HaHafeng
b64896a307
Summary: - PostgreSQL database migration to RDS completed (90MB SQL, 11 schemas) - Frontend Nginx Docker image built and pushed to ACR (v1.0, ~50MB) - Python microservice Docker image built and pushed to ACR (v1.0, 1.12GB) - Created 3 deployment documentation files Docker Configuration Files: - frontend-v2/Dockerfile: Multi-stage build with nginx:alpine - frontend-v2/.dockerignore: Optimize build context - frontend-v2/nginx.conf: SPA routing and API proxy - frontend-v2/docker-entrypoint.sh: Dynamic env injection - extraction_service/Dockerfile: Multi-stage build with Aliyun Debian mirror - extraction_service/.dockerignore: Optimize build context - extraction_service/requirements-prod.txt: Production dependencies (removed Nougat) Deployment Documentation: - docs/05-部署文档/00-部署进度总览.md: One-stop deployment status overview - docs/05-部署文档/07-前端Nginx-SAE部署操作手册.md: Frontend deployment guide - docs/05-部署文档/08-PostgreSQL数据库部署操作手册.md: Database deployment guide - docs/00-系统总体设计/00-系统当前状态与开发指南.md: Updated with deployment status Database Migration: - RDS instance: pgm-2zex1m2y3r23hdn5 (2C4G, PostgreSQL 15.0) - Database: ai_clinical_research - Schemas: 11 business schemas migrated successfully - Data: 3 users, 2 projects, 1204 literatures verified - Backup: rds_init_20251224_154529.sql (90MB) Docker Images: - Frontend: crpi-cd5ij4pjt65mweeo.cn-beijing.personal.cr.aliyuncs.com/ai-clinical/ai-clinical_frontend-nginx:v1.0 - Python: crpi-cd5ij4pjt65mweeo.cn-beijing.personal.cr.aliyuncs.com/ai-clinical/python-extraction:v1.0 Key Achievements: - Resolved Docker Hub network issues (using generic tags) - Fixed 30 TypeScript compilation errors - Removed Nougat OCR to reduce image size by 1.5GB - Used Aliyun Debian mirror to resolve apt-get network issues - Implemented multi-stage builds for optimization Next Steps: - Deploy Python microservice to SAE - Build Node.js backend Docker image - Deploy Node.js backend to SAE - Deploy frontend Nginx to SAE - End-to-end verification testing Status: Docker images ready, SAE deployment pending
70 lines
1.4 KiB
Python
70 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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