imdb-lstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a1fd7ee-d486-4e60-82ef-b8b98744db26",
   "metadata": {},
   "source": [
    "#### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d3241ed-4b46-4167-9b10-276f65c02f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-04 02:27:53.659567: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1735937873.670448  169809 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1735937873.673735  169809 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-01-04 02:27:53.686264: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from numpy import array\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.preprocessing.text import one_hot,Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dropout, Dense\n",
    "from keras.layers import Flatten, GlobalMaxPooling1D, Embedding, Conv1D, LSTM\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d36f9e20-6cf4-4752-957e-b2034cefd700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 11040445708177024046\n",
      "xla_global_id: -1\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 9379840000\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 16016060118127100581\n",
      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:06:00.0, compute capability: 8.6\"\n",
      "xla_global_id: 416903419\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1735937875.195060  169809 gpu_device.cc:2022] Created device /device:GPU:0 with 8945 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:06:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecb0d18c-558d-47e2-a98d-3ca84d6eee14",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This disables GPU , forced to CPU\n",
    "\n",
    "# import os\n",
    "# os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66d2c28-9b53-45cc-a2d5-85856697eb4e",
   "metadata": {},
   "source": [
    "## Preparing and Processing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b597f983-dd73-4f71-9e4b-6563b9daa4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_reviews = pd.read_csv(\"a1_IMDB_Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85602612-293c-4f30-8bf4-a25c5be065cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f867ae82-5769-48be-9628-bf766f5389c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment\n",
       "0  One of the other reviewers has mentioned that ...  positive\n",
       "1  A wonderful little production. <br /><br />The...  positive\n",
       "2  I thought this was a wonderful way to spend ti...  positive\n",
       "3  Basically there's a family where a little boy ...  negative\n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49015041-95f3-494f-8b09-39b6a0e0353c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review       0\n",
       "sentiment    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5bae193-7b25-4100-8da2-129ec51e40d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(x='sentiment', data=movie_reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53f08197-46f7-4c55-89fc-357a738ca8cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A wonderful little production. <br /><br />The filming technique is very unassuming- very old-time-BBC fashion and gives a comforting, and sometimes discomforting, sense of realism to the entire piece. <br /><br />The actors are extremely well chosen- Michael Sheen not only \"has got all the polari\" but he has all the voices down pat too! You can truly see the seamless editing guided by the references to Williams\\' diary entries, not only is it well worth the watching but it is a terrificly written and performed piece. A masterful production about one of the great master\\'s of comedy and his life. <br /><br />The realism really comes home with the little things: the fantasy of the guard which, rather than use the traditional \\'dream\\' techniques remains solid then disappears. It plays on our knowledge and our senses, particularly with the scenes concerning Orton and Halliwell and the sets (particularly of their flat with Halliwell\\'s murals decorating every surface) are terribly well done.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews['review'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e6cba90-3f91-44f4-a896-890b7fcfbb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you see this review, it has html tags or any unecessary symbols we have to delete it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cbac18-91c3-46b0-87cd-a90ee65f671a",
   "metadata": {},
   "source": [
    "## Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4555f4c-f411-43c5-a89d-56a957933f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "TAG_RE = re.compile(r'<[^>]+>')\n",
    "\n",
    "def remove_tags(text):\n",
    "    return TAG_RE.sub('', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d839d39-c7fa-4f47-93f3-edc68e4ab4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jd1t/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "188bd000-c16c-4ac6-b896-0281d361e2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(sen):    \n",
    "    sentence = sen.lower()\n",
    "\n",
    "    # Remove html tags\n",
    "    sentence = remove_tags(sentence)\n",
    "\n",
    "    # Remove punctuations and numbers\n",
    "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
    "\n",
    "    # Single character removal\n",
    "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)  \n",
    "\n",
    "    # Remove multiple spaces\n",
    "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
    "\n",
    "    # Remove Stopwords\n",
    "    pattern = re.compile(r'\\b(' + r'|'.join(stopwords.words('english')) + r')\\b\\s*')\n",
    "    sentence = pattern.sub('', sentence)\n",
    "\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0807e003-e71f-4e6c-a3d5-a03987b3c22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "sentences = list(movie_reviews['review'])\n",
    "for sen in sentences:\n",
    "    X.append(preprocess_text(sen))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2424d16c-2564-4a7f-b103-8a0a4a041a94",
   "metadata": {},
   "source": [
    "### Converting using Binary Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac47e541-4baa-4c0f-a7b6-3906ffd3d26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = movie_reviews['sentiment']\n",
    "\n",
    "y = np.array(list(map(lambda x: 1 if x==\"positive\" else 0, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ade7d19d-3bbd-4937-9707-d4189419e67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## No need to explain this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd3b8000-0d32-4010-b08e-68172280d5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfc5c1dc-9963-4bf5-a244-cc53e900662a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenizes the text data, converting the text into sequences of integers based on the word frequency.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea16ec6e-f59c-46ff-9e07-4cb6bbd219c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_tokenizer = Tokenizer()\n",
    "word_tokenizer.fit_on_texts(X_train)\n",
    "\n",
    "X_train = word_tokenizer.texts_to_sequences(X_train)\n",
    "X_test = word_tokenizer.texts_to_sequences(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f033ab15-4f52-4d47-bba8-935266b99602",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculates size of vocal i.e. total no of unique words\n",
    "# adding 1, well because word index should not be greater than vocab size in embedding layer \n",
    "# https://stackoverflow.com/questions/67293182/reason-for-adding-1-to-word-index-for-sequence-modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e7c6d81-226d-4725-b770-c32ef2bdbf7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92394"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_length = len(word_tokenizer.word_index) + 1\n",
    "\n",
    "vocab_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdc0690a-1e6c-4990-acfb-0a3a15956e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to ensure they are all of equal length that is 100 here and adding to each sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2caeb806-6ec4-4ebd-8c6e-693f3829ee85",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen = 100\n",
    "\n",
    "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
    "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e215ff28-bdd8-4cc9-8b94-c5e4534af54f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# used Glove here, maps each word to its vector "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34cc89ed-a220-4fed-bdc2-87bdb8b7d13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import asarray\n",
    "from numpy import zeros\n",
    "\n",
    "embeddings_dictionary = dict()\n",
    "glove_file = open('a2_glove.6B.100d.txt', encoding=\"utf8\")\n",
    "\n",
    "for line in glove_file:\n",
    "    records = line.split()\n",
    "    word = records[0]\n",
    "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
    "    embeddings_dictionary [word] = vector_dimensions\n",
    "glove_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e504b7f0-905a-4258-9369-d572acd79d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct embedding matrix which here is 100 dim vector from glove , this matrix will provide word representations udring traingin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b21c2e52-057e-40e0-8352-46fd927c0cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = zeros((vocab_length, 100))\n",
    "for word, index in word_tokenizer.word_index.items():\n",
    "    embedding_vector = embeddings_dictionary.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[index] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94552001-8a12-4e70-ae55-eef34a9325bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92394, 100)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad095926-7309-49df-b18a-d6434d817c1a",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd4079b2-48c2-4efa-8754-e701dd561621",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c7a8237-52a4-4c51-9226-94f31672887c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Sequential LSTM with Glove embedding layer followed by lstm layer and finally sigmoid activation for binary classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0942a296-c60b-499e-8ce9-508684ee75a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1735937898.050518  169809 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8945 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:06:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "lstm_model = Sequential()\n",
    "embedding_layer = Embedding(vocab_length, 100, weights=[embedding_matrix], trainable=False)\n",
    "\n",
    "lstm_model.add(embedding_layer)\n",
    "lstm_model.add(LSTM(128))\n",
    "\n",
    "lstm_model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e878c7be-248a-4139-9af2-6e013229452f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiles model with adam optimizer, accuracy as metric \n",
    "# Prints the model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46c1a47d-3e3f-461f-a3ae-639c34b58f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ ?                      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">9,239,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ ?                      │     \u001b[38;5;34m9,239,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,239,400</span> (35.25 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,239,400\u001b[0m (35.25 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,239,400</span> (35.25 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m9,239,400\u001b[0m (35.25 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
