/**
 * Context module — AI text generation + token estimation
 * Ported from apps/context/apps/server
 *
 * CRUD for spaces/documents handled by mana-sync.
 */

import { Hono } from 'hono';
import { consumeCredits, validateCredits } from '@mana/shared-hono/credits';
import type { AuthVariables } from '@mana/shared-hono';

const LLM_URL = process.env.MANA_LLM_URL || 'http://localhost:3025';
const CRAWLER_URL = process.env.MANA_CRAWLER_URL || 'http://localhost:3023';
const DEFAULT_SUMMARY_MODEL = process.env.MANA_LLM_DEFAULT_MODEL || 'gemma3:4b';

const routes = new Hono<{ Variables: AuthVariables }>();

// ─── URL Import (crawler → optional LLM summary → document) ──

const DEEP_MAX_PAGES = 20;
const CRAWL_POLL_INTERVAL_MS = 1500;
const CRAWL_TIMEOUT_MS = 90_000;

/**
 * Local LLMs love to wrap Markdown in ```markdown fences or prepend
 * a "Hier ist die Zusammenfassung:" preamble. Strip those so the
 * output renders correctly when dropped into the Kontext document.
 */
function sanitizeSummary(raw: string): string {
	let s = raw.trim();
	// Strip a leading ```markdown / ```md / ``` fence and its closing ```.
	const fenceMatch = s.match(/^```(?:markdown|md)?\s*\n([\s\S]*?)\n?```\s*$/i);
	if (fenceMatch) s = fenceMatch[1].trim();
	// Drop a single-line preamble that ends with a colon (LLM chatter).
	const lines = s.split('\n');
	if (lines.length > 2 && /^[^#\n].{0,80}:\s*$/.test(lines[0].trim())) {
		s = lines.slice(1).join('\n').trim();
	}
	// Demote a solitary leading H1 to H2 so it doesn't clash with our
	// section header that the frontend prepends.
	s = s.replace(/^#\s+/, '## ');
	return s;
}

async function pollCrawlJob(jobId: string) {
	const deadline = Date.now() + CRAWL_TIMEOUT_MS;
	while (Date.now() < deadline) {
		await new Promise((r) => setTimeout(r, CRAWL_POLL_INTERVAL_MS));
		const res = await fetch(`${CRAWLER_URL}/api/v1/crawl/${jobId}`);
		if (!res.ok) throw new Error(`crawl status ${res.status}`);
		const job = (await res.json()) as { status: string; error?: string };
		if (job.status === 'completed') return;
		if (job.status === 'failed') throw new Error(job.error || 'crawl failed');
	}
	throw new Error('crawl timeout');
}

routes.post('/import-url', async (c) => {
	const userId = c.get('userId');
	const {
		url,
		mode = 'single',
		summarize = false,
	} = (await c.req.json()) as {
		url?: string;
		mode?: 'single' | 'deep';
		summarize?: boolean;
	};

	if (!url || !/^https?:\/\//i.test(url)) {
		return c.json({ error: 'valid http(s) url required' }, 400);
	}

	const creditCost = summarize ? 5 : 1;
	const validation = await validateCredits(userId, 'AI_CONTEXT_IMPORT_URL', creditCost);
	if (!validation.hasCredits) {
		return c.json(
			{
				error: 'Insufficient credits',
				required: creditCost,
				available: validation.availableCredits,
			},
			402
		);
	}

	try {
		const crawlBody = {
			startUrl: url,
			config: {
				maxDepth: mode === 'deep' ? 3 : 0,
				maxPages: mode === 'deep' ? DEEP_MAX_PAGES : 1,
				rateLimit: 2,
				respectRobots: true,
				outputFormat: 'markdown',
			},
		};

		const startRes = await fetch(`${CRAWLER_URL}/api/v1/crawl`, {
			method: 'POST',
			headers: { 'Content-Type': 'application/json' },
			body: JSON.stringify(crawlBody),
		});
		if (!startRes.ok) return c.json({ error: 'crawler unreachable' }, 502);
		const { jobId } = (await startRes.json()) as { jobId: string };

		await pollCrawlJob(jobId);

		const resultsRes = await fetch(
			`${CRAWLER_URL}/api/v1/crawl/${jobId}/results?page=1&limit=${DEEP_MAX_PAGES}`
		);
		if (!resultsRes.ok) return c.json({ error: 'crawl results failed' }, 502);
		const results = (await resultsRes.json()) as {
			results: Array<{
				url: string;
				title?: string | null;
				markdown?: string | null;
				content?: string | null;
				depth: number;
			}>;
		};
		const items = (results.results || []).filter((it) => it.markdown || it.content);
		if (items.length === 0) return c.json({ error: 'no content crawled' }, 422);

