How to visualise an interaction effect using ggplot with method = glmmTMB::glmmTMB and point weights

I am running an analysis in R on the effect of canopy cover (OverheadCover) and the number of carcasses placed on the same location (CarcassNumber) on the proportion of carrion eaten by birds (ProportionBirdsScavenging). The interaction effect OverheadCover * CarcassNumber is significant and I would like visualise this using ggplot like explained here: https://sebastiansauer.github.io/vis_interaction_effects/. I won't be using method = "lm" like in the example, but method = glmmTMB::glmmTMB. I've added the extra arguments formula = and method.args = to make sure R computes the smooth correctly.

This is how it should look, but I prefer the graph to be made with ggplot because then all my graphs will be in the same style.

glmm_interaction <- glmmTMB(ProportionBirdsScavenging ~ OverheadCover * CarcassNumber + (1|Area), data = data_both, beta_family(link = "logit"), weights = pointWeight_scaled)
plot_model(glmm_interaction, type = "int", ci.lvl = 0.682) # conf. int. of 68.3% -> ± standard error

This is the code I'm trying to run, but I can't get it to work. It keeps giving me errors, like object 'pointWeight_scaled' not found. Anyone an idea what I'm doing wrong here?

qplot(x = OverheadCover, y = ProportionBirdsScavenging, color = CarcassNumber, data = data_both) +
  geom_smooth(method = glmmTMB::glmmTMB,
              formula = ProportionBirdsScavenging ~ OverheadCover * CarcassNumber,
              method.args = list(data = data_both, beta_family(link = "logit"), weights = pointWeight_scaled))

I know that it might be easier to just individually run the models and plot them on the same graph. I've done that, and it works. However, my calculated standard errors are larger than the ones in the plot_model(), so I wanted to see how these standard errors look if R does all the work, hence my intention to plot it this way.