    "print(lstm_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e4113c9-d0c4-4c3a-9a0b-5ce6ac4589d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model having batch size of 128 for 6 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db76e450-34e3-48d5-a656-dfefbfe6dfd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1735937899.840461  169975 cuda_dnn.cc:529] Loaded cuDNN version 90501\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - acc: 0.6818 - loss: 0.5876 - val_acc: 0.8084 - val_loss: 0.4235\n",
      "Epoch 2/6\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - acc: 0.8064 - loss: 0.4304 - val_acc: 0.8311 - val_loss: 0.3890\n",
      "Epoch 3/6\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - acc: 0.8268 - loss: 0.3930 - val_acc: 0.8413 - val_loss: 0.3739\n",
      "Epoch 4/6\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - acc: 0.8388 - loss: 0.3737 - val_acc: 0.8389 - val_loss: 0.3578\n",
      "Epoch 5/6\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - acc: 0.8600 - loss: 0.3301 - val_acc: 0.8610 - val_loss: 0.3250\n",
      "Epoch 6/6\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - acc: 0.8698 - loss: 0.3087 - val_acc: 0.8529 - val_loss: 0.3407\n"
     ]
    }
   ],
   "source": [
    "lstm_model_history = lstm_model.fit(X_train, y_train, batch_size=128, epochs=6, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee1d152c-086a-4292-9666-e23de01291ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluates returning loss and accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bc34fd20-3302-4467-a205-effbf7bd956f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - acc: 0.8542 - loss: 0.3426 \n"
     ]
    }
   ],
   "source": [
    "score = lstm_model.evaluate(X_test, y_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d41375d0-ac20-408a-880a-8b9fbaff9e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary above has ?? because i didnt had yet passed the input "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e84ec706-8fd6-432f-b0dc-4f42e4aba940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)        │     <span style=\"color: #00af00; text-decoration-color: #00af00\">9,239,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">117,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │     \u001b[38;5;34m9,239,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │       \u001b[38;5;34m117,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m1\u001b[0m)               │           \u001b[38;5;34m129\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,591,533</span> (36.59 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,591,533\u001b[0m (36.59 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,377</span> (458.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,377\u001b[0m (458.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,239,400</span> (35.25 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m9,239,400\u001b[0m (35.25 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">234,756</span> (917.02 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m234,756\u001b[0m (917.02 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(lstm_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a40f9710-445a-4a8b-a43b-4e69dfe4ff73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Self explainotary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6cfaf419-cd8f-4005-9fb7-65e034a36898",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.34347790479660034\n",
      "Test Accuracy: 0.8529000282287598\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ebd1c566-2db5-4fd5-8dff-36a3f89d8513",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "28fe3bd4-c8c1-45a1-8429-016d83fe3e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lstm_model_history.history['acc'])\n",
    "plt.plot(lstm_model_history.history['val_acc'])\n",
    "\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train','test'], loc='center right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91e21042-708b-487c-ac20-11091abae66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lstm_model_history.history['loss'])\n",
    "plt.plot(lstm_model_history.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6f6c6536-ac25-4d9d-a3b4-0a38fffb9972",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Just Testing Purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b21876e2-931a-4606-8928-984cadb96e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = [\n",
    "    \"This movie was a thrilling rollercoaster from start to finish, filled with unexpected twists and emotional depth.\",\n",
    "    \"The plot was predictable and lacked originality. The characters were flat, making it hard to connect with them.\",\n",
    "    \"Visually stunning and highly creative, but the pacing was slow, which made it hard to stay engaged throughout.\",\n",
    "    \"An absolute masterpiece! The acting was superb, and the storyline was both gripping and emotionally powerful.\",\n",
    "    \"A good movie overall, but it suffered from some clichés. Still enjoyable for fans of the genre.\"\n",
    "]\n",
    "\n",
    "ratings = [8, 1, 9, 10, 6]\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Review': reviews,\n",
    "    'Rating': ratings\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2ae78a9-8e87-4b4a-96fd-fbcade0ef68c",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_reviews = df['Review']\n",
    "unseen_processed = []\n",
    "for review in unseen_reviews:\n",
    "  review = preprocess_text(review)\n",
