mitai-jinkendo/backend/routers/nutrition.py
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ROLLBACK: complete removal of broken feature enforcement system
Reverts all feature enforcement changes (commits 3745ebd, cbad50a, cd4d912, 8415509)
to restore original working functionality.

Issues caused by feature enforcement implementation:
- Export buttons disappeared and never reappeared
- KI analysis counter not incrementing
- New analyses not saving
- Pipeline appearing twice
- Many core features broken

Restored files to working state before enforcement implementation (commit 0210844):
- Backend: auth.py, insights.py, exportdata.py, importdata.py, nutrition.py, activity.py
- Frontend: Analysis.jsx, SettingsPage.jsx, api.js
- Removed: FeatureGate.jsx, useFeatureAccess.js

The original simple AI limit system (ai_enabled, ai_limit_day) is now active again.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-20 15:19:56 +01:00

134 lines
6.1 KiB
Python

"""
Nutrition Tracking Endpoints for Mitai Jinkendo
Handles nutrition data, FDDB CSV import, correlations, and weekly aggregates.
"""
import csv
import io
import uuid
from typing import Optional
from datetime import datetime
from fastapi import APIRouter, HTTPException, UploadFile, File, Header, Depends
from db import get_db, get_cursor, r2d
from auth import require_auth
from routers.profiles import get_pid
router = APIRouter(prefix="/api/nutrition", tags=["nutrition"])
# ── Helper ────────────────────────────────────────────────────────────────────
def _pf(s):
"""Parse float from string (handles comma decimal separator)."""
try: return float(str(s).replace(',','.').strip())
except: return 0.0
# ── Endpoints ─────────────────────────────────────────────────────────────────
@router.post("/import-csv")
async def import_nutrition_csv(file: UploadFile=File(...), x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Import FDDB nutrition CSV."""
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), delimiter=';')
days: dict = {}
count = 0
for row in reader:
rd = row.get('datum_tag_monat_jahr_stunde_minute','').strip().strip('"')
if not rd: continue
try:
p = rd.split(' ')[0].split('.')
iso = f"{p[2]}-{p[1]}-{p[0]}"
except: continue
days.setdefault(iso,{'kcal':0,'fat_g':0,'carbs_g':0,'protein_g':0})
days[iso]['kcal'] += _pf(row.get('kj',0))/4.184
days[iso]['fat_g'] += _pf(row.get('fett_g',0))
days[iso]['carbs_g'] += _pf(row.get('kh_g',0))
days[iso]['protein_g'] += _pf(row.get('protein_g',0))
count+=1
inserted=0
with get_db() as conn:
cur = get_cursor(conn)
for iso,vals in days.items():
kcal=round(vals['kcal'],1); fat=round(vals['fat_g'],1)
carbs=round(vals['carbs_g'],1); prot=round(vals['protein_g'],1)
cur.execute("SELECT id FROM nutrition_log WHERE profile_id=%s AND date=%s",(pid,iso))
if cur.fetchone():
cur.execute("UPDATE nutrition_log SET kcal=%s,protein_g=%s,fat_g=%s,carbs_g=%s WHERE profile_id=%s AND date=%s",
(kcal,prot,fat,carbs,pid,iso))
else:
cur.execute("INSERT INTO nutrition_log (id,profile_id,date,kcal,protein_g,fat_g,carbs_g,source,created) VALUES (%s,%s,%s,%s,%s,%s,%s,'csv',CURRENT_TIMESTAMP)",
(str(uuid.uuid4()),pid,iso,kcal,prot,fat,carbs))
inserted+=1
return {"rows_parsed":count,"days_imported":inserted,
"date_range":{"from":min(days) if days else None,"to":max(days) if days else None}}
@router.get("")
def list_nutrition(limit: int=365, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get nutrition entries for current profile."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute(
"SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date DESC LIMIT %s", (pid,limit))
return [r2d(r) for r in cur.fetchall()]
@router.get("/correlations")
def nutrition_correlations(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get nutrition data correlated with weight and body fat."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date",(pid,))
nutr={r['date']:r2d(r) for r in cur.fetchall()}
cur.execute("SELECT date,weight FROM weight_log WHERE profile_id=%s ORDER BY date",(pid,))
wlog={r['date']:r['weight'] for r in cur.fetchall()}
cur.execute("SELECT date,lean_mass,body_fat_pct FROM caliper_log WHERE profile_id=%s ORDER BY date",(pid,))
cals=sorted([r2d(r) for r in cur.fetchall()],key=lambda x:x['date'])
all_dates=sorted(set(list(nutr)+list(wlog)))
mi,last_cal,cal_by_date=0,{},{}
for d in all_dates:
while mi<len(cals) and cals[mi]['date']<=d: last_cal=cals[mi]; mi+=1
if last_cal: cal_by_date[d]=last_cal
result=[]
for d in all_dates:
if d not in nutr and d not in wlog: continue
row={'date':d}
if d in nutr: row.update({k:float(nutr[d][k]) if nutr[d][k] is not None else None for k in ['kcal','protein_g','fat_g','carbs_g']})
if d in wlog: row['weight']=float(wlog[d])
if d in cal_by_date:
lm = cal_by_date[d].get('lean_mass')
bf = cal_by_date[d].get('body_fat_pct')
row['lean_mass']=float(lm) if lm is not None else None
row['body_fat_pct']=float(bf) if bf is not None else None
result.append(row)
return result
@router.get("/weekly")
def nutrition_weekly(weeks: int=16, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get nutrition data aggregated by week."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date DESC LIMIT %s",(pid,weeks*7))
rows=[r2d(r) for r in cur.fetchall()]
if not rows: return []
wm={}
for d in rows:
wk=datetime.strptime(d['date'],'%Y-%m-%d').strftime('%Y-W%V')
wm.setdefault(wk,[]).append(d)
result=[]
for wk in sorted(wm):
en=wm[wk]; n=len(en)
def avg(k): return round(sum(float(e.get(k) or 0) for e in en)/n,1)
result.append({'week':wk,'days':n,'kcal':avg('kcal'),'protein_g':avg('protein_g'),'fat_g':avg('fat_g'),'carbs_g':avg('carbs_g')})
return result