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{ useState, useEffect, useRef, useMemo } from 'react'
const API = ''
const DIFF_ORDER = ['easy', 'medium', 'hard', 'expert']
const DIFF_COLOR = { easy:'#22c55e', medium:'#6366f1', hard:'#f59e0b', expert:'#ef4444' }
const DIFF_BG = { easy:'#f0fdf4', medium:'#eef2ff', hard:'#fffbeb', expert:'#fef2f2' }
const COMP_COLORS = ['#6366f1','#0ea5e9','#22c55e','#f59e0b','#f43f5e']
const COMP_KEYS = ['weighted_completion','deadline_adherence',
'energy_efficiency','dependency_bonus','interruption_bonus']
const COMP_LABELS = ['Weighted Completion Γ0.60','Deadline Adherence Γ0.22',
'Energy Efficiency Γ0.10','Dependency Bonus Γ0.05',
'Interruption Bonus Γ0.03']
const BASELINE = { easy:0.856, medium:0.523, hard:0.301, expert:0.221 }
const LLM_TARGET = { easy:0.88, medium:0.58, hard:0.37, expert:0.27 }
// ββ Helpers ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
function trailingAvg(arr, w = 10) {
return arr.map((_, i) => {
const sl = arr.slice(Math.max(0, i - w + 1), i + 1)
return sl.reduce((s, v) => s + v, 0) / sl.length
})
}
function arrAvg(arr) {
return arr.length ? arr.reduce((s, v) => s + v, 0) / arr.length : 0
}
// ββ GRPO Training Chart (matches the reference image) βββββββββββββββββββββββββ
function GRPOTrainingChart({ curve }) {
const data = useMemo(() => {
if (!curve || curve.length < 10) return null
const n = curve.length
const means = curve.map(d => d.mean)
const maxes = curve.map(d => d.max ?? d.mean)
const mins = curve.map(d => d.min ?? d.mean)
const sm = trailingAvg(means, 10)
const yLo = Math.min(-0.01, ...mins) - 0.005
const yHi = Math.max(...maxes) + 0.01
// Phase averages
const t1 = Math.floor(n / 3), t2 = Math.floor((2 * n) / 3)
const early = arrAvg(means.slice(0, t1))
const middle = arrAvg(means.slice(t1, t2))
const late = arrAvg(means.slice(t2))
return { n, means, maxes, mins, sm, yLo, yHi,
startMean: means[0], endMean: means[n - 1],
peakMean: Math.max(...means),
early, middle, late }
}, [curve])
if (!data) return null
const { n, means, maxes, mins, sm,
yLo, yHi, startMean, endMean, peakMean,
early, middle, late } = data
// SVG layout
const W = 880, PAD = { t: 24, r: 20, b: 38, l: 52 }
const cW = W - PAD.l - PAD.r
const H1 = 220
const cH = H1 - PAD.t - PAD.b
const x = i => PAD.l + (i / Math.max(n - 1, 1)) * cW
const y = v => PAD.t + cH - ((v - yLo) / (yHi - yLo)) * cH
const fp = v => v.toFixed(1)
// Polyline points (generated once via useMemo already)
const bandPts = curve.map((d, i) => `${fp(x(i))},${fp(y(d.min))}`).join(' ')
+ ' '
+ [...curve].reverse().map((d, i) => `${fp(x(n - 1 - i))},${fp(y(d.max))}`).join(' ')
const rawPts = means.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
const smPts = sm.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
const y0 = y(0)
const yStart = y(startMean)
// Y-axis ticks
const yTicks = []
for (let v = -0.10; v <= yHi + 0.01; v = Math.round((v + 0.05) * 100) / 100) {
if (v >= yLo) yTicks.push(v)
}
// X-axis ticks (every 200 or 100 steps)
const xStep = n > 500 ? 200 : 100
const xTicks = []
for (let i = 0; i < n; i += xStep) xTicks.push(i)
if (xTicks[xTicks.length - 1] !== n - 1) xTicks.push(n - 1)
const phases = [
{ label: 'Early', sub: '(first 1/3)', val: early, fill: '#fca5a5' },
{ label: 'Middle', sub: '(second 1/3)', val: middle, fill: '#fcd34d' },
{ label: 'Late', sub: '(final 1/3)', val: late, fill: '#86efac' },
]
const maxPhase = Math.max(early, middle, late)
// Phase bar SVG dimensions
const BW = W, BH = 130
const BPAD = { t: 10, b: 44, l: PAD.l, r: PAD.r }
const bcW = BW - BPAD.l - BPAD.r
const bcH = BH - BPAD.t - BPAD.b
const barSlot = bcW / phases.length
const barW = barSlot * 0.52
return (
<div style={{ background:'#fff', border:'1px solid #e2e8f0', borderRadius:14,
padding:'18px 16px 12px', marginBottom:16 }}>
{/* Chart title */}
<div style={{ textAlign:'center', marginBottom:12 }}>
<div style={{ fontSize:15, fontWeight:800, color:'#1e3a8a', letterSpacing:'.01em' }}>
β‘ StressTest β GRPO Training Reward Curve
</div>
<div style={{ fontSize:12, color:'#475569', marginTop:2 }}>
Cognitive Load Manager Β· Meta OpenEnv Hackathon
</div>
<div style={{ fontSize:11, color:'#94a3b8', marginTop:1 }}>
Episode Reward Over Training (mean Β± range per step)
</div>
</div>
{/* ββ Top chart SVG ββ */}