    "  unseen_processed.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f800c3dd-597b-4781-862b-c7fb80e7a999",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_tokenized = word_tokenizer.texts_to_sequences(unseen_processed)\n",
    "unseen_padded = pad_sequences(unseen_tokenized, padding='post', maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4be60a40-09ad-4d97-9ca9-1153225fa2e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.97152036],\n",
       "       [0.00647334],\n",
       "       [0.97158855],\n",
       "       [0.97170264],\n",
       "       [0.34913337]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sentiments = lstm_model.predict(unseen_padded)\n",
    "\n",
    "unseen_sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed9f261a-cbd2-4f89-9d1d-cbee0213e77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Predicted Sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This movie was a thrilling rollercoaster from ...</td>\n",
       "      <td>8</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The plot was predictable and lacked originalit...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Visually stunning and highly creative, but the...</td>\n",
       "      <td>9</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>An absolute masterpiece! The acting was superb...</td>\n",
       "      <td>10</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A good movie overall, but it suffered from som...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Rating  \\\n",
       "0  This movie was a thrilling rollercoaster from ...       8   \n",
       "1  The plot was predictable and lacked originalit...       1   \n",
       "2  Visually stunning and highly creative, but the...       9   \n",
       "3  An absolute masterpiece! The acting was superb...      10   \n",
       "4  A good movie overall, but it suffered from som...       6   \n",
       "\n",
       "   Predicted Sentiments  \n",
       "0                   9.7  \n",
       "1                   0.1  \n",
       "2                   9.7  \n",
       "3                   9.7  \n",
       "4                   3.5  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predicted Sentiments'] = np.round(unseen_sentiments*10,1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "afd6db8c-78f5-4e42-bcb5-2a5eebda5cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now the saving part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9583099d-ade0-49a7-95bd-f5d35c487698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lstm_model.save(r\"./lstm_imdb_review.h5\", save_format='h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37ea55bb-55b0-4dde-a165-ccdfd4e18e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lstm_model.save('./lstm-imdb-review.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "331cb21b-f69b-489e-818c-967ef1c11dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Under is all just testing with different input that's it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ad7f5b91-8e71-4008-b277-f54c39872e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = \"\"\"It is no wonder that the film has such a high rating, it is quite literally breathtaking. What can I say that hasn't been said before? Not much, it's the story, the acting, the premise, but most of all, this movie is about how it makes you feel. Sometimes you watch a film, and can't remember it days later, this film lives with you, once you've seen it, you don't forget.\n",
    "\n",
    "The ultimate story of friendship, of hope, and of life, and overcoming adversity.\n",
    "\n",
    "I understand why so many class this as the best film of all time, it isn't mine, but I get it. If you haven't seen it, or haven't seen it for some time, you need to watch it, it's amazing. 10/10.\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f9e6bd9a-a1d0-4023-966b-d15b635836ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews.append(review_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7442b1b2-1d9e-4908-9a57-0865f1d7fd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = [8, 1, 9, 10, 6,10]\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Review': reviews,\n",
    "    'Rating': ratings\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6f64af0a-35e0-463a-9cab-a185301537ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This movie was a thrilling rollercoaster from ...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The plot was predictable and lacked originalit...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Visually stunning and highly creative, but the...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>An absolute masterpiece! The acting was superb...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A good movie overall, but it suffered from som...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>It is no wonder that the film has such a high ...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Rating\n",
       "0  This movie was a thrilling rollercoaster from ...       8\n",
       "1  The plot was predictable and lacked originalit...       1\n",
       "2  Visually stunning and highly creative, but the...       9\n",
       "3  An absolute masterpiece! The acting was superb...      10\n",
       "4  A good movie overall, but it suffered from som...       6\n",
       "5  It is no wonder that the film has such a high ...      10"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dfd258d4-982a-40d8-a288-bf8f7ca20aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_reviews = df['Review']\n",
    "unseen_processed = []\n",
    "for review in unseen_reviews:\n",
    "  review = preprocess_text(review)\n",
