feat(analytics): add Python NeighborhoodScore service + NestJS HTTP proxy (TEC-2756)

- libs/ai-services: new POST /neighborhood/score router computing weighted
  6-axis livability score from per-category POI counts; algorithm versioned
  for future iteration (sigmoid curves, percentile thresholds).
- apps/api: HttpNeighborhoodScoreService proxies to Python first, falls back
  to PrismaNeighborhoodScoreService when AI service unavailable. Mirrors the
  HttpAVMService pattern. Existing GET /analytics/neighborhoods/:district/score
  endpoint and CQRS handler now flow through the proxy.
- AnalyticsModule binds Http variant by default, retains Prisma variant as
  injectable fallback.
- Tests: 5 pytest cases for Python heuristic, 4 vitest cases for HTTP proxy
  fallback behaviour.

Co-Authored-By: Paperclip <noreply@paperclip.ing>

libs/ai-services/app/services/neighborhood_service.py

@@ -0,0 +1,71 @@
+import logging
+
+from app.models.neighborhood import (
+    NeighborhoodPOICounts,
+    NeighborhoodScoreRequest,
+    NeighborhoodScoreResponse,
+)
+
+logger = logging.getLogger(__name__)
+
+ALGORITHM_VERSION = "neighborhood-heuristic-v1"
+
+# Sum = 100. Mirrors NestJS PrismaNeighborhoodScoreServiceImpl for fallback parity.
+CATEGORY_WEIGHTS: dict[str, int] = {
+    "education": 20,
+    "healthcare": 20,
+    "transport": 20,
+    "shopping": 15,
+    "greenery": 15,
+    "safety": 10,
+}
+
+# Count yielding a 10/10 sub-score. Calibrated against HCMC/HN audit benchmarks.
+MAX_COUNTS: dict[str, int] = {
+    "education": 15,
+    "healthcare": 8,
+    "transport": 12,
+    "shopping": 10,
+    "greenery": 6,
+    "safety": 4,
+}
+
+
+class NeighborhoodScoreService:
+    """Stateless scoring algorithm.
+
+    NestJS owns the PostGIS radius query and passes per-category counts.
+    This service applies the weighting + capping curve so the algorithm
+    can evolve independently of the persistence layer.
+    """
+
+    def score(self, req: NeighborhoodScoreRequest) -> NeighborhoodScoreResponse:
+        counts = req.poi_counts
+        sub_scores = self._sub_scores(counts)
+        total = sum(
+            CATEGORY_WEIGHTS[cat] * sub_scores[cat] / 10.0 for cat in CATEGORY_WEIGHTS
+        )
+
+        return NeighborhoodScoreResponse(
+            district=req.district,
+            city=req.city,
+            education_score=sub_scores["education"],
+            healthcare_score=sub_scores["healthcare"],
+            transport_score=sub_scores["transport"],
+            shopping_score=sub_scores["shopping"],
+            greenery_score=sub_scores["greenery"],
+            safety_score=sub_scores["safety"],
+            total_score=round(total, 1),
+            poi_counts=counts.model_dump(),
+            algorithm_version=ALGORITHM_VERSION,
+        )
+
+    def _sub_scores(self, counts: NeighborhoodPOICounts) -> dict[str, float]:
+        raw = counts.model_dump()
+        return {
+            cat: round(min(10.0, raw[cat] / MAX_COUNTS[cat] * 10.0), 2)
+            for cat in CATEGORY_WEIGHTS
+        }
+
+
+neighborhood_score_service = NeighborhoodScoreService()