Welcome to Snapill!

Snapill is the first app that leverages advanced Computer Vision techniques like homography with your common LLM. The combination allows for robustness and covers several user failure points (mainly: bad lighting). Snapill allows a patient to scan their medication vial, and automatically generates important metadata like prescription name, expiration date, dosage, and possible times to take the pill. Furthermore, we believe awareness is a strong factor for increased patient safety, so we incorporated the Cerebras AI Inference models to allow users to "chat" with their medications (we weren't joking when we alluded to SnapChat). Users are able to request more information about the drug through the chat, along with a personal vanguard that scans for incompatibility issues between medications. Finally, Snapill provides reminders when it is time for users to take their medications

Snapill is a combination of a react-native app ran on the user's phone that connects to a Flask backend hosted on Heroku. Here, we would like to credit this blog, which we used the homography technique from. This allows us to use homemade openCV functions while calling APIs such as Roboflow, Cerebras, and Firebase. User data, auth, and storage is all stored through firebase, allowing for simple public downloads from other APIs. We tested the app using both an Android simulator and a physical iPhone. The backend is hosted on heroku. We also used Roboflow API for label recognition model training and OCR on warped images.