ht-docker-ai/test/test.bankstatements.combined.ts

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';

// Service URLs
const OLLAMA_URL = 'http://localhost:11434';
const PADDLEOCR_VL_URL = 'http://localhost:8000';

// Models
const MINICPM_MODEL = 'openbmb/minicpm-v4.5:q8_0';
const PADDLEOCR_VL_MODEL = 'paddleocr-vl';

// Prompt for MiniCPM-V visual extraction
const MINICPM_EXTRACT_PROMPT = `/nothink
You are a bank statement parser. Extract EVERY transaction from the table.

Read the Amount column carefully:
- "- 21,47 €" means DEBIT, output as: -21.47
- "+ 1.000,00 €" means CREDIT, output as: 1000.00
- European format: comma = decimal point

For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}

Do not skip any rows. Return ONLY the JSON array, no explanation.`;

// Prompt for PaddleOCR-VL table extraction
const PADDLEOCR_VL_TABLE_PROMPT = `Table Recognition:`;

// Post-processing prompt to convert PaddleOCR-VL output to JSON
const PADDLEOCR_VL_CONVERT_PROMPT = `/nothink
Convert the following bank statement table data to JSON.

Read the Amount values carefully:
- "- 21,47 €" means DEBIT, output as: -21.47
- "+ 1.000,00 €" means CREDIT, output as: 1000.00
- European format: comma = decimal point

For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}

Return ONLY the JSON array, no explanation.

Table data:
---
{TABLE_DATA}
---`;

interface ITransaction {
  date: string;
  counterparty: string;
  amount: number;
}

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

  try {
    execSync(
      `convert -density 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
      { stdio: 'pipe' }
    );

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

    for (const file of files) {
      const imagePath = path.join(tempDir, file);
      const imageData = fs.readFileSync(imagePath);
      images.push(imageData.toString('base64'));
    }

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

/**
 * Extract using MiniCPM-V via Ollama
 */
async function extractWithMiniCPM(images: string[], passLabel: string): Promise<ITransaction[]> {
  const payload = {
    model: MINICPM_MODEL,
    prompt: MINICPM_EXTRACT_PROMPT,
    images,
    stream: true,
    options: {
      num_predict: 16384,
      temperature: 0.1,
    },
  };

  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(payload),
  });

  if (!response.ok) {
    throw new Error(`Ollama API error: ${response.status}`);
  }

  const reader = response.body?.getReader();
  if (!reader) {
    throw new Error('No response body');
  }

  const decoder = new TextDecoder();
  let fullText = '';
  let lineBuffer = '';

  console.log(`[${passLabel}] Extracting with MiniCPM-V...`);

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value, { stream: true });
    const lines = chunk.split('\n').filter((l) => l.trim());

    for (const line of lines) {
      try {
        const json = JSON.parse(line);
        if (json.response) {
          fullText += json.response;
          lineBuffer += json.response;

          if (lineBuffer.includes('\n')) {
            const parts = lineBuffer.split('\n');
            for (let i = 0; i < parts.length - 1; i++) {
              console.log(parts[i]);
            }
            lineBuffer = parts[parts.length - 1];
          }
        }
      } catch {
        // Skip invalid JSON lines
      }
    }
  }

  if (lineBuffer) {
    console.log(lineBuffer);
  }
  console.log('');

  const startIdx = fullText.indexOf('[');
  const endIdx = fullText.lastIndexOf(']') + 1;

  if (startIdx < 0 || endIdx <= startIdx) {
    throw new Error('No JSON array found in response');
  }

  return JSON.parse(fullText.substring(startIdx, endIdx));
}

/**
 * Extract table using PaddleOCR-VL via OpenAI-compatible API
 */
async function extractTableWithPaddleOCRVL(imageBase64: string): Promise<string> {
  const payload = {
    model: PADDLEOCR_VL_MODEL,
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'image_url',
            image_url: { url: `data:image/png;base64,${imageBase64}` },
          },
          {
            type: 'text',
            text: PADDLEOCR_VL_TABLE_PROMPT,
          },
        ],
      },
    ],
    temperature: 0.0,
    max_tokens: 8192,
  };

  const response = await fetch(`${PADDLEOCR_VL_URL}/v1/chat/completions`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(payload),
  });

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

  const data = await response.json();
  return data.choices?.[0]?.message?.content || '';
}

