How can i test the performance of a model when the test data contains seen and unseen data
The test of your model should always be done exclusively on unseen data. That is fundamental to asses your model's capacity to generalize and predict observations it has never seen.
If you test it using already seen data, that is like cheating, and overfitting will be 100% guaranteed.

Repeat the test only on unseen data, and check the difference between train and test performances. If differences are big, then you have an overfitting problem. There are several ways to fight overfitting, but they depend on the nature of your model. Could you please provide some information on it?