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Domyślnie \"gpt-image-1\"\n// (obsługuje edits + input_fidelity). Jeśli Twoje konto edytuje na \"gpt-image-2\",\n// zmień EDIT_MODEL niżej. Jeśli dostaniesz 400 o nieznanym parametrze —\n// usuń najpierw output_compression, potem input_fidelity.\n\nimport { blob } from \"https://esm.town/v/std/blob\";\n\nconst MODEL = \"gpt-image-2\"; // generacja (bez zmian)\nconst EDIT_MODEL = \"gpt-image-1\"; // edycja (/v1/images/edits)\nlet SELF_ORIGIN = \"\";\n\nasync function generateImage(prompt: string, size: string) {\n const key = Deno.env.get(\"OPENAI_API_KEY\");\n if (!key) return { err: \"Brak OPENAI_API_KEY.\" };\n const res = await fetch(\"https://api.openai.com/v1/images/generations\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${key}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: MODEL,\n prompt,\n size: size || \"1024x1024\",\n n: 1,\n output_format: \"webp\",\n output_compression: 50,\n }),\n });\n if (!res.ok) {\n return {\n err: `Błąd OpenAI (${res.status}): ${(await res.text()).slice(0, 400)}`,\n };\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) return { err: \"Brak obrazu.\" };\n return { b64 };\n}\n\n// --- sniff typu obrazu po magic-bytes (dla multipart File) ---\nfunction sniff(bytes: Uint8Array): { mime: string; ext: string } {\n if (\n bytes.length > 12 && bytes[0] === 0x52 && bytes[1] === 0x49 &&\n bytes[2] === 0x46 && bytes[3] === 0x46 && bytes[8] === 0x57 &&\n bytes[9] === 0x45 && bytes[10] === 0x42 && bytes[11] === 0x50\n ) return { mime: \"image/webp\", ext: \"webp\" };\n if (\n bytes[0] === 0x89 && bytes[1] === 0x50 && bytes[2] === 0x4e &&\n bytes[3] === 0x47\n ) {\n return { mime: \"image/png\", ext: \"png\" };\n }\n if (bytes[0] === 0xff && bytes[1] === 0xd8) {\n return { mime: \"image/jpeg\", ext: \"jpg\" };\n }\n return { mime: \"image/png\", ext: \"png\" };\n}\n\n// --- \"image\" (URL / id / data-URL / base64) → bajty ---\nasync function resolveBytes(image: string): Promise {\n if (!image) throw new Error(\"image is required\");\n if (image.startsWith(\"http://\") || image.startsWith(\"https://\")) {\n const r = await fetch(image);\n if (!r.ok) throw new Error(\"fetch url \" + r.status);\n return new Uint8Array(await r.arrayBuffer());\n }\n if (image.startsWith(\"data:\")) {\n const b64 = image.split(\",\")[1] ?? \"\";\n return Uint8Array.from(atob(b64), (c) => c.charCodeAt(0));\n }\n // może to id zapisanego obrazu\n try {\n const b = await blob.get(`img_${image}`);\n if (b) return new Uint8Array(await b.arrayBuffer());\n } catch { /* nie id — lecimy dalej */ }\n // ostatecznie czysty base64\n return Uint8Array.from(atob(image), (c) => c.charCodeAt(0));\n}\n\n// --- RDZEŃ EDYCJI ---\nasync function editImage(\n image: string,\n prompt: string,\n mask?: string,\n size?: string,\n) {\n const key = Deno.env.get(\"OPENAI_API_KEY\");\n if (!key) return { err: \"Brak OPENAI_API_KEY.\" };\n let bytes: Uint8Array;\n try {\n bytes = await resolveBytes(image);\n } catch (e) {\n return { err: \"Nie udało się wczytać obrazu wejściowego: \" + String(e) };\n }\n const { mime, ext } = sniff(bytes);\n const form = new FormData();\n form.append(\"model\", EDIT_MODEL);\n form.append(\"image\", new File([bytes], `image.${ext}`, { type: mime }));\n form.append(\"prompt\", prompt);\n form.append(\"input_fidelity\", \"high\"); // zachowaj wejście maksymalnie\n form.append(\"output_format\", \"webp\");\n form.append(\"output_compression\", \"50\");\n if (size) form.append(\"size\", size);\n if (mask) {\n try {\n const mb = await resolveBytes(mask);\n form.append(\"mask\", new File([mb], \"mask.png\", { type: \"image/png\" }));\n } catch { /* maska opcjonalna */ }\n }\n const res = await fetch(\"https://api.openai.com/v1/images/edits\", {\n method: \"POST\",\n headers: { \"Authorization\": `Bearer ${key}` }, // NIE ustawiaj Content-Type\n body: form,\n });\n if (!res.ok) {\n return {\n err: `Błąd OpenAI edits (${res.status}): ${\n (await res.text()).slice(0, 400)\n }`,\n };\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) return { err: \"Brak obrazu (edit).