test if all predictors in a glm object are factors

I'm trying to develop some graphic methods for glm objects, but they
only apply for models
where all predictors are discrete factors.  How can I test for this in a
function, given the
glm model object?

That is, I want something that will serve as an equivalent of
is.discrete.glm() in the following
context:

myplot.glm <-
function(model, ...) {
   if (!inherits(model,"glm")) stop("requires a glm object")
   if (!is.discrete.glm(model)) stop("only factors are allowed")
...
}

A small example, for count data, a poisson glm:

GSS <- data.frame(
 expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")),
 count=c(279,165,73,47,225,191))

mod.glm <- glm(count ~ sex + party, family = poisson, data = GSS)

So, the model terms are sex and party, both factors.  Peeking inside
mod.glm, I
can find

mod.glm$xlevels

$sex
[1] "female" "male"
$party
[1] "dem"   "indep" "rep"
and, in str(mod.glm$model) I see

str(mod.glm$model)

'data.frame':   6 obs. of  3 variables:
$ count: num  279 165 73 47 225 191
$ sex  : Factor w/ 2 levels "female","male": 1 2 1 2 1 2
$ party: Factor w/ 3 levels "dem","indep",..: 1 1 2 2 3 3
- attr(*, "terms")=Classes 'terms', 'formula' length 3 count ~ sex + party
 ....

so this is a keeper.  Can someone help me improve on the following
is.discrete.glm() function.
It works for mod.glm, but isn't very general ;-)

is.discrete.glm <- function(model) {
   TRUE
}

is.discrete.glm(mod.glm)