Fix release blockers and deployment build

apps/jobs/services/feedback.py

@@ -1,18 +1,15 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from datetime import timedelta
 from typing import Any
 
 from django.db import transaction
-from django.utils import timezone
 
-from apps.jobs.models import Application, Feedback, JobPosting, ScoreRun
+from apps.jobs.models import Feedback, JobPosting, ScoreRun
 from apps.jobs.services.applications import apply_application_on_feedback
 from apps.profiles.models import SearchProfile
 from apps.profiles.services import apply_feedback_delta
 
-
 LEARNING_MIN_SAMPLES = 2
 LEARNING_DELTA_BY_ACTION = {
     Feedback.Action.INTERESTING: 1.0,
@@ -47,15 +44,21 @@ def _latest_score_run(profile: SearchProfile, job: JobPosting) -> ScoreRun | Non
 
 
 def _best_signal_feature(score_run: ScoreRun | None) -> str:
+    if score_run is None:
+        return "content"
     components = {
-        key: value for key, value in (score_run.components or {}).items() if key in LEARNING_FEATURES
+        key: value
+        for key, value in (score_run.components or {}).items()
+        if key in LEARNING_FEATURES
     }
     if not components:
         return "content"
     return max(components, key=components.get)
 
 
-def _classify_hide_signal(profile: SearchProfile, score_run: ScoreRun | None, reason: str) -> _LearningSignal:
+def _classify_hide_signal(
+    profile: SearchProfile, score_run: ScoreRun | None, reason: str
+) -> _LearningSignal:
     normalized = _normalize_reason_text(reason)
     if not normalized:
         return _LearningSignal(
@@ -71,7 +74,9 @@ def _classify_hide_signal(profile: SearchProfile, score_run: ScoreRun | None, re
             reason_code="non_learning_title",
             learnable=False,
         )
-    if any(token in normalized for token in ("afstand", "afstands", "km", "locatie", "verplaatsing")):
+    if any(
+        token in normalized for token in ("afstand", "afstands", "km", "locatie", "verplaatsing")
+    ):
         return _LearningSignal(
             feature="",
             delta=0.0,
@@ -128,7 +133,9 @@ def _learning_signal_count(profile: SearchProfile, feature: str) -> int:
     return count
 
 
-def _apply_learning_metadata(feedback: Feedback, signal: _LearningSignal, *, samples: int, applied: bool) -> None:
+def _apply_learning_metadata(
+    feedback: Feedback, signal: _LearningSignal, *, samples: int, applied: bool
+) -> None:
     metadata: dict[str, Any] = dict(feedback.metadata or {})
     metadata["learning"] = {
         "status": "applied" if applied else "queued",
@@ -187,7 +194,11 @@ def record_feedback(
         action=action,
         reason=reason[:200],
     )
-    signal = _classify_learning_signal(profile=profile, job=job, action=action, reason=reason) if profile else None
+    signal = (
+        _classify_learning_signal(profile=profile, job=job, action=action, reason=reason)
+        if profile
+        else None
+    )
     if signal:
         _evaluate_learning(profile=profile, feedback=feedback, signal=signal)
     if action == Feedback.Action.APPLIED: