F1 Score Calculation using sklearn.metrices in different from theory

I had created my first AI model, and I need to test the accuracy of the data model. I used the inbuilt confusion_matrix() function and then the classification_report() to get the accuracy related fields for the developed model. Just to satisfy my curiosity, i calculated F1 Score using the f1_score() and to my astonishment it was not same as received using the classification_report(). I am not sure whether f1_score() is wrongly calculated or whether there is any issue in the classification report details from the python code. Need someone's help to understand this difference.

Confusion Matrix (From Python Code):
94	13
15	32
Classification Report :
			precision    recall  f1-score   support

           0       0.86      0.88      0.87       107
           1       0.71      0.68      0.70        47

    accuracy                           0.82       154
   macro avg       0.79      0.78      0.78       154
weighted avg       0.82      0.82      0.82       154

F1 Score (Calculated using sklearn.metrics.f1_score()):