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{ tool } from '@strands-agents/sdk'\nimport { z } from 'zod'\n\nfunction getKey(): string | null {\n try {\n const raw = localStorage.getItem('careless-v2-settings')\n const s = raw ? JSON.parse(raw) : {}\n // Prefer dedicated openaiApiKey, fall back to main key only if provider is openai\n return s.openaiApiKey || (s.provider === 'openai' ? s.apiKey : null) || null\n } catch { return null }\n}\n\n/**\n * Persist a base64 image to IndexedDB so it survives reloads and can be referenced\n * from conversation history. Returns an id we embed in markdown.\n */\nasync function storeImage(b64: string, mime: string, prompt: string): Promise {\n const id = `img_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`\n try {\n const db = await openImageDb()\n const tx = db.transaction('images', 'readwrite')\n tx.objectStore('images').put({ id, b64, mime, prompt, createdAt: Date.now() })\n await new Promise((res, rej) => { tx.oncomplete = () => res(); tx.onerror = () => rej(tx.error) })\n } catch (e) {\n console.warn('[openai-image] IDB store failed, image is session-only', e)\n }\n return id\n}\n\nfunction openImageDb(): Promise {\n return new Promise((resolve, reject) => {\n const req = indexedDB.open('careless-images', 1)\n req.onupgradeneeded = () => {\n const db = req.result\n if (!db.objectStoreNames.contains('images')) db.createObjectStore('images', { keyPath: 'id' })\n }\n req.onsuccess = () => resolve(req.result)\n req.onerror = () => reject(req.error)\n })\n}\n\nconst GPT_IMAGE_MODELS = ['gpt-image-1.5', 'gpt-image-1', 'gpt-image-1-mini']\nconst GPT_IMAGE_SIZES = ['auto', '1024x1024', '1536x1024', '1024x1536'] as const\nconst DALL_E_3_SIZES = ['1024x1024', '1792x1024', '1024x1792'] as const\n\nexport const generateImageTool = tool({\n name: 'generate_image',\n description:\n 'Generate an image from a text prompt via OpenAI. Supports gpt-image-1.5 (default, best), gpt-image-1, gpt-image-1-mini, dall-e-3, dall-e-2. Returns a data-URL embedded in markdown so it renders inline.',\n inputSchema: z.object({\n prompt: z.string().min(1).max(32000).describe('Text description of the image'),\n model: z\n .enum(['gpt-image-1.5', 'gpt-image-1', 'gpt-image-1-mini', 'dall-e-3', 'dall-e-2'])\n .optional()\n .describe('Model. Default: gpt-image-1.5 (recommended).'),\n size: z\n .string()\n .optional()\n .describe('Size. gpt-image: auto|1024x1024|1536x1024|1024x1536. dall-e-3: 1024x1024|1792x1024|1024x1792. Default: 1024x1024.'),\n quality: z\n .enum(['auto', 'low', 'medium', 'high', 'standard', 'hd'])\n .optional()\n .describe('Quality. gpt-image: auto|low|medium|high. dall-e-3: standard|hd. Default: auto.'),\n format: z.enum(['png', 'jpeg', 'webp']).optional().describe('Output format (gpt-image only). Default: png.'),\n background: z.enum(['transparent', 'opaque', 'auto']).optional().describe('Background (gpt-image only). Default: auto. Transparent requires png/webp.'),\n compression: z.number().min(0).max(100).optional().describe('Compression 0-100 for jpeg/webp (gpt-image only).'),\n n: z.number().min(1).max(4).optional().describe('Number of images (1-4). Default: 1.'),\n }),\n callback: async (input) => {\n try {\n const key = getKey()\n if (!key) {\n return JSON.stringify({\n status: 'error',\n error: 'OpenAI API key required. Add it in Settings → OpenAI.',\n })\n }\n\n const model = input.model || 'gpt-image-1.5'\n const isGptImage = GPT_IMAGE_MODELS.includes(model)\n const isDallE3 = model === 'dall-e-3'\n\n // Build body based on model family\n const body: Record = {\n model,\n prompt: input.prompt,\n n: input.n || 1,\n }\n\n // Size handling\n if (input.size) {\n body.size = input.size\n } else {\n body.size = '1024x1024'\n }\n\n // Quality handling — map generic values to model-specific\n if (input.quality) {\n if (isDallE3 && !['standard', 'hd'].includes(input.quality)) {\n body.quality = 'standard'\n } else if (isGptImage && !['auto', 'low', 'medium', 'high'].includes(input.quality)) {\n body.quality = 'auto'\n } else {\n body.quality = input.quality\n }\n }\n\n // GPT-image-only params\n if (isGptImage) {\n if (input.format) body.output_format = input.format\n if (input.background) body.background = input.background\n if (input.compression !