import * as os from "node:os";
import * as path from "node:path";
import { getAntigravityUserAgent, getEnvApiKey, type Model } from "@oh-my-pi/pi-ai";
import {
	CODEX_BASE_URL,
	getCodexAccountId,
	OPENAI_HEADER_VALUES,
	OPENAI_HEADERS,
	URL_PATHS,
} from "@oh-my-pi/pi-ai/providers/openai-codex/constants";
import {
	$env,
	isEnoent,
	parseImageMetadata,
	prompt,
	ptree,
	readSseJson,
	Snowflake,
	untilAborted,
} from "@oh-my-pi/pi-utils";
import * as z from "zod/v4";
import packageJson from "../../package.json" with { type: "json" };
import { isAuthenticated, type ModelRegistry } from "../config/model-registry";
import type { CustomTool } from "../extensibility/custom-tools/types";
import imageGenDescription from "../prompts/tools/image-gen.md" with { type: "text" };
import { resolveReadPath } from "./path-utils";

const DEFAULT_MODEL = "gemini-3-pro-image-preview";
const DEFAULT_OPENROUTER_MODEL = "google/gemini-3-pro-image-preview";
const DEFAULT_ANTIGRAVITY_MODEL = "gemini-3-pro-image";
const IMAGE_TIMEOUT = 3 * 60 * 1000; // 3 minutes
const MAX_IMAGE_SIZE = 35 * 1024 * 1024;
const DEFAULT_OPENAI_BASE_URL = "https://api.openai.com/v1";
const OPENAI_IMAGE_OUTPUT_FORMAT = "webp";
const OPENAI_IMAGE_MIME_TYPE = "image/webp";

const ANTIGRAVITY_ENDPOINT = "https://daily-cloudcode-pa.sandbox.googleapis.com";
const IMAGE_SYSTEM_INSTRUCTION =
	"You are an AI image generator. Generate images based on user descriptions. Focus on creating high-quality, visually appealing images that match the user's request.";

type ImageProvider = "antigravity" | "gemini" | "openai" | "openai-codex" | "openrouter";
interface ImageApiKey {
	provider: ImageProvider;
	apiKey: string;
	projectId?: string;
	model?: Model;
}

const responseModalitySchema = z.enum(["IMAGE", "TEXT"] as const);
const aspectRatioSchema = z.enum(["1:1", "3:4", "4:3", "9:16", "16:9"] as const).describe("aspect ratio");
const imageSizeSchema = z.enum(["1024x1024", "1536x1024", "1024x1536"] as const).describe("image size");

const inputImageSchema = z
	.object({
		path: z.string().describe("input image path").optional(),
		data: z.string().describe("base64 image data").optional(),
		mime_type: z.string().describe("mime type").optional(),
	})
	.strict();

const baseImageSchema = z
	.object({
		subject: z.string().describe("main subject"),
		action: z.string().describe("what subject is doing").optional(),
		scene: z.string().describe("location or environment").optional(),
		composition: z.string().describe("camera angle and framing").optional(),
		lighting: z.string().describe("lighting setup").optional(),
		style: z.string().describe("artistic style").optional(),
		text: z.string().describe("text to render").optional(),
		changes: z.array(z.string()).describe("edits to make").optional(),
		aspect_ratio: aspectRatioSchema.optional(),
		image_size: imageSizeSchema.optional(),
		input: z.array(inputImageSchema).describe("input images").optional(),
	})
	.strict();

export const imageGenSchema = baseImageSchema;
export type ImageGenParams = z.infer<typeof imageGenSchema>;
export type GeminiResponseModality = z.infer<typeof responseModalitySchema>;

/**
 * Assembles a structured prompt from the provided parameters.
 * For generation: builds "subject, action, scene. composition. lighting. camera. style."
 * For edits: appends change instructions and preserve directives.
 */
function assemblePrompt(params: ImageGenParams): string {
	const parts: string[] = [];

	// Core subject line: subject + action + scene
	const subjectParts = [params.subject];
	if (params.action) subjectParts.push(params.action);
	if (params.scene) subjectParts.push(params.scene);
	parts.push(subjectParts.join(", "));

	// Technical details as separate sentences
	if (params.composition) parts.push(params.composition);
	if (params.lighting) parts.push(params.lighting);
	if (params.style) parts.push(params.style);

	// Join with periods for sentence structure
	let prompt = `${parts.map(p => p.replace(/[.!,;:]+$/, "")).join(". ")}.`;

	// Text rendering specs
	if (params.text) {
		prompt += `\n\nText: ${params.text}`;
	}