    "  unseen_processed.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4a579573-aaba-46bc-9a6a-99744a578967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 100)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_tokenized = word_tokenizer.texts_to_sequences(unseen_processed)\n",
    "unseen_padded = pad_sequences(unseen_tokenized, padding='post', maxlen=maxlen)\n",
    "unseen_padded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "83e2b29c-c86f-40eb-a3ae-0c80ca62705d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.97152036],\n",
       "       [0.00647334],\n",
       "       [0.97158855],\n",
       "       [0.97170264],\n",
       "       [0.34913337],\n",
       "       [0.9714597 ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sentiments = lstm_model.predict(unseen_padded)\n",
    "\n",
    "unseen_sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "67abf9d5-0e67-411c-a93c-302d724e47ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Predicted Sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This movie was a thrilling rollercoaster from ...</td>\n",
       "      <td>8</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The plot was predictable and lacked originalit...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Visually stunning and highly creative, but the...</td>\n",
       "      <td>9</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>An absolute masterpiece! The acting was superb...</td>\n",
       "      <td>10</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A good movie overall, but it suffered from som...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>It is no wonder that the film has such a high ...</td>\n",
       "      <td>10</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Rating  \\\n",
       "0  This movie was a thrilling rollercoaster from ...       8   \n",
       "1  The plot was predictable and lacked originalit...       1   \n",
       "2  Visually stunning and highly creative, but the...       9   \n",
       "3  An absolute masterpiece! The acting was superb...      10   \n",
       "4  A good movie overall, but it suffered from som...       6   \n",
       "5  It is no wonder that the film has such a high ...      10   \n",
       "\n",
       "   Predicted Sentiments  \n",
       "0                   9.7  \n",
       "1                   0.1  \n",
       "2                   9.7  \n",
       "3                   9.7  \n",
       "4                   3.5  \n",
       "5                   9.7  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predicted Sentiments'] = np.round(unseen_sentiments*10,1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fc1470d9-925a-4a3a-a0b4-f2f43155ec01",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_model.save('lstm123.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eaddab11-ea75-4f29-b88f-aac1dfaa31e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = \"\"\"It is no wonder that the film has such a high rating, it is quite literally breathtaking. What can I say that hasn't been said before? Not much, it's the story, the acting, the premise, but most of all, this movie is about how it makes you feel. Sometimes you watch a film, and can't remember it days later, this film lives with you, once you've seen it, you don't forget.\n",
    "\n",
    "The ultimate story of friendship, of hope, and of life, and overcoming adversity.\n",
    "\n",
    "I understand why so many class this as the best film of all time, it isn't mine, but I get it. If you haven't seen it, or haven't seen it for some time, you need to watch it, it's amazing. 10/10.\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b443c41e-7390-46ed-b4f7-5ad4829bf4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "review = preprocess_text(review_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8ceca410-5c7c-4ec7-bdcb-557f000379ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_tokenized = word_tokenizer.texts_to_sequences([unseen_processed])\n",
    "unseen_padded = pad_sequences(unseen_tokenized, padding='post', maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "19d5c88c-8d4f-40cd-8139-e3f8a716b75f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.72549963]], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sentiments = lstm_model.predict(unseen_padded)\n",
    "unseen_sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "201bc300-3cd0-4b65-8972-f08a26fb654e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.3]], dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(unseen_sentiments*10,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "97b1cfe3-4687-4ac9-9b9e-5ff87b748780",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of padded sequence: (1, 100)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of padded sequence:\", unseen_padded.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b18867e6-97f7-4bbd-8560-c65647a3cc89",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_review = pd.read_csv('./userrating.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c107050d-c584-4d16-9e42-6e38a7ef9ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_review.drop(columns=user_review.columns[0],axis=\"1\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "65d5eb53-f9ac-42e1-ab53-7ce0833c5117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the early scenes in \"Pulp Fiction\" feat...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I like the bit with the cheeseburger. It makes...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This is Tarantino's masterpiece, there's no ot...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I can only speak for myself, but I had never s...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pulp Fiction is the most original, rule breaki...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Before I saw this I assumed it was probably ov...