Hardening follow-up: N+1 elimination, type validation, diversify fix

Additional fixes from parallel hardening streams:

- exercise/serializers: remove unused WorkoutEquipment import, add prefetch docs
- generator/serializers: N+1 fix in GeneratedWorkoutDetailSerializer (inline workout dict, prefetch-aware supersets)
- generator/services/plan_builder: eliminate redundant .save() after .create() via single create_kwargs dict
- generator/services/workout_generator: proper type-match validation for HIIT/cardio/core/flexibility; fix diversify type count to account for removed entry
- generator/views: request-level caching for get_registered_user helper; prefetch chain for accept_workout
- superset/serializers: guard against dangling FK in SupersetExerciseSerializer
- workout/helpers: use prefetched data instead of re-querying per superset

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

exercise/serializers.py

@@ -1,7 +1,6 @@
 from rest_framework import serializers
 from .models import *
 from muscle.models import ExerciseMuscle
-from equipment.models import WorkoutEquipment
 from equipment.serializers import WorkoutEquipmentSerializer
 
 class ExerciseMuscleSerializer(serializers.ModelSerializer):
@@ -26,13 +25,13 @@ class ExerciseSerializer(serializers.ModelSerializer):
         fields = '__all__'
 
     def get_muscles(self, obj):
-        # Use prefetched data if available, avoiding N+1 queries
+        # Use prefetched related manager if available (avoids N+1 queries)
-        if hasattr(obj, '_prefetched_objects_cache') and 'exercise_muscle_exercise' in obj._prefetched_objects_cache:
+        # Callers should use .prefetch_related('exercise_muscle_exercise__muscle')
-            return [{'muscle': em.muscle_id, 'name': em.muscle.name} for em in obj.exercise_muscle_exercise.all()]
+        objs = obj.exercise_muscle_exercise.all()
-        return list(obj.exercise_muscle_exercise.values('muscle', name=models.F('muscle__name')))
+        return ExerciseMuscleSerializer(objs, many=True).data
 
     def get_equipment(self, obj):
-        # Use prefetched data if available, avoiding N+1 queries
+        # Use prefetched related manager if available (avoids N+1 queries)
-        if hasattr(obj, '_prefetched_objects_cache') and 'workout_exercise_workout' in obj._prefetched_objects_cache:
+        # Callers should use .prefetch_related('workout_exercise_workout__equipment')
-            return [{'equipment': we.equipment_id, 'name': we.equipment.name} for we in obj.workout_exercise_workout.all()]
+        objs = obj.workout_exercise_workout.all()
-        return list(obj.workout_exercise_workout.values('equipment', name=models.F('equipment__name')))
+        return WorkoutEquipmentSerializer(objs, many=True).data

generator/serializers.py

@@ -330,18 +330,29 @@ class GeneratedWorkoutDetailSerializer(serializers.ModelSerializer):
 
     def get_workout_detail(self, obj):
         if obj.workout:
-            return WorkoutDetailSerializer(obj.workout).data
+            return {
+                'id': obj.workout.id,
+                'name': obj.workout.name,
+                'description': obj.workout.description,
+                'estimated_time': obj.workout.estimated_time,
+            }
         return None
 
     def get_supersets(self, obj):
-        if obj.workout:
+        if not obj.workout:
+            return []
+        # Use prefetched data if available (via workout__superset_workout prefetch),
+        # otherwise fall back to a query with its own prefetch
+        workout = obj.workout
+        if hasattr(workout, '_prefetched_objects_cache') and 'superset_workout' in workout._prefetched_objects_cache:
+            superset_qs = sorted(workout.superset_workout.all(), key=lambda s: s.order)
+        else:
             superset_qs = Superset.objects.filter(
-                workout=obj.workout
+                workout=workout
             ).prefetch_related(
-                'supersetexercise_set__exercise',
+                'superset_exercises__exercise',
             ).order_by('order')
-            return SupersetSerializer(superset_qs, many=True).data
+        return SupersetSerializer(superset_qs, many=True).data
-        return []
 
 
 # ============================================================

generator/services/plan_builder.py

@@ -96,26 +96,27 @@ class PlanBuilder:
 
                     order = ex_spec.get('order', 1)
 
-                    superset_exercise = SupersetExercise.objects.create(
+                    # Build kwargs for create, including optional fields,
-                        superset=superset,
+                    # so we don't need a separate .save() after .create().
-                        exercise=exercise_obj,
+                    create_kwargs = {
-                        order=order,
+                        'superset': superset,
-                    )
+                        'exercise': exercise_obj,
+                        'order': order,
+                    }
 