	// Edit mode: changes and preserve directives
	if (params.changes?.length) {
		prompt += `\n\nChanges:\n${params.changes.map(c => `- ${c}`).join("\n")}`;
	}

	return prompt;
}

interface GeminiInlineData {
	data?: string;
	mimeType?: string;
}

interface GeminiPart {
	text?: string;
	inlineData?: GeminiInlineData;
}

interface GeminiCandidate {
	content?: { parts?: GeminiPart[] };
}

interface GeminiSafetyRating {
	category?: string;
	probability?: string;
}

interface GeminiPromptFeedback {
	blockReason?: string;
	safetyRatings?: GeminiSafetyRating[];
}

interface GeminiUsageMetadata {
	promptTokenCount?: number;
	candidatesTokenCount?: number;
	totalTokenCount?: number;
}

interface GeminiGenerateContentResponse {
	candidates?: GeminiCandidate[];
	promptFeedback?: GeminiPromptFeedback;
	usageMetadata?: GeminiUsageMetadata;
}

interface OpenAIResponsesUsage {
	input_tokens?: number;
	output_tokens?: number;
	total_tokens?: number;
}

type ImageUsageMetadata = GeminiUsageMetadata | OpenAIResponsesUsage;

type OpenAIImageAction = "edit" | "generate";

interface OpenAIInputTextContent {
	type: "input_text";
	text: string;
}

interface OpenAIInputImageContent {
	type: "input_image";
	detail: "auto";
	image_url: string;
}

type OpenAIInputContent = OpenAIInputTextContent | OpenAIInputImageContent;

interface OpenAIImageGenerationTool {
	type: "image_generation";
	action: OpenAIImageAction;
	output_format: typeof OPENAI_IMAGE_OUTPUT_FORMAT;
	size?: string;
}

interface OpenAIHostedImageRequest {
	model: string;
	instructions?: string;
	input: Array<{ role: "user"; content: OpenAIInputContent[] }>;
	tools: OpenAIImageGenerationTool[];
	tool_choice: { type: "image_generation" };
	store: false;
	stream?: boolean;
}

interface OpenAIImageGenerationCall {
	id?: string;
	type: "image_generation_call";
	result?: string;
	revised_prompt?: string;
	status?: string;
}

interface OpenAIOutputText {
	type: "output_text" | "refusal";
	text?: string;
	refusal?: string;
}

interface OpenAIOutputMessage {
	id?: string;
	type: "message";
	content?: OpenAIOutputText[];
}

type OpenAIResponseOutput = OpenAIImageGenerationCall | OpenAIOutputMessage;

interface OpenAIHostedImageResponse {
	output?: OpenAIResponseOutput[];
	usage?: OpenAIResponsesUsage;
	error?: { code?: string; message?: string };
}

interface OpenAISseEvent {
	type?: string;
	item?: OpenAIResponseOutput;
	response?: OpenAIHostedImageResponse;
	code?: string;
	message?: string;
	error?: { code?: string; message?: string };
}

interface OpenAIHostedImageResult {
	images: InlineImageData[];
	responseText?: string;
	revisedPrompt?: string;
	usage?: OpenAIResponsesUsage;
}

interface OpenRouterImageUrl {
	url: string;
}

interface OpenRouterContentPart {
	type: "text" | "image_url";
	text?: string;
	image_url?: OpenRouterImageUrl;
}

interface OpenRouterMessage {
	content?: string | OpenRouterContentPart[];
	images?: Array<string | { image_url?: OpenRouterImageUrl }>;
}

interface OpenRouterChoice {
	message?: OpenRouterMessage;
}

interface OpenRouterResponse {
	choices?: OpenRouterChoice[];
}

interface AntigravityRequest {
	project: string;
	model: string;
	request: {
		contents: Array<{ role: "user"; parts: Array<{ text?: string; inlineData?: InlineImageData }> }>;
		systemInstruction?: { parts: Array<{ text: string }> };
		generationConfig?: {
			responseModalities?: GeminiResponseModality[];
			imageConfig?: { aspectRatio?: string; imageSize?: string };
			candidateCount?: number;
		};
		safetySettings?: Array<{ category: string; threshold: string }>;
	};
	requestType?: string;
	userAgent?: string;
	requestId?: string;
}

interface AntigravityResponseChunk {
	response?: {
		candidates?: Array<{
			content?: {
				role: string;
				parts?: Array<{
					text?: string;
					inlineData?: { mimeType?: string; data?: string };
				}>;
			};
		}>;
		usageMetadata?: GeminiUsageMetadata;
	};
}

interface ImageGenToolDetails {
	provider: ImageProvider;
	model: string;
	imageCount: number;
	imagePaths: string[];
	images: InlineImageData[];
	responseText?: string;
	promptFeedback?: GeminiPromptFeedback;
	revisedPrompt?: string;
	usage?: ImageUsageMetadata;
}

interface ImageInput {
	path?: string;
	data?: string;
	mime_type?: string;
}

interface InlineImageData {
	data: string;
	mimeType: string;
}

function normalizeDataUrl(data: string): { data: string; mimeType?: string } {
	const match = data.match(/^data:([^;]+);base64,(.+)$/);
	if (!match) return { data };
	return { data: match[2] ?? "", mimeType: match[1] };
}

function resolveOpenRouterModel(model: string): string {
	return model.includes("/") ? model : `google/${model}`;
}

function toDataUrl(image: InlineImageData): string {
	return `data:${image.mimeType};base64,${image.data}`;
}

