feat(recipes): add manual checkpoint reconnect for broken recipe entries

Checkpoint entries that cannot be restored by download (deleted,
unresolvable hash, or name-only remnants with no CivitAI identifiers)
now get the same remediation chain LoRAs already had:

- scanner: parameterized reconnect-suggestion ranking, update/restore/
  set-hash-invalid for the checkpoint entry, and clear hashInvalid on
  rematch write-back (was only done for LoRAs)
- persistence/handlers/routes: reconnect/restore/reconnect-suggestions/
  mark-hash-invalid endpoints under /api/lm/recipe/checkpoint/*
- modal: checkpoint reconnect UI (deleted/hash-invalid badges, inline
  form with suggestions, undo for reconnected entries); download
  failures mark the hash invalid only on explicit unresolvable signals
  (not found/deleted/404/410), matching the LoRA rule
- css: checkpoint undo button shares the LoRA undo styles
- i18n: the 14 new keys translated in all 9 locales

py/services/recipe_scanner.py

@@ -265,6 +265,49 @@ class RecipeScanner:
     ) -> list[dict[str, Any]]:
         """Rank local LoRAs as reconnect candidates for a broken recipe entry.
 
+        Thin wrapper over ``_suggest_reconnect_candidates`` scoped to the
+        LoRA library (see it for the ranking contract).
+        """
+        return await self._suggest_reconnect_candidates(
+            entry=entry,
+            recipe_base_model=recipe_base_model,
+            query=query,
+            limit=limit,
+            is_checkpoint=False,
+        )
+
+    async def suggest_checkpoint_reconnect_candidates(
+        self,
+        *,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        query: Optional[str] = None,
+        limit: int = 5,
+    ) -> list[dict[str, Any]]:
+        """Rank local checkpoints as reconnect candidates for a broken entry.
+
+        Thin wrapper over ``_suggest_reconnect_candidates`` scoped to the
+        checkpoint library (see it for the ranking contract).
+        """
+        return await self._suggest_reconnect_candidates(
+            entry=entry,
+            recipe_base_model=recipe_base_model,
+            query=query,
+            limit=limit,
+            is_checkpoint=True,
+        )
+
+    async def _suggest_reconnect_candidates(
+        self,
+        *,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        query: Optional[str] = None,
+        limit: int = 5,
+        is_checkpoint: bool,
+    ) -> list[dict[str, Any]]:
+        """Rank local models as reconnect candidates for a broken recipe entry.
+
         Identity signals (same hash / same CivitAI model version) outrank
         similarity signals (filename / model name fuzzy match). A confident
         base-model mismatch (both sides known and different) is a hard
@@ -286,11 +329,11 @@ class RecipeScanner:
         if limit <= 0 or not isinstance(entry, dict):
             return []
 
-        lora_scanner = self._lora_scanner
-        if lora_scanner is None:
+        scanner = self._checkpoint_scanner if is_checkpoint else self._lora_scanner
+        if scanner is None:
             return []
 
-        data = await lora_scanner.get_cached_data()
+        data = await scanner.get_cached_data()
         recipe_bm = (recipe_base_model or "").strip().casefold()
 
         def _base_model_known_mismatch(item: dict[str, Any]) -> bool:
@@ -315,7 +358,7 @@ class RecipeScanner:
             # entry without a usable hash — same rule as the filename cache.
             if not (item.get("sha256") or "").strip():
                 continue
-            if not self._is_type_compatible(item, is_checkpoint=False):
+            if not self._is_type_compatible(item, is_checkpoint=is_checkpoint):
                 continue
             if _base_model_known_mismatch(item):
                 continue
@@ -351,14 +394,17 @@ class RecipeScanner:
                 if (
                     isinstance(hit, dict)
                     and (hit.get("sha256") or "").strip()
-                    and self._is_type_compatible(hit, is_checkpoint=False)
+                    and self._is_type_compatible(hit, is_checkpoint=is_checkpoint)
                     and not _base_model_known_mismatch(hit)
                 ):
                     _consider(hit, 1.0 + _base_model_adjustment(hit), "same_hash")
 
