feat(api): translate GIAS codes to names at the query boundary

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

backend/data_loader.py

@@ -21,6 +21,38 @@ from .models import (
     FactDeprivation, FactFinance, FactPupilCharacteristics,
 )
 from .schemas import SCHOOL_TYPE_MAP
+from .gias_codes import (
+    ADMISSIONS_POLICY,
+    ESTABLISHMENT_STATUS,
+    PHASE_OF_EDUCATION,
+    RELIGIOUS_CHARACTER,
+    SCHOOL_TYPE,
+    translate,
+)
+
+# mart code column -> (API name column, dictionary)
+_GIAS_CODE_COLUMNS = {
+    "phase_code": ("phase", PHASE_OF_EDUCATION),
+    "school_type_code": ("school_type", SCHOOL_TYPE),
+    "status_code": ("status", ESTABLISHMENT_STATUS),
+    "religious_character_code": ("religious_denomination", RELIGIOUS_CHARACTER),
+    "admissions_policy_code": ("admissions_policy", ADMISSIONS_POLICY),
+}
+
+
+def translate_gias_code_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Map GIAS code columns to today's name columns (API contract).
+
+    Runs immediately after pd.read_sql so every downstream consumer —
+    filters, PHASE_GROUPS, payloads, /api/filters — keeps seeing names.
+    DataFrames without the code columns (old schema, test fixtures) pass
+    through unchanged.
+    """
+    for code_col, (name_col, mapping) in _GIAS_CODE_COLUMNS.items():
+        if code_col in df.columns:
+            df[name_col] = df[code_col].map(lambda c: translate(c, mapping))
+    return df
+
 
 _postcode_cache: Dict[str, Tuple[float, float]] = {}
 _typesense_client = None
@@ -121,16 +153,16 @@ _MAIN_QUERY = text("""
     SELECT
         s.urn,
         s.school_name,
-        s.phase,
-        s.school_type,
+        s.phase_code,
+        s.school_type_code,
         s.academy_trust_name   AS trust_name,
         s.academy_trust_uid    AS trust_uid,
-        s.religious_character  AS religious_denomination,
+        s.religious_character_code,
         s.gender,
         s.age_range,
         s.has_sixth_form,
-        s.status,
-        s.admissions_policy,
+        s.status_code,
+        s.admissions_policy_code,
         s.capacity,
         s.total_pupils         AS gias_total_pupils,
         s.headteacher_name,
@@ -256,6 +288,8 @@ def load_school_data_as_dataframe() -> pd.DataFrame:
     if df.empty:
         return df
 
+    df = translate_gias_code_columns(df)
+
     # Build address string
     df["address"] = df.apply(
         lambda r: ", ".join(