Lars
7d6fdab812
- Updated the `_import_activity` function to include new metrics: duration_min, kcal_active, kcal_resting, hr_avg, hr_max, and distance_km during CSV imports. - Modified the `insert_activity_csv_minimal` function to accept and store these additional metrics in the activity log. - Enhanced the `run_activity_post_write_hooks_import` function to utilize the new metrics for auto-evaluation after activity imports. - Updated the activity import router to pass the new metrics from the CSV file to the database functions, ensuring comprehensive data handling. - Improved frontend handling of activity entry forms to accommodate the new metrics, enhancing user experience during activity log edits.
692 lines
26 KiB
Python
692 lines
26 KiB
Python
"""
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Activity Tracking Endpoints for Mitai Jinkendo
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Handles workout/activity logging, statistics, and Apple Health CSV import.
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"""
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import csv
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import io
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import uuid
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import logging
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import re
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import calendar
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from datetime import date
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from typing import Optional
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from fastapi import APIRouter, HTTPException, UploadFile, File, Header, Depends, Query
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from db import get_db, get_cursor, r2d
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from auth import require_auth, check_feature_access, increment_feature_usage
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from models import ActivityEntry, ActivityMetricsReplace
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from routers.profiles import get_pid
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from feature_logger import log_feature_usage
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from quality_filter import get_quality_filter_sql
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from data_layer.activity_persistence_orchestrator import (
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get_mappable_activity_field_catalog,
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insert_activity_from_entry,
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run_activity_post_write_hooks,
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update_activity_from_entry,
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find_activity_duplicate_id,
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update_activity_columns,
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insert_activity_csv_minimal,
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run_activity_post_write_hooks_import,
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new_activity_id,
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)
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from data_layer.activity_time_normalize import normalize_activity_start
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router = APIRouter(prefix="/api/activity", tags=["activity"])
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logger = logging.getLogger(__name__)
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_MONTH_RE = re.compile(r"^(\d{4})-(\d{2})$")
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def _month_date_bounds(ym: str) -> tuple[date, date]:
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m = _MONTH_RE.match((ym or "").strip())
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if not m:
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raise HTTPException(status_code=400, detail="month muss YYYY-MM sein")
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y, mo = int(m.group(1)), int(m.group(2))
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if mo < 1 or mo > 12:
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raise HTTPException(status_code=400, detail="Ungültiger Monat")
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last = calendar.monthrange(y, mo)[1]
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return date(y, mo, 1), date(y, mo, last)
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_ACTIVITY_DEDUP_WINDOW = """
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PARTITION BY al.profile_id, al.date,
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COALESCE(al.activity_type, ''),
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COALESCE(al.start_time::text, ''),
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COALESCE(ROUND(al.duration_min::numeric, 1), '-999999'::numeric),
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COALESCE(ROUND(al.kcal_active::numeric, 1), '-999999'::numeric)
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ORDER BY al.created DESC NULLS LAST, al.id DESC
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"""
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def _activity_rows_after_list_query(cur):
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rows = []
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for r in cur.fetchall():
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d = r2d(r)
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if not d:
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continue
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d.pop("_dup_rn", None)
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rows.append(d)
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return rows
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# Evaluation import with error handling (Phase 1.2)
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try:
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from evaluation_helper import evaluate_and_save_activity
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EVALUATION_AVAILABLE = True
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except Exception as e:
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logger.warning(f"[AUTO-EVAL] Evaluation system not available: {e}")
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EVALUATION_AVAILABLE = False
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evaluate_and_save_activity = None
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@router.get("")
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def list_activity(
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limit: int = Query(200, ge=1, le=50_000),
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offset: int = Query(0, ge=0, le=100_000, description="SQL OFFSET für Pagination"),
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days: Optional[int] = Query(None, ge=1, le=4000, description="Nur Einträge mit date >= HEUTE − days (Kalendertage)"),
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month: Optional[str] = Query(
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None,
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description='Kalendermonat "YYYY-MM" (ganzer Monat; schließt days und offset aus)',
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),
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skip_quality_filter: bool = Query(
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False,
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description="True = alle Einträge des Profils (ohne quality_label-Filter). Für /activity Erfassung.",
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),
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collapse_duplicate_sessions: bool = Query(
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False,
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description="True = Sessions mit gleichem Datum/Typ/Startzeit/Dauer/Kcal falten (neueste Zeile behalten).",
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),
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session: dict = Depends(require_auth),
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):
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"""Get activity entries for current profile. Optional *days* filter by calendar window (not the same as *limit*)."""