-                    # Assign optional fields exactly like add_workout does
                     if ex_spec.get('weight') is not None:
-                        superset_exercise.weight = ex_spec['weight']
+                        create_kwargs['weight'] = ex_spec['weight']
 
                     if ex_spec.get('reps') is not None:
-                        superset_exercise.reps = ex_spec['reps']
+                        create_kwargs['reps'] = ex_spec['reps']
                         rep_duration = exercise_obj.estimated_rep_duration or 3.0
                         superset_total_time += ex_spec['reps'] * rep_duration
 
                     if ex_spec.get('duration') is not None:
-                        superset_exercise.duration = ex_spec['duration']
+                        create_kwargs['duration'] = ex_spec['duration']
                         superset_total_time += ex_spec['duration']
 
-                    superset_exercise.save()
+                    SupersetExercise.objects.create(**create_kwargs)
 
                 # ---- 4. Update superset estimated_time ----
                 # Store total time including all rounds and rest between rounds

generator/services/workout_generator.py

@@ -1398,18 +1398,31 @@ class WorkoutGenerator:
                 break
 
             replaced = False
+            removed_type = (result[idx].get('split_type') or 'full_body').strip().lower()
+            removed_sig = self._split_signature(result[idx])
             for candidate in candidates:
                 candidate_type = (candidate.get('split_type') or 'full_body').strip().lower()
                 candidate_sig = self._split_signature(candidate)
-                current_sig = self._split_signature(result[idx])
+                if candidate_sig == removed_sig:
-                if candidate_sig == current_sig:
                     continue
 
-                new_type_count = type_counts[candidate_type] + (0 if candidate_type == (result[idx].get('split_type') or 'full_body').strip().lower() else 1)
+                # Account for the removal of the old entry when counting
+                # the new type: subtract 1 for the removed type if it
+                # matches the candidate type, add 1 for the candidate.
+                if candidate_type == removed_type:
+                    new_type_count = type_counts[candidate_type]  # net zero: -1 removed +1 added
+                else:
+                    new_type_count = type_counts[candidate_type] + 1
                 if new_type_count > max_same_type:
                     continue
 
-                if sig_counts[candidate_sig] >= max_same_signature:
+                # Same accounting for signatures: the removed signature
+                # frees a slot, so only block if the candidate sig count
+                # (after removing the old entry) is still at max.
+                effective_sig_count = sig_counts[candidate_sig]
+                if candidate_sig == removed_sig:
+                    effective_sig_count -= 1
+                if effective_sig_count >= max_same_signature:
                     continue
 
                 result[idx] = dict(candidate)
@@ -2987,7 +3000,12 @@ class WorkoutGenerator:
             return []
 
         wt_name_lower = workout_type.name.strip().lower()
+        wt_key = _normalize_type_key(wt_name_lower)
         is_strength = wt_name_lower in STRENGTH_WORKOUT_TYPES
+        is_hiit = wt_key == 'high_intensity_interval_training'
+        is_cardio = wt_key == 'cardio'
+        is_core = wt_key == 'core_training'
+        is_flexibility = wt_key == 'flexibility'
         threshold = GENERATION_RULES['workout_type_match_pct']['value']
 
         total_exercises = 0
@@ -3001,7 +3019,33 @@ class WorkoutGenerator:
                 if is_strength:
                     if getattr(ex, 'is_weight', False) or getattr(ex, 'is_compound', False):
                         matching_exercises += 1
+                elif is_hiit:
+                    # HIIT: favor high HR, compound, or duration-capable exercises
+                    hr = getattr(ex, 'hr_elevation_rating', None) or 0
+                    if hr >= 5 or getattr(ex, 'is_compound', False) or getattr(ex, 'is_duration', False):
+                        matching_exercises += 1
+                elif is_cardio:
+                    # Cardio: favor duration-capable or high-HR exercises
+                    hr = getattr(ex, 'hr_elevation_rating', None) or 0
+                    if getattr(ex, 'is_duration', False) or hr >= 5:
+                        matching_exercises += 1
+                elif is_core:
+                    # Core: check if exercise targets core muscles
+                    muscles = (getattr(ex, 'muscle_groups', '') or '').lower()
+                    patterns = (getattr(ex, 'movement_patterns', '') or '').lower()
+                    if any(tok in muscles for tok in ('core', 'abs', 'oblique')):
+                        matching_exercises += 1
+                    elif 'core' in patterns or 'anti' in patterns:
+                        matching_exercises += 1
+                elif is_flexibility:
+                    # Flexibility: favor duration-based, stretch/mobility exercises
+                    patterns = (getattr(ex, 'movement_patterns', '') or '').lower()
+                    if getattr(ex, 'is_duration', False) or any(
+                        tok in patterns for tok in ('stretch', 'mobility', 'yoga', 'flexibility')
+                    ):
+                        matching_exercises += 1
                 else:
+                    # Unknown type -- count all as matching (no false negatives)
                     matching_exercises += 1
 
