Lars
04306a7fef
Implemented global quality_filter_level in user profiles for consistent data filtering across all views (Dashboard, History, Charts, KI-Pipeline). Backend changes: - Migration 016: Add quality_filter_level column to profiles table - quality_filter.py: Centralized helper functions for SQL filtering - insights.py: Apply global filter in _get_profile_data() - activity.py: Apply global filter in list_activity() Frontend changes: - SettingsPage.jsx: Add Datenqualität section with 4-level selector - History.jsx: Use global quality filter from profile context Filter levels: all, quality (good+excellent+acceptable), very_good (good+excellent), excellent (only excellent) Closes #31 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
461 lines
20 KiB
Python
461 lines
20 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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from typing import Optional
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from fastapi import APIRouter, HTTPException, UploadFile, File, Header, Depends
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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
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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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# 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 = APIRouter(prefix="/api/activity", tags=["activity"])
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logger = logging.getLogger(__name__)
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@router.get("")
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def list_activity(limit: int=200, x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Get activity entries for current profile."""
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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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# Issue #31: Apply global quality filter
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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)
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cur.execute(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
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LIMIT %s
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""", (pid, limit))
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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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d = e.model_dump()
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute("""INSERT INTO activity_log
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(id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
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hr_avg,hr_max,distance_km,rpe,source,notes,created)
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VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)""",
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(eid,pid,d['date'],d['start_time'],d['end_time'],d['activity_type'],d['duration_min'],
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d['kcal_active'],d['kcal_resting'],d['hr_avg'],d['hr_max'],d['distance_km'],
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d['rpe'],d['source'],d['notes']))
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# Phase 1.2: Auto-evaluation after INSERT
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if EVALUATION_AVAILABLE:
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# Load the activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Evaluated activity {eid} on INSERT")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to evaluate activity {eid}: {eval_error}")
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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.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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d = e.model_dump()
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cur = get_cursor(conn)
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cur.execute(f"UPDATE activity_log SET {', '.join(f'{k}=%s' for k in d)} WHERE id=%s AND profile_id=%s",
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list(d.values())+[eid,pid])
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# Phase 1.2: Auto-evaluation after UPDATE
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if EVALUATION_AVAILABLE:
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# Load the updated activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Re-evaluated activity {eid} on UPDATE")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to re-evaluate activity {eid}: {eval_error}")
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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.get("/stats")
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def activity_stats(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Get activity statistics (last 30 entries)."""
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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(
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"SELECT * FROM activity_log WHERE profile_id=%s ORDER BY date DESC LIMIT 30", (pid,))
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rows = [r2d(r) for r in cur.fetchall()]
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if not rows: return {"count":0,"total_kcal":0,"total_min":0,"by_type":{}}
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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']; 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 {"count":len(rows),"total_kcal":round(total_kcal),"total_min":round(total_min),"by_type":by_type}
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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)
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"""
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with get_db() as conn:
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cur = get_cursor(conn)
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# Try user-specific mapping first
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if profile_id:
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cur.execute("""
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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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""", (activity_type, profile_id))
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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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# Try global mapping
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cur.execute("""
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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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""", (activity_type,))
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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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@router.get("/uncategorized")
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def list_uncategorized_activities(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Get activities without assigned training type, grouped by activity_type."""
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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("""
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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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""", (pid,))
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return [r2d(r) for r in cur.fetchall()]
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@router.post("/bulk-categorize")
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def bulk_categorize_activities(
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data: dict,
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x_profile_id: Optional[str]=Header(default=None),
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session: dict=Depends(require_auth)
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):
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"""
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Bulk update training type for activities.
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Also saves the mapping to activity_type_mappings for future imports.