\" };\n return { b64 };\n}\n\n// --- zapis base64(webp) → {id,url} (jak w generate batch) ---\nasync function storeWebpB64(b64: string): Promise<{ id: string; url: string }> {\n const imgId = crypto.randomUUID();\n const bytes = Uint8Array.from(atob(b64), (c) => c.charCodeAt(0));\n await blob.set(`img_${imgId}`, new Blob([bytes], { type: \"image/webp\" }));\n return { id: imgId, url: `${SELF_ORIGIN}/img/${imgId}` };\n}\n\n// bytes → base64 (kawałkami, bezpieczne dla dużych)\nfunction bytesToB64(bytes: Uint8Array): string {\n let binary = \"\";\n const chunk = 0x8000;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode(...bytes.subarray(i, i + chunk));\n }\n return btoa(binary);\n}\n\nconst TOOLS = [\n {\n name: \"generate_image\",\n description:\n \"Generuje obraz przez OpenAI gpt-image-2 i zwraca go jako lekki WebP base64 w polu tekstowym, aby asystent mógł zapisać plik i wyświetlić go automatycznie. Świetny do infografik, ilustracji, grafik z tekstem.\",\n inputSchema: {\n type: \"object\",\n properties: {\n prompt: { type: \"string\", description: \"Opis obrazu\" },\n size: {\n type: \"string\",\n description:\n \"np. 1024x1024, 1536x1024, 1024x1536, 1536x2048 (2K), 2160x3840 (4K)\",\n },\n },\n required: [\"prompt\"],\n },\n },\n {\n name: \"generate_images_batch\",\n description:\n \"★ SZYBKIE: generuje WIELE obrazów równolegle (Promise.all). Zwraca lekką listę {index, id, url} — BEZ base64. base64 przez get_image(id) / get_images(ids).\",\n inputSchema: {\n type: \"object\",\n properties: {\n prompts: {\n type: \"array\",\n items: { type: \"string\" },\n description: \"Lista promptów\",\n },\n size: { type: \"string\", default: \"1024x1536\" },\n max_concurrent: {\n type: \"number\",\n default: 4,\n description: \"Max równoległych (≤7)\",\n },\n },\n required: [\"prompts\"],\n },\n },\n {\n name: \"edit_image\",\n description:\n \"★ EDYCJA: bierze obraz wejściowy i dodaje/zmienia TYLKO to, co opisuje prompt, zachowując resztę bez zmian (input_fidelity high). 'image' = URL (z generate/edit), id zapisanego obrazu, data-URL lub base64. Idealne do kart 'krok po kroku' (panel N = panel N-1 + jeden element) — wynik podajesz jako 'image' kolejnej edycji. Zwraca {id,url}; base64 przez get_image(id).\",\n inputSchema: {\n type: \"object\",\n properties: {\n image: {\n type: \"string\",\n description: \"Obraz wejściowy: URL / id / data-URL / base64\",\n },\n prompt: {\n type: \"string\",\n description:\n \"Co dodać, np. 'add two pointed cat ears on top; do not change anything else'\",\n },\n mask: {\n type: \"string\",\n description:\n \"Opcjonalna maska PNG (przezroczyste = obszar do edycji)\",\n },\n size: { type: \"string\", description: \"np. 1024x1536\" },\n },\n required: [\"image\", \"prompt\"],\n },\n },\n {\n name: \"edit_images_batch\",\n description:\n \"★ Wiele edycji równolegle. Każdy element edits[]: {image, prompt, mask?}. Zwraca {index,id,url}. base64 przez get_images(ids).\",\n inputSchema: {\n type: \"object\",\n properties: {\n edits: {\n type: \"array\",\n items: {\n type: \"object\",\n properties: {\n image: { type: \"string\" },\n prompt: { type: \"string\" },\n mask: { type: \"string\" },\n },\n required: [\"image\", \"prompt\"],\n },\n description: \"Lista edycji\",\n },\n size: { type: \"string\", default: \"1024x1536\" },\n max_concurrent: {\n type: \"number\",\n default: 4,\n description: \"Max równoległych (≤7)\",\n },\n },\n required: [\"edits\"],\n },\n },\n {\n name: \"get_images\",\n description:\n \"★ Zwraca base64 (WebP) WIELU obrazów naraz wg listy id. JEDNO wywołanie. Duża odpowiedź trafia do tool_results na dysku.