{/* Watermark */} {window.CHISEL_WATERMARK_SRC && (

)} {head} {/* Colorful score hero */}

{CRITERIA.map((c, i) => { const v = r.scores[c.key]; return (

{c.label.split(' ')[0]}

{v}

); })}

The Verdict

“{r.verdict}”

Performance by criterion

{CRITERIA.map((c, i) => { const v = r.scores[c.key]; const g = gradeOf(v); return (

{c.label}

{v} {g.g}

{c.desc}

); })}

✓ Strengths

{r.strengths.map((s, i) => (

+{s}

))}

△ To improve

{r.weaknesses.map((s, i) => (

→{s}

))}

{/* AI score ring */}

{/* AI vs Self comparison — enhanced */}

Score Comparison

{/* AI */}

AI Evaluation

{typeof r.scores.overall === 'number' ? r.scores.overall.toFixed(1) : r.scores.overall}

/10

{r.grade.g} · {r.grade.label}

{/* Self */}

Self Assessment

{typeof r.selfScore === 'number' ? r.selfScore.toFixed(1) : (r.selfScore || '—')}

/10

{(() => { const diff = parseFloat(r.selfScore) - parseFloat(r.scores.overall); if (isNaN(diff)) return null; const label = diff > 1.5 ? 'Overestimates' : diff < -1.5 ? 'Underestimates' : 'Accurate self-view'; const tone = diff > 1.5 ? 'warn' : diff < -1.5 ? 'info' : 'ok'; return (

{diff > 0 ? '+' : ''}{diff.toFixed(1)} · {label}

); })()}

{/* Bar comparison row */} {(() => { const ai = parseFloat(r.scores.overall) || 0; const self = parseFloat(r.selfScore) || 0; return (

{[{ label: 'AI score', val: ai, color: 'var(--accent)' }, { label: 'Self score', val: self, color: 'var(--ink-3)' }] .map(({ label, val, color }) => (

{label} {val.toFixed(1)} / 10

))}

); })()} {/* Verdict band — prominent */}

{r.grade.tone === 'ok' ? '✓' : r.grade.tone === 'warn' ? '△' : '✗'}

{r.grade.label}

{r.grade.g}

{r.grade.tone === 'ok' ? 'Performance is above the cohort benchmark. Strengths are consistent across dimensions.' : r.grade.tone === 'warn' ? 'Performance is approaching the benchmark. Targeted practice on weak dimensions will bridge the gap.' : 'Performance is below benchmark. Coaching and structured practice are recommended before the next attempt.'}

Vs. cohort

This student Cohort avg

Voice telemetry

{[ { k: 'Duration', v: `${Math.floor(r.duration / 60)}:${String(r.duration % 60).padStart(2,'0')}`, sub: 'mm:ss', c: '--viz-2' }, { k: 'Words', v: r.words, sub: 'total', c: '--viz-4' }, { k: 'Pace', v: `${r.wpm}`, sub: 'wpm', c: '--viz-1' }, { k: 'Fillers', v: r.fillers, sub: '"um", "like"…', c: '--viz-5' }, { k: 'Pause rate', v: r.pauseRate.toFixed(1), sub: 'per min', c: '--viz-3' }, ].map((s, i) => (

{s.k}

{s.v}

{s.sub}

))}

); } // ── Coaching — concrete, actionable improvements from content analysis ─ function CoachingSection({ r }) { const weakestKey = r.weakest_dimension || (() => { // fallback: compute from scores const entries = CRITERIA.map((c) => ({ key: c.key, val: r.scores[c.key] })); return entries.sort((a, b) => a.val - b.val)[0]?.key || 'clarity'; })(); const weakestCrit = CRITERIA.find((c) => c.key === weakestKey) || CRITERIA[2]; const weakestVal = r.scores[weakestKey] || 0; const colorIdx = CRITERIA.findIndex((c) => c.key === weakestKey); // ── Annotated excerpts (real from AI, fallback empty) ───────────────────── const excerpts = r.annotated_excerpts || []; // ── Coaching drills (real from AI) ──────────────────────────────────────── const drills = r.coaching_drills || []; // ── Content density (real from AI + local analysis) ─────────────────────── const cd = r.content_density || {}; // ── High-leverage lifts — computed from actual metrics ──────────────────── const fillerRate = r.duration > 0 ? ((cd.fillers || r.fillers || 0) / (r.duration / 60)).toFixed(1) : (cd.fillers || r.fillers || 0); const targetWpm = r.wpm < 130 ? 140 : r.wpm > 170 ? 155 : '145'; const leverage = [ { icon: '↓', metric: 'Fillers', from: `${fillerRate}/min`, to: '≤2/min', lift: '+5 Delivery', tone: 'viz-5' }, { icon: '↑', metric: 'Pace', from: `${r.wpm || '—'} wpm`, to: `${targetWpm} wpm`, lift: '+3 Voice', tone: 'viz-1' }, { icon: '↑', metric: 'Specificity', from: `${weakestVal}/100`, to: '80/100', lift: '+4 Content', tone: 'viz-4' }, ]; const flagStyle = (kind) => ({ background: kind === 'filler' ? '#fde2e2' : kind === 'hedge' ? '#fef3c7' : '#e0e7ff', color: kind === 'filler' ? '#991b1b' : kind === 'hedge' ? '#92400e' : '#3730a3', borderBottom: '1.5px solid currentColor', padding: '0 2px', margin: '0 1px', borderRadius: '2px', fontWeight: 500, }); return (

Coaching — Suggested revisions

How to take this from {r.grade.g} to the next grade up.

Three changes, applied to the actual transcript, with the projected score lift each would produce.

{/* High-leverage lifts */}

{leverage.map((l, i) => (

{l.icon}

{l.metric}

{l.from} → {l.to}

{l.lift}

))}

{/* Annotated transcript excerpts */} {excerpts.length > 0 && ( <>

Annotated excerpts — problem passages, rewritten

{excerpts.map((t, i) => { const tone = i === excerpts.length - 1 ? 'ok' : 'warn'; // Build highlighted original const flags = t.highlights || []; let parts = [{ text: t.original || '', flag: null }]; flags.forEach((f) => { parts = parts.flatMap((p) => { if (p.flag || !f.text) return [p]; const idx = p.text.toLowerCase().indexOf(f.text.toLowerCase()); if (idx < 0) return [p]; return [ { text: p.text.slice(0, idx), flag: null }, { text: p.text.slice(idx, idx + f.text.length), flag: f.kind }, { text: p.text.slice(idx + f.text.length), flag: null }, ].filter((x) => x.text); }); }); return (

{t.tag}

{t.timestamp || `${i}:00`}

Original

"{parts.map((p, j) => p.flag ? {p.text} : {p.text})}"

✓ Rewrite

"{t.rewrite}"

Why: {t.why}

); })}

)} {/* Legend + drills */}

Highlight legend

fillers {cd.filler_words?.length ? `"${cd.filler_words.slice(0,3).join('", "')}"` : '"um", "like"'} — remove entirely

hedges {cd.hedge_words?.length ? `"${cd.hedge_words.slice(0,3).join('", "')}"` : '"kind of", "I think"'} — weaken claims

vague {cd.vague_phrases?.length ? `"${cd.vague_phrases.slice(0,2).join('", "')}"` : '"a lot of", "this thing"'} — replace with evidence

Content density

{cd.fillers ?? r.fillers ?? 0} FILLERS · {cd.hedges ?? 0} HEDGES · {cd.vague ?? 0} VAGUE

Detected across {r.words} words of transcript.

{drills.length > 0 && (

Drills for {weakestCrit.label.toLowerCase()} — your weakest dimension ({weakestVal}/100)

