refactor(phase): merge KS2+KS4 into fact_performance, fix all phase inconsistencies

Root cause: the UNION ALL query in data_loader.py produced two rows per
all-through school per year (one KS2, one KS4), with drop_duplicates()
silently discarding the KS4 row. Fixes:

- New dbt mart `fact_performance`: FULL OUTER JOIN of fact_ks2_performance
  and fact_ks4_performance on (urn, year). One row per school per year.
  All-through schools have both KS2 and KS4 columns populated.
- data_loader.py: replace 175-line UNION ALL with a simple JOIN to
  fact_performance. No more duplicate rows or drop_duplicates needed.
- sync_typesense.py: single LATERAL JOIN to fact_performance instead of
  two separate KS2/KS4 joins.
- app.py: remove drop_duplicates (no longer needed); add PHASE_GROUPS
  constant so all-through/middle schools appear in primary and secondary
  filter results (were previously invisible to both); scope result_filters
  gender/admissions_policies to secondary schools only.
- HomeView.tsx: isSecondaryView is now majority-based (not "any secondary")
  and isMixedView shows both sort option sets for mixed result sets.
- school/[slug]/page.tsx: all-through schools route to SchoolDetailView
  (renders both SATs + GCSE sections) instead of SecondarySchoolDetailView
  (KS4-only). Dedicated SEO metadata for all-through schools.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app.py

@@ -35,6 +35,14 @@ from .utils import clean_for_json
 # Values to exclude from filter dropdowns (empty strings, non-applicable labels)
 EXCLUDED_FILTER_VALUES = {"", "Not applicable", "Does not apply"}
 
+# Maps user-facing phase filter values to the GIAS PhaseOfEducation values they include.
+# All-through schools appear in both primary and secondary results.
+PHASE_GROUPS: dict[str, set[str]] = {
+    "primary": {"primary", "middle deemed primary", "all-through"},
+    "secondary": {"secondary", "middle deemed secondary", "all-through", "16 plus"},
+    "all-through": {"all-through"},
+}
+
 BASE_URL = "https://schoolcompare.co.uk"
 MAX_SLUG_LENGTH = 60
 
@@ -343,20 +351,13 @@ async def get_schools(
             )
         df_latest = df_latest.merge(prev_rwm, on="urn", how="left")
 
-    # Phase filter
+    # Phase filter — uses PHASE_GROUPS so all-through/middle schools appear
+    # in the correct phase(s) rather than being invisible to both filters.
     if phase:
-        phase_lower = phase.lower()
-        if phase_lower in ("primary", "secondary", "all-through", "all_through"):
-            # Map param values to GIAS phase strings (partial match)
-            phase_map = {
-                "primary": "primary",
-                "secondary": "secondary",
-                "all-through": "all-through",
-                "all_through": "all-through",
-            }
-            phase_substr = phase_map[phase_lower]
-            schools_df_phase_mask = df_latest["phase"].str.lower().str.contains(phase_substr, na=False)
-            df_latest = df_latest[schools_df_phase_mask]
+        phase_lower = phase.lower().replace("_", "-")
+        allowed = PHASE_GROUPS.get(phase_lower)
+        if allowed:
+            df_latest = df_latest[df_latest["phase"].str.lower().isin(allowed)]
 
     # Secondary-specific filters (after phase filter)
     if gender:
@@ -389,7 +390,8 @@ async def get_schools(
         for c in SCHOOL_COLUMNS + location_cols + result_cols
         if c in df_latest.columns
     ]
-    schools_df = df_latest[available_cols].drop_duplicates(subset=["urn"])
+    # fact_performance guarantees one row per (urn, year); df_latest has one row per urn.
+    schools_df = df_latest[available_cols]
 
     # Location-based search (uses pre-geocoded data from database)
     search_coords = None
@@ -458,13 +460,16 @@ async def get_schools(
             schools_df["school_type"].str.lower() == school_type.lower()
         ]
 
-    # Compute result-scoped filter values (before pagination)
+    # Compute result-scoped filter values (before pagination).
+    # Gender and admissions are secondary-only filters — scope them to schools
+    # with KS4 data so they don't appear for purely primary result sets.
+    _sec_mask = schools_df["attainment_8_score"].notna() if "attainment_8_score" in schools_df.columns else pd.Series(False, index=schools_df.index)
     result_filters = {
         "local_authorities": clean_filter_values(schools_df["local_authority"]) if "local_authority" in schools_df.columns else [],
         "school_types": clean_filter_values(schools_df["school_type"]) if "school_type" in schools_df.columns else [],
         "phases": clean_filter_values(schools_df["phase"]) if "phase" in schools_df.columns else [],
-        "genders": clean_filter_values(schools_df["gender"]) if "gender" in schools_df.columns else [],
-        "admissions_policies": clean_filter_values(schools_df["admissions_policy"]) if "admissions_policy" in schools_df.columns else [],
+        "genders": clean_filter_values(schools_df.loc[_sec_mask, "gender"]) if "gender" in schools_df.columns and _sec_mask.any() else [],
+        "admissions_policies": clean_filter_values(schools_df.loc[_sec_mask, "admissions_policy"]) if "admissions_policy" in schools_df.columns and _sec_mask.any() else [],
     }
 
     # Pagination