/**
 * AI Cluster & Hyperscale DC Announcement Scraper
 *
 * Monitors RSS feeds from:
 *   - DataCenterKnowledge (datacenterknowledge.com)
 *   - The Register (theregister.com)
 *   - ServeTheHome (servethehome.com)
 *   - DataCenter Dynamics (datacenterdynamics.com)
 *   - Blocks & Files (blocksandfiles.com)
 *
 * Extracts announcements about:
 *   - AI clusters (xAI, Meta, AWS, Microsoft, Google)
 *   - New data center builds with scale indicators
 *   - Network tech mentions (400G, 800G, InfiniBand, RoCE)
 *
 * Each announced AI cluster = predictable transceiver demand
 * with 3-9 month deployment lag.
 */

import * as cheerio from "cheerio";
import { pool } from "../utils/db";
import { logger } from "../utils/logger";
import { contentHash } from "../utils/hash";

interface Announcement {
  company: string;
  title: string;
  summary: string;
  announced_date: string | null;
  scale_mw: number | null;
  scale_servers: number | null;
  network_speed: string | null;
  estimated_transceivers: number | null;
  deployment_date: string | null;
  location: string | null;
  source_url: string;
  source_name: string;
}

const RSS_FEEDS = [
  { url: "https://www.datacenterknowledge.com/rss.xml",       name: "DataCenterKnowledge" },
  { url: "https://www.datacenterdynamics.com/en/rss/",        name: "DataCenter Dynamics" },
  { url: "https://www.theregister.com/data_centre/rss/",      name: "The Register" },
  { url: "https://blocksandfiles.com/feed/",                   name: "Blocks & Files" },
  { url: "https://www.nextplatform.com/feed/",                 name: "Next Platform" },
  { url: "https://www.servethehome.com/feed/",                 name: "ServeTheHome" },
];

const COMPANY_PATTERNS: Array<{ pattern: RegExp; name: string }> = [
  { pattern: /\bxAI\b/i,                    name: "xAI" },
  { pattern: /\bMeta\b.*?(AI|data center)/i, name: "Meta" },
  { pattern: /\bOpenAI\b/i,                  name: "OpenAI" },
  { pattern: /\bAWS\b|\bAmazon\b.*?cloud/i,  name: "Amazon (AWS)" },
  { pattern: /\bMicrosoft\b.*?(Azure|AI)/i,  name: "Microsoft" },
  { pattern: /\bGoogle\b.*?(Cloud|DeepMind)/i, name: "Google" },
  { pattern: /\bOracle\b.*?cloud/i,          name: "Oracle Cloud" },
  { pattern: /\bCoreWeave\b/i,               name: "CoreWeave" },
  { pattern: /\bLambda\b.*?(Labs|cloud)/i,   name: "Lambda Labs" },
  { pattern: /\bNVIDIA\b.*?supercomputer/i,  name: "NVIDIA" },
  { pattern: /\bApple\b.*?data center/i,     name: "Apple" },
];

const AI_KEYWORDS = [
  "GPU cluster", "AI supercomputer", "AI infrastructure", "data center",
  "hyperscale", "AI campus", "GPU server", "AI factory", "compute cluster",
  "400G", "800G", "InfiniBand", "RoCE", "co-packaged optics",
];

const SCALE_PATTERNS = [
  { pattern: /(\d+(?:\.\d+)?)\s*(?:MW|megawatt)/i, key: "mw" },
  { pattern: /(\d[\d,]*)\s*(?:GPU|H100|H200|A100|B200)/i, key: "gpus" },
  { pattern: /\$(\d+(?:\.\d+)?)\s*(?:billion|B)\b/i, key: "usd_billion" },
  { pattern: /(\d[\d,]*)\s*(?:server|node)/i, key: "servers" },
];

const NETWORK_PATTERNS = [
  { pattern: /\b800G\b/i,              value: "800G" },
  { pattern: /\b400G\b/i,              value: "400G" },
  { pattern: /\bInfiniBand NDR\b/i,    value: "IB-NDR-400G" },
  { pattern: /\bInfiniBand HDR\b/i,    value: "IB-HDR-200G" },
  { pattern: /\bInfiniBand\b/i,        value: "InfiniBand" },
  { pattern: /\bRoCEv2?\b/i,           value: "RoCE-Ethernet" },
  { pattern: /\b100G\b/i,              value: "100G" },
];

