/**
 * Invoice extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
 *
 * Stage 1: Nanonets-OCR2-3B converts ALL document pages to markdown (stop after completion)
 * Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
 *
 * This approach avoids GPU contention by running services sequentially.
 */
import { tap, expect } from '@git.zone/tstest/tapbundle';
import * as fs from 'fs';
import * as path from 'path';
import { execSync } from 'child_process';
import * as os from 'os';
import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
import { SmartAi } from '@push.rocks/smartai';
import { DualAgentOrchestrator } from '@push.rocks/smartagent';

const NANONETS_URL = 'http://localhost:8000/v1';
const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';

const OLLAMA_URL = 'http://localhost:11434';
const EXTRACTION_MODEL = 'gpt-oss:20b';

// Persistent cache directory for storing markdown between runs
const MD_CACHE_DIR = path.join(process.cwd(), '.nogit/invoices-md');

// SmartAi instance for Ollama with optimized settings
const smartAi = new SmartAi({
  ollama: {
    baseUrl: OLLAMA_URL,
    model: EXTRACTION_MODEL,
    defaultOptions: {
      num_ctx: 32768,   // Larger context for long invoices + thinking
      temperature: 0,   // Deterministic for JSON extraction
    },
    defaultTimeout: 600000,  // 10 minute timeout for large documents
  },
});

// DualAgentOrchestrator for structured task execution
let orchestrator: DualAgentOrchestrator;

interface IInvoice {
  invoice_number: string;
  invoice_date: string;
  vendor_name: string;
  currency: string;
  net_amount: number;
  vat_amount: number;
  total_amount: number;
}

interface IImageData {
  base64: string;
  width: number;
  height: number;
  pageNum: number;
}

interface ITestCase {
  name: string;
  pdfPath: string;
  jsonPath: string;
  markdownPath?: string;
}

// Nanonets-specific prompt for document OCR to markdown
const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
Return the tables in html format.
Return the equations in LaTeX representation.
If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;

// JSON extraction prompt for GPT-OSS 20B (sent AFTER the invoice text is provided)
const JSON_EXTRACTION_PROMPT = `Extract key fields from the invoice. Return ONLY valid JSON.

WHERE TO FIND DATA:
- invoice_number, invoice_date, vendor_name: Look in the HEADER section at the TOP of PAGE 1 (near "Invoice no.", "Invoice date:", "Rechnungsnummer")
- net_amount, vat_amount, total_amount: Look in the SUMMARY section at the BOTTOM (look for "Total", "Amount due", "Gesamtbetrag")

RULES:
1. invoice_number: Extract ONLY the value (e.g., "R0015632540"), NOT the label "Invoice no."
2. invoice_date: Convert to YYYY-MM-DD format (e.g., "14/04/2022" → "2022-04-14")
3. vendor_name: The company issuing the invoice
4. currency: EUR, USD, or GBP
5. net_amount: Total before tax
6. vat_amount: Tax amount
7. total_amount: Final total with tax

JSON only:
{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}

Double check for valid JSON syntax.

`;

// Constants for smart batching
const PATCH_SIZE = 14;  // Qwen2.5-VL uses 14x14 patches

/**
 * Estimate visual tokens for an image based on dimensions
 */
function estimateVisualTokens(width: number, height: number): number {
  return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
}

/**
 * Process images one page at a time for reliability
 */
function batchImages(images: IImageData[]): IImageData[][] {
  // One page per batch for reliable processing
  return images.map(img => [img]);
}

/**
 * Convert PDF to JPEG images using ImageMagick with dimension tracking
 */
function convertPdfToImages(pdfPath: string): IImageData[] {
  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
  const outputPattern = path.join(tempDir, 'page-%d.jpg');

  try {
    execSync(
      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
      { stdio: 'pipe' }
    );

    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
    const images: IImageData[] = [];

    for (let i = 0; i < files.length; i++) {
      const file = files[i];
      const imagePath = path.join(tempDir, file);
      const imageData = fs.readFileSync(imagePath);

