Fix release blockers and deployment build

apps/jobs/services/scoring.py

@@ -1,15 +1,16 @@
 from __future__ import annotations
 
+from collections.abc import Iterable
 from dataclasses import dataclass
 from difflib import SequenceMatcher
-from typing import Any, Iterable
+from typing import Any
 
 from django.db import transaction
 
+from apps.jobs.models import JobPosting, ScoreRun
 from apps.jobs.services.ai import AiAnalysis, AiAnalysisCache, analyze_job_text
 from apps.jobs.services.distance import estimate_commute, haversine_km
 from apps.jobs.services.geocoding import resolve_cached_location
-from apps.jobs.models import JobPosting, ScoreRun
 from apps.profiles.models import SearchProfile
 
 from .normalization import normalize_token
@@ -106,7 +107,9 @@ def _profile_reference(profile: SearchProfile) -> _GeoReference | None:
 def _title_fit(job: JobPosting, profile: SearchProfile) -> float:
     if not profile.desired_titles:
         return 0.65
-    return max(_similarity(job.normalized_title, desired_title) for desired_title in profile.desired_titles)
+    return max(
+        _similarity(job.normalized_title, desired_title) for desired_title in profile.desired_titles
+    )
 
 
 def _skill_fit(job: JobPosting, profile: SearchProfile) -> tuple[float, list[str], list[str]]:
@@ -191,9 +194,15 @@ def _hard_exclusions(
     ):
         reasons.append(f"Uitgesloten regio: {job.region or job.municipality}")
 
-    if distance.exact_distance_km is not None and distance.exact_distance_km > distance_limit:
-        if job.workplace_type != "remote":
-            reasons.append(f"Afstand {distance.exact_distance_km:.0f} km boven maximum {profile.max_distance_km} km")
+    if (
+        distance.exact_distance_km is not None
+        and distance.exact_distance_km > distance_limit
+        and job.workplace_type != "remote"
+    ):
+        reasons.append(
+            f"Afstand {distance.exact_distance_km:.0f} km boven maximum "
+            f"{profile.max_distance_km} km"
+        )
 
     max_commute_minutes = profile.hard_rules.get("max_commute_minutes")
     try:
@@ -206,7 +215,8 @@ def _hard_exclusions(
         and distance.commute_minutes > max_commute_limit
     ):
         reasons.append(
-            f"Geschatte reistijd {distance.commute_minutes} minuten boven limiet van {max_commute_limit}"
+            f"Geschatte reistijd {distance.commute_minutes} minuten boven limiet van "
+            f"{max_commute_limit}"
         )
 
     excluded_skills = {
@@ -244,7 +254,9 @@ def _ai_feature_score(features: dict[str, Any]) -> float:
         "unknown": 0.03,
         "": 0.03,
     }.get(seniority, 0.05)
-    score = 0.5 * (1.0 - support_ratio) + 0.25 * (1.0 - consultancy_ratio) + 0.15 * (1.0 - travel_ratio)
+    score = (
+        0.5 * (1.0 - support_ratio) + 0.25 * (1.0 - consultancy_ratio) + 0.15 * (1.0 - travel_ratio)
+    )
     return max(0.0, min(1.0, score + seniority_boost))
 
 
@@ -373,7 +385,10 @@ def calculate_score(job: JobPosting, profile: SearchProfile) -> ScoreResult:
         positives.append("Herkenbare skills: " + ", ".join(present_skills[:6]))
     if job.direct_employer and not job.recruiter:
         positives.append("Rechtstreekse werkgeversbron.")
-    if distance.exact_distance_km is not None and distance.exact_distance_km <= profile.max_distance_km:
+    if (
+        distance.exact_distance_km is not None
+        and distance.exact_distance_km <= profile.max_distance_km
+    ):
         positives.append(f"Binnen de ingestelde afstand ({distance.exact_distance_km:.0f} km).")
     if (
         distance.exact_distance_km is None
@@ -382,13 +397,18 @@ def calculate_score(job: JobPosting, profile: SearchProfile) -> ScoreResult:
     ):
         estimate_label = "geschatte" if distance.commute_estimate else "ingeschatte"
         concerns.append(
-            f"Schatting: {estimate_label} reistijd ca. {distance.commute_minutes} min (conservatief)."
+            f"Schatting: {estimate_label} reistijd ca. {distance.commute_minutes} min "
+            "(conservatief)."
         )
     if support_ratio >= 0.5:
         concerns.append("Vacature bevat sterke first-line/helpdesksignalen.")
     if missing_skills:
         concerns.append("Niet duidelijk teruggevonden: " + ", ".join(missing_skills[:6]))
-    if distance.exact_distance_km is None and distance.has_distance_data and job.workplace_type != "remote":
+    if (
+        distance.exact_distance_km is None
+        and distance.has_distance_data
+        and job.workplace_type != "remote"
+    ):
         concerns.append("Afstand kon nog niet exact betrouwbaar worden berekend.")
     if not job.compensation:
         concerns.append("Salaris of barema is niet vermeld.")
@@ -436,7 +456,9 @@ def calculate_score(job: JobPosting, profile: SearchProfile) -> ScoreResult:
                 "warnings": ai_analysis.warnings,
                 "features": ai_analysis.features,
                 "weight_requested": float(profile.weights.get("ai", 0) or 0),
-                "weight_applied": ai_weight if ai_analysis.status == AiAnalysisCache.Status.OK else 0.0,
+                "weight_applied": ai_weight
+                if ai_analysis.status == AiAnalysisCache.Status.OK
+                else 0.0,
             },
         },
         model_version=ai_analysis.model,
@@ -451,9 +473,7 @@ def _iter_active_profiles(profile_id: int | None):
     return profiles
 
 
-def rescore_jobs_with_profiles(
-    jobs: Iterable[JobPosting], *, profile_id: int | None = None
-) -> int:
+def rescore_jobs_with_profiles(jobs: Iterable[JobPosting], *, profile_id: int | None = None) -> int:
     count = 0
     for profile in _iter_active_profiles(profile_id):
         for job in jobs: