxMZVgzygUzKD6onIqpPLHBE9ETScoqwNPY8AGBG3/ZwbWohciIiIsPHAkdEtVahVGFi9GmUVqjwrHszDPNvIXYkIqIGgQWOiGrty/2XkHi9AFamRlj2ujckEu46JSJ6GljgiKhWkm4U4PO4iwCA+f07wsHaVOREREQNBwscEWmtpFyJ8OjTqFAJ6NPRAf19ncSORETUoLDAEZHWPv3jAi5kF8G2kQkWDujIXadERE8ZCxwRaeXE1Xx88/dlAMDi17zRtJFc5ERERA0PCxwR1VhxaQUmRidCEIA3/JrjZU97sSMRETVILHBEVGOLdqcgPf8unBubYU6Ip9hxiIgaLBY4IqqRAxdy8ePxdADA8kHesDI1FjkREVHDxQJHRI9VcLccU7YkAgDe7e6G7q1tRU5ERNSwscAR0WNF7ExCtqIUrWwtMDW4ndhxiIgaPBY4Inqk389mYvvpm5BKgJWDfWBmIhM7EhFRg8cCR0TVyi0sxYxtZwEAHzzfBp1aNBE5ERERASxwRFQNQRAwfetZ3L5bDk9HK3z0UluxIxER0f9hgSOiKm1JuI4/U7JhIpMicogPTIz444KISFfwJzIRVXL99l3M+zUZADDhZXe0c7ASORERET2MBY6INKhUAibHnEFRaQX8XJvgvWdbiR2JiIj+gwWOiDRsOnoVRy/fgpmxDCsH+UAm5QfVExHpGhY4IlK7lFuEJb+fBwDM6NsObrYWIiciIqKqsMAREQCgQqlCeHQiSitU6NXWFm8/4yp2JCIiqgYLHBEBANYcuITEjDuwNDXCsje8IZFw1ykRka7SiQK3evVquLm5wdTUFP7+/oiPj6927PPPPw+JRFLp9sorr6jHvPvuu5XuDw4OfhpPhUgvnbtZgM/iLgIA5vXrAEdrM5ETERHRoxiJHSAqKgrh4eFYs2YN/P39sWrVKgQFBSE1NRV2dnaVxm/duhVlZWXqr2/dugUfHx8MGjRIY1xwcDA2bNig/loul9ffkyDSY6UVSoRHJaJcKSC4gwMGdnIWOxIRET2G6DNwkZGRGD16NMLCwuDp6Yk1a9bA3Nwc69evr3K8jY0NHBwc1Lc//vgD5ubmlQqcXC7XGNekCT8CiKgqn/5xEanZhbBtZIJPBnbkrlMiIj0gaoErKytDQkICAgMD1cukUikCAwNx9OjRGm1j3bp1ePPNN2FhoXm23P79+2FnZwcPDw+MGTMGt27dqnYbpaWlUCgUGjeihiDhWj6+OXgJALBooBeaNuJMNRGRPhC1wOXl5UGpVMLe3l5jub29PbKysh67fnx8PJKSkjBq1CiN5cHBwfjuu+8QFxeHpUuX4sCBA+jTpw+USmWV21m8eDGsra3VNxcXl9o/KSI9cbesAuHRiVAJwOudm6N3BwexIxERUQ2Jfgzck1i3bh28vLzQrVs3jeVvvvmm+u9eXl7w9vZG69atsX//frz00kuVtjN9+nSEh4erv1YoFCxxZPAW7z6Pa7fuwsnaFBH9PMWOQ0REWhB1Bs7W1hYymQzZ2dkay7Ozs+Hg8OjZgOLiYmzevBkjR4587OO0atUKtra2SEtLq/J+uVwOKysrjRuRIfv7Yi6+P3YNALB8kA+sTI1FTkRERNoQtcCZmJjAz88PcXFx6mUqlQpxcXEICAh45LoxMTEoLS3F22+//djHuX79Om7dugVHR8cnzkyk7wrulWNyzBkAwPAAV/RoYytyIiIi0pboZ6GGh4dj7dq12LRpE1JSUjBmzBgUFxcjLCwMABAaGorp06dXWm/dunUYMGAAmjZtqrG8qKgIkydPxrFjx3D16lXExcWhf//+aNOmDYKCgp7KcyLSZfN2nkOWogQtbS0wrU97seMQEVEtiH4M3JAhQ5Cbm4s5c+YgKysLvr6+iI2NVZ/YkJ6eDqlUs2empqbi0KFD2Lt3b6XtyWQynDlzBps2bcKdO3fg5OSE3r17Y8GCBbwWHDV4sUmZ2HrqBqQSYOVgH5iZyMSOREREtSARBEEQO4SuUSgUsLa2RkFBAY+HI4ORW1iKoFUHkV9chg+eb40pwe3EjkREVKca0vu36LtQiaj+CYKAGdvOIr+4DO0cLDE+sK3YkYiI6AmwwBE1AL+cvIE/krNhLJPg0yG+kBtx1ykRkT5jgSMycDfu3MO8necAAB8HuqO9o2HvViAiaghY4IgMmEolYMqWRBSWVqBTi8Z4/9lWYkciIqI6wAJHZMC+P3YNh9NuwdRYisjBvjCS8SVPRGQI+NOcyEBdzi3C4t9TAADT+7RHS1sLkRMREVFdYYEjMkAVShUmxiSipFyFnm1s8c4zrmJHIiKiOsQCR2SAvj54GafS78DS1AjL3vCGVCoROxIREdUhFjgiA5N8U4FVf14AAMwN6QCnxmYiJyIiorrGAkdkQEorlAiPPo1ypYDenvZ4rbOz2JGIiKgesMARGZDP/ryI81mFaGphgkWveUEi4a5TIiJDxAJHZCASrt3GmgOXAACfDPSCbSO5yImIiKi+sMARGYC7ZRWYFJMIlQC81skZwR0dxI5ERET1iAWOyAAs+f08ruQVw9HaFBH9Oogdh4iI6hkLHJGeO3QxD98dvQYAWPaGN6zNjEVORERE9Y0FjkiPFdwrx+QtiQCAd55xRa+2zURORERETwMLHJEem/frOWQWlMCtqTmm920ndhwiInpKWOCI9NSec1nYevIGpBJg5WAfmJsYiR2JiIieEhY4Ij2UV1SKGVvPAgDee7Y1/FxtRE5ERERPEwsckZ4RBAEzt53FreIytHOwxISX24odiYiInjIWOCI9s+3UDew5lw1jmQQrB/tAbiQTOxIRET1lLHBEeuTmnXuI2HkOAPBxoDs6OFmLnIiIiMTAAkekJwRBwNRfzqCwpAKdWjTG+8+2EjsSERGJhAWOSE/8cOwa/r6YB1NjKVYO8oGRjC9fIqKGiu8ARHrgal4xFu0+DwCYFtwOrZo1EjkRERGJiQWOSMcpVQImxiTiXrkS3Vs3RWiAm9iRiIhIZCxwRDrum4OXkXDtNizlRlg+yAdSqUTsSEREJDIWOCIdlpKpwKd/XAAAzAnxhHNjM5ETERGRLmCBI9JRZRUqhEcnokypQmB7e7zh11zsSEREpCNY4Ih01GdxF5CSqYCNhQkWv+YFiYS7TomI6D4WOCIddDL9Nr7afwkA8MmAjmhmKRc5ERER6RIWOCIdc69MiUnRiVAJwABfJ/TxchQ7EhER6RgWOCIdszT2PC7nFcPByhTz+nUUOw4REekgFjgiHXI4LQ8bj1wFACx9wxvW5sbiBiIiIp3EAkekIxQl5ZgckwgAGObfAs+5NxM5ERER6SoWOCIdMf/XZNwsKEELG3PM6Nte7DhERKTDWOCIdMAfydnYknAdEgkQOdgHFnIjsSMREZEO04kCt3r1ari5ucHU1BT+/v6Ij4+vduzzzz8PiURS6fbKK6+oxwiCgDlz5sDR0RFmZmYIDAzExYsXn8ZTIdLaraJSTN96BgDw3rOt0MXNRuRERESk67QucG5ubpg/fz7S09PrJEBUVBTCw8MRERGBkydPwsfHB0FBQcjJyaly/NatW5GZmam+JSUlQSaTYdCgQeoxy5Ytw+eff441a9bg+PHjsLCwQFBQEEpKSuokM1FdEQQBs7YnIa+oDB72lgh/2V3sSEREpAe0LnAff/wxtm7dilatWuHll1/G5s2bUVpaWusAkZGRGD16NMLCwuDp6Yk1a9bA3Nwc69evr3K8jY0NHBwc1Lc//vgD5ubm6gInCAJWrVqFWbNmoX///vD29sZ3332HmzdvYvv27bXOSVQfdpy+id+TsmAklWDlYB/IjWRiRyIiIj1QqwJ3+vRpxMfHo3379vjwww/h6OiIcePG4eTJk1ptq6ysDAkJCQgMDPw3kFSKwMBAHD16tEbbWLduHd58801YWFgAAK5cuYKsrCyNbVpbW8Pf37/abZaWlkKhUGjciOpbVkEJ5uxIAgCMf6ktOjpbi5yIiIj0Ra2PgevcuTM+//xz3Lx5ExEREfj222/RtWtX+Pr6Yv369RAE4bHbyMvLg1KphL29vcZye3t7ZGVlPXb9+Ph4JCUlYdSoUeplD9bTZpuLFy+GtbW1+ubi4vLYxyZ6EoIgYMovZ6AoqYCPS2OMeb612JGIiEiP1LrAlZeXIzo6Gv369cPEiRPRpUsXfPvtt3j99dcxY8YMDBs2rC5zVmndunXw8vJCt27dnmg706dPR0FBgfqWkZFRRwmJqvbj8XQcvJALuZEUKwf5wEimE+cTERGRntD6WgUnT57Ehg0b8PPPP0MqlSI0NBSffvop2rVrpx4zcOBAdO3a9bHbsrW1hUwmQ3Z2tsby7OxsODg4PHLd4uJibN68GfPnz9dY/mC97OxsODr++xmS2dnZ8PX1rXJbcrkccjk/LJyejqt5xfhkVwoAYGpwO7SxayRyIiIi0jda/9rftWtXXLx4EV999RVu3LiBFStWaJQ3AGjZsiXefPPNx27LxMQEfn5+iIuLUy9TqVSIi4tDQEDAI9eNiYlBaWkp3n777UqP7eDgoLFNhUKB48ePP3abRPVNqRIwKSYR98qVCGjVFO92dxM7EhER6SGtZ+AuX74MV1fXR46xsLDAhg0barS98PBwDB8+HF26dEG3bt2watUqFBcXIywsDAAQGhoKZ2dnLF68WGO9devWYcCAAWjatKnGcolEgo8//hgLFy5E27Zt0bJlS8yePRtOTk4YMGBAzZ8oUT1Y+/dl/HPtNhrJjbB8kDekUonYkYiISA9pXeBycnKQlZUFf39/jeXHjx+HTCZDly5dtNrekCFDkJubizlz5iArKwu+vr6IjY1Vn4SQnp4OqVRzojA1NRWHDh3C3r17q9zmlClTUFxcjPfeew937txBz549ERsbC1NTU62yEdWl81kKRO69AACY86onmjcxFzkRERHpK4lQk9NFH9KtWzdMmTIFb7zxhsbyrVu3YunSpTh+/HidBhSDQqGAtbU1CgoKYGVlJXYcMgBlFSoMWH0YyZkKvNTODt8O7wKJhLNvRER1qSG9f2t9DFxycjI6d+5caXmnTp2QnJxcJ6GIDM3/23cRyZkKNDE3xuLXvVjeiIjoiWhd4ORyeaWzRgEgMzMTRkb8AG6i/zqdcQdf7r8EAFg4wAt2ltyVT0RET0brAte7d2/1ddMeuHPnDmbMmIGXX365TsMR6buSciXCo09DqRLQz8cJr3g7Pn4lIiKix9B6ymzFihV49tln4erqik6dOgEATp8+DXt7e3z//fd1HpBIny2NPY/LucWws5Rjfv8OYschIiIDoXWBc3Z2xpkzZ/Djjz8iMTERZmZmCAsLw9ChQ2FsbFwfGYn00pFLedhw+CoAYNkb3mhsbiJuICIiMhi1OmjNwsIC7733Xl1nITIYhSXlmBxzBgDwln8LPO9hJ3IiIiIyJLU+6yA5ORnp6ekoKyvTWN6vX78nDkWk7xb8lowbd+6hhY05ZvZtL3YcIiIyMLX6JIaBAwfi7NmzkEgkeHAZuQeXRVAqlXWbkEjP/Jmcjeh/rkMiAVYM8oGFnGdnExFR3dL6LNTx48ejZcuWyMnJgbm5Oc6dO4eDBw+iS5cu2L9/fz1EJNIf+cVlmLb1LABgdK9W6NbSRuRERERkiLSeGjh69Cj27dsHW1tbSKVSSKVS9OzZE4sXL8ZHH32EU6dO1UdOIp0nCAJmb09CXlEp3O0bIfxld7EjERGRgdJ6Bk6pVMLS0hIAYGtri5s3bwIAXF1dkZqaWrfpiPTIzsSb2HU2E0ZSCSIH+8LUWCZ2JCIiMlBaz8B17NgRiYmJaNmyJfz9/bFs2TKYmJjgm2++QatWreojI5HOyyooweztSQCAD19si47O1iInIiIiQ6Z1gZs1axaKi4sBAPPnz8err76KXr16oWnTpoiKiqrzgES6ThAETP3lDBQlFfBubo0PXmgtdiQiIjJwWhe4oKAg9d/btGmD8+fPIz8/H02aNOEHdFOD9FN8Og5cyIWJkRSRg31gLNP6yAQiIiKtaPVOU15eDiMjIyQlJWkst7GxYXmjBunarWJ8sisFADAlyANt7CxFTkRERA2BVgXO2NgYLVq04LXeiAAoVQImxSTibpkS/i1tMKJHS7EjERFRA6H1vp6ZM2dixowZyM/Pr488RHpj3aHLOHH1NixMZFgxyAdSKWehiYjo6dD6GLgvvvgCaWlpcHJygqurKywsLDTuP3nyZJ2FI9JVF7ILsWLPBQDA7Fc94WJjLnIiIiJqSLQucAMGDKiHGET6o1ypQnj0aZQpVXixnR2GdHUROxIRETUwWhe4iIiI+shBpDf+3740JN1QoLG5MZa85sUTeIiI6Knj9Q6ItJCYcQer/0oDACwc0BF2VqYiJyIiooZI6xk4qVT6yBkHnqFKhqqkXInw6NNQqgSE+DjhVW8nsSMREVEDpXWB27Ztm8bX5eXlOHXqFDZt2oR58+bVWTAiXbN8Tyou5RbDzlKOBf07iB2HiIgaMK0LXP/+/Sste+ONN9ChQwdERUVh5MiRdRKMSJccu3wL6w9fAQAsfd0bjc1NRE5EREQNWZ0dA/fMM88gLi6urjZHpDOKSiswKSYRggAM7eaCF9rZiR2JiIgauDopcPfu3cPnn38OZ2fnutgckU5Z+Fsyrt++BxcbM8x8xVPsOERERNrvQv3vh9YLgoDCwkKYm5vjhx9+qNNwRGLbdz4bm09kQCIBVrzhg0ZyrV8yREREdU7rd6NPP/1Uo8BJpVI0a9YM/v7+aNKkSZ2GIxLT7eIyTP3lLABgZI+W8G/VVORERERE92ld4N599916iEGke2btSEJuYSna2DXCpCAPseMQERGpaX0M3IYNGxATE1NpeUxMDDZt2lQnoYjEtjPxJnadyYRMKkHkYB+YGsvEjkRERKSmdYFbvHgxbG1tKy23s7PDokWL6iQUkZiyFSWYvT0JADDuhTbwbt5Y3EBERET/oXWBS09PR8uWLSstd3V1RXp6ep2EIhKLIAiY+ssZFNwrh5ezNca92EbsSERERJVoXeDs7Oxw5syZSssTExPRtCkP8ib9tvlEBvan5sLESIrIwT4wlvHjgomISPdo/e40dOhQfPTRR/jrr7+gVCqhVCqxb98+jB8/Hm+++WZ9ZCR6KjLy72Lhb8kAgMm9PdDW3lLkRERERFXT+izUBQsW4OrVq3jppZdgZHR/dZVKhdDQUB4DR3pLpRIwMSYRxWVKdGtpgxE9Kx8mQEREpCu0LnAmJiaIiorCwoULcfr0aZiZmcHLywuurq71kY/oqVh/+Arir+TDwkSGlYN8IJNKHr8SERGRSGp9Wfm2bduibdu2dZmFSBQXswuxbE8qAGDWq55wsTEXOREREdGjaX0M3Ouvv46lS5dWWr5s2TIMGjRI6wCrV6+Gm5sbTE1N4e/vj/j4+EeOv3PnDsaOHQtHR0fI5XK4u7tj9+7d6vvnzp0LiUSicWvXrp3WuahhKFeqEB6diLIKFZ73aIY3u7qIHYmIiOixtC5wBw8eRN++fSst79OnDw4ePKjVtqKiohAeHo6IiAicPHkSPj4+CAoKQk5OTpXjy8rK8PLLL+Pq1avYsmULUlNTsXbtWjg7O2uM69ChAzIzM9W3Q4cOaZWLGo7Vf6Xh7I0CWJsZY+nr3hofE0dERKSrtN6FWlRUBBMTk0rLjY2NoVAotNpWZGQkRo8ejbCwMADAmjVrsGvXLqxfvx7Tpk2rNH79+vXIz8/HkSNHYGxsDABwc3OrNM7IyAgODg5aZaGG5+z1AnyxLw0AsGBAR9hbmYqciIiIqGa0noHz8vJCVFRUpeWbN2+Gp6dnjbdTVlaGhIQEBAYG/htGKkVgYCCOHj1a5To7d+5EQEAAxo4dC3t7e3Ts2BGLFi2CUqnUGHfx4kU4OTmhVatWGDZs2GMvMFxaWgqFQqFxI8NWUq7EhOjTqFAJeMXbEf18nMSOREREVGNaz8DNnj0br732Gi5duoQXX3wRABAXF4effvoJW7ZsqfF28vLyoFQqYW9vr7Hc3t4e58+fr3Kdy5cvY9++fRg2bBh2796NtLQ0fPDBBygvL0dERAQAwN/fHxs3boSHhwcyMzMxb9489OrVC0lJSbC0rPq6XosXL8a8efNqnJ3034o9qUjLKUIzSzkW9u8odhwiIiKtaF3gQkJCsH37dixatAhbtmyBmZkZfHx8sG/fPtjY2NRHRjWVSgU7Ozt88803kMlk8PPzw40bN7B8+XJ1gevTp496vLe3N/z9/eHq6oro6GiMHDmyyu1Onz4d4eHh6q8VCgVcXHgwu6E6dvkW1h2+AgBY+roXmlhUPiSAiIhIl9XqMiKvvPIKXnnlFQD3y87PP/+MSZMmISEhodLuzOrY2tpCJpMhOztbY3l2dna1x685OjrC2NgYMplMvax9+/bIyspCWVlZlcfmNW7cGO7u7khLS6s2i1wuh1wur1Fu0m9FpRWYFJMIQQCGdHHBi+3sH78SERGRjqn1Bz0ePHgQw4cPh5OTE1auXIkXX3wRx44dq/H6JiYm8PPzQ1xcnHqZSqVCXFwcAgICqlynR48eSEtLg0qlUi+7cOECHB0dqyxvwP2TLi5dugRHR8caZyPD9cmuZFy/fQ/Ojc0w69X2YschIiKqFa0KXFZWFpYsWYK2bdti0KBBsLKyQmlpKbZv344lS5aga9euWj14eHg41q5di02bNiElJQVjxoxBcXGx+qzU0NBQTJ8+XT1+zJgxyM/Px/jx43HhwgXs2rULixYtwtixY9VjJk2ahAMHDuDq1as4cuQIBg4cCJlMhqFDh2qVjQzPX+dz8HN8BgBgxSAfWJoai5yIiIiodmq8CzUkJAQHDx7EK6+8glWrViE4OBgymQxr1qyp9YMPGTIEubm5mDNnDrKysuDr64vY2Fj1iQ3p6emQSv/tmC4uLtizZw8mTJgAb29vODs7Y/z48Zg6dap6zPXr1zF06FDcunULzZo1Q8+ePXHs2DE0a9as1jlJ/925W4apv5wBAIzo0RIBrZuKnIiIiKj2JIIgCDUZaGRkhI8++ghjxozR+AgtY2NjJCYmanUJEV2nUChgbW2NgoICWFlZiR2H6sCHP5/Cr4k30bqZBXZ91AumxrLHr0RERHqlIb1/13gX6qFDh1BYWAg/Pz/4+/vjiy++QF5eXn1mI6oTv525iV8Tb0ImlSBysC/LGxER6b0aF7hnnnkGa9euRWZmJt5//31s3rwZTk5OUKlU+OOPP1BYWFifOYlqJUdRglnbkwAAY59vDR+XxuIGIiIiqgNan4VqYWGBESNG4NChQzh79iwmTpyIJUuWwM7ODv369auPjES1IggCpm09izt3y9HR2QrjXmz7+JWIiIj0QK0vIwIAHh4eWLZsGa5fv46ff/65rjIR1YnofzKw73wOTIykiBzsCxOjJ/p2JyIi0hl18o4mk8kwYMAA7Ny5sy42R/TEMvLvYv6vyQCASb3d4W5f9ceoERER6SNOSZDBUakETIpJRHGZEt3cbDCyZyuxIxEREdUpFjgyOBuOXMXxK/kwN5FhxSAfyKQSsSMRERHVKRY4MihpOYVYFnseADDzlfZo0dRc5ERERER1jwWODEa5UoXw6ESUVqjwnHszvNWthdiRiIiI6gULHBmML/+6hDPXC2BlaoSlr3tDIuGuUyIiMkwscGQQzl4vwP/bdxEAsGBARzhYm4qciIiIqP6wwJHeKylXIjz6NCpUAvp6OaCfj5PYkYiIiOoVCxzpvcg/LuBiThFsG8mxcIAXd50SEZHBY4EjvRZ/JR9r/74MAFjymhdsLExETkRERFT/WOBIbxWXVmBSTCIEARjk1xyBnvZiRyIiInoqWOBIb32yOwXp+Xfh3NgMc0I8xY5DRET01LDAkV7an5qDn46nAwCWD/KGpamxyImIiIieHhY40jsFd8sx9ZczAICwHm7o3tpW5ERERERPFwsc6Z05O5OQrShFq2YWmBrcTuw4RERETx0LHOmV3WczseP0TcikEkQO9oWpsUzsSERERE8dCxzpjZzCEszcdhYA8MHzreHr0ljcQERERCJhgSO9IAgCZmw9i9t3y9HByQofvthW7EhERESiYYEjvRCTcB1/puTARCZF5GBfmBjxW5eIiBouvguSzsvIv4v5vyYDAMJ7u8PDwVLkREREROJigSOdplIJmLwlEUWlFeji2gSje7USOxIREZHoWOBIp208chXHLufDzFiGFYN8IJPyg+qJiIhY4EhnpeUUYWnseQDAjFfaw83WQuREREREuoEFjnRShVKFiTGJKK1QoVdbW7zt30LsSERERDqDBY500lf7LyEx4w4sTY2w7A1vSCTcdUpERPQACxzpnKQbBfgs7iIAYH7/DnC0NhM5ERERkW5hgSOdUlqhxMToRFSoBAR3cMAAX2exIxEREekcFjjSKZF/XEBqdiFsG5ngk4EdueuUiIioCixwpDP+uZqPbw5eBgAsGuiFpo3kIiciIiLSTSxwpBOKSyswMSYRggC84dccvTs4iB2JiIhIZ7HAkU5Y/HsKrt26C+fGZpgT4il2HCIiIp3GAkeiO3ghFz8cSwcALH/DG1amxiInIiIi0m0scCSqgrvlmLLlDADg3e5u6N7GVuREREREuk/0Ard69Wq4ubnB1NQU/v7+iI+Pf+T4O3fuYOzYsXB0dIRcLoe7uzt27979RNsk8cz99RyyFCVoZWuBqcHtxI5DRESkF0QtcFFRUQgPD0dERAROnjwJHx8fBAUFIScnp8rxZWVlePnll3H16lVs2bIFqampWLt2LZydnWu9TRLP72czse3UDUglwIrBPjAzkYkdiYiISC9IBEEQxHpwf39/dO3aFV988QUAQKVSwcXFBR9++CGmTZtWafyaNWuwfPlynD9/HsbGVR8npe02q6JQKGBtbY2CggJYWVnV8tnRo+QWlqL3pwdw+245xr7QGpODOPtGRERPpiG9f4s2A1dWVoaEhAQEBgb+G0YqRWBgII4ePVrlOjt37kRAQADGjh0Le3t7dOzYEYsWLYJSqaz1NgGgtLQUCoVC40b1RxAETN96FrfvlqO9oxXGv+QudiQiIiK9IlqBy8vLg1KphL29vcZye3t7ZGVlVbnO5cuXsWXLFiiVSuzevRuzZ8/GypUrsXDhwlpvEwAWL14Ma2tr9c3FxeUJnx09ypaE6/gzJRvGMgkiB/vAxEj0QzGJiIj0il69c6pUKtjZ2eGbb76Bn58fhgwZgpkzZ2LNmjVPtN3p06ejoKBAfcvIyKijxPRfN+7cw/xfkwEAE152R3tHw57iJiIiqg9GYj2wra0tZDIZsrOzNZZnZ2fDwaHqq/A7OjrC2NgYMtm/B7u3b98eWVlZKCsrq9U2AUAul0Mu58c21TeVSsDkmEQUllagc4vGeP/Z1mJHIiIi0kuizcCZmJjAz88PcXFx6mUqlQpxcXEICAiocp0ePXogLS0NKpVKvezChQtwdHSEiYlJrbZJT893R6/iyKVbMDOWYeVgX8ik/KB6IiKi2hB1F2p4eDjWrl2LTZs2ISUlBWPGjEFxcTHCwsIAAKGhoZg+fbp6/JgxY5Cfn4/x48fjwoUL2LVrFxYtWoSxY8fWeJskjsu5RVgSex4AML1vO7S0tRA5ERERkf4SbRcqAAwZMgS5ubmYM2cOsrKy4Ovri9jYWPVJCOnp6ZBK/+2YLi4u2LNnDyZMmABvb284Oztj/PjxmDp1ao23SU9fhVKF8OhElJSr0LONLd72dxU7EhERkV4T9TpwuqohXUfmaVj9VxqW70mFpakR9nz8LJwam4kdiYiIDFBDev/Wq7NQSf8k31Rg1Z8XAADz+nVgeSMiIqoDLHBUb0orlAiPPo1ypYCgDvYY2Mn58SsRERHRY7HAUb1Z9edFnM8qRFMLEywa6AWJhGedEhER1QUWOKoXCdfy8fWBSwCARa95oWkjXmePiIiorrDAUZ27W1aBidGJUAnAa52dEdSh+osoExERkfZY4KjOLd59Hldv3YWjtSkiQjqIHYeIiMjgsMBRnfr7Yi6+P3YNALD8DR9YmxmLnIiIiMjwsMBRnSm4V47JMWcAAKEBrujZ1lbkRERERIaJBY7qzLyd55ClKIFbU3NM69NO7DhEREQGiwWO6kRsUha2nroBqQRYOdgX5iaifkobERGRQWOBoyeWV1SKmdvOAgDef641/FybiJyIiIjIsLHA0RMRBAEztp7FreIytHOwxMeBbcWOREREZPBY4OiJbD15A3uTs2EskyBysC/kRjKxIxERERk8FjiqtZt37mHuznMAgI8D3eHpZCVyIiIiooaBBY5qRaUSMGXLGRSWVqBTi8Z4/9lWYkciIiJqMFjgqFZ+OH4Nh9LyYGosxcpBPjCS8VuJiIjoaeG7LmntSl4xFu1OAQBM79MerZo1EjkRERFRw8ICR1pRqgRMjD6NknIVerRpineecRU7EhERUYPDAkda+frgJZxMvwNLuRGWv+EDqVQidiQiIqIGhwWOaiwlU4FP/7gAAIjo1wFOjc1ETkRERNQwscBRjZRWKDEh6jTKlQJe9rTH652dxY5ERETUYLHAUY189udFnM8qhI2FCRa/5gWJhLtOiYiIxMICR4+VcO021hy4BABYNLAjbBvJRU5ERETUsLHA0SPdLavApJhEqARgYCdnBHd0FDsSERFRg8cCR4+09PfzuJJXDAcrU8zt10HsOERERAQWOHqEw2l52HT0GgBg2RvesDYzFjkRERERASxwVA1FSTkmxyQCAN5+pgWedW8mciIiIiJ6gAWOqjRvZzJuFpTAtak5ZvRtL3YcIiIieggLHFWy91wWfjl5HRIJsHKQD8xNjMSORERERA9hgSMNt4pKMWPbWQDAe8+2Qhc3G5ETERER0X+xwJGaIAiYuS0JeUVl8LC3RPjL7mJHIiIioiqwwJHa9tM3EHsuC8YyCSKH+EBuJBM7EhEREVWBBY4AAJkF9zBnxzkAwPiX2qKDk7XIiYiIiKg6LHAEQRAwZcsZFJZUwNelMf73XGuxIxEREdEjsMARfjiejr8v5sHUWIqVg31gJOO3BRERkS7jO3UDdzWvGIt2pQAApga3Q+tmjURORERERI/DAteAKVUCJsYk4l65EgGtmmJ4gJvYkYiIiKgGdKLArV69Gm5ubjA1NYW/vz/i4+OrHbtx40ZIJBKNm6mpqcaYd999t9KY4ODg+n4aeuebg5eRcO02GsmNsHyQN6RSidiRiIiIqAZEv8R+VFQUwsPDsWbNGvj7+2PVqlUICgpCamoq7OzsqlzHysoKqamp6q8lksrFIzg4GBs2bFB/LZfL6z68HjufpcCnf1wAAMwJ8UTzJuYiJyIiIqKaEn0GLjIyEqNHj0ZYWBg8PT2xZs0amJubY/369dWuI5FI4ODgoL7Z29tXGiOXyzXGNGnSpD6fhl4pq1BhQlQiypQqBLa3wyC/5mJHIiIiIi2IWuDKysqQkJCAwMBA9TKpVIrAwEAcPXq02vWKiorg6uoKFxcX9O/fH+fOnas0Zv/+/bCzs4OHhwfGjBmDW7duVbu90tJSKBQKjZsh+zzuIlIyFWhiboxFr3lVOYNJREREukvUApeXlwelUllpBs3e3h5ZWVlVruPh4YH169djx44d+OGHH6BSqdC9e3dcv35dPSY4OBjfffcd4uLisHTpUhw4cAB9+vSBUqmscpuLFy+GtbW1+ubi4lJ3T1LHnEq/jS/3pwEAPhnoBTtL08esQURERLpG9GPgtBUQEICAgAD11927d0f79u3x9ddfY8GCBQCAN998U32/l5cXvL290bp1a+zfvx8vvfRSpW1Onz4d4eHh6q8VCoVBlrh7ZUpMjE6ESgD6+zqhr5ej2JGIiIioFkSdgbO1tYVMJkN2drbG8uzsbDg4ONRoG8bGxujUqRPS0tKqHdOqVSvY2tpWO0Yul8PKykrjZoiWxp7H5bxi2FvJMb9fR7HjEBERUS2JWuBMTEzg5+eHuLg49TKVSoW4uDiNWbZHUSqVOHv2LBwdq59Nun79Om7duvXIMYbuSFoeNh65CgBY+ro3rM2NxQ1EREREtSb6Wajh4eFYu3YtNm3ahJSUFIwZMwbFxcUICwsDAISGhmL69Onq8fPnz8fevXtx+fJlnDx5Em+//TauXbuGUaNGAbh/gsPkyZNx7NgxXL16FXFxcejfvz/atGmDoKAgUZ6j2BQl5Zi85QwA4C3/Fnjeo+rLsxAREZF+EP0YuCFDhiA3Nxdz5sxBVlYWfH19ERsbqz6xIT09HVLpvz3z9u3bGD16NLKystCkSRP4+fnhyJEj8PT0BADIZDKcOXMGmzZtwp07d+Dk5ITevXtjwYIFDfZacAt+TcaNO/fQwsYcM/u2FzsOERERPSGJIAiC2CF0jUKhgLW1NQoKCvT+eLg/k7Mx6rt/IJEA0e8HoKubjdiRiIiI6oUhvX8/jui7UKn+5BeXYdrWswCA93q1YnkjIiIyECxwBkoQBMzcdhZ5RaVwt2+ECS+7ix2JiIiI6ggLnIHacfomfk/KgpFUgsjBvjA1lokdiYiIiOoIC5wByioowZwdSQCAj15qi47O1iInIiIiorrEAmdgBEHAlF/OQFFSAZ/m1vjg+dZiRyIiIqI6xgJnYH48no6DF3IhN5Ji5WBfGMn4X0xERGRo+O5uQK7dKsai3SkAgCnB7dDGrpHIiYiIiKg+sMAZCKVKwMToRNwtU+KZVjYI6+4mdiQiIiKqJyxwBuLbvy/jn2u30UhuhOVv+EAqlYgdiYiIiOoJC5wBSM0qxMq9FwAAs19tDxcbc5ETERERUX1igdNzZRUqhEefRplShRfb2WFwFxexIxEREVE9Y4HTc1/su4hzNxVobG6MJa95QSLhrlMiIiJDxwKnxxIz7mD1/ksAgIUDOsLOylTkRERERPQ0sMDpqZJyJcKjT0OpEtDPxwmvejuJHYmIiIieEhY4PbUsNhWXcothZynH/P4dxI5DRERETxELnB46eukW1h++AgBY+oY3GpubiJyIiIiIniYWOD1TWFKOSTGJAICh3VrgBQ87kRMRERHR08YCp2cW/JaMG3fuwcXGDDNfaS92HCIiIhIBC5we+TM5G9H/XIdEAqwc5ItGciOxIxEREZEIWOD0RH5xGaZtPQsAGNWzJbq1tBE5EREREYmFBU4PCIKA2duTkFdUirZ2jTCxt4fYkYiIiEhELHB6YGfiTew6mwkjqQSRg31haiwTOxIRERGJiAVOx2UrSjBnxzkAwLgX28CrubXIiYiIiEhsLHA6TBAETNlyBgX3yuHlbI2xL7QROxIRERHpABY4HfZzfAYOXMiFiZEUkYN9YCzjfxcRERGxwOms9Ft3sXBXMgBgSpAH2tpbipyIiIiIdAULnA5SqgRMiknE3TIlurW0wYgeLcWORERERDqEBU4HrT90BfFX82FhIsPKQT6QSiViRyIiIiIdwgKnYy5kF2L53lQAwOxXPeFiYy5yIiIiItI1LHA6pFypQnj0aZRVqPBiOzsM6eoidiQiIiLSQSxwOuSLfWlIuqFAY3NjLHnNCxIJd50SERFRZSxwOuLM9Tv44q80AMCC/h1hZ2UqciIiIiLSVSxwOqCkXIkJUaehVAl41dsRIT5OYkciIiIiHcYCpwOW70nFpdxiNLOUY0H/jmLHISIiIh3HAieyY5dvYf3hKwCAZa97o4mFiciJiIiISNexwImoqLQCk2ISIQjAm11d8EI7O7EjERERkR5ggRPRwt+Scf32PTRvYoZZr3qKHYeIiIj0hE4UuNWrV8PNzQ2mpqbw9/dHfHx8tWM3btwIiUSicTM11TxjUxAEzJkzB46OjjAzM0NgYCAuXrxY30/jsZQqAUcv3cKO0zfw5V8XsflEBiQSYMUgHzSSG4kdj4iIiPSE6K0hKioK4eHhWLNmDfz9/bFq1SoEBQUhNTUVdnZV71K0srJCamqq+uv/Xi9t2bJl+Pzzz7Fp0ya0bNkSs2fPRlBQEJKTkyuVvaclNikT835NRmZBicbyFz3s8EyrpqJkIiIiIv0k+gxcZGQkRo8ejbCwMHh6emLNmjUwNzfH+vXrq11HIpHAwcFBfbO3t1ffJwgCVq1ahVmzZqF///7w9vbGd999h5s3b2L79u1P4RlVFpuUiTE/nKxU3gBg3/kcxCZlipCKiIiI9JWoBa6srAwJCQkIDAxUL5NKpQgMDMTRo0erXa+oqAiurq5wcXFB//79ce7cOfV9V65cQVZWlsY2ra2t4e/v/8ht1helSsC8X5MhPGLMvF+ToVQ9agQRERHRv0QtcHl5eVAqlRozaABgb2+PrKysKtfx8PDA+vXrsWPHDvzwww9QqVTo3r07rl+/DgDq9bTZZmlpKRQKhcatrsRfya9y5u0BAUBmQQnir+TX2WMSERGRYRN9F6q2AgICEBoaCl9fXzz33HPYunUrmjVrhq+//rrW21y8eDGsra3VNxeXuvsQ+ZzC6stbbcYRERERiVrgbG1tIZPJkJ2drbE8OzsbDg4ONdqGsbExOnXqhLS0+58j+mA9bbY5ffp0FBQUqG8ZGRnaPpVq2VnW7KSJmo4jIiIiErXAmZiYwM/PD3FxceplKpUKcXFxCAgIqNE2lEolzp49C0dHRwBAy5Yt4eDgoLFNhUKB48ePV7tNuVwOKysrjVtd6dbSBo7WppBUc78EgKO1Kbq1tKmzxyQiIiLDJvou1PDwcKxduxabNm1CSkoKxowZg+LiYoSFhQEAQkNDMX36dPX4+fPnY+/evbh8+TJOnjyJt99+G9euXcOoUaMA3D9D9eOPP8bChQuxc+dOnD17FqGhoXBycsKAAQOe+vOTSSWICLl/kd7/lrgHX0eEeEImra7iEREREWkS/TpwQ4YMQW5uLubMmYOsrCz4+voiNjZWfRJCeno6pNJ/e+bt27cxevRoZGVloUmTJvDz88ORI0fg6fnvJxlMmTIFxcXFeO+993Dnzh307NkTsbGxol0DLrijI756u3Ol68A5WJsiIsQTwR0dRclFRERE+kkiCAKvX/EfCoUC1tbWKCgoqNPdqUqVgPgr+cgpLIGd5f3dppx5IyIiqhv19f6ti0SfgWtIZFIJAlrzUxeIiIjoyYh+DBwRERERaYcFjoiIiEjPsMARERER6RkWOCIiIiI9wwJHREREpGdY4IiIiIj0DAscERERkZ5hgSMiIiLSMyxwRERERHqGn8RQhQefLqZQKEROQkRERDX14H27IXxKKAtcFQoLCwEALi4uIichIiIibRUWFsLa2lrsGPWKH2ZfBZVKhZs3b8LS0hISSd1+2LxCoYCLiwsyMjIM/oN2iXQRX4NE4qrP16AgCCgsLISTkxOkUsM+SowzcFWQSqVo3rx5vT6GlZUV3zyIRMTXIJG46us1aOgzbw8Ydj0lIiIiMkAscERERER6hgXuKZPL5YiIiIBcLhc7ClGDxNcgkbj4GqwbPImBiIiISM9wBo6IiIhIz7DAEREREekZFjgiIiIiPcMCpwU3NzesWrVK/bVEIsH27dtFy1OXtH0u7777LgYMGFBveajh2b9/PyQSCe7cuSN2FK1dvXoVEokEp0+frvE6zz//PD7++ON6y0T6QZ+/73XR3Llz4evrK3YMrV/fGzduROPGjbV6DL0ocO+++y4kEon61rRpUwQHB+PMmTOi5srMzESfPn3q9TE2btwIiUSC9u3bV7ovJiYGEokEbm5u9ZqB6Ek9/Pqt6jZ37tx6e+y5c+dCIpEgODi40n3Lly+HRCLB888/X2+PTw2X2N/3ulBkdM2DX7ZkMhlu3LihcV9mZiaMjIwgkUhw9epVcQJqQS8KHAAEBwcjMzMTmZmZiIuLg5GREV599VVRMzk4ODyV06AtLCyQk5ODo0ePaixft24dWrRoUe+PT/SkHrx2MzMzsWrVKlhZWWksmzRpUr0+vqOjI/766y9cv35dY/n69ev5GqJ6I/b3vRgEQUBFRYXYMR7L2dkZ3333ncayTZs2wdnZWaRE2tObAieXy+Hg4AAHBwf4+vpi2rRpyMjIQG5urnrM1KlT4e7uDnNzc7Rq1QqzZ89GeXm5+v7ExES88MILsLS0hJWVFfz8/PDPP/+o7z906BB69eoFMzMzuLi44KOPPkJxcXG1mR7e7fig1W/duhUvvPACzM3N4ePjU6l0afsYAGBkZIS33noL69evVy+7fv069u/fj7feeqvS+K+++gqtW7eGiYkJPDw88P3332vcf/HiRTz77LMwNTWFp6cn/vjjj0rbyMjIwODBg9G4cWPY2Nigf//+evEbCemmB69dBwcHWFtbQyKRaCxr1KiRemxCQgK6dOkCc3NzdO/eHampqRrb2rFjBzp37gxTU1O0atUK8+bNe+wbhp2dHXr37o1Nmzaplx05cgR5eXl45ZVXNMaqVCrMnz8fzZs3h1wuh6+vL2JjYzXGxMfHo1OnTjA1NUWXLl1w6tSpSo+ZlJSEPn36oFGjRrC3t8c777yDvLy8Gv+bkf4T+/v+YQ8Oe1m0aBHs7e3RuHFjzJ8/HxUVFZg8eTJsbGzQvHlzbNiwQb3Og/e1zZs3o3v37jA1NUXHjh1x4MAB9ZgHu4B///13+Pn5QS6X49ChQygtLcVHH30EOzs7mJqaomfPnjhx4gSA+6+x5s2b46uvvtLIeOrUKUilUly7dg0AcOfOHYwaNQrNmjWDlZUVXnzxRSQmJmqss2TJEtjb28PS0hIjR45ESUlJjf49hg8frvFcAWDDhg0YPnx4pbEHDhxAt27dIJfL4ejoiGnTpmn82xcXFyM0NBSNGjWCo6MjVq5cWWkbpaWlmDRpEpydnWFhYQF/f3/s37+/RlmrozcF7mFFRUX44Ycf0KZNGzRt2lS93NLSEhs3bkRycjI+++wzrF27Fp9++qn6/mHDhqF58+Y4ceIEEhISMG3aNBgbGwMALl26hODgYLz++us4c+YMoqKicOjQIYwbN06rbDNnzsSkSZNw+vRpuLu7Y+jQoer/6Cd5jBEjRiA6Ohp3794FcH/XanBwMOzt7TXGbdu2DePHj8fEiRORlJSE999/H2FhYfjrr78A3H/hvPbaazAxMcHx48exZs0aTJ06VWMb5eXlCAoKgqWlJf7++28cPnwYjRo1QnBwMMrKyrT69yDS1syZM7Fy5Ur8888/MDIywogRI9T3/f333wgNDcX48eORnJyMr7/+Ghs3bsQnn3zy2O2OGDECGzduVH+9fv16DBs2DCYmJhrjPvvsM6xcuRIrVqzAmTNnEBQUhH79+uHixYsA7v/8efXVV+Hp6YmEhATMnTu30kzKnTt38OKLL6JTp074559/EBsbi+zsbAwePPgJ/mXIkNXX9/3D9u3bh5s3b+LgwYOIjIxEREQEXn31VTRp0gTHjx/H//73P7z//vuVZqonT56MiRMn4tSpUwgICEBISAhu3bqlMWbatGlYsmQJUlJS4O3tjSlTpuCXX37Bpk2bcPLkSbRp0wZBQUHIz8+HVCrF0KFD8dNPP2ls48cff0SPHj3g6uoKABg0aBBycnLw+++/IyEhAZ07d8ZLL72E/Px8AEB0dDTmzp2LRYsW4Z9//oGjoyO+/PLLGv1b9OvXD7dv38ahQ4cA3J9cuX37NkJCQjTG3bhxA3379kXXrl2RmJiIr776CuvWrcPChQs1/n0OHDiAHTt2YO/evdi/fz9OnjypsZ1x48bh6NGj2Lx5M86cOYNBgwYhODhY/XOlVgQ9MHz4cEEmkwkWFhaChYWFAEBwdHQUEhISHrne8uXLBT8/P/XXlpaWwsaNG6scO3LkSOG9997TWPb3338LUqlUuHfvniAIguDq6ip8+umn6vsBCNu2bRMEQRCuXLkiABC+/fZb9f3nzp0TAAgpKSk1foz/2rBhg2BtbS0IgiD4+voKmzZtElQqldC6dWthx44dwqeffiq4urqqx3fv3l0YPXq0xjYGDRok9O3bVxAEQdizZ49gZGQk3LhxQ33/77//rvFcvv/+e8HDw0NQqVTqMaWlpYKZmZmwZ88eQRDu/5/079+/ysxEj/Lw9/TD/vrrLwGA8Oeff6qX7dq1SwCgfn289NJLwqJFizTW+/777wVHR8dqHy8iIkLw8fERysrKBDs7O+HAgQNCUVGRYGlpKSQmJgrjx48XnnvuOfV4Jycn4ZNPPtHYRteuXYUPPvhAEARB+Prrr4WmTZtqvGa/+uorAYBw6tQpQRAEYcGCBULv3r01tpGRkSEAEFJTUwVBEITnnntOGD9+fLW5ybCI9X3/wPDhwwVXV1dBqVSql3l4eAi9evVSf11RUSFYWFgIP//8syAI/76vLVmyRD2mvLxcaN68ubB06VKN/Nu3b1ePKSoqEoyNjYUff/xRvaysrExwcnISli1bJgiCIJw6dUqQSCTCtWvXBEEQBKVSKTg7OwtfffWVIAj33xutrKyEkpISjefVunVr4euvvxYEQRACAgLUr8sH/P39NZ73fz14TqdOnRI+/vhjISwsTBAEQQgLCxMmTJggnDp1SgAgXLlyRRAEQZgxY0al98PVq1cLjRo1EpRKpVBYWCiYmJgI0dHR6vtv3bolmJmZqV/f165dE2Qymcb7riDc/3+dPn26IAjVf388it7MwL3wwgs4ffo0Tp8+jfj4eAQFBaFPnz7qqVYAiIqKQo8ePdRT07NmzUJ6err6/vDwcIwaNQqBgYFYsmQJLl26pL4vMTERGzduRKNGjdS3oKAgqFQqXLlypcY5vb291X93dHQEAOTk5NTJY4wYMQIbNmzAgQMHUFxcjL59+1Yak5KSgh49emgs69GjB1JSUtT3u7i4wMnJSX1/QECAxvjExESkpaXB0tJSndPGxgYlJSUa/2ZE9eFxr6H58+drvIZGjx6NzMxM9ex0dYyNjfH2229jw4YNiImJgbu7u8ZjAYBCocDNmzcf+xry9vaGqamp+v6qXkN//fWXRs527doBAF9DVKX6+r5/WIcOHSCV/vu2b29vDy8vL/XXMpkMTZs2VT/uAw9/fxsZGaFLly7q18MDXbp0Uf/90qVLKC8v13gdGRsbo1u3bur1fH190b59e/Us3IEDB5CTk4NBgwapn3NRURGaNm2q8byvXLmifg2lpKTA39+/2qyPM2LECMTExCArKwsxMTEas54PpKSkICAgABKJRL2sR48eKCoqwvXr13Hp0iWUlZVp5LCxsYGHh4f667Nnz0KpVMLd3V3juRw4cOCJfh4Y1XrNp8zCwgJt2rRRf/3tt9/C2toaa9euxcKFC3H06FEMGzYM8+bNQ1BQEKytrbF582aNfdFz587FW2+9hV27duH3339HREQENm/ejIEDB6KoqAjvv/8+Pvroo0qPrc1Bzg92yQJQ/4erVCoAeOLHGDZsGKZMmYK5c+finXfegZFR/fz3FRUVwc/PDz/++GOl+5o1a1Yvj0n0wONeQ/PmzcNrr71Wab2HC1V1RowYAX9/fyQlJVX5w7quFBUVISQkBEuXLq1034M3Z6KH1ef3fVWP8eBxqlr24HG1YWFhofU6w4YNw08//YRp06bhp59+QnBwsPqwqKKiIjg6OlZ5nJi2l9uojpeXF9q1a4ehQ4eiffv26Nixo1aXAqqpoqIiyGQyJCQkQCaTadz38HGQ2tKbAvdfEokEUqkU9+7dA3D/gGRXV1fMnDlTPebh2bkH3N3d4e7ujgkTJmDo0KHYsGEDBg4ciM6dOyM5OVmjJNa1J30MGxsb9OvXD9HR0VizZk2VY9q3b4/Dhw9rHIh5+PBheHp6qu/PyMhAZmam+o3k2LFjlXJGRUXBzs4OVlZWtcpKVB86d+6M1NTUWr+GOnTogA4dOuDMmTNVngBkZWUFJycnHD58GM8995x6+eHDh9GtWzcA919D33//PUpKStRvnlW9hn755Re4ubnV2y9a1HA86ff9kzp27BieffZZAEBFRQUSEhIeeez2g5PoDh8+rD6erby8HCdOnNC4Ntpbb72FWbNmISEhAVu2bNF4X+vcuTOysrJgZGRU7aWy2rdvj+PHjyM0NFQjqzZGjBiBDz74oNIJFQ8/xi+//AJBENTF+vDhw7C0tETz5s1hY2MDY2NjHD9+XD0Rc/v2bVy4cEH9M6RTp05QKpXIyclBr169tMr3KHqzC7W0tBRZWVnIyspCSkoKPvzwQ/VvuQDQtm1bpKenY/Pmzbh06RI+//xzbNu2Tb3+vXv3MG7cOOzfvx/Xrl3D4cOHceLECfX11aZOnYojR45g3LhxOH36NC5evIgdO3ZofRLDo9TFY2zcuBF5eXnq3TH/NXnyZGzcuBFfffUVLl68iMjISGzdulV9kHVgYCDc3d0xfPhwJCYm4u+//9YovcD934psbW3Rv39//P3337hy5Qr279+Pjz76qNLBrURP05w5c/Ddd99h3rx5OHfuHFJSUrB582bMmjWrxtvYt28fMjMzq/0tfvLkyVi6dCmioqKQmpqKadOm4fTp0xg/fjyA+286EokEo0ePRnJyMnbv3o0VK1ZobGPs2LHIz8/H0KFDceLECVy6dAl79uxBWFgYlEplrZ8/NUx18X3/JFavXo1t27bh/PnzGDt2LG7fvv3IGWwLCwuMGTMGkydPRmxsLJKTkzF69GjcvXsXI0eOVI9zc3ND9+7dMXLkSCiVSvTr1099X2BgIAICAjBgwADs3bsXV69exZEjRzBz5kz11SPGjx+P9evXY8OGDbhw4QIiIiJw7tw5rZ7b6NGjkZubi1GjRlV5/wcffICMjAx8+OGHOH/+PHbs2IGIiAiEh4dDKpWiUaNGGDlyJCZPnox9+/YhKSkJ7777rsauand3dwwbNgyhoaHYunUrrly5gvj4eCxevBi7du3