async function loadImageFromUrl(imageUrl: string, signal?: AbortSignal): Promise<InlineImageData> {
	if (imageUrl.startsWith("data:")) {
		const normalized = normalizeDataUrl(imageUrl.trim());
		if (!normalized.mimeType) {
			throw new Error("mime_type is required when providing raw base64 data.");
		}
		if (!normalized.data) {
			throw new Error("Image data is empty.");
		}
		return { data: normalized.data, mimeType: normalized.mimeType };
	}

	const response = await fetch(imageUrl, { signal });
	if (!response.ok) {
		const rawText = await response.text();
		throw new Error(`Image download failed (${response.status}): ${rawText}`);
	}
	const contentType = response.headers.get("content-type")?.split(";")[0];
	if (!contentType?.startsWith("image/")) {
		throw new Error(`Unsupported image type from URL: ${imageUrl}`);
	}
	const buffer = await response.bytes();
	return { data: buffer.toBase64(), mimeType: contentType };
}

function collectOpenRouterResponseText(message: OpenRouterMessage | undefined): string | undefined {
	if (!message) return undefined;
	if (typeof message.content === "string") {
		const trimmed = message.content.trim();
		return trimmed.length > 0 ? trimmed : undefined;
	}
	if (Array.isArray(message.content)) {
		const texts = message.content
			.filter(part => part.type === "text")
			.map(part => part.text)
			.filter((text): text is string => Boolean(text));
		const combined = texts.join("\n").trim();
		return combined.length > 0 ? combined : undefined;
	}
	return undefined;
}

function extractOpenRouterImageUrls(message: OpenRouterMessage | undefined): string[] {
	const urls: string[] = [];
	if (!message) return urls;
	for (const image of message.images ?? []) {
		if (typeof image === "string") {
			urls.push(image);
			continue;
		}
		if (image.image_url?.url) {
			urls.push(image.image_url.url);
		}
	}
	if (Array.isArray(message.content)) {
		for (const part of message.content) {
			if (part.type === "image_url" && part.image_url?.url) {
				urls.push(part.image_url.url);
			}
		}
	}
	return urls;
}

/** Preferred provider set via settings (default: auto) */
let preferredImageProvider: ImageProvider | "auto" = "auto";

/** Set the preferred image provider from settings */
export function setPreferredImageProvider(provider: ImageProvider | "auto"): void {
	preferredImageProvider = provider;
}

interface ParsedAntigravityCredentials {
	accessToken: string;
	projectId: string;
}

function parseAntigravityCredentials(raw: string): ParsedAntigravityCredentials | null {
	try {
		const parsed = JSON.parse(raw) as { token?: string; projectId?: string };
		if (parsed.token && parsed.projectId) {
			return { accessToken: parsed.token, projectId: parsed.projectId };
		}
	} catch {
		// Invalid JSON
	}
	return null;
}

async function findAntigravityCredentials(modelRegistry: ModelRegistry): Promise<ImageApiKey | null> {
	const apiKey = await modelRegistry.getApiKeyForProvider("google-antigravity");
	if (!apiKey) return null;

	const parsed = parseAntigravityCredentials(apiKey);
	if (!parsed) return null;

	return {
		provider: "antigravity",
		apiKey: parsed.accessToken,
		projectId: parsed.projectId,
	};
}

async function findOpenAIHostedImageCredentials(
	modelRegistry: ModelRegistry | undefined,
	activeModel: Model | undefined,
	sessionId?: string,
): Promise<ImageApiKey | null> {
	if (!modelRegistry || !isOpenAIHostedImageModel(activeModel)) return null;
	const apiKey = await modelRegistry.getApiKey(activeModel, sessionId);
	if (!isAuthenticated(apiKey)) return null;
	return {
		provider: getOpenAIHostedImageProvider(activeModel),
		apiKey,
		model: activeModel,
	};
}

async function findImageApiKey(
	modelRegistry?: ModelRegistry,
	activeModel?: Model,
	sessionId?: string,
): Promise<ImageApiKey | null> {
	// If a specific provider is preferred, try it first.
	if (preferredImageProvider === "openai") {
		const openAI = await findOpenAIHostedImageCredentials(modelRegistry, activeModel, sessionId);
		if (openAI) return openAI;
		// Fall through to auto-detect if preferred provider key not found.
	} else if (preferredImageProvider === "antigravity" && modelRegistry) {
		const antigravity = await findAntigravityCredentials(modelRegistry);
		if (antigravity) return antigravity;
		// Fall through to auto-detect if preferred provider key not found.
	} else if (preferredImageProvider === "gemini") {
		const geminiKey = getEnvApiKey("google");
		if (geminiKey) return { provider: "gemini", apiKey: geminiKey };
		const googleKey = $env.GOOGLE_API_KEY;
		if (googleKey) return { provider: "gemini", apiKey: googleKey };
		// Fall through to auto-detect if preferred provider key not found.
	} else if (preferredImageProvider === "openrouter") {
		const openRouterKey = getEnvApiKey("openrouter");
		if (openRouterKey) return { provider: "openrouter", apiKey: openRouterKey };
		// Fall through to auto-detect if preferred provider key not found.
	}

