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import os
import sys
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
# 1. --- Import config ---
try:
import config
except ImportError:
print("Error: Could not import config.py.")
sys.exit(1)
# Suppress TensorFlow logs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def create_test_generator():
"""
Creates the Keras Data Generator for the test set.
"""
print("Creating Test Data Generator...")
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
directory=config.TEST_DIR,
target_size=(config.TARGET_IMAGE_SIZE, config.TARGET_IMAGE_SIZE),
batch_size=config.BATCH_SIZE,
class_mode='binary',
shuffle=False # IMPORTANT: Must be False for evaluation
)
return test_generator
def plot_confusion_matrix(y_true, y_pred, save_path):
"""Plots and saves a confusion matrix."""
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=['Fake', 'Real'],
yticklabels=['Fake', 'Real'])
plt.title('Confusion Matrix')
plt.ylabel('Actual Label')
plt.xlabel('Predicted Label')
plt.savefig(save_path)
print(f"Confusion matrix saved to {save_path}")
def main():
print("--- Starting Final Model Evaluation ---")
# 1. Load our test data
test_gen = create_test_generator()
# 2. Load our BEST model (the fine-tuned one)
model_path = os.path.join(config.MODEL_DIR, "finetuned_model.h5")
if not os.path.exists(model_path):
print(f"Error: Fine-tuned model not found at {model_path}")
print("Run finetune.py first.")
return
print(f"Loading final model from {model_path}...")
model = tf.keras.models.load_model(model_path)
# 3. Get model's performance on the test set
print("Evaluating model on test set...")
results = model.evaluate(test_gen, verbose=1)
print("\n--- Test Set Evaluation Metrics ---")
print(f"Test Loss: {results[0]:.4f}")
print(f"Test Accuracy: {results[1]:.4f}")
print(f"Test AUC: {results[2]:.4f}")
# 4. Get predictions for the entire test set
print("Generating predictions for classification report...")
# This gets the raw probabilities (e.g., 0.1, 0.8, 0.9)
y_pred_probs = model.predict(test_gen, verbose=1)
# We convert probabilities to class labels (0 or 1)
y_pred_classes = (y_pred_probs > 0.5).astype(int)
# 5. Get the true labels
y_true = test_gen.classes
# 6. Get the class names
class_names = list(test_gen.class_indices.keys())
# 7. Print Classification Report
print("\n--- Classification Report ---")
print(classification_report(y_true, y_pred_classes, target_names=class_names))
# 8. Save Confusion Matrix
cm_path = os.path.join(config.RESULTS_DIR, "confusion_matrix.png")
plot_confusion_matrix(y_true, y_pred_classes, cm_path)
if __name__ == "__main__":
# Create the results directory if it doesn't exist
os.makedirs(config.RESULTS_DIR, exist_ok=True)
main() |