Video Captioning

This pipeline accepts short video clips and generates four stylistically distinct captions per clip: formal, sarcastic, humorous-tech, and humorous-non-tech. The architecture is deliberately decoupled into four stages. First, OpenCV extracts keyframes and Whisper transcribes audio. Second, Minimax M3 via Fireworks AI analyzes frames and transcript together, producing a structured JSON scene description covering setting, subjects, actions, notable details, and audio context. Third, this scene JSON — not the raw video — is passed to four tone-specific prompts to generate captions, ensuring all four are factually grounded in identical scene facts while differing only in expression. Fourth, a self-judge loop powered by the same model scores each caption on accuracy and tone fit, mirroring the hackathon's own LLM-judge evaluation criterion and surfacing weak captions before submission. The Streamlit demo UI supports three modes: a results dashboard showing all processed clips with self-judge scores, a live video upload tab, and a URL/Google Drive input tab for downloading and processing clips on demand. The entire pipeline is containerized via Docker, with the pre-built image publicly available on Docker Hub for one-command deployment without any local setup.