	// Auto-detect: GPT hosted image generation, then Antigravity, OpenRouter, Gemini.
	const openAI = await findOpenAIHostedImageCredentials(modelRegistry, activeModel, sessionId);
	if (openAI) return openAI;

	if (modelRegistry) {
		const antigravity = await findAntigravityCredentials(modelRegistry);
		if (antigravity) return antigravity;
	}

	const openRouterKey = getEnvApiKey("openrouter");
	if (openRouterKey) return { provider: "openrouter", apiKey: openRouterKey };

	const geminiKey = getEnvApiKey("google");
	if (geminiKey) return { provider: "gemini", apiKey: geminiKey };

	const googleKey = $env.GOOGLE_API_KEY;
	if (googleKey) return { provider: "gemini", apiKey: googleKey };

	return null;
}

async function loadImageFromPath(imagePath: string, cwd: string): Promise<InlineImageData> {
	const resolved = resolveReadPath(imagePath, cwd);
	try {
		const buffer = await Bun.file(resolved).bytes();
		if (buffer.length > MAX_IMAGE_SIZE) {
			throw new Error(`Image file too large: ${imagePath}`);
		}

		const metadata = parseImageMetadata(buffer);
		const mimeType = metadata?.mimeType;
		if (!mimeType) {
			throw new Error(`Unsupported image type: ${imagePath}`);
		}

		return { data: buffer.toBase64(), mimeType };
	} catch (err) {
		if (isEnoent(err)) throw new Error(`Image file not found: ${imagePath}`);
		throw err;
	}
}

async function resolveInputImage(input: ImageInput, cwd: string): Promise<InlineImageData> {
	if (input.path) {
		return loadImageFromPath(input.path, cwd);
	}

	if (input.data) {
		const normalized = normalizeDataUrl(input.data.trim());
		const mimeType = normalized.mimeType ?? input.mime_type;
		if (!mimeType) {
			throw new Error("mime_type is required when providing raw base64 data.");
		}
		if (!normalized.data) {
			throw new Error("Image data is empty.");
		}
		return { data: normalized.data, mimeType };
	}

	throw new Error("input_images entries must include either path or data.");
}

function getExtensionForMime(mimeType: string): string {
	const map: Record<string, string> = {
		"image/png": "png",
		"image/jpeg": "jpg",
		"image/gif": "gif",
		"image/webp": "webp",
	};
	return map[mimeType] ?? "png";
}

async function saveImageToTemp(image: InlineImageData): Promise<string> {
	const ext = getExtensionForMime(image.mimeType);
	const filename = `omp-image-${Snowflake.next()}.${ext}`;
	const filepath = path.join(os.tmpdir(), filename);
	await Bun.write(filepath, Buffer.from(image.data, "base64"));
	return filepath;
}

async function saveImagesToTemp(images: InlineImageData[]): Promise<string[]> {
	return Promise.all(images.map(saveImageToTemp));
}

function buildResponseSummary(
	provider: ImageProvider,
	model: string,
	imagePaths: string[],
	responseText: string | undefined,
): string {
	const lines = [`Provider: ${provider}`, `Model: ${model}`, `Generated ${imagePaths.length} image(s):`];
	for (const p of imagePaths) {
		lines.push(`  ${p}`);
	}
	if (responseText) {
		lines.push("", responseText.trim());
	}
	return lines.join("\n");
}

function collectResponseText(parts: GeminiPart[]): string | undefined {
	const texts = parts.map(part => part.text).filter((text): text is string => Boolean(text));
	const combined = texts.join("\n").trim();
	return combined.length > 0 ? combined : undefined;
}

function collectInlineImages(parts: GeminiPart[]): InlineImageData[] {
	const images: InlineImageData[] = [];
	for (const part of parts) {
		const data = part.inlineData?.data;
		const mimeType = part.inlineData?.mimeType;
		if (!data || !mimeType) continue;
		images.push({ data, mimeType });
	}
	return images;
}

function isOpenAIHostedImageModel(model: Model | undefined): model is Model {
	if (!model) return false;
	if (model.provider !== "openai" && model.provider !== "openai-codex") return false;
	if (model.api !== "openai-responses" && model.api !== "openai-codex-responses") return false;
	const modelId = model.id.toLowerCase();
	return modelId.startsWith("gpt-") || modelId === "o3" || modelId.startsWith("o3-");
}

function getOpenAIHostedImageProvider(model: Model): ImageProvider {
	return model.api === "openai-codex-responses" || model.provider === "openai-codex" ? "openai-codex" : "openai";
}

function resolveOpenAIImageSize(aspectRatio: string | undefined, imageSize: string | undefined): string | undefined {
	if (imageSize) return imageSize;
	switch (aspectRatio) {
		case "1:1":
			return "1024x1024";
		case "3:4":
		case "9:16":
			return "1024x1536";
		case "4:3":
		case "16:9":
			return "1536x1024";
		default:
			return undefined;
	}
}

function buildOpenAIHostedImageRequest(
	model: Model,
	promptText: string,
	params: ImageGenParams,
	inputImages: InlineImageData[],
	stream: boolean,
): OpenAIHostedImageRequest {
	const content: OpenAIInputContent[] = [{ type: "input_text", text: promptText }];
	for (const image of inputImages) {
		content.push({ type: "input_image", detail: "auto", image_url: toDataUrl(image) });
	}