\",\n inputSchema: {\n type: \"object\",\n properties: {\n ids: {\n type: \"array\",\n items: { type: \"string\" },\n description: \"Lista id obrazów\",\n },\n },\n required: [\"ids\"],\n },\n },\n {\n name: \"get_image\",\n description:\n \"Zwraca base64 (WebP) JEDNEGO obrazu wg id (fallback / retry). Do wielu użyj get_images.\",\n inputSchema: {\n type: \"object\",\n properties: {\n id: { type: \"string\", description: \"id obrazu\" },\n },\n required: [\"id\"],\n },\n },\n];\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n SELF_ORIGIN = url.origin;\n\n if (req.method === \"GET\" && url.pathname.startsWith(\"/img/\")) {\n const id = url.pathname.slice(\"/img/\".length);\n try {\n const b = await blob.get(`img_${id}`);\n return new Response(await b.arrayBuffer(), {\n headers: { \"content-type\": \"image/webp\" },\n });\n } catch {\n return new Response(\"not found\", { status: 404 });\n }\n }\n\n if (req.method === \"GET\") {\n return new Response(\n \"OpenAI Obrazy v4.0 — generate + edit. batch zwraca id+url, base64 przez get_image(id). POST /mcp\",\n );\n }\n\n let body: any;\n try {\n body = await req.json();\n } catch {\n return Response.json({\n jsonrpc: \"2.0\",\n id: null,\n error: { code: -32700, message: \"Parse error\" },\n });\n }\n\n const { id, method, params } = body;\n const ok = (result: any) => Response.json({ jsonrpc: \"2.0\", id, result });\n\n if (method === \"initialize\") {\n return ok({\n protocolVersion: \"2024-11-05\",\n capabilities: { tools: {} },\n serverInfo: { name: \"openai-obrazy-v4\", version: \"4.0.0\" },\n });\n }\n if (method?.startsWith?.(\"notifications/\")) {\n return new Response(null, { status: 202 });\n }\n if (method === \"tools/list\") return ok({ tools: TOOLS });\n\n if (method === \"tools/call\") {\n // ── generate_image (single) ──\n if (params?.name === \"generate_image\") {\n const { b64, err } = await generateImage(\n params?.arguments?.prompt ?? \"\",\n params?.arguments?.size ?? \"1024x1024\",\n );\n if (err) {\n return ok({ content: [{ type: \"text\", text: err }], isError: true });\n }\n return ok({\n content: [{\n type: \"text\",\n text: `OBRAZ_WEBP_BASE64_POCZATEK\\n${b64}\\nOBRAZ_WEBP_BASE64_KONIEC`,\n }],\n });\n }\n\n // ── generate_images_batch ──\n if (params?.name === \"generate_images_batch\") {\n const prompts: string[] = params?.arguments?.prompts ?? [];\n if (!prompts.length) {\n return ok({\n content: [{ type: \"text\", text: \"BŁĄD: Brak promptów.\" }],\n isError: true,\n });\n }\n const size = params?.arguments?.size ?? \"1024x1536\";\n const maxC = Math.min(\n Number(params?.arguments?.max_concurrent ?? 4),\n prompts.length,\n );\n const results: Array<\n { index: number; id: string; url: string; ok: boolean }\n > = new Array(prompts.length);\n const queue = prompts.map((p, i) => ({ p, i }));\n const worker = async () => {\n while (queue.length > 0) {\n const item = queue.shift();\n if (!item) break;\n let b64: string | undefined, err: string | undefined;\n try {\n ({ b64, err } = await generateImage(item.p, size));\n } catch (e) {\n err = String(e);\n }\n if (err || !b64) {\n await new Promise((r) => setTimeout(r, 2000));\n ({ b64, err } = await generateImage(item.p, size));\n }\n if (!b64) {\n results[item.i] = { index: item.i, id: \"\", url: \"\", ok: false };\n continue;\n }\n const s = await storeWebpB64(b64);\n results[item.i] = { index: item.i, id: s.id, url: s.url, ok: true };\n }\n };\n await Promise.all(Array(maxC).fill(null).map(() => worker()));\n const output = JSON.stringify({\n count: prompts.length,\n ok: results.filter((r) => r.ok).length,\n failed: results.filter((r) => !r.ok).map((r) => r.index),\n images: results.map((r) => ({ index: r.index, id: r.id, url: r.url })),\n hint: \"Pobierz base64 każdego obrazu przez get_image(id).\",\n });\n return ok({ content: [{ type: \"text\", text: output }] });\n }\n\n // ── edit_image (single) ── NOWE\n if (params?.name === \"edit_image\") {\n const a = params?.arguments ?? {};\n if (!a.image || !a.prompt) {\n return ok({\n content: [{ type: \"text\", text: \"BŁĄD: wymagane image i prompt.