      // Get image dimensions using identify command
      const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
      const [width, height] = dimensions.split(' ').map(Number);

      images.push({
        base64: imageData.toString('base64'),
        width,
        height,
        pageNum: i + 1,
      });
    }

    return images;
  } finally {
    fs.rmSync(tempDir, { recursive: true, force: true });
  }
}

/**
 * Convert a batch of pages to markdown using Nanonets-OCR-s
 */
async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
  const startTime = Date.now();
  const pageNums = batch.map(img => img.pageNum).join(', ');

  // Build content array with all images first, then the prompt
  const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];

  for (const img of batch) {
    content.push({
      type: 'image_url',
      image_url: { url: `data:image/jpeg;base64,${img.base64}` },
    });
  }

  // Add prompt with page separator instruction if multiple pages
  const promptText = batch.length > 1
    ? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
    : NANONETS_OCR_PROMPT;

  content.push({ type: 'text', text: promptText });

  const response = await fetch(`${NANONETS_URL}/chat/completions`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer dummy',
    },
    body: JSON.stringify({
      model: NANONETS_MODEL,
      messages: [{
        role: 'user',
        content,
      }],
      max_tokens: 4096 * batch.length,  // Scale output tokens with batch size
      temperature: 0.0,
    }),
    signal: AbortSignal.timeout(600000),  // 10 minute timeout for OCR
  });

  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);

  if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
  }

  const data = await response.json();
  let responseContent = (data.choices?.[0]?.message?.content || '').trim();

  // For single-page batches, add page marker if not present
  if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
    responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
  }

  console.log(`        Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
  return responseContent;
}

/**
 * Convert all pages of a document to markdown using smart batching
 */
async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
  const batches = batchImages(images);
  console.log(`      [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);

  const markdownParts: string[] = [];

  for (let i = 0; i < batches.length; i++) {
    const batch = batches[i];
    const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
    console.log(`        Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
    const markdown = await convertBatchToMarkdown(batch);
    markdownParts.push(markdown);
  }

  const fullMarkdown = markdownParts.join('\n\n');
  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
  return fullMarkdown;
}

/**
 * Stop Nanonets container
 */
function stopNanonets(): void {
  console.log('    [Docker] Stopping Nanonets container...');
  try {
    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
    execSync('sleep 5', { stdio: 'pipe' });
    console.log('    [Docker] Nanonets stopped');
  } catch {
    console.log('    [Docker] Nanonets was not running');
  }
}

/**
 * Ensure GPT-OSS 20B model is available
 */
async function ensureExtractionModel(): Promise<boolean> {
  try {
    const response = await fetch(`${OLLAMA_URL}/api/tags`);
    if (response.ok) {
      const data = await response.json();
      const models = data.models || [];
      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
        return true;
      }
    }
  } catch {
    return false;
  }

  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
  });

  return pullResponse.ok;
}

/**
 * Parse amount from string (handles European format)
 */
function parseAmount(s: string | number | undefined): number {
  if (s === undefined || s === null) return 0;
  if (typeof s === 'number') return s;
  const match = s.match(/([\d.,]+)/);
  if (!match) return 0;
  const numStr = match[1];
  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
    ? numStr.replace(/\./g, '').replace(',', '.')
    : numStr.replace(/,/g, '');
  return parseFloat(normalized) || 0;
}

/**
 * Extract invoice number from potentially verbose response
 */
function extractInvoiceNumber(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const patterns = [
    /\b([A-Z]{2,3}\d{10,})\b/i,
    /\b([A-Z]\d{8,})\b/i,
    /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,
    /\b(\d{7,})\b/,
  ];
  for (const pattern of patterns) {
    const match = clean.match(pattern);
    if (match) return match[1];
  }
  return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
}