		items.sort((a, b) => a.depth - b.depth);
		const root = items[0];
		const pageTitle = root.title || new URL(url).hostname;

		let content: string;
		if (mode === 'deep' && items.length > 1) {
			content = items
				.map((it) => `# ${it.title || it.url}\n\n_${it.url}_\n\n${it.markdown || it.content}`)
				.join('\n\n---\n\n');
		} else {
			content = root.markdown || root.content || '';
		}

		if (summarize) {
			const summaryRes = await fetch(`${LLM_URL}/api/v1/chat/completions`, {
				method: 'POST',
				headers: { 'Content-Type': 'application/json' },
				body: JSON.stringify({
					model: DEFAULT_SUMMARY_MODEL,
					max_tokens: 2000,
					messages: [
						{
							role: 'system',
							content:
								'Du bist ein Assistent, der Web-Inhalte in strukturierte Kontext-Dokumente zusammenfasst. ' +
								'Antworte ausschließlich in sauberem Markdown. Gliedere in H2-Abschnitte: ' +
								'"## Überblick", "## Kernaussagen", "## Details". Nutze die Sprache der Quelle. ' +
								'Schreibe die Antwort direkt, ohne Einleitung ("Hier ist…"), ohne Schlussformel, ' +
								'und OHNE Code-Fences (```) um die Antwort.',
						},
						{
							role: 'user',
							content: `Quelle: ${url}\n\n${content.slice(0, 60_000)}`,
						},
					],
				}),
			});
			if (summaryRes.ok) {
				const data = (await summaryRes.json()) as {
					choices?: Array<{ message?: { content?: string } }>;
				};
				const raw = data.choices?.[0]?.message?.content?.trim();
				if (raw) {
					content = sanitizeSummary(raw);
				}
			} else {
				return c.json({ error: 'summary failed' }, 502);
			}
		}

		await consumeCredits(
			userId,
			'AI_CONTEXT_IMPORT_URL',
			creditCost,
			`URL import (${mode}${summarize ? ' + summary' : ''})`
		);

		return c.json({
			title: pageTitle,
			content,
			sourceUrl: url,
			crawlMode: mode,
			crawledAt: new Date().toISOString(),
			pageCount: items.length,
		});
	} catch (err) {
		const message = err instanceof Error ? err.message : 'import failed';
		return c.json({ error: message }, 500);
	}
});

// ─── AI Generation (server-only: mana-llm) ──────────────────

routes.post('/ai/generate', async (c) => {
	const userId = c.get('userId');
	const { prompt, documents, model, maxTokens } = await c.req.json();

	if (!prompt) return c.json({ error: 'prompt required' }, 400);

	// Validate credits
	const validation = await validateCredits(userId, 'AI_CONTEXT_GENERATE', 5);
	if (!validation.hasCredits) {
		return c.json(
			{ error: 'Insufficient credits', required: 5, available: validation.availableCredits },
			402
		);
	}

	try {
		// Build messages with document context
		const messages: Array<{ role: string; content: string }> = [];

		if (documents?.length) {
			const contextText = documents
				.map((d: { title: string; content: string }) => `--- ${d.title} ---\n${d.content}`)
				.join('\n\n');
			messages.push({
				role: 'system',
				content: `Verwende diese Dokumente als Kontext:\n\n${contextText}`,
			});
		}

		messages.push({ role: 'user', content: prompt });

		const res = await fetch(`${LLM_URL}/api/v1/chat/completions`, {
			method: 'POST',
			headers: { 'Content-Type': 'application/json' },
			body: JSON.stringify({
				messages,
				model: model || 'gemma3:4b',
				max_tokens: maxTokens || 2000,
			}),
		});

		if (!res.ok) return c.json({ error: 'AI generation failed' }, 502);

		const data = await res.json();
		const content = data.choices?.[0]?.message?.content || '';
		const tokensUsed = data.usage?.total_tokens || 0;

		// Consume credits
		await consumeCredits(userId, 'AI_CONTEXT_GENERATE', 5, `AI generation (${tokensUsed} tokens)`);

		return c.json({ content, tokensUsed, model: model || 'gemma3:4b' });
	} catch (_err) {
		return c.json({ error: 'Generation failed' }, 500);
	}
});

routes.post('/ai/estimate', async (c) => {
	const { prompt, documents } = await c.req.json();
	const charCount =
		(prompt?.length || 0) +
		(documents || []).reduce(
			(sum: number, d: { content: string }) => sum + (d.content?.length || 0),
			0
		);
	const estimatedTokens = Math.ceil(charCount / 4);
	return c.json({ estimatedTokens, estimatedCost: 5 });
});

export { routes as contextRoutes };