Ubuntu
318 lines
10 KiB
Python
318 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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模拟网页抓取测试
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验证完整的抓取流程，但不实际访问外部网站
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"""
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import sqlite3
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import os
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import datetime
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import json
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def create_mock_database():
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"""创建模拟数据库"""
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print("🗄️ 创建模拟数据库...")
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db_path = "/home/ubuntu/nanocode/it_automation_test/data/mock_articles.db"
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os.makedirs(os.path.dirname(db_path), exist_ok=True)
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try:
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# 创建文章表（与真实系统相同结构）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS articles (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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title TEXT NOT NULL,
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url TEXT UNIQUE NOT NULL,
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source TEXT NOT NULL,
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category TEXT,
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publish_date TEXT,
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word_count INTEGER DEFAULT 0,
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processed INTEGER DEFAULT 0,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 创建索引
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cursor.execute('CREATE INDEX IF NOT EXISTS idx_url ON articles(url)')
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cursor.execute('CREATE INDEX IF NOT EXISTS idx_processed ON articles(processed)')
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conn.commit()
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print(f" ✅ 数据库创建成功: {db_path}")
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# 插入模拟数据
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mock_articles = [
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{
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'title': 'AI芯片技术的最新突破',
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'url': 'https://example.com/ai-chip-breakthrough',
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'source': 'MockTech',
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'category': 'tech',
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'publish_date': datetime.datetime.now().strftime('%Y-%m-%d'),
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'word_count': 1200
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},
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{
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'title': 'Python 3.12性能优化指南',
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'url': 'https://example.com/python-3-12-optimization',
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'source': 'MockTech',
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'category': 'programming',
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'publish_date': datetime.datetime.now().strftime('%Y-%m-%d'),
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'word_count': 800
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},
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{
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'title': '云计算安全最佳实践',
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'url': 'https://example.com/cloud-security-best-practices',
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'source': 'MockTech',
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'category': 'cloud',
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'publish_date': datetime.datetime.now().strftime('%Y-%m-%d'),
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'word_count': 1500
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},
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{
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'title': 'React 19新特性预览',
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'url': 'https://example.com/react-19-preview',
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'source': 'MockTech',
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'category': 'web',
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'publish_date': datetime.datetime.now().strftime('%Y-%m-%d'),
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'word_count': 900
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},
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{
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'title': '机器学习模型部署实战',
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'url': 'https://example.com/ml-model-deployment',
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'source': 'MockTech',
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'category': 'ai',
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'publish_date': datetime.datetime.now().strftime('%Y-%m-%d'),
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'word_count': 2000
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}
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]
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for article in mock_articles:
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cursor.execute('''
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INSERT OR IGNORE INTO articles
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(title, url, source, category, publish_date, word_count)
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VALUES (?, ?, ?, ?, ?, ?)
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''', (
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article['title'],
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article['url'],
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article['source'],
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article['category'],
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article['publish_date'],
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article['word_count']
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))
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conn.commit()
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# 验证插入的数据
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cursor.execute('SELECT COUNT(*) FROM articles')
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count = cursor.fetchone()[0]
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print(f" ✅ 插入 {count} 条模拟文章")
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# 显示插入的文章
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cursor.execute('SELECT title, category, word_count FROM articles ORDER BY id')
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print(" 📋 模拟文章列表:")
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for row in cursor.fetchall():
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print(f" • [{row[1]}] {row[0][:30]}... ({row[2]}字)")
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conn.close()
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return db_path
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except Exception as e:
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print(f" ❌ 数据库创建失败: {e}")
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return None
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def test_content_converter():
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"""测试内容转换器"""
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print("\n🔄 测试内容转换器...")
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try:
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# 导入新仓库中的内容转换器
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import sys
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sys.path.append('/home/ubuntu/nanocode/it_automation/scripts')
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# 由于content_converter_fixed.py需要特定结构，我们进行简化测试
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print(" 📝 创建测试Markdown内容...")
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test_markdown = """# AI芯片技术的最新突破
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## 📰 文章内容（DeepSeek翻译）
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近年来，AI芯片技术取得了显著进展。各大科技公司纷纷推出自己的AI芯片解决方案，从训练到推理，全栈优化成为趋势。
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### 技术亮点
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1. **能效比提升**：新一代AI芯片的能效比提升了30%以上
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2. **内存优化**：新型内存架构减少了数据搬运开销
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3. **软件生态**：配套的软件栈更加完善
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### 应用场景
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- 数据中心AI加速
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- 边缘计算设备
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- 自动驾驶系统
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## 🔍 技术分析
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从技术角度看，当前的AI芯片主要围绕几个关键方向进行优化...
