1c61b80731
- Add rules_engine.py with quantitative rules for all 8 workout types - Add quality gate retry loop in generate_single_workout() - Expand calibrate_structure_rules to all 120 combinations (8 types × 5 goals × 3 sections) - Wire WeeklySplitPattern DB records into _pick_weekly_split() - Enforce movement patterns from WorkoutStructureRule in exercise selection - Add straight-set strength support (single main lift, 4-6 rounds) - Add modality consistency check for duration-dominant workout types - Add InjuryStep component to onboarding and preferences - Add sibling exercise exclusion in regenerate and preview_day endpoints - Display generator warnings on dashboard - Expand fix_rep_durations, fix_exercise_flags, fix_movement_pattern_typo - Add audit_exercise_data and check_rules_drift management commands - Add Next.js frontend with dashboard, onboarding, preferences, history pages - Add generator app with ML-powered workout generation pipeline - 96 new tests across 7 test modules Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
150 lines
5.3 KiB
Python
150 lines
5.3 KiB
Python
import logging
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from workout.models import Workout
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from superset.models import Superset, SupersetExercise
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logger = logging.getLogger(__name__)
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class PlanBuilder:
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"""
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Creates Django ORM objects (Workout, Superset, SupersetExercise) from
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a workout specification dict. Follows the exact same creation pattern
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used by the existing ``add_workout`` view.
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"""
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def __init__(self, registered_user):
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self.registered_user = registered_user
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def create_workout_from_spec(self, workout_spec):
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"""
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Create a full Workout with Supersets and SupersetExercises.
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Parameters
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----------
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workout_spec : dict
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Expected shape::
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{
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'name': 'Upper Push + Core',
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'description': 'Generated workout targeting chest ...',
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'supersets': [
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{
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'name': 'Warm Up',
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'rounds': 1,
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'exercises': [
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{
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'exercise': <Exercise instance>,
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'duration': 30,
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'order': 1,
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},
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{
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'exercise': <Exercise instance>,
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'reps': 10,
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'weight': 50,
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'order': 2,
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},
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],
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},
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...
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],
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}
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Returns
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-------
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Workout
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The fully-persisted Workout instance with all child objects.
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"""
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# ---- 1. Create the Workout ----
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workout = Workout.objects.create(
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name=workout_spec.get('name', 'Generated Workout'),
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description=workout_spec.get('description', ''),
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registered_user=self.registered_user,
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)
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workout.save()
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workout_total_time = 0
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superset_order = 1
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# ---- 2. Create each Superset ----
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for ss_spec in workout_spec.get('supersets', []):
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ss_name = ss_spec.get('name', f'Set {superset_order}')
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rounds = ss_spec.get('rounds', 1)
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exercises = ss_spec.get('exercises', [])
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superset = Superset.objects.create(
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workout=workout,
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name=ss_name,
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rounds=rounds,
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order=superset_order,
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rest_between_rounds=ss_spec.get('rest_between_rounds', 45),
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)
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superset.save()
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superset_total_time = 0
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# ---- 3. Create each SupersetExercise ----
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for ex_spec in exercises:
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exercise_obj = ex_spec.get('exercise')
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if exercise_obj is None:
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logger.warning(
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"Skipping exercise entry with no exercise object in "
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"superset '%s'", ss_name,
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)
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continue
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order = ex_spec.get('order', 1)
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superset_exercise = SupersetExercise.objects.create(
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superset=superset,
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exercise=exercise_obj,
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order=order,
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)
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# Assign optional fields exactly like add_workout does
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if ex_spec.get('weight') is not None:
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superset_exercise.weight = ex_spec['weight']
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if ex_spec.get('reps') is not None:
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superset_exercise.reps = ex_spec['reps']
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rep_duration = exercise_obj.estimated_rep_duration or 3.0
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superset_total_time += ex_spec['reps'] * rep_duration
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if ex_spec.get('duration') is not None:
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superset_exercise.duration = ex_spec['duration']
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superset_total_time += ex_spec['duration']
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superset_exercise.save()
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# ---- 4. Update superset estimated_time ----
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# Store total time including all rounds and rest between rounds
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rest_between_rounds = ss_spec.get('rest_between_rounds', 45)
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rest_time = rest_between_rounds * max(0, rounds - 1)
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superset.estimated_time = (superset_total_time * rounds) + rest_time
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superset.save()
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# Accumulate into workout total (use the already-calculated superset time)
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workout_total_time += superset.estimated_time
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superset_order += 1
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# Add transition time between supersets
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# (matches GENERATION_RULES['rest_between_supersets'] in workout_generator)
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superset_count = superset_order - 1
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if superset_count > 1:
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rest_between_supersets = 30
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workout_total_time += rest_between_supersets * (superset_count - 1)
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# ---- 5. Update workout estimated_time ----
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workout.estimated_time = workout_total_time
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workout.save()
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logger.info(
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"Created workout '%s' (id=%s) with %d supersets, est. %ds",
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workout.name,
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workout.pk,
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superset_order - 1,
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workout_total_time,
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)
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return workout
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