\" }],\n isError: true,\n });\n }\n const { b64, err } = await editImage(a.image, a.prompt, a.mask, a.size);\n if (err || !b64) {\n return ok({\n content: [{ type: \"text\", text: err || \"Brak obrazu.\" }],\n isError: true,\n });\n }\n const s = await storeWebpB64(b64);\n return ok({\n content: [{\n type: \"text\",\n text: JSON.stringify({\n id: s.id,\n url: s.url,\n hint: \"base64 przez get_image(id).\",\n }),\n }],\n });\n }\n\n // ── edit_images_batch ── NOWE\n if (params?.name === \"edit_images_batch\") {\n const edits: Array<{ image: string; prompt: string; mask?: string }> =\n params?.arguments?.edits ?? [];\n if (!edits.length) {\n return ok({\n content: [{ type: \"text\", text: \"BŁĄD: brak edits.\" }],\n isError: true,\n });\n }\n const size = params?.arguments?.size;\n const maxC = Math.min(\n Number(params?.arguments?.max_concurrent ?? 4),\n edits.length,\n );\n const results: Array<\n { index: number; id: string; url: string; ok: boolean }\n > = new Array(edits.length);\n const queue = edits.map((e, i) => ({ e, i }));\n const worker = async () => {\n while (queue.length > 0) {\n const item = queue.shift();\n if (!item) break;\n let r = await editImage(\n item.e.image,\n item.e.prompt,\n item.e.mask,\n size,\n );\n if (r.err || !r.b64) {\n await new Promise((rs) => setTimeout(rs, 2000));\n r = await editImage(item.e.image, item.e.prompt, item.e.mask, size);\n }\n if (!r.b64) {\n results[item.i] = { index: item.i, id: \"\", url: \"\", ok: false };\n continue;\n }\n const s = await storeWebpB64(r.b64);\n results[item.i] = { index: item.i, id: s.id, url: s.url, ok: true };\n }\n };\n await Promise.all(Array(maxC).fill(null).map(() => worker()));\n const output = JSON.stringify({\n count: edits.length,\n ok: results.filter((r) => r.ok).length,\n failed: results.filter((r) => !r.ok).map((r) => r.index),\n images: results.map((r) => ({ index: r.index, id: r.id, url: r.url })),\n hint: \"Pobierz base64 przez get_images(ids).\",\n });\n return ok({ content: [{ type: \"text\", text: output }] });\n }\n\n // ── get_images(ids[]) ──\n if (params?.name === \"get_images\") {\n const ids: string[] = params?.arguments?.ids ?? [];\n if (!ids.length) {\n return ok({\n content: [{ type: \"text\", text: \"BŁĄD: brak ids.\" }],\n isError: true,\n });\n }\n const images = await Promise.all(ids.map(async (imgId) => {\n try {\n const b = await blob.get(`img_${imgId}`);\n const bytes = new Uint8Array(await b.arrayBuffer());\n return { id: imgId, b64: bytesToB64(bytes), ok: true };\n } catch {\n return { id: imgId, b64: \"\", ok: false };\n }\n }));\n const output = JSON.stringify({\n count: images.length,\n ok: images.filter((i) => i.ok).length,\n failed: images.filter((i) => !i.ok).map((i) => i.id),\n images,\n });\n return ok({ content: [{ type: \"text\", text: output }] });\n }\n\n // ── get_image(id) ──\n if (params?.name === \"get_image\") {\n const imgId = params?.arguments?.id ?? \"\";\n if (!imgId) {\n return ok({\n content: [{ type: \"text\", text: \"BŁĄD: brak id.\" }],\n isError: true,\n });\n }\n try {\n const b = await blob.get(`img_${imgId}`);\n const bytes = new Uint8Array(await b.arrayBuffer());\n const b64 = bytesToB64(bytes);\n return ok({\n content: [{\n type: \"text\",\n text:\n `OBRAZ_WEBP_BASE64_POCZATEK\\n${b64}\\nOBRAZ_WEBP_BASE64_KONIEC`,\n }],\n });\n } catch {\n return ok({\n content: [{\n type: \"text\",\n text: `BŁĄD: nie znaleziono obrazu ${imgId}.`,\n }],\n isError: true,\n });\n }\n }\n\n return Response.json({\n jsonrpc: \"2.0\",\n id,\n error: { code: -32602, message: \"Nieznane narzędzie\" },\n });\n }\n\n return Response.json({\n jsonrpc: \"2.0\",\n id,\n error: { code: -32601, message: \"Method not found\" },\n });\n}","// ============================================================\n// CHATGPT (OpenAI) — MCP serwer (Val Town / Deno) v2.0\n// Generacja gpt-image-2 + edycja + WIZJA (gpt-5.5 / gpt-4o) + batch (pLimit)\n// Hosting obrazów w blob, serwowane pod /img/. ID trwałe między edycjami kodu.