StyleCap: Grounded Video Captioning

StyleCap is a containerized video-captioning agent built for AMD Hackathon ACT II, Track 2. For each short clip it downloads the video, samples frames with ffmpeg, and runs a two-phase anti-hallucination pipeline: (1) a vision model extracts a strict JSON fact sheet from what is actually visible; (2) a text model generates captions in only the requested styles—formal, sarcastic, humorous-tech, and humorous-non-tech—from those facts alone. A batched internal judge picks the best of two candidates per style. A graceful degradation ladder and schema repair guarantee valid JSON output even under timeout pressure. The graded Docker image (ghcr.io/quantumbyte-01/amd-track2-captioner:v34) uses Fireworks serverless Kimi K2.6 for grounding and GLM 5.2 for styling and judging, with the API key baked in per Track 2 rules. Parallel workers and per-clip budgets keep 12 clips inside the 10-minute limit. For the Gemma partner prize, the identical pipeline runs end-to-end on google/gemma-3-12b-it served via vLLM on AMD Instinct MI300X (ROCm)—grounding, styling, and judging—without Fireworks Gemma deployment. Eight public validation clips complete in 86 seconds; evidence is in GEMMA_RESULTS.md and work/gemma_results.json. Demo video shows live rocm-smi GPU load and caption generation on the pod.