Plotting ROC Curve for Multiclass

Hi, I'm trying to make a multiclass ROC Curve and i think i'm close to it but it doesn't work. I've spent quite a while to figure out where the problem is, yet it came to nothing. I'm curious could someone who had done it before can help me ?

Firstly I have the classes below.

template=templateSVM('Standardize',true,'KernelFunction','gaussian')
MultiClassSVM=fitcecoc(winedataset,'quality','Learners',template,'FitPosterior',true)
yPredicted=predict(MultiClassSVM,winedataset)
fprintf(sprintf('Training accuracy: %0.2f%%', 100*mean(yPredicted==winedataset.quality)));
confusionchart(winedataset.quality,yPredicted,'RowSummary','row-normalized')

MultiClassSVM.ClassNames
ans =

     3
     4
     5
     6
     7
     8   

This is my confusion matrix. Furthermore, I'm using the code below to create a ROC curve.

[~,score_svm] = resubPredict(MultiClassSVM);

diffscore=zeros;
for i=1:size(score_svm,1)
    temp=score_svm(i,:); 
    diffscore(i,:)=temp(2)-max([temp(1),temp(3),temp(4),temp(5),temp(6)]); 
end

[X,Y,T,~,OPTROCPT,suby,subnames] = perfcurve(winedataset.quality,diffscore,4)
plot(X,Y)
hold on
plot(OPTROCPT(1),OPTROCPT(2),'ro')  
xlabel('False positive rate') 
ylabel('True positive rate')
title('ROC for Classification by SVM')
fprintf("Area Under the Curve (AUC) = %f",AUCsvm) 
hold off

Well the curve is supposed to have a value like 0.94 yet it shows 1. I probably made a mistake while calculating diffscore but i'm not quite sure of it. I would appreciate if anyone who has done it before could help.