[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_199":200,"_201":202},"routes/_app.x.$handle.$name",{"_103":102,"_38":51,"_112":93,"_33":113,"_13":14,"_114":-5,"_11":115,"_116":117,"_118":119,"_120":121,"_122":123,"_26":-5,"_124":-5,"_125":-5,"_126":31,"_127":128,"_129":65,"_130":-5,"_131":-5,"_132":133},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":-5},"project",{"_49":50,"_38":51,"_44":52,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":69,"_70":71,"_72":-5,"_73":-5,"_74":-5,"_75":-5,"_76":77,"_78":77,"_79":80,"_81":80,"_82":80,"_83":77,"_84":77,"_85":86,"_87":88,"_89":90,"_91":31},"version",11,"changes",[25],"aiChatInfo",{"_20":21,"_22":-5,"_23":-5,"_24":-5},"userId","aiChatMessageId","e416b4f9-9044-4a1f-a4d0-b16ae4c898f7","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle",{"_26":27,"_28":29,"_30":31,"_32":31,"_33":34,"_35":34},"file",{"_36":37,"_38":34,"_39":40,"_41":43,"_44":45,"_46":14,"_47":48,"_30":31},"previous",{"_36":37,"_38":34,"_39":40,"_41":42},"deleted",false,"deletedHere","path","hf.ts","latestPath","fileId","4373a594-5f99-11f1-9f91-1607ee4eb77e","name","fileType","script","content","// HuggingFace router calls. Every request bills to the org in ORG via X-HF-Bill-To.\n\nconst HF_TOKEN = Deno.env.get(\"HF_TOKEN\");\nconst BILL_TO = Deno.env.get(\"ORG\") || \"hugging-science\";\nconst ROUTER = \"https://router.huggingface.co/v1\";\n\nfunction billingHeaders(extra: Record = {}) {\n return {\n \"Authorization\": `Bearer ${HF_TOKEN}`,\n \"X-HF-Bill-To\": BILL_TO,\n ...extra,\n };\n}\n\nexport type ChatMessage = { role: \"system\" | \"user\" | \"assistant\"; content: string };\n\nexport interface ChatOptions {\n messages: ChatMessage[];\n model?: string;\n temperature?: number;\n max_tokens?: number;\n top_p?: number;\n}\n\n// Single-turn helper: pass a string prompt, get a string back.\nexport async function chat(opts: ChatOptions) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"openai/gpt-oss-120b:groq\";\n const body: Record = {\n model,\n messages: opts.messages,\n stream: false,\n };\n if (opts.temperature != null) body.temperature = opts.temperature;\n if (opts.max_tokens != null) body.max_tokens = opts.max_tokens;\n if (opts.top_p != null) body.top_p = opts.top_p;\n\n const res = await fetch(`${ROUTER}/chat/completions`, {\n method: \"POST\",\n headers: billingHeaders({ \"Content-Type\": \"application/json\" }),\n body: JSON.stringify(body),\n });\n\n const text = await res.text();\n if (!res.ok) {\n throw new HfError(`chat request failed (${res.status})`, res.status, text);\n }\n\n const data = JSON.parse(text);\n const message = data.choices?.[0]?.message?.content ?? \"\";\n return {\n model: data.model ?? model,\n reply: message,\n usage: data.usage ?? null,\n raw: data,\n };\n}\n\n// Text-to-image. Returns raw image bytes + the content type the provider gave us.\nexport async function textToImage(opts: {\n prompt: string;\n model?: string;\n negative_prompt?: string;\n}) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"Tongyi-MAI/Z-Image-Turbo\";\n const payload: Record = { inputs: opts.prompt };\n if (opts.negative_prompt) {\n payload.parameters = { negative_prompt: opts.negative_prompt };\n }\n\n // The router exposes per-model inference under /models/.