Move character slot label policy

cast_context.py

@@ -2,6 +2,11 @@ from __future__ import annotations
 
 from typing import Any, Callable
 
+try:
+    from . import character_config as character_policy
+except ImportError:  # Allows local smoke tests with top-level imports.
+    import character_config as character_policy
+
 
 Choose = Callable[[Any, list[tuple[str, str, str]]], tuple[str, str, str]]
 
@@ -37,6 +42,30 @@ def cast_summary_phrase(women_count: int, men_count: int) -> str:
     return f"{women_count} {women_label}, {men_count} {men_label}, {person_count} total adults"
 
 
+def explicit_character_slot_label(slot: dict[str, Any]) -> str:
+    label = str(slot.get("label") or "").strip().upper()
+    if label in character_policy.CHARACTER_LABEL_CHOICES and label != "AUTO_CHAIN":
+        return label
+    return ""
+
+
+def character_slot_label_map(slots: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
+    label_map: dict[str, dict[str, Any]] = {}
+    letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+    for subject_type, prefix in (("woman", "Woman"), ("man", "Man")):
+        subject_slots = [slot for slot in slots if slot.get("subject_type") == subject_type]
+        auto_slots = [slot for slot in subject_slots if not explicit_character_slot_label(slot)]
+        for index, slot in enumerate(reversed(auto_slots)):
+            if index >= len(letters):
+                break
+            label_map[f"{prefix} {letters[index]}"] = slot
+        for slot in subject_slots:
+            explicit = explicit_character_slot_label(slot)
+            if explicit:
+                label_map[f"{prefix} {explicit}"] = slot
+    return label_map
+
+
 def configured_cast_context(women_count: int, men_count: int) -> dict[str, str]:
     women_count = max(0, int(women_count))
     men_count = max(0, int(men_count))