/* Model Insights — renders everything from /static/data/model_card.json */

const pretty = (s) => s.replace(/_/g, ' ');
const f1class = (v) => (v >= 0.8 ? 'good' : v >= 0.5 ? 'mid' : 'bad');

fetch('/static/data/model_card.json')
  .then(r => r.json())
  .then(render)
  .catch(() => {
    document.getElementById('kpis').innerHTML =
      '<p class="muted">Could not load model_card.json</p>';
  });

function render(card) {
  const m = card.metrics, ds = card.dataset, mdl = card.model, hp = card.hyperparameters;

  // ── KPIs ──
  const kpis = [
    { v: (m.accuracy * 100).toFixed(1) + '%', l: 'Accuracy' },
    { v: m.macro_f1.toFixed(3), l: 'Macro F1' },
    { v: ds.classes, l: 'Classes' },
    { v: ds.total_images.toLocaleString(), l: 'Images' },
    { v: mdl.trainable_params_millions + 'M', l: 'Parameters' },
    { v: mdl.weights_size_mb + 'MB', l: 'Model size' },
  ];
  document.getElementById('kpis').innerHTML = kpis.map(k =>
    `<div class="kpi rise"><b>${k.v}</b><span>${k.l}</span></div>`).join('');

  // ── Pipeline ──
  document.getElementById('pipeline').innerHTML = card.pipeline.map(p =>
    `<div class="pipe-step"><b>${p.step}</b><span>${p.detail}</span></div>`).join('');

  // ── Dataset donut (train vs test) ──
  const trainPct = Math.round(ds.train_images / ds.total_images * 100);
  const accent = getComputedStyle(document.documentElement).getPropertyValue('--accent').trim() || '#2EA866';
  document.getElementById('donut').style.background =
    `conic-gradient(${accent} 0 ${trainPct}%, var(--bg-tint) ${trainPct}% 100%)`;
  document.getElementById('donutLegend').innerHTML = `
    <div><i style="background:${accent}"></i>Train · ${ds.train_images.toLocaleString()}</div>
    <div><i style="background:var(--bg-tint)"></i>Test · ${ds.test_images.toLocaleString()}</div>`;
  document.getElementById('dsFacts').innerHTML = `
    <div><b>${ds.classes}</b><span>dish classes</span></div>
    <div><b>${ds.per_class}</b><span>images / class</span></div>
    <div><b>${ds.split}</b><span>split</span></div>
    <div><b>${ds.balanced ? 'Balanced' : 'Imbalanced'}</b><span>distribution</span></div>`;

  document.getElementById('augChips').innerHTML =
    card.augmentations.map(a => `<span class="pill">${a}</span>`).join('');

  // ── Model card + hyperparameters ──
  const rows = (obj) => Object.entries(obj).map(([k, v]) =>
    `<tr><td>${k}</td><td>${v}</td></tr>`).join('');
  document.getElementById('modelSpec').innerHTML = rows({
    'Name': mdl.published_as,
    'Architecture': mdl.architecture,
    'Framework': mdl.framework,
    'Parameters': mdl.trainable_params_millions + ' M',
    'Weights size': mdl.weights_size_mb + ' MB',
    'Input': mdl.input,
    'Output': mdl.output,
  });
  document.getElementById('hpSpec').innerHTML = rows({
    'Epochs': hp.epochs,
    'Learning rate': hp.learning_rate,
    'Train batch size': hp.train_batch_size,
    'Eval batch size': hp.eval_batch_size,
    'Weight decay': hp.weight_decay,
    'Warmup steps': hp.warmup_steps,
    'Optimizer steps': hp.optimizer_steps,
    'Runtime': hp.runtime_min + ' min (GPU)',
  });

  // ── Train compare (baseline vs final) ──
  document.getElementById('trainCompare').innerHTML = `
    <div class="tc"><b>${(m.baseline_accuracy * 100).toFixed(1)}%</b><span>Baseline accuracy</span></div>
    <div class="tc final"><b>${(m.accuracy * 100).toFixed(1)}%</b><span>After fine-tuning</span></div>
    <div class="tc"><b>${m.macro_f1.toFixed(3)}</b><span>Macro F1</span></div>`;
  document.getElementById('curveNote').textContent =
    'Per-epoch loss/accuracy curves are not plotted: the training run only logged baseline and final metrics. Enable epoch logging in the notebook to capture full curves.';

  // ── Per-class F1 bars ──
  const sorted = [...card.per_class].sort((a, b) => b.f1 - a.f1);
  renderBars('bestBars', sorted.slice(0, 10));
  renderBars('worstBars', sorted.slice(-10).reverse());
  renderBars('allBars', sorted);

  // ── Limitations ──
  document.getElementById('limits').innerHTML =
    card.limitations.map(l => `<li>${l}</li>`).join('');

  // animate bar widths after paint
  requestAnimationFrame(() => requestAnimationFrame(() => {
    document.querySelectorAll('.bar-fill').forEach(el => { el.style.width = el.dataset.w + '%'; });
  }));
}

function renderBars(id, items) {
  document.getElementById(id).innerHTML = items.map(c => `
    <div class="bar-row">
      <span class="name" title="${pretty(c.label)}">${pretty(c.label)}</span>
      <div class="bar-track"><div class="bar-fill ${f1class(c.f1)}" data-w="${(c.f1 * 100).toFixed(0)}"></div></div>
      <span class="val">${c.f1.toFixed(2)}</span>
    </div>`).join('');
}