- Introduced a layout engine for auto-layout computation, including padding resolution and fit-content size calculations. - Added a node creator for various drawing tools (rectangle, frame, ellipse, line, text) to streamline object creation. - Implemented object modification handling to sync changes back to the document store, improving interaction with Fabric.js. - Enhanced text measurement utilities for better width and height estimation, including support for CJK characters. - Added new API endpoints for AI icon retrieval and validation, supporting multimodal content analysis.
160 lines
4.6 KiB
TypeScript
160 lines
4.6 KiB
TypeScript
import { defineEventHandler, readBody, setResponseHeaders } from 'h3'
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import { resolveClaudeCli } from '../../utils/resolve-claude-cli'
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import { writeFile, unlink, mkdtemp } from 'node:fs/promises'
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import { tmpdir } from 'node:os'
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import { join } from 'node:path'
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interface ValidateBody {
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system: string
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message: string
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imageBase64: string
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model?: string
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provider?: string
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}
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/**
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* Vision-based validation endpoint.
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* Accepts a base64 PNG screenshot and a text prompt, sends multimodal
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* content blocks for analysis.
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*
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* - Anthropic API key: uses SDK multimodal content blocks directly.
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* - Agent SDK fallback: saves screenshot to temp file, asks Claude Code
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* to read it via its built-in Read tool.
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*/
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export default defineEventHandler(async (event) => {
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const body = await readBody<ValidateBody>(event)
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if (!body?.system || !body?.message || !body?.imageBase64) {
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setResponseHeaders(event, { 'Content-Type': 'application/json' })
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return { error: 'Missing required fields: system, message, imageBase64' }
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}
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// Try Anthropic SDK first (direct multimodal support)
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const apiKey = process.env.ANTHROPIC_API_KEY
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if (apiKey) {
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try {
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return await validateViaAnthropicSDK(apiKey, body, body.model)
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} catch {
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// Fall through to Agent SDK
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}
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}
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// Fallback: Agent SDK — save screenshot to temp file, let Claude read it
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try {
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return await validateViaAgentSDK(body, body.model)
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} catch (error) {
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const message = error instanceof Error ? error.message : 'Unknown error'
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return { error: message }
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}
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})
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async function validateViaAnthropicSDK(
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apiKey: string,
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body: ValidateBody,
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model?: string,
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): Promise<{ text: string; skipped?: boolean }> {
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const { default: Anthropic } = await import('@anthropic-ai/sdk')
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const client = new Anthropic({ apiKey })
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// Strip data URL prefix if present
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let base64Data = body.imageBase64
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const dataUrlPrefix = 'data:image/png;base64,'
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if (base64Data.startsWith(dataUrlPrefix)) {
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base64Data = base64Data.slice(dataUrlPrefix.length)
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}
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const response = await client.messages.create({
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model: model || 'claude-sonnet-4-5-20250929',
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max_tokens: 4096,
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system: body.system,
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messages: [
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{
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role: 'user',
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content: [
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{
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type: 'image',
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source: { type: 'base64', media_type: 'image/png', data: base64Data },
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},
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{
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type: 'text',
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text: body.message,
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},
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],
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},
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],
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})
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const textBlock = response.content.find((b: { type: string }) => b.type === 'text')
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return { text: textBlock && 'text' in textBlock ? textBlock.text : '' }
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}
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/**
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* Agent SDK fallback: save screenshot to a temp PNG file, then ask Claude
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* Code to read it (Claude Code's Read tool supports images natively).
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*/
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async function validateViaAgentSDK(
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body: ValidateBody,
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model?: string,
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): Promise<{ text: string; skipped?: boolean; error?: string }> {
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// Save base64 image to temp file
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let base64Data = body.imageBase64
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const dataUrlPrefix = 'data:image/png;base64,'
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if (base64Data.startsWith(dataUrlPrefix)) {
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base64Data = base64Data.slice(dataUrlPrefix.length)
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}
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const tempDir = await mkdtemp(join(tmpdir(), 'openpencil-validate-'))
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const tempPath = join(tempDir, 'screenshot.png')
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try {
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await writeFile(tempPath, Buffer.from(base64Data, 'base64'))
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const { query } = await import('@anthropic-ai/claude-agent-sdk')
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const env = { ...process.env } as Record<string, string | undefined>
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delete env.CLAUDECODE
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const claudePath = resolveClaudeCli()
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// Prompt Claude Code to read the temp image and analyze it
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const prompt = `Read the image file at "${tempPath}" and analyze it as a UI design screenshot.
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${body.message}
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${body.system}
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Output ONLY the JSON object, no markdown fences, no explanation.`
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const q = query({
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prompt,
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options: {
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model: model || 'claude-sonnet-4-6',
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maxTurns: 2,
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tools: [],
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plugins: [],
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permissionMode: 'plan',
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persistSession: false,
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env,
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...(claudePath ? { pathToClaudeCodeExecutable: claudePath } : {}),
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},
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})
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for await (const message of q) {
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if (message.type === 'result') {
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if (message.subtype === 'success') {
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return { text: message.result }
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}
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const errors = 'errors' in message ? (message.errors as string[]) : []
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return { error: errors.join('; ') || `Query ended with: ${message.subtype}`, text: '' }
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}
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}
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return { text: '', skipped: true }
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} finally {
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// Clean up temp file
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try {
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await unlink(tempPath)
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} catch { /* ignore */ }
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}
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}
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