\n// WIELOWĄTKOWOŚĆ wszędzie gdzie LISTA.\n// ENV: OPENAI_API_KEY (potwierdź nazwę sekretu w swoim valu!)\n// ============================================================\nimport { blob } from \"https://esm.town/v/std/blob\";\n\nconst IMG_MODEL_DEFAULT = \"gpt-image-2\"; // SOTA, 4K, snapshot gpt-image-2-2026-04-21\nconst VIS_MODEL_DEFAULT = \"gpt-5.5\"; // widzi obrazy (vision IN), tekst OUT; fallback gpt-4o\nconst IMG_MODELS: Record = {\n \"image2\": \"gpt-image-2\",\n \"gpt-image-2\": \"gpt-image-2\",\n \"image1\": \"gpt-image-1\",\n};\nconst VIS_MODELS: Record = {\n \"5.5\": \"gpt-5.5\",\n \"pro\": \"gpt-5.5-pro\",\n \"5.4\": \"gpt-5.4\",\n \"4o\": \"gpt-4o\",\n \"mini\": \"gpt-5.4-mini\",\n};\nconst resolveImg = (m?: string) =>\n !m ? IMG_MODEL_DEFAULT : (IMG_MODELS[m] ?? m);\nconst resolveVis = (m?: string) =>\n !m ? VIS_MODEL_DEFAULT : (VIS_MODELS[m] ?? m);\n\nconst RETRY = {\n maxAttempts: 6,\n baseMs: 800,\n factor: 1.8,\n capMs: 20_000,\n retryStatuses: new Set([408, 409, 425, 429, 500, 502, 503, 504]),\n};\nconst TIMEOUT_MS = 120_000;\nconst DEFAULT_CONCURRENCY = 4, MAX_CONCURRENCY = 8; // gpt-image bywa wolny -> ostrożny default\nconst sleep = (ms: number) => new Promise((r) => setTimeout(r, ms));\nconst jitter = (ms: number) => Math.round(ms * (0.7 + Math.random() * 0.6));\n\nclass OErr extends Error {\n status: number;\n retryable: boolean;\n retryAfterMs?: number;\n constructor(m: string, s: number, r: boolean) {\n super(m);\n this.status = s;\n this.retryable = r;\n }\n}\n\nfunction pLimit(limit: number) {\n let active = 0;\n const q: (() => void)[] = [];\n const next = () => {\n active--;\n q.shift()?.();\n };\n return (fn: () => Promise) =>\n new Promise((res, rej) => {\n const run = () => {\n active++;\n fn().then(res, rej).finally(next);\n };\n active < limit ? run() : q.push(run);\n });\n}\nasync function withRetry(fn: () => Promise): Promise {\n let a = 0, wait = RETRY.baseMs, last: unknown;\n while (a < RETRY.maxAttempts) {\n try {\n return await fn();\n } catch (e) {\n const o = e as OErr;\n if (!o.retryable) throw e;\n if (++a >= RETRY.maxAttempts) break;\n await sleep(o.retryAfterMs ?? jitter(Math.min(wait, RETRY.capMs)));\n wait *= RETRY.factor;\n last = e;\n }\n }\n throw last;\n}\nconst KEY = () => {\n const k = Deno.env.get(\"OPENAI_API_KEY\");\n if (!k) throw new OErr(\"brak OPENAI_API_KEY w ENV\", 500, false);\n return k;\n};\n\n// zapis obrazu do blob -> {id,url}\nlet SELF_ORIGIN = \"\";\nasync function storeImage(b64: string, mime = \"image/png\") {\n const id = crypto.randomUUID().replace(/-/g, \"\");\n await blob.setJSON(`img:${id}`, { b64, mime });\n return { id, url: `${SELF_ORIGIN}/img/${id}` };\n}\nasync function loadBytes(\n image: string,\n): Promise<{ bytes: Uint8Array; mime: string }> {\n // image = http(s) URL | data-URL | val id | surowy base64\n if (/^https?:/i.test(image)) {\n const r = await fetch(image);\n if (!r.ok) {\n throw new OErr(\n `pobranie obrazu HTTP ${r.status}`,\n r.status,\n RETRY.retryStatuses.has(r.status),\n );\n }\n return {\n bytes: new Uint8Array(await r.arrayBuffer()),\n mime: r.headers.get(\"content-type\") || \"image/png\",\n };\n }\n if (image.startsWith(\"data:\")) {\n const mime = image.slice(5, image.indexOf(\";\")) || \"image/png\";\n const b = image.replace(/^data:[^,]+,/, \"\");\n return { bytes: Uint8Array.from(atob(b), (c) => c.charCodeAt(0)), mime };\n }\n const o = await blob.getJSON(`img:${image}`);\n if (o) {\n return {\n bytes: Uint8Array.from(atob(o.b64), (c) => c.charCodeAt(0)),\n mime: o.mime || \"image/png\",\n };\n }\n return {\n bytes: Uint8Array.from(atob(image), (c) => c.charCodeAt(0)),\n mime: \"image/png\",\n }; // surowy base64\n}\n\n// === GENERACJA (images/generations) ===\nasync function genOne(\n o: { prompt: string; model?: string; size?: string; quality?: string },\n) {\n return await withRetry(async () => {\n const ctrl = new AbortController();\n const t = setTimeout(() => ctrl.abort(), TIMEOUT_MS);\n let res: Response;\n try {\n 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({\n model: resolveImg(o.model),\n prompt: o.prompt,\n n: 1,\n ...