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>To put this in context, I am 34 years old and ...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I don't get as much out of Pulp Fiction as eve...</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My oh my.  \"Pulp Fiction\" is one of those roll...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I don't understand why this is the 8th highest...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Viewers are taken on a ride through three diff...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pulp Fiction may be the single best film ever ...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>This is my favorite film of all time. Every se...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>I heard so many people claim that this movie i...</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tarantino is without a doubt one of the best d...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The proof of the pudding, they say, is in the ...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Conflicted on this one. I like a lot of elemen...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>This movie is such a masterpiece for many reas...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>It's not a film it's a experience. The charact...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Review  Rating\n",
       "0   One of the early scenes in \"Pulp Fiction\" feat...    10.0\n",
       "1   I like the bit with the cheeseburger. It makes...    10.0\n",
       "2   This is Tarantino's masterpiece, there's no ot...    10.0\n",
       "3   I can only speak for myself, but I had never s...    10.0\n",
       "4   Pulp Fiction is the most original, rule breaki...    10.0\n",
       "5   Before I saw this I assumed it was probably ov...    10.0\n",
       "6   To put this in context, I am 34 years old and ...    10.0\n",
       "7   I don't get as much out of Pulp Fiction as eve...     7.0\n",
       "8   My oh my.  \"Pulp Fiction\" is one of those roll...     NaN\n",
       "9   I don't understand why this is the 8th highest...     4.0\n",
       "10  Viewers are taken on a ride through three diff...    10.0\n",
       "11  Pulp Fiction may be the single best film ever ...    10.0\n",
       "12  This is my favorite film of all time. Every se...    10.0\n",
       "13  I heard so many people claim that this movie i...     6.0\n",
       "14  Tarantino is without a doubt one of the best d...     NaN\n",
       "15  The proof of the pudding, they say, is in the ...     5.0\n",
       "16  Conflicted on this one. I like a lot of elemen...     8.0\n",
       "17  This movie is such a masterpiece for many reas...    10.0\n",
       "18  It's not a film it's a experience. The charact...    10.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "08ea67e9-a96a-4afc-9911-00f9c3b1e387",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_reviews = user_review['Review']\n",
    "unseen_processed = []\n",
    "for review in unseen_reviews:\n",
    "  review = preprocess_text(review)\n",
    "  unseen_processed.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d04dc269-c6c6-4a51-a723-e883ec32bef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 100)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_tokenized = word_tokenizer.texts_to_sequences(unseen_processed)\n",
    "unseen_padded = pad_sequences(unseen_tokenized, padding='post', maxlen=maxlen)\n",
    "unseen_padded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4424eae7-7e63-4733-9b32-b3025da07663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n"
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       "array([[0.92900187],\n",
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     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sentiments = lstm_model.predict(unseen_padded)\n",
    "\n",
    "unseen_sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c47caa61-f443-4cc8-b31f-b291177048d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Predicted Sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>One of the early scenes in \"Pulp Fiction\" feat...</td>\n",
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       "      <td>9.3</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>I like the bit with the cheeseburger. It makes...</td>\n",
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       "      <td>7.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This is Tarantino's masterpiece, there's no ot...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
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       "    <tr>\n",
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       "      <td>I can only speak for myself, but I had never s...</td>\n",
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       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Pulp Fiction is the most original, rule breaki...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>Before I saw this I assumed it was probably ov...</td>\n",
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       "      <td>To put this in context, I am 34 years old and ...</td>\n",
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       "      <td>7.6</td>\n",
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       "      <th>7</th>\n",
       "      <td>I don't get as much out of Pulp Fiction as eve...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My oh my.  \"Pulp Fiction\" is one of those roll...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I don't understand why this is the 8th highest...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Viewers are taken on a ride through three diff...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pulp Fiction may be the single best film ever ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
