feat: replace infer_target_type with capability-based model selection and validation

bulkgen/config.py

@@ -10,7 +10,7 @@ import yaml
 from pydantic import BaseModel, model_validator
 
 if TYPE_CHECKING:
-    from bulkgen.providers.models import ModelInfo
+    from bulkgen.providers.models import Capability, ModelInfo
 
 
 class TargetType(enum.Enum):
@@ -57,39 +57,95 @@ class ProjectConfig(BaseModel):
         return self
 
 
-def infer_target_type(target_name: str) -> TargetType:
-    """Infer whether a target produces an image or text from its file extension."""
+def infer_required_capabilities(
+    target_name: str, target: TargetConfig
+) -> frozenset[Capability]:
+    """Infer the capabilities a model must have based on filename and config.
+
+    Raises :class:`ValueError` for unsupported file extensions.
+    """
+    from bulkgen.providers.models import Capability
+
     suffix = Path(target_name).suffix.lower()
+    caps: set[Capability] = set()
+
     if suffix in IMAGE_EXTENSIONS:
+        caps.add(Capability.TEXT_TO_IMAGE)
+        if target.reference_images:
+            caps.add(Capability.REFERENCE_IMAGES)
+        if target.control_images:
+            caps.add(Capability.CONTROL_IMAGES)
+    elif suffix in TEXT_EXTENSIONS:
+        caps.add(Capability.TEXT_GENERATION)
+        all_input_names = list(target.inputs) + list(target.reference_images)
+        if any(Path(n).suffix.lower() in IMAGE_EXTENSIONS for n in all_input_names):
+            caps.add(Capability.VISION)
+    else:
+        msg = f"Cannot infer target type for '{target_name}': unsupported extension '{suffix}'"
+        raise ValueError(msg)
+
+    return frozenset(caps)
+
+
+def target_type_from_capabilities(capabilities: frozenset[Capability]) -> TargetType:
+    """Derive the target type from a set of required capabilities."""
+    from bulkgen.providers.models import Capability
+
+    if Capability.TEXT_TO_IMAGE in capabilities:
         return TargetType.IMAGE
-    if suffix in TEXT_EXTENSIONS:
-        return TargetType.TEXT
-    msg = f"Cannot infer target type for '{target_name}': unsupported extension '{suffix}'"
-    raise ValueError(msg)
+    return TargetType.TEXT
 
 
 def resolve_model(
     target_name: str, target: TargetConfig, defaults: Defaults
 ) -> ModelInfo:
-    """Return the effective model for a target (explicit or default by type).
+    """Return the effective model for a target, validated against required capabilities.
 
-    Raises :class:`ValueError` if the resolved model name is not in the registry.
+    If the target specifies an explicit model, it is validated to have all
+    required capabilities.  Otherwise the type-appropriate default is tried
+    first; if it lacks a required capability the first capable model of the
+    same type is selected.
+
+    Raises :class:`ValueError` if no suitable model can be found.
     """
     from bulkgen.providers.models import ALL_MODELS
 
+    required = infer_required_capabilities(target_name, target)
+    target_type = target_type_from_capabilities(required)
+
     if target.model is not None:
-        model_name = target.model
-    else:
-        target_type = infer_target_type(target_name)
-        model_name = (
-            defaults.image_model
-            if target_type is TargetType.IMAGE
-            else defaults.text_model
-        )
+        # Explicit model — look up and validate.
+        for model in ALL_MODELS:
+            if model.name == target.model:
+                missing = required - frozenset(model.capabilities)
+                if missing:
+                    names = ", ".join(sorted(missing))
+                    msg = f"Model '{target.model}' for target '{target_name}' lacks required capabilities: {names}"
+                    raise ValueError(msg)
+                return model
+        msg = f"Unknown model '{target.model}' for target '{target_name}'"
+        raise ValueError(msg)
+
+    # No explicit model — try the default first, then fall back.
+    default_name = (
+        defaults.image_model if target_type is TargetType.IMAGE else defaults.text_model
+    )
     for model in ALL_MODELS:
-        if model.name == model_name:
+        if model.name == default_name:
+            if required <= frozenset(model.capabilities):
+                return model
+            break
+
+    # Default lacks capabilities — find the first capable model of the same type.
+    model_type = "image" if target_type is TargetType.IMAGE else "text"
+    for model in ALL_MODELS:
+        if model.type == model_type and required <= frozenset(model.capabilities):
             return model
-    msg = f"Unknown model '{model_name}' for target '{target_name}'"
+
+    names = ", ".join(sorted(required))
+    msg = (
+        f"No model found for target '{target_name}' with required capabilities: {names}"
+    )
     raise ValueError(msg)