curl -X POST "https://api.llmgateway.io/v1/images/generations" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3-pro-image-preview",
    "prompt": "A cute cat wearing a tiny top hat",
    "n": 1,
    "size": "1024x1024"
  }'

import OpenAI from "openai";
import { writeFileSync } from "fs";

const client = new OpenAI({
	baseURL: "https://api.llmgateway.io/v1",
	apiKey: process.env.LLM_GATEWAY_API_KEY,
});

const response = await client.images.generate({
	model: "gemini-3-pro-image-preview",
	prompt: "A futuristic city skyline at sunset with flying cars",
	n: 1,
	size: "1024x1024",
});

response.data.forEach((image, i) => {
	if (image.b64_json) {
		const buf = Buffer.from(image.b64_json, "base64");
		writeFileSync(`image-${i}.png`, buf);
	}
});

import { createLLMGateway } from "@llmgateway/ai-sdk-provider";
import { generateImage } from "ai";
import { writeFileSync } from "fs";

const llmgateway = createLLMGateway({
	apiKey: process.env.LLM_GATEWAY_API_KEY,
});

const result = await generateImage({
	model: llmgateway.image("gemini-3-pro-image-preview"),
	prompt:
		"A cozy cabin in a snowy mountain landscape at night with aurora borealis",
	size: "1024x1024",
	n: 1,
});

result.images.forEach((image, i) => {
	const buf = Buffer.from(image.base64, "base64");
	writeFileSync(`image-${i}.png`, buf);
});

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3-pro-image-preview",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a cute golden retriever puppy playing in a sunny meadow"
      }
    ]
  }'

{
	"id": "chatcmpl-1756234109285",
	"object": "chat.completion",
	"created": 1756234109,
	"model": "gemini-3-pro-image-preview",
	"choices": [
		{
			"index": 0,
			"message": {
				"role": "assistant",
				"content": "Here's an image of a cute dog for you: ",
				"images": [
					{
						"type": "image_url",
						"image_url": {
							"url": "data:image/png;base64,<base64_encoded_image_data>"
						}
					}
				]
			},
			"finish_reason": "stop"
		}
	],
	"usage": {
		"prompt_tokens": 8,
		"completion_tokens": 1303,
		"total_tokens": 1311
	}
}

import { streamText, type UIMessage, convertToModelMessages } from "ai";
import { createLLMGateway } from "@llmgateway/ai-sdk-provider";

interface ChatRequestBody {
	messages: UIMessage[];
}

export async function POST(req: Request) {
	const body = await req.json();

	const { messages }: ChatRequestBody = body;

	const llmgateway = createLLMGateway({
		apiKey: "llmgateway_api_key",
		baseUrl: "https://api.llmgateway.io/v1",
	});

	try {
		const result = streamText({
			model: llmgateway.chat("gemini-3-pro-image-preview"),
			messages: convertToModelMessages(messages),
		});

		return result.toUIMessageStreamResponse();
	} catch {
		return new Response(
			JSON.stringify({ error: "LLM Gateway Chat request failed" }),
			{
				status: 500,
			},
		);
	}
}

"use client";

import { useState, useRef } from "react";
import { useChat } from "@ai-sdk/react";
import { parseImagePartToDataUrl } from "@/lib/image-utils";
import {
	PromptInput,
	PromptInputBody,
	PromptInputButton,
	PromptInputSubmit,
	PromptInputTextarea,
	PromptInputToolbar,
} from "@/components/ai-elements/prompt-input";
import {
	Conversation,
	ConversationContent,
} from "@/components/ai-elements/conversation";
import { Image } from "@/components/ai-elements/image";
import { Loader } from "@/components/ai-elements/loader";
import { Message, MessageContent } from "@/components/ai-elements/message";
import { Response } from "@/components/ai-elements/response";

export const ChatUI = () => {
	const textareaRef = useRef<HTMLTextAreaElement | null>(null);
	const [text, setText] = useState("");
	const { messages, status, stop, regenerate, sendMessage } = useChat();

	return (
		<>
			<div className="flex-1 overflow-y-auto px-4 pb-24">
				<Conversation>
					<ConversationContent>
						{messages.length === 0 ? (
							<div className="mb-6 text-center">
								<h2 className="text-3xl font-semibold tracking-tight">
									How can I help you?
								</h2>
							</div>
						) : (
							messages.map((m, messageIndex) => {
								const isLastMessage = messageIndex === messages.length - 1;

								if (m.role === "assistant") {
									const textContent = m.parts
										.filter((p) => p.type === "text")
										.map((p) => p.text)
										.join("");
									// Combine all image parts (both image_url and file types)
									const imageParts = m.parts.filter(
										(p) =>
											p.type === "file" && p.mediaType?.startsWith("image/"),
									);

									return (
										<div key={m.id}>
											{textContent ? <Response>{textContent}</Response> : null}
											{imageParts.length > 0 ? (
												<div className="mt-3 grid grid-cols-1 gap-3 sm:grid-cols-2">
													{imageParts.map((part, idx: number) => {
														const { base64Only, mediaType } =
															parseImagePartToDataUrl(part);

