--- a
+++ b/ensemble_methods.py
@@ -0,0 +1,37 @@
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import Input
+from tensorflow.keras.preprocessing import image
+
+test_image_path = 'X-ray Images/train/Pneumonia-Bacterial/Pneumonia-Bacterial (1).jpg'
+img = image.load_img(test_image_path, target_size=(224, 224))
+img_array = image.img_to_array(img)
+img_array = np.expand_dims(img_array, axis=0)
+
+# Load the trained models
+cnn_model = load_model('Deep Learning Models/CNN-CUSTOM.h5')
+vgg16_model = load_model('Deep Learning Models/Feature-Extraction-VGG16.h5')
+vgg19_model = load_model('Deep Learning Models/VGG19.h5')
+resnet50_model = load_model('Deep Learning Models/ResNet50.h5')
+
+# Create input tensor
+input_tensor = Input(shape=(224, 224, 3))
+
+# Preprocess the image
+img_array = img_array / 255.0  # Normalize pixel values
+
+# Make predictions with each model
+cnn_predictions = cnn_model.predict(img_array)
+vgg16_predictions = vgg16_model.predict(img_array)
+vgg19_predictions = vgg19_model.predict(img_array)
+resnet50_predictions = resnet50_model.predict(img_array)
+
+# Combine predictions using majority voting
+ensemble_predictions = np.argmax(cnn_predictions + vgg16_predictions + vgg19_predictions + resnet50_predictions, axis=1)
+
+class_name = ['Bacterial Pneumonia', 'Normal', 'Viral Pneumonia']
+predicted_label = class_name[ensemble_predictions[0]]
+
+print("Predicted Label: ", predicted_label)
+
+img.show()