	const size = resolveOpenAIImageSize(params.aspect_ratio, params.image_size);
	const tool: OpenAIImageGenerationTool = {
		type: "image_generation",
		action: inputImages.length > 0 ? "edit" : "generate",
		output_format: OPENAI_IMAGE_OUTPUT_FORMAT,
		...(size ? { size } : {}),
	};

	return {
		model: model.id,
		input: [{ role: "user", content }],
		tools: [tool],
		tool_choice: { type: "image_generation" },
		store: false,
		...(stream
			? {
					instructions:
						"You are an AI image generator. Generate images based on user descriptions. Focus on creating high-quality, visually appealing images that match the user's request.",
				}
			: {}),
		...(stream ? { stream: true } : {}),
	};
}

function createOpenAIInlineImage(data: string): InlineImageData {
	const bytes = Buffer.from(data, "base64");
	const mimeType = parseImageMetadata(bytes)?.mimeType ?? OPENAI_IMAGE_MIME_TYPE;
	return { data, mimeType };
}

function collectOpenAIHostedImageResult(response: OpenAIHostedImageResponse): OpenAIHostedImageResult {
	const images: InlineImageData[] = [];
	const textParts: string[] = [];
	let revisedPrompt: string | undefined;

	for (const output of response.output ?? []) {
		if (output.type === "image_generation_call") {
			if (output.result) {
				images.push(createOpenAIInlineImage(output.result));
			}
			if (output.revised_prompt) {
				revisedPrompt = output.revised_prompt;
			}
			continue;
		}

		for (const part of output.content ?? []) {
			if (part.type === "output_text" && part.text) {
				textParts.push(part.text);
			} else if (part.type === "refusal" && part.refusal) {
				textParts.push(part.refusal);
			}
		}
	}

	const responseText = textParts.join("\n").trim();
	return {
		images,
		revisedPrompt,
		responseText: responseText.length > 0 ? responseText : undefined,
		usage: response.usage,
	};
}

function getOpenAIResponseErrorMessage(rawText: string): string {
	try {
		const parsed = JSON.parse(rawText) as { error?: { message?: string } };
		return parsed.error?.message ?? rawText;
	} catch {
		return rawText;
	}
}

function getOpenAIBaseUrl(model: Model): string {
	const fallback =
		model.api === "openai-codex-responses" || model.provider === "openai-codex"
			? CODEX_BASE_URL
			: DEFAULT_OPENAI_BASE_URL;
	return (model.baseUrl || fallback).replace(/\/+$/, "");
}

function getOpenAIResponsesUrl(model: Model): string {
	const baseUrl = getOpenAIBaseUrl(model);
	if (model.api !== "openai-codex-responses" && model.provider !== "openai-codex") {
		return `${baseUrl}/responses`;
	}
	const baseWithSlash = baseUrl.endsWith("/") ? baseUrl : `${baseUrl}/`;
	return new URL(URL_PATHS.RESPONSES.slice(1), baseWithSlash)
		.toString()
		.replace(URL_PATHS.RESPONSES, URL_PATHS.CODEX_RESPONSES);
}

function buildOpenAIImageHeaders(model: Model, apiKey: string, sessionId: string | undefined): Headers {
	const headers = new Headers(model.headers ?? {});
	headers.set("Content-Type", "application/json");
	headers.set("Authorization", `Bearer ${apiKey}`);

	if (model.api === "openai-codex-responses" || model.provider === "openai-codex") {
		const accountId = getCodexAccountId(apiKey);
		if (!accountId) {
			throw new Error("Failed to extract accountId from OpenAI Codex token");
		}
		headers.delete("x-api-key");
		headers.set(OPENAI_HEADERS.ACCOUNT_ID, accountId);
		headers.set(OPENAI_HEADERS.BETA, OPENAI_HEADER_VALUES.BETA_RESPONSES);
		headers.set(OPENAI_HEADERS.ORIGINATOR, OPENAI_HEADER_VALUES.ORIGINATOR_CODEX);
		headers.set("User-Agent", `pi/${packageJson.version} (${os.platform()} ${os.release()}; ${os.arch()})`);
		if (sessionId) {
			headers.set(OPENAI_HEADERS.CONVERSATION_ID, sessionId);
			headers.set(OPENAI_HEADERS.SESSION_ID, sessionId);
		}
	}

	return headers;
}

async function parseOpenAIHostedImageSse(response: Response, signal?: AbortSignal): Promise<OpenAIHostedImageResult> {
	if (!response.body) {
		throw new Error("No response body");
	}

	const fallbackOutput: OpenAIResponseOutput[] = [];
	let completedResponse: OpenAIHostedImageResponse | undefined;

	for await (const event of readSseJson<OpenAISseEvent>(response.body, signal)) {
		if (event.type === "error") {
			const message = event.error?.message ?? event.message ?? "OpenAI image request failed";
			throw new Error(message);
		}
		if (event.type === "response.failed") {
			const message = event.response?.error?.message ?? "OpenAI image request failed";
			throw new Error(message);
		}
		if (event.type === "response.output_item.done" && event.item) {
			fallbackOutput.push(event.item);
		}
		if ((event.type === "response.completed" || event.type === "response.done") && event.response) {
			completedResponse = event.response;
		}
	}

