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Will Miao
4064ea7d3a
`_build_prompt_context()` was only reached when the LLM was configured, so a user with no provider got nothing at all from a linked model source — no preview, no example images, no author summary, no tags — even though all of that is deterministic data from a public API. Split the model-card fetch into `_load_source_card()`, which runs for every source-backed enrichment, and have the post-processor apply its result whether or not the LLM runs. The prompt is then built from the already-fetched card rather than re-fetching it. Invoking "Enrich Metadata with AI" still always calls the provider; a model source supplying a description, images and tags is not treated as a reason to skip it, since the LLM's summary and notes are richer and an action that silently does not call out to the provider would be unpredictable. The site data acts as a fallback for the gaps the LLM leaves. Add `base_model_resolver.resolve_base_model()` to map the site's own names (`krea/Krea-2-Turbo`, `KREA_2_TURBO`) onto the canonical vocabulary, used only when the LLM returns no base model. It is strictly conservative — exact normalised matching plus a bounded set of variant suffixes, and it only ever returns a name that is already in the vocabulary — so an uncertain hint defers to the LLM instead of writing a plausible-looking wrong value.