classification using zero-inflated negative binomial mixture model

Hi,
   I want using zero-inflated negative binomial regression model to
classify data(a vector of data), that is I want know each observed value is
more likely belong to the "zero" or "count" distribution(better with
relative probability). My data is some like:

   count site samp

 12909    1    1

   602     1    2

    50      1    3

  1218    1     4

 91291   1     5

while "count" is the data with a mixture of "zero" and "non-zero"
distribution I want know, and "site", "samp" are two prediction valuables
with additive effect(but I am not interested in it).

  I have tried the zeroinfl function of pscl package to fit zero-inflated
negative binomial regression. But it  can not give you the classification
result of "count".  Can anyone help with some indication of how to do it or
other tools that can do this job ??