feat(recipes): explain empty LoRA lists with collapsible "Why no LoRAs?" panel

Record import provenance on every recipe: a new import_info block
(channel, machine-readable no-LoRA reason, diagnostic details) built at
import time across all channels (batch import, single URL, local file,
upload, widget save, re-imports) and persisted in the recipe JSON plus
the SQLite persistent cache (new import_info_json column with ALTER
TABLE migration).

The recipe modal renders the empty LoRA list with a collapsed details
panel showing the import method, the reason (CivitAI API returned no
LoRA resource data, API meta missing, no embedded metadata, ComfyUI
workflow metadata, video, unparsable format), and recorded diagnostics.
Legacy recipes without import_info fall back to heuristics labeled as
inferred. Genuine no-LoRA generations show no panel.

CivitAI images are always classified by API meta shape: the onsite
generator writes A1111-style EXIF without LoRA references, so parsed
EXIF cannot prove "no LoRAs used".

Adds recipes.resources.noLoras* i18n keys (all 10 locales) plus
frontend vitest and backend pytest coverage.

py/services/recipes/persistence_service.py

@@ -21,6 +21,7 @@ from ...utils.base_model import (
 from ...utils.utils import calculate_recipe_fingerprint
 from ..pending_delete_service import get_pending_delete_service
 from .errors import RecipeNotFoundError, RecipeValidationError
+from .import_info import CHANNEL_UPLOAD, CHANNEL_WIDGET, build_import_info
 
 
 @dataclass(frozen=True)
@@ -134,6 +135,22 @@ class RecipePersistenceService:
         if metadata.get("source_path"):
             recipe_data["source_path"] = metadata.get("source_path")
 
+        # Persist import provenance. Batch import / re-import paths pass a
+        # prebuilt import_info; frontend-driven saves (upload, single URL,
+        # local path) carry the analysis payload's diagnostics, from which
+        # import_info is derived here.
+        import_info = metadata.get("import_info")
+        if not isinstance(import_info, dict):
+            diagnostics = metadata.get("diagnostics")
+            if isinstance(diagnostics, dict):
+                import_info = build_import_info(
+                    diagnostics.get("channel") or CHANNEL_UPLOAD,
+                    diagnostics,
+                    loras_data,
+                )
+        if isinstance(import_info, dict) and import_info:
+            recipe_data["import_info"] = import_info
+
         nsfw_level = metadata.get("preview_nsfw_level")
         if nsfw_level is not None and isinstance(nsfw_level, int):
             recipe_data["preview_nsfw_level"] = nsfw_level
@@ -731,6 +748,9 @@ class RecipePersistenceService:
             # Widget saves re-encode an in-memory tensor to PNG/WebP with no
             # embedded metadata chunks, so a workflow can never be present.
             "has_workflow": False,
+            # Widget saves read LoRAs straight from the current workflow; an
+            # empty list means the workflow used no LoRAs.
+            "import_info": build_import_info(CHANNEL_WIDGET, None, loras_data),
         }
         if checkpoint_entry:
             recipe_data["checkpoint"] = checkpoint_entry