65acefd2a6* feat(studio): infinite scroll for recommended models list The model selector showed a hard cap of 4 GGUFs + 4 safetensors in the Recommended section. Users who wanted to browse more had to search manually on Hugging Face. Backend: increase the default model pool from 8+8 to 40+40 (the HF fetch already pulls 80, so no extra network cost). Frontend: replace the static 4+4 cap with on-demand lazy loading. A page counter tracks how many groups of 4 to show per category. An IntersectionObserver on a sentinel div at the bottom of the list increments the page when the user scrolls down. Models are interleaved in groups of 4 GGUFs then 4 hub models per page for a balanced view. Key implementation details: - Callback ref for the sentinel so the observer attaches reliably on first popover open (useRef would miss the initial mount) - Observer disconnects after each fire and re-attaches via useEffect with a 100ms layout delay to prevent runaway page loading - VRAM info fetched incrementally via useRecommendedModelVram on the visible slice only - recommendedSet uses visible IDs so HF search dedup stays correct * refactor: address review feedback on recommended infinite scroll - Simplify visibleRecommendedIds: use findIndex to locate the GGUF/hub split point instead of re-filtering the entire array each time. recommendedIds is already sorted GGUF-first, so a single slice is enough. - Fix VRAM refetch churn: pass the full recommendedIds (stable across page increments) to useRecommendedModelVram instead of the growing visibleRecommendedIds slice. The hook derives its stableKey from the sorted+joined input, so passing the same pool on every page avoids redundant HF modelInfo requests. |
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| .. | ||
| __init__.py | ||
| audio_codecs.py | ||
| defaults.py | ||
| inference.py | ||
| llama_cpp.py | ||
| orchestrator.py | ||
| tools.py | ||
| worker.py | ||