phase 1

modelgen/types.py

@@ -5,6 +5,7 @@ Type mappings for each output format.
 Used by generators to convert Python types to target framework types.
 """
 
+import dataclasses as dc
 from typing import Any, Callable, get_args
 
 # =============================================================================
@@ -39,8 +40,12 @@ DJANGO_SPECIAL: dict[str, str] = {
 def _get_list_inner(type_hint: Any) -> str:
     """Get inner type of List[T] for Pydantic."""
     args = get_args(type_hint)
-    if args and args[0] in (str, int, float, bool):
-        return {str: "str", int: "int", float: "float", bool: "bool"}[args[0]]
+    if args:
+        inner = args[0]
+        if inner in (str, int, float, bool):
+            return {str: "str", int: "int", float: "float", bool: "bool"}[inner]
+        if isinstance(inner, type) and dc.is_dataclass(inner):
+            return inner.__name__
     return "str"
 
 
@@ -54,6 +59,7 @@ PYDANTIC_RESOLVERS: dict[Any, Callable[[Any], str]] = {
     "dict": lambda _: "Dict[str, Any]",
     "list": lambda base: f"List[{_get_list_inner(base)}]",
     "enum": lambda base: base.__name__,
+    "dataclass": lambda base: base.__name__,
 }
 
 # =============================================================================
@@ -72,6 +78,8 @@ def _resolve_ts_list(base: Any) -> str:
             return "number[]"
         elif inner is bool:
             return "boolean[]"
+        elif isinstance(inner, type) and dc.is_dataclass(inner):
+            return f"{inner.__name__}[]"
     return "string[]"
 
 
@@ -85,6 +93,7 @@ TS_RESOLVERS: dict[Any, Callable[[Any], str]] = {
     "dict": lambda _: "Record<string, unknown>",
     "list": _resolve_ts_list,
     "enum": lambda base: base.__name__,
+    "dataclass": lambda base: base.__name__,
 }
 
 # =============================================================================