"""
测试改进的CMA提取逻辑（使用模拟数据）
"""
import re
import logging

logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger(__name__)

# 模拟OCR结果（基于之前成功运行的结果）
mock_ocr_results = {
    "YDQ23_001838.pdf": {
        "texts": [
            "广东产品质量监督检验研究院",
            "210020349096",  # 正确的CMA码
            "CNASL0153",
            "440023010130",  # 报告编号（干扰项）
            "TESTING"
        ],
        "scores": [0.95, 1.00, 0.92, 0.99, 0.98]
    }
}

def extract_cma_smart(ocr_texts, ocr_scores, pdf_name):
    """
    改进的CMA码提取逻辑：
    1. 优先选择以"2"开头的12位数字
    2. 如果没有，选择置信度最高的
    """
    pattern = re.compile(r'\d{11,12}')

    logger.info(f"\nProcessing {pdf_name}...")
    logger.info(f"OCR texts: {len(ocr_texts)} lines")

    # 查找所有11-12位数字
    candidates = []
    for i, (text, score) in enumerate(zip(ocr_texts, ocr_scores)):
        matches = pattern.findall(text.replace(" ", ""))
        for num in matches:
            candidates.append({
                'code': num,
                'confidence': float(score),
                'text': text,
                'line': i
            })

    if not candidates:
        logger.warning("No 11-12 digit numbers found")
        return {'success': False, 'code': None, 'method': 'no_candidates'}

    logger.info(f"Found {len(candidates)} candidates:")
    for c in candidates:
        logger.info(f"  - {c['code']} (conf: {c['confidence']:.2f}, from line {c['line']})")

    # 优先选择以"2"开头的
    candidates_starting_with_2 = [c for c in candidates if c['code'].startswith('2')]

    if candidates_starting_with_2:
        candidates_starting_with_2.sort(key=lambda x: x['confidence'], reverse=True)
        best = candidates_starting_with_2[0]
        logger.info(f"✓ Selected (starts with '2'): {best['code']} (confidence: {best['confidence']:.2f})")
        return {
            'success': True,
            'code': best['code'],
            'confidence': best['confidence'],
            'method': 'template_matching_smart'
        }
    else:
        candidates.sort(key=lambda x: x['confidence'], reverse=True)
        best = candidates[0]
        logger.info(f"✓ Selected (highest confidence): {best['code']} (confidence: {best['confidence']:.2f})")
        return {
            'success': True,
            'code': best['code'],
            'confidence': best['confidence'],
            'method': 'fullpage_ocr'
        }

# 测试
print("="*80)
print("TESTING IMPROVED CMA EXTRACTION LOGIC")
print("="*80)

data = mock_ocr_results["YDQ23_001838.pdf"]
result = extract_cma_smart(data["texts"], data["scores"], "YDQ23_001838.pdf")

print("\n" + "="*80)
print("RESULT")
print("="*80)
print(f"Success: {result['success']}")
print(f"CMA Code: {result['code']}")
print(f"Method: {result['method']}")
print(f"Confidence: {result['confidence']:.2f}")

expected = "210020349096"
if result['code'] == expected:
    print(f"\n✓✓✓ CORRECT! Expected: {expected}, Got: {result['code']}")
    print("The improved logic correctly prioritizes '2'-prefixed CMA codes!")
else:
    print(f"\n✗✗✗ WRONG! Expected: {expected}, Got: {result['code']}")

print("="*80)