Create app.py

app.py

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+    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()