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Codebase hardening: 102 fixes across 35+ files

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Deep audit identified 106 findings; 102 fixed, 4 deferred. Covers 8 areas:

- Settings & deploy: env-gated DEBUG/SECRET_KEY, HTTPS headers, gunicorn, celery worker
- Auth (registered_user): password write_only, request.data fixes, transaction safety, proper HTTP status codes
- Workout app: IDOR protection, get_object_or_404, prefetch_related N+1 fixes, transaction.atomic
- Video/scripts: path traversal sanitization, HLS trigger guard, auth on cache wipe
- Models (exercise/equipment/muscle/superset): null-safe __str__, stable IDs, prefetch support
- Generator views: helper for registered_user lookup, logger.exception, bulk_update, transaction wrapping
- Generator core (rules/selector/generator): push-pull ratio, type affinity normalization, modality checks, side-pair exact match, word-boundary regex, equipment cache clearing
- Generator services (plan_builder/analyzer/normalizer): transaction.atomic, muscle cache, bulk_update, glutes classification fix

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Trey t
2026-02-27 22:29:14 -06:00
parent 63b57a83ab
commit c80c66c2e5
58 changed files with 3363 additions and 1049 deletions
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33
generator/services/workout_analyzer.py
View File

@@ -27,6 +27,7 @@ from typing import Dict, List, Optional, Set, Tuple
import numpy as np
from django.db import transaction
from django.db.models import Count, Prefetch, Q
from exercise.models import Exercise
@@ -225,14 +226,15 @@ class WorkoutAnalyzer:
print(' Workout Analyzer - ML Pattern Extraction')
print('=' * 64)
self._clear_existing_patterns()
self._step1_populate_workout_types()
self._step2_extract_workout_data()
self._step3_extract_muscle_group_splits()
self._step4_extract_weekly_split_patterns()
self._step5_extract_workout_structure_rules()
self._step6_extract_movement_pattern_ordering()
self._step7_ensure_full_rule_coverage()
with transaction.atomic():
self._clear_existing_patterns()
self._step1_populate_workout_types()
self._step2_extract_workout_data()
self._step3_extract_muscle_group_splits()
self._step4_extract_weekly_split_patterns()
self._step5_extract_workout_structure_rules()
self._step6_extract_movement_pattern_ordering()
self._step7_ensure_full_rule_coverage()
print('\n' + '=' * 64)
print(' Analysis complete.')
@@ -1325,16 +1327,19 @@ class WorkoutAnalyzer:
},
}
# Prefetch all existing rules into an in-memory set to avoid
# N exists() queries (one per workout_type x section x goal combination).
existing_rules = set(
WorkoutStructureRule.objects.values_list(
'workout_type_id', 'section_type', 'goal_type'
)
)
created = 0
for wt in workout_types:
for section in all_sections:
for goal in all_goals:
exists = WorkoutStructureRule.objects.filter(
workout_type=wt,
section_type=section,
goal_type=goal,
).exists()
if not exists:
if (wt.pk, section, goal) not in existing_rules:
defaults = dict(section_defaults[section])
# Apply goal adjustments
base_params = {