app.py

    import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import VGG16
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Set up data directories
data_dir = "/kaggle/input/deepfake-and-real-images/Dataset"
train_dir = f"{data_dir}/Train"
val_dir = f"{data_dir}/Validation"
test_dir = f"{data_dir}/Test"

# Data augmentation and preprocessing
datagen = ImageDataGenerator(rescale=1.0/255)

train_generator = datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)

val_generator = datagen.flow_from_directory(
    val_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)

test_generator = datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    shuffle=False
)

# Load pre-trained VGG16 model
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
base_model.trainable = False  # Freeze the base model

# Build the model
model = keras.Sequential([
    base_model,
    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(1, activation='sigmoid')
])

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

# Train the model
history = model.fit(
    train_generator,
    validation_data=val_generator,
    epochs=10
)

# Save the model
model.save("real_fake_classifier_vgg16.h5")

# Evaluate on test data
test_loss, test_acc = model.evaluate(test_generator)
print(f"Test Accuracy: {test_acc:.4f}")

# Plot training history
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()