SKu/D9KbAxcbGwtHREY6OjvD398eJEycQExOjvgBnv379MGHCBIwbNw6+vr44cuQIZs+erV5fJpPh1q1bCA0Nhbu7OwYPHow+ffpg3rx5AO4ff3DgwAFcuHABvXr1QqdOnTBnzhyNY8WeVF08hpmZmcaZt/81YMAAfPbZZ1ixYgU6dOiAr7/+Ghs2bFD/O0mlUmzbtg337t1Dt27dMGrUqEpnMpmbm+PgwYNo0aIFXnvtNbRv3159ejZn5EhMQUFB+O2337B371507doVzzzzDD799FP1b/k1YWFh8chdMB999BHCw8MxceJEeHl5ITY2Fjt37kTbtm0B3N/l8euvv+Ls2bPo1KkTZs6cWWlX6YNZPKVSid69e8PLywsff/wxGjdurPGDnagm6uL7/kksWbIES5YsgY+PDw4dOoSdO3fC1tb2seu8/vrreOedd9C5c2ekpaVhz549aNKkica4YcOGITExEQMHDoSZmZl6uUQiwe7du/Hss88iLCwM7u7uePPNN3Ht2jX11ReGDBmC2bNnY8qUKfDz88O1a9cwZswYrZ6bkZERbG1tq50pd3Z2xu7duxEfHw8fHx/873//w8iRIzXK8/Lly9GrVy+EhIQgMDAQPXv2hJ+fn8Z2NmzYgNDQUEycOBEeHh4YMGAATpw48UTXoZQIgiDUem0iIiIySFevXkXLli1x6tQpfqqDDuKvgkRERER6hgWOiIiISM9wFyoRERGRnuEMHBEREZGeYYEjIiIi0jMscERERER6hgWOiIiISM+wwBERERHpGRY4IiIiIj3DAkdERESkZ1jgiIiIiPQMCxwRERGRnvn/fIPrd6II45sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Graph\n",
    "plt.plot([baseline_acc, the_model_acc, improved_model_acc], marker='o')\n",
    "plt.xticks([0, 1, 2], ['Baseline Model', 'The Model', 'The Improved Model'])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "043e1753",
   "metadata": {},
   "source": [
    "## Predicting the model for new review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "645e44d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Masking, LSTM, Dense\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the pre-trained Word2Vec model\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")\n",
    "\n",
    "# Define the function to embed a sentence with the pre-trained Word2Vec model\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Define the function to preprocess a new movie review\n",
    "def preprocess_review(review):\n",
    "    # Tokenize the review\n",
    "    review = text_to_word_sequence(review)\n",
    "    # Embed the review with the pre-trained Word2Vec model\n",
    "    review_embedded = embed_sentence_with_TF(word2vec_transfer, review)\n",
    "    # Pad the embedded review\n",
    "    review_padded = pad_sequences([review_embedded], dtype='float32', padding='post', maxlen=200)\n",
    "    return review_padded\n",
    "\n",
    "# Load the trained model\n",
    "model = Sequential()\n",
    "model.add(Masking())\n",
    "model.add(LSTM(20, activation='tanh'))\n",
    "model.add(Dense(15, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "model = load_model('my_model.h5')\n",
    "def predict_sentiment(review):\n",
    "    # Preprocess the review\n",
    "    review_padded = preprocess_review(review)\n",
    "    # Predict the sentiment\n",
    "    sentiment = model.predict(review_padded)[0][0]\n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "faf5685a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 721ms/step\n",
      "Positive review\n"
     ]
    }
   ],
   "source": [
    "review = input(\"Enter a review:\")\n",
    "sentiment = predict_sentiment(review)\n",
    "\n",
    "if sentiment > 0.5:\n",
    "    print(\"Positive review\")\n",
    "elif sentiment == 0.5:\n",
    "    print(\"Neutral review\")\n",
    "else:\n",
    "    print(\"Negative review\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d5f2bc35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gradio in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (3.33.1)\n",
      "Requirement already satisfied: aiofiles in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (23.1.0)\n",
      "Requirement already satisfied: aiohttp in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (3.8.4)\n",
      "Requirement already satisfied: altair>=4.2.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (4.2.0)\n",
      "Requirement already satisfied: fastapi in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.96.0)\n",
      "Requirement already satisfied: ffmpy in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.3.0)\n",
      "Requirement already satisfied: gradio-client>=0.2.4 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.2.5)\n",
      "Requirement already satisfied: httpx in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.23.0)\n",
      "Requirement already satisfied: huggingface-hub>=0.14.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.15.1)\n",
      "Requirement already satisfied: jinja2 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (3.1.2)\n",
      "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (2.2.0)\n",
      "Requirement already satisfied: markupsafe in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (2.1.1)\n",
