diff --git a/train_model_to_recognize_patterns.md b/train_model_to_recognize_patterns.md
index dddf7c1..525bf28 100644
--- a/train_model_to_recognize_patterns.md
+++ b/train_model_to_recognize_patterns.md
@@ -57,5 +57,36 @@ model.fit(np.array(X_train), np.array(y_train), epochs=10, batch_size=32)
                                                                            
 print("Model trained!")                              
 ```
-
-
+## Using the recognition model 
+```python                                                                                                                                              
+import cv2                                                                 
+from tensorflow.keras.models import load_model  
+        
+# Load trained model from disk                                                                                                                         
+model_path = 'path/to/trained/model.h5'                                                                                                                
+model = load_model(model_path)                                             
+                                                                                                                                                       
+# Capture video from webcam or load pre-existing image                     
+cap = cv2.VideoCapture(0)                                                  
+while True:                                                                                                                                            
+    ret, frame = cap.read()                                                                                                                            
+    if not ret:                      
+        break                                                                                                                                          
+                                                                                                                                                       
+    # Convert frame to grayscale and resize to 224x224 pixels                                                                                          
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)                   
+    resized_frame = cv2.resize(gray_frame, (224, 224))                                                                                                 
+                                                                           
+    # Add batch dimension for prediction                                                                                                               
+    input_data = np.array([resized_frame])                                 
+                                                                                                                                                       
+    # Get output probabilities for each facial expression                  
+    output_probabilities = model.predict(input_data)                                                                                                   
+                                                                                                                                                       
+    # Print the top 3 predicted expressions with their probabilities                                                                                   
+    print(np.argsort(-output_probabilities[0])[:3], output_probabilities[0][:3])                                                                       
+                                                                                                                                                       
+# Release^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A resources^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A and^[[A^[[A^[[A exit                                           
+cap.release()                                                                                                                                          
+cv2.destroyAllWindows()                                                
+```