Temu Gigi

Temu Gigi is a fullstack web and mobile-integrated application designed to support dental students in their clinical practice by streamlining the process of finding and diagnosing patients. The platform leverages a machine learning model to analyze uploaded dental images and detect potential oral diseases, helping students make more accurate diagnoses. The frontend is built with React and styled using Tailwind CSS, while the backend uses Express.js and is deployed via Google Cloud Run for scalable and reliable performance. Data is stored in a managed Cloud SQL database, and the architecture follows a microservices pattern to ensure modularity and maintainability. Additional features include secured RESTful APIs tested using Postman, JWT-based authentication, image upload and analysis pipeline, patient-student matching system, appointment scheduling, and user role management. Machine learning integration is handled through an endpoint that processes images and returns diagnostic predictions. This project demonstrates an effective combination of cloud computing, fullstack development, and AI integration for a real-world healthcare use case.