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5d10875 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | /** Evaluation flywheel: suites created from validated failures (governance
* fields + contamination-guard badges) and the seqeval regression hook. */
import { useState } from 'react';
import Box from '@mui/material/Box';
import Button from '@mui/material/Button';
import Chip from '@mui/material/Chip';
import Paper from '@mui/material/Paper';
import Table from '@mui/material/Table';
import TableBody from '@mui/material/TableBody';
import TableCell from '@mui/material/TableCell';
import TableHead from '@mui/material/TableHead';
import TableRow from '@mui/material/TableRow';
import Typography from '@mui/material/Typography';
import type { EvaluationSuite, RegressionSuiteResult } from '../../types/agentic';
import { runRegression } from '../../services/agentic';
import { fmtCi, KV, PanelTitle } from './common';
const DECISION_COLORS: Record<string, string> = {
PASS: '#1b5e20',
REGRESSION: '#b71c1c',
INSUFFICIENT_EVIDENCE: '#4e342e',
};
export default function FlywheelPanel({ suites }: { suites: EvaluationSuite[] }) {
const [regression, setRegression] = useState<RegressionSuiteResult[] | null>(null);
const [running, setRunning] = useState(false);
const evaluate = async () => {
setRunning(true);
try {
const res = await runRegression();
setRegression(res.suites);
} finally {
setRunning(false);
}
};
return (
<Box sx={{ display: 'flex', flexDirection: 'column', gap: 1.5 }}>
<Paper variant="outlined" sx={{ p: 1.5, bgcolor: '#161b22' }}>
<PanelTitle title="Evaluation Suites from Validated Failures" origin="deterministic" />
{suites.length === 0 ? (
<Typography variant="caption" sx={{ color: '#8a949e' }}>
No suites yet — suites are created by the LEARNING FLYWHEEL stage after a failure is
human-validated.
</Typography>
) : (
suites.map((s) => (
<Paper key={s.suite_id} variant="outlined" sx={{ p: 1, mb: 1, bgcolor: '#12171d' }}>
<Box sx={{ display: 'flex', alignItems: 'center', gap: 0.75, flexWrap: 'wrap', mb: 0.5 }}>
<Typography variant="caption" sx={{ fontWeight: 800, flex: 1 }}>
{s.name} <span style={{ color: '#8a949e' }}>v{s.version}</span>
</Typography>
<Chip size="small" label={s.approval_status.toUpperCase()} sx={{ height: 18, fontSize: 10, fontWeight: 700, bgcolor: s.approval_status === 'approved' ? '#1b5e20' : '#37474f', color: '#fff' }} />
<Chip size="small" label="CONTAMINATION GUARD" sx={{ height: 18, fontSize: 10, fontWeight: 800, bgcolor: '#4a148c', color: '#ce93d8' }} />
{s.governance_overrides.length > 0 ? (
<Chip size="small" label={`${s.governance_overrides.length} recorded override(s)`} sx={{ height: 18, fontSize: 10, bgcolor: '#4e342e', color: '#ffcc80' }} />
) : null}
</Box>
<KV k="Creation reason" v={s.creation_reason} />
<KV k="Source failures" v={s.source_failures.join(', ')} mono />
<KV k="Taxonomy tags" v={s.taxonomy_tags.join(', ')} mono />
<KV k="Sampling policy" v={s.sampling_policy} />
<KV k="Coverage" v={JSON.stringify(s.coverage)} mono />
<KV k="Known limitations" v={s.known_limitations.join('; ')} />
<KV
k="Members"
v={`${s.members.length} (training-eligible: ${s.members.filter((m) => m.training_eligible).length} — requires explicit recorded override)`}
/>
</Paper>
))
)}
</Paper>
<Paper variant="outlined" sx={{ p: 1.5, bgcolor: '#161b22' }}>
<PanelTitle
title="Regression Hook — candidate vs baseline per suite (stats: seqeval)"
origin="deterministic"
extra={
<Button size="small" variant="contained" disabled={running} onClick={evaluate}>
{running ? 'Evaluating…' : 'Evaluate candidate'}
</Button>
}
/>
{regression ? (
<Table size="small">
<TableHead>
<TableRow>
{['Suite', 'n', 'Baseline', 'Candidate', 'Δ', 'Δ CI (anytime-valid)', 'Decision'].map((h) => (
<TableCell key={h} sx={{ fontSize: 11, fontWeight: 800, color: '#8a949e' }}>
{h}
</TableCell>
))}
</TableRow>
</TableHead>
<TableBody>
{regression.map((r) => (
<TableRow key={r.suite}>
<TableCell sx={{ fontSize: 11.5 }}>{r.suite}</TableCell>
<TableCell sx={{ fontSize: 11.5, fontFamily: 'monospace' }}>{r.n}</TableCell>
<TableCell sx={{ fontSize: 11.5, fontFamily: 'monospace' }}>{r.baseline_rate ?? '—'}</TableCell>
<TableCell sx={{ fontSize: 11.5, fontFamily: 'monospace' }}>{r.candidate_rate ?? '—'}</TableCell>
<TableCell sx={{ fontSize: 11.5, fontFamily: 'monospace' }}>{r.delta ?? '—'}</TableCell>
<TableCell sx={{ fontSize: 11.5, fontFamily: 'monospace' }}>{fmtCi(r.delta_ci)}</TableCell>
<TableCell>
<Chip size="small" label={r.decision} sx={{ height: 18, fontSize: 10, fontWeight: 800, bgcolor: DECISION_COLORS[r.decision] ?? '#37474f', color: '#fff' }} />
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
) : (
<Typography variant="caption" sx={{ color: '#8a949e' }}>
Run the evaluation to compare the candidate model against the general,
historical-regression, rare-event, safety-critical and failure-derived suites.
INSUFFICIENT_EVIDENCE is reported as-is — it is never a pass.
</Typography>
)}
</Paper>
</Box>
);
}
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