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Body: {
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"activity_type": "Running",
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"training_type_id": 1,
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"training_category": "cardio",
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"training_subcategory": "running"
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}
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"""
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pid = get_pid(x_profile_id)
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activity_type = data.get('activity_type')
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training_type_id = data.get('training_type_id')
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training_category = data.get('training_category')
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training_subcategory = data.get('training_subcategory')
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if not activity_type or not training_type_id:
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raise HTTPException(400, "activity_type and training_type_id required")
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with get_db() as conn:
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cur = get_cursor(conn)
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# Update existing activities
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cur.execute("""
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UPDATE activity_log
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SET training_type_id = %s,
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training_category = %s,
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training_subcategory = %s
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WHERE profile_id = %s
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AND activity_type = %s
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AND training_type_id IS NULL
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""", (training_type_id, training_category, training_subcategory, pid, activity_type))
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updated_count = cur.rowcount
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# Phase 1.2: Auto-evaluation after bulk categorization
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if EVALUATION_AVAILABLE:
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# Load all activities that were just updated and evaluate them
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE profile_id = %s
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AND activity_type = %s
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AND training_type_id = %s
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""", (pid, activity_type, training_type_id))
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activities_to_evaluate = cur.fetchall()
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evaluated_count = 0
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for activity_row in activities_to_evaluate:
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activity_dict = dict(activity_row)
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try:
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evaluate_and_save_activity(cur, activity_dict["id"], activity_dict, training_type_id, pid)
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evaluated_count += 1
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except Exception as eval_error:
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logger.warning(f"[AUTO-EVAL] Failed to evaluate bulk-categorized activity {activity_dict['id']}: {eval_error}")
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logger.info(f"[AUTO-EVAL] Evaluated {evaluated_count}/{updated_count} bulk-categorized activities")
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# Save mapping for future imports (upsert)
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cur.execute("""
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INSERT INTO activity_type_mappings (activity_type, training_type_id, profile_id, source, updated_at)
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VALUES (%s, %s, %s, 'bulk', CURRENT_TIMESTAMP)
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ON CONFLICT (activity_type, profile_id)
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DO UPDATE SET
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training_type_id = EXCLUDED.training_type_id,
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source = 'bulk',
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updated_at = CURRENT_TIMESTAMP
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""", (activity_type, training_type_id, pid))
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logger.info(f"[MAPPING] Saved bulk mapping: {activity_type} → training_type_id {training_type_id} (profile {pid})")
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return {"updated": updated_count, "activity_type": activity_type, "mapping_saved": True}
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@router.post("/import-csv")
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async def import_activity_csv(file: UploadFile=File(...), x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
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"""Import Apple Health workout CSV with automatic training type mapping."""
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pid = get_pid(x_profile_id)
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raw = await file.read()
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try: text = raw.decode('utf-8')
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except: text = raw.decode('latin-1')
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if text.startswith('\ufeff'): text = text[1:]
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if not text.strip(): raise HTTPException(400,"Leere Datei")
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reader = csv.DictReader(io.StringIO(text))
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inserted = skipped = 0
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with get_db() as conn:
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cur = get_cursor(conn)
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for row in reader:
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wtype = row.get('Workout Type','').strip()
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start = row.get('Start','').strip()
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if not wtype or not start: continue
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try: date = start[:10]
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except: continue
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dur = row.get('Duration','').strip()
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duration_min = None
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if dur:
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try:
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p = dur.split(':')
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duration_min = round(int(p[0])*60+int(p[1])+int(p[2])/60,1)
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except: pass
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def kj(v):
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try: return round(float(v)/4.184) if v else None
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except: return None
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def tf(v):
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try: return round(float(v),1) if v else None