														if (!base64Only) {
															return null;
														}

														return (
															<Image
																key={idx}
																base64={base64Only}
																mediaType={mediaType}
																alt={part.name || "Generated image"}
															/>
														);
													})}
												</div>
											) : null}
											{isLastMessage &&
												(status === "submitted" || status === "streaming") && (
													<Loader />
												)}
										</div>
									);
								} else {
									return (
										<Message key={m.id} from={m.role}>
											<MessageContent variant="flat">
												{m.parts.map((p, i) => {
													if (p.type === "text") {
														return <div key={i}>{p.text}</div>;
													}
													return null;
												})}
											</MessageContent>
											{isLastMessage &&
												(status === "submitted" || status === "streaming") && (
													<Loader />
												)}
										</Message>
									);
								}
							})
						)}
					</ConversationContent>
				</Conversation>
			</div>
			<div className="sticky bottom-0 left-0 right-0 px-4 pb-[max(env(safe-area-inset-bottom),1rem)] pt-2 bg-gradient-to-t from-background via-background/95 to-transparent backdrop-blur supports-[backdrop-filter]:bg-background/60">
				<PromptInput
					aria-disabled={status === "streaming"}
					onSubmit={async (message) => {
						if (status === "streaming") {
							return;
						}

						try {
							const textContent = message.text ?? "";
							if (!textContent.trim()) {
								return;
							}

							setText(""); // Clear input immediately

							const parts = [{ type: "text", text: textContent }];

							// Call sendMessage which will handle adding the user message and API request
							sendMessage({
								role: "user",
								parts,
							});
						} catch (error) {
							// Throw error here
						}
					}}
				>
					<PromptInputBody>
						<PromptInputTextarea
							ref={textareaRef}
							value={text}
							onChange={(e) => setText(e.currentTarget.value)}
							placeholder="Message"
						/>
					</PromptInputBody>
					<PromptInputToolbar>
						<div className="flex items-center gap-2">
							{status === "streaming" ? (
								<PromptInputButton onClick={() => stop()} variant="ghost">
									Stop
								</PromptInputButton>
							) : null}
							<PromptInputSubmit
								status={status === "streaming" ? "streaming" : "ready"}
							/>
						</div>
					</PromptInputToolbar>
				</PromptInput>
			</div>
		</>
	);
};

/**
 * Parses a file object containing image data and returns a properly formatted data URL
 * and normalized media type.
 *
 * Handles:
 * - Normalizing mediaType from various property names (mediaType, mime_type)
 * - Detecting existing data: URLs
 * - Detecting base64-looking content
 * - Stripping whitespace from base64 content
 * - Building proper data:...;base64,... URLs
 */
export function parseImageFile(file: {
	url?: string;
	mediaType?: string;
	mime_type?: string;
}): { dataUrl: string; mediaType: string } {
	const mediaType = file.mediaType || file.mime_type || "image/png";
	let url = String(file.url || "");

	const isDataUrl = url.startsWith("data:");
	const looksLikeBase64 =
		!isDataUrl && /^[A-Za-z0-9+/=\s]+$/.test(url.slice(0, 200));

	if (looksLikeBase64) {
		url = url.replace(/\s+/g, "");
	}

	const dataUrl = isDataUrl
		? url
		: looksLikeBase64
			? `data:${mediaType};base64,${url}`
			: url;

	return { dataUrl, mediaType };
}

/**
 * Extracts base64-only content from a data URL.
 * Returns empty string if the input is not a valid data URL.
 */
export function extractBase64FromDataUrl(dataUrl: string): string {
	if (!dataUrl.startsWith("data:")) {
		return "";
	}

	const comma = dataUrl.indexOf(",");
	return comma >= 0 ? dataUrl.slice(comma + 1) : "";
}

/**
 * Parses an image part (either image_url or file type) and returns
 * dataUrl, base64Only, and mediaType ready for rendering.
 *
 * Handles error cases gracefully by returning empty base64Only string
 * when parsing fails, allowing the renderer to skip invalid images.
 */
export function parseImagePartToDataUrl(part: any): {
	dataUrl: string;
	base64Only: string;
	mediaType: string;
} {
	try {
		// Handle image_url parts
		if (part.type === "image_url" && part.image_url?.url) {
			const url = part.image_url.url;
			const mediaType = "image/png"; // Default for image_url parts

			if (url.startsWith("data:")) {
				// Extract media type from data URL if present
				const match = url.match(/data:([^;]+)/);
				const extractedMediaType = match?.[1] || mediaType;
				return {
					dataUrl: url,
					base64Only: extractBase64FromDataUrl(url),
					mediaType: extractedMediaType,
				};
			}

			return {
				dataUrl: url,
				base64Only: "",
				mediaType,
			};
		}

		// Handle file parts (AI SDK format)
		if (part.type === "file") {
			const { dataUrl, mediaType } = parseImageFile(part);
			return {
				dataUrl,
				base64Only: extractBase64FromDataUrl(dataUrl),
				mediaType,
			};
		}

		return {
			dataUrl: "",
			base64Only: "",
			mediaType: "image/png",
		};
	} catch {
		return {
			dataUrl: "",
			base64Only: "",
			mediaType: "image/png",
		};
	}
}

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3-pro-image-preview",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a mountain landscape at sunset"
      }
    ],
    "image_config": {
      "aspect_ratio": "16:9",
      "image_size": "4K"
    }
  }'

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "alibaba/qwen-image-plus",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a mountain landscape at sunset"
      }
    ],
    "image_config": {
      "image_size": "1024x1536",
      "n": 1,
      "seed": 42
    }
  }'

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai/cogview-4",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a futuristic city skyline"
      }
    ],
    "image_config": {
      "image_size": "1024x1024"
    }
  }'

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bytedance/seedream-4-5",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a futuristic cyberpunk city at night"
      }
    ],
    "image_config": {
      "image_size": "2048x2048"
    }
  }'