ggpredict error: $ operator not defined for this S4 class - r

Question

I have formulated a mixed linear regression model that I would like to plot.
I wanted to do so by generating predictive values with ggPredict from
ggiraphExtra .
My model is as follows:
lme9v <- lmer(Log(Visit) ~ Slope_degree*Path_dist * AS_sa + (1|Location), data= merged_dataset_05, REML=F)
*
note that Log(Visit) is actually notated with backtick escapes.
ggPredict(lme9v, interactive=TRUE)
However, I get the error:
Error: $ operator not defined for this S4 class
What causes the error to occur and what should I do differently?
This is my code:
structure(list(Station = structure(1:6, .Label = c("BL0102S",
"BL0102V", "BL01R", "BL01S", "BL0405-2R", "BL0405-2S", "BL0405R",
"BL0405S", "BL1112S", "BL1112V", "BL11R", "BL11S", "BL1314-2R",
"BL1314-2S", "BL1516S", "BL1516V", "D0405S", "D0405V", "DF0405S",
"DF0405V", "DF04S", "DF04V", "DF0708S", "DF0708V", "DF07R", "DF07S",
"DF0809R", "DF0809S", "DF12R", "DF12S", "DF14S", "DF14V", "FM06R",
"FM06S", "FM0708S", "FM0708V", "FM0910R", "FM0910S", "FM1415R",
"FM1415S", "TB0405S", "TB0405V", "TB12S", "TB12V", "WMW0102R",
"WMW0102S", "WMW0203S", "WMW0203V", "WMW02S", "WMW02V"), class = "factor"),
Geom = structure(c(2L, 1L, 1L, 2L, 1L, 2L), .Label = c("L",
"S"), class = "factor"), Slope_degree = c(24L, 5L, 0L, 23L,
5L, 35L), MDD = c(5, 5, 4.7, 4.7, 4.3, 5.3), CC = c(84L,
83L, 82L, 80L, 78L, 74L), CT = c(61L, 15L, 7L, 33L, 22L,
30L), Corrected_10m = c(10.9, 10, 10, 10.9, 10, 12.2), Av_litter_depth = c(2.89,
4, 7, 4.22, 3.83, 3.58), Content = c(20.34, 23.26, 23.23,
23.74, 17.5, 20.24), Slope_radian = c(0.41887902, 0.087266463,
0, 0.401425728, 0.087266463, 0.610865238), AB_a = c(0L, 0L,
0L, 0L, 1L, 0L), AB_sa = c(9L, 2L, 0L, 1L, 5L, 0L), AS_a = c(0L,
0L, 0L, 0L, 3L, 0L), AS_sa = c(2L, 1L, 0L, 0L, 2L, 0L), Days_deployed = c(15L,
15L, 14L, 14L, 14L, 14L), Count = c(23L, 29L, 9L, 20L, 85L,
43L), Tottime = c(295, 318, 66, 199, 1386, 745), Path_dist = c(659.4047198,
1021.11078342, 516.51545352, 997.8758996, 988.18342935, 957.66932416
), Count_rate = c(9.2, 11.6, 4.10334346504559, 9.11854103343465,
42.358803986711, 17.3854447439353), Time_use = c(118, 127.2,
30.0911854103343, 90.7294832826748, 690.697674418605, 301.212938005391
), `Log(Time)` = c(4.77068462446567, 4.84576065090602, 3.40423228535731,
4.50788236805538, 6.53770220909723, 5.70781744986838), `Log(Visit)` = c(2.21920348405499,
2.45100509811232, 1.4118021206671, 2.21030981688487, 3.7461762858377,
2.85563334718238), Location = c(1, 1, 2, 2, 3, 3)), row.names = c(NA,
-6L), groups = structure(list(Station = structure(1:6, .Label = c("BL0102S",
"BL0102V", "BL01R", "BL01S", "BL0405-2R", "BL0405-2S", "BL0405R",
"BL0405S", "BL1112S", "BL1112V", "BL11R", "BL11S", "BL1314-2R",
"BL1314-2S", "BL1516S", "BL1516V", "D0405S", "D0405V", "DF0405S",
"DF0405V", "DF04S", "DF04V", "DF0708S", "DF0708V", "DF07R", "DF07S",
"DF0809R", "DF0809S", "DF12R", "DF12S", "DF14S", "DF14V", "FM06R",
"FM06S", "FM0708S", "FM0708V", "FM0910R", "FM0910S", "FM1415R",
"FM1415S", "TB0405S", "TB0405V", "TB12S", "TB12V", "WMW0102R",
"WMW0102S", "WMW0203S", "WMW0203V", "WMW02S", "WMW02V"), class = "factor"),
Geom = structure(c(2L, 1L, 1L, 2L, 1L, 2L), .Label = c("L",
"S"), class = "factor"), Slope_degree = c(24L, 5L, 0L, 23L,
5L, 35L), MDD = c(5, 5, 4.7, 4.7, 4.3, 5.3), CC = c(84L,
83L, 82L, 80L, 78L, 74L), CT = c(61L, 15L, 7L, 33L, 22L,
30L), Corrected_10m = c(10.9, 10, 10, 10.9, 10, 12.2), Av_litter_depth = c(2.89,
4, 7, 4.22, 3.83, 3.58), Content = c(20.34, 23.26, 23.23,
23.74, 17.5, 20.24), Slope_radian = c(0.41887902, 0.087266463,
0, 0.401425728, 0.087266463, 0.610865238), AB_a = c(0L, 0L,
0L, 0L, 1L, 0L), AB_sa = c(9L, 2L, 0L, 1L, 5L, 0L), AS_a = c(0L,
0L, 0L, 0L, 3L, 0L), AS_sa = c(2L, 1L, 0L, 0L, 2L, 0L), .rows = list(
1L, 2L, 3L, 4L, 5L, 6L)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), na.action = structure(c(`31` = 31L,
`32` = 32L, `47` = 47L, `48` = 48L), class = "omit"))