Deep Learning accuracy vs Confusion Matrix accuracy

I am working on deep learning with fer2013 dataset.
After training the model I got val_precision: 0.9168 (precision: 0.8492)

Epoch 67/100
238/238 [==============================] - 31s 130ms/step - loss: 1.5087 - tp: 2622.4142 - fp: 474.9121 - tn: 45584.3013 - fn: 5054.1213 - accuracy: 0.8972 - precision: 0.8492 - recall: 0.3410 - auc: 0.9042 - prc: 0.6758 - val_loss: 0.9754 - val_tp: 1389.0000 - val_fp: 126.0000 - val_tn: 22698.0000 - val_fn: 2415.0000 - val_accuracy: 0.9046 - **val_precision: 0.9168** - val_recall: 0.3651 - val_auc: 0.9235 - val_prc: 0.7276
Restoring model weights from the end of the best epoch.
Epoch 00067: early stopping

But when I output the Confusion Matrix I get precision of 0.13 - 0.18 ...
I have some misunderstanding - why is my precision so different ?
Is this confusion matrix part has errors ?

Here is my notebook - https://www.kaggle.com/code/prilia/emotion-recognition-with-resnet50-7emotions/notebook

Please help