You could use the [scikit-learn classification report][1]. To convert your labels into a numerical or binary format take a look at the [scikit-learn label encoder][2].

    y_pred = model.predict(x_test, batch_size=64, verbose=1)
    y_pred_bool = np.argmax(y_pred, axis=1)
    
    print(classification_report(y_test, y_pred_bool))

which gives you (output copied from the scikit-learn example):

                 precision  recall   f1-score    support

     class 0       0.50      1.00      0.67         1
     class 1       0.00      0.00      0.00         1
     class 2       1.00      0.67      0.80         3


  [1]: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
  [2]: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html