(o.size ? { size: o.size } : {}),\n ...(o.quality ? { quality: o.quality } : {}),\n }),\n signal: ctrl.signal,\n });\n } catch (e) {\n throw new OErr(`network: ${(e as Error).message}`, 0, true);\n } finally {\n clearTimeout(t);\n }\n if (!res.ok) {\n const b = await res.text().catch(() => \"\");\n throw new OErr(\n `HTTP ${res.status}: ${b.slice(0, 300)}`,\n res.status,\n RETRY.retryStatuses.has(res.status),\n );\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) throw new OErr(\"brak b64_json w odpowiedzi\", 200, false);\n return await storeImage(b64, \"image/png\");\n });\n}\n// === EDYCJA (images/edits, input_fidelity high) ===\nasync function editOne(\n o: { image: string; prompt: string; model?: string; size?: string },\n) {\n return await withRetry(async () => {\n const { bytes, mime } = await loadBytes(o.image);\n const fd = new FormData();\n fd.append(\"model\", resolveImg(o.model));\n fd.append(\"prompt\", o.prompt);\n fd.append(\"input_fidelity\", \"high\");\n if (o.size) fd.append(\"size\", o.size);\n fd.append(\n \"image\",\n new Blob([bytes], { type: mime || \"image/png\" }),\n \"image.png\",\n );\n const ctrl = new AbortController();\n const t = setTimeout(() => ctrl.abort(), TIMEOUT_MS);\n let res: Response;\n try {\n res = await fetch(\"https://api.openai.com/v1/images/edits\", {\n method: \"POST\",\n headers: { Authorization: `Bearer ${KEY()}` },\n body: fd,\n signal: ctrl.signal,\n });\n } catch (e) {\n throw new OErr(`network: ${(e as Error).message}`, 0, true);\n } finally {\n clearTimeout(t);\n }\n if (!res.ok) {\n const b = await res.text().catch(() => \"\");\n throw new OErr(\n `HTTP ${res.status}: ${b.slice(0, 300)}`,\n res.status,\n RETRY.retryStatuses.has(res.status),\n );\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) throw new OErr(\"brak b64_json w odpowiedzi\", 200, false);\n return await storeImage(b64, \"image/png\");\n });\n}\n// === WIZJA (chat/completions, obraz -> tekst/JSON) ===\nasync function visionOne(\n o: {\n prompt: string;\n model?: string;\n image_b64?: string;\n image_url?: string;\n mime?: string;\n as_json?: boolean;\n },\n) {\n return await withRetry(async () => {\n let imgUrl = o.image_url;\n if (!imgUrl && o.image_b64) {\n const b = o.image_b64.replace(/^data:[^,]+,/, \"\");\n imgUrl = `data:${o.mime || \"image/png\"};base64,${b}`;\n }\n if (!imgUrl) throw new OErr(\"podaj image_b64 albo image_url\", 400, false);\n const body: any = {\n model: resolveVis(o.model),\n messages: [{\n role: \"user\",\n content: [{ type: \"text\", text: o.prompt }, {\n type: \"image_url\",\n image_url: { url: imgUrl },\n }],\n }],\n ...(o.as_json ? { response_format: { type: \"json_object\" } } : {}),\n };\n const ctrl = new AbortController();\n const t = setTimeout(() => ctrl.abort(), TIMEOUT_MS);\n let res: Response;\n try {\n res = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${KEY()}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(body),\n signal: ctrl.signal,\n });\n } catch (e) {\n throw new OErr(`network: ${(e as Error).message}`, 0, true);\n } finally {\n clearTimeout(t);\n }\n if (!res.ok) {\n const b = await res.text().catch(() => \"\");\n throw new OErr(\n `HTTP ${res.status}: ${b.slice(0, 300)}`,\n res.status,\n RETRY.retryStatuses.has(res.status),\n );\n }\n const data = await res.json();\n const out = data?.choices?.