Parkinson's disease (PD), a commonly occurring neurodegenerative disease, affects millions worldwide. One approach to detecting PD is observing variations in an individual's speech patterns, such as tone, jitter, shimmer, and pitch. In this demo, we present PidiBuddy, a smartphone-based system that detects PD based on the user's voice data. To reduce privacy concerns and dependency on background infrastructure and facilitate usage by naive users, PidiBuddy runs end-to-end on the smartphone. It collects short speech segments, extracts features, and infers PD (each step happens in situ) using a Random Forest-based machine learning model. Before deploying the model on the device, we trained the model offline using a publicly available speech dataset comprising a set of MFCC (Mel-Frequency Cepstral Coefficients) related speech features. The initial findings from the system are promising in terms of PD detection performance, system parameters, and system usability, all of which we aim to improve further in our ongoing work.