mitai-jinkendo/backend/routers/activity.py

271 lines
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refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
"""
Activity Tracking Endpoints for Mitai Jinkendo
Handles workout/activity logging, statistics, and Apple Health CSV import.
"""
import csv
import io
import uuid
import logging
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
from typing import Optional
from fastapi import APIRouter, HTTPException, UploadFile, File, Header, Depends
from db import get_db, get_cursor, r2d
from auth import require_auth, check_feature_access, increment_feature_usage
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
from models import ActivityEntry
from routers.profiles import get_pid
from feature_logger import log_feature_usage
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
router = APIRouter(prefix="/api/activity", tags=["activity"])
logger = logging.getLogger(__name__)
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
@router.get("")
def list_activity(limit: int=200, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get activity entries for current profile."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute(
"SELECT * FROM activity_log WHERE profile_id=%s ORDER BY date DESC, start_time DESC LIMIT %s", (pid,limit))
return [r2d(r) for r in cur.fetchall()]
@router.post("")
def create_activity(e: ActivityEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Create new activity entry."""
pid = get_pid(x_profile_id)
# Phase 4: Check feature access and ENFORCE
access = check_feature_access(pid, 'activity_entries')
log_feature_usage(pid, 'activity_entries', access, 'create')
if not access['allowed']:
logger.warning(
f"[FEATURE-LIMIT] User {pid} blocked: "
f"activity_entries {access['reason']} (used: {access['used']}, limit: {access['limit']})"
)
raise HTTPException(
status_code=403,
detail=f"Limit erreicht: Du hast das Kontingent für Aktivitätseinträge überschritten ({access['used']}/{access['limit']}). "
f"Bitte kontaktiere den Admin oder warte bis zum nächsten Reset."
)
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
eid = str(uuid.uuid4())
d = e.model_dump()
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("""INSERT INTO activity_log
(id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
hr_avg,hr_max,distance_km,rpe,source,notes,created)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)""",
(eid,pid,d['date'],d['start_time'],d['end_time'],d['activity_type'],d['duration_min'],
d['kcal_active'],d['kcal_resting'],d['hr_avg'],d['hr_max'],d['distance_km'],
d['rpe'],d['source'],d['notes']))
# Phase 2: Increment usage counter (always for new entries)
increment_feature_usage(pid, 'activity_entries')
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
return {"id":eid,"date":e.date}
@router.put("/{eid}")
def update_activity(eid: str, e: ActivityEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Update existing activity entry."""
pid = get_pid(x_profile_id)
with get_db() as conn:
d = e.model_dump()
cur = get_cursor(conn)
cur.execute(f"UPDATE activity_log SET {', '.join(f'{k}=%s' for k in d)} WHERE id=%s AND profile_id=%s",
list(d.values())+[eid,pid])
return {"id":eid}
@router.delete("/{eid}")
def delete_activity(eid: str, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Delete activity entry."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("DELETE FROM activity_log WHERE id=%s AND profile_id=%s", (eid,pid))
return {"ok":True}
@router.get("/stats")
def activity_stats(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get activity statistics (last 30 entries)."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute(
"SELECT * FROM activity_log WHERE profile_id=%s ORDER BY date DESC LIMIT 30", (pid,))
rows = [r2d(r) for r in cur.fetchall()]
if not rows: return {"count":0,"total_kcal":0,"total_min":0,"by_type":{}}
total_kcal=sum(float(r.get('kcal_active') or 0) for r in rows)
total_min=sum(float(r.get('duration_min') or 0) for r in rows)
by_type={}
for r in rows:
t=r['activity_type']; by_type.setdefault(t,{'count':0,'kcal':0,'min':0})
by_type[t]['count']+=1
by_type[t]['kcal']+=float(r.get('kcal_active') or 0)
by_type[t]['min']+=float(r.get('duration_min') or 0)
return {"count":len(rows),"total_kcal":round(total_kcal),"total_min":round(total_min),"by_type":by_type}
def get_training_type_for_apple_health(workout_type: str):
"""
Map Apple Health workout type to training_type_id + category + subcategory.
Returns: (training_type_id, category, subcategory) or (None, None, None)
"""
with get_db() as conn:
cur = get_cursor(conn)
# Mapping: Apple Health Workout Type → training_type subcategory
mapping = {
'running': 'running',
'walking': 'walk',
'hiking': 'walk',
'cycling': 'cycling',
'swimming': 'swimming',
'traditional strength training': 'hypertrophy',
'functional strength training': 'functional',
'high intensity interval training': 'hiit',
'yoga': 'yoga',
'martial arts': 'technique',
'boxing': 'sparring',
'rowing': 'rowing',
'dance': 'dance',
'core training': 'functional',
'flexibility': 'static',
'cooldown': 'regeneration',
'meditation': 'meditation',
'mindfulness': 'mindfulness',
}
subcategory = mapping.get(workout_type.lower())
if not subcategory:
return (None, None, None)
# Find training_type_id by subcategory
cur.execute("""
SELECT id, category, subcategory
FROM training_types
WHERE LOWER(subcategory) = %s
LIMIT 1
""", (subcategory,))
row = cur.fetchone()
if row:
return (row['id'], row['category'], row['subcategory'])
return (None, None, None)
@router.get("/uncategorized")
def list_uncategorized_activities(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get activities without assigned training type, grouped by activity_type."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("""
SELECT activity_type, COUNT(*) as count,
MIN(date) as first_date, MAX(date) as last_date
FROM activity_log
WHERE profile_id=%s AND training_type_id IS NULL
GROUP BY activity_type
ORDER BY count DESC
""", (pid,))
return [r2d(r) for r in cur.fetchall()]
@router.post("/bulk-categorize")
def bulk_categorize_activities(
data: dict,
x_profile_id: Optional[str]=Header(default=None),
session: dict=Depends(require_auth)
):
"""
Bulk update training type for activities.
