smsspam.py

import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from keras.models import load_model
from tensorflow.keras.models import save_model
from tensorflow.keras.preprocessing.text import Tokenizer
import pickle

dataset = pd.read_csv(r"SMSSpamCollection.txt",sep='\t',names=['label','message'])

dataset['label'] = dataset['label'].map( {'spam': 1, 'ham': 0} )
X = dataset['message'].values
y = dataset['label'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
tokeniser = tf.keras.preprocessing.text.Tokenizer()
tokeniser.fit_on_texts(X_train)
encoded_train = tokeniser.texts_to_sequences(X_train)
encoded_test = tokeniser.texts_to_sequences(X_test)

max_length = 10
padded_train = tf.keras.preprocessing.sequence.pad_sequences(encoded_train, maxlen=max_length, padding='post')
padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=max_length, padding='post')

vocab_size = len(tokeniser.word_index)+1

# define the model

model=tf.keras.models.Sequential([
   tf.keras.layers.Embedding(input_dim=vocab_size,output_dim= 24, input_length=max_length),
   tf.keras.layers.SimpleRNN(24, return_sequences=False),
   tf.keras.layers.Dense(64, activation='relu'),
   tf.keras.layers.Dense(32, activation='relu'),
   tf.keras.layers.Dense(1, activation='sigmoid')
])

# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# summarize the model

early_stop = tf.keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', patience=10)


# fit the model
model.fit(x=padded_train,
         y=y_train,
         epochs=50,
         validation_data=(padded_test, y_test),
         callbacks=[early_stop]
         )

preds = (model.predict(padded_test) > 0.5).astype("int32")

model_filename = "spam_model.h5"
model.save(model_filename)

# Save the tokenizer using pickle
tokeniser_filename = "spam_tokeniser.pkl"
with open(tokeniser_filename, 'wb') as tokeniser_file:
    pickle.dump(tokeniser, tokeniser_file)