feat(ux): 8 UX improvements — simpler home, advanced filters, phase tabs, 4-line rows

1. Simpler home page: only search box on landing, no filter dropdowns
2. Advanced filters: hidden behind toggle on results page, auto-open if active
3. Per-school phase rendering: each row renders based on its own data
4. Taller 4-line rows with context line (type, age range, denomination, gender)
5. Result-scoped filters: dropdown values reflect current search results
6. Fix blank filter values: exclude empty strings and "Not applicable"
7. Rankings: Primary/Secondary phase tabs with phase-specific metrics
8. Compare: Primary/Secondary tabs with school counts and phase metrics

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

backend/app.py

@@ -32,6 +32,17 @@ from .data_loader import get_data_info as get_db_info
 from .schemas import METRIC_DEFINITIONS, RANKING_COLUMNS, SCHOOL_COLUMNS
 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"}
+
+
+def clean_filter_values(series: pd.Series) -> list[str]:
+    """Return sorted unique values from a Series, excluding NaN and junk labels."""
+    return sorted(
+        v for v in series.dropna().unique().tolist()
+        if v not in EXCLUDED_FILTER_VALUES
+    )
+
 
 # =============================================================================
 # SECURITY MIDDLEWARE & HELPERS
@@ -380,6 +391,15 @@ async def get_schools(
             schools_df["school_type"].str.lower() == school_type.lower()
         ]
 
+    # Compute result-scoped filter values (before pagination)
+    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 [],
+    }
+
     # Pagination
     total = len(schools_df)
     start_idx = (page - 1) * page_size
@@ -392,6 +412,7 @@ async def get_schools(
         "page": page,
         "page_size": page_size,
         "total_pages": (total + page_size - 1) // page_size if page_size > 0 else 0,
+        "result_filters": result_filters,
         "location_info": {
             "postcode": postcode,
             "radius": radius * 1.60934,  # Convert miles to km for frontend display
@@ -507,7 +528,11 @@ async def compare_schools(
                     "urn": urn,
                     "school_name": latest.get("school_name", ""),
                     "local_authority": latest.get("local_authority", ""),
+                    "school_type": latest.get("school_type", ""),
                     "address": latest.get("address", ""),
+                    "phase": latest.get("phase", ""),
+                    "attainment_8_score": float(latest["attainment_8_score"]) if pd.notna(latest.get("attainment_8_score")) else None,
+                    "rwm_expected_pct": float(latest["rwm_expected_pct"]) if pd.notna(latest.get("rwm_expected_pct")) else None,
                 },
                 "yearly_data": clean_for_json(school_data),
             }
@@ -529,15 +554,15 @@ async def get_filter_options(request: Request):
         }
 
     # Phases: return values from data, ordered sensibly
-    phases = sorted(df["phase"].dropna().unique().tolist()) if "phase" in df.columns else []
+    phases = clean_filter_values(df["phase"]) if "phase" in df.columns else []
 
     secondary_df = df[df["attainment_8_score"].notna()] if "attainment_8_score" in df.columns else df.iloc[0:0]
-    genders = sorted(secondary_df["gender"].dropna().unique().tolist()) if "gender" in secondary_df.columns else []
-    admissions_policies = sorted(secondary_df["admissions_policy"].dropna().unique().tolist()) if "admissions_policy" in secondary_df.columns else []
+    genders = clean_filter_values(secondary_df["gender"]) if "gender" in secondary_df.columns else []
+    admissions_policies = clean_filter_values(secondary_df["admissions_policy"]) if "admissions_policy" in secondary_df.columns else []
 
     return {
-        "local_authorities": sorted(df["local_authority"].dropna().unique().tolist()),
-        "school_types": sorted(df["school_type"].dropna().unique().tolist()),
+        "local_authorities": clean_filter_values(df["local_authority"]) if "local_authority" in df.columns else [],
+        "school_types": clean_filter_values(df["school_type"]) if "school_type" in df.columns else [],
         "years": sorted(df["year"].dropna().unique().tolist()),
         "phases": phases,
         "genders": genders,
@@ -641,6 +666,9 @@ async def get_rankings(
     local_authority: Optional[str] = Query(
         None, description="Filter by local authority", max_length=100
     ),
+    phase: Optional[str] = Query(
+        None, description="Filter by phase: primary or secondary", max_length=20
+    ),
 ):
     """Get school rankings by a specific metric."""
     # Sanitize local authority input
@@ -670,6 +698,12 @@ async def get_rankings(
     if local_authority:
         df = df[df["local_authority"].str.lower() == local_authority.lower()]
 
+    # Filter by phase
+    if phase == "primary" and "rwm_expected_pct" in df.columns:
+        df = df[df["rwm_expected_pct"].notna()]
+    elif phase == "secondary" and "attainment_8_score" in df.columns:
+        df = df[df["attainment_8_score"].notna()]
+
     # Sort and rank (exclude rows with no data for this metric)
     df = df.dropna(subset=[metric])
     total = len(df)