	return collectOpenAIHostedImageResult(
		completedResponse?.output?.length
			? completedResponse
			: { output: fallbackOutput, usage: completedResponse?.usage },
	);
}

async function generateOpenAIHostedImage(
	apiKey: string,
	model: Model,
	params: ImageGenParams,
	inputImages: InlineImageData[],
	signal: AbortSignal | undefined,
	sessionId: string | undefined,
): Promise<OpenAIHostedImageResult> {
	const promptText = assemblePrompt(params);
	const stream = model.api === "openai-codex-responses" || model.provider === "openai-codex";
	const requestBody = buildOpenAIHostedImageRequest(model, promptText, params, inputImages, stream);
	const response = await fetch(getOpenAIResponsesUrl(model), {
		method: "POST",
		headers: buildOpenAIImageHeaders(model, apiKey, sessionId),
		body: JSON.stringify(requestBody),
		signal,
	});

	if (!response.ok) {
		const errorText = await response.text();
		throw new Error(`OpenAI image request failed (${response.status}): ${getOpenAIResponseErrorMessage(errorText)}`);
	}

	const contentType = response.headers.get("content-type") ?? "";
	if (stream || contentType.includes("text/event-stream")) {
		return parseOpenAIHostedImageSse(response, signal);
	}

	const data = (await response.json()) as OpenAIHostedImageResponse;
	return collectOpenAIHostedImageResult(data);
}

function combineParts(response: GeminiGenerateContentResponse): GeminiPart[] {
	const parts: GeminiPart[] = [];
	for (const candidate of response.candidates ?? []) {
		const candidateParts = candidate.content?.parts ?? [];
		parts.push(...candidateParts);
	}
	return parts;
}

function buildAntigravityRequest(
	prompt: string,
	model: string,
	projectId: string,
	aspectRatio: string | undefined,
	imageSize: string | undefined,
	inputImages: InlineImageData[],
): AntigravityRequest {
	const parts: Array<{ text?: string; inlineData?: InlineImageData }> = [];
	for (const image of inputImages) {
		parts.push({ inlineData: image });
	}
	parts.push({ text: prompt });

	const imageConfig = aspectRatio || imageSize ? { aspectRatio: aspectRatio, imageSize: imageSize } : undefined;

	return {
		project: projectId,
		model,
		request: {
			contents: [{ role: "user", parts }],
			systemInstruction: { parts: [{ text: IMAGE_SYSTEM_INSTRUCTION }] },
			generationConfig: {
				responseModalities: ["IMAGE"],
				imageConfig,
				candidateCount: 1,
			},
			safetySettings: [
				{ category: "HARM_CATEGORY_HARASSMENT", threshold: "BLOCK_ONLY_HIGH" },
				{ category: "HARM_CATEGORY_HATE_SPEECH", threshold: "BLOCK_ONLY_HIGH" },
				{ category: "HARM_CATEGORY_SEXUALLY_EXPLICIT", threshold: "BLOCK_ONLY_HIGH" },
				{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", threshold: "BLOCK_ONLY_HIGH" },
				{ category: "HARM_CATEGORY_CIVIC_INTEGRITY", threshold: "BLOCK_ONLY_HIGH" },
			],
		},
		requestType: "agent",
		requestId: `agent-${Date.now()}-${Math.random().toString(36).slice(2, 11)}`,
		userAgent: "antigravity",
	};
}

interface AntigravitySseResult {
	images: InlineImageData[];
	text: string[];
	usage?: GeminiUsageMetadata;
}

async function parseAntigravitySseForImage(response: Response, signal?: AbortSignal): Promise<AntigravitySseResult> {
	if (!response.body) {
		throw new Error("No response body");
	}

	const textParts: string[] = [];
	const images: InlineImageData[] = [];
	let usage: GeminiUsageMetadata | undefined;

	for await (const chunk of readSseJson<AntigravityResponseChunk>(response.body, signal)) {
		const responseData = chunk.response;
		if (!responseData) continue;
		if (!responseData.candidates) continue;
		for (const candidate of responseData.candidates) {
			const parts = candidate.content?.parts;
			if (!parts) continue;
			for (const part of parts) {
				if (part.text) {
					textParts.push(part.text);
				}
				const inlineData = part.inlineData;
				if (inlineData?.data && inlineData.mimeType) {
					images.push({ data: inlineData.data, mimeType: inlineData.mimeType });
				}
			}
		}
		if (responseData.usageMetadata) {
			usage = responseData.usageMetadata;
		}
	}