\n const res = await fetch(`https://router.huggingface.co/models/${model}`, {\n method: \"POST\",\n headers: billingHeaders({ \"Content-Type\": \"application/json\" }),\n body: JSON.stringify(payload),\n });\n\n if (!res.ok) {\n const errText = await res.text();\n throw new HfError(`image request failed (${res.status})`, res.status, errText);\n }\n\n const contentType = res.headers.get(\"content-type\") || \"image/png\";\n const bytes = new Uint8Array(await res.arrayBuffer());\n return { bytes, contentType, model };\n}\n\nexport class HfError extends Error {\n status: number;\n detail: string;\n constructor(message: string, status: number, detail: string) {\n super(message);\n this.name = \"HfError\";\n this.status = status;\n this.detail = detail;\n }\n}\n","// HuggingFace router calls. Every request bills to the org in ORG via X-HF-Bill-To.\n\nconst HF_TOKEN = Deno.env.get(\"HF_TOKEN\");\nconst BILL_TO = Deno.env.get(\"ORG\") || \"hugging-science\";\nconst ROUTER = \"https://router.huggingface.co/v1\";\n\nfunction billingHeaders(extra: Record = {}) {\n return {\n \"Authorization\": `Bearer ${HF_TOKEN}`,\n \"X-HF-Bill-To\": BILL_TO,\n ...extra,\n };\n}\n\nexport type ChatMessage = { role: \"system\" | \"user\" | \"assistant\"; content: string };\n\nexport interface ChatOptions {\n messages: ChatMessage[];\n model?: string;\n temperature?: number;\n max_tokens?: number;\n top_p?: number;\n}\n\n// Single-turn helper: pass a string prompt, get a string back.\nexport async function chat(opts: ChatOptions) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"openai/gpt-oss-120b:groq\";\n const body: Record = {\n model,\n messages: opts.messages,\n stream: false,\n };\n if (opts.temperature != null) body.temperature = opts.temperature;\n if (opts.max_tokens != null) body.max_tokens = opts.max_tokens;\n if (opts.top_p != null) body.top_p = opts.top_p;\n\n const res = await fetch(`${ROUTER}/chat/completions`, {\n method: \"POST\",\n headers: billingHeaders({ \"Content-Type\": \"application/json\" }),\n body: JSON.stringify(body),\n });\n\n const text = await res.text();\n if (!res.ok) {\n throw new HfError(`chat request failed (${res.status})`, res.status, text);\n }\n\n const data = JSON.parse(text);\n const message = data.choices?.[0]?.message?.content ?? \"\";\n return {\n model: data.model ?? model,\n reply: message,\n usage: data.usage ?? null,\n raw: data,\n };\n}\n\n// Text-to-image. Uses the official HF inference client so provider routing\n// (replicate/fal/etc.) is handled for us. Billing goes to the org via billTo.\n// Returns raw image bytes + the content type the provider gave us.\nexport async function textToImage(opts: {\n prompt: string;\n model?: string;\n provider?: string;\n negative_prompt?: string;\n}) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"Tongyi-MAI/Z-Image-Turbo\";\n const provider = (opts.provider || \"replicate\") as any;\n\n const { InferenceClient } = await import(\"npm:@huggingface/inference\");\n const client = new InferenceClient(HF_TOKEN, { billTo: BILL_TO });\n\n try {\n const result = await client.textToImage({\n provider,\n model,\n inputs: opts.prompt,\n parameters: opts.negative_prompt ? { negative_prompt: opts.negative_prompt } : undefined,\n });\n\n // The client returns a Blob.\n const blob = result as Blob;\n const contentType = blob.type || \"image/png\";\n const bytes = new Uint8Array(await blob.arrayBuffer());\n return { bytes, contentType, model, provider };\n } catch (e) {\n const msg = (e as Error)?.message || String(e);\n throw new HfError(`image request failed: ${msg}`, 502, msg);\n }\n}\n\nexport class HfError extends Error {\n status: number;\n detail: string;\n constructor(message: string, status: number, detail: string) {\n super(message);\n this.name = \"HfError\";\n this.status = status;\n this.detail = detail;\n }\n}\n","createdAt",["D",1780524842321],"branchVersion","branchId","81f1664b-7a2e-4ba3-86b4-5e255cb6a15b","id","0ef5210b-4901-491d-bbc8-6bbf286fe6c3","aimodels",["D",1780519334637],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Simple JSON API endpoint","imageUrl","1d9535d6-12d5-4534-9252-5a7b8e5c0385","pinnedFileId","b70c0526-5f8c-11f1-9921-1607ee4eb77e","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","019e8f38-77d6-75c1-bcad-454198a838b4","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_103":102,"_111":107},"org",{"_103":102,"_104":105,"_106":107,"_19":61,"_108":109,"_110":31},"branch",{"_13":92,"_38":93,"_49":48,"_94":50,"_95":-5,"_96":-5,"_97":-5,"_98":-5,"_99":-5,"_100":51,"_101":102},"isOwned",23,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","aibruh","handle","displayName","f","avatar","https://lh3.googleusercontent.com/a/ACg8ocIuS4-SwJiruuCEmR_xeJNChwrND1xAByncZGFgp2IbjLxu2A=s96-c","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_49":50,"_38":51,"_44":195,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":69,"_70":71,"_72":-5,"_73":-5,"_74":-5,"_75":-5,"_76":77,"_78":77,"_79":80,"_81":80,"_82":80,"_83":77,"_84":77,"_85":196,"_87":197,"_89":198,"_91":31},"tree",[178,179,180,181],"files",[178,179,180,181],"counts",{"_167":69,"_168":158,"_169":158,"_170":171,"_172":173},"contents",[134,135,136,137],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_49":160,"_38":161,"_36":162,"_141":-5,"_47":48,"_46":163,"_143":31,"_44":164,"_13":69,"_39":26,"_41":165,"_30":31,"_146":-5,"_33":166},{"_49":156,"_38":34,"_36":37,"_141":-5,"_47":48,"_46":14,"_143":65,"_44":157,"_13":158,"_39":40,"_41":43,"_30":31,"_146":-5,"_33":159},{"_49":148,"_38":149,"_36":63,"_141":-5,"_47":48,"_46":150,"_143":31,"_44":151,"_13":142,"_39":152,"_41":153,"_30":31,"_146":154,"_33":155},{"_49":138,"_38":139,"_36":140,"_141":-5,"_47":48,"_46":142,"_143":31,"_44":144,"_13":69,"_39":40,"_41":145,"_30":31,"_146":-5,"_33":147},"921e73d3-255a-43d4-ab93-a2066a1a0a5e","upload.ts","462a2df8-5f99-11f1-b814-1607ee4eb77e","parentId",6,"latest",["D",1780524733688],"// Pushes raw image bytes to the jerrycans file host and returns a public URL.