[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_155":156,"_157":158},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_104":85,"_27":105,"_13":14,"_106":-5,"_11":107,"_108":109,"_110":111,"_112":113,"_114":115,"_20":-5,"_116":-5,"_117":-5,"_118":25,"_119":120,"_121":122,"_123":-5,"_124":-5,"_125":126},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",17,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","b17d7cd6-548b-11f1-813b-ee650bb23af1","name","fileType","http","content","// ============================================================\n// CHATGPT (OpenAI) — MCP serwer (Val Town / Deno) v3.0 ★ GAME-CHANGER EDIT\n// Generacja gpt-image-2 + EDYCJA Z MASKĄ + WIZJA + batch (pLimit)\n// Hosting obrazów w blob, serwowane pod /img/. ID trwałe między edycjami kodu.\n// ENV: OPENAI_API_KEY (opcjonalnie GEMINI_ORIGIN dla edycji finałów z banana-pro)\n//\n// ZMIANY v3.0 (vs v2.0):\n// 1) FIX KRYTYCZNY: input_fidelity wysyłane TYLKO dla gpt-image-1\n// (gpt-image-2 go NIE wspiera -> wcześniej HTTP 400 na każdej edycji image2).\n// 2) MASKA (inpainting): nowy param `mask` w edit_image / edit_images_batch.\n// - `mask` = url|data-url|id|base64 (jak `image`)\n// - `mask_white_erases:true` -> val sam konwertuje czarno-białą maskę\n// (BIAŁE = obszar do zmiany) na poprawną alfa-maskę OpenAI\n// (OpenAI edytuje tam, gdzie maska jest PRZEZROCZYSTA). Auto-resize do obrazu.\n// 3) `erase_mode:true` -> val dokleja twardą klauzulę ochrony konturu\n// (idealne do kart how-to-draw: \"wymaż tylko X, reszta piksel-w-piksel\").\n// 4) `fallback_model` -> przy nieretryowalnym błędzie modelu próbuje drugiego.\n// 5) batch generacji honoruje `quality`.\n// ============================================================\nimport { blob } from \"https://esm.town/v/std/blob\";\n\nconst IMG_MODEL_DEFAULT = \"gpt-image-2\";\nconst VIS_MODEL_DEFAULT = \"gpt-5.5\";\nconst IMG_MODELS: Record = {\n \"image2\": \"gpt-image-2\",\n \"gpt-image-2\": \"gpt-image-2\",\n \"image1\": \"gpt-image-1\",\n \"gpt-image-1\": \"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\n// modele które WSPIERAJĄ input_fidelity (reszta dostaje HTTP 400, więc nie wysyłamy)\nconst SUPPORTS_INPUT_FIDELITY = new Set([\"gpt-image-1\"]);\n\n// klauzula ochrony konturu doklejana w erase_mode (karty how-to-draw)\nconst ERASE_GUARD =\n \" Output the SAME bold black line drawing on a pure white background. \" +\n \"Keep EVERY other line pixel-for-pixel identical — do NOT redraw, move, thicken, \" +\n \"re-center, restyle or add anything. No color, no shading.\";\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;\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}\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}\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\nlet SELF_ORIGIN = \"\";\n\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/${id}` };\n}\n\n// image = http(s) URL | data-URL | val id | \"gem:\" | surowy base64\nasync function loadBytes(\n image: string,\n): Promise<{ bytes: Uint8Array; mime: string }> {\n // opcjonalny most do bloba Gemini (edycja finałów z banana-pro):\n // podaj \"gem:\" i ustaw ENV GEMINI_ORIGIN na origin vala Gemini, który serwuje /img/\n if (image.startsWith(\"gem:\")) {\n const gorigin = Deno.env.get(\"GEMINI_ORIGIN\");\n if (!gorigin) {\n throw new OErr(\n \"podano gem: ale brak ENV GEMINI_ORIGIN (origin vala Gemini)\",\n 400,\n false,\n );\n }\n const gid = image.slice(4);\n return await loadBytes(`${gorigin.replace(/\\/$/, \"\")}/img/${gid}`);\n }\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 };\n}\n\n// === MASKA: konwersja czarno-białej maski (BIAŁE=zmień) na alfa-maskę OpenAI ===\n// OpenAI edytuje tam, gdzie maska jest PRZEZROCZYSTA (alpha=0).