mitai-jinkendo/backend/routers/weight.py

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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
"""
Weight Tracking Endpoints for Mitai Jinkendo
Handles weight log CRUD operations and statistics.
"""
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, Header, Depends, HTTPException
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 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 WeightEntry
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/weight", tags=["weight"])
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_weight(limit: int=365, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get weight entries for current profile."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute(
"SELECT * FROM weight_log WHERE profile_id=%s ORDER BY date DESC LIMIT %s", (pid,limit))
return [r2d(r) for r in cur.fetchall()]
@router.post("")
def upsert_weight(e: WeightEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Create or update weight entry (upsert by date)."""
pid = get_pid(x_profile_id)
# Phase 4: Check feature access and ENFORCE
access = check_feature_access(pid, 'weight_entries')
# Structured logging (always)
log_feature_usage(pid, 'weight_entries', access, 'create')
# BLOCK if limit exceeded
if not access['allowed']:
logger.warning(
f"[FEATURE-LIMIT] User {pid} blocked: "
f"weight_entries {access['reason']} (used: {access['used']}, limit: {access['limit']})"
)
raise HTTPException(
status_code=403,
detail=f"Limit erreicht: Du hast das Kontingent für Gewichtseinträge 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
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("SELECT id FROM weight_log WHERE profile_id=%s AND date=%s", (pid,e.date))
ex = cur.fetchone()
is_new_entry = not ex
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
if ex:
# UPDATE existing entry
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
cur.execute("UPDATE weight_log SET weight=%s,note=%s WHERE id=%s", (e.weight,e.note,ex['id']))
wid = ex['id']
else:
# INSERT new entry
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
wid = str(uuid.uuid4())
cur.execute("INSERT INTO weight_log (id,profile_id,date,weight,note,created) VALUES (%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)",
(wid,pid,e.date,e.weight,e.note))
# Phase 2: Increment usage counter (only for new entries)
increment_feature_usage(pid, 'weight_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":wid,"date":e.date,"weight":e.weight}
@router.put("/{wid}")
def update_weight(wid: str, e: WeightEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Update existing weight entry."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("UPDATE weight_log SET date=%s,weight=%s,note=%s WHERE id=%s AND profile_id=%s",
(e.date,e.weight,e.note,wid,pid))
return {"id":wid}
@router.delete("/{wid}")
def delete_weight(wid: str, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Delete weight entry."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("DELETE FROM weight_log WHERE id=%s AND profile_id=%s", (wid,pid))
return {"ok":True}
@router.get("/stats")
def weight_stats(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
"""Get weight statistics (last 90 days)."""
pid = get_pid(x_profile_id)
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("SELECT date,weight FROM weight_log WHERE profile_id=%s ORDER BY date DESC LIMIT 90", (pid,))
rows = cur.fetchall()
if not rows: return {"count":0,"latest":None,"prev":None,"min":None,"max":None,"avg_7d":None}
w=[float(r['weight']) for r in rows]
return {"count":len(rows),"latest":{"date":rows[0]['date'],"weight":float(rows[0]['weight'])},
"prev":{"date":rows[1]['date'],"weight":float(rows[1]['weight'])} if len(rows)>1 else None,
"min":min(w),"max":max(w),"avg_7d":round(sum(w[:7])/min(7,len(w)),2)}