Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import load_model\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the tokenizer\n",
    "with open('tokenizer.pkl', 'rb') as f:\n",
    "    word_index = pickle.load(f)\n",
    "\n",
    "reverse_word_index = {value: key for key, value in word_index.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "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_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\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_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ simple_rnn_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ simple_rnn_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,352</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</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_3 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │     \u001b[38;5;34m1,280,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ simple_rnn_4 (\u001b[38;5;33mSimpleRNN\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ simple_rnn_5 (\u001b[38;5;33mSimpleRNN\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m12,352\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\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\">1,325,315</span> (5.06 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,325,315\u001b[0m (5.06 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\">1,325,313</span> (5.06 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,325,313\u001b[0m (5.06 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "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\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "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\">2</span> (12.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2\u001b[0m (12.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading the pre-trained model with relu activation function \n",
    "\n",
    "model = load_model('SimpleRnn_imdb.h5')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         0.00513334,  0.003085  , -0.00249286,  0.00532153], dtype=float32),\n",
       " array([[-0.05110557],\n",
       "        [-0.244057  ],\n",
       "        [ 0.18051171],\n",
       "        [ 0.12740801],\n",
       "        [-0.2156148 ],\n",
       "        [ 0.02380658],\n",
       "        [ 0.1193834 ],\n",
       "        [-0.00653258],\n",
       "        [-0.04764251],\n",
       "        [ 0.26247868],\n",
       "        [ 0.24724859],\n",
       "        [-0.15372354],\n",
       "        [-0.3322009 ],\n",
       "        [ 0.02434802],\n",
       "        [ 0.29946527],\n",
       "        [ 0.08313432],\n",
       "        [ 0.07563789],\n",
       "        [ 0.02219537],\n",
       "        [-0.24446787],\n",
       "        [ 0.2952812 ],\n",
       "        [-0.05473496],\n",
       "        [-0.01423078],\n",
       "        [-0.00640337],\n",
       "        [-0.22757655],\n",
       "        [ 0.06938802],\n",
       "        [ 0.25693768],\n",
       "        [ 0.33546388],\n",
       "        [ 0.19946557],\n",
       "        [-0.05499836],\n",
       "        [ 0.2282488 ],\n",
       "        [-0.15937154],\n",
       "        [-0.01625277],\n",
       "        [ 0.3046259 ],\n",
       "        [-0.04975113],\n",
       "        [ 0.0024904 ],\n",
       "        [-0.26206756],\n",
       "        [ 0.13297532],\n",
       "        [-0.05773066],\n",
       "        [ 0.02219504],\n",
       "        [-0.05515486],\n",
       "        [ 0.08635706],\n",
       "        [-0.22169515],\n",
       "        [ 0.21811104],\n",
       "        [ 0.07157247],\n",
       "        [-0.05207359],\n",
       "        [-0.30274707],\n",
       "        [ 0.11990856],\n",
       "        [-0.24746378],\n",
       "        [-0.00701342],\n",
       "        [ 0.00546527],\n",
       "        [ 0.18971944],\n",
       "        [-0.24405015],\n",
       "        [-0.30942383],\n",
       "        [-0.26765558],\n",
       "        [-0.20984215],\n",
       "        [-0.29457542],\n",
       "        [-0.23165186],\n",
       "        [ 0.18152124],\n",
       "        [ 0.30183637],\n",
       "        [ 0.26660398],\n",
       "        [-0.00543671],\n",
       "        [-0.22644238],\n",
       "        [-0.14288236],\n",
       "        [-0.15753081]], dtype=float32),\n",
       " array([0.00785794], dtype=float32)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to decode reviews\n",
    "def decode_review(encoded_review):\n",
    "    return ' '.join([reverse_word_index.get(i - 3, '?') for i in encoded_review])\n",
    "\n",
    "# Function to preprocess user input\n",
    "def preprocess_text(text):\n",
    "    words = text.lower().split()\n",
    "    encoded_review = [word_index.get(word, 2) + 3 for word in words]\n",
    "    padded_review = sequence.pad_sequences([encoded_review], maxlen=500)  # Match training input length\n",
    "    return padded_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_sentiment(review):\n",
    "    preprocessed_input = preprocess_text(review)\n",
    "    prediction = model.predict(preprocessed_input)[0][0]  # Extract scalar value\n",
    "\n",
    "    # Define thresholds for classification\n",
    "    if prediction >= 0.6:\n",
    "        sentiment = 'Positive'\n",
    "    elif prediction <= 0.4:\n",
    "        sentiment = 'Negative'\n",
    "    else:\n",
    "        sentiment = 'Neutral'\n",
    "\n",
    "    return sentiment, prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User Input And Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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 81ms/step\n",
      "Review: The movie had some good moments, but overall, it was just okay.\n",
      "Sentiment: Positive | Prediction Score: 0.6006\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "Review: Not bad, but not great either. A decent watch for a lazy afternoon.\n",
      "Sentiment: Neutral | Prediction Score: 0.5389\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "Review: There were parts I enjoyed, but the ending was disappointing.\n",
      "Sentiment: Neutral | Prediction Score: 0.5614\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "Review: It had a good concept, but the execution could have been better.\n",
      "Sentiment: Neutral | Prediction Score: 0.4590\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "Review: Some scenes were good, but overall, it felt a bit underwhelming.\n",
      "Sentiment: Neutral | Prediction Score: 0.5762\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_reviews = [\n",
    "    \"The movie had some good moments, but overall, it was just okay.\",\n",
    "    \"Not bad, but not great either. A decent watch for a lazy afternoon.\",\n",
    "    \"There were parts I enjoyed, but the ending was disappointing.\",\n",
    "    \"It had a good concept, but the execution could have been better.\",\n",
    "    \"Some scenes were good, but overall, it felt a bit underwhelming.\"\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Running Predictions\n",
    "for review in test_reviews:\n",
    "    sentiment, score = predict_sentiment(review)\n",
    "    print(f'Review: {review}')\n",
    "    print(f'Sentiment: {sentiment} | Prediction Score: {score:.4f}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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