how to implement SVM inplace of KNN in face recognition

I tried to implement SVM in place of KNN but got stuck. In KNN we compare the distance from the nearest neighbor with a threshold value. How to do these in SVM?
I made changes here.
svm_clf = SVC(gamma='auto', kernel='rbf', C=20) svm_clf.fit(X, y)

but don't know what changes i need to do here:
closest_distances= knn_clf.kneighbors(faces_encodings, n_neighbors=1)

is_recognized = [closest_distances[0][i][0] <= DIST_THRESH for i in range(len(X_faces_loc))]
return [(pred, loc) if rec else ("N/A", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_faces_loc, is_recognized)]

Start with the existing KNN recognition flow shown around knn_clf.kneighbors, DIST_THRESH, and knn_clf.predict, then compare it with the SVC setup using svm_clf.fit(X, y). Determine how recognition and rejection should work with the SVM approach; done means the face-recognition results preserve the expected prediction and location behavior without the KNN-only distance logic.