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# Immer das Profil der gültigen Session (X-Profile-Id wird hier nicht verwendet).
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pid = str(session["profile_id"])
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with get_db() as conn:
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cur = get_cursor(conn)
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# Issue #31: Qualitätsfilter — auf der Erfassungsseite /activity abschaltbar (skip_quality_filter)
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if skip_quality_filter:
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quality_filter = ""
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quality_filter_al = ""
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else:
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cur.execute("SELECT * FROM profiles WHERE id=%s", (pid,))
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profile = r2d(cur.fetchone())
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quality_filter = get_quality_filter_sql(profile or {}, "")
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quality_filter_al = get_quality_filter_sql(profile or {}, "al.")
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if month:
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if days is not None:
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raise HTTPException(status_code=400, detail="month und days schließen sich aus")
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if offset != 0:
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raise HTTPException(status_code=400, detail="month und offset schließen sich aus")
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d0, d1 = _month_date_bounds(month)
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if collapse_duplicate_sessions:
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cur.execute(
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f"""
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SELECT d.* FROM (
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SELECT al.*, ROW_NUMBER() OVER (
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{_ACTIVITY_DEDUP_WINDOW}
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) AS _dup_rn
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FROM activity_log al
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WHERE al.profile_id = %s
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{quality_filter_al}
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AND al.date >= %s AND al.date <= %s
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) d
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WHERE d._dup_rn = 1
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ORDER BY d.date DESC, d.start_time DESC NULLS LAST, d.id DESC
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LIMIT %s
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""",
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(pid, d0, d1, limit),
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)
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return _activity_rows_after_list_query(cur)
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cur.execute(
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f"""
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SELECT * FROM activity_log
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WHERE profile_id=%s
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{quality_filter}
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AND date >= %s AND date <= %s
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ORDER BY date DESC, start_time DESC NULLS LAST, id DESC
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LIMIT %s
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""",
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(pid, d0, d1, limit),
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)
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return [r2d(r) for r in cur.fetchall()]
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if days is not None:
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if collapse_duplicate_sessions:
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cur.execute(
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f"""
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SELECT d.* FROM (
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SELECT al.*, ROW_NUMBER() OVER (
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{_ACTIVITY_DEDUP_WINDOW}
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) AS _dup_rn
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FROM activity_log al
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WHERE al.profile_id = %s
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{quality_filter_al}
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AND al.date >= (CURRENT_DATE - %s::integer)
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) d
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WHERE d._dup_rn = 1
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ORDER BY d.date DESC, d.start_time DESC NULLS LAST, d.id DESC
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LIMIT %s OFFSET %s
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""",
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(pid, days, limit, offset),
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)
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return _activity_rows_after_list_query(cur)
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cur.execute(
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f"""
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SELECT * FROM activity_log
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WHERE profile_id=%s
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{quality_filter}
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AND date >= (CURRENT_DATE - %s::integer)
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ORDER BY date DESC, start_time DESC NULLS LAST, id DESC
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LIMIT %s OFFSET %s
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""",
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(pid, days, limit, offset),
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)
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else:
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if collapse_duplicate_sessions:
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cur.execute(
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f"""
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SELECT d.* FROM (
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SELECT al.*, ROW_NUMBER() OVER (
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{_ACTIVITY_DEDUP_WINDOW}
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) AS _dup_rn
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FROM activity_log al
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WHERE al.profile_id = %s
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{quality_filter_al}
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) d
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WHERE d._dup_rn = 1
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ORDER BY d.date DESC, d.start_time DESC NULLS LAST, d.id DESC
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LIMIT %s OFFSET %s
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""",
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(pid, limit, offset),
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)
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return _activity_rows_after_list_query(cur)
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cur.execute(
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f"""
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SELECT * FROM activity_log
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WHERE profile_id=%s
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{quality_filter}
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ORDER BY date DESC, start_time DESC NULLS LAST, id DESC
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LIMIT %s OFFSET %s
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""",
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(pid, limit, offset),
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)
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return [r2d(r) for r in cur.fetchall()]
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@router.post("")
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def create_activity(e: ActivityEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Create new activity entry."""