== undefined && (input.format === 'jpeg' || input.format === 'webp')) {\n body.output_compression = input.compression\n }\n }\n\n // DALL-E 2/3 need explicit b64_json response_format (GPT-image always returns b64)\n if (!isGptImage) {\n body.response_format = 'b64_json'\n }\n\n const res = await fetch('https://api.openai.com/v1/images/generations', {\n method: 'POST',\n headers: {\n Authorization: `Bearer ${key}`,\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(body),\n })\n\n const data = await res.json()\n if (!res.ok) {\n return JSON.stringify({\n status: 'error',\n error: data.error?.message || `HTTP ${res.status}`,\n code: data.error?.code,\n type: data.error?.type,\n })\n }\n\n const images = data.data || []\n if (!images.length) {\n return JSON.stringify({ status: 'error', error: 'No images returned' })\n }\n\n const mime = `image/${(data.output_format || input.format || 'png')}`\n const results = await Promise.all(\n images.map(async (img: { b64_json?: string; url?: string; revised_prompt?: string }) => {\n const b64 = img.b64_json\n if (!b64) {\n // Shouldn't happen for our configured request but handle gracefully\n return { url: img.url, revised_prompt: img.revised_prompt }\n }\n const id = await storeImage(b64, mime, input.prompt)\n const dataUrl = `data:${mime};base64,${b64}`\n return { id, dataUrl, revised_prompt: img.revised_prompt }\n })\n )\n\n // Build markdown so the agent's response renders images inline.\n // The MarkdownRenderer's handler will wire click-to-fullscreen.\n const markdown = results\n .map((r, i) => {\n const alt = (r as any).revised_prompt || input.prompt\n const src = (r as any).dataUrl || (r as any).url\n return `![${alt.replace(/[\\[\\]]/g, '').slice(0, 120)}](${src})`\n })\n .join('\\n\\n')\n\n return JSON.stringify({\n status: 'success',\n model,\n size: body.size,\n count: results.length,\n usage: data.usage,\n images: results.map((r: any) => ({\n id: r.id,\n revised_prompt: r.revised_prompt,\n // Do NOT echo dataUrl back to agent context — it would blow up tokens.\n // The markdown below already embeds it for rendering.\n has_data_url: !!r.dataUrl,\n url: r.url,\n })),\n markdown,\n hint: 'Include the `markdown` field in your reply so the user sees the image.',\n })\n } catch (err: unknown) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\nexport const editImageTool = tool({\n name: 'edit_image',\n description:\n 'Edit or combine existing images via OpenAI gpt-image-1.5 / gpt-image-1. Pass 1-16 images as data URLs or https URLs plus a prompt describing the edit.',\n inputSchema: z.object({\n prompt: z.string().min(1).describe('Description of the desired edit'),\n images: z\n .array(z.string())\n .min(1)\n .max(16)\n .describe('Array of data URLs (data:image/...) or https URLs of input images'),\n model: z\n .enum(['gpt-image-1.5', 'gpt-image-1', 'gpt-image-1-mini'])\n .optional()\n .describe('Model. Default: gpt-image-1.5.'),\n size: z.enum(GPT_IMAGE_SIZES).optional().describe('Output size. Default: auto.'),\n quality: z.enum(['auto', 'low', 'medium', 'high']).optional().describe('Default: auto.'),\n format: z.enum(['png', 'jpeg', 'webp']).optional(),\n background: z.enum(['transparent', 'opaque', 'auto']).optional(),\n mask: z.string().optional().describe('Optional mask as data URL or https URL (alpha channel indicates edit region)'),\n n: z.number().min(1).max(4).optional(),\n }),\n callback: async (input) => {\n try {\n const key = getKey()\n if (!key) return JSON.stringify({ status: 'error', error: 'OpenAI API key required' })\n\n const model = input.model || 'gpt-image-1.5'\n\n // /v1/images/edits with JSON body + images[] array is supported on newer GPT-image models\n const body: Record = {\n model,\n prompt: input.prompt,\n images: input.images.map((url) => ({ image_url: url })),\n n: input.n || 1,\n }\n if (input.mask) body.mask = { image_url: input.mask }\n if (input.size) body.size = input.size\n if (input.quality) body.quality = input.quality\n if (input.format) body.output_format = input.format\n if (input.background) body.background = input.background\n\n const res = await fetch('https://api.openai.com/v1/images/edits', {\n method: 'POST',\n headers: {\n Authorization: `Bearer ${key}`,\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(body),\n })\n\n const data = await res.json()\n if (!res.ok) {\n return JSON.stringify({\n status: 'error',\n error: data.error?.message || `HTTP ${res.status}`,\n code: data.error?.code,\n })\n }\n\n const images = data.data || []\n const mime = `image/${data.output_format || input.format || 'png'}`\n const results = await Promise.all(\n images.map(async (img: { b64_json?: string }) => {\n if (!img.b64_json) return null\n const id = await storeImage(img.b64_json, mime, `edit: ${input.prompt}`)\n return { id, dataUrl: `data:${mime};base64,${img.b64_json}` }\n })\n )\n\n const markdown = results\n .filter(Boolean)\n .map((r: any) => `![${input.prompt.replace(/[\\[\\]]/g, '').slice(0, 120)}](${r.dataUrl})`)\n .join('\\n\\n')\n\n return JSON.stringify({\n status: 'success',\n model,\n count: results.filter(Boolean).length,\n usage: data.usage,\n images: results.filter(Boolean).map((r: any) => ({ id: r.id })),\n markdown,\n hint: 'Include the `markdown` in your reply to render the edited image.',\n })\n } catch (err) {\n return JSON.stringify({ status: 'error', error: (err as Error).message })\n }\n },\n})\n\nexport const OPENAI_IMAGE_TOOLS = [generateImageTool, editImageTool]\n"]}