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import type { ModelFitScore } from "@/lib/modelfit";
const FIT_COLOR: Record<string, string> = {
comfortable: "text-emerald-400",
tight: "text-amber-400",
not_recommended: "text-orange-400",
impossible: "text-red-400",
};
const LABEL_SHORT: Record<string, string> = {
"Excellent fit": "★★★",
"Recommended": "★★",
"Usable with limits": "★",
"Not recommended": "–",
"Does not fit": "✗",
};
export function ComparisonTable({ scores }: { scores: ModelFitScore[] }) {
if (scores.length === 0) {
return (
<p className="text-sm text-zinc-500">
No models scored yet. Use Model Search to select models and compute scores.
</p>
);
}
function exportCSV() {
const header = [
"model_id", "quantization", "overall", "hardware_fit", "speed_fit",
"rag_fit", "task_fit", "deployment_fit", "label", "vram_gb", "disk_gb",
"tok_per_sec_measured", "fit_level",
].join(",");
const rows = scores.map((s) => {
const re = s.resource_estimate;
return [
JSON.stringify(s.model_id),
s.best_quantization,
s.overall_score,
s.hardware_fit,
s.speed_fit,
s.rag_fit,
s.task_fit,
s.deployment_fit,
JSON.stringify(s.label),
re?.estimated_vram_gb ?? "",
re?.estimated_disk_gb ?? "",
s.benchmark?.avg_tok_per_sec ?? "",
re?.fit_level ?? "",
].join(",");
});
const csv = [header, ...rows].join("\n");
const blob = new Blob([csv], { type: "text/csv" });
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = "modelfit-comparison.csv";
a.click();
URL.revokeObjectURL(url);
}
function exportJSON() {
const blob = new Blob([JSON.stringify(scores, null, 2)], { type: "application/json" });
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = "modelfit-comparison.json";
a.click();
URL.revokeObjectURL(url);
}
function exportMarkdown() {
const header = `| Model | Quant | Score | HW | Speed | RAG | VRAM est. | Tok/s | Fit | Label |`;
const sep = `|-------|-------|------:|---:|------:|----:|----------:|------:|-----|-------|`;
const rows = scores.map((s) => {
const re = s.resource_estimate;
const model = s.model_id.replace(/^(ollama:|hf:|local:)/, "");
const toks = s.benchmark?.avg_tok_per_sec != null ? String(s.benchmark.avg_tok_per_sec) : "—";
const vram = re ? `${re.estimated_vram_gb} GB` : "—";
const fit = re?.fit_level?.replace("_", " ") ?? "unknown";
const speed = `${Math.round(s.speed_fit)}${s.estimate_used ? "*" : ""}`;
return `| ${model} | ${s.best_quantization} | ${Math.round(s.overall_score)} | ${Math.round(s.hardware_fit)} | ${speed} | ${Math.round(s.rag_fit)} | ${vram} | ${toks} | ${fit} | ${s.label} |`;
});
const md = [`## Auralynq ModelFit Comparison`, ``, header, sep, ...rows, ``, `\\* speed score uses estimates — run benchmark for measured tok/s`].join("\n");
const blob = new Blob([md], { type: "text/markdown" });
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = "modelfit-comparison.md";
a.click();
URL.revokeObjectURL(url);
}
return (
<div className="space-y-3">
<div className="flex gap-2 justify-end">
<button
onClick={exportCSV}
className="px-3 py-1 text-xs rounded border border-zinc-700 text-zinc-400 hover:text-zinc-200"
>
Export CSV
</button>
<button
onClick={exportJSON}
className="px-3 py-1 text-xs rounded border border-zinc-700 text-zinc-400 hover:text-zinc-200"
>
Export JSON
</button>
<button
onClick={exportMarkdown}
className="px-3 py-1 text-xs rounded border border-zinc-700 text-zinc-400 hover:text-zinc-200"
>
Export Markdown
</button>
</div>
<div className="overflow-x-auto">
<table className="w-full text-xs text-zinc-300 border-collapse">
<thead>
<tr className="text-left text-[10px] text-zinc-500 border-b border-zinc-800">
<th className="pb-2 pr-3">Model</th>
<th className="pb-2 pr-3">Quant</th>
<th className="pb-2 pr-3 text-right">Score</th>
<th className="pb-2 pr-3 text-right">HW</th>
<th className="pb-2 pr-3 text-right">Speed</th>
<th className="pb-2 pr-3 text-right">RAG</th>
<th className="pb-2 pr-3">VRAM est.</th>
<th className="pb-2 pr-3">Disk est.</th>
<th className="pb-2 pr-3">Tok/s</th>
<th className="pb-2 pr-3">Fit</th>
<th className="pb-2">Label</th>
</tr>
</thead>
<tbody>
{scores.map((s) => {
const re = s.resource_estimate;
return (
<tr key={s.model_id} className="border-b border-zinc-800/40 hover:bg-zinc-800/30">
<td className="py-1.5 pr-3 font-mono text-zinc-100 max-w-[160px] truncate">
{s.model_id.replace(/^(ollama:|hf:|local:)/, "")}
</td>
<td className="py-1.5 pr-3 font-mono">{s.best_quantization}</td>
<td className="py-1.5 pr-3 text-right font-bold text-zinc-100">
{Math.round(s.overall_score)}
</td>
<td className="py-1.5 pr-3 text-right">{Math.round(s.hardware_fit)}</td>
<td className="py-1.5 pr-3 text-right">
{Math.round(s.speed_fit)}
{s.estimate_used && <span className="text-zinc-600 ml-0.5">*</span>}
</td>
<td className="py-1.5 pr-3 text-right">{Math.round(s.rag_fit)}</td>
<td className="py-1.5 pr-3 font-mono">
{re ? `${re.estimated_vram_gb} GB` : "—"}
<span className="text-zinc-600 ml-0.5">est.</span>
</td>
<td className="py-1.5 pr-3 font-mono">
{re ? `${re.estimated_disk_gb} GB` : "—"}
</td>
<td className="py-1.5 pr-3 font-mono">
{s.benchmark?.avg_tok_per_sec != null ? (
<span className="text-emerald-400">{s.benchmark.avg_tok_per_sec}</span>
) : (
<span className="text-zinc-600 italic">not run</span>
)}
</td>
<td className={`py-1.5 pr-3 font-mono ${FIT_COLOR[re?.fit_level ?? ""] ?? ""}`}>
{re?.fit_level?.replace("_", " ") ?? "unknown"}
</td>
<td className="py-1.5 text-zinc-300">
{LABEL_SHORT[s.label] ?? ""} {s.label}
</td>
</tr>
);
})}
</tbody>
</table>
<p className="text-[10px] text-zinc-600 mt-1">
* speed score uses estimates — run benchmark for measured tok/s
</p>
</div>
</div>
);
}
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