	return { images, text: textParts, usage };
}

export const imageGenTool: CustomTool<typeof imageGenSchema, ImageGenToolDetails> = {
	name: "generate_image",
	label: "GenerateImage",
	strict: false,
	description: prompt.render(imageGenDescription),
	parameters: imageGenSchema,
	async execute(_toolCallId, params, _onUpdate, ctx, signal) {
		return untilAborted(signal, async () => {
			const sessionId = ctx.sessionManager.getSessionId();
			const apiKey = await findImageApiKey(ctx.modelRegistry, ctx.model, sessionId);
			if (!apiKey) {
				throw new Error(
					"No image API credentials found. Use a GPT Responses/Codex model with OpenAI credentials, login with google-antigravity, or set OPENROUTER_API_KEY, GEMINI_API_KEY, or GOOGLE_API_KEY.",
				);
			}

			const provider = apiKey.provider;
			const model =
				provider === "openai" || provider === "openai-codex"
					? (apiKey.model?.id ?? "gpt")
					: provider === "antigravity"
						? DEFAULT_ANTIGRAVITY_MODEL
						: provider === "openrouter"
							? DEFAULT_OPENROUTER_MODEL
							: DEFAULT_MODEL;
			const resolvedModel = provider === "openrouter" ? resolveOpenRouterModel(model) : model;
			const cwd = ctx.sessionManager.getCwd();

			const resolvedImages: InlineImageData[] = [];
			if (params.input?.length) {
				for (const input of params.input) {
					resolvedImages.push(await resolveInputImage(input, cwd));
				}
			}

			const requestSignal = ptree.combineSignals(signal, IMAGE_TIMEOUT);

			if (provider === "openai" || provider === "openai-codex") {
				if (!apiKey.model) {
					throw new Error("Missing active GPT model for OpenAI image generation");
				}

				const parsed = await generateOpenAIHostedImage(
					apiKey.apiKey,
					apiKey.model,
					params,
					resolvedImages,
					requestSignal,
					sessionId,
				);

				if (parsed.images.length === 0) {
					const messageText = parsed.responseText ? `\n\n${parsed.responseText}` : "";
					return {
						content: [{ type: "text", text: `No image data returned.${messageText}` }],
						details: {
							provider,
							model,
							imageCount: 0,
							imagePaths: [],
							images: [],
							responseText: parsed.responseText,
							revisedPrompt: parsed.revisedPrompt,
							usage: parsed.usage,
						},
					};
				}

				const imagePaths = await saveImagesToTemp(parsed.images);

				return {
					content: [
						{ type: "text", text: buildResponseSummary(provider, model, imagePaths, parsed.responseText) },
					],
					details: {
						provider,
						model,
						imageCount: parsed.images.length,
						imagePaths,
						images: parsed.images,
						responseText: parsed.responseText,
						revisedPrompt: parsed.revisedPrompt,
						usage: parsed.usage,
					},
				};
			}

			if (provider === "antigravity") {
				if (!apiKey.projectId) {
					throw new Error("Missing projectId in antigravity credentials");
				}

				const prompt = assemblePrompt(params);
				const requestBody = buildAntigravityRequest(
					prompt,
					model,
					apiKey.projectId,
					params.aspect_ratio,
					params.image_size,
					resolvedImages,
				);

				const response = await fetch(`${ANTIGRAVITY_ENDPOINT}/v1internal:streamGenerateContent?alt=sse`, {
					method: "POST",
					headers: {
						Authorization: `Bearer ${apiKey.apiKey}`,
						"Content-Type": "application/json",
						Accept: "text/event-stream",
						"User-Agent": getAntigravityUserAgent(),
					},
					body: JSON.stringify(requestBody),
					signal: requestSignal,
				});

				if (!response.ok) {
					const errorText = await response.text();
					let message = errorText;
					try {
						const parsed = JSON.parse(errorText) as { error?: { message?: string } };
						message = parsed.error?.message ?? message;
					} catch {
						// Keep raw text.
					}
					throw new Error(`Antigravity image request failed (${response.status}): ${message}`);
				}

				const parsed = await parseAntigravitySseForImage(response, requestSignal);
				const responseText = parsed.text.length > 0 ? parsed.text.join(" ") : undefined;

				if (parsed.images.length === 0) {
					const messageText = responseText ? `\n\n${responseText}` : "";
					return {
						content: [{ type: "text", text: `No image data returned.${messageText}` }],
						details: {
							provider,
							model,
							imageCount: 0,
							imagePaths: [],
							images: [],
							responseText,
							usage: parsed.usage,
						},
					};
				}

				const imagePaths = await saveImagesToTemp(parsed.images);

				return {
					content: [{ type: "text", text: buildResponseSummary(provider, model, imagePaths, responseText) }],
					details: {
						provider,
						model,
						imageCount: parsed.images.length,
						imagePaths,
						images: parsed.images,
						responseText,
						usage: parsed.usage,
					},
				};
			}

			if (provider === "openrouter") {
				const prompt = assemblePrompt(params);
				const contentParts: OpenRouterContentPart[] = [{ type: "text", text: prompt }];
				for (const image of resolvedImages) {
					contentParts.push({ type: "image_url", image_url: { url: toDataUrl(image) } });
				}

				const requestBody = {
					model: resolvedModel,
					messages: [{ role: "user" as const, content: contentParts }],
				};