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pid = get_pid(x_profile_id)
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# Phase 4: Check feature access and ENFORCE
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access = check_feature_access(pid, 'activity_entries')
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log_feature_usage(pid, 'activity_entries', access, 'create')
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if not access['allowed']:
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logger.warning(
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f"[FEATURE-LIMIT] User {pid} blocked: "
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f"activity_entries {access['reason']} (used: {access['used']}, limit: {access['limit']})"
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)
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raise HTTPException(
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status_code=403,
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detail=f"Limit erreicht: Du hast das Kontingent für Aktivitätseinträge überschritten ({access['used']}/{access['limit']}). "
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f"Bitte kontaktiere den Admin oder warte bis zum nächsten Reset."
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)
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eid = str(uuid.uuid4())
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with get_db() as conn:
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cur = get_cursor(conn)
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insert_activity_from_entry(cur, pid, eid, e)
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run_activity_post_write_hooks(cur, pid, eid)
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# Phase 2: Increment usage counter (always for new entries)
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increment_feature_usage(pid, 'activity_entries')
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return {"id":eid,"date":e.date}
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@router.get("/stats")
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def activity_stats(
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skip_quality_filter: bool = Query(
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False,
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description="True = Statistik-Kacheln ohne Profil-Qualitätsfilter (passend zur /activity-Liste).",
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),
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session: dict = Depends(require_auth),
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):
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"""Get activity statistics (last 30 entries)."""
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pid = str(session["profile_id"])
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with get_db() as conn:
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cur = get_cursor(conn)
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if skip_quality_filter:
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quality_filter = ""
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else:
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cur.execute("SELECT * FROM profiles WHERE id=%s", (pid,))
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profile = r2d(cur.fetchone())
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quality_filter = get_quality_filter_sql(profile or {}, "")
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cur.execute(
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f"SELECT COUNT(*)::bigint AS c FROM activity_log WHERE profile_id=%s {quality_filter}",
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(pid,),
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)
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total_in_profile = int(cur.fetchone()["c"])
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if skip_quality_filter:
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cur.execute(
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f"""
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SELECT d.* FROM (
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SELECT al.*, ROW_NUMBER() OVER (
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{_ACTIVITY_DEDUP_WINDOW}
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) AS _dup_rn
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FROM activity_log al
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WHERE al.profile_id = %s
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) d
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WHERE d._dup_rn = 1
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ORDER BY d.date DESC, d.start_time DESC NULLS LAST, d.id DESC
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LIMIT 30
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""",
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(pid,),
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)
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rows = _activity_rows_after_list_query(cur)
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else:
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cur.execute(
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f"""
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SELECT * FROM activity_log
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WHERE profile_id=%s {quality_filter}
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ORDER BY date DESC, start_time DESC NULLS LAST, id DESC
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LIMIT 30
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""",
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(pid,),
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)
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rows = [r2d(r) for r in cur.fetchall()]
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if not rows:
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return {
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"count": 0,
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"sample_size": 0,
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"total_in_profile": total_in_profile,
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"total_kcal": 0,
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"total_min": 0,
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"by_type": {},
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}
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total_kcal = sum(float(r.get("kcal_active") or 0) for r in rows)
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total_min = sum(float(r.get("duration_min") or 0) for r in rows)
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by_type = {}
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for r in rows:
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t = r["activity_type"]
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by_type.setdefault(t, {"count": 0, "kcal": 0, "min": 0})
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by_type[t]["count"] += 1
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by_type[t]["kcal"] += float(r.get("kcal_active") or 0)
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by_type[t]["min"] += float(r.get("duration_min") or 0)
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return {
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"count": len(rows),
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"sample_size": len(rows),
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"total_in_profile": total_in_profile,
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"total_kcal": round(total_kcal),
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"total_min": round(total_min),
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"by_type": by_type,
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}
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@router.get("/uncategorized")
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def list_uncategorized_activities(
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session: dict = Depends(require_auth),
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):
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"""Get activities without assigned training type, grouped by activity_type."""
