While commonly used for communication, recently, WiFi is increasingly being used for sensing. In particular, wireless signals are altered (i.e., absorbed, reflected, and attenuated) by the human body and objects in the environment. This can be perceived by an observer to infer information on the environment and hence, to “see” over WiFi. So far, works in this area have led to a variety of custom software tools – each designed for a specific purpose. Moreover, given how scattered the literature is, it is difficult to even identify all processing steps or functionalities necessary for WiFi sensing. To the best of our knowledge, there has been no effort towards a generic solution that helps promote further research and boost new applications in the area. With this as a motivation, we propose WirelessEye, a freely available, generic software framework that allows bootstrapping WiFi sensing systems on low-cost hardware, such as a Raspberry Pi. WirelessEye consolidates all necessary processing steps in a single framework, from collecting and visualizing data to executing different machine learning models in real-time for the purpose of comparison. This way, researchers and practitioners can focus on aspects of their research/applications rather than dealing with the many implementation hurdles of WiFi sensing.