SMS SPAM USING RNN.ipynb

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    "df=pd.read_csv('spam.csv',encoding='latin-1')"
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      "C:\\Users\\djdeb\\AppData\\Local\\Temp\\ipykernel_12640\\3257931024.py:3: SettingWithCopyWarning: \n",
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "df['label']=le.fit_transform(df['label'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8147769",
   "metadata": {},
   "source": [
    "# Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bddc4834",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\djdeb\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\djdeb\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\djdeb\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import string\n",
    "\n",
    "# Download required NLTK data if not already downloaded\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "def preprocess_text(sentence):\n",
    "    # Convert to lowercase\n",
    "    sentence = sentence.lower()\n",
    "    \n",
    "    # Remove punctuation\n",
    "    sentence = sentence.translate(str.maketrans('', '', string.punctuation))\n",
    "    \n",
    "    # Remove extra whitespaces\n",
    "    sentence = ' '.join(sentence.split())\n",
    "    \n",
    "    # Tokenize the sentence\n",
    "    tokens = word_tokenize(sentence)\n",
    "    \n",
    "    # Lemmatize the tokens\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
    "    \n",
    "    # Join the tokens back into a sentence\n",
    "    sentence = ' '.join(tokens)\n",
    "    \n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9eb502e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is not an example sentence 1945\n"
     ]
    }
   ],
   "source": [
    "sentence = \"This is not an example sentence. 1945\"\n",
    "preprocessed_sentence = preprocess_text(sentence)\n",
    "print(preprocessed_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7cf97d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\djdeb\\AppData\\Local\\Temp\\ipykernel_12640\\3726493619.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['text']=df['text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "df['text']=df['text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96cdf6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>go until jurong point crazy available only in ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>free entry in 2 a wkly comp to win fa cup fina...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>u dun say so early hor u c already then say</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nah i dont think he go to usf he life around h...</td>\n",
       "      <td>0</td>\n",
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       "      <th>5567</th>\n",
       "      <td>this is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>will ì b going to esplanade fr home</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>pity wa in mood for that soany other suggestion</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>the guy did some bitching but i acted like id ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>5571</th>\n",
       "      <td>rofl it true to it name</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  label\n",
       "0     go until jurong point crazy available only in ...      0\n",
       "1                               ok lar joking wif u oni      0\n",
       "2     free entry in 2 a wkly comp to win fa cup fina...      1\n",
       "3           u dun say so early hor u c already then say      0\n",
       "4     nah i dont think he go to usf he life around h...      0\n",
       "...                                                 ...    ...\n",
       "5567  this is the 2nd time we have tried 2 contact u...      1\n",
       "5568                will ì b going to esplanade fr home      0\n",
       "5569    pity wa in mood for that soany other suggestion      0\n",
       "5570  the guy did some bitching but i acted like id ...      0\n",
       "5571                            rofl it true to it name      0\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d43cf1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(df,open('sms_cleanedData.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "448bda07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Load the SavedModel\n",
    "loaded_model = tf.saved_model.load('sms-spam-model')\n",
    "\n",
    "# Get the actual model\n",
    "exact_model = loaded_model.signatures['serving_default']\n",
    "\n",
    "# Now you can use the exact_model for inference or other purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "823afafb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cbc612da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.src.legacy_tf_layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ae3252bc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras.src.legacy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[52], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtokenizer (1).pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m----> 4\u001b[0m     tokenizer \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'keras.src.legacy'"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open('tokenizer (1).pkl', 'rb') as f:\n",
    "    tokenizer = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5edeae57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f0a93ae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9845814d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the model on the padded input\n",
    "output = exact_model(padded_input)\n",
    "\n",
    "# Get the predicted value\n",
    "predicted_value = output['output'].numpy()  # Assuming the output is a dictionary with an 'output' key\n",
    "\n",
    "print(predicted_value)"
   ]
  }
 ],
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