             version_id = entry.get("modelVersionId") or entry.get("id")
             if version_id is not None:
-                hit = self._get_lora_from_version_index(str(version_id))
+                if is_checkpoint:
+                    hit = self._get_checkpoint_from_version_index(str(version_id))
+                else:
+                    hit = self._get_lora_from_version_index(str(version_id))
                 if (
                     isinstance(hit, dict)
                     and (hit.get("sha256") or "").strip()
@@ -367,7 +413,12 @@ class RecipeScanner:
                     _consider(hit, 0.95 + _base_model_adjustment(hit), "same_version")
 
         filename_source = query_text or (entry.get("file_name") or "")
-        name_source = query_text or (entry.get("modelName") or "")
+        # Parser-style checkpoint entries carry the model name under ``name``,
+        # widget-style ones under ``modelName`` — try both for checkpoints.
+        if is_checkpoint:
+            name_source = query_text or (entry.get("name") or entry.get("modelName") or "")
+        else:
+            name_source = query_text or (entry.get("modelName") or "")
         norm_filename_source = self._normalize_filename_key(filename_source)
         name_source_cf = name_source.casefold()
         # Substring hits floor the similarity ratio, but only for meaningful
@@ -1496,6 +1547,7 @@ class RecipeScanner:
         identifier key when neither identifier form exists).
         """
         entry["isDeleted"] = False
+        entry["hashInvalid"] = False
 
         new_hash = (item.get("sha256") or "").lower()
         if new_hash:
@@ -3290,6 +3342,19 @@ class RecipeScanner:
 
         return await self._lora_scanner.find_models_by_name(name, base_model=base_model)
 
+    async def find_local_checkpoints_by_name(
+        self, name: str, base_model: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
+        """Return every local checkpoint matching ``name`` (used to explain lookup misses)."""
+
+        checkpoint_scanner = getattr(self, "_checkpoint_scanner", None)
+        if not checkpoint_scanner or not name:
+            return []
+
+        return await checkpoint_scanner.find_models_by_name(
+            name, base_model=base_model
+        )
+
     async def get_local_lora_by_hash(self, hash_value: str) -> Optional[Dict[str, Any]]:
         """Lookup a local LoRA through the scanner's hash index."""
 
@@ -4036,6 +4101,214 @@ class RecipeScanner:
         updated_lora = self._enrich_lora_entry(dict(lora_entry))
         return recipe_data, updated_lora
 
