#代码执行

import { writeFileSync } from "node:fs";

const BASE_URL = "http://127.0.0.1:18091";
const SESSION = "analysis";
const sb = new Aio(BASE_URL);

// 1. The dataset, written through the file plane.
await sb.post("/v2/fs/write", {
  path: "/tmp/analysis/sales.csv",
  content:
    "month,revenue,cost\n2026-01,120,80\n2026-02,135,82\n2026-03,150,90\n" +
    "2026-04,142,95\n2026-05,168,99\n2026-06,180,104\n",
});

// 2. Name a session, then load and compute in it.
const first = await sb.post("/v2/code/execute", {
  language: "python",
  session_id: SESSION,
  code: `
import pandas as pd
df = pd.read_csv("/tmp/analysis/sales.csv")
df["margin"] = df.revenue - df.cost
print(df.margin.describe())
`,
});
console.log(first.outputs[0].text);
// -> "count     6.000000\nmean     57.500000\nstd      13.546217\n…"

// 3. Plot in the same session, and save the chart into the sandbox.
await sb.post("/v2/code/execute", {
  language: "python",
  session_id: SESSION,
  code: `
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
df.plot(x="month", y=["revenue", "cost"], kind="bar", figsize=(6, 3))
plt.tight_layout()
plt.savefig("/tmp/analysis/plot.png", dpi=120)
`,
});

// 4. The chart is a sandbox file now; the agent sees it on the file plane.
const listed = await sb.get("/v2/fs/list?path=/tmp/analysis");
console.log(listed.files.map((f: any) => [f.name, f.size]));

// 5. Download the bytes: attachment; filename="plot.png".
const dl = await fetch(`${BASE_URL}/v2/fs/download?path=/tmp/analysis/plot.png`);
const bytes = Buffer.from(await dl.arrayBuffer());
console.log(bytes.length);
writeFileSync("margin.png", bytes);

await sb.delete(`/v2/code/sessions/${SESSION}`);

import { writeFileSync } from "node:fs";

import { SandboxClient } from "@agent-infra/sandbox";

const BASE_URL = "http://127.0.0.1:18091";
// The SDK's default timeout is 60 s; kernel start can be slower, so raise it.
const client = new SandboxClient({ environment: BASE_URL, timeoutInSeconds: 120 });

// 1. The dataset, written through the file plane.
await client.file.writeFile({
  file: "/tmp/analysis/sales.csv",
  content:
    "month,revenue,cost\n2026-01,120,80\n2026-02,135,82\n2026-03,150,90\n" +
    "2026-04,142,95\n2026-05,168,99\n2026-06,180,104\n",
});

// 2. Create a session, then load and compute in it.
const session = (await client.jupyter.createSession()) as any;
const sessionId = session.body.data.session_id;

const first = (await client.jupyter.executeCode({
  session_id: sessionId,
  code: `
import pandas as pd
df = pd.read_csv("/tmp/analysis/sales.csv")
df["margin"] = df.revenue - df.cost
print(df.margin.describe())
`,
} as any)) as any;
console.log(first.body.data.outputs[0].text);
// -> "count     6.000000\nmean     57.500000\nstd      13.546217\n…"

// 3. Plot in the same session, and save the chart into the sandbox.
await client.jupyter.executeCode({
  session_id: sessionId,
  code: `
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
df.plot(x="month", y=["revenue", "cost"], kind="bar", figsize=(6, 3))
plt.tight_layout()
plt.savefig("/tmp/analysis/plot.png", dpi=120)
`,
} as any);

// 4. The chart is a sandbox file now; the agent sees it on the file plane.
const listed = (await client.file.listPath({ path: "/tmp/analysis" })) as any;
console.log(listed.body.data.files.map((f: any) => [f.name, f.size]));

// 5. Download the bytes: attachment; filename="plot.png".
const dl = (await client.file.downloadFile({ path: "/tmp/analysis/plot.png" })) as any;
const bytes = Buffer.from(await dl.body.arrayBuffer());
console.log(bytes.length);
writeFileSync("margin.png", bytes);

await client.jupyter.deleteSession(sessionId);

const BASE_URL = "http://127.0.0.1:18091";
const sb = new Aio(BASE_URL);

const py = await sb.post("/v2/code/execute", {
  language: "python",
  code: "print(sum([1,2,3]))",
});
const js = await sb.post("/v2/code/execute", {
  language: "javascript",
  code: "console.log([1,2,3].reduce((a,b)=>a+b))",
});
console.log(py.stdout.trim(), js.stdout.trim());
// -> 6 6

import { SandboxClient } from "@agent-infra/sandbox";

const BASE_URL = "http://127.0.0.1:18091";
const client = new SandboxClient({ environment: BASE_URL });

const py = (await client.code.executeCode({
  language: "python",
  code: "print(sum([1,2,3]))",
} as any)) as any;
const js = (await client.code.executeCode({
  language: "javascript",
  code: "console.log([1,2,3].reduce((a,b)=>a+b))",
} as any)) as any;
console.log(py.body.data.stdout.trim(), js.body.data.stdout.trim());
// -> 6 6

const BASE_URL = "http://127.0.0.1:18091";
const sb = new Aio(BASE_URL);

await sb.post("/v2/code/execute", {
  language: "python",
  code: "x = 21",
  session_id: "s1",
});
const r = await sb.post("/v2/code/execute", {
  language: "python",
  code: "print(x * 2)",
  session_id: "s1",
});
console.log(r.stdout.trim());
// -> 42

import { SandboxClient } from "@agent-infra/sandbox";

const BASE_URL = "http://127.0.0.1:18091";
const client = new SandboxClient({ environment: BASE_URL });

await client.code.executeCode({
  language: "python",
  code: "x = 21",
  session_id: "s1",
} as any);
const r = (await client.code.executeCode({
  language: "python",
  code: "print(x * 2)",
  session_id: "s1",
} as any)) as any;
console.log(r.body.data.stdout.trim());
// -> 42

const BASE_URL = "http://127.0.0.1:18091";
const sb = new Aio(BASE_URL);

await sb.post("/v2/code/execute", {
  language: "javascript",
  code: "globalThis.n = 41",
  session_id: "s1",
});
const r = await sb.post("/v2/code/execute", {
  language: "javascript",
  code: "console.log(n + 1)",
  session_id: "s1",
});
console.log(r.stdout.trim());
// -> 42

import { SandboxClient } from "@agent-infra/sandbox";

const BASE_URL = "http://127.0.0.1:18091";
const client = new SandboxClient({ environment: BASE_URL });

await client.nodejs.executeCode({ code: "globalThis.n = 41", session_id: "s1" } as any);
const r = (await client.nodejs.executeCode({
  code: "console.log(n + 1)",
  session_id: "s1",
} as any)) as any;
console.log(r.body.data.stdout.trim());
// -> 42