/**
 * Convert PaddleOCR-VL table output to transactions using MiniCPM-V
 */
async function convertTableToTransactions(
  tableData: string,
  passLabel: string
): Promise<ITransaction[]> {
  const prompt = PADDLEOCR_VL_CONVERT_PROMPT.replace('{TABLE_DATA}', tableData);

  const payload = {
    model: MINICPM_MODEL,
    prompt,
    stream: true,
    options: {
      num_predict: 16384,
      temperature: 0.1,
    },
  };

  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(payload),
  });

  if (!response.ok) {
    throw new Error(`Ollama API error: ${response.status}`);
  }

  const reader = response.body?.getReader();
  if (!reader) {
    throw new Error('No response body');
  }

  const decoder = new TextDecoder();
  let fullText = '';

  console.log(`[${passLabel}] Converting table data to JSON...`);

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value, { stream: true });
    const lines = chunk.split('\n').filter((l) => l.trim());

    for (const line of lines) {
      try {
        const json = JSON.parse(line);
        if (json.response) {
          fullText += json.response;
        }
      } catch {
        // Skip invalid JSON lines
      }
    }
  }

  const startIdx = fullText.indexOf('[');
  const endIdx = fullText.lastIndexOf(']') + 1;

  if (startIdx < 0 || endIdx <= startIdx) {
    throw new Error('No JSON array found in response');
  }

  return JSON.parse(fullText.substring(startIdx, endIdx));
}

/**
 * Extract using PaddleOCR-VL (table recognition) + conversion
 */
async function extractWithPaddleOCRVL(
  images: string[],
  passLabel: string
): Promise<ITransaction[]> {
  console.log(`[${passLabel}] Extracting tables with PaddleOCR-VL...`);

  // Extract table data from each page
  const tableDataParts: string[] = [];
  for (let i = 0; i < images.length; i++) {
    console.log(`[${passLabel}] Processing page ${i + 1}/${images.length}...`);
    const tableData = await extractTableWithPaddleOCRVL(images[i]);
    if (tableData.trim()) {
      tableDataParts.push(`--- Page ${i + 1} ---\n${tableData}`);
    }
  }

  const combinedTableData = tableDataParts.join('\n\n');
  console.log(`[${passLabel}] Got ${combinedTableData.length} chars of table data`);

  // Convert to transactions
  return convertTableToTransactions(combinedTableData, passLabel);
}

/**
 * Create a hash of transactions for comparison
 */
function hashTransactions(transactions: ITransaction[]): string {
  return transactions
    .map((t) => `${t.date}|${t.amount.toFixed(2)}`)
    .sort()
    .join(';');
}

/**
 * Check if PaddleOCR-VL service is available
 */
async function isPaddleOCRVLAvailable(): Promise<boolean> {
  try {
    const response = await fetch(`${PADDLEOCR_VL_URL}/health`, {
      method: 'GET',
      signal: AbortSignal.timeout(5000),
    });
    return response.ok;
  } catch {
    return false;
  }
}

/**
 * Extract with dual-VLM consensus
 * Strategy:
 *   Pass 1 = MiniCPM-V visual extraction
 *   Pass 2 = PaddleOCR-VL table recognition (if available)
 *   Pass 3+ = MiniCPM-V visual (fallback)
 */
async function extractWithConsensus(
  images: string[],
  maxPasses: number = 5
): Promise<ITransaction[]> {
  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
  const hashCounts: Map<string, number> = new Map();

  const addResult = (transactions: ITransaction[], passLabel: string): number => {
    const hash = hashTransactions(transactions);
    results.push({ transactions, hash });
    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
    console.log(
      `[${passLabel}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`
    );
    return hashCounts.get(hash)!;
  };

  // Check if PaddleOCR-VL is available
  const paddleOCRVLAvailable = await isPaddleOCRVLAvailable();
  if (paddleOCRVLAvailable) {
    console.log('[Setup] PaddleOCR-VL service available - using dual-VLM consensus');
  } else {
    console.log('[Setup] PaddleOCR-VL not available - using MiniCPM-V only');
  }

  // Pass 1: MiniCPM-V visual extraction
  try {
    const pass1Result = await extractWithMiniCPM(images, 'Pass 1 MiniCPM-V');
    addResult(pass1Result, 'Pass 1 MiniCPM-V');
  } catch (err) {
    console.log(`[Pass 1] Error: ${err}`);
  }

