Analyze data với AI Analyze data with AI

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

yarn create cloudflare analyze-data --template=cloudflare/sandbox-sdk/examples/minimal

pnpm create cloudflare@latest analyze-data --template=cloudflare/sandbox-sdk/examples/minimal

cd analyze-data

npm i @anthropic-ai/sdk

yarn add @anthropic-ai/sdk

pnpm add @anthropic-ai/sdk

bun add @anthropic-ai/sdk

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 },

        );

      }


      async function streamToBase64(stream) {

        const blob = await new Response(stream).blob();

        const buffer = await blob.arrayBuffer();

        const bytes = new Uint8Array(buffer);


        // Convert to base64

        let binary = '';

        for (let i = 0; i < bytes.length; i++) {

          binary += String.fromCharCode(bytes[i]);

        }

        return btoa(binary);

      }


      // Check for generated chart

      let chart = null;

      try {

        const { content, mimeType } = await sandbox.readFile("/workspace/chart.png", {

          encoding: "none"

        });

        chart = `data:${mimeType};base64,${await streamToBase64(content)}`;

      } 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

echo "ANTHROPIC_API_KEY=your_api_key_here\nSANDBOX_TRANSPORT=rpc" > .dev.vars

# 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

npm run dev

curl -X POST http://localhost:8787 \

  -F "[email protected]" \

  -F "question=What is the average rating by year?"

{

  "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..."

}

npx wrangler deploy

npx wrangler secret put ANTHROPIC_API_KEY

{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"/directory/","name":"Directory"}},{"@type":"ListItem","position":2,"item":{"@id":"/sandbox/","name":"Sandbox SDK"}},{"@type":"ListItem","position":3,"item":{"@id":"/sandbox/tutorials/","name":"Tutorials"}},{"@type":"ListItem","position":4,"item":{"@id":"/sandbox/tutorials/analyze-data-with-ai/","name":"Analyze data with AI"}}]}