I developed a robust REST API using Python and the FastAPI framework to facilitate the submission of roof images directly from homeowners. The primary function of the API is to accept and process these images seamlessly, enabling users to quickly obtain an assessment of their roof's condition. Once an image is received, it is fed into a custom machine learning model developed with scikit-learn. This model was meticulously trained to analyze the visual characteristics of roofs, identifying key indicators that distinguish between scenarios requiring a simple repair and those necessitating a complete replacement.

The system is designed to extract relevant features from each image, scrutinizing elements such as wear patterns, structural anomalies, and potential degradation markers that may not be immediately apparent to the naked eye. By leveraging the efficient processing capabilities of FastAPI, the API is capable of handling multiple requests concurrently, ensuring a responsive experience for users. The combination of FastAPI’s high-performance routing and Python’s flexibility allowed for the creation of a scalable solution that could adapt to varying levels of user demand.

Overall, the project not only streamlines the process of roof condition evaluation but also provides homeowners with actionable insights into the state of their properties. The integration of a custom machine learning pipeline with a modern RESTful service highlights the potential of using advanced data analysis to deliver practical, real-world benefits, ultimately empowering roof owners to make informed decisions regarding necessary maintenance or upgrades.