--- a
+++ b/test_lstm.py
@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+"""Test_LSTM.ipynb
+
+**
+ * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.
+ *
+ * Written by Ankan Ghosh Dastider and Farhan Sadik.
+ *
+ * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.
+ */
+
+"""
+
+model = load_model('') #Link CNN model weight directory
+model_lstm = load_model('') #Link LSTM model weight directory
+
+data_dir_test = '' #Link test video
+test_dir = os.path.join(data_dir_test)
+
+test_data = []
+for file in sorted(os.listdir(test_dir)):
+    # print(file)
+    test_data.append(['{}'.format(file)])
+        
+test_on = pd.DataFrame(test_data, columns=['File'])
+test_on.head()
+
+IMAGE_SIZE = 128
+NUM_FRAMES = test_on.shape[0]
+
+def read_image_test(filepath):
+    return cv2.imread(os.path.join(data_dir_test, filepath)) # Loading a color image is the default flag
+# Resize image to target size
+def resize_image(newimage, image_size):
+    return cv2.resize(newimage.copy(), image_size, interpolation=cv2.INTER_AREA)
+
+X_Test = np.zeros((NUM_FRAMES, IMAGE_SIZE, IMAGE_SIZE, 3))
+Y_Test = np.zeros((NUM_FRAMES, 1))
+
+for i, file in tqdm(enumerate(test_on['File'].values)):
+    newimage = read_image_test(file)
+    if newimage is not None:
+        X_Test[i] = resize_image(newimage, (IMAGE_SIZE, IMAGE_SIZE))
+        match = re.search('Score(\d)',file)
+        score = int(match.group(1))
+        Y_Test[i] = score
+        #print(file)
+        #print(score)
+
+Y_Test = to_categorical(Y_Test, num_classes=4)
+# print(Y_Test)
+# Normalize the data
+X_Test = X_Test / 255.
+print('X_Test Shape: {}'.format(X_Test.shape))
+print('Y_Test Shape: {}'.format(Y_Test.shape))
+
+output = np.zeros((1, NUM_FRAMES, 64))
+
+specific_layer_output = K.function([model.layers[0].input], [model.get_layer('dropout_35').output])
+layer_output = specific_layer_output([X_Test])[0]
+#print(layer_output.shape)
+#print(layer_output)
+output[0] = layer_output
+
+print('Output from CNN Shape: {}'.format(output.shape))
+#custom3 = model.predict(X_Test)
+#print(custom3)
+
+score_types = ['Score 0', 'Score 1', 'Score 2', 'Score 3']
+Y_pred_test = model_lstm.predict(output)
+Y_pred_test = np.reshape(Y_pred_test, (Y_pred_test.shape[1], Y_pred_test.shape[2]))
+Y_pred_test = np.argmax(Y_pred_test, axis=1)
+
+Y_true_test = np.argmax(Y_Test, axis=1)
+
+#print(Y_pred_lstm.shape)
+#print(Y_Val_LSTM.shape)
+
+cm = confusion_matrix(Y_true_test, Y_pred_test)
+plt.figure(figsize=(12, 12))
+ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=score_types, yticklabels=score_types)
+ax.set_ylabel('Actual', fontsize=40)
+ax.set_xlabel('Predicted', fontsize=40)
+
+final_loss, final_accuracy = model.evaluate(X_Test, Y_Test)
+print('Final Loss: {}, Final Accuracy: {}'.format(final_loss, final_accuracy))
+