+    async def update_checkpoint_entry(
+        self,
+        recipe_id: str,
+        *,
+        target_name: str,
+        target_checkpoint: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Update the checkpoint entry within a recipe (manual reconnect).
+
+        Mirrors :meth:`update_lora_entry`: the pre-update entry is snapshotted
+        under ``reconnectSnapshot`` so the association can be restored later,
+        then the matched local checkpoint is written back following the same
+        pinned key set as ``_write_rematch_checkpoint_entry``. ``file_name``
+        keeps the user-entered ``target_name`` (the same convention as the
+        LoRA reconnect), while hash/name/version/baseModel/identifier are
+        refreshed from the local item. The fingerprint is untouched — it is
+        computed over LoRAs only.
+
+        Returns:
+            The updated recipe data and the refreshed checkpoint metadata.
+        """
+        if target_name is None:
+            raise ValueError("target_name must be provided")
+
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError(
+                    "Recipe has no checkpoint entry to reconnect"
+                )
+
+            # Snapshot the pre-update state so the association can be restored
+            # later (undo reconnect). Never nest snapshots.
+            snapshot = {
+                key: copy.deepcopy(value)
+                for key, value in checkpoint.items()
+                if key != "reconnectSnapshot"
+            }
+            checkpoint["isDeleted"] = False
+            checkpoint["hashInvalid"] = False
+            checkpoint["file_name"] = target_name
+
+            if target_checkpoint is not None:
+                sha_value = target_checkpoint.get("sha256") or target_checkpoint.get(
+                    "sha"
+                )
+                if sha_value:
+                    checkpoint["hash"] = sha_value.lower()
+
+                self._write_rematch_checkpoint_entry(checkpoint, target_checkpoint)
+
+                # The write-back only refreshes keys the entry already has;
+                # a manual reconnect must also backfill the display keys so a
+                # sparse parser-style entry renders properly after the swap.
+                if not checkpoint.get("name") and target_checkpoint.get("model_name"):
+                    checkpoint["name"] = target_checkpoint["model_name"]
+                civitai = target_checkpoint.get("civitai") or {}
+                civ_name = civitai.get("name")
+                if not checkpoint.get("version") and civ_name:
+                    checkpoint["version"] = civ_name
+                if (
+                    not checkpoint.get("baseModel")
+                    and target_checkpoint.get("base_model")
+                ):
+                    checkpoint["baseModel"] = target_checkpoint["base_model"]
+
+            checkpoint["reconnectSnapshot"] = snapshot
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        # Update FTS index
+        self._update_fts_index_for_recipe(recipe_data, "update")
+
+        # Update persistent SQLite cache
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        updated_checkpoint = dict(checkpoint)
+        if target_checkpoint is not None:
+            preview_url = target_checkpoint.get("preview_url")
+            if preview_url:
+                updated_checkpoint["preview_url"] = config.get_preview_static_url(
+                    preview_url
+                )
+            if target_checkpoint.get("file_path"):
+                updated_checkpoint["localPath"] = target_checkpoint["file_path"]
+
+        updated_checkpoint = self._enrich_checkpoint_entry(updated_checkpoint)
+        return recipe_data, updated_checkpoint
+
+    async def restore_checkpoint_entry(
+        self,
+        recipe_id: str,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Restore the checkpoint entry to its pre-reconnect snapshot.
+
+        Reverses :meth:`update_checkpoint_entry`: the entry saved under
+        ``reconnectSnapshot`` becomes the checkpoint again and the snapshot is
+        dropped. Returns the updated recipe data and the restored checkpoint
+        metadata.
+        """
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError(
+                    "Recipe has no checkpoint entry to restore"
+                )
+
+            snapshot = checkpoint.get("reconnectSnapshot")
+            if not isinstance(snapshot, dict):
+                raise RecipeValidationError(
+                    "Checkpoint entry has no reconnect snapshot to restore"
+                )
+
+            restored_entry = copy.deepcopy(snapshot)
+            restored_entry.pop("reconnectSnapshot", None)
+            recipe_data["checkpoint"] = restored_entry
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        # Update FTS index
+        self._update_fts_index_for_recipe(recipe_data, "update")
+
+        # Update persistent SQLite cache
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        restored_checkpoint = self._enrich_checkpoint_entry(dict(restored_entry))
+        return recipe_data, restored_checkpoint
+
+    async def set_checkpoint_entry_hash_invalid(
+        self,
+        recipe_id: str,
+        hash_invalid: bool,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Set the ``hashInvalid`` flag on the recipe's checkpoint entry.
+
+        ``hashInvalid`` records that the entry's hash could not be resolved
+        on CivitAI (e.g. a download attempt returned "Model not found").
+        Marking it makes the entry an unresolved rematch candidate without
+        touching its stored hash/file_name.
+
+        Returns:
+            The updated recipe data and the refreshed checkpoint metadata.
+        """
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError("Checkpoint entry is not a dict")
+
+            checkpoint["hashInvalid"] = bool(hash_invalid)
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        updated_checkpoint = self._enrich_checkpoint_entry(dict(checkpoint))
+        return recipe_data, updated_checkpoint
+
     async def get_recipes_for_lora(self, lora_hash: str) -> List[Dict[str, Any]]:
         """Return recipes that reference a given LoRA hash."""