Implement backend caching and add GIF benchmarking and plotting tools#2
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Add scripts/bench_gifs.py (comprehensive GIF moderation pipeline benchmark) and scripts/plot_results.py (generate media/perf_stages.png and media/perf_latency.png from bench_results.jsonl). Update docs/performance.md to document installing bench deps and the new bench/plot workflow. Tweak scripts/bench_decode.py wording to clarify that ViT inference dominates wall-clock (~91%) and adjust the delta message. bench_gifs runs the real prescreen pipeline (precise backend stubbed), outputs per-GIF JSONL and scaling/timing analysis.
Add caching for constructed backends to avoid reloading large model weights per invocation: introduce _cache, _cache_lock, _construct, and a cache-aware load_backend (with cache=False bypass). Export clear_backend_cache() to allow tests or users to drop cached backends. Update package exports to include clear_backend_cache. Update CLI to build a Config and create a single Scanner.from_config() (reusing the constructed backend) and then scan each path with that scanner, handling BackendUnavailableError up-front. Also update two performance images (media/perf_latency.png, media/perf_stages.png).
Update project version in pyproject.toml and synchronize the __version__ fallback values in src/pyframe/__init__.py to 0.3.0 in preparation for a new release.
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