I think there is some misinterpretation happening regarding both the graph and how the Precision, Recall F1-score variables from the presented confusion matrix are calculated.
Starting with the confusion matrix, the Precision is 100% but actually the Recall is at 75% (6/(6+2)). So the F1 score is actually 85.7% still "great" generally speaking but not "more than 97%". Focusing now on the P-R curve shown: we can see it conveys similar information. From the confusion matrix we can see that we have 8 Positive examples. Effectively up until reaching 62.5% (5/8) we have 100% Precision, i.e. we detect the first 4 Positive examples fine using a very high threshold but we have lower our threshold to something close to 0.33 (judging from the greenish colour) to detect our next Positive example and increase our Recall further. That nevertheless drops our Precision significantly because we accumulate our False Positive somewhere around there too. As we further lower our threshold to values close to 0, we get some bouncy behaviour as our Precision is artificially inflated at times but realistically we get lower and lower Precision up until the point we reach 100% Recall.
As a conclusion, an average precision score of 0.81.9% seems ball-park correct for this classifier. The confusion matrix (most likely calculated a threshold of 0.5) also concurs to that with a F1 score of about 85.7%.