\n\nconst UPLOAD_ENDPOINT = \"https://jerrrycans-file.hf.space/upload\";\nconst PUBLIC_BASE = \"https://jerrycans-file.hf.space\";\n\nfunction extFromContentType(contentType: string): string {\n if (contentType.includes(\"jpeg\") || contentType.includes(\"jpg\")) return \"jpg\";\n if (contentType.includes(\"webp\")) return \"webp\";\n if (contentType.includes(\"gif\")) return \"gif\";\n return \"png\";\n}\n\nexport async function uploadImage(bytes: Uint8Array, contentType: string): Promise {\n const ext = extFromContentType(contentType);\n const filename = `gen-${Date.now()}.${ext}`;\n\n const form = new FormData();\n form.append(\"file\", new Blob([bytes], { type: \"application/octet-stream\" }), filename);\n form.append(\"originalExtension\", ext);\n\n const res = await fetch(UPLOAD_ENDPOINT, { method: \"POST\", body: form });\n if (!res.ok) {\n throw new Error(`upload failed (${res.status})`);\n }\n\n const result = await res.json();\n let url: string = result.url ?? \"\";\n if (url && !url.startsWith(\"http\")) {\n url = PUBLIC_BASE + url;\n }\n if (!url) throw new Error(\"upload host returned no url\");\n return url;\n}\n","label",[139],"ddd18de5-1233-4eba-9a5a-b201b8a3ce72","main.ts",10,["D",1780524811332],"http","import { chat, HfError, textToImage } from \"./hf.ts\";\nimport { uploadImage } from \"./upload.ts\";\nimport { parseVal, readFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nconst JSON_HEADERS = { \"Content-Type\": \"application/json\" };\n\nfunction json(data: unknown, status = 200) {\n return new Response(JSON.stringify(data, null, 2), {\n status,\n headers: JSON_HEADERS,\n });\n}\n\nfunction errorJson(message: string, status = 400, detail?: unknown) {\n return json({ error: message, detail: detail ?? null }, status);\n}\n\n// Reads a JSON body, tolerating empty bodies.\nasync function readBody(req: Request): Promise> {\n const text = await req.text();\n if (!text.trim()) return {};\n try {\n return JSON.parse(text);\n } catch {\n throw new Error(\"Request body is not valid JSON\");\n }\n}\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const path = url.pathname.replace(/\\/+$/, \"\") || \"/\";\n\n // ---- Docs page ----\n if (path === \"/\" && req.method === \"GET\") {\n const html = await readFile(\"/docs.html\", import.meta.url);\n return new Response(html, { headers: { \"Content-Type\": \"text/html; charset=utf-8\" } });\n }\n\n // ---- View source ----\n if (path === \"/source\") {\n return new Response(null, {\n status: 302,\n headers: { Location: parseVal().links.self.val },\n });\n }\n\n // ---- Health check ----\n if (path === \"/health\") {\n return json({ ok: true, time: new Date().toISOString() });\n }\n\n // ---- Chat (text) ----\n if (path === \"/v1/chat\") {\n if (req.method === \"GET\") {\n const prompt = url.searchParams.get(\"prompt\") || url.searchParams.get(\"q\");\n if (!prompt) {\n return errorJson(\"Pass a prompt: /v1/chat?prompt=hello\", 400);\n }\n return await handleChat({\n messages: [{ role: \"user\", content: prompt }],\n model: url.searchParams.get(\"model\") || undefined,\n });\n }\n\n if (req.method === \"POST\") {\n let body: Record;\n try {\n body = await readBody(req);\n } catch (e) {\n return errorJson((e as Error).message, 400);\n }\n\n // Accept either { prompt } or full { messages: [...] }.