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Analyze data with AI

Build an AI-powered data analysis system that accepts CSV uploads, uses Claude to generate Python analysis code, executes it in sandboxes, and returns visualizations.

Time to complete: 25 minutes

Prerequisites

  1. Sign up for a Cloudflare account ↗.
  2. Install Node.js ↗.

Node.js version manager

Use a Node version manager like Volta ↗ or nvm ↗ to avoid permission issues and change Node.js versions. Wrangler, discussed later in this guide, requires a Node version of 16.17.0 or later.

You'll also need:

1. Create your project

Create a new Sandbox SDK project:

Terminal window
npm create cloudflare@latest -- analyze-data --template=cloudflare/sandbox-sdk/examples/minimal
Terminal window
cd analyze-data

2. Install dependencies

Terminal window
npm i @anthropic-ai/sdk

3. Build the analysis handler

Replace src/index.ts:

TypeScript
import { getSandbox, proxyToSandbox, type Sandbox } from "@cloudflare/sandbox";
import Anthropic from "@anthropic-ai/sdk";


export { Sandbox } from "@cloudflare/sandbox";


interface Env {
  Sandbox: DurableObjectNamespace<Sandbox>;
  ANTHROPIC_API_KEY: string;
}


export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const proxyResponse = await proxyToSandbox(request, env);
    if (proxyResponse) return proxyResponse;


    if (request.method !== "POST") {
      return Response.json(
        { error: "POST CSV file and question" },
        { status: 405 },
      );
    }


    try {
      const formData = await request.formData();
      const csvFile = formData.get("file") as File;
      const question = formData.get("question") as string;


      if (!csvFile || !question) {
        return Response.json(
          { error: "Missing file or question" },
          { status: 400 },
        );
      }


      // Upload CSV to sandbox
      const sandbox = getSandbox(env.Sandbox, `analysis-${Date.now()}`);
      const csvPath = "/workspace/data.csv";
      await sandbox.writeFile(csvPath, await csvFile.text());


      // Analyze CSV structure
      const structure = await sandbox.exec(
        `python3 -c "import pandas as pd; df = pd.read_csv('${csvPath}'); print(f'Rows: {len(df)}'); print(f'Columns: {list(df.columns)[:5]}')"`,
      );


      if (!structure.success) {
        return Response.json(
          { error: "Failed to read CSV", details: structure.stderr },
          { status: 400 },
        );
      }


      // Generate analysis code with Claude
      const code = await generateAnalysisCode(
        env.ANTHROPIC_API_KEY,
        csvPath,
        question,
        structure.stdout,
      );


      // Write and execute the analysis code
      await sandbox.writeFile("/workspace/analyze.py", code);
      const result = await sandbox.exec("python /workspace/analyze.py");


      if (!result.success) {
        return Response.json(
          { error: "Analysis failed", details: result.stderr },
          { status: 500 },
        );
      }


      // Check for generated chart
      let chart = null;
      try {
        const chartFile = await sandbox.readFile("/workspace/chart.png");
        const buffer = new Uint8Array(chartFile.content);
        chart = `data:image/png;base64,${btoa(String.fromCharCode(...buffer))}`;
      } catch {
        // No chart generated
      }


      await sandbox.destroy();


      return Response.json({
        success: true,
        output: result.stdout,
        chart,
        code,
      });
    } catch (error: any) {
      return Response.json({ error: error.message }, { status: 500 });
    }
  },
};


async function generateAnalysisCode(
  apiKey: string,
  csvPath: string,
  question: string,
  csvStructure: string,
): Promise<string> {
  const anthropic = new Anthropic({ apiKey });


  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-5",
    max_tokens: 2048,
    messages: [
      {
        role: "user",
        content: `CSV at ${csvPath}:
${csvStructure}


Question: "${question}"


Generate Python code that:
- Reads CSV with pandas
- Answers the question
- Saves charts to /workspace/chart.png if helpful
- Prints findings to stdout


Use pandas, numpy, matplotlib.`,
      },
    ],
    tools: [
      {
        name: "generate_python_code",
        description: "Generate Python code for data analysis",
        input_schema: {
          type: "object",
          properties: {
            code: { type: "string", description: "Complete Python code" },
          },
          required: ["code"],
        },
      },
    ],
  });


  for (const block of response.content) {
    if (block.type === "tool_use" && block.name === "generate_python_code") {
      return (block.input as { code: string }).code;
    }
  }


  throw new Error("Failed to generate code");
}

4. Set up local environment variables

Create a .dev.vars file in your project root for local development:

Terminal window
echo "ANTHROPIC_API_KEY=your_api_key_here" > .dev.vars

Replace your_api_key_here with your actual API key from the Anthropic Console ↗.

5. Test locally

Download a sample CSV:

Terminal window
# Create a test CSV
echo "year,rating,title
2020,8.5,Movie A
2021,7.2,Movie B
2022,9.1,Movie C" > test.csv

Start the dev server:

Terminal window
npm run dev

Test with curl:

Terminal window
curl -X POST http://localhost:8787 \
  -F "file=@test.csv" \
  -F "question=What is the average rating by year?"

Response:

{
  "success": true,
  "output": "Average ratings by year:\n2020: 8.5\n2021: 7.2\n2022: 9.1",
  "chart": "data:image/png;base64,...",
  "code": "import pandas as pd\nimport matplotlib.pyplot as plt\n..."
}

6. Deploy

Deploy your Worker:

Terminal window
npx wrangler deploy

Then set your Anthropic API key as a production secret:

Terminal window
npx wrangler secret put ANTHROPIC_API_KEY

Paste your API key from the Anthropic Console ↗ when prompted.

What you built

An AI data analysis system that:

  • Uploads CSV files to sandboxes
  • Uses Claude's tool calling to generate analysis code
  • Executes Python with pandas and matplotlib
  • Returns text output and visualizations

Next steps