				const response = await fetch("https://openrouter.ai/api/v1/chat/completions", {
					method: "POST",
					headers: {
						"Content-Type": "application/json",
						Authorization: `Bearer ${apiKey.apiKey}`,
						"HTTP-Referer": "https://omp.sh/",
						"X-OpenRouter-Title": "Oh-My-Pi",
						"X-OpenRouter-Categories": "cli-agent",
					},
					body: JSON.stringify(requestBody),
					signal: requestSignal,
				});

				const rawText = await response.text();
				if (!response.ok) {
					let message = rawText;
					try {
						const parsed = JSON.parse(rawText) as { error?: { message?: string } };
						message = parsed.error?.message ?? message;
					} catch {
						// Keep raw text.
					}
					throw new Error(`OpenRouter image request failed (${response.status}): ${message}`);
				}

				const data = JSON.parse(rawText) as OpenRouterResponse;
				const message = data.choices?.[0]?.message;
				const responseText = collectOpenRouterResponseText(message);
				const imageUrls = extractOpenRouterImageUrls(message);
				const inlineImages: InlineImageData[] = [];
				for (const imageUrl of imageUrls) {
					inlineImages.push(await loadImageFromUrl(imageUrl, requestSignal));
				}

				if (inlineImages.length === 0) {
					const messageText = responseText ? `\n\n${responseText}` : "";
					return {
						content: [{ type: "text", text: `No image data returned.${messageText}` }],
						details: {
							provider,
							model: resolvedModel,
							imageCount: 0,
							imagePaths: [],
							images: [],
							responseText,
						},
					};
				}

				const imagePaths = await saveImagesToTemp(inlineImages);

				return {
					content: [
						{ type: "text", text: buildResponseSummary(provider, resolvedModel, imagePaths, responseText) },
					],
					details: {
						provider,
						model: resolvedModel,
						imageCount: inlineImages.length,
						imagePaths,
						images: inlineImages,
						responseText,
					},
				};
			}

			const parts = [] as Array<{ text?: string; inlineData?: InlineImageData }>;
			for (const image of resolvedImages) {
				parts.push({ inlineData: image });
			}
			parts.push({ text: assemblePrompt(params) });

			const generationConfig: {
				responseModalities: GeminiResponseModality[];
				imageConfig?: { aspectRatio?: string; imageSize?: string };
			} = {
				responseModalities: ["IMAGE"],
			};

			if (params.aspect_ratio || params.image_size) {
				generationConfig.imageConfig = {
					aspectRatio: params.aspect_ratio,
					imageSize: params.image_size,
				};
			}

			const requestBody = {
				contents: [{ role: "user" as const, parts }],
				generationConfig,
			};

			const response = await fetch(
				`https://generativelanguage.googleapis.com/v1beta/models/${encodeURIComponent(model)}:generateContent`,
				{
					method: "POST",
					headers: {
						"Content-Type": "application/json",
						"x-goog-api-key": apiKey.apiKey,
					},
					body: JSON.stringify(requestBody),
					signal: requestSignal,
				},
			);

			const rawText = await response.text();
			if (!response.ok) {
				let message = rawText;
				try {
					const parsed = JSON.parse(rawText) as { error?: { message?: string } };
					message = parsed.error?.message ?? message;
				} catch {
					// Keep raw text.
				}
				throw new Error(`Gemini image request failed (${response.status}): ${message}`);
			}

			const data = JSON.parse(rawText) as GeminiGenerateContentResponse;
			const responseParts = combineParts(data);
			const responseText = collectResponseText(responseParts);
			const inlineImages = collectInlineImages(responseParts);

			if (inlineImages.length === 0) {
				const blocked = data.promptFeedback?.blockReason
					? `Blocked: ${data.promptFeedback.blockReason}`
					: "No image data returned.";
				return {
					content: [{ type: "text", text: `${blocked}${responseText ? `\n\n${responseText}` : ""}` }],
					details: {
						provider,
						model,
						imageCount: 0,
						imagePaths: [],
						images: [],
						responseText,
						promptFeedback: data.promptFeedback,
						usage: data.usageMetadata,
					},
				};
			}

			const imagePaths = await saveImagesToTemp(inlineImages);

			return {
				content: [{ type: "text", text: buildResponseSummary(provider, model, imagePaths, responseText) }],
				details: {
					provider,
					model,
					imageCount: inlineImages.length,
					imagePaths,
					images: inlineImages,
					responseText,
					promptFeedback: data.promptFeedback,
					usage: data.usageMetadata,
				},
			};
		});
	},
};

export async function getImageGenTools(
	modelRegistry?: ModelRegistry,
	activeModel?: Model,
): Promise<Array<CustomTool<typeof imageGenSchema, ImageGenToolDetails>>> {
	const apiKey = await findImageApiKey(modelRegistry, activeModel);
	if (!apiKey) return [];
	return [imageGenTool];
}

export async function getImageGenToolsWithRegistry(
	modelRegistry: ModelRegistry,
	activeModel?: Model,
): Promise<Array<CustomTool<typeof imageGenSchema, ImageGenToolDetails>>> {
	const apiKey = await findImageApiKey(modelRegistry, activeModel);
	if (!apiKey) return [];
	return [imageGenTool];
}