  // Pass 2: PaddleOCR-VL table recognition (if available)
  if (paddleOCRVLAvailable) {
    try {
      const pass2Result = await extractWithPaddleOCRVL(images, 'Pass 2 PaddleOCR-VL');
      const count = addResult(pass2Result, 'Pass 2 PaddleOCR-VL');
      if (count >= 2) {
        console.log('[Consensus] MiniCPM-V and PaddleOCR-VL extractions match!');
        return pass2Result;
      }
    } catch (err) {
      console.log(`[Pass 2 PaddleOCR-VL] Error: ${err}`);
    }
  }

  // Pass 3+: Continue with MiniCPM-V visual passes
  const startPass = paddleOCRVLAvailable ? 3 : 2;
  for (let pass = startPass; pass <= maxPasses; pass++) {
    try {
      const transactions = await extractWithMiniCPM(images, `Pass ${pass} MiniCPM-V`);
      const count = addResult(transactions, `Pass ${pass} MiniCPM-V`);

      if (count >= 2) {
        console.log(`[Consensus] Reached after ${pass} passes`);
        return transactions;
      }

      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
    } catch (err) {
      console.log(`[Pass ${pass}] Error: ${err}`);
    }
  }

  // No consensus reached - return the most common result
  let bestHash = '';
  let bestCount = 0;
  for (const [hash, count] of hashCounts) {
    if (count > bestCount) {
      bestCount = count;
      bestHash = hash;
    }
  }

  if (!bestHash) {
    throw new Error('No valid results obtained');
  }

  const best = results.find((r) => r.hash === bestHash)!;
  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
  return best.transactions;
}

/**
 * Compare extracted transactions against expected
 */
function compareTransactions(
  extracted: ITransaction[],
  expected: ITransaction[]
): { matches: number; total: number; errors: string[] } {
  const errors: string[] = [];
  let matches = 0;

  for (let i = 0; i < expected.length; i++) {
    const exp = expected[i];
    const ext = extracted[i];

    if (!ext) {
      errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
      continue;
    }

    const dateMatch = ext.date === exp.date;
    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;

    if (dateMatch && amountMatch) {
      matches++;
    } else {
      errors.push(
        `Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
      );
    }
  }

  if (extracted.length > expected.length) {
    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
  }

  return { matches, total: expected.length, errors };
}

/**
 * Find all test cases (PDF + JSON pairs) in .nogit/
 */
function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
  const testDir = path.join(process.cwd(), '.nogit');
  if (!fs.existsSync(testDir)) {
    return [];
  }

  const files = fs.readdirSync(testDir);
  const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];

  for (const pdf of pdfFiles) {
    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;
}

// Tests

tap.test('should connect to Ollama API', async () => {
  const response = await fetch(`${OLLAMA_URL}/api/tags`);
  expect(response.ok).toBeTrue();
  const data = await response.json();
  expect(data.models).toBeArray();
});

tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
  const response = await fetch(`${OLLAMA_URL}/api/tags`);
  const data = await response.json();
  const modelNames = data.models.map((m: { name: string }) => m.name);
  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
});

tap.test('should check PaddleOCR-VL availability', async () => {
  const available = await isPaddleOCRVLAvailable();
  console.log(`PaddleOCR-VL available: ${available}`);
  // This test passes regardless - PaddleOCR-VL is optional
  expect(true).toBeTrue();
});

// Dynamic test for each PDF/JSON pair
const testCases = findTestCases();
for (const testCase of testCases) {
  tap.test(`should extract transactions from ${testCase.name}`, async () => {
    // Load expected transactions
    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
    console.log(`\n=== ${testCase.name} ===`);
    console.log(`Expected: ${expected.length} transactions`);

    // Convert PDF to images
    console.log('Converting PDF to images...');
    const images = convertPdfToImages(testCase.pdfPath);
    console.log(`Converted: ${images.length} pages\n`);

    // Extract with dual-VLM consensus
    const extracted = await extractWithConsensus(images);
    console.log(`\nFinal: ${extracted.length} transactions`);

    // Compare results
    const result = compareTransactions(extracted, expected);
    console.log(`Accuracy: ${result.matches}/${result.total}`);

    if (result.errors.length > 0) {
      console.log('Errors:');
      result.errors.forEach((e) => console.log(`  - ${e}`));
    }

    // Assert high accuracy
    const accuracy = result.matches / result.total;
    expect(accuracy).toBeGreaterThan(0.95);
    expect(extracted.length).toEqual(expected.length);
  });
}

export default tap.start();