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pid = str(session["profile_id"])
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""
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SELECT activity_type, COUNT(*) as count,
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MIN(date) as first_date, MAX(date) as last_date
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FROM activity_log
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WHERE profile_id=%s AND training_type_id IS NULL
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GROUP BY activity_type
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ORDER BY count DESC
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""",
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(pid,),
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)
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return [r2d(r) for r in cur.fetchall()]
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@router.get("/mappable-fields")
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def get_activity_mappable_fields(session: dict = Depends(require_auth)):
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"""
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Vollständiger Katalog für Import-Mappings (activity_log-Kernfelder + alle aktiven training_parameters).
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Werte für Keys ohne Schema zur konkreten Session werden beim Import ignoriert.
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"""
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pid = str(session["profile_id"])
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with get_db() as conn:
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cur = get_cursor(conn)
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return get_mappable_activity_field_catalog(cur, pid)
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@router.put("/{eid}")
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def update_activity(eid: str, e: ActivityEntry, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Update existing activity entry."""
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pid = get_pid(x_profile_id)
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with get_db() as conn:
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cur = get_cursor(conn)
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update_activity_from_entry(cur, pid, eid, e)
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run_activity_post_write_hooks(cur, pid, eid)
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return {"id":eid}
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@router.delete("/{eid}")
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def delete_activity(eid: str, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Delete activity entry."""
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pid = get_pid(x_profile_id)
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute("DELETE FROM activity_log WHERE id=%s AND profile_id=%s", (eid,pid))
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return {"ok":True}
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@router.put("/{eid}/metrics")
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def replace_activity_metrics(
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eid: str,
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body: ActivityMetricsReplace,
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session: dict = Depends(require_auth),
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):
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"""
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Voller Ersatz der EAV-Session-Metriken (siehe ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE.md).
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"""
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from data_layer.activity_session_metrics import (
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ActivitySessionMetricsError,
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replace_activity_session_metrics,
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)
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pid = str(session["profile_id"])
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payload = [m.model_dump() for m in body.metrics]
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try:
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with get_db() as conn:
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cur = get_cursor(conn)
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metrics = replace_activity_session_metrics(cur, pid, eid, payload)
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conn.commit()
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except ActivitySessionMetricsError as err:
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raise HTTPException(err.status_code, err.detail) from err
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return {"id": eid, "metrics": metrics}
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@router.get("/{eid}")
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def get_activity_session(
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eid: str,
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session: dict = Depends(require_auth),
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):
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"""Session-Kopf + aufgelöstes Schema + EAV-Metriken (Layer 1)."""
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from data_layer.activity_session_metrics import (
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ActivitySessionMetricsError,
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get_activity_session_logical_unit,
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)
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from data_layer.utils import serialize_dates
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pid = str(session["profile_id"])
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try:
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with get_db() as conn:
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cur = get_cursor(conn)
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unit = get_activity_session_logical_unit(cur, pid, eid)
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except ActivitySessionMetricsError as err:
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raise HTTPException(err.status_code, err.detail) from err
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unit["header"] = serialize_dates(unit["header"])
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return unit
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def get_training_type_for_activity_with_cursor(cur, activity_type: str, profile_id: str | None = None):
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"""
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Wie get_training_type_for_activity, aber mit bestehendem Cursor (z. B. Universal-CSV-Import).
