Automatically generating humor similar to given example jokes is a challenging task. Text generators are often either learned and hard to interpret, or created by hand using techniques such as grammars and templates. In this demo, we show GITTA (Grammar Induction using a Template Tree Approach) and GOOFER (Generator Of One-liners From Examples with Ratings). GITTA is a grammar induction algorithm for learning interpretable grammars for generative purposes. It uses the novel notion of template trees to discover latent templates in corpora to derive these generative grammars. GOOFER is a general framework for computational humor that learns generative schemas and parameterizations from rated example jokes. This framework generalizes previous generative schemas to allow for machine learning models to be used in them. Together, GITTA and GOOFER form a basis for learning interpretable, generative models from only a few examples. Additionally, it also allows tweaking the models to user preference. From only a limited number of example jokes, they can figure out the general template and how to fill it to create jokes similar to the given jokes.