      "Requirement already satisfied: matplotlib in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (3.5.3)\n",
      "Requirement already satisfied: mdit-py-plugins<=0.3.3 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.3.3)\n",
      "Requirement already satisfied: numpy in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (1.23.4)\n",
      "Requirement already satisfied: orjson in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (3.9.0)\n",
      "Requirement already satisfied: pandas in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (1.4.4)\n",
      "Requirement already satisfied: pillow in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (9.1.1)\n",
      "Requirement already satisfied: pydantic in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (1.9.2)\n",
      "Requirement already satisfied: pydub in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.25.1)\n",
      "Requirement already satisfied: pygments>=2.12.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (2.13.0)\n",
      "Requirement already satisfied: python-multipart in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.0.6)\n",
      "Requirement already satisfied: pyyaml in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (5.4.1)\n",
      "Requirement already satisfied: requests in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (2.28.1)\n",
      "Requirement already satisfied: semantic-version in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (2.10.0)\n",
      "Requirement already satisfied: typing-extensions in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (4.4.0)\n",
      "Requirement already satisfied: uvicorn>=0.14.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (0.22.0)\n",
      "Requirement already satisfied: websockets>=10.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio) (11.0.3)\n",
      "Requirement already satisfied: entrypoints in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from altair>=4.2.0->gradio) (0.4)\n",
      "Requirement already satisfied: jsonschema>=3.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from altair>=4.2.0->gradio) (4.16.0)\n",
      "Requirement already satisfied: toolz in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from altair>=4.2.0->gradio) (0.12.0)\n",
      "Requirement already satisfied: fsspec in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio-client>=0.2.4->gradio) (2022.10.0)\n",
      "Requirement already satisfied: packaging in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from gradio-client>=0.2.4->gradio) (21.3)\n",
      "Requirement already satisfied: filelock in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface-hub>=0.14.0->gradio) (3.12.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface-hub>=0.14.0->gradio) (4.64.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
      "Requirement already satisfied: linkify-it-py<3,>=1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (2.0.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from pandas->gradio) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from pandas->gradio) (2022.1)\n",
      "Requirement already satisfied: click>=7.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from uvicorn>=0.14.0->gradio) (8.1.3)\n",
      "Requirement already satisfied: h11>=0.8 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from uvicorn>=0.14.0->gradio) (0.12.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (22.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (2.1.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (6.0.4)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (4.0.2)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (1.3.3)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from aiohttp->gradio) (1.3.1)\n",
      "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from fastapi->gradio) (0.27.0)\n",
      "Requirement already satisfied: certifi in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from httpx->gradio) (2022.12.7)\n",
      "Requirement already satisfied: sniffio in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from httpx->gradio) (1.3.0)\n",
      "Requirement already satisfied: rfc3986[idna2008]<2,>=1.3 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from httpx->gradio) (1.5.0)\n",
      "Requirement already satisfied: httpcore<0.16.0,>=0.15.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from httpx->gradio) (0.15.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from matplotlib->gradio) (0.10.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from matplotlib->gradio) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from matplotlib->gradio) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from matplotlib->gradio) (2.4.7)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->gradio) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->gradio) (1.26.12)\n",
