--- title: Analyze data with AI description: Upload CSV files, generate analysis code with Claude, and return visualizations. image: https://developers.cloudflare.com/dev-products-preview.png --- > Documentation Index > Fetch the complete documentation index at: https://developers.cloudflare.com/sandbox/llms.txt > Use this file to discover all available pages before exploring further. [Skip to content](#%5Ftop) # Analyze data with AI **Last reviewed:** 7 months ago 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 ↗](https://dash.cloudflare.com/sign-up/workers-and-pages). 2. Install [Node.js ↗](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm). Node.js version manager Use a Node version manager like [Volta ↗](https://volta.sh/) or [nvm ↗](https://github.com/nvm-sh/nvm) to avoid permission issues and change Node.js versions. [Wrangler](https://developers.cloudflare.com/workers/wrangler/install-and-update/), discussed later in this guide, requires a Node version of `16.17.0` or later. You'll also need: * An [Anthropic API key ↗](https://console.anthropic.com/) for Claude * [Docker ↗](https://www.docker.com/) running locally ## 1\. Create your project Create a new Sandbox SDK project: npm yarn pnpm ``` 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 ``` Terminal window ``` cd analyze-data ``` ## 2\. Install dependencies npm yarn pnpm bun ``` npm i @anthropic-ai/sdk ``` ``` yarn add @anthropic-ai/sdk ``` ``` pnpm add @anthropic-ai/sdk ``` ``` bun add @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; ANTHROPIC_API_KEY: string; } export default { async fetch(request: Request, env: Env): Promise { 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 { 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 ↗](https://console.anthropic.com/). Note The `.dev.vars` file is automatically gitignored and only used during local development with `npm run dev`. ## 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 ↗](https://console.anthropic.com/) when prompted. Warning Wait 2-3 minutes after first deployment for container provisioning. ## 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 * [Code Interpreter API](https://developers.cloudflare.com/sandbox/api/interpreter/) \- Use the built-in code interpreter * [File operations](https://developers.cloudflare.com/sandbox/guides/manage-files/) \- Advanced file handling * [Streaming output](https://developers.cloudflare.com/sandbox/guides/streaming-output/) \- Real-time progress updates ```json {"@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"}}]} ```