[0]?.message?.content ?? \"\";\n if (!out.trim()) throw new OErr(\"pusta odpowiedź\", 200, false);\n return out.trim();\n });\n}\n\n// --- BATCH (pLimit) ---\nasync function batch(\n items: any[],\n conc: number,\n fn: (it: any, i: number) => Promise,\n) {\n const lim = pLimit(Math.max(1, Math.min(MAX_CONCURRENCY, conc)));\n const out = await Promise.all(items.map((it, index) =>\n lim(async () => {\n try {\n return { index, ok: true, ...(await fn(it, index) as any) };\n } catch (e) {\n return { index, ok: false, error: (e as Error).message };\n }\n })\n ));\n out.sort((a: any, b: any) => a.index - b.index);\n return out;\n}\n\nconst TOOLS = [\n {\n name: \"generate_image\",\n description:\n \"Generuje obraz (gpt-image-2). Zwraca {id,url}. base64 przez get_image(id).\",\n inputSchema: {\n type: \"object\",\n properties: {\n prompt: { type: \"string\" },\n model: { type: \"string\" },\n size: { type: \"string\" },\n quality: { type: \"string\" },\n },\n required: [\"prompt\"],\n },\n },\n {\n name: \"generate_images_batch\",\n description:\n \"★ WIELOWĄTKOWO generuje LISTĘ promptów (pLimit). Zwraca [{index,id,url}]. base64 przez get_images.\",\n inputSchema: {\n type: \"object\",\n properties: {\n prompts: { type: \"array\", items: { type: \"string\" } },\n model: { type: \"string\" },\n size: { type: \"string\" },\n max_concurrent: { type: \"number\" },\n },\n required: [\"prompts\"],\n },\n },\n {\n name: \"edit_image\",\n description:\n \"Edytuje obraz (gpt-image-2, input_fidelity high). image = url | data-url | id | base64. Zwraca {id,url}.\",\n inputSchema: {\n type: \"object\",\n properties: {\n image: { type: \"string\" },\n prompt: { type: \"string\" },\n model: { type: \"string\" },\n size: { type: \"string\" },\n },\n required: [\"image\", \"prompt\"],\n },\n },\n {\n name: \"edit_images_batch\",\n description:\n \"★ WIELOWĄTKOWO edytuje LISTĘ (pLimit). edits=[{image,prompt,model,size}]. Zwraca [{index,id,url}].\",\n inputSchema: {\n type: \"object\",\n properties: {\n edits: { type: \"array\", items: { type: \"object\" } },\n max_concurrent: { type: \"number\" },\n },\n required: [\"edits\"],\n },\n },\n {\n name: \"get_image\",\n description: \"Zwraca base64 JEDNEGO obrazu wg id.\",\n inputSchema: {\n type: \"object\",\n properties: { id: { type: \"string\" } },\n required: [\"id\"],\n },\n },\n {\n name: \"get_images\",\n description:\n \"★ Zwraca DUŻY payload {images:[{id,b64,mime}]} dla ids (zrzut na dysk).\",\n inputSchema: {\n type: \"object\",\n properties: { ids: { type: \"array\", items: { type: \"string\" } } },\n required: [\"ids\"],\n },\n },\n {\n name: \"vision_plan\",\n description:\n \"★ WIZJA: obraz + prompt -> TEKST/JSON (gpt-5.5/gpt-4o). image_b64 ALBO image_url. as_json=true -> JSON.\",\n inputSchema: {\n type: \"object\",\n properties: {\n prompt: { type: \"string\" },\n model: { type: \"string\" },\n image_b64: { type: \"string\" },\n image_url: { type: \"string\" },\n mime: { type: \"string\" },\n as_json: { type: \"boolean\" },\n },\n required: [\"prompt\"],\n },\n },\n {\n name: \"vision_plan_batch\",\n description:\n \"★ WIELOWĄTKOWO analizuje LISTĘ obrazów (pLimit). items=[{prompt,model,image_b64|image_url,mime,as_json}]. Wyniki w kolejności.\",\n inputSchema: {\n type: \"object\",\n properties: {\n items: { type: \"array\", items: { type: \"object\" } },\n concurrency: { type: \"number\" },\n },\n required: [\"items\"],\n },\n },\n];\n\nasync function dispatch(name: string, a: any): Promise {\n if (name === \"generate_image\") {\n const r = await genOne({\n prompt: String(a?.prompt ?? \"\"),\n model: a?.model,\n size: a?.size,\n quality: a?.quality,\n });\n return JSON.stringify(r);\n }\n if (name === \"generate_images_batch\") {\n const r = await batch(\n (a?.prompts ?? []).map((p: string) => ({ prompt: p })),\n Number(a?.max_concurrent) || DEFAULT_CONCURRENCY,\n (it) =>\n genOne({ prompt: String(it.prompt), model: a?.model, size: a?.size }),\n );\n return