model.py

import pandas as pd
import numpy as np
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, Conv1D, GlobalMaxPooling1D, Dense
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

# Loading sentiment dataset (update the data loading part)
df = pd.read_csv('amazon.csv')

# Data preprocessing
tokenizer = Tokenizer(num_words=5000, oov_token='<OOV>')
tokenizer.fit_on_texts(df['reviewText'])
X = tokenizer.texts_to_sequences(df['reviewText'])
X = pad_sequences(X, maxlen=100)

# Encode sentiment labels
y = df['Positive']

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the CNN model
model = Sequential()
model.add(Embedding(input_dim=5000, output_dim=128, input_length=100))
model.add(Conv1D(128, 5, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(1, activation='sigmoid'))

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

# Train the model and obtain the training history
history = model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test, y_test))

# Plot the learning curve
plt.figure(figsize=(12, 6))

# Plot training & validation accuracy values
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(['Train', 'Test'], loc='upper left')

# Plot training & validation loss values
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(['Train', 'Test'], loc='upper left')

plt.show()

# Evaluate the model on the test set
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}")

# Make predictions on the entire dataset
y_pred = model.predict(X)

# Convert predicted probabilities to binary labels (0 or 1)
y_pred_binary = [1 if pred >= 0.5 else 0 for pred in y_pred]

# Calculate the confusion matrix
conf_matrix = confusion_matrix(y, y_pred_binary)

# Plot the confusion matrix
disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=['Negative', 'Positive'])
disp.plot(cmap=plt.cm.Blues, values_format='d')

plt.title('Confusion Matrix')
plt.show()