Body: {
"activity_type": "Running",
"training_type_id": 1,
"training_category": "cardio",
"training_subcategory": "running"
}
"""
pid = get_pid(x_profile_id)
activity_type = data.get('activity_type')
training_type_id = data.get('training_type_id')
training_category = data.get('training_category')
training_subcategory = data.get('training_subcategory')
if not activity_type or not training_type_id:
raise HTTPException(400, "activity_type and training_type_id required")
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("""
UPDATE activity_log
SET training_type_id = %s,
training_category = %s,
training_subcategory = %s
WHERE profile_id = %s
AND activity_type = %s
AND training_type_id IS NULL
""", (training_type_id, training_category, training_subcategory, pid, activity_type))
updated_count = cur.rowcount
return {"updated": updated_count, "activity_type": activity_type}
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
@router.post("/import-csv")
async def import_activity_csv(file: UploadFile=File(...), x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Import Apple Health workout CSV with automatic training type mapping."""
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
pid = get_pid(x_profile_id)
raw = await file.read()
try: text = raw.decode('utf-8')
except: text = raw.decode('latin-1')
if text.startswith('\ufeff'): text = text[1:]
if not text.strip(): raise HTTPException(400,"Leere Datei")
reader = csv.DictReader(io.StringIO(text))
inserted = skipped = 0
with get_db() as conn:
cur = get_cursor(conn)
for row in reader:
wtype = row.get('Workout Type','').strip()
start = row.get('Start','').strip()
if not wtype or not start: continue
try: date = start[:10]
except: continue
dur = row.get('Duration','').strip()
duration_min = None
if dur:
try:
p = dur.split(':')
duration_min = round(int(p[0])*60+int(p[1])+int(p[2])/60,1)
except: pass
def kj(v):
try: return round(float(v)/4.184) if v else None
except: return None
def tf(v):
try: return round(float(v),1) if v else None
except: return None
# Map Apple Health workout type to training_type_id
training_type_id, training_category, training_subcategory = get_training_type_for_apple_health(wtype)
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
try:
cur.execute("""INSERT INTO activity_log
(id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
hr_avg,hr_max,distance_km,source,training_type_id,training_category,training_subcategory,created)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'apple_health',%s,%s,%s,CURRENT_TIMESTAMP)""",
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
(str(uuid.uuid4()),pid,date,start,row.get('End',''),wtype,duration_min,
kj(row.get('Aktive Energie (kJ)','')),kj(row.get('Ruheeinträge (kJ)','')),
tf(row.get('Durchschn. Herzfrequenz (count/min)','')),
tf(row.get('Max. Herzfrequenz (count/min)','')),
tf(row.get('Distanz (km)','')),
training_type_id,training_category,training_subcategory))
refactor: modular backend architecture with 14 router modules Phase 2 Complete - Backend Refactoring: - Extracted all endpoints to dedicated router modules - main.py: 1878 → 75 lines (-96% reduction) - Created modular structure for maintainability Router Structure (60 endpoints total): ├── auth.py - 7 endpoints (login, logout, password reset) ├── profiles.py - 7 endpoints (CRUD + current user) ├── weight.py - 5 endpoints (tracking + stats) ├── circumference.py - 4 endpoints (body measurements) ├── caliper.py - 4 endpoints (skinfold tracking) ├── activity.py - 6 endpoints (workouts + Apple Health import) ├── nutrition.py - 4 endpoints (diet + FDDB import) ├── photos.py - 3 endpoints (progress photos) ├── insights.py - 8 endpoints (AI analysis + pipeline) ├── prompts.py - 2 endpoints (AI prompt management) ├── admin.py - 7 endpoints (user management) ├── stats.py - 1 endpoint (dashboard stats) ├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export) └── importdata.py - 1 endpoint (ZIP import) Core modules maintained: - db.py: PostgreSQL connection + helpers - auth.py: Auth functions (hash, verify, sessions) - models.py: 11 Pydantic models Benefits: - Self-contained modules with clear responsibilities - Easier to navigate and modify specific features - Improved code organization and readability - 100% functional compatibility maintained - All syntax checks passed Updated CLAUDE.md with new architecture documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
inserted+=1
except: skipped+=1
return {"inserted":inserted,"skipped":skipped,"message":f"{inserted} Trainings importiert"}