  The physical processes responsible for shaping how galaxies form and quench
over time leave imprints on both the spatial (galaxy morphology) and temporal
(star formation history; SFH) tracers that we use to study galaxies. While the
morphology-SFR connection is well studied, the correlation with past star
formation activity is not as well understood. To quantify this we present
Katachi, an interpretable convolutional neural network (CNN) framework that
learns the connection between the factors regulating star formation in galaxies
on different spatial and temporal scales. Katachi is trained on 9904 galaxies
at 0.02$<$z$<$0.1 in the SDSS-IV MaNGA DR17 sample to predict stellar mass
(M$_*$; RMSE 0.22 dex), current star formation rate (SFR; RMSE 0.31 dex) and
half-mass time (t$_{50}$; RMSE 0.23 dex). This information allows us to
reconstruct non-parametric SFHs for each galaxy from \textit{gri} imaging
alone. To quantify the morphological features informing the SFH predictions we
use SHAP (SHapley Additive exPlanations). We recover the expected trends of
M$_*$ governed by the growth of galaxy bulges, and SFR correlating with spiral
arms and other star-forming regions. We also find the SHAP maps of D4000 are
more complex than those of M$_*$ and SFR, and that morphology is correlated
with t$_{50}$ even at fixed mass and SFR. Katachi serves as a scalable public
framework to predict galaxy properties from large imaging surveys including
Rubin, Roman, and Euclid, with large datasets of high SNR imaging across
limited photometric bands.