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Vermeidet verschachteltes get_db() — bei maxconn=1 sonst Deadlock auf dem Connection-Pool.
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"""
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if profile_id:
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cur.execute(
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"""
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SELECT m.training_type_id, t.category, t.subcategory
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FROM activity_type_mappings m
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JOIN training_types t ON m.training_type_id = t.id
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WHERE m.activity_type = %s AND m.profile_id = %s
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LIMIT 1
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""",
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(activity_type, profile_id),
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)
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row = cur.fetchone()
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if row:
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return (row["training_type_id"], row["category"], row["subcategory"])
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cur.execute(
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"""
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SELECT m.training_type_id, t.category, t.subcategory
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FROM activity_type_mappings m
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JOIN training_types t ON m.training_type_id = t.id
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WHERE m.activity_type = %s AND m.profile_id IS NULL
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LIMIT 1
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""",
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(activity_type,),
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)
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row = cur.fetchone()
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if row:
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return (row["training_type_id"], row["category"], row["subcategory"])
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return (None, None, None)
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def get_training_type_for_activity(activity_type: str, profile_id: str = None):
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"""
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Map activity_type to training_type_id using database mappings.
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Priority:
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1. User-specific mapping (profile_id)
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2. Global mapping (profile_id = NULL)
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3. No mapping found → returns (None, None, None)
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|
||
Returns: (training_type_id, category, subcategory) or (None, None, None)
|
||
"""
|
||
with get_db() as conn:
|
||
cur = get_cursor(conn)
|
||
return get_training_type_for_activity_with_cursor(cur, activity_type, profile_id)
|
||
|
||
|
||
@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.
|
||
|
||
Also saves the mapping to activity_type_mappings for future imports.
|
||
|
||
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)
|
||
|
||
# Update existing activities
|
||
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
|
||
|
||
# Phase 1.2: Auto-evaluation after bulk categorization
|
||
if EVALUATION_AVAILABLE:
|
||
# Load all activities that were just updated and evaluate them
|
||
cur.execute("""
|
||
SELECT id, profile_id, date, training_type_id, duration_min,
|
||
hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
|
||
rpe, pace_min_per_km, cadence, elevation_gain
|
||
FROM activity_log
|
||
WHERE profile_id = %s
|
||
AND activity_type = %s
|
||
AND training_type_id = %s
|
||
""", (pid, activity_type, training_type_id))
|
||
|
||
activities_to_evaluate = cur.fetchall()
|
||
evaluated_count = 0
|
||
for activity_row in activities_to_evaluate:
|
||
activity_dict = dict(activity_row)
|
||
try:
|
||
evaluate_and_save_activity(cur, activity_dict["id"], activity_dict, training_type_id, pid)
|
||
evaluated_count += 1
|
||
except Exception as eval_error:
|
||
logger.warning(f"[AUTO-EVAL] Failed to evaluate bulk-categorized activity {activity_dict['id']}: {eval_error}")
|
||
|
||
logger.info(f"[AUTO-EVAL] Evaluated {evaluated_count}/{updated_count} bulk-categorized activities")
|
||
|
||
# Save mapping for future imports (upsert)
|
||
cur.execute("""
|
||
INSERT INTO activity_type_mappings (activity_type, training_type_id, profile_id, source, updated_at)
|
||
VALUES (%s, %s, %s, 'bulk', CURRENT_TIMESTAMP)
|
||
ON CONFLICT (activity_type, profile_id)
|
||
DO UPDATE SET
|
||
training_type_id = EXCLUDED.training_type_id,
|
||
source = 'bulk',
|
||
updated_at = CURRENT_TIMESTAMP
|
||
""", (activity_type, training_type_id, pid))
|
||
|
||
logger.info(f"[MAPPING] Saved bulk mapping: {activity_type} → training_type_id {training_type_id} (profile {pid})")
|
||
|
||
return {"updated": updated_count, "activity_type": activity_type, "mapping_saved": True}
|
||
|
||
|
||
@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)):
|
||
"""
|
||
Legacy-Upload (Apple Health Workout-CSV-Spaltennamen).