\n let messages = body.messages as any;\n if (!messages && typeof body.prompt === \"string\") {\n messages = [{ role: \"user\", content: body.prompt }];\n }\n if (!Array.isArray(messages) || messages.length === 0) {\n return errorJson(\"Provide 'prompt' (string) or 'messages' (array)\", 400);\n }\n\n return await handleChat({\n messages,\n model: typeof body.model === \"string\" ? body.model : undefined,\n temperature: typeof body.temperature === \"number\" ? body.temperature : undefined,\n max_tokens: typeof body.max_tokens === \"number\" ? body.max_tokens : undefined,\n top_p: typeof body.top_p === \"number\" ? body.top_p : undefined,\n });\n }\n\n return errorJson(\"Use GET or POST on /v1/chat\", 405);\n }\n\n // ---- Image (text-to-image, uploaded to host) ----\n if (path === \"/v1/image\") {\n let prompt: string | null = null;\n let model: string | undefined;\n let negative_prompt: string | undefined;\n let raw = url.searchParams.get(\"raw\") === \"true\" || url.searchParams.get(\"raw\") === \"1\";\n\n if (req.method === \"GET\") {\n prompt = url.searchParams.get(\"prompt\") || url.searchParams.get(\"q\");\n model = url.searchParams.get(\"model\") || undefined;\n negative_prompt = url.searchParams.get(\"negative_prompt\") || undefined;\n } else if (req.method === \"POST\") {\n let body: Record;\n try {\n body = await readBody(req);\n } catch (e) {\n return errorJson((e as Error).message, 400);\n }\n prompt = typeof body.prompt === \"string\" ? body.prompt : null;\n model = typeof body.model === \"string\" ? body.model : undefined;\n negative_prompt = typeof body.negative_prompt === \"string\" ? body.negative_prompt : undefined;\n if (body.raw === true) raw = true;\n } else {\n return errorJson(\"Use GET or POST on /v1/image\", 405);\n }\n\n if (!prompt) {\n return errorJson(\"Provide a 'prompt' describing the image\", 400);\n }\n\n try {\n const img = await textToImage({ prompt, model, negative_prompt });\n\n // raw=true streams the bytes straight back instead of uploading.\n if (raw) {\n return new Response(img.bytes, { headers: { \"Content-Type\": img.contentType } });\n }\n\n const uploadedUrl = await uploadImage(img.bytes, img.contentType);\n return json({\n prompt,\n model: img.model,\n url: uploadedUrl,\n content_type: img.contentType,\n });\n } catch (e) {\n return handleError(e);\n }\n }\n\n return errorJson(`Unknown route: ${path}`, 404);\n}\n\nasync function handleChat(opts: Parameters[0]): Promise {\n try {\n const result = await chat(opts);\n return json({\n model: result.model,\n reply: result.reply,\n usage: result.usage,\n });\n } catch (e) {\n return handleError(e);\n }\n}\n\nfunction handleError(e: unknown): Response {\n if (e instanceof HfError) {\n return errorJson(e.message, e.status >= 400 && e.status < 600 ? e.status : 502, e.detail);\n }\n return errorJson((e as Error).message || \"Something went wrong\", 500);\n}\n","aimodel",[149],"4144359c-8b24-4560-bd29-5db192a3e915",["D",1780524842321],1,[34],"4bf35fda-25fb-4488-a18b-6a11e09593eb","docs.html","7104038c-5f99-11f1-9d94-1607ee4eb77e",8,["D",1780524805567],"\n\n\n\n\nAI Models API\n\n\n\n

