def roc_auc_single(df, model):
    
    import sklearn.metrics as metrics
    
    num_label = {'ok': 1, 'nok' : 0}
    Y_test = df_test['class'].copy().map(num_label).astype('int')
    
    df.reset()
    predictions = model.predict(df, steps=len(df), verbose=0)
    pred_labels= np.where(predictions>0.5, 1, 0)
    
    roc_auc = metrics.roc_auc_score(Y_test, predictions)
    print('ROC_AUC: ', roc_auc)

    fpr, tpr, thresholds = metrics.roc_curve(Y_test, predictions)

    plt.plot(fpr, tpr, label = 'ROC_AUC = %0.3f' % roc_auc)

    plt.xlabel("False Positive Rate", fontsize= 12)
    plt.ylabel("True Positive Rate", fontsize= 12)
    plt.legend(loc="lower right")

    fig1 = plt.gcf()
    plt.show()
    plt.draw()
    fig1.savefig('roc_auc_single.png', dpi=50)