|
||
Persistenz läuft über activity_persistence_orchestrator — gleiche Schicht wie Universal-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))
|
||
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
|
||
workout_date, workout_start_t = normalize_activity_start(start)
|
||
if not workout_date:
|
||
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 activity_type to training_type_id using database mappings
|
||
training_type_id, training_category, training_subcategory = get_training_type_for_activity(wtype, pid)
|
||
|
||
kcal_a = kj(row.get("Aktive Energie (kJ)", ""))
|
||
kcal_r = kj(row.get("Ruheeinträge (kJ)", ""))
|
||
hr_av = tf(row.get("Durchschn. Herzfrequenz (count/min)", ""))
|
||
hr_mx = tf(row.get("Max. Herzfrequenz (count/min)", ""))
|
||
dist_km = tf(row.get("Distanz (km)", ""))
|
||
try:
|
||
existing_id = find_activity_duplicate_id(cur, pid, workout_date, workout_start_t)
|
||
if existing_id:
|
||
update_activity_columns(
|
||
cur,
|
||
pid,
|
||
str(existing_id),
|
||
{
|
||
"start_time": workout_start_t,
|
||
"end_time": row.get("End", "") or None,
|
||
"activity_type": wtype,
|
||
"duration_min": duration_min,
|
||
"kcal_active": kcal_a,
|
||
"kcal_resting": kcal_r,
|
||
"hr_avg": hr_av,
|
||
"hr_max": hr_mx,
|
||
"distance_km": dist_km,
|
||
"training_type_id": training_type_id,
|
||
"training_category": training_category,
|
||
"training_subcategory": training_subcategory,
|
||
},
|
||
)
|
||
skipped += 1
|
||
run_activity_post_write_hooks_import(
|
||
cur,
|
||
pid,
|
||
str(existing_id),
|
||
workout_date=workout_date,
|
||
training_type_id=training_type_id,
|
||
duration_min=duration_min,
|
||
hr_avg=hr_av,
|
||
hr_max=hr_mx,
|
||
distance_km=dist_km,
|
||
kcal_active=kcal_a,
|
||
kcal_resting=kcal_r,
|
||
)
|
||
else:
|
||
new_id = new_activity_id()
|
||
insert_activity_csv_minimal(
|
||
cur,
|
||
pid,
|
||
new_id,
|
||
date_iso=workout_date,
|
||
start_time=workout_start_t,
|
||
end_time=row.get("End", "") or None,
|
||
activity_type=wtype,
|
||
duration_min=duration_min,
|
||
kcal_active=kcal_a,
|
||
kcal_resting=kcal_r,
|
||
hr_avg=hr_av,
|
||
hr_max=hr_mx,
|
||
distance_km=dist_km,
|
||
training_type_id=training_type_id,
|
||
training_category=training_category,
|
||
training_subcategory=training_subcategory,
|
||
source="apple_health",
|
||
)
|
||
inserted += 1
|
||
run_activity_post_write_hooks_import(
|
||
cur,
|
||
pid,
|
||
new_id,
|
||
workout_date=workout_date,
|
||
training_type_id=training_type_id,
|
||
duration_min=duration_min,
|
||
hr_avg=hr_av,
|
||
hr_max=hr_mx,
|
||
distance_km=dist_km,
|
||
kcal_active=kcal_a,
|
||
kcal_resting=kcal_r,
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"Import row failed: {e}")
|
||
skipped+=1
|
||
return {"inserted":inserted,"skipped":skipped,"message":f"{inserted} Trainings importiert"}
|