HaHafeng
decff0bb1f
Summary: - Successfully deployed complete system to Aliyun SAE (2025-12-25) - All services running: Python microservice + Node.js backend + Frontend Nginx + CLB - Public access available at http://8.140.53.236/ Major Achievements: 1. Python microservice deployed (v1.0, internal IP: 172.17.173.66:8000) 2. Node.js backend deployed (v1.3, internal IP: 172.17.173.73:3001) - Fixed 4 critical issues: bash path, config directory, pino-pretty, ES Module 3. Frontend Nginx deployed (v1.0, internal IP: 172.17.173.72:80) 4. CLB load balancer configured (public IP: 8.140.53.236) New Documentation (9 docs): - 11-Node.js backend SAE deployment config checklist (21 env vars) - 12-Node.js backend SAE deployment operation manual - 13-Node.js backend image fix record (config directory) - 14-Node.js backend pino-pretty fix - 15-Node.js backend deployment success summary - 16-Frontend Nginx deployment success summary - 17-Complete deployment practical manual 2025 edition (1800 lines) - 18-Deployment documentation usage guide - 19-Daily update quick operation manual (670 lines) Key Fixes: - Environment variable name correction: EXTRACTION_SERVICE_URL (not PYTHON_SERVICE_URL) - Dockerfile fix: added COPY config ./config - Logger configuration: conditional pino-pretty for dev only - Health check fix: ES Module compatibility (require -> import) Updated Files: - System status document updated with full deployment info - Deployment progress overview updated with latest IPs - All 3 Docker services' Dockerfiles and configs refined Verification: - All health checks passed - Tool C 7 features working correctly - Literature screening module functional - Response time < 1 second BREAKING CHANGE: Node.js backend internal IP changed from 172.17.173.71 to 172.17.173.73 Closes #deployment-milestone
73 lines
1.4 KiB
Python
73 lines
1.4 KiB
Python
"""简单的代码执行测试"""
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import requests
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import json
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# 测试数据
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test_data = [
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{"patient_id": "P001", "age": 25, "gender": "男"},
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{"patient_id": "P002", "age": 65, "gender": "女"},
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{"patient_id": "P003", "age": 45, "gender": "男"},
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]
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# 测试代码
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test_code = """
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df['age_group'] = df['age'].apply(lambda x: '老年' if x > 60 else '非老年')
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print(f"处理完成，共 {len(df)} 行")
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"""
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print("=" * 60)
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print("测试: Pandas代码执行")
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print("=" * 60)
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try:
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response = requests.post(
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"http://localhost:8000/api/dc/execute",
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json={"data": test_data, "code": test_code},
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timeout=10
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)
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print(f"\n状态码: {response.status_code}")
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result = response.json()
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print(json.dumps(result, indent=2, ensure_ascii=False))
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if result.get("success"):
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print("\n✅ 代码执行成功！")
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print(f"结果数据: {len(result.get('result_data', []))} 行")
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print(f"执行时间: {result.get('execution_time', 0):.3f}秒")
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print(f"\n打印输出:\n{result.get('output', '')}")
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print(f"\n结果数据示例:")
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for row in result.get('result_data', [])[:3]:
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print(f" {row}")
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else:
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print(f"\n❌ 代码执行失败: {result.get('error')}")
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except Exception as e:
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print(f"\n❌ 测试异常: {str(e)}")
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