[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_194":195,"_196":197},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":-7,"_18":19,"_17":-7,"_148":-5,"_20":149,"_22":150,"_151":152,"_153":154,"_26":155,"_24":156,"_157":-5,"_158":-5,"_159":65,"_160":161,"_162":59,"_163":-5,"_164":-5,"_165":166},"routes/_app.x.$handle.$name.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":-5,"_29":-5,"_30":-5,"_31":-5,"_32":-5},"handle","aibruh","name","aimodels","branchName","main","version","path","docs.html","project",{"_33":91,"_13":14,"_44":92,"_93":-5,"_94":65,"_95":96,"_97":96,"_98":99,"_100":-5,"_101":102,"_103":71,"_104":59,"_105":-5,"_106":-5,"_107":108,"_109":110,"_111":-5,"_112":-5,"_113":-5,"_114":-5,"_115":116,"_117":116,"_118":119,"_120":119,"_121":119,"_122":116,"_123":116,"_124":125,"_126":127,"_128":129,"_130":65},"tree",[50,51,52,53],"file",{"_33":34,"_35":36,"_37":38,"_13":19,"_39":40,"_41":24,"_17":42,"_43":-5,"_44":45,"_46":47,"_48":-5,"_18":49},"contents",[],"upstream","pull","ahead","behind","envVars","id","e53528ce-2773-4249-bd42-7fd9063d3857","branchId","81f1664b-7a2e-4ba3-86b4-5e255cb6a15b","fileId","0296f6ec-5f9a-11f1-957d-1607ee4eb77e","content","\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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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 } 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