<svg width="100%" viewBox={`0 0 ${W} ${H1}`}
style={{ display:'block', overflow:'visible' }}>
{/* Y-axis grid + labels */}
{yTicks.map(v => (
<g key={v}>
<line x1={PAD.l} y1={y(v)} x2={W - PAD.r} y2={y(v)}
stroke="#e8edf5" strokeWidth="0.7" strokeDasharray="4 3"/>
<text x={PAD.l - 5} y={y(v) + 3.5} fontSize="9"
fill="#94a3b8" textAnchor="end">
{v >= 0 ? `+${v.toFixed(2)}` : v.toFixed(2)}
</text>
</g>
))}
{/* min/max band */}
<polygon points={bandPts} fill="#6366f112" stroke="none"/>
<polyline points={maxes.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')}
fill="none" stroke="#6366f130" strokeWidth="0.7"/>
<polyline points={mins.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')}
fill="none" stroke="#6366f130" strokeWidth="0.7"/>
{/* Zero baseline (red dashed) */}
{y0 >= PAD.t && y0 <= PAD.t + cH && (
<line x1={PAD.l} y1={y0} x2={W - PAD.r} y2={y0}
stroke="#ef4444" strokeWidth="1" strokeDasharray="5 4" opacity="0.6"/>
)}
{/* Start reward baseline (gray dotted) */}
<line x1={PAD.l} y1={yStart} x2={W - PAD.r} y2={yStart}
stroke="#94a3b8" strokeWidth="0.8" strokeDasharray="3 3" opacity="0.7"/>
{/* Raw mean (thin) */}
<polyline points={rawPts} fill="none"
stroke="#818cf8" strokeWidth="0.9" opacity="0.45"/>
{/* Smoothed mean (thick) */}
<polyline points={smPts} fill="none"
stroke="#1d4ed8" strokeWidth="2.5"
strokeLinejoin="round" strokeLinecap="round"/>
{/* X-axis ticks */}
{xTicks.map(i => (
<g key={i}>
<line x1={x(i)} y1={PAD.t + cH} x2={x(i)} y2={PAD.t + cH + 4}
stroke="#cbd5e1" strokeWidth="1"/>
<text x={x(i)} y={PAD.t + cH + 14} fontSize="9"
fill="#94a3b8" textAnchor="middle">{i}</text>
</g>
))}
{/* X-axis label */}
<text x={PAD.l + cW / 2} y={H1 - 2} fontSize="10"
fill="#64748b" textAnchor="middle">Training Step</text>
{/* Start annotation */}
<circle cx={x(0)} cy={y(sm[0])} r="4" fill="#94a3b8"/>
<text x={x(0) + 9} y={y(sm[0]) - 6} fontSize="9" fill="#6b7280" fontWeight="600">
Start: {startMean >= 0 ? '+' : ''}{startMean.toFixed(4)}
</text>
{/* End annotation */}
<circle cx={x(n - 1)} cy={y(sm[n - 1])} r="4.5" fill="#1d4ed8"/>
<text x={x(n - 1) - 10} y={y(sm[n - 1]) - 8} fontSize="9"
fill="#1d4ed8" textAnchor="end" fontWeight="600">
End: {endMean >= 0 ? '+' : ''}{endMean.toFixed(4)}
</text>
{/* Chart border */}
<rect x={PAD.l} y={PAD.t} width={cW} height={cH}
fill="none" stroke="#e2e8f0" strokeWidth="1"/>
</svg>
{/* Legend */}
<div style={{ display:'flex', gap:16, justifyContent:'center',
margin:'6px 0 10px', flexWrap:'wrap', fontSize:10, color:'#64748b' }}>
{[
{ color:'#6366f112', stroke:'#6366f130', label:'min/max range', band:true },
{ color:'#818cf8', label:'raw mean', opacity:0.5 },
{ color:'#1d4ed8', label:'smoothed (window=10)', bold:true },
{ color:'#ef4444', label:'zero baseline', dash:true },
{ color:'#94a3b8', label:'start reward', dash:true },
].map(l => (
<span key={l.label} style={{ display:'flex', alignItems:'center', gap:5 }}>
{l.band ? (
<svg width="26" height="10" style={{ verticalAlign:'middle' }}>
<rect x="0" y="2" width="26" height="6" fill={l.color}/>
<line x1="0" y1="5" x2="26" y2="5" stroke={l.stroke} strokeWidth="0.8"/>
</svg>
) : (
<svg width="26" height="10" style={{ verticalAlign:'middle' }}>
<line x1="0" y1="5" x2="26" y2="5"
stroke={l.color}
strokeWidth={l.bold ? '2.5' : '1'}
strokeDasharray={l.dash ? '4 3' : undefined}
opacity={l.opacity ?? 1}/>
</svg>
)}
{l.label}
</span>
))}
</div>
{/* Phase bar subtitle */}
<div style={{ textAlign:'center', fontSize:11, color:'#64748b', marginBottom:4 }}>
Average Reward by Training Phase (Early β Late shows improvement)
</div>
{/* ββ Phase bar chart SVG ββ */}
<svg width="100%" viewBox={`0 0 ${BW} ${BH}`}
style={{ display:'block', maxHeight:130 }}>
{phases.map((p, i) => {
const bH = (p.val / maxPhase) * bcH
const bX = BPAD.l + i * barSlot + (barSlot - barW) / 2
const bY = BPAD.t + bcH - bH
return (
<g key={p.label}>
{/* Bar */}
<rect x={bX} y={bY} width={barW} height={bH}
fill={p.fill} rx="4" opacity="0.88"/>
{/* Value label above bar */}
<text x={bX + barW / 2} y={bY - 5} fontSize="10"
fill="#1f2937" fontWeight="700" textAnchor="middle">
+{p.val.toFixed(4)}
</text>
{/* Phase label */}
<text x={bX + barW / 2} y={BPAD.t + bcH + 16} fontSize="9.5"
fill="#374151" textAnchor="middle" fontWeight="600">{p.label}</text>
<text x={bX + barW / 2} y={BPAD.t + bcH + 28} fontSize="8.5"
fill="#9ca3af" textAnchor="middle">{p.sub}</text>
</g>
)
})}
{/* Y=0 baseline */}
<line x1={BPAD.l} y1={BPAD.t + bcH} x2={BW - BPAD.r} y2={BPAD.t + bcH}
stroke="#d1d5db" strokeWidth="1"/>
</svg>
{/* Summary stats row */}
<div style={{ display:'flex', gap:12, justifyContent:'center', flexWrap:'wrap',
marginTop:10, paddingTop:10, borderTop:'1px solid #f1f5f9' }}>
{[
{ l:'Total Steps', v: n.toLocaleString(), c:'#6366f1' },
{ l:'Start', v: `+${startMean.toFixed(4)}`, c:'#6b7280' },
{ l:'End', v: `+${endMean.toFixed(4)}`, c:'#1d4ed8' },
{ l:'Total Gain', v: `+${(endMean-startMean).toFixed(4)}`, c:'#16a34a' },
{ l:'Peak Mean', v: `+${peakMean.toFixed(4)}`, c:'#22c55e' },
].map(s => (
<div key={s.l} style={{ textAlign:'center', minWidth:80 }}>
<div style={{ fontSize:9, color:'#94a3b8', textTransform:'uppercase',
letterSpacing:'.06em', marginBottom:2 }}>{s.l}</div>
<div style={{ fontSize:14, fontWeight:800, color:s.c }}>{s.v}</div>
</div>
))}
</div>
</div>
)
}
// ββ Before vs After Training β single overlay chart βββββββββββββββββββββββββββ
function BeforeAfterComparisonChart({ curve }) {
const N = Math.min(120, Math.floor(curve.length / 5))
const beforeSlice = curve.slice(0, N)
const afterSlice = curve.slice(curve.length - N)
const bMeans = beforeSlice.map(d => d.mean)
const aMeans = afterSlice.map(d => d.mean)
const smB = trailingAvg(bMeans, 8)
const smA = trailingAvg(aMeans, 8)
const allVals = [...bMeans, ...aMeans]
const yLo = Math.min(-0.01, ...allVals) - 0.005
const yHi = Math.max(...allVals) + 0.015
const W = 880, PAD = { t:28, r:20, b:40, l:54 }, H = 200
const cW = W - PAD.l - PAD.r
const cH = H - PAD.t - PAD.b
const x = i => PAD.l + (i / Math.max(N - 1, 1)) * cW
const y = v => PAD.t + cH - ((v - yLo) / (yHi - yLo)) * cH
const fp = v => v.toFixed(1)
const bSmPts = smB.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
const aSmPts = smA.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
const bRawPts = bMeans.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
const aRawPts = aMeans.map((v, i) => `${fp(x(i))},${fp(y(v))}`).join(' ')
// Filled area under each smoothed line
const bFill = `${fp(x(0))},${fp(y(yLo))} ${bSmPts} ${fp(x(N-1))},${fp(y(yLo))}`
const aFill = `${fp(x(0))},${fp(y(yLo))} ${aSmPts} ${fp(x(N-1))},${fp(y(yLo))}`
const avgB = arrAvg(bMeans), avgA = arrAvg(aMeans)
const gain = avgA - avgB
const gainPct = avgB !== 0 ? ((gain / Math.abs(avgB)) * 100).toFixed(1) : 'β'
const yTicks = []
for (let v = -0.05; v <= yHi + 0.01; v = Math.round((v + 0.05) * 100) / 100) {
if (v >= yLo) yTicks.push(v)
}
const xLabels = [0, Math.floor(N/4), Math.floor(N/2), Math.floor(3*N/4), N-1]
return (
<div style={{ background:'#fff', border:'1px solid #e2e8f0', borderRadius:14,
padding:'18px 16px 14px', marginBottom:16 }}>
{/* Header row */}
<div style={{ display:'flex', justifyContent:'space-between',
alignItems:'flex-start', marginBottom:14, flexWrap:'wrap', gap:12 }}>
<div>
<div style={{ fontSize:14, fontWeight:800, color:'#0f172a', marginBottom:3 }}>
π Before vs After Training β Reward Comparison
</div>
<div style={{ fontSize:11, color:'#64748b', lineHeight:1.5 }}>
First <b>{N} steps</b> (pre-convergence) vs Last <b>{N} steps</b> (post-GRPO) β both smoothed with window=8
</div>
</div>
{/* Stat chips */}
<div style={{ display:'flex', gap:8, flexShrink:0 }}>
{[
{ label:'Before avg', val:`+${avgB.toFixed(4)}`,
bg:'#fff1f2', fg:'#e11d48', border:'#fecdd3' },
{ label:'After avg', val:`+${avgA.toFixed(4)}`,
bg:'#f0fdf4', fg:'#15803d', border:'#bbf7d0' },
{ label:'Gain', val:`+${gain.toFixed(4)}`,
bg:'#eff6ff', fg:'#1d4ed8', border:'#bfdbfe' },
{ label:'Improvement', val:`${gainPct}%`,
bg:'#fdf4ff', fg:'#7e22ce', border:'#e9d5ff' },
].map(s => (
<div key={s.label} style={{ background:s.bg,
border:`1px solid ${s.border}`, borderRadius:10,
padding:'8px 14px', textAlign:'center', minWidth:72 }}>
<div style={{ fontSize:9, color:s.fg, textTransform:'uppercase',
letterSpacing:'.06em', marginBottom:2 }}>{s.label}</div>
<div style={{ fontSize:15, fontWeight:800, color:s.fg }}>{s.val}</div>
</div>
))}
</div>
</div>
{/* Chart SVG */}
<svg width="100%" viewBox={`0 0 ${W} ${H}`}
style={{ display:'block', overflow:'visible' }}>
{/* Grid + Y ticks */}
{yTicks.map(v => (
<g key={v}>
<line x1={PAD.l} y1={y(v)} x2={W - PAD.r} y2={y(v)}
