150 lines
5.3 KiB
Python
150 lines
5.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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測試優化後的翻譯映射邏輯 - 優先使用原始DIFY翻譯
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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# 設定編碼
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sys.stdout.reconfigure(encoding='utf-8')
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from pathlib import Path
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from app import create_app
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def test_prioritized_mapping():
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"""測試優化後的翻譯映射邏輯"""
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print("=" * 80)
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print("測試優化後的翻譯映射邏輯")
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print("預期: 應該優先使用原始DIFY翻譯 (ROW 449)")
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print("=" * 80)
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app = create_app()
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with app.app_context():
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from sqlalchemy import text as sql_text
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from app import db
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from app.services.translation_service import ExcelParser
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# 取得Excel提取的D2文字
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original_file = Path(r"C:\Users\EGG\WORK\data\user_scrip\TOOL\Document_translator_V2\uploads\98158984-f335-44f5-a0b4-88fb8ccd5d78") / "original_panjit_98158984.xlsx"
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if not original_file.exists():
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print("❌ 測試檔案不存在")
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return
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parser = ExcelParser(str(original_file))
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segments = parser.extract_text_segments()
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d2_extracted = None
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for segment in segments:
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if "WB inline" in segment:
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d2_extracted = segment
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break
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if not d2_extracted:
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print("❌ 沒有找到D2相關內容")
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return
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print(f"1. Excel提取的D2文字:")
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print(f" {repr(d2_extracted)}")
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# 2. 測試新的聯合查詢邏輯
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print(f"\n2. 測試新的聯合查詢邏輯")
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print("-" * 60)
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target_language = 'ko'
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normalized_text = d2_extracted.replace('\n', ' ').replace('\r', ' ').strip()
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print(f"標準化文字: {repr(normalized_text)}")
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result = db.session.execute(sql_text("""
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SELECT translated_text, created_at, 'exact' as match_type
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FROM dt_translation_cache
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WHERE source_text = :exact_text AND target_language = :lang
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UNION ALL
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SELECT translated_text, created_at, 'normalized' as match_type
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FROM dt_translation_cache
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WHERE REPLACE(REPLACE(TRIM(source_text), '\n', ' '), '\r', ' ') = :norm_text
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AND target_language = :lang
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AND source_text != :exact_text
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ORDER BY created_at ASC
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LIMIT 1
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"""), {'exact_text': d2_extracted, 'norm_text': normalized_text, 'lang': target_language})
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row = result.fetchone()
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if row:
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print(f"✅ 聯合查詢找到翻譯:")
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print(f" 翻譯內容: {repr(row[0][:50])}...")
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print(f" 創建時間: {row[1]}")
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print(f" 匹配類型: {row[2]}")
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# 檢查這是原始DIFY翻譯還是手動翻譯
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if "와이어 본딩" in row[0]:
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print(f" 🎯 這是原始DIFY翻譯！(特徵: 와이어 본딩)")
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success = True
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elif "연결" in row[0]:
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print(f" ✋ 這是手動補充翻譯 (特徵: 연결)")
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success = False
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else:
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print(f" ❓ 無法判斷翻譯來源")
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success = False
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else:
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print(f"❌ 聯合查詢沒有找到任何翻譯")
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success = False
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# 3. 查看所有可能的翻譯記錄
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print(f"\n3. 查看所有相關的翻譯記錄 (用於對比)")
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print("-" * 60)
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all_result = db.session.execute(sql_text("""
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SELECT id, translated_text, created_at, 'exact' as match_type
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FROM dt_translation_cache
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WHERE source_text = :exact_text AND target_language = :lang
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UNION ALL
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SELECT id, translated_text, created_at, 'normalized' as match_type
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FROM dt_translation_cache
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WHERE REPLACE(REPLACE(TRIM(source_text), '\n', ' '), '\r', ' ') = :norm_text
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AND target_language = :lang
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AND source_text != :exact_text
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ORDER BY created_at ASC
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"""), {'exact_text': d2_extracted, 'norm_text': normalized_text, 'lang': target_language})
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all_rows = all_result.fetchall()
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for i, (row_id, trans, created_at, match_type) in enumerate(all_rows, 1):
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print(f"選項{i}: ROW {row_id} ({match_type}匹配, {created_at})")
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print(f" 翻譯: {repr(trans[:40])}...")
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if row_id == 449:
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print(f" 🎯 這是原始DIFY翻譯")
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elif row_id == 514:
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print(f" ✋ 這是手動補充翻譯")
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# 4. 結果評估
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print(f"\n4. 結果評估")
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print("-" * 60)
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if success:
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print(f"🎉 成功！新邏輯正確地優先選擇了原始DIFY翻譯")
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print(f" 現在重新生成韓文Excel檔案應該會使用原始翻譯")
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else:
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print(f"⚠️ 邏輯需要進一步調整")
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print(f" 可能需要檢查SQL查詢或排序邏輯")
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print(f"\n" + "=" * 80)
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print("優化後映射邏輯測試完成！")
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print("=" * 80)
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if __name__ == "__main__":
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test_prioritized_mapping() |