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       "      <td>This is my favorite film of all time. Every se...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>I heard so many people claim that this movie i...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tarantino is without a doubt one of the best d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The proof of the pudding, they say, is in the ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Conflicted on this one. I like a lot of elemen...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>This movie is such a masterpiece for many reas...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>It's not a film it's a experience. The charact...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Review  Rating  \\\n",
       "0   One of the early scenes in \"Pulp Fiction\" feat...    10.0   \n",
       "1   I like the bit with the cheeseburger. It makes...    10.0   \n",
       "2   This is Tarantino's masterpiece, there's no ot...    10.0   \n",
       "3   I can only speak for myself, but I had never s...    10.0   \n",
       "4   Pulp Fiction is the most original, rule breaki...    10.0   \n",
       "5   Before I saw this I assumed it was probably ov...    10.0   \n",
       "6   To put this in context, I am 34 years old and ...    10.0   \n",
       "7   I don't get as much out of Pulp Fiction as eve...     7.0   \n",
       "8   My oh my.  \"Pulp Fiction\" is one of those roll...     NaN   \n",
       "9   I don't understand why this is the 8th highest...     4.0   \n",
       "10  Viewers are taken on a ride through three diff...    10.0   \n",
       "11  Pulp Fiction may be the single best film ever ...    10.0   \n",
       "12  This is my favorite film of all time. Every se...    10.0   \n",
       "13  I heard so many people claim that this movie i...     6.0   \n",
       "14  Tarantino is without a doubt one of the best d...     NaN   \n",
       "15  The proof of the pudding, they say, is in the ...     5.0   \n",
       "16  Conflicted on this one. I like a lot of elemen...     8.0   \n",
       "17  This movie is such a masterpiece for many reas...    10.0   \n",
       "18  It's not a film it's a experience. The charact...    10.0   \n",
       "\n",
       "    Predicted Sentiments  \n",
       "0                    9.3  \n",
       "1                    7.9  \n",
       "2                    9.7  \n",
       "3                    9.9  \n",
       "4                    9.7  \n",
       "5                    9.5  \n",
       "6                    7.6  \n",
       "7                    9.7  \n",
       "8                    9.8  \n",
       "9                    9.7  \n",
       "10                   7.5  \n",
       "11                   9.7  \n",
       "12                   9.7  \n",
       "13                   9.7  \n",
       "14                   9.7  \n",
       "15                   1.4  \n",
       "16                   9.7  \n",
       "17                   9.7  \n",
       "18                   6.8  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_review['Predicted Sentiments'] = np.round(unseen_sentiments*10,1)\n",
    "user_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e53304ac-123b-4125-824d-bdcf0672e079",
   "metadata": {},
   "outputs": [],
   "source": [
    "dftest = pd.read_csv(r'./main/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5545c4d9-4a82-43c6-ab70-8d87b8e26514",
   "metadata": {},
   "outputs": [
    {
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       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Interstellar - ReviewCertain things in life ar...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>I hadn't seen this but movie and caught it on ...</td>\n",
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       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>It's difficult to put into words how powerful ...</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>I understand Mr. Nolan purposely made the dial...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Was this movie terrible? No, but for as much p...</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>The film begins by establishing at his own rhy...</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Even though I'm a big fan of Christofer Nolan,...</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>If you saw \"sunshine\" and thought \"wow, good t...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "    Unnamed: 0                                        user_review  user_score\n",
       "0            0  Sometimes I just need to see the start. Or end...        10.0\n",
       "1            1  After watching this insane movie in the theatr...        10.0\n",
       "2            2  A lot has been said and written about Interste...        10.0\n",
       "3            3  Interstellar is a movie like no other. Unlike ...        10.0\n",
       "4            4  Amongst the best movies of all time. The story...        10.0\n",
       "5            5  This movie was the best written, acted, visual...        10.0\n",
       "6            6  I think just about everything has been said ab...        10.0\n",
       "7            7  I judge a movie by how long it takes me to rea...        10.0\n",
       "8            8  A science-fiction masterpiece. Nolan executes ...        10.0\n",
       "9            9  I was extremely lucky to get the chance to see...         NaN\n",
       "10          10  I will keep this short for the sake of not spo...         6.0\n",
       "11          11  All is amazing. I can't describe anything. It'...        10.0\n",
