I suggest using a generalized additive model to plot the smooths for the predictor (similar to geom_smooth()). For example:
library(modeldata)
library(mgcv)
#> Loading required package: nlme
#> This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
data("two_class_dat")
mod <- gam(Class ~ s(A) + s(B), data = two_class_dat, family = binomial)
# The `trans` argument puts them back on the probability scale
plot(mod, trans = function(x) binomial()$linkinv(x), pages = 1)

Created on 2021-01-20 by the reprex package (v0.3.0)