Spam_dnn.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Flatten, Dense
import pickle

# Load the dataset
txt_file_path = 'SMSSpamCollection.txt'

# Initialize empty lists to store labels and messages
labels = []
messages = []

# Read the text file line by line and extract labels and messages
try:
    with open(txt_file_path, 'r', encoding='utf-8') as file:
        for line in file:
            parts = line.strip().split('\t')
            if len(parts) == 2:
                label, message = parts
                labels.append(label)
                messages.append(message)

    # Create a DataFrame from the lists
    dataset = pd.DataFrame({'label': labels, 'message': messages})
    # Print the first few rows of the dataframe to check if data is loaded successfully
    print(dataset.head())
except Exception as e:
    print(f"Error reading text file: {e}")
# Assuming your dataset has 'label' and 'message' columns
X = dataset['message'].values
y = dataset['label'].map({'spam': 1, 'ham': 0}).values

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Tokenize the text data
max_words = 10000
tokenizer = Tokenizer(num_words=max_words, oov_token='<OOV>')
tokenizer.fit_on_texts(X_train)
sequences_train = tokenizer.texts_to_sequences(X_train)
sequences_test = tokenizer.texts_to_sequences(X_test)

# Pad sequences to a fixed length
max_sequence_length = 200
X_train_padded = pad_sequences(sequences_train, maxlen=max_sequence_length, padding='post')
X_test_padded = pad_sequences(sequences_test, maxlen=max_sequence_length, padding='post')

# Build the DNN model
model = Sequential()
model.add(Embedding(input_dim=max_words, output_dim=64, input_length=max_sequence_length))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

# Train the model
model.fit(X_train_padded, y_train, epochs=5, batch_size=32, validation_split=0.2)

# Evaluate the model on the test set
y_pred = (model.predict(X_test_padded) > 0.5).astype("int32")

# Print classification report and accuracy
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
print("Accuracy:", accuracy_score(y_test, y_pred))

# Save the model
model.save('spam_dnn_model.h5')

# Save the tokenizer
with open('tokenizer_dnn.pkl', 'wb') as tokenizer_file:
    tokenizer.word_index = {e: i for e, i in tokenizer.word_index.items() if i <= max_words}
    pickle.dump(tokenizer, tokenizer_file)