feat(ollama): enhance model pull with improved error handling, persistence, and UI

- Add catalog metadata lookup for better error messages with hints (e.g., for missing manifests or tags)
- Persist downloaded model references in saved remote models for future reuse
- Replace searchRemoteModels import with getSavedRemoteModels in search route
- Enhance pull progress UI to display download bytes, digest, and fallback message when data is unavailable
- Improve error payload with model details, URL, and tags count to guide users toward valid tags or models

app/api/system/status/route.ts

@@ -0,0 +1,108 @@
+import { NextResponse } from "next/server";
+import os from "os";
+
+export async function GET() {
+  try {
+    const totalMem = os.totalmem();
+    const freeMem = os.freemem();
+    const usedMem = totalMem - freeMem;
+    const load = os.loadavg();
+    const memUsage = process.memoryUsage();
+
+    // Base com os/fallback
+    const baseInfo: any = {
+      os: {
+        platform: os.platform(),
+        arch: os.arch(),
+        release: os.release(),
+        uptimeSec: os.uptime(),
+      },
+      cpu: {
+        model: os.cpus()?.[0]?.model || "unknown",
+        cores: os.cpus()?.length || 0,
+        speed: os.cpus()?.[0]?.speed || 0,
+      },
+      memory: {
+        total: totalMem,
+        free: freeMem,
+        used: usedMem,
+        modules: [] as Array<{ size: number; clockSpeed?: number; type?: string }>,
+      },
+      gpus: [] as Array<{ index: number; name: string; vendor?: string; vramMB?: number; bus?: string }>,
+      metrics: {
+        loadAvg: { "1m": load[0] ?? 0, "5m": load[1] ?? 0, "15m": load[2] ?? 0 },
+        process: {
+          rss: memUsage.rss,
+          heapUsed: memUsage.heapUsed,
+          heapTotal: memUsage.heapTotal,
+          external: memUsage.external,
+        },
+      },
+    };
+
+    // Tentar usar systeminformation, se disponível
+    try {
+      // Import dinâmico sem acionar o bundler (evita Module not found)
+      // eslint-disable-next-line @typescript-eslint/no-implied-eval
+      const dynImport: (m: string) => Promise<any> = new Function(
+        "m",
+        "return import(m)"
+      ) as any;
+      const si = await dynImport("systeminformation");
+
+      const [cpu, osInfo, memLayout, graphics, currentLoad] = await Promise.all([
+        si.cpu(),
+        si.osInfo(),
+        si.memLayout(),
+        si.graphics(),
+        si.currentLoad(),
+      ]);
+
+      baseInfo.os = {
+        platform: osInfo.platform || baseInfo.os.platform,
+        arch: os.arch(),
+        release: osInfo.release || baseInfo.os.release,
+        uptimeSec: baseInfo.os.uptimeSec,
+        distro: osInfo.distro,
+      };
+      baseInfo.cpu = {
+        model: cpu.brand || baseInfo.cpu.model,
+        cores: cpu.cores || baseInfo.cpu.cores,
+        physicalCores: cpu.physicalCores,
+        speed: Number(cpu.speed) || baseInfo.cpu.speed,
+      };
+      baseInfo.memory.modules = (memLayout || []).map((m: any) => ({ size: m.size, clockSpeed: m.clockSpeed, type: m.type }));
+      baseInfo.gpus = (graphics?.controllers || []).map((c: any, idx: number) => ({ index: idx, name: c.model || c.vendor || "GPU", vendor: c.vendor, vramMB: c.vram, bus: c.bus }));
+      baseInfo.metrics.loadAvg = {
+        "1m": currentLoad?.avgload ?? baseInfo.metrics.loadAvg["1m"],
+        "5m": baseInfo.metrics.loadAvg["5m"],
+        "15m": baseInfo.metrics.loadAvg["15m"],
+      };
+    } catch {
+      // sem systeminformation, seguimos com fallback
+    }
+
+    // Fallback adicional para detectar GPUs no Windows via PowerShell
+    try {
+      if ((!baseInfo.gpus || baseInfo.gpus.length === 0) && (baseInfo.os.platform === "win32" || os.platform() === "win32")) {
+        const { execFileSync } = await import("child_process");
+        const out = execFileSync("powershell", [
+          "-NoProfile",
+          "-Command",
+          "Get-CimInstance Win32_VideoController | Select-Object Name,AdapterRAM,PNPDeviceID | ConvertTo-Json -Depth 3"
+        ], { encoding: "utf8", timeout: 2000 });
+        const parsed = JSON.parse(out);
+        const arr = Array.isArray(parsed) ? parsed : [parsed];
+        baseInfo.gpus = arr.map((g: any, idx: number) => ({ index: idx, name: g.Name || "GPU", vendor: undefined, vramMB: g.AdapterRAM ? Math.round(g.AdapterRAM / 1024 / 1024) : undefined, bus: g.PNPDeviceID }));
+      }
+    } catch {
+      // ignore fallback errors
+    }
+
+    return NextResponse.json({ success: true, info: baseInfo });
+  } catch (e) {
+    return NextResponse.json({ success: false, error: String(e) }, { status: 500 });
+  }
+}
+
+