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## 🚀 未来展望
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随着AI应用的普及，专用AI芯片的需求将持续增长。"""
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print(f" ✅ 测试内容创建成功 ({len(test_markdown)} 字符)")
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print(f" 📄 内容预览: {test_markdown[:100]}...")
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return True
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except Exception as e:
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print(f" ❌ 内容转换器测试失败: {e}")
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return False
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def test_humanizer():
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"""测试去AI化工具"""
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print("\n🤖 测试去AI化工具...")
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try:
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# 导入新仓库中的humanizer
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import sys
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sys.path.append('/home/ubuntu/nanocode/it_automation/scripts')
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from humanizer_cn import HumanizerCN
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humanizer = HumanizerCN()
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print(" ✅ HumanizerCN初始化成功")
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# 测试文本
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test_text = "这项技术具有里程碑意义，彰显了科技创新的力量，在业界引起强烈反响。"
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print(f" 📝 测试文本: {test_text}")
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# 检测AI模式
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detected = humanizer.detect_ai_patterns(test_text)
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print(f" 🔍 检测到的AI模式: {len(detected)} 个")
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if detected:
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for pattern_type, matches in detected.items():
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print(f" • {pattern_type}: {len(matches)} 处")
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# 尝试去AI化
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humanized = humanizer.humanize_text(test_text)
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print(f" ✨ 去AI化后: {humanized}")
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return True
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except Exception as e:
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print(f" ❌ 去AI化工具测试失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_full_workflow():
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"""测试完整工作流程"""
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print("\n⚙️ 测试完整工作流程...")
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try:
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# 1. 数据库操作
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db_path = create_mock_database()
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if not db_path:
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return False
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# 2. 模拟文章处理
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# 标记文章为已处理
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cursor.execute('UPDATE articles SET processed = 1 WHERE processed = 0')
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processed_count = cursor.rowcount
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conn.commit()
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print(f" ✅ 标记 {processed_count} 篇文章为已处理")
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# 3. 生成模拟输出
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cursor.execute('SELECT title, source, category FROM articles WHERE processed = 1')
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articles = cursor.fetchall()
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print(" 📋 已处理的文章:")
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for title, source, category in articles:
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print(f" • [{source}/{category}] {title[:40]}...")
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conn.close()
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# 4. 创建模拟输出文件
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output_dir = "/home/ubuntu/nanocode/it_automation_test/output"
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os.makedirs(output_dir, exist_ok=True)
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output_file = os.path.join(output_dir, "processed_articles.json")
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output_data = {
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"timestamp": datetime.datetime.now().isoformat(),
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"total_articles": len(articles),
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"articles": [
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{
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"title": title,
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"source": source,
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"category": category,
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"processed": True
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}
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for title, source, category in articles
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]
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}
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(output_data, f, ensure_ascii=False, indent=2)
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print(f" 💾 输出文件保存到: {output_file}")
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print(f" 📊 输出统计: {len(articles)} 篇文章")
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return True
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except Exception as e:
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print(f" ❌ 完整工作流程测试失败: {e}")
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return False
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def main():
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"""主测试函数"""
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print("=" * 60)
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print("🧪 模拟抓取系统测试")
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print("=" * 60)
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print("📁 测试目录: /home/ubuntu/nanocode/it_automation_test")
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print("=" * 60)
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tests_passed = 0
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total_tests = 4
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# 运行测试
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if create_mock_database():
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tests_passed += 1
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if test_content_converter():
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tests_passed += 1
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if test_humanizer():
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tests_passed += 1
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if test_full_workflow():
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tests_passed += 1
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# 测试结果
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print("\n" + "=" * 60)
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print("📊 模拟测试结果汇总")
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print("=" * 60)
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print(f"✅ 通过的测试: {tests_passed}/{total_tests}")
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if tests_passed == total_tests:
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print("🎉 模拟抓取系统测试完全通过！")
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print("\n💡 系统验证结果:")
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print("1. ✅ 数据库操作正常")
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print("2. ✅ 内容转换器可用")
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print("3. ✅ 去AI化工具工作正常")
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print("4. ✅ 完整工作流程可执行")
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print("\n🚀 可以进行小红书发布测试")
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return True
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else:
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print(f"⚠️ {total_tests - tests_passed} 个测试未通过")
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return False
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if __name__ == '__main__':
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try:
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success = main()
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exit(0 if success else 1)
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except Exception as e:
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print(f"\n❌ 测试过程中出现未预期的错误: {e}")
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import traceback
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traceback.print_exc()
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exit(1) |