/**
 * Extract date (YYYY-MM-DD) from response
 */
function extractDate(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
  if (isoMatch) return isoMatch[1];
  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
  if (dmyMatch) {
    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
  }
  return clean.replace(/[^\d-]/g, '').trim();
}

/**
 * Extract currency
 */
function extractCurrency(s: string | undefined): string {
  if (!s) return 'EUR';
  const upper = s.toUpperCase();
  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
  if (upper.includes('USD') || upper.includes('$')) return 'USD';
  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
  return 'EUR';
}

/**
 * Try to extract valid JSON from a response string
 */
function tryExtractJson(response: string): Record<string, unknown> | null {
  // Remove thinking tags
  let clean = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();

  // Try code block
  const codeBlockMatch = clean.match(/```(?:json)?\s*([\s\S]*?)```/);
  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : clean;

  try {
    return JSON.parse(jsonStr);
  } catch {
    // Try to find JSON object
    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
    if (jsonMatch) {
      try {
        return JSON.parse(jsonMatch[0]);
      } catch {
        return null;
      }
    }
    return null;
  }
}

/**
 * Extract invoice from markdown using smartagent DualAgentOrchestrator
 * Validates JSON and retries if invalid
 */
async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
  const startTime = Date.now();
  const maxRetries = 2;

  console.log(`      [${queryId}] Invoice: ${markdown.length} chars`);

  // Build the extraction task with document context
  const taskPrompt = `Extract the invoice data from this document and output ONLY the JSON:

${markdown}

${JSON_EXTRACTION_PROMPT}`;

  try {
    let result = await orchestrator.run(taskPrompt);
    let elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
    console.log(`      [${queryId}] Status: ${result.status}, Iterations: ${result.iterations} (${elapsed}s)`);

    // Try to parse JSON from result
    let jsonData: Record<string, unknown> | null = null;
    let responseText = result.result || '';

    if (result.success && responseText) {
      jsonData = tryExtractJson(responseText);
    }

    // Fallback: try parsing from history
    if (!jsonData && result.history?.length > 0) {
      const lastMessage = result.history[result.history.length - 1];
      if (lastMessage?.content) {
        responseText = lastMessage.content;
        jsonData = tryExtractJson(responseText);
      }
    }

    // If JSON is invalid, retry with correction request
    let retries = 0;
    while (!jsonData && retries < maxRetries) {
      retries++;
      console.log(`      [${queryId}] Invalid JSON, requesting correction (retry ${retries}/${maxRetries})...`);

      result = await orchestrator.continueTask(
        `Your response was not valid JSON. Please output ONLY the JSON object with no markdown, no explanation, no thinking tags. Just the raw JSON starting with { and ending with }. Format:
{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}`
      );

      elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
      console.log(`      [${queryId}] Retry ${retries}: ${result.status} (${elapsed}s)`);

      responseText = result.result || '';
      if (responseText) {
        jsonData = tryExtractJson(responseText);
      }

      if (!jsonData && result.history?.length > 0) {
        const lastMessage = result.history[result.history.length - 1];
        if (lastMessage?.content) {
          responseText = lastMessage.content;
          jsonData = tryExtractJson(responseText);
        }
      }
    }

    if (!jsonData) {
      console.log(`      [${queryId}] Failed to get valid JSON after ${retries} retries`);
      return null;
    }

    console.log(`      [${queryId}] Valid JSON extracted`);
    return {
      invoice_number: extractInvoiceNumber(String(jsonData.invoice_number || '')),
      invoice_date: extractDate(String(jsonData.invoice_date || '')),
      vendor_name: String(jsonData.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
      currency: extractCurrency(String(jsonData.currency || '')),
      net_amount: parseAmount(jsonData.net_amount as string | number),
      vat_amount: parseAmount(jsonData.vat_amount as string | number),
      total_amount: parseAmount(jsonData.total_amount as string | number),
    };
  } catch (error) {
    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
    console.log(`      [${queryId}] ERROR: ${error} (${elapsed}s)`);
    throw error;
  }
}

