def recognize(image_path, database, model):
    """
    Implements face recognition by finding who is the person on the image_path image.
    
    Arguments:
    image_path -- path to an image
    database -- database containing image encodings along with the name of the person on the image
    model -- your Inception model instance in Keras
    
    Returns:
    min_dist -- the minimum distance between image_path encoding and the encodings from the database
    identity -- string, the name prediction for the person on image_path
    """
    
    ## Compute the target "encoding" for the image. Use img_path_to_encoding()
    encoding = img_path_to_encoding(image_path, model)
    
    ## Find the closest encoding by initializing "min_dist" to a large value, 100.
    min_dist = 100
    
    # Loop over the database dictionary's names and encodings.
    for (name, db_enc) in database.items():
        
        # Compute L2 distance between the target "encoding" and the current "emb" from the database.
        dist = np.linalg.norm(encoding-db_enc)

        # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. 
        if dist < min_dist:
            min_dist = dist
            identity = name

    if min_dist > 0.7:
        print("Not in the database.")
    else:
        print ("Welcome ==> " + str(identity) + ". Distance :" + str(min_dist))
        
    return min_dist, identity