Computing the band gap of a semiconductor material often requires laborious manual curve fitting to Tauc plots that result in human error and consume time. Current band gap computing software from literature still requires each sample to be processed one by one, which becomes infeasible for large numbers of samples produced by highthroughput synthesis tools. We develop a band gap computation tool that takes in the vision-segmented hyperspectral data, Φ, for each deposited sample and accurately computes each band gap autonomously. This developed process is agnostic to the number of samples and will scale up to higher numbers of samples, accelerating the material characterization process.