Publish curated VacatureRadar source snapshot

apps/profiles/services.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+from django.db import transaction
+
+from apps.jobs.services.geocoding import LocationMatchResult, resolve_cached_location
+
+from .models import ProfileRevision, SearchProfile
+
+
+@transaction.atomic
+def resolve_profile_home_location(profile: SearchProfile) -> LocationMatchResult:
+    """Resolve a user-entered postal code without exposing coordinate fields in the UI."""
+    query = profile.home_postal_code.strip()
+    result = (
+        resolve_cached_location(query) if query else LocationMatchResult(query=query, location=None)
+    )
+    location = result.location
+    profile.home_municipality = location.municipality or "" if location else ""
+    profile.home_latitude = location.point.latitude if location and location.point else None
+    profile.home_longitude = location.point.longitude if location and location.point else None
+    profile.save(
+        update_fields=[
+            "home_municipality",
+            "home_latitude",
+            "home_longitude",
+            "updated_at",
+        ]
+    )
+    return result
+
+
+@transaction.atomic
+def save_profile_revision(profile: SearchProfile, *, reason: str) -> ProfileRevision:
+    latest = profile.revisions.order_by("-version").first()
+    next_version = (latest.version if latest else 0) + 1
+    if profile.version != next_version:
+        profile.version = next_version
+        profile.save(update_fields=["version", "updated_at"])
+    return ProfileRevision.objects.create(
+        profile=profile,
+        version=next_version,
+        snapshot=profile.snapshot(),
+        reason=reason,
+    )
+
+
+@transaction.atomic
+def apply_feedback_delta(
+    profile: SearchProfile,
+    feature: str,
+    delta: float,
+    *,
+    min_weight: float = 0.0,
+    max_weight: float = 40.0,
+) -> SearchProfile:
+    if not profile.learning_enabled:
+        return profile
+    weights = dict(profile.weights)
+    current = float(weights.get(feature, 0.0))
+    weights[feature] = round(max(min_weight, min(max_weight, current + delta)), 2)
+    profile.weights = weights
+    profile.save(update_fields=["weights", "updated_at"])
+    save_profile_revision(profile, reason=f"feedback_delta:{feature}:{delta:+.2f}")
+    return profile