/**
 * Extract invoice (single pass - GPT-OSS is more reliable)
 */
async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
  console.log(`    [${docName}] Extracting...`);
  const invoice = await extractInvoiceFromMarkdown(markdown, docName);
  if (!invoice) {
    return {
      invoice_number: '',
      invoice_date: '',
      vendor_name: '',
      currency: 'EUR',
      net_amount: 0,
      vat_amount: 0,
      total_amount: 0,
    };
  }
  console.log(`    [${docName}] Extracted: ${invoice.invoice_number}`);
  return invoice;
}

/**
 * Normalize date to YYYY-MM-DD
 */
function normalizeDate(dateStr: string | null): string {
  if (!dateStr) return '';
  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;

  const monthMap: Record<string, string> = {
    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
  };

  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
  if (match) {
    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
  }

  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
  if (match) {
    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
  }

  return dateStr;
}

/**
 * Compare extracted invoice against expected
 */
function compareInvoice(
  extracted: IInvoice,
  expected: IInvoice
): { match: boolean; errors: string[] } {
  const errors: string[] = [];

  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
  if (extNum !== expNum) {
    errors.push(`invoice_number: exp "${expected.invoice_number}", got "${extracted.invoice_number}"`);
  }

  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
    errors.push(`invoice_date: exp "${expected.invoice_date}", got "${extracted.invoice_date}"`);
  }

  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
    errors.push(`total_amount: exp ${expected.total_amount}, got ${extracted.total_amount}`);
  }

  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
    errors.push(`currency: exp "${expected.currency}", got "${extracted.currency}"`);
  }

  return { match: errors.length === 0, errors };
}

/**
 * Find all test cases
 */
function findTestCases(): ITestCase[] {
  const testDir = path.join(process.cwd(), '.nogit/invoices');
  if (!fs.existsSync(testDir)) return [];

  const files = fs.readdirSync(testDir);
  const testCases: ITestCase[] = [];

  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
    const baseName = pdf.replace('.pdf', '');
    const jsonFile = `${baseName}.json`;
    if (files.includes(jsonFile)) {
      testCases.push({
        name: baseName,
        pdfPath: path.join(testDir, pdf),
        jsonPath: path.join(testDir, jsonFile),
      });
    }
  }

  return testCases.sort((a, b) => a.name.localeCompare(b.name));
}

// ============ TESTS ============

const testCases = findTestCases();
console.log(`\nFound ${testCases.length} invoice test cases\n`);

// Ensure cache directory exists
if (!fs.existsSync(MD_CACHE_DIR)) {
  fs.mkdirSync(MD_CACHE_DIR, { recursive: true });
}

// -------- STAGE 1: OCR with Nanonets --------

tap.test('Stage 1: Convert invoices to markdown (with caching)', async () => {
  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');

  // Check which invoices need OCR conversion
  const needsConversion: ITestCase[] = [];
  let cachedCount = 0;

  for (const tc of testCases) {
    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
    if (fs.existsSync(mdPath)) {
      cachedCount++;
      tc.markdownPath = mdPath;
      console.log(`  [CACHED] ${tc.name} - using cached markdown`);
    } else {
      needsConversion.push(tc);
    }
  }

  console.log(`\n  Summary: ${cachedCount} cached, ${needsConversion.length} need conversion\n`);

  if (needsConversion.length === 0) {
    console.log('  All invoices already cached, skipping Nanonets OCR\n');
    return;
  }

  // Start Nanonets only if there are files to convert
  console.log('  Starting Nanonets for OCR conversion...\n');
  const ok = await ensureNanonetsOcr();
  expect(ok).toBeTrue();

  // Convert only the invoices that need conversion
  for (const tc of needsConversion) {
    console.log(`\n    === ${tc.name} ===`);

    const images = convertPdfToImages(tc.pdfPath);
    console.log(`      Pages: ${images.length}`);

    const markdown = await convertDocumentToMarkdown(images, tc.name);