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except: return None
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# Map activity_type to training_type_id using database mappings
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training_type_id, training_category, training_subcategory = get_training_type_for_activity(wtype, pid)
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try:
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# Check if entry already exists (duplicate detection by date + start_time)
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cur.execute("""
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SELECT id FROM activity_log
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WHERE profile_id = %s AND date = %s AND start_time = %s
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""", (pid, date, start))
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existing = cur.fetchone()
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if existing:
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# Update existing entry (e.g., to add training type mapping)
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existing_id = existing['id']
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cur.execute("""
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UPDATE activity_log
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SET end_time = %s,
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activity_type = %s,
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duration_min = %s,
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kcal_active = %s,
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kcal_resting = %s,
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hr_avg = %s,
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hr_max = %s,
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distance_km = %s,
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training_type_id = %s,
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training_category = %s,
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training_subcategory = %s
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WHERE id = %s
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""", (
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row.get('End',''), wtype, duration_min,
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kj(row.get('Aktive Energie (kJ)','')),
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kj(row.get('Ruheeinträge (kJ)','')),
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tf(row.get('Durchschn. Herzfrequenz (count/min)','')),
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tf(row.get('Max. Herzfrequenz (count/min)','')),
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tf(row.get('Distanz (km)','')),
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training_type_id, training_category, training_subcategory,
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existing_id
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))
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skipped += 1 # Count as skipped (not newly inserted)
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# Phase 1.2: Auto-evaluation after CSV import UPDATE
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if EVALUATION_AVAILABLE and training_type_id:
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try:
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# Build activity dict for evaluation
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activity_dict = {
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"id": existing_id,
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"profile_id": pid,
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"date": date,
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"training_type_id": training_type_id,
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"duration_min": duration_min,
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"hr_avg": tf(row.get('Durchschn. Herzfrequenz (count/min)','')),
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"hr_max": tf(row.get('Max. Herzfrequenz (count/min)','')),
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"distance_km": tf(row.get('Distanz (km)','')),
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"kcal_active": kj(row.get('Aktive Energie (kJ)','')),
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"kcal_resting": kj(row.get('Ruheeinträge (kJ)','')),
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"rpe": None,
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"pace_min_per_km": None,
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"cadence": None,
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"elevation_gain": None
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}
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evaluate_and_save_activity(cur, existing_id, activity_dict, training_type_id, pid)
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logger.debug(f"[AUTO-EVAL] Re-evaluated updated activity {existing_id}")
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except Exception as eval_error:
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logger.warning(f"[AUTO-EVAL] Failed to re-evaluate updated activity {existing_id}: {eval_error}")
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else:
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# Insert new entry
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new_id = str(uuid.uuid4())
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cur.execute("""INSERT INTO activity_log
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(id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
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hr_avg,hr_max,distance_km,source,training_type_id,training_category,training_subcategory,created)
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VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'apple_health',%s,%s,%s,CURRENT_TIMESTAMP)""",
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(new_id,pid,date,start,row.get('End',''),wtype,duration_min,
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kj(row.get('Aktive Energie (kJ)','')),kj(row.get('Ruheeinträge (kJ)','')),
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tf(row.get('Durchschn. Herzfrequenz (count/min)','')),
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tf(row.get('Max. Herzfrequenz (count/min)','')),
|
|
tf(row.get('Distanz (km)','')),
|
|
training_type_id,training_category,training_subcategory))
|
|
inserted+=1
|
|
|
|
# Phase 1.2: Auto-evaluation after CSV import INSERT
|
|
if EVALUATION_AVAILABLE and training_type_id:
|
|
try:
|
|
# Build activity dict for evaluation
|
|
activity_dict = {
|
|
"id": new_id,
|
|
"profile_id": pid,
|
|
"date": date,
|
|
"training_type_id": training_type_id,
|
|
"duration_min": duration_min,
|
|
"hr_avg": tf(row.get('Durchschn. Herzfrequenz (count/min)','')),
|
|
"hr_max": tf(row.get('Max. Herzfrequenz (count/min)','')),
|
|
"distance_km": tf(row.get('Distanz (km)','')),
|
|
"kcal_active": kj(row.get('Aktive Energie (kJ)','')),
|
|
"kcal_resting": kj(row.get('Ruheeinträge (kJ)','')),
|
|
"rpe": None,
|
|
"pace_min_per_km": None,
|
|
"cadence": None,
|
|
"elevation_gain": None
|
|
}
|
|
evaluate_and_save_activity(cur, new_id, activity_dict, training_type_id, pid)
|
|
logger.debug(f"[AUTO-EVAL] Evaluated imported activity {new_id}")
|
|
except Exception as eval_error:
|
|
logger.warning(f"[AUTO-EVAL] Failed to evaluate imported activity {new_id}: {eval_error}")
|
|
except Exception as e:
|
|
logger.warning(f"Import row failed: {e}")
|
|
skipped+=1
|
|
return {"inserted":inserted,"skipped":skipped,"message":f"{inserted} Trainings importiert"}
|