         violations = []

generator/views.py

@@ -275,7 +275,9 @@ def accept_workout(request, workout_id):
     """
     registered_user = get_registered_user(request)
     generated_workout = get_object_or_404(
-        GeneratedWorkout.objects.select_related('workout', 'workout_type'),
+        GeneratedWorkout.objects.select_related('workout', 'workout_type').prefetch_related(
+            'workout__superset_workout__superset_exercises__exercise',
+        ),
         pk=workout_id,
         plan__registered_user=registered_user,
     )

superset/serializers.py

@@ -12,13 +12,26 @@ class SupersetExerciseSerializer(serializers.ModelSerializer):
         fields = '__all__'
 
     def get_exercise(self, obj):
-        data = ExerciseSerializer(obj.exercise, many=False).data
+        try:
-        return data
+            exercise = obj.exercise
+        except (Exercise.DoesNotExist, AttributeError):
+            return None
+        if exercise is None:
+            return None
+        return ExerciseSerializer(exercise, many=False).data
 
     def get_unique_id(self, obj):
         return f"{obj.pk}-{obj.superset_id}" if hasattr(obj, 'superset_id') else str(obj.pk)
 
 class SupersetSerializer(serializers.ModelSerializer):
+    """Serializer for Superset with nested exercises.
+
+    For optimal performance, callers should prefetch related data:
+        Superset.objects.prefetch_related(
+            'superset_exercises__exercise__exercise_muscle_exercise__muscle',
+            'superset_exercises__exercise__workout_exercise_workout__equipment',
+        )
+    """
     exercises = serializers.SerializerMethodField()
 
     class Meta:
@@ -30,5 +43,4 @@ class SupersetSerializer(serializers.ModelSerializer):
             return []
         # Use prefetched data if available via superset_exercises related manager
         objs = obj.superset_exercises.all().order_by('order')
-        data = SupersetExerciseSerializer(objs, many=True).data
+        return SupersetExerciseSerializer(objs, many=True).data
-        return data

workout/helpers.py

@@ -62,13 +62,14 @@ def create_all_exercise_list_for_workout(workout):
                             audio_queues.append(next_up_data)
                     
                     elif x < superset.rounds - 1:
-                        first_exercise = supersetExercises.first()
+                        first_exercise = supersetExercises[0] if supersetExercises else None
-                        next_up_data = {
+                        if first_exercise is not None:
-                            "audio_url": first_exercise.exercise.audio_url().lower(),
+                            next_up_data = {
-                            "play_at": 7
+                                "audio_url": first_exercise.exercise.audio_url().lower(),
-                        }
+                                "play_at": 7
+                            }
 
-                        audio_queues.append(next_up_data)
+                            audio_queues.append(next_up_data)
 
                     elif len(supersets) > superset_count+1:
                         next_superset = supersets[superset_count+1]
@@ -76,12 +77,13 @@ def create_all_exercise_list_for_workout(workout):
                         next_superset_exercises = sorted(next_superset.supersetexercise_set.all(), key=lambda se: se.order)
                         next_supersetExercises = next_superset_exercises[0] if next_superset_exercises else None
 
-                        next_up_data = {
+                        if next_supersetExercises is not None:
-                            "audio_url": next_supersetExercises.exercise.audio_url().lower(),
+                            next_up_data = {
-                            "play_at": 7
+                                "audio_url": next_supersetExercises.exercise.audio_url().lower(),
-                        }
+                                "play_at": 7
+                            }
 
-                        audio_queues.append(next_up_data)
+                            audio_queues.append(next_up_data)
 
                 ser_data["audio_queues"] = audio_queues
                 all_superset_exercise.append(ser_data)