stroke="#e8edf5" strokeWidth="0.7" strokeDasharray="4 3"/>
<text x={PAD.l - 5} y={y(v) + 3.5} fontSize="9"
fill="#94a3b8" textAnchor="end">
{v >= 0 ? `+${v.toFixed(2)}` : v.toFixed(2)}
</text>
</g>
))}
{/* Zero line */}
<line x1={PAD.l} y1={y(0)} x2={W - PAD.r} y2={y(0)}
stroke="#94a3b8" strokeWidth="0.8" strokeDasharray="3 3" opacity="0.6"/>
{/* Filled areas */}
<polygon points={bFill} fill="#f43f5e0e" stroke="none"/>
<polygon points={aFill} fill="#22c55e0e" stroke="none"/>
{/* Raw lines (thin, behind) */}
<polyline points={bRawPts} fill="none"
stroke="#f43f5e" strokeWidth="0.8" opacity="0.28"/>
<polyline points={aRawPts} fill="none"
stroke="#16a34a" strokeWidth="0.8" opacity="0.28"/>
{/* Smoothed lines (prominent) */}
<polyline points={bSmPts} fill="none"
stroke="#f43f5e" strokeWidth="2.8"
strokeLinejoin="round" strokeLinecap="round"/>
<polyline points={aSmPts} fill="none"
stroke="#16a34a" strokeWidth="2.8"
strokeLinejoin="round" strokeLinecap="round"/>
{/* Average reference lines */}
<line x1={PAD.l} y1={y(avgB)} x2={W - PAD.r} y2={y(avgB)}
stroke="#f43f5e" strokeWidth="1" strokeDasharray="6 4" opacity="0.5"/>
<line x1={PAD.l} y1={y(avgA)} x2={W - PAD.r} y2={y(avgA)}
stroke="#16a34a" strokeWidth="1" strokeDasharray="6 4" opacity="0.5"/>
{/* Avg labels on right */}
<text x={W - PAD.r + 3} y={y(avgB) + 3.5} fontSize="8.5"
fill="#f43f5e" fontWeight="600">avg</text>
<text x={W - PAD.r + 3} y={y(avgA) + 3.5} fontSize="8.5"
fill="#16a34a" fontWeight="600">avg</text>
{/* Start/end dots */}
<circle cx={x(0)} cy={y(smB[0])} r="4" fill="#f43f5e"/>
<circle cx={x(N-1)} cy={y(smB[N-1])} r="4" fill="#f43f5e"/>
<circle cx={x(0)} cy={y(smA[0])} r="4" fill="#16a34a"/>
<circle cx={x(N-1)} cy={y(smA[N-1])} r="4.5" fill="#16a34a"/>
{/* X-axis */}
{xLabels.map(i => (
<g key={i}>
<line x1={x(i)} y1={PAD.t + cH} x2={x(i)} y2={PAD.t + cH + 4}
stroke="#cbd5e1" strokeWidth="1"/>
<text x={x(i)} y={PAD.t + cH + 14} fontSize="9"
fill="#94a3b8" textAnchor="middle">{i}</text>
</g>
))}
<text x={PAD.l + cW / 2} y={H - 2} fontSize="10"
fill="#64748b" textAnchor="middle">Steps (relative within window)</text>
{/* Chart border */}
<rect x={PAD.l} y={PAD.t} width={cW} height={cH}
fill="none" stroke="#e2e8f0" strokeWidth="1"/>
</svg>
{/* Legend */}
<div style={{ display:'flex', gap:24, justifyContent:'center',
marginTop:8, fontSize:11, color:'#64748b', flexWrap:'wrap' }}>
<span style={{ display:'flex', alignItems:'center', gap:6 }}>
<svg width="24" height="10"><line x1="0" y1="5" x2="24" y2="5"
stroke="#f43f5e" strokeWidth="2.8"/></svg>
Before Training (steps 0β{N})
</span>
<span style={{ display:'flex', alignItems:'center', gap:6 }}>
<svg width="24" height="10"><line x1="0" y1="5" x2="24" y2="5"
stroke="#16a34a" strokeWidth="2.8"/></svg>
After Training (steps {curve.length - N}β{curve.length})
</span>
<span style={{ display:'flex', alignItems:'center', gap:6 }}>
<svg width="24" height="10"><line x1="0" y1="5" x2="24" y2="5"
stroke="#94a3b8" strokeWidth="1" strokeDasharray="4 3"/></svg>
Phase average
</span>
</div>
</div>
)
}
// ββ Scoring Formula Card (standalone, visually rich) βββββββββββββββββββββββββ
const FORMULA_ITEMS = [
{ key:'completion', label:'Task Completion', weight:0.60, color:'#6366f1',
desc:'Fraction of tasks fully completed, weighted by priority' },
{ key:'deadline', label:'Deadline Adherence', weight:0.22, color:'#0ea5e9',
desc:'Bonus for finishing before deadline; penalty for missing it' },
{ key:'energy', label:'Energy Efficiency', weight:0.10, color:'#22c55e',
desc:'Penalises high worker fatigue and stress spikes' },
{ key:'dependency', label:'Dependency Bonus', weight:0.05, color:'#f59e0b',
desc:'Reward for respecting task dependency order' },
{ key:'interrupt', label:'Interruption Bonus', weight:0.03, color:'#f43f5e',
desc:'Reward for minimising context-switching interruptions' },
]
function ScoringFormulaCard() {
return (
<div style={{ background:'#fff', border:'1px solid #e2e8f0',
borderRadius:14, padding:'20px 20px 16px', marginBottom:16 }}>
{/* Title */}
<div style={{ marginBottom:16 }}>
<div style={{ fontSize:14, fontWeight:800, color:'#0f172a', marginBottom:4 }}>
π Reward Scoring Formula
</div>
<div style={{ fontSize:11, color:'#64748b' }}>
Each action is scored on 5 dimensions. Weights reflect cognitive-load research priorities.
</div>
</div>
{/* Stacked weight bar */}
<div style={{ marginBottom:16 }}>
<div style={{ fontSize:10, color:'#94a3b8', fontWeight:700, textTransform:'uppercase',
letterSpacing:'.07em', marginBottom:6 }}>Weight distribution</div>
<div style={{ display:'flex', height:18, borderRadius:99, overflow:'hidden',
boxShadow:'0 1px 4px #0001' }}>
{FORMULA_ITEMS.map(it => (
<div key={it.key} title={`${it.label}: Γ${it.weight}`}
style={{ width:`${it.weight * 100}%`, background:it.color,
display:'flex', alignItems:'center', justifyContent:'center' }}>
{it.weight >= 0.10 && (
<span style={{ fontSize:9, color:'#fff', fontWeight:800 }}>
{(it.weight * 100).toFixed(0)}%
</span>
)}
</div>
))}
</div>
{/* Labels under bar */}
<div style={{ display:'flex', marginTop:5 }}>
{FORMULA_ITEMS.map(it => (
<div key={it.key} style={{ width:`${it.weight * 100}%`,
textAlign:'center', fontSize:8.5, color:it.color, fontWeight:700,
overflow:'hidden', whiteSpace:'nowrap' }}>
{it.weight >= 0.08 ? it.label.split(' ')[0] : ''}
</div>
))}
</div>
</div>
{/* Component cards */}
<div style={{ display:'grid', gridTemplateColumns:'repeat(5,1fr)', gap:10,
marginBottom:14 }}>
{FORMULA_ITEMS.map(it => (
<div key={it.key} style={{
background:`${it.color}08`,
border:`1.5px solid ${it.color}30`,
borderRadius:12, padding:'12px 12px 10px',
position:'relative', overflow:'hidden',
}}>
{/* Coloured top accent bar */}
<div style={{ position:'absolute', top:0, left:0, right:0,
height:4, background:it.color, borderRadius:'12px 12px 0 0' }}/>
{/* Weight badge */}
<div style={{ display:'inline-flex', alignItems:'center',
background:it.color, color:'#fff', borderRadius:99,
padding:'2px 9px', fontSize:13, fontWeight:900,
marginBottom:8, marginTop:2 }}>
Γ{it.weight.toFixed(2)}
</div>
<div style={{ fontSize:11, fontWeight:700, color:'#1e293b',
marginBottom:4, lineHeight:1.3 }}>
{it.label}
</div>
<div style={{ fontSize:10, color:'#64748b', lineHeight:1.4 }}>
{it.desc}
</div>
</div>
))}
</div>
{/* Formula expression */}
<div style={{ background:'#f8fafc', borderRadius:10,
padding:'12px 16px', border:'1px solid #e2e8f0' }}>
<div style={{ fontSize:10, color:'#94a3b8', fontWeight:700,
textTransform:'uppercase', letterSpacing:'.07em', marginBottom:6 }}>
Formula
</div>
<div style={{ fontFamily:'monospace', fontSize:12, lineHeight:1.8,
display:'flex', flexWrap:'wrap', gap:'0 4px', alignItems:'center' }}>
<span style={{ color:'#0f172a', fontWeight:700 }}>score =</span>
{FORMULA_ITEMS.map((it, idx) => (
<span key={it.key}>
<span style={{ color:it.color, fontWeight:800 }}>
{it.key === 'completion' ? 'completion' :
it.key === 'deadline' ? 'deadline' :
it.key === 'energy' ? 'energy' :
it.key === 'dependency' ? 'dep' : 'interrupt'}Γ{it.weight}
</span>
{idx < FORMULA_ITEMS.length - 1 &&
<span style={{ color:'#94a3b8' }}> + </span>}
</span>
))}
<span style={{ color:'#94a3b8', marginLeft:6 }}>β (0.01, 0.99)</span>
</div>
</div>
</div>
)
}
// ββ Tiny SVG charts (used by benchmark section) ββββββββββββββββββββββββββββββββ
function LineChart({ data, color = '#6366f1', height = 120 }) {
if (!data || !data.length) return (
<div style={{ height, display:'flex', alignItems:'center',
justifyContent:'center', color:'#cbd5e1', fontSize:12 }}>No data yet</div>
)
const W = Math.max(data.length * 18, 300)
const lo = Math.min(...data), hi = Math.max(...data)
const sp = hi === lo ? 1 : hi - lo
const py = v => (height - 14) - ((v - lo) / sp) * (height - 26) + 7
const pts = data.map((v, i) => `${i * 18 + 9},${py(v)}`).join(' ')
return (
<svg width="100%" height={height} viewBox={`0 0 ${W} ${height}`}
preserveAspectRatio="none" style={{ display:'block' }}>
<line x1="0" y1={py(0)} x2={W} y2={py(0)}
stroke="#e2e8f0" strokeWidth="1" strokeDasharray="4 3"/>
<polyline
points={[`0,${height}`,
...data.map((v, i) => `${i * 18 + 9},${py(v)}`),
`${(data.length - 1) * 18 + 9},${height}`].join(' ')}
fill={color + '18'} stroke="none"/>
<polyline points={pts} fill="none" stroke={color}
strokeWidth="2.5" strokeLinejoin="round" strokeLinecap="round"/>
<circle cx={(data.length - 1) * 18 + 9} cy={py(data[data.length - 1])}
r="4.5" fill={color}/>
</svg>
)
}
// Small band chart (used by demo training live view)
function BandChart({ curve, height = 140 }) {
if (!curve || !curve.length) return (
<div style={{ height, display:'flex', alignItems:'center',
justifyContent:'center', color:'#cbd5e1', fontSize:12 }}>
Training data will appear here
</div>
)
const means = curve.map(d => d.mean)
const maxes = curve.map(d => d.max ?? d.mean)
const mins = curve.map(d => d.min ?? d.mean)
const W = Math.max(curve.length * 18, 300)
const lo = Math.min(...mins), hi = Math.max(...maxes)
const sp = hi === lo ? 1 : hi - lo
const py = v => (height - 14) - ((v - lo) / sp) * (height - 26) + 7
const bandPts = [
...mins.map((v, i) => `${i * 18 + 9},${py(v)}`),
...[...maxes].reverse().map((v, i) =>
`${(curve.length - 1 - i) * 18 + 9},${py(v)}`),
].join(' ')
const meanPts = means.map((v, i) => `${i * 18 + 9},${py(v)}`).join(' ')
return (
<svg width="100%" height={height} viewBox={`0 0 ${W} ${height}`}
preserveAspectRatio="none" style={{ display:'block' }}>
<line x1="0" y1={py(0)} x2={W} y2={py(0)}
stroke="#e2e8f0" strokeWidth="1" strokeDasharray="4 3"/>
<polyline points={bandPts} fill="#6366f118" stroke="none"/>
<polyline points={maxes.map((v, i) => `${i * 18 + 9},${py(v)}`).join(' ')}
fill="none" stroke="#6366f140" strokeWidth="1"/>
<polyline points={mins.map((v, i) => `${i * 18 + 9},${py(v)}`).join(' ')}
fill="none" stroke="#6366f140" strokeWidth="1"/>
<polyline points={meanPts} fill="none" stroke="#6366f1"
strokeWidth="2.5" strokeLinejoin="round" strokeLinecap="round"/>
<circle cx={(curve.length - 1) * 18 + 9} cy={py(means[means.length - 1])}
r="4.5" fill="#6366f1"/>
</svg>
)
}
// ββ Before/After grouped bar βββββββββββββββββββββββββββββββββββββββββββββββββββ
function BeforeAfterBars({ before, after }) {
if (!before && !after) return (
<div style={{ color:'#94a3b8', fontSize:13, textAlign:'center', padding:24 }}>
Run demo training to see before/after comparison
</div>
)
return (
<div style={{ display:'grid', gridTemplateColumns:'repeat(4,1fr)', gap:12 }}>
{DIFF_ORDER.map(d => {
const bv = before?.[d] ?? null
const av = after?.[d] ?? null
const bPct = bv != null ? `${Math.min(100, bv * 100).toFixed(0)}%` : '0%'
const aPct = av != null ? `${Math.min(100, av * 100).toFixed(0)}%` : '0%'
const tPct = `${Math.min(100, LLM_TARGET[d] * 100).toFixed(0)}%`
return (
<div key={d} style={{ background: DIFF_BG[d],
border:`1px solid ${DIFF_COLOR[d]}33`, borderRadius:12, padding:'14px 16px' }}>
<div style={{ fontWeight:700, textTransform:'capitalize',
color:DIFF_COLOR[d], fontSize:15, marginBottom:10 }}>{d}</div>
<BarRow label="Before (random)" pct={bPct} color="#94a3b8"
val={bv != null ? bv.toFixed(4) : 'β'} />
<BarRow label="After (trained)" pct={aPct} color={DIFF_COLOR[d]}
val={av != null ? av.toFixed(4) : 'β'} glow />
<BarRow label="LLM Target" pct={tPct} color="#6366f1"
val={LLM_TARGET[d].toFixed(3)} dashed />
{av != null && bv != null && (
<div style={{ marginTop:8, fontSize:11, fontWeight:700,
color: av > bv ? '#16a34a' : '#ef4444' }}>
{av > bv ? 'β²' : 'βΌ'}
{av > bv ? '+' : ''}{(av - bv).toFixed(4)} vs before
</div>
)}
</div>
)
})}
</div>
)
}
function BarRow({ label, pct, color, val, dashed, glow }) {
return (
<div style={{ marginBottom:6 }}>
<div style={{ display:'flex', justifyContent:'space-between',
fontSize:11, color:'#64748b', marginBottom:2 }}>
<span>{label}</span>
<span style={{ fontWeight:700 }}>{val}</span>
</div>
<div style={{ height:7, background:'#f1f5f9', borderRadius:99 }}>
<div style={{
height:7, borderRadius:99, width:pct,
background: dashed ? 'transparent' : color,
border: dashed ? `2px dashed ${color}` : 'none',
boxShadow: glow ? `0 0 6px ${color}88` : 'none',
transition:'width .7s ease',
}}/>
</div>
</div>
)
}
function SectionHeader({ children, action }) {
return (
<div style={{ display:'flex', justifyContent:'space-between', alignItems:'center',
marginBottom:12 }}>
<div style={{ fontSize:11, fontWeight:700, color:'#94a3b8',
textTransform:'uppercase', letterSpacing:'.08em' }}>{children}</div>
{action}
</div>
)
}
// ββ Demo training progress section ββββββββββββββββββββββββββββββββββββββββββββ
function DemoTrainingProgress({ state, onStart }) {
const { running, status, current_step, total_steps, curve,
before, after, metadata, error } = state
const pct = total_steps > 0
? Math.round((current_step / total_steps) * 100) : 0
const lastEntry = curve && curve.length ? curve[curve.length - 1] : null
const meanTrace = (curve || []).map(d => d.mean)
return (
<div style={{ background:'#fff', border:'1px solid #e2e8f0',
borderRadius:14, padding:20, marginBottom:16 }}>
<div style={{ display:'flex', justifyContent:'space-between',
alignItems:'flex-start', marginBottom:14 }}>
<div>
<div style={{ fontSize:14, fontWeight:800, color:'#0f172a', marginBottom:4 }}>
π§ͺ Demo Training β Random β Heuristic Agent
</div>
<div style={{ fontSize:12, color:'#64748b', lineHeight:1.6, maxWidth:500 }}>
Simulates GRPO reward progression on the HF Space (no GPU required).
Runs {total_steps} steps. Results saved to{' '}
<code style={{ background:'#f1f5f9', padding:'1px 5px',
borderRadius:4, fontSize:11 }}>reward_curve.json</code>.
</div>
</div>
<div style={{ display:'flex', gap:8, flexShrink:0, marginLeft:16 }}>
{['medium','hard','expert'].map(d => (
<button key={d} onClick={() => onStart(d)} disabled={running}
style={{ padding:'8px 14px', borderRadius:8, border:'none',
background: running ? '#e2e8f0' : DIFF_BG[d],
color: running ? '#94a3b8' : DIFF_COLOR[d],
fontWeight:700, fontSize:12, cursor: running ? 'not-allowed':'pointer',
textTransform:'capitalize' }}>
{running ? 'β³' : 'βΆ'} {d}
</button>
))}
</div>
</div>
{status !== 'idle' && (
<div style={{ marginBottom:14 }}>
<div style={{ display:'flex', justifyContent:'space-between',
fontSize:12, color:'#64748b', marginBottom:6 }}>
<span style={{ fontWeight:600,
color: status==='completed'?'#16a34a': status==='error'?'#ef4444':'#6366f1' }}>
{status==='running' && `①Training⦠step ${current_step}/${total_steps}`}
{status==='completed' && `β
Training complete β ${total_steps} steps`}
{status==='error' && `β ${error}`}
</span>
<span>{pct}%</span>
</div>
<div style={{ height:10, background:'#f1f5f9', borderRadius:99, overflow:'hidden' }}>
<div style={{
height:10, borderRadius:99,
width:`${status==='completed' ? 100 : pct}%`,
background: status==='completed' ? '#22c55e' : '#6366f1',
transition:'width .4s ease',
boxShadow:'0 0 8px #6366f166',
}}/>
</div>
</div>
)}
{(running || status==='completed') && (
<div style={{ display:'flex', gap:10, flexWrap:'wrap', marginBottom:14 }}>
{[
{ l:'Step', v:`${current_step}/${total_steps}`, c:'#6366f1' },
{ l:'Last Mean', v: lastEntry ? lastEntry.mean.toFixed(4) : 'β',
c: lastEntry && lastEntry.mean >= 0 ? '#16a34a':'#ef4444' },
{ l:'Last Max', v: lastEntry ? lastEntry.max.toFixed(4) : 'β', c:'#22c55e' },
{ l:'Difficulty', v: metadata?.difficulty ?? 'β', c:'#0ea5e9' },
].map(s => (
<div key={s.l} style={{ background:'#f8fafc', borderRadius:8,
padding:'7px 12px', textAlign:'center', minWidth:64 }}>
<div style={{ fontSize:9, color:'#94a3b8', textTransform:'uppercase',
marginBottom:2 }}>{s.l}</div>
<div style={{ fontSize:13, fontWeight:800, color:s.c }}>{s.v}</div>
</div>
))}
</div>
)}
{meanTrace.length > 0 && (
<div style={{ border:'1px solid #f1f5f9', borderRadius:10,
background:'#fafafa', overflow:'hidden', marginBottom:14 }}>
<div style={{ padding:'8px 12px 0', fontSize:10, color:'#94a3b8',
fontWeight:700, textTransform:'uppercase' }}>
Live Reward Curve (mean Β± band)
</div>
<BandChart curve={curve} height={130}/>
</div>
)}
{/* Before/After when demo completes */}
{status === 'completed' && (before || after) && (
<div style={{ marginTop:8 }}>
<SectionHeader>Before vs After β Score Comparison</SectionHeader>
<BeforeAfterBars before={before} after={after}/>
</div>
)}
{status === 'idle' && (
<div style={{ textAlign:'center', padding:'18px 0',
color:'#94a3b8', fontSize:13 }}>
Click <b>βΆ medium</b> / <b>βΆ hard</b> / <b>βΆ expert</b> to start demo training.
</div>
)}
</div>
)
}
// ββ Benchmark section ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
function BenchmarkSection() {
const [data, setData] = useState(null)
const [running, setRunning] = useState(false)
const [sel, setSel] = useState('medium')
const run = async () => {
setRunning(true); setData(null)
try {
const r = await fetch(`${API}/benchmark`)
if (r.ok) setData(await r.json())
} catch(e) { console.error(e) }
finally { setRunning(false) }
}
const selD = data?.[sel]
const card = (ex = {}) => ({
background:'#fff', border:'1px solid #e2e8f0',
borderRadius:14, padding:16, marginBottom:16, ...ex,
})
return (
<>
<div style={card()}>
<div style={{ display:'flex', justifyContent:'space-between',
alignItems:'center', marginBottom:14 }}>
<div>
<SectionHeader>Heuristic Agent Benchmark</SectionHeader>
<p style={{ fontSize:12, color:'#64748b', margin:0, lineHeight:1.5 }}>
Runs the deterministic heuristic on all 4 difficulties (seed=42).
</p>
</div>
<button onClick={run} disabled={running}
style={{ background: running ? '#94a3b8':'#6366f1', color:'#fff',
border:'none', borderRadius:10, padding:'10px 22px',
fontWeight:700, fontSize:13, cursor:running ? 'not-allowed':'pointer',
marginLeft:20, whiteSpace:'nowrap' }}>
{running ? 'β³ Runningβ¦' : 'βΆ Run Benchmarks'}
</button>
</div>
<div style={{ display:'grid', gridTemplateColumns:'repeat(4,1fr)', gap:10 }}>
{DIFF_ORDER.map(d => {
const score = data?.[d]?.score
const bPct = `${Math.min(100, BASELINE[d] * 100).toFixed(0)}%`
const sPct = score != null ? `${Math.min(100, score * 100).toFixed(0)}%` : '0%'
return (
<div key={d} style={{ background:DIFF_BG[d],
border:`1px solid ${DIFF_COLOR[d]}33`, borderRadius:12,
padding:'12px 14px' }}>
<div style={{ fontWeight:700, color:DIFF_COLOR[d], fontSize:14,
textTransform:'capitalize', marginBottom:8 }}>{d}</div>
<BarRow label="Achieved" pct={sPct} color={DIFF_COLOR[d]}
val={score != null ? score.toFixed(4) : 'β'} glow={score != null}/>
<BarRow label="Baseline" pct={bPct} color="#94a3b8"
val={BASELINE[d].toFixed(3)}/>
</div>
)
})}
</div>
</div>
{data && (
<div style={card()}>
<div style={{ display:'flex', gap:6, marginBottom:14 }}>
{DIFF_ORDER.map(d => (
<button key={d} onClick={() => setSel(d)}
style={{ padding:'7px 16px', borderRadius:8, border:'none',
background: sel === d ? DIFF_COLOR[d] : DIFF_BG[d],
color: sel === d ? '#fff' : DIFF_COLOR[d],
fontWeight:700, fontSize:13, cursor:'pointer',
textTransform:'capitalize' }}>{d}</button>
))}
</div>
{selD && !selD.error && (
<div style={{ display:'grid', gridTemplateColumns:'1fr 1fr', gap:16 }}>
<div>
<SectionHeader>Stats β {sel}</SectionHeader>
<div style={{ display:'grid', gridTemplateColumns:'1fr 1fr',
gap:8, marginBottom:14 }}>
{[
{ l:'Score', v: selD.score?.toFixed(4) },
{ l:'Total Reward', v: selD.total_reward?.toFixed(3) },
{ l:'Steps', v: selD.steps },
{ l:'Tasks Done', v:`${selD.tasks_done}/${selD.tasks_total}`},
{ l:'Avg Energy', v: selD.avg_energy?.toFixed(3) },
{ l:'Deadlines', v:`${selD.deadlines_met}/${selD.deadlines_total}`},
].map(s => (
<div key={s.l} style={{ background:'#f8fafc',
borderRadius:8, padding:'8px 12px' }}>
<div style={{ fontSize:10, color:'#94a3b8', marginBottom:2 }}>{s.l}</div>
<div style={{ fontSize:14, fontWeight:700, color:'#0f172a' }}>{s.v}</div>
</div>
))}
</div>
{selD.components && (
<>
<SectionHeader>Score Components</SectionHeader>
<ComponentBar components={selD.components}/>
</>
)}
</div>
<div>
<SectionHeader>Step Rewards</SectionHeader>
<div style={{ border:'1px solid #f1f5f9', borderRadius:8,
background:'#fafafa', overflow:'hidden', marginBottom:10 }}>
<LineChart data={selD.step_rewards} color={DIFF_COLOR[sel]} height={100}/>
</div>
<SectionHeader>Energy / Stress</SectionHeader>
<div style={{ border:'1px solid #f1f5f9', borderRadius:8,
background:'#fafafa', overflow:'hidden' }}>
<svg width="100%" height={90}
viewBox={`0 0 ${Math.max((selD.energy_trace || []).length * 18, 300)} 90`}
preserveAspectRatio="none" style={{ display:'block' }}>
<polyline
points={(selD.energy_trace || []).map((v, i) => `${i * 18 + 9},${80 - v * 70}`).join(' ')}
fill="none" stroke="#22c55e" strokeWidth="2" strokeLinejoin="round"/>
<polyline
points={(selD.stress_trace || []).map((v, i) => `${i * 18 + 9},${80 - v * 70}`).join(' ')}
fill="none" stroke="#f59e0b" strokeWidth="2"
strokeLinejoin="round" strokeDasharray="5 3"/>
</svg>
</div>
<div style={{ fontSize:10, color:'#94a3b8', marginTop:4 }}>
<span style={{ color:'#22c55e', fontWeight:700 }}>β</span> Energy 
<span style={{ color:'#f59e0b', fontWeight:700 }}>β</span> Stress
</div>
</div>
</div>
)}
</div>
)}
<ScoringFormulaCard/>
</>
)
}
function ComponentBar({ components }) {
const total = COMP_KEYS.reduce((s, k) => s + (components[k] || 0), 0)
return (
<div>
<div style={{ display:'flex', height:22, borderRadius:6, overflow:'hidden', marginBottom:6 }}>
{COMP_KEYS.map((k, i) => {
const v = components[k] || 0
const pct = total > 0 ? (v / total) * 100 : 0
return <div key={k} title={`${COMP_LABELS[i]}: ${v.toFixed(4)}`}
style={{ width:`${pct}%`, background:COMP_COLORS[i], minWidth: pct > 2 ? 2 : 0 }}/>
})}
</div>
<div style={{ display:'flex', flexWrap:'wrap', gap:'4px 10px' }}>
{COMP_KEYS.map((k, i) => (
<span key={k} style={{ fontSize:10, color:'#475569',
display:'flex', alignItems:'center', gap:3 }}>
<span style={{ width:8, height:8, borderRadius:2,
background:COMP_COLORS[i], display:'inline-block' }}/>
{COMP_LABELS[i].split(' ')[0]}: <b>{(components[k] || 0).toFixed(4)}</b>
</span>
))}
</div>
</div>
)
}
// ββ Main TrainingDashboard βββββββββββββββββββββββββββββββββββββββββββββββββββββ
export default function TrainingDashboard() {
const [activeTab, setActiveTab] = useState('training')
const [trainState, setTrainState] = useState({
running:false, status:'idle', current_step:0, total_steps:25,
difficulty:'medium', curve:[], before:null, after:null,
metadata:null, error:null,
})
const [savedLog, setSavedLog] = useState(null)
const esRef = useRef(null)
// Load saved training log on mount
useEffect(() => {
fetch(`${API}/training-log`)
.then(r => r.ok ? r.json() : null)
.then(d => { if (d) setSavedLog(d) })
.catch(() => {})
}, [])
const startTraining = async (difficulty) => {
if (trainState.running) return
await fetch(`${API}/train/start?difficulty=${difficulty}&steps=25`, { method:'POST' })
if (esRef.current) { esRef.current.close(); esRef.current = null }
const es = new EventSource(`${API}/train/stream`)
esRef.current = es
es.onmessage = (ev) => {
const d = JSON.parse(ev.data)
setTrainState(d)
if (d.status === 'completed' || d.status === 'error') {
fetch(`${API}/training-log`)
.then(r => r.ok ? r.json() : null)
.then(saved => { if (saved) setSavedLog(saved) })
.catch(() => {})
es.close(); esRef.current = null
}
}
es.onerror = () => { es.close(); esRef.current = null }
}
useEffect(() => () => { if (esRef.current) esRef.current.close() }, [])
// The saved log curve β show GRPO chart if it has many steps (real training data)
const savedCurve = savedLog?.curve ?? []
const hasRealTrainingData = savedCurve.length > 100
const TABS = [
{ id:'training', label:'π§ͺ Training Progress' },
{ id:'benchmark', label:'π Benchmarks' },
]
return (
<div>
<div style={{ display:'flex', gap:4, marginBottom:20 }}>
{TABS.map(t => (
<button key={t.id} onClick={() => setActiveTab(t.id)}
style={{ padding:'9px 20px', borderRadius:10, border:'none',
background: activeTab === t.id ? '#0f172a' : '#e2e8f0',
color: activeTab === t.id ? '#fff' : '#64748b',
fontWeight:700, fontSize:13, cursor:'pointer' }}>
{t.label}
</button>
))}
</div>
{activeTab === 'training' && (
<>
{/* Real GRPO training chart β shown when actual data exists */}
{hasRealTrainingData && (
<>
<GRPOTrainingChart curve={savedCurve}/>
<BeforeAfterComparisonChart curve={savedCurve}/>
</>
)}
{/* Demo training controls */}
<DemoTrainingProgress state={trainState} onStart={startTraining}/>
{/* Scoring formula β always visible */}
<ScoringFormulaCard/>
{/* No data placeholder */}
{!hasRealTrainingData && trainState.status === 'idle' && (
<div style={{ background:'linear-gradient(135deg,#f8fafc 0%,#eef2ff 100%)',
border:'1px solid #e2e8f0', borderRadius:14, padding:28,
textAlign:'center', color:'#94a3b8', fontSize:14 }}>
<div style={{ fontSize:36, marginBottom:10 }}>π</div>
<div style={{ fontWeight:800, color:'#475569', fontSize:15, marginBottom:6 }}>
GRPO Training Charts will appear here
</div>
<div style={{ fontSize:13, lineHeight:1.8, maxWidth:440,
margin:'0 auto', color:'#64748b' }}>
The full training chart + before/after comparison
(<b>1,116 steps</b>) load automatically from{' '}
<code style={{ background:'#e0e7ff', color:'#4f46e5',
padding:'1px 6px', borderRadius:4, fontSize:11 }}>
reward_curve.json
</code>
.<br/>
Or click <b>βΆ medium</b> above to run a quick 25-step demo.
</div>
</div>
)}
</>
)}
{activeTab === 'benchmark' && <BenchmarkSection/>}
</div>
)
} |