// Rough estimate: 1 GPU server ≈ 8 transceivers for 400G networking
function estimateTransceivers(servers: number | null, mw: number | null, networkSpeed: string | null): number | null {
  if (!servers && !mw) return null;
  const serverEstimate = servers ?? (mw ? Math.round(mw * 300) : 0); // ~300 servers/MW
  const perServer = networkSpeed?.includes("800G") ? 16 : 8;
  return Math.round(serverEstimate * perServer);
}

async function parseRssFeed(feedUrl: string, sourceName: string): Promise<Announcement[]> {
  const res = await fetch(feedUrl, {
    headers: { "User-Agent": "TIP-DataCollector/1.0 contact@context-x.org" },
    signal: AbortSignal.timeout(15000),
  });
  if (!res.ok) throw new Error(`RSS fetch failed: ${res.status}`);
  const xml = await res.text();
  const $ = cheerio.load(xml, { xmlMode: true });

  const announcements: Announcement[] = [];

  $("item").each((_, el) => {
    const title   = $(el).find("title").first().text().trim();
    const link    = $(el).find("link").first().text().trim() || $(el).find("guid").first().text().trim();
    const desc    = $(el).find("description").first().text().replace(/<[^>]+>/g, " ").trim();
    const pubDate = $(el).find("pubDate").first().text().trim();

    const fullText = `${title} ${desc}`;

    // Check if this article is relevant
    const isRelevant = AI_KEYWORDS.some(kw => fullText.toLowerCase().includes(kw.toLowerCase()));
    if (!isRelevant) return;

    // Extract company
    let company = "Unknown";
    for (const cp of COMPANY_PATTERNS) {
      if (cp.pattern.test(fullText)) { company = cp.name; break; }
    }

    // Extract scale
    let scaleMw: number | null = null;
    let scaleServers: number | null = null;
    for (const sp of SCALE_PATTERNS) {
      const m = fullText.match(sp.pattern);
      if (m) {
        const v = parseFloat(m[1].replace(/,/g, ""));
        if (sp.key === "mw") scaleMw = v;
        else if (sp.key === "gpus" || sp.key === "servers") scaleServers = Math.round(v / 8) * 8; // normalize
        else if (sp.key === "usd_billion") scaleServers = Math.round(v * 2000); // rough estimate
      }
    }

    // Extract network speed
    let networkSpeed: string | null = null;
    for (const np of NETWORK_PATTERNS) {
      if (np.pattern.test(fullText)) { networkSpeed = np.value; break; }
    }

    // Parse date
    let announcedDate: string | null = null;
    if (pubDate) {
      try { announcedDate = new Date(pubDate).toISOString().split("T")[0]; } catch { /* ignore */ }
    }

    const summary = desc.substring(0, 500);

    announcements.push({
      company,
      title: title.substring(0, 300),
      summary,
      announced_date: announcedDate,
      scale_mw: scaleMw,
      scale_servers: scaleServers,
      network_speed: networkSpeed,
      estimated_transceivers: estimateTransceivers(scaleServers, scaleMw, networkSpeed),
      deployment_date: null, // extracted from text in future
      location: null,
      source_url: link,
      source_name: sourceName,
    });
  });

  return announcements;
}

export async function scrapeAiClusters(): Promise<void> {
  logger.info("AI cluster announcement scraper starting");
  let newItems = 0;

  for (const feed of RSS_FEEDS) {
    try {
      logger.info(`Fetching: ${feed.name}`);
      await new Promise(r => setTimeout(r, 1000));

      const announcements = await parseRssFeed(feed.url, feed.name);
      logger.info(`${feed.name}: ${announcements.length} relevant articles found`);

      for (const a of announcements) {
        const hash = contentHash(`${a.title}${a.source_url}`);
        try {
          await pool.query(`
            INSERT INTO ai_cluster_announcements
              (company, title, summary, announced_date, scale_mw, scale_servers,
               network_speed, estimated_transceivers, source_url, source_name, content_hash)
            VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11)
            ON CONFLICT (content_hash) DO NOTHING
          `, [
            a.company, a.title, a.summary, a.announced_date,
            a.scale_mw, a.scale_servers, a.network_speed,
            a.estimated_transceivers, a.source_url, a.source_name, hash,
          ]);
          newItems++;
        } catch { /* duplicate */ }
      }
    } catch (err) {
      logger.warn(`AI cluster feed failed: ${feed.name}`, { err });
    }
  }

  logger.info(`AI cluster scraper done — ${newItems} items recorded`);
}