\n// Tu: piksel jasny (białe pole użytkownika) -> alpha=0 (edytuj); ciemny -> alpha=255 (zostaw).\n// Maska jest resize'owana do wymiarów obrazu. Wymaga ImageScript (Deno).\nasync function maskWhiteToAlphaPng(\n maskBytes: Uint8Array,\n targetW: number,\n targetH: number,\n invert = false,\n): Promise {\n const { Image } = await import(\n \"https://deno.land/x/imagescript@1.2.15/mod.ts\"\n );\n let m = await Image.decode(maskBytes);\n if (m.width !== targetW || m.height !== targetH) {\n m = m.resize(targetW, targetH);\n }\n const out = new Image(targetW, targetH);\n for (let y = 1; y <= targetH; y++) {\n for (let x = 1; x <= targetW; x++) {\n const px = m.getPixelAt(x, y); // RGBA uint32\n const r = (px >>> 24) & 0xff,\n g = (px >>> 16) & 0xff,\n b = (px >>> 8) & 0xff;\n const lum = 0.299 * r + 0.587 * g + 0.114 * b;\n let erase = lum > 127; // jasny = obszar do zmiany\n if (invert) erase = !erase;\n const alpha = erase ? 0 : 255; // 0 = edytuj (przezroczyste), 255 = zostaw\n out.setPixelAt(x, y, Image.rgbaToColor(0, 0, 0, alpha));\n }\n }\n return await out.encode();\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\n// === EDYCJA (images/edits) — model-aware fidelity, MASKA, erase_mode, fallback ===\nasync function editOnce(\n o: {\n image: string;\n prompt: string;\n model?: string;\n size?: string;\n mask?: string;\n mask_white_erases?: boolean;\n mask_invert?: boolean;\n input_fidelity?: string; // override; jeśli model nie wspiera -> pomijane\n erase_mode?: boolean;\n },\n modelOverride?: string,\n) {\n const model = modelOverride ?? resolveImg(o.model);\n const { bytes, mime } = await loadBytes(o.image);\n const prompt = o.erase_mode ? (o.prompt + ERASE_GUARD) : o.prompt;\n\n const fd = new FormData();\n fd.append(\"model\", model);\n fd.append(\"prompt\", prompt);\n if (o.size) fd.append(\"size\", o.size);\n\n // input_fidelity TYLKO dla modeli, które go wspierają (fix krytyczny v3.0)\n if (SUPPORTS_INPUT_FIDELITY.has(model)) {\n fd.append(\"input_fidelity\", o.input_fidelity || \"high\");\n }\n\n fd.append(\n \"image\",\n new Blob([bytes], { type: mime || \"image/png\" }),\n \"image.png\",\n );\n\n // MASKA (opcjonalnie)\n if (o.mask) {\n const mk = await loadBytes(o.mask);\n let maskBytes = mk.bytes;\n if (o.mask_white_erases) {\n // wykryj wymiary obrazu bazowego, by zrównać maskę\n const { Image } = await import(\n \"https://deno.land/x/imagescript@1.2.15/mod.ts\"\n );\n const base = await Image.decode(bytes);\n maskBytes = await maskWhiteToAlphaPng(\n maskBytes,\n base.width,\n base.height,\n Boolean(o.mask_invert),\n );\n }\n fd.append(\n \"mask\",\n new Blob([maskBytes], { type: \"image/png\" }),\n \"mask.png\",\n );\n }\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}: ${b.slice(0, 300)}`,\n res.status,\n RETRY.retryStatuses.has(res.status),\n );\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) throw new OErr(\"brak b64_json w odpowiedzi\", 200, false);\n return await storeImage(b64, \"image/png\");\n}\n\nasync function editOne(o: any) {\n return await withRetry(async () => {\n try {\n return await editOnce(o);\n } catch (e) {\n // fallback modelu przy nieretryowalnym błędzie modelu (np. 400 specyficzne dla modelu)\n const oe = e as OErr;\n if (\n o.fallback_model && !oe.retryable && oe.status >= 400 && oe.status < 500\n ) {\n return await editOnce(o, resolveImg(o.fallback_model));\n }\n throw e;\n }\n });\n}\n\n// === WIZJA (chat/completions, obraz -> tekst/JSON) ===\nasync function visionOne(\n o: {\n prompt: string;\n model?: string;\n image_b64?: string;\n image_url?: string;\n mime?: string;\n as_json?: boolean;\n },\n) {\n return await withRetry(async () => {\n let imgUrl = o.image_url;\n if (!imgUrl && o.image_b64) {\n const b = o.image_b64.replace(/^data:[^,]+,/, \"\");\n imgUrl = `data:${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\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 quality: { type: \"string\" },\n max_concurrent: { type: \"number\" },\n },\n required: [\"prompts\"],\n },\n },\n {\n name: \"edit_image\",\n description:\n \"Edytuje obraz. image = url | data-url | id | base64 | gem:. \" +\n \"★ MASKA: mask (jak image) + mask_white_erases:true (BIAŁE=obszar do zmiany, val konwertuje na alfę). \" +\n \"erase_mode:true dokleja klauzulę ochrony konturu (karty how-to-draw). \" +\n \"model: image1/image2 (input_fidelity auto, tylko image1). fallback_model opcjonalnie. 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 mask: { type: \"string\" },\n mask_white_erases: { type: \"boolean\" },\n mask_invert: { type: \"boolean\" },\n input_fidelity: { type: \"string\" },\n erase_mode: { type: \"boolean\" },\n fallback_model: { type: \"string\" },\n },\n required: [\"image\", \"prompt\"],\n },\n },\n {\n name: \"edit_images_batch\",\n description: \"★ WIELOWĄTKOWO edytuje LISTĘ (pLimit). \" +\n \"edits=[{image,prompt,model,size,mask,mask_white_erases,mask_invert,erase_mode,fallback_model}]. \" +\n \"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({\n prompt: String(it.prompt),\n model: a?.model,\n size: a?.size,\n quality: a?.quality,\n }),\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 mask: a?.mask,\n mask_white_erases: Boolean(a?.mask_white_erases),\n mask_invert: Boolean(a?.mask_invert),\n input_fidelity: a?.input_fidelity,\n erase_mode: Boolean(a?.erase_mode),\n fallback_model: a?.fallback_model,\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 mask: it.mask,\n mask_white_erases: Boolean(it.mask_white_erases),\n mask_invert: Boolean(it.mask_invert),\n input_fidelity: it.input_fidelity,\n erase_mode: Boolean(it.erase_mode),\n fallback_model: it.fallback_model,\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: ${name}`, 400, false);\n}\n\nconst ok = (id: any, result: any) =>\n Response.json({ jsonrpc: \"2.0\", id, result });\nconst er = (id: any, code: number, message: string) =>\n Response.json({ jsonrpc: \"2.0\", id, error: { code, message } });\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n SELF_ORIGIN = url.origin;\n if (req.method === \"GET\" && url.pathname.startsWith(\"/img/\")) {\n const id = url.pathname.slice(5);\n const o = await blob.getJSON(`img:${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 v3.0 — działa. Obrazy: ${IMG_MODEL_DEFAULT}. Wizja: ${VIS_MODEL_DEFAULT}. ` +\n `Edycja z maską (mask + mask_white_erases) i erase_mode aktywne.`,\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: \"3.0\" },\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 }],\n isError: true,\n });\n }\n }\n return er(id, -32601, \"Method not found\");\n}","// ============================================================\n// CHATGPT (OpenAI) — MCP serwer (Val Town / Deno) v3.1 ★ MASKA NAPRAWIONA\n// Generacja gpt-image-2 + EDYCJA Z MASKĄ + WIZJA + batch (pLimit)\n// Hosting obrazów w blob, serwowane pod /img/. ID trwałe między edycjami kodu.\n// ENV: OPENAI_API_KEY (opcjonalnie GEMINI_ORIGIN dla edycji finałów z banana-pro)\n//\n// ZMIANY v3.1 (vs v3.0):\n// ★ FIX KRYTYCZNY MASKI: ImageScript wywalał \"png: failed to decode (-8)\"\n// na poprawnych PNG. Wyrzucony CAŁKOWICIE. Zastąpiony:\n// - pngSize(): wymiary z nagłówka IHDR (zero zależności, zawsze działa),\n// - UPNG.js (esm.sh): niezawodny dekoder/enkoder PNG (każdy wariant),\n// - ręczny nearest-neighbor resize maski do rozmiaru obrazu bazowego.\n// Teraz mask_white_erases:true działa na image1 i image2.