import { useState, useEffect } from 'react'
import { useNavigate, useLocation } from 'react-router-dom'
import {
LineChart, Line, BarChart, Bar,
XAxis, YAxis, Tooltip, ResponsiveContainer, CartesianGrid,
ReferenceLine, PieChart, Pie, Cell
} from 'recharts'
import { ChevronRight, Brain, ChevronDown, ChevronUp } from 'lucide-react'
import { api } from '../utils/api'
import { getBfCategory } from '../utils/calc'
import { getInterpretation, getStatusColor, getStatusBg } from '../utils/interpret'
import Markdown from '../utils/Markdown'
import dayjs from 'dayjs'
import 'dayjs/locale/de'
dayjs.locale('de')
function rollingAvg(arr, key, window=7) {
return arr.map((d,i) => {
const s = arr.slice(Math.max(0,i-window+1),i+1).map(x=>x[key]).filter(v=>v!=null)
return s.length ? {...d,[`${key}_avg`]:Math.round(s.reduce((a,b)=>a+b)/s.length*10)/10} : d
})
}
const fmtDate = d => dayjs(d).format('DD.MM')
function NavToCaliper() {
const nav = useNavigate()
return
}
function NavToCircum() {
const nav = useNavigate()
return
}
function EmptySection({ text, to, toLabel }) {
const nav = useNavigate()
return (
🤖 KI-AUSWERTUNGEN
{slugs.map(slug=>(
{relevant.length===0 && (
Noch keine Auswertung. Klicke oben um eine zu erstellen.
)}
{relevant.map(ins=>(
setExpanded(expanded===ins.id?null:ins.id)}>
{dayjs(ins.created).format('DD. MMM YYYY, HH:mm')} · {LABELS[ins.scope]||ins.scope}
{expanded===ins.id?
:}
{expanded===ins.id &&
}
))}
)
}
// ── Period selector ───────────────────────────────────────────────────────────
function PeriodSelector({ value, onChange }) {
const opts = [{v:30,l:'30 Tage'},{v:90,l:'90 Tage'},{v:180,l:'6 Monate'},{v:365,l:'1 Jahr'},{v:9999,l:'Alles'}]
return (
{opts.map(o=>(
))}
)
}
// ── Body Section (Weight + Composition combined) ──────────────────────────────
function BodySection({ weights, calipers, circs, profile, insights, onRequest, loadingSlug, filterActiveSlugs }) {
const [period, setPeriod] = useState(90)
const sex = profile?.sex||'m'
const height = profile?.height||178
const cutoff = dayjs().subtract(period,'day').format('YYYY-MM-DD')
const filtW = [...(weights||[])].sort((a,b)=>a.date.localeCompare(b.date))
.filter(d=>period===9999||d.date>=cutoff)
const filtCal = (calipers||[]).filter(d=>period===9999||d.date>=cutoff)
const filtCir = (circs||[]).filter(d=>period===9999||d.date>=cutoff)
const hasWeight = filtW.length >= 2
const hasCal = filtCal.length >= 1
const hasCir = filtCir.length >= 1
if (!hasWeight && !hasCal && !hasCir) return (
)
// ── Weight chart ──
const withAvg = rollingAvg(filtW,'weight')
const withAvg14= rollingAvg(filtW,'weight',14)
const wCd = withAvg.map((d,i)=>({
date:fmtDate(d.date),
weight:d.weight,
avg7: d.weight_avg,
avg14: withAvg14[i]?.weight_avg,
}))
const ws = filtW.map(w=>w.weight)
const minW = ws.length ? Math.min(...ws) : null
const maxW = ws.length ? Math.max(...ws) : null
const avgAll = ws.length ? Math.round(ws.reduce((a,b)=>a+b)/ws.length*10)/10 : null
const trendPeriods = [7,30,90].map(days=>{
const cut = dayjs().subtract(days,'day').format('YYYY-MM-DD')
const per = filtW.filter(d=>d.date>=cut)
if (per.length<2) return null
const diff = Math.round((per[per.length-1].weight-per[0].weight)*10)/10
return {label:`${days}T`,diff,count:per.length}
}).filter(Boolean)
// ── Caliper chart ──
const bfCd = [...filtCal].filter(c=>c.body_fat_pct).reverse().map(c=>({
date:fmtDate(c.date),bf:c.body_fat_pct,lean:c.lean_mass,fat:c.fat_mass
}))
const latestCal = filtCal[0]
const prevCal = filtCal[1]
const latestCir = filtCir[0]
const latestW2 = filtW[filtW.length-1]
const bfCat = latestCal?.body_fat_pct ? getBfCategory(latestCal.body_fat_pct,sex) : null
// ── Circ chart ──
const cirCd = [...filtCir].filter(c=>c.c_waist||c.c_hip).reverse().map(c=>({
date:fmtDate(c.date),waist:c.c_waist,hip:c.c_hip,belly:c.c_belly
}))
// ── Indicators ──
const whr = latestCir?.c_waist&&latestCir?.c_hip ? Math.round(latestCir.c_waist/latestCir.c_hip*100)/100 : null
const whtr = latestCir?.c_waist&&height ? Math.round(latestCir.c_waist/height*100)/100 : null
// ── Rules ──
const combined = {
...(latestCal||{}),
c_waist:latestCir?.c_waist, c_hip:latestCir?.c_hip,
weight:latestW2?.weight
}
const rules = getInterpretation(combined, profile, prevCal||null)
return (
{/* Summary stats */}
{latestW2 &&
{latestW2.weight} kg
Aktuell
}
{latestCal?.body_fat_pct &&
{latestCal.body_fat_pct}%
KF {bfCat?.label}
}
{latestCal?.lean_mass &&
{latestCal.lean_mass} kg
Mager
}
{whr &&
}
{whtr &&
}
{/* Weight chart – 3 lines like WeightScreen */}
{hasWeight && (
Gewicht · {filtW.length} Einträge
{avgAll && }
{profile?.goal_weight && }
[`${v} kg`,n==='weight'?'Täglich':n==='avg7'?'Ø 7 Tage':'Ø 14 Tage']}/>
● Täglich
Ø 7T
Ø 14T
Ø Gesamt
{/* Trend tiles */}
{trendPeriods.length>0 && (
{trendPeriods.map(({label,diff})=>(
0?'var(--warn)':'var(--border)'}`}}>
0?'var(--warn)':'var(--text3)'}}>
{diff>0?'+':''}{diff} kg
{label}
))}
{minW &&
}
)}
)}
{/* KF + Magermasse chart */}
{bfCd.length>=2 && (
[`${v}${n==='bf'?'%':' kg'}`,n==='bf'?'KF%':'Mager']}/>
{profile?.goal_bf_pct && }
KF%
Mager kg
{profile?.goal_bf_pct && Ziel KF}
)}
{/* Circ trend */}
{cirCd.length>=2 && (
[`${v} cm`,n]}/>
{cirCd.some(d=>d.belly) && }
)}
{/* WHR / WHtR detail */}
{(whr||whtr) && (
{whr &&
{whr}
WHR
Taille ÷ Hüfte
Ziel <{sex==='m'?'0,90':'0,85'}
{whr<(sex==='m'?0.90:0.85)?'✓ Günstig':'⚠️ Erhöht'}
}
{whtr &&
{whtr}
WHtR
Taille ÷ Körpergröße
Ziel <0,50
{whtr<0.5?'✓ Optimal':'⚠️ Erhöht'}
}
)}
{rules.length>0 && (
BEWERTUNG
{rules.map((item,i)=>
)}
)}
)
}
// ── Nutrition Section ─────────────────────────────────────────────────────────
function NutritionSection({ nutrition, weights, profile, insights, onRequest, loadingSlug, filterActiveSlugs }) {
const [period, setPeriod] = useState(30)
if (!nutrition?.length) return (
)
const cutoff = dayjs().subtract(period,'day').format('YYYY-MM-DD')
const filtN = nutrition.filter(d=>period===9999||d.date>=cutoff)
const sorted = [...filtN].sort((a,b)=>a.date.localeCompare(b.date))
if (!filtN.length) return (
)
const n = filtN.length
const avgKcal = Math.round(filtN.reduce((s,d)=>s+(d.kcal||0),0)/n)
const avgProtein = Math.round(filtN.reduce((s,d)=>s+(d.protein_g||0),0)/n*10)/10
const avgFat = Math.round(filtN.reduce((s,d)=>s+(d.fat_g||0),0)/n*10)/10
const avgCarbs = Math.round(filtN.reduce((s,d)=>s+(d.carbs_g||0),0)/n*10)/10
const latestW = weights?.[0]?.weight||80
const ptLow = Math.round(latestW*1.6)
const ptHigh = Math.round(latestW*2.2)
const proteinOk = avgProtein>=ptLow
// Stacked macro bar (daily)
const cdMacro = sorted.map(d=>({
date: fmtDate(d.date),
Protein: Math.round(d.protein_g||0),
KH: Math.round(d.carbs_g||0),
Fett: Math.round(d.fat_g||0),
kcal: Math.round(d.kcal||0),
}))
// Pie
const totalMacroKcal = avgProtein*4+avgCarbs*4+avgFat*9
const pieData = [
{name:'Protein',value:Math.round(avgProtein*4/totalMacroKcal*100),color:'#1D9E75'},
{name:'KH', value:Math.round(avgCarbs*4/totalMacroKcal*100), color:'#D4537E'},
{name:'Fett', value:Math.round(avgFat*9/totalMacroKcal*100), color:'#378ADD'},
]
// Weekly macro bars
const weeklyMap={}
filtN.forEach(d=>{
const wk=dayjs(d.date).format('YYYY-WW')
const weekNum = (() => { const dt=new Date(d.date); dt.setHours(0,0,0,0); dt.setDate(dt.getDate()+4-(dt.getDay()||7)); const y=new Date(dt.getFullYear(),0,1); return Math.ceil(((dt-y)/86400000+1)/7) })()
if(!weeklyMap[wk]) weeklyMap[wk]={label:'KW'+weekNum,n:0,protein:0,carbs:0,fat:0,kcal:0}
weeklyMap[wk].protein+=d.protein_g||0; weeklyMap[wk].carbs+=d.carbs_g||0
weeklyMap[wk].fat+=d.fat_g||0; weeklyMap[wk].kcal+=d.kcal||0; weeklyMap[wk].n++
})
const weeklyData=Object.values(weeklyMap).slice(-12).map(w=>({
label:w.label,
Protein:Math.round(w.protein/w.n),
KH:Math.round(w.carbs/w.n),
Fett:Math.round(w.fat/w.n),
kcal:Math.round(w.kcal/w.n),
}))
// Rules
const macroRules=[]
if(!proteinOk) macroRules.push({status:'bad',icon:'🥩',category:'Protein',
title:`Unterversorgung: ${avgProtein}g/Tag (Ziel ${ptLow}–${ptHigh}g)`,
detail:`1,6–2,2g/kg KG. Fehlend: ~${ptLow-Math.round(avgProtein)}g täglich. Konsequenz: Muskelverlust bei Defizit.`,
value:avgProtein+'g'})
else macroRules.push({status:'good',icon:'🥩',category:'Protein',
title:`Gut: ${avgProtein}g/Tag (Ziel ${ptLow}–${ptHigh}g)`,
detail:`Ausreichend für Muskelerhalt und -aufbau.`,value:avgProtein+'g'})
const protPct=Math.round(avgProtein*4/totalMacroKcal*100)
if(protPct<20) macroRules.push({status:'warn',icon:'📊',category:'Makro-Anteil',
title:`Protein-Anteil niedrig: ${protPct}% der Kalorien`,
detail:`Empfehlung: 25–35%. Aktuell: ${protPct}% P / ${Math.round(avgCarbs*4/totalMacroKcal*100)}% KH / ${Math.round(avgFat*9/totalMacroKcal*100)}% F`,
value:protPct+'%'})
return (
{[['Ø Kalorien',avgKcal+' kcal','#EF9F27'],['Ø Protein',avgProtein+'g',proteinOk?'#1D9E75':'#D85A30'],
['Ø Fett',avgFat+'g','#378ADD'],['Ø KH',avgCarbs+'g','#D4537E'],
['Einträge',n+' T','var(--text3)']].map(([l,v,c])=>(
))}
{/* Stacked macro bars (daily) */}
Makroverteilung täglich (g) · {sorted[0]?.date?.slice(0,7)} – {sorted[sorted.length-1]?.date?.slice(0,7)}
[`${v}g`,n]}/>
Protein
KH
Fett
Protein-Ziel
{/* Pie + macro breakdown */}
Ø Makroverteilung · {n} Tage ({sorted[0]?.date?.slice(0,10)} – {sorted[sorted.length-1]?.date?.slice(0,10)})
{pieData.map((e,i)=>| )}
|
[`${v}%`,n]}/>
{pieData.map(p=>(
{p.name}
{p.value}%
{Math.round(p.name==='Protein'?avgProtein:p.name==='KH'?avgCarbs:avgFat)}g
{p.name==='Protein' &&
{proteinOk?'✓':'⚠️'} Ziel {ptLow}g
}
))}
Gesamt: {avgKcal} kcal/Tag
{/* Weekly stacked bars */}
{weeklyData.length>=2 && (
Makros pro Woche (Ø g/Tag)
[`${v}g`,n]}/>
)}
BEWERTUNG
{macroRules.map((item,i)=>
)}
)
}
// ── Activity Section ──────────────────────────────────────────────────────────
function ActivitySection({ activities, insights, onRequest, loadingSlug, filterActiveSlugs }) {
const [period, setPeriod] = useState(30)
if (!activities?.length) return (
)
const cutoff = dayjs().subtract(period,'day').format('YYYY-MM-DD')
const filtA = activities.filter(d=>period===9999||d.date>=cutoff)
const byDate={}
filtA.forEach(a=>{ byDate[a.date]=(byDate[a.date]||0)+(a.kcal_active||0) })
const cd=Object.entries(byDate).sort((a,b)=>a[0].localeCompare(b[0])).map(([date,kcal])=>({date:fmtDate(date),kcal:Math.round(kcal)}))
const totalKcal=Math.round(filtA.reduce((s,a)=>s+(a.kcal_active||0),0))
const totalMin =Math.round(filtA.reduce((s,a)=>s+(a.duration_min||0),0))
const hrData =filtA.filter(a=>a.hr_avg)
const avgHr =hrData.length?Math.round(hrData.reduce((s,a)=>s+a.hr_avg,0)/hrData.length):null
const types={}; filtA.forEach(a=>{ types[a.activity_type]=(types[a.activity_type]||0)+1 })
const topTypes=Object.entries(types).sort((a,b)=>b[1]-a[1])
const daysWithAct=new Set(filtA.map(a=>a.date)).size
const totalDays=Math.min(period,dayjs().diff(dayjs(filtA[filtA.length-1]?.date),'day')+1)
const consistency=totalDays>0?Math.round(daysWithAct/totalDays*100):0
const actRules=[{
status:consistency>=70?'good':consistency>=40?'warn':'bad',
icon:'📅', category:'Konsistenz',
title:`${consistency}% aktive Tage (${daysWithAct}/${Math.min(period,30)} Tage)`,
detail:consistency>=70?'Ausgezeichnete Regelmäßigkeit.':consistency>=40?'Ziel: 4–5 Einheiten/Woche.':'Mehr Regelmäßigkeit empfohlen.',
value:consistency+'%'
}]
return (
{[['Trainings',filtA.length,'var(--text1)'],['Kcal',totalKcal,'#EF9F27'],
['Stunden',Math.round(totalMin/60*10)/10,'#378ADD'],
avgHr?['Ø HF',avgHr+' bpm','#D85A30']:null].filter(Boolean).map(([l,v,c])=>(
))}
Aktive Kalorien / Tag
[`${v} kcal`]}/>
Trainingsarten
{topTypes.map(([type,count])=>(
BEWERTUNG
{actRules.map((item,i)=>
)}
)
}
// ── Correlation Section ───────────────────────────────────────────────────────
function CorrelationSection({ corrData, insights, profile, onRequest, loadingSlug, filterActiveSlugs }) {
const filtered = (corrData||[]).filter(d=>d.kcal&&d.weight)
if (filtered.length < 5) return (
)
const sex = profile?.sex||'m'
const height = profile?.height||178
const latestW = filtered[filtered.length-1]?.weight||80
const age = profile?.dob ? Math.floor((Date.now()-new Date(profile.dob))/(365.25*24*3600*1000)) : 35
const bmr = sex==='m' ? 10*latestW+6.25*height-5*age+5 : 10*latestW+6.25*height-5*age-161
const tdee = Math.round(bmr*1.4) // light activity baseline
// Chart 1: Kcal vs Weight
const kcalVsW = rollingAvg(filtered.map(d=>({...d,date:fmtDate(d.date)})),'kcal')
// Chart 2: Protein vs Lean Mass (only days with both)
const protVsLean = filtered.filter(d=>d.protein_g&&d.lean_mass)
.map(d=>({date:fmtDate(d.date),protein:d.protein_g,lean:d.lean_mass}))
// Chart 3: Activity kcal vs Weight change
const actVsW = filtered.filter(d=>d.weight)
.map((d,i,arr)=>{
const prev = arr[i-1]
return {
date: fmtDate(d.date),
weight: d.weight,
weightDelta: prev ? Math.round((d.weight-prev.weight)*10)/10 : null,
kcal: d.kcal||0,
}
}).filter(d=>d.weightDelta!==null)
// Chart 4: Calorie balance (intake - estimated TDEE)
const balance = filtered.map(d=>({
date: fmtDate(d.date),
balance: Math.round((d.kcal||0) - tdee),
}))
const balWithAvg = rollingAvg(balance,'balance')
const avgBalance = Math.round(balance.reduce((s,d)=>s+d.balance,0)/balance.length)
// ── Correlation insights ──
const corrInsights = []
// 1. Kcal → Weight correlation
if (filtered.length >= 14) {
const highKcal = filtered.filter(d=>d.kcal>tdee+200)
const lowKcal = filtered.filter(d=>d.kcal=3 && lowKcal.length>=3) {
const avgWHigh = Math.round(highKcal.reduce((s,d)=>s+d.weight,0)/highKcal.length*10)/10
const avgWLow = Math.round(lowKcal.reduce((s,d)=>s+d.weight,0)/lowKcal.length*10)/10
corrInsights.push({
icon:'📊', status: avgWLow < avgWHigh ? 'good' : 'warn',
title: avgWLow < avgWHigh
? `Kalorienreduktion wirkt: Ø ${avgWLow}kg bei Defizit vs. ${avgWHigh}kg bei Überschuss`
: `Kein klarer Kalorieneffekt auf Gewicht erkennbar`,
detail: `Tage mit Überschuss (>${tdee+200} kcal): Ø ${avgWHigh}kg · Tage mit Defizit (<${tdee-200} kcal): Ø ${avgWLow}kg`,
})
}
}
// 2. Protein → Lean mass
if (protVsLean.length >= 3) {
const ptLow = Math.round(latestW*1.6)
const highProt = protVsLean.filter(d=>d.protein>=ptLow)
const lowProt = protVsLean.filter(d=>d.protein=2 && lowProt.length>=2) {
const avgLH = Math.round(highProt.reduce((s,d)=>s+d.lean,0)/highProt.length*10)/10
const avgLL = Math.round(lowProt.reduce((s,d)=>s+d.lean,0)/lowProt.length*10)/10
corrInsights.push({
icon:'🥩', status: avgLH >= avgLL ? 'good' : 'warn',
title: `Hohe Proteinzufuhr (≥${ptLow}g): Ø ${avgLH}kg Mager · Niedrig: Ø ${avgLL}kg`,
detail: `${highProt.length} Messpunkte mit hoher vs. ${lowProt.length} mit niedriger Proteinzufuhr verglichen.`,
})
}
}
// 3. Avg balance
corrInsights.push({
icon: avgBalance < -100 ? '✅' : avgBalance > 200 ? '⬆️' : '➡️',
status: avgBalance < -100 ? 'good' : avgBalance > 300 ? 'warn' : 'good',
title: `Ø Kalorienbilanz: ${avgBalance>0?'+':''}${avgBalance} kcal/Tag`,
detail: `Geschätzter TDEE: ${tdee} kcal (Mifflin-St Jeor ×1,4). ${
avgBalance<-500?'Starkes Defizit – Muskelerhalt durch ausreichend Protein sicherstellen.':
avgBalance<-100?'Moderates Defizit – ideal für Fettabbau bei Muskelerhalt.':
avgBalance>300?'Kalorienüberschuss – günstig für Muskelaufbau, Fettzunahme möglich.':
'Nahezu ausgeglichen – Gewicht sollte stabil bleiben.'}`,
})
return (
{/* Chart 1: Kcal vs Weight */}
📉 Kalorien (Ø 7T) vs. Gewicht
[`${Math.round(v)} ${n==='weight'?'kg':'kcal'}`,n==='kcal_avg'?'Ø Kalorien':'Gewicht']}/>
Gestrichelt: geschätzter TDEE {tdee} kcal · — Kalorien · — Gewicht
{/* Chart 2: Calorie balance */}
⚖️ Kalorienbilanz (Aufnahme − TDEE {tdee} kcal)
[`${v>0?'+':''}${v} kcal`,n==='balance_avg'?'Ø 7T Bilanz':'Tagesbilanz']}/>
Über 0 = Überschuss · Unter 0 = Defizit · Ø {avgBalance>0?'+':''}{avgBalance} kcal/Tag
{/* Chart 3: Protein vs Lean Mass */}
{protVsLean.length >= 3 && (
🥩 Protein vs. Magermasse
[`${v}${n==='protein'?'g':' kg'}`,n==='protein'?'Protein':'Mager']}/>
— Protein g/Tag · ● Magermasse kg
)}
{/* Correlation insights */}
{corrInsights.length > 0 && (
KORRELATIONSAUSSAGEN
{corrInsights.map((item,i) => (
{item.icon}
{item.title}
{item.detail}
))}
ℹ️ TDEE-Schätzung basiert auf Mifflin-St Jeor ×1,4 (leicht aktiv). Für genauere Werte Aktivitätsdaten erfassen.
)}
)
}
// ── Photo Grid ────────────────────────────────────────────────────────────────
function PhotoGrid() {
const [photos,setPhotos]=useState([])
const [big,setBig]=useState(null)
useEffect(()=>{ api.listPhotos().then(setPhotos) },[])
if(!photos.length) return
return (
<>
{big&&setBig(null)}>
}
{photos.map(p=>(
})
setBig(p.id)} alt=""/>
{p.date?.slice(0,10)||p.created?.slice(0,10)}
))}
>
)
}
// ── Main ──────────────────────────────────────────────────────────────────────
const TABS = [
{ id:'body', label:'⚖️ Körper' },
{ id:'nutrition', label:'🍽️ Ernährung' },
{ id:'activity', label:'🏋️ Aktivität' },
{ id:'correlation', label:'🔗 Korrelation' },
{ id:'photos', label:'📷 Fotos' },
]
export default function History() {
const location = useLocation?.() || {}
const [tab, setTab] = useState((location.state?.tab)||'body')
const [weights, setWeights] = useState([])
const [calipers, setCalipers] = useState([])
const [circs, setCircs] = useState([])
const [nutrition, setNutrition] = useState([])
const [activities, setActivities] = useState([])
const [corrData, setCorrData] = useState([])
const [insights, setInsights] = useState([])
const [prompts, setPrompts] = useState([])
const [profile, setProfile] = useState(null)
const [loading, setLoading] = useState(true)
const [loadingSlug,setLoadingSlug]= useState(null)
const loadAll = () => Promise.all([
api.listWeight(365), api.listCaliper(), api.listCirc(),
api.listNutrition(90), api.listActivity(200),
api.nutritionCorrelations(), api.latestInsights(), api.getProfile(),
api.listPrompts(),
]).then(([w,ca,ci,n,a,corr,ins,p,pr])=>{
setWeights(w); setCalipers(ca); setCircs(ci)
setNutrition(n); setActivities(a); setCorrData(corr)
setInsights(Array.isArray(ins)?ins:[]); setProfile(p)
setPrompts(Array.isArray(pr)?pr:[])
setLoading(false)
})
useEffect(()=>{ loadAll() },[])
const requestInsight = async (slug) => {
setLoadingSlug(slug)
try {
const result = await api.runInsight(slug)
// result is already JSON, not a Response object
const ins = await api.latestInsights()
setInsights(Array.isArray(ins)?ins:[])
} catch(e){
alert('KI-Fehler: '+e.message)
}
finally{ setLoadingSlug(null) }
}
if(loading) return
// Filter active prompts
const activeSlugs = prompts.filter(p=>p.active).map(p=>p.slug)
const filterActiveSlugs = (slugs) => slugs.filter(s=>activeSlugs.includes(s))
const sp={insights,onRequest:requestInsight,loadingSlug,filterActiveSlugs}
return (
Verlauf & Auswertung
{TABS.map(t=>(
))}
{tab==='body' &&
}
{tab==='nutrition' &&
}
{tab==='activity' &&
}
{tab==='correlation' &&
}
{tab==='photos' &&
}
)
}