JSON.stringify(r);\n }\n if (name === \"edit_image\") {\n const r = await editOne({\n image: String(a?.image ?? \"\"),\n prompt: String(a?.prompt ?? \"\"),\n model: a?.model,\n size: a?.size,\n });\n return JSON.stringify(r);\n }\n if (name === \"edit_images_batch\") {\n const r = await batch(\n a?.edits ?? [],\n Number(a?.max_concurrent) || DEFAULT_CONCURRENCY,\n (it) =>\n editOne({\n image: String(it.image),\n prompt: String(it.prompt),\n model: it.model,\n size: it.size,\n }),\n );\n return JSON.stringify(r);\n }\n if (name === \"get_image\") {\n const o = await blob.getJSON(`img:${a.id}`);\n if (!o) throw new OErr(`brak obrazu ${a.id}`, 404, false);\n return JSON.stringify({ id: a.id, b64: o.b64, mime: o.mime });\n }\n if (name === \"get_images\") {\n const ids: string[] = a?.ids ?? [];\n const images: any[] = [];\n for (const id of ids) {\n const o = await blob.getJSON(`img:${id}`);\n if (o) images.push({ id, b64: o.b64, mime: o.mime });\n }\n let payload = JSON.stringify({ images });\n const TARGET = 1150000;\n if (payload.length < TARGET) {\n payload = JSON.stringify({\n images,\n _pad: \" \".repeat(TARGET - payload.length),\n });\n }\n return payload;\n }\n if (name === \"vision_plan\") {\n return await visionOne({\n prompt: String(a?.prompt ?? \"\"),\n model: a?.model,\n image_b64: a?.image_b64,\n image_url: a?.image_url,\n mime: a?.mime,\n as_json: Boolean(a?.as_json),\n });\n }\n if (name === \"vision_plan_batch\") {\n const r = await batch(\n a?.items ?? [],\n Number(a?.concurrency) || DEFAULT_CONCURRENCY,\n async (it) => ({\n text: await visionOne({\n prompt: String(it?.prompt ?? \"\"),\n model: it?.model,\n image_b64: it?.image_b64,\n image_url: it?.image_url,\n mime: it?.mime,\n as_json: Boolean(it?.as_json),\n }),\n }),\n );\n return JSON.stringify(r, null, 2);\n }\n throw new OErr(`Nieznane narzędzie: ${name}`, 400, false);\n}\n\n// --- TRANSPORT (MCP JSON-RPC + serwowanie /img/) ---\nconst ok = (id: any, result: any) =>\n Response.json({ jsonrpc: \"2.0\", id, result });\nconst er = (id: any, code: number, message: string) =>\n Response.json({ jsonrpc: \"2.0\", id, error: { code, message } });\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n SELF_ORIGIN = url.origin;\n // serwowanie obrazów\n if (req.method === \"GET\" && url.pathname.startsWith(\"/img/\")) {\n const id = url.pathname.slice(5);\n const o = await blob.getJSON(`img:${id}`);\n if (!o) return new Response(\"not found\", { status: 404 });\n const bin = Uint8Array.from(atob(o.b64), (c) => c.charCodeAt(0));\n return new Response(bin, {\n headers: {\n \"Content-Type\": o.mime || \"image/png\",\n \"Cache-Control\": \"public, max-age=31536000\",\n },\n });\n }\n if (req.method === \"GET\") {\n return new Response(\n `ChatGPT (OpenAI) MCP v2.0 — działa. Obrazy: ${IMG_MODEL_DEFAULT}. Wizja: ${VIS_MODEL_DEFAULT}. Tooly: generate_image, generate_images_batch, edit_image, edit_images_batch, get_image, get_images, vision_plan, vision_plan_batch.`,\n { status: 200 },\n );\n }\n let body: any = {};\n try {\n body = await req.json();\n } catch {\n return er(null, -32700, \"Parse error\");\n }\n const { id, method, params } = body;\n if (method === \"initialize\") {\n return ok(id, {\n protocolVersion: \"2024-11-05\",\n capabilities: { tools: {} },\n serverInfo: { name: \"chatgpt\", version: \"2.0\" },\n });\n }\n if (\n method === \"notifications/initialized\" ||\n method?.startsWith?.(\"notifications/\")\n ) return new Response(null, { status: 202 });\n if (method === \"tools/list\") return ok(id, { tools: TOOLS });\n if (method === \"tools/call\") {\n try {\n return ok(id, {\n content: [{\n type: \"text\",\n text: await dispatch(params?.name, params?.arguments ?? {}),\n }],\n });\n } catch (e) {\n return ok(id, {\n content: [{\n type: \"text\",\n text: `BŁĄD OpenAI: ${(e as Error).message}`,\n 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Diff · v15