\n//\n// ZMIANY v3.0 (vs v2.0):\n// 1) FIX: input_fidelity wysyłane TYLKO dla gpt-image-1\n// (gpt-image-2 go NIE wspiera -> wcześniej HTTP 400 na każdej edycji image2).\n// 2) MASKA (inpainting): param `mask` w edit_image / edit_images_batch.\n// - `mask` = url|data-url|id|base64 (jak `image`)\n// - `mask_white_erases:true` -> val konwertuje czarno-białą maskę\n// (BIAŁE = obszar do zmiany) na alfa-maskę OpenAI (PRZEZROCZYSTE = edytuj).\n// - `mask_invert:true` -> odwraca znaczenie.\n// 3) `erase_mode:true` -> twarda klauzula ochrony konturu (karty how-to-draw).\n// 4) `fallback_model` -> przy nieretryowalnym błędzie próbuje drugiego modelu.\n// 5) batch generacji honoruje `quality`.\n// ============================================================\nimport { blob } from \"https://esm.town/v/std/blob\";\n\nconst IMG_MODEL_DEFAULT = \"gpt-image-2\";\nconst VIS_MODEL_DEFAULT = \"gpt-5.5\";\nconst IMG_MODELS: Record = {\n \"image2\": \"gpt-image-2\",\n \"gpt-image-2\": \"gpt-image-2\",\n \"image1\": \"gpt-image-1\",\n \"gpt-image-1\": \"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\n// modele które WSPIERAJĄ input_fidelity (reszta dostaje HTTP 400, więc nie wysyłamy)\nconst SUPPORTS_INPUT_FIDELITY = new Set([\"gpt-image-1\"]);\n\n// klauzula ochrony konturu doklejana w erase_mode (karty how-to-draw)\nconst ERASE_GUARD =\n \" Output the SAME bold black line drawing on a pure white background. \" +\n \"Keep EVERY other line pixel-for-pixel identical — do NOT redraw, move, thicken, \" +\n \"re-center, restyle or add anything. No color, no shading.\";\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;\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}\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}\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\nlet SELF_ORIGIN = \"\";\n\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}\n\n// image = http(s) URL | data-URL | val id | \"gem:\" | surowy base64\nasync function loadBytes(\n image: string,\n): Promise<{ bytes: Uint8Array; mime: string }> {\n // opcjonalny most do bloba Gemini (edycja finałów z banana-pro):\n // podaj \"gem:\" i ustaw ENV GEMINI_ORIGIN na origin vala Gemini, który serwuje /img/\n if (image.startsWith(\"gem:\")) {\n const gorigin = Deno.env.get(\"GEMINI_ORIGIN\");\n if (!gorigin) {\n throw new OErr(\n \"podano gem: ale brak ENV GEMINI_ORIGIN (origin vala Gemini)\",\n 400,\n false,\n );\n }\n const gid = image.slice(4);\n return await loadBytes(`${gorigin.replace(/\\/$/, \"\")}/img/${gid}`);\n }\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 };\n}\n\n// === MASKA: konwersja czarno-białej maski (BIAŁE=zmień) na alfa-maskę OpenAI ===\n// OpenAI edytuje tam, gdzie maska jest PRZEZROCZYSTA (alpha=0).\n// Tu: piksel jasny (białe pole użytkownika) -> alpha=0 (edytuj); ciemny -> alpha=255 (zostaw).\n//\n// FIX v3.1: ImageScript (deno.land/x/imagescript) wywalał \"png: failed to decode (-8)\"\n// na poprawnych PNG (grayscale i RGB). Zastąpione UPNG.js (czysty JS, dekoduje KAŻDY\n// wariant PNG) + ręczne nearest-neighbor resize. Niezawodne, bez WASM.\n\n// czytanie wymiarów PNG prosto z nagłówka IHDR (bez żadnej biblioteki — 100% pewne)\nfunction pngSize(bytes: Uint8Array): { w: number; h: number } | null {\n // sygnatura PNG + IHDR: width @ offset 16 (BE u32), height @ offset 20 (BE u32)\n if (\n bytes.length < 24 || bytes[0] !== 0x89 || bytes[1] !== 0x50 ||\n bytes[2] !== 0x4e || bytes[3] !