Video Captioning API Router

This project was developed as a containerized solution for the AMD Developer Hackathon: ACT II, specifically targeting the Track 2 Video Captioning challenge. The primary objective was to build a robust, scalable, and highly lightweight backend application that acts as the core routing layer for interacting with the Fireworks AI API. Built using Python and FastAPI, the architecture is designed to handle automated video captioning requests and route them to generate summaries across four distinct styles: formal, sarcastic, humorous-tech, and humorous-non-tech. Due to the strict hackathon requirement for all submissions to be fully cloud-ready and containerized, this application is packaged using an ultra-efficient Alpine Linux Docker image. This ensures that the environment footprint remains incredibly small (under a few megabytes), allowing for lightning-fast deployment onto AMD Developer Cloud infrastructure without the heavy overhead typically associated with local machine learning environments. While the initial architecture explored heavy computer vision pipelines and local model routing, the final submission prioritizes a streamlined, API-first approach to maximize deployment speed and perfectly align with the constraints of the Fireworks AI API track. The backend is fully Dockerized, pre-configured with the necessary REST endpoints, and ready to be wired directly to the remote Fireworks models to generate and return the stylized captions seamlessly.