       "12          12  \"Love is the one thing we're capable of percei...         5.0\n",
       "13          13  Interstellar - ReviewCertain things in life ar...        10.0\n",
       "14          14  I hadn't seen this but movie and caught it on ...        10.0\n",
       "15          15  There is not even a single day I don't think o...        10.0\n",
       "16          16  I have been a cinema lover for years, read a l...        10.0\n",
       "17          17  It's difficult to put into words how powerful ...        10.0\n",
       "18          18  I understand Mr. Nolan purposely made the dial...         NaN\n",
       "19          19  Was this movie terrible? No, but for as much p...         7.0\n",
       "20          20  The film begins by establishing at his own rhy...        10.0\n",
       "21          21  Even though I'm a big fan of Christofer Nolan,...         7.0\n",
       "22          22  If you saw \"sunshine\" and thought \"wow, good t...         4.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e479c903-7da2-4b53-9d51-6001b0577aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_reviews = dftest['user_review']\n",
    "unseen_processed = []\n",
    "for review in unseen_reviews:\n",
    "  review = preprocess_text(review)\n",
    "  unseen_processed.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3b23ddca-e780-409c-b94e-702dcfb3a4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 100)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_tokenized = word_tokenizer.texts_to_sequences(unseen_processed)\n",
    "unseen_padded = pad_sequences(unseen_tokenized, padding='post', maxlen=maxlen)\n",
    "unseen_padded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0c962286-b3bb-44f9-8680-83c189cc2c3f",
   "metadata": {},
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       "      <td>9.7</td>\n",
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       "      <td>\"Love is the one thing we're capable of percei...</td>\n",
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       "      <td>0.2</td>\n",
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       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Interstellar - ReviewCertain things in life ar...</td>\n",
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       "      <td>8.2</td>\n",
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       "      <td>It's difficult to put into words how powerful ...</td>\n",
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       "    Unnamed: 0                                        user_review  user_score  \\\n",
       "0            0  Sometimes I just need to see the start. Or end...        10.0   \n",
       "1            1  After watching this insane movie in the theatr...        10.0   \n",
       "2            2  A lot has been said and written about Interste...        10.0   \n",
       "3            3  Interstellar is a movie like no other. Unlike ...        10.0   \n",
       "4            4  Amongst the best movies of all time. The story...        10.0   \n",
       "5            5  This movie was the best written, acted, visual...        10.0   \n",
       "6            6  I think just about everything has been said ab...        10.0   \n",
       "7            7  I judge a movie by how long it takes me to rea...        10.0   \n",
       "8            8  A science-fiction masterpiece. Nolan executes ...        10.0   \n",
       "9            9  I was extremely lucky to get the chance to see...         NaN   \n",
       "10          10  I will keep this short for the sake of not spo...         6.0   \n",
       "11          11  All is amazing. I can't describe anything. It'...        10.0   \n",
       "12          12  \"Love is the one thing we're capable of percei...         5.0   \n",
       "13          13  Interstellar - ReviewCertain things in life ar...        10.0   \n",
       "14          14  I hadn't seen this but movie and caught it on ...        10.0   \n",
       "15          15  There is not even a single day I don't think o...        10.0   \n",
       "16          16  I have been a cinema lover for years, read a l...        10.0   \n",
       "17          17  It's difficult to put into words how powerful ...        10.0   \n",
       "18          18  I understand Mr. Nolan purposely made the dial...         NaN   \n",
       "19          19  Was this movie terrible? No, but for as much p...         7.0   \n",
       "20          20  The film begins by establishing at his own rhy...        10.0   \n",
       "21          21  Even though I'm a big fan of Christofer Nolan,...         7.0   \n",
       "22          22  If you saw \"sunshine\" and thought \"wow, good t...         4.0   \n",
       "\n",
       "    Predicted Sentiments  \n",
       "0                    9.7  \n",
       "1                    9.0  \n",
       "2                    9.3  \n",
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       "4                    9.7  \n",
       "5                    9.7  \n",
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       "7                    8.9  \n",
       "8                    9.7  \n",
       "9                   10.0  \n",
       "10                   9.9  \n",
       "11                   9.7  \n",
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       "14                   9.7  \n",
       "15                   9.7  \n",
       "16                   9.2  \n",
       "17                   9.8  \n",
       "18                   0.1  \n",
       "19                   7.8  \n",
       "20                   7.3  \n",
       "21                   0.1  \n",
       "22                   0.9  "
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    "dftest"
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