== 0x47\n ) return null;\n const dv = new DataView(bytes.buffer, bytes.byteOffset, bytes.byteLength);\n return { w: dv.getUint32(16), h: dv.getUint32(20) };\n}\n\n// UPNG: solidny dekoder/enkoder PNG w czystym JS\nasync function loadUPNG() {\n const mod = await import(\"https://esm.sh/upng-js@2.1.0\");\n return (mod as any).default ?? mod;\n}\n\n// dekoduj DOWOLNY png -> RGBA8 (Uint8Array length = w*h*4) + wymiary\nasync function decodeToRGBA(\n bytes: Uint8Array,\n): Promise<{ w: number; h: number; rgba: Uint8Array }> {\n const UPNG = await loadUPNG();\n const img = UPNG.decode(bytes.buffer.slice(\n bytes.byteOffset,\n bytes.byteOffset + bytes.byteLength,\n ));\n const frames = UPNG.toRGBA8(img); // tablica ArrayBuffer-ów (klatki)\n const rgba = new Uint8Array(frames[0]);\n return { w: img.width, h: img.height, rgba };\n}\n\nasync function maskWhiteToAlphaPng(\n maskBytes: Uint8Array,\n targetW: number,\n targetH: number,\n invert = false,\n): Promise {\n const UPNG = await loadUPNG();\n const { w: mw, h: mh, rgba } = await decodeToRGBA(maskBytes);\n\n // wynik: RGBA, czarne piksele; alpha=0 tam gdzie \"zmień\", 255 gdzie \"zostaw\"\n const out = new Uint8Array(targetW * targetH * 4);\n for (let y = 0; y < targetH; y++) {\n // nearest-neighbor mapowanie maski na rozmiar obrazu bazowego\n const sy = mh === targetH ? y : Math.min(mh - 1, (y * mh / targetH) | 0);\n for (let x = 0; x < targetW; x++) {\n const sx = mw === targetW ? x : Math.min(mw - 1, (x * mw / targetW) | 0);\n const si = (sy * mw + sx) * 4;\n const r = rgba[si], g = rgba[si + 1], b = rgba[si + 2];\n const lum = 0.299 * r + 0.587 * g + 0.114 * b;\n let erase = lum > 127; // jasny (białe pole) = obszar do zmiany\n if (invert) erase = !erase;\n const di = (y * targetW + x) * 4;\n out[di] = 0;\n out[di + 1] = 0;\n out[di + 2] = 0;\n out[di + 3] = erase ? 0 : 255; // 0 = edytuj (przezroczyste), 255 = zostaw\n }\n }\n // UPNG.encode(frames[], w, h, cnum) — cnum=0 = bezstratny RGBA\n const png = UPNG.encode([out.buffer], targetW, targetH, 0);\n return new Uint8Array(png);\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\n// === EDYCJA (images/edits) — model-aware fidelity, MASKA, erase_mode, fallback ===\nasync function editOnce(\n o: {\n image: string;\n prompt: string;\n model?: string;\n size?: string;\n mask?: string;\n mask_white_erases?: boolean;\n mask_invert?: boolean;\n input_fidelity?: string; // override; jeśli model nie wspiera -> pomijane\n erase_mode?: boolean;\n },\n modelOverride?: string,\n) {\n const model = modelOverride ?? resolveImg(o.model);\n const { bytes, mime } = await loadBytes(o.image);\n const prompt = o.erase_mode ? (o.prompt + ERASE_GUARD) : o.prompt;\n\n const fd = new FormData();\n fd.append(\"model\", model);\n fd.append(\"prompt\", prompt);\n if (o.size) fd.append(\"size\", o.size);\n\n // input_fidelity TYLKO dla modeli, które go wspierają (fix krytyczny v3.0)\n if (SUPPORTS_INPUT_FIDELITY.has(model)) {\n fd.append(\"input_fidelity\", o.input_fidelity || \"high\");\n }\n\n fd.append(\n \"image\",\n new Blob([bytes], { type: mime || \"image/png\" }),\n \"image.png\",\n );\n\n // MASKA (opcjonalnie)\n if (o.mask) {\n const mk = await loadBytes(o.mask);\n let maskBytes = mk.bytes;\n if (o.mask_white_erases) {\n // wykryj wymiary obrazu bazowego, by zrównać maskę (bez ImageScript)\n let bw = 0, bh = 0;\n const hdr = pngSize(bytes);\n if (hdr) {\n bw = hdr.w;\n bh = hdr.h;\n } else {\n const dec = await decodeToRGBA(bytes); // fallback dla nie-PNG (jpg itp.)\n bw = dec.w;\n bh = dec.h;\n }\n maskBytes = await maskWhiteToAlphaPng(\n maskBytes,\n bw,\n bh,\n Boolean(o.mask_invert),\n );\n }\n fd.append(\n \"mask\",\n new Blob([maskBytes], { type: \"image/png\" }),\n \"mask.png\",\n );\n }\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}: ${b.slice(0, 300)}`,\n res.status,\n RETRY.retryStatuses.has(res.status),\n );\n }\n const data = await res.json();\n const b64 = data?.data?.