predict does not attempt to invert transformations to left hand side

Let's say I have a model like this:

model = "log(y) ~ s"
fit = ols(model, data=data).fit()

Presumably I am trying to predict y from s. I am taking the log of the left hand side so that I believe that I now have a linear model. Ultimately though, I want the forecast value of y for a given value of s.

If I use fit.predict(s), what I am given are predictions for log(y) as opposed to y itself. Is there any way (for some subset of transformations for which the inverse is known) to tell the predict that I would like to predict y? Something like: fit.predict(s, 'y')

Start at the fit.predict entry point and trace how the formula model="log(y) ~ s" is represented during prediction. Determine which left-hand-side transformations could have a known inverse, then define and test behavior for requesting predictions on the original y scale. Done means supported transformations return y-scale forecasts without changing the existing default behavior.