    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
    fs.writeFileSync(mdPath, markdown);
    tc.markdownPath = mdPath;
    console.log(`      Saved: ${mdPath}`);
  }

  console.log(`\n  Stage 1 complete: ${needsConversion.length} invoices converted to markdown\n`);
});

tap.test('Stage 1: Stop Nanonets', async () => {
  stopNanonets();
  await new Promise(resolve => setTimeout(resolve, 3000));
  expect(isContainerRunning('nanonets-test')).toBeFalse();
});

// -------- STAGE 2: Extraction with GPT-OSS 20B --------

tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
  console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');

  const ollamaOk = await ensureMiniCpm();
  expect(ollamaOk).toBeTrue();

  const extractionOk = await ensureExtractionModel();
  expect(extractionOk).toBeTrue();

  // Initialize SmartAi and DualAgentOrchestrator
  console.log('  [SmartAgent] Starting SmartAi...');
  await smartAi.start();

  console.log('  [SmartAgent] Creating DualAgentOrchestrator...');
  orchestrator = new DualAgentOrchestrator({
    smartAiInstance: smartAi,
    defaultProvider: 'ollama',
    guardianPolicyPrompt: `
      JSON EXTRACTION POLICY:
      - APPROVE all JSON extraction tasks
      - This is a read-only operation - no file system or network access needed
      - The task is to extract structured data from document text
    `,
    driverSystemMessage: `You are a precise JSON extraction assistant. Your only job is to extract invoice data from documents.

CRITICAL RULES:
1. Output ONLY valid JSON - no markdown, no explanations, no thinking
2. Use the exact format requested
3. If you cannot find a value, use empty string "" or 0 for numbers

When done, wrap your JSON in <task_complete></task_complete> tags.`,
    maxIterations: 3,
    // Enable streaming for real-time progress visibility
    onToken: (token, source) => {
      if (source === 'driver') {
        process.stdout.write(token);
      }
    },
  });

  // No tools needed for JSON extraction
  console.log('  [SmartAgent] Starting orchestrator...');
  await orchestrator.start();
  console.log('  [SmartAgent] Ready for extraction');
});

let passedCount = 0;
let failedCount = 0;
const processingTimes: number[] = [];

for (const tc of testCases) {
  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
    const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
    console.log(`\n    === ${tc.name} ===`);
    console.log(`    Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);

    const startTime = Date.now();

    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
    if (!fs.existsSync(mdPath)) {
      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
    }
    const markdown = fs.readFileSync(mdPath, 'utf-8');
    console.log(`    Markdown: ${markdown.length} chars`);

    const extracted = await extractInvoice(markdown, tc.name);

    const elapsedMs = Date.now() - startTime;
    processingTimes.push(elapsedMs);

    console.log(`    Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);

    const result = compareInvoice(extracted, expected);

    if (result.match) {
      passedCount++;
      console.log(`    Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
    } else {
      failedCount++;
      console.log(`    Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
      result.errors.forEach(e => console.log(`      - ${e}`));
    }

    expect(result.match).toBeTrue();
  });
}

tap.test('Summary', async () => {
  // Cleanup orchestrator and SmartAi
  if (orchestrator) {
    console.log('\n  [SmartAgent] Stopping orchestrator...');
    await orchestrator.stop();
  }
  console.log('  [SmartAgent] Stopping SmartAi...');
  await smartAi.stop();

  const totalInvoices = testCases.length;
  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;

  console.log(`\n========================================`);
  console.log(`   Invoice Summary (Nanonets + GPT-OSS 20B)`);
  console.log(`========================================`);
  console.log(`  Stage 1:   Nanonets-OCR-s (doc -> md)`);
  console.log(`  Stage 2:   GPT-OSS 20B + SmartAgent (md -> JSON)`);
  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
  console.log(`----------------------------------------`);
  console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
  console.log(`========================================\n`);
  console.log(`  Cache location: ${MD_CACHE_DIR}\n`);
});

export default tap.start();