      "Requirement already satisfied: six in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from cycler>=0.10->matplotlib->gradio) (1.16.0)\n",
      "Requirement already satisfied: anyio==3.* in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from httpcore<0.16.0,>=0.15.0->httpx->gradio) (3.6.2)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.18.1)\n",
      "Requirement already satisfied: uc-micro-py in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio) (1.0.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7daa2754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: huggingface_hub in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (0.15.1)\n",
      "Requirement already satisfied: filelock in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (3.12.0)\n",
      "Requirement already satisfied: fsspec in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (2022.10.0)\n",
      "Requirement already satisfied: requests in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (2.28.1)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (4.64.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (5.4.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (4.4.0)\n",
      "Requirement already satisfied: packaging>=20.9 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from huggingface_hub) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from packaging>=20.9->huggingface_hub) (2.4.7)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->huggingface_hub) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->huggingface_hub) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->huggingface_hub) (1.26.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages (from requests->huggingface_hub) (2022.12.7)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade huggingface_hub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "30d4a32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:30: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  super().__init__(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:30: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
      "  super().__init__(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.text import text_to_word_sequence\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from gensim.models import KeyedVectors\n",
    "from tensorflow.keras.models import load_model\n",
    "# Load the pre-trained Word2Vec model\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")\n",
    "\n",
    "# Define the function to embed a sentence with the pre-trained Word2Vec model\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Define the function to preprocess a new movie review\n",
    "def preprocess_review(review):\n",
    "    # Tokenize the review\n",
    "    review = text_to_word_sequence(review)\n",
    "    # Embed the review with the pre-trained Word2Vec model\n",
    "    review_embedded = embed_sentence_with_TF(word2vec_transfer, review)\n",
    "    # Pad the embedded review\n",
    "    review_padded = pad_sequences([review_embedded], dtype='float32', padding='post', maxlen=200)\n",
    "    return review_padded\n",
    "\n",
    "# Load the trained model\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "def predict_sentiment(review):\n",
    "    # Preprocess the review\n",
    "    review_padded = preprocess_review(review)\n",
    "    # Predict the sentiment\n",
    "    sentiment = model.predict(review_padded)[0][0]\n",
    "    if sentiment > 0.5:\n",
    "        return \"Positive\"\n",
    "    elif sentiment == 0.5:\n",
    "        return \"Neutral\"\n",
    "    else:\n",
    "        return \"Negative\"\n",
    "\n",
    "# Create a Gradio interface\n",
    "inputs = gr.inputs.Textbox(lines=5, label=\"Input Text\")\n",
    "outputs = gr.outputs.Textbox(label=\"Sentiment\")\n",
    "title = \"Sentiment Analysis\"\n",
    "description = \"Enter a text and get the sentiment prediction.\"\n",
    "gr.Interface(fn=predict_sentiment, inputs=inputs, outputs=outputs, title=title, description=description).launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfaff667",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "toc": {
   "base_numbering": "1",
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}