{drills.map((d, i) => (

{String(i + 1).padStart(2, '0')}

{d.title || d.t}

{d.description || d.d}

))}

)}

); } // ── Transcript section ──────────────────────────────────── function TranscriptSection({ r }) { const [open, setOpen] = React.useState(true); // open by default so it appears in PDF const [copied, setCopied] = React.useState(false); const raw = (r.transcript || '').trim(); if (!raw) return null; // Build filler/hedge/vague word sets from content_density const cd = r.content_density || {}; const fillerSet = new Set((cd.filler_words || []).map((w) => w.toLowerCase())); const hedgeSet = new Set((cd.hedge_words || []).map((w) => w.toLowerCase())); const vagueSet = new Set((cd.vague_phrases || []).map((w) => w.toLowerCase())); // Tokenise preserving whitespace / punctuation for highlighting const tokens = raw.split(/(\s+)/); const getHighlight = (tok) => { const clean = tok.replace(/[^a-zA-Z]/g, '').toLowerCase(); if (fillerSet.has(clean)) return { bg: '#fde2e2', color: '#991b1b', title: 'Filler word' }; if (hedgeSet.has(clean)) return { bg: '#fef3c7', color: '#92400e', title: 'Hedge word' }; if (vagueSet.has(clean)) return { bg: '#e0e7ff', color: '#3730a3', title: 'Vague word' }; return null; }; const handleCopy = () => { navigator.clipboard.writeText(raw).then(() => { setCopied(true); setTimeout(() => setCopied(false), 2000); }); }; // Split into paragraphs on double newline, or wrap at ~80 words per paragraph const paragraphs = raw.includes('\n') ? raw.split(/\n{2,}/).filter(Boolean) : (() => { const words = raw.split(' '); const chunks = []; for (let i = 0; i < words.length; i += 80) chunks.push(words.slice(i, i + 80).join(' ')); return chunks; })(); const wordCount = raw.split(/\s+/).filter(Boolean).length; const charCount = raw.length; return (

{/* Section header — clickable to collapse */}

setOpen((o) => !o)}>

Audio to Text · Speech Recognition

Full Speech Transcript

{wordCount.toLocaleString()} words · {charCount.toLocaleString()} chars {fillerSet.size + hedgeSet.size + vagueSet.size > 0 && ' · annotated'}

{open && (fillerSet.size > 0 || hedgeSet.size > 0 || vagueSet.size > 0) && (

{fillerSet.size > 0 && filler} {hedgeSet.size > 0 && hedge} {vagueSet.size > 0 && vague}

)}

{/* Transcript body */} {open && (

{/* Dark sub-header */}

Recorded speech · automatically transcribed {wordCount.toLocaleString()} words

{/* Text paragraphs */}

{paragraphs.map((para, pi) => { const paraTokens = para.split(/(\s+)/); return (

{paraTokens.map((tok, ti) => { if (/^\s+$/.test(tok)) return tok; const hl = getHighlight(tok); return hl ? ( {tok} ) : tok; })}

); })}

{/* Stats footer */}

{[ { k: 'Words', v: wordCount.toLocaleString() }, { k: 'Pace', v: `${r.wpm || '—'} wpm` }, { k: 'Duration', v: `${Math.floor((r.duration || 0) / 60)}:${String((r.duration || 0) % 60).padStart(2, '0')}` }, { k: 'Fillers', v: cd.fillers ?? r.fillers ?? 0 }, { k: 'Hedges', v: cd.hedges ?? 0 }, ].map((s) => ( {s.v} {' '}{s.k.toLowerCase()} ))}

)}

); } // ── Dashboard layout (widgety) ──────────────────────────── function ReportDashboard({ r, head, cohort, critValues, axes }) { return (

{head}

{r.grade.g} · {r.grade.label}

Criteria breakdown

{CRITERIA.map((c) => ( ))}

Vs. cohort

Verdict

{r.verdict}

Strengths

{r.strengths.map((s, i) =>

+ {s}

)}

To improve

{r.weaknesses.map((s, i) =>

− {s}

)}

Telemetry

{[ { k: 'Duration', v: `${Math.floor(r.duration / 60)}:${String(r.duration % 60).padStart(2,'0')}` }, { k: 'Words', v: r.words }, { k: 'Pace (wpm)', v: r.wpm }, { k: 'Fillers', v: r.fillers }, { k: 'Pause/min', v: r.pauseRate.toFixed(1) }, { k: 'Self-score', v: r.selfScore }, ].map((s, i) => (

{s.k}

{s.v}

))}

); } // ── Briefing (compact one-pager) ─────────────────────────── function ReportBriefing({ r, head, cohort, critValues, axes }) { return (

{head}

Verdict

{r.verdict}

{r.duration}s· {r.words} words· {r.wpm} wpm· {r.fillers} fillers

{CRITERIA.map((c) => { const v = r.scores[c.key]; return (

{c.label}

{v}

); })}

); } Object.assign(window, { ReportView });

{r.topic}