Ubuntu
164 lines
4.4 KiB
Python
164 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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Anti-AI Detector 测试脚本
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"""
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import sys
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sys.path.insert(0, '/home/ubuntu/.nanobot/workspace/skills/anti-ai-detector')
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from humanize import AntiAIDetector
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from it_integration import ITContentHumanizer, humanize_for_it_news, humanize_for_xiaohongshu
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def test_chinese():
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"""测试中文去AI化"""
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print("="*60)
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print("测试1: 中文去AI化")
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print("="*60)
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text = """
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值得注意的是，这项技术非常重要。我们可以看到，随着AI的发展，
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越来越多的应用场景被开发出来。首先，它提高了效率；其次，
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它降低了成本；最后，它改变了工作方式。综上所述，
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这项技术毫无疑问具有极其重要的意义。
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"""
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detector = AntiAIDetector()
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result = detector.humanize_cn(text)
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print("原文:")
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print(text.strip())
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print("\n去AI化后:")
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print(result)
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print()
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def test_english():
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"""测试英文去AI化"""
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print("="*60)
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print("测试2: 英文去AI化 (Stop Slop)")
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print("="*60)
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text = """
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Here's the thing: building great products is hard. It turns out that
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most teams struggle with alignment. Not because they lack talent.
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Because they lack clarity. Let that sink in.
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The uncomfortable truth is that most advice is generic. Let me be clear:
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speed without direction is just chaos. We need to navigate the complex
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landscape of product development and leverage our resources effectively.
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"""
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detector = AntiAIDetector()
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result = detector.humanize_en(text)
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print("原文:")
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print(text.strip())
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print("\n去AI化后:")
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print(result)
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print()
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def test_xiaohongshu():
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"""测试小红书风格"""
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print("="*60)
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print("测试3: 小红书风格优化")
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print("="*60)
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text = """
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姐妹们！今天给大家分享一个宝藏神器！🔥🔥🔥
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1️⃣ 首先，这个产品真的绝绝子！
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2️⃣ 其次，效果YYDS！
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3️⃣ 最后，闭眼入不踩雷！
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强烈推荐大家码住这个干货！✅✅✅
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#好物推荐 #必买清单 #宝藏神器
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"""
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detector = AntiAIDetector()
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result = detector.humanize_xhs(text)
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print("原文:")
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print(text.strip())
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print("\n优化后:")
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print(result)
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print()
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def test_it_integration():
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"""测试IT资讯集成"""
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print("="*60)
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print("测试4: IT资讯项目集成")
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print("="*60)
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content = {
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'title': '深度解析：揭秘AI技术的十大不为人知的秘密',
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'translated': """
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值得注意的是，这项技术非常重要。我们可以看到，随着AI的发展，
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越来越多的应用场景被开发出来。
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""",
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'summary': """
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首先，AI提高了效率；其次，它降低了成本；最后，它改变了工作方式。
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综上所述，这项技术毫无疑问具有极其重要的意义。
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""",
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'platform': 'xiaohongshu'
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}
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humanizer = ITContentHumanizer()
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result = humanizer.full_pipeline(content)
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print("原标题:", content['title'])
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print("优化标题:", result['title'])
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print("\n优化总结:")
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print(result['summary'])
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print()
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def test_analysis():
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"""测试AI痕迹分析"""
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print("="*60)
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print("测试5: AI痕迹风险分析")
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print("="*60)
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text = """
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Here's the thing: 值得注意的是，这项技术非常重要。
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我们可以看到，随着AI的发展，越来越多的应用场景被开发出来。
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Let that sink in. 首先，它提高了效率；其次，它降低了成本。
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"""
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humanizer = ITContentHumanizer()
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risk = humanizer.analyze_risk(text)
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print("分析文本:")
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print(text.strip())
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print(f"\n风险等级: {risk['risk_level']}")
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print(f"得分: {risk['score']}")
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print("建议:")
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for s in risk['suggestions']:
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print(f" - {s}")
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print()
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def main():
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"""运行所有测试"""
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print("\n")
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print("╔" + "="*58 + "╗")
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print("║" + " "*15 + "Anti-AI Detector 测试" + " "*20 + "║")
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print("╚" + "="*58 + "╝")
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print("\n")
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test_chinese()
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test_english()
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test_xiaohongshu()
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test_it_integration()
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test_analysis()
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print("="*60)
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print("所有测试完成！")
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print("="*60)
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if __name__ == '__main__':
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main()
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