[0]?.b64_json;\n if (!b64) throw new OErr(\"brak b64_json w odpowiedzi\", 200, false);\n return await storeImage(b64, \"image/png\");\n}\n\nasync function editOne(o: any) {\n return await withRetry(async () => {\n try {\n return await editOnce(o);\n } catch (e) {\n // fallback modelu przy nieretryowalnym błędzie modelu (np. 400 specyficzne dla modelu)\n const oe = e as OErr;\n if (\n o.fallback_model && !oe.retryable && oe.status >= 400 && oe.status < 500\n ) {\n return await editOnce(o, resolveImg(o.fallback_model));\n }\n throw e;\n }\n });\n}\n\n// === WIZJA (chat/completions, obraz -> tekst/JSON) ===\nasync function visionOne(\n o: {\n prompt: string;\n model?: string;\n image_b64?: string;\n image_url?: string;\n mime?: string;\n as_json?: boolean;\n },\n) {\n return await withRetry(async () => {\n let imgUrl = o.image_url;\n if (!imgUrl && o.image_b64) {\n const b = o.image_b64.replace(/^data:[^,]+,/, \"\");\n imgUrl = `data:${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\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 quality: { type: \"string\" },\n max_concurrent: { type: \"number\" },\n },\n required: [\"prompts\"],\n },\n },\n {\n name: \"edit_image\",\n description:\n \"Edytuje obraz. image = url | data-url | id | base64 | gem:. \" +\n \"★ MASKA: mask (jak image) + mask_white_erases:true (BIAŁE=obszar do zmiany, val konwertuje na alfę). \" +\n \"erase_mode:true dokleja klauzulę ochrony konturu (karty how-to-draw). \" +\n \"model: image1/image2 (input_fidelity auto, tylko image1). fallback_model opcjonalnie. 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 mask: { type: \"string\" },\n mask_white_erases: { type: \"boolean\" },\n mask_invert: { type: \"boolean\" },\n input_fidelity: { type: \"string\" },\n erase_mode: { type: \"boolean\" },\n fallback_model: { type: \"string\" },\n },\n required: [\"image\", \"prompt\"],\n },\n },\n {\n name: \"edit_images_batch\",\n description: \"★ WIELOWĄTKOWO edytuje LISTĘ (pLimit). \" +\n \"edits=[{image,prompt,model,size,mask,mask_white_erases,mask_invert,erase_mode,fallback_model}]. \" +\n \"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({\n prompt: String(it.prompt),\n model: a?.model,\n size: a?.size,\n quality: a?.quality,\n }),\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 mask: a?.mask,\n mask_white_erases: Boolean(a?.mask_white_erases),\n mask_invert: Boolean(a?.mask_invert),\n input_fidelity: a?.input_fidelity,\n erase_mode: Boolean(a?.erase_mode),\n fallback_model: a?.fallback_model,\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 mask: it.mask,\n mask_white_erases: Boolean(it.mask_white_erases),\n mask_invert: Boolean(it.mask_invert),\n input_fidelity: it.input_fidelity,\n erase_mode: Boolean(it.erase_mode),\n fallback_model: it.fallback_model,\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: ${name}`, 400, false);\n}\n\nconst ok = (id: any, result: any) =>\n Response.json({ jsonrpc: \"2.0\", id, result });\nconst er = (id: any, code: number, message: string) =>\n Response.json({ jsonrpc: \"2.0\", id, error: { code, message } });\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n SELF_ORIGIN = url.origin;\n if (req.method === \"GET\" && url.pathname.startsWith(\"/img/\")) {\n const id = url.pathname.slice(5);\n const o = await blob.getJSON(`img:${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 v3.0 — działa. Obrazy: ${IMG_MODEL_DEFAULT}. Wizja: ${VIS_MODEL_DEFAULT}. ` +\n `Edycja z maską (mask + mask_white_erases) i erase_mode aktywne.`,\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: \"3.0\" },\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 }],\n isError: true,\n });\n }\n }\n 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Diff · v17