AI Models API

Text and image generation over a single HTTP endpoint. No SDK, no keys to pass — just hit the URLs below.

\n\n

\n This API gives you two things: a chat endpoint that returns text from a language model,\n and an image endpoint that turns a text prompt into an image and hands you back a hosted URL.\n Everything speaks JSON. You can call it from a browser, curl, a phone app, or any backend.\n

\n\n

\n There's no auth on these routes — the server holds the model token internally, so you only\n deal with prompts and responses. If you're building a public app, put your own rate limiting in front of it.\n

\n\n

Base URL

All routes are relative to the deployment origin:

loading…

\n\n

Quick start

The fastest way to see it work — open this in your browser:

loading…

\n\n \n

Chat

Send a prompt, get text back. There are two ways to call it.

\n\n

\n GET\n /v1/chat?prompt=...\n

Good for quick tests and simple one-liners. Put your question in the prompt query parameter.

curl \"$BASE/v1/chat?prompt=What%20is%20the%20capital%20of%20France%3F\"

\n\n

\n POST\n /v1/chat\n

Use this when you want a system prompt, a full conversation, or generation settings. Send JSON.

\n\n

Body fields

\n \n \n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	Shortcut for a single user message. Use this or `messages`.
`messages`	array	Full chat history. Each item is `{ \"role\": \"system\" \| \"user\" \| \"assistant\", \"content\": \"...\" }`.
`model`	string	optional · defaults to `openai/gpt-oss-120b:groq`
`temperature`	number	optional · 0–2, higher is more random
`max_tokens`	number	optional · cap on response length
`top_p`	number	optional · nucleus sampling

\n\n

Example — single prompt

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"Give me three names for a coffee shop.\"\n  }'

\n\n

Example — full conversation

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"openai/gpt-oss-120b:groq\",\n    \"temperature\": 0.7,\n    \"messages\": [\n      { \"role\": \"system\", \"content\": \"You answer in one short sentence.\" },\n      { \"role\": \"user\", \"content\": \"Why is the sky blue?\" }\n    ]\n  }'

\n\n

Response

{\n  \"model\": \"openai/gpt-oss-120b\",\n  \"reply\": \"Sunlight scatters off air molecules, and blue light scatters most.\",\n  \"usage\": {\n    \"prompt_tokens\": 24,\n    \"completion_tokens\": 14,\n    \"total_tokens\": 38\n  }\n}

\n\n \n

Image

\n Describe an image, get back a hosted URL you can drop into an <img> tag.\n The server generates the image, uploads it for you, and returns the link.\n

\n\n

\n GET\n /v1/image?prompt=...\n

\n POST\n /v1/image\n

\n\n

Body / query fields

\n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	What you want to see. Required.
`model`	string	optional · defaults to `Tongyi-MAI/Z-Image-Turbo`
`negative_prompt`	string	optional · things to avoid in the image
`raw`	boolean	optional · if true, returns the raw image bytes instead of a JSON URL

\n\n

Example

curl -X POST \"$BASE/v1/image\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"An astronaut riding a horse, cinematic lighting\"\n  }'

\n\n

Response

{\n  \"prompt\": \"An astronaut riding a horse, cinematic lighting\",\n  \"model\": \"Tongyi-MAI/Z-Image-Turbo\",\n  \"url\": \"https://jerrycans-file.hf.space/files/gen-1717000000000.png\",\n  \"content_type\": \"image/png\"\n}

\n\n

Want the image bytes directly (e.g. to pipe into a file)? Add ?raw=true:

curl \"$BASE/v1/image?prompt=a%20red%20bicycle&raw=true\" --output bike.png

\n\n \n

Calling it from code

\n\n

JavaScript

const base = \"$BASE\";\n\n// chat\nconst chat = await fetch(`${base}/v1/chat`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"Write a haiku about the sea.\" }),\n}).then(r => r.json());\nconsole.log(chat.reply);\n\n// image\nconst img = await fetch(`${base}/v1/image`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"A lighthouse at dusk\" }),\n}).then(r => r.json());\nconsole.log(img.url);

\n\n

Python

import requests\n\nbase = \"$BASE\"\n\nchat = requests.post(f\"{base}/v1/chat\", json={\n    \"prompt\": \"Explain gravity to a five year old.\"\n}).json()\nprint(chat[\"reply\"])\n\nimg = requests.post(f\"{base}/v1/image\", json={\n    \"prompt\": \"A fox in a snowy forest\"\n}).json()\nprint(img[\"url\"])

\n\n \n

Errors

Failures come back as JSON with the matching HTTP status:

{\n  \"error\": \"Provide a 'prompt' describing the image\",\n  \"detail\": null\n}

\n \n \n \n \n \n \n \n

Status	Meaning
`400`	Bad input — missing prompt, malformed JSON, etc.
`404`	Route doesn't exist.
`405`	Wrong HTTP method for that route.
`429`	Rate limited upstream — slow down and retry.
`5xx`	Model or upload host had a problem. The `detail` field usually says what.

\n\n

Endpoints at a glance

\n \n \n \n \n \n \n

Route	Method	Does
`/`	GET	This page.
`/health`	GET	Liveness check.
`/v1/chat`	GET / POST	Text generation.
`/v1/image`	GET / POST	Image generation → hosted URL.

\n\n \n

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Diff · v11