model.evaluate is giving good accuracy and loss, but the confusion matrix & ROC is off

The model is giving 0.9 acc, and 0.3 loss, but when I draw the confusion matrix, all the anomalies are labeled FPs, it does not make sense to me and ROC curve is straight line thru the middle. How do I interpret it? Loss and Acc curves look normal though. Could someone point me in a good direction?

​

`testing_datagen = ImageDataGenerator(rescale=1/255)`

`testing_generator = testing_datagen.flow_from_directory(`

`TESTINGDIR,`

`classes = ['anomaly', 'normal'],`

`batch_size=1,`

`class_mode='binary',`

`shuffle=True`

`,color_mode="rgb")`

​

`model.evaluate(testing_generator)`

`STEP_SIZE_TEST=testing_generator.n//testing_generator.batch_size`

`testing_generator.reset()`

`preds = …

accuracy deeplearning giving good loss roc