bluemouse

#!/usr/bin/env python3 """ Project Exporter - 項目導出模組 將生成的代碼、架構圖、文檔打包成 ZIP 文件 """ import io import json import zipfile from typing import Dict, List, Any from datetime import datetime def export_project(project_data: Dict[str, Any]) -> bytes: """ 導出完整項目為 ZIP Args: project_data: { "name": "項目名稱", "code": {...}, # 代碼文件 (來自 code_generator) "diagrams": [...], # 架構圖 (來自 diagram_generator) "cost": {...}, # 成本估算 (來自 cost_estimator) "validation": {...} # 驗證結果 (來自 MCP Server, 可選) } Returns: ZIP 文件的二進制數據 """ # 創建內存中的 ZIP 文件 zip_buffer = io.BytesIO() with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file: # 1. 添加代碼文件 if 'code' in project_data and 'files' in project_data['code']: for filename, content in project_data['code']['files'].items(): zip_file.writestr(f"code/{filename}", content) # 2. 添加架構圖 (Mermaid 格式) if 'diagrams' in project_data: for i, diagram in enumerate(project_data['diagrams']): diagram_type = diagram.get('type', f'diagram_{i}') mermaid_content = diagram.get('mermaid', '') zip_file.writestr( f"diagrams/{diagram_type}.mmd", mermaid_content ) # 3. 添加成本估算報告 if 'cost' in project_data: cost_report = generate_cost_report(project_data['cost']) zip_file.writestr("docs/COST_ESTIMATION.md", cost_report) # 4. 添加驗證報告 (如果有) if 'validation' in project_data: validation_report = generate_validation_report(project_data['validation']) zip_file.writestr("docs/VALIDATION_REPORT.md", validation_report) # 5. 生成並添加 README readme = generate_readme(project_data) zip_file.writestr("README.md", readme) # 6. 生成並添加安裝指南 tutorial = generate_tutorial(project_data) zip_file.writestr("TUTORIAL.md", tutorial) # 7. 添加配置文件 if 'code' in project_data and 'setup_instructions' in project_data['code']: setup = project_data['code']['setup_instructions'] zip_file.writestr("INSTALLATION.md", setup) # 8. 添加項目元數據 metadata = { "project_name": project_data.get('name', 'Generated Project'), "generated_at": datetime.now().isoformat(), "generator": "Blueprint Little Mouse v1.0", "framework": project_data.get('code', {}).get('framework', 'Django'), "version": project_data.get('code', {}).get('version', '4.2') } zip_file.writestr("project.json", json.dumps(metadata, indent=2, ensure_ascii=False)) # 返回 ZIP 文件的二進制數據 zip_buffer.seek(0) return zip_buffer.read() def generate_readme(project_data: Dict[str, Any]) -> str: """ 生成 README.md Args: project_data: 項目數據 Returns: README.md 內容 """ project_name = project_data.get('name', 'Generated Project') framework = project_data.get('code', {}).get('framework', 'Django') readme = f"""# {project_name} > 由 Blueprint Little Mouse 自動生成 ## 項目概述 這是一個使用 {framework} 框架開發的項目,由 AI 驅動的架構生成系統自動創建。 ## 特性 """ # 添加模組列表 if 'modules' in project_data: readme += "### 核心模組\n\n" for module in project_data['modules']: name = module.get('name', '未命名模組') desc = module.get('description', '無描述') readme += f"- **{name}**: {desc}\n" readme += "\n" # 添加技術棧 readme += f"""## 技術棧 - **框架**: {framework} - **Python**: 3.9+ - **數據庫**: PostgreSQL (推薦) / SQLite (開發) - **API**: Django REST Framework ## 快速開始 ### 1. 安裝依賴 ```bash pip install -r code/requirements.txt ``` ### 2. 配置數據庫 ```bash # 創建數據庫遷移 python manage.py makemigrations # 運行遷移 python manage.py migrate ``` ### 3. 創建超級用戶 ```bash python manage.py createsuperuser ``` ### 4. 啟動服務器 ```bash python manage.py runserver ``` 訪問 http://localhost:8000 查看應用。 ## 項目結構 ``` . ├── code/ # 源代碼 │ ├── models.py # 數據模型 │ ├── views.py # API 視圖 │ ├── serializers.py # 序列化器 │ ├── urls.py # URL 路由 │ ├── admin.py # Admin 配置 │ ├── tests.py # 測試用例 │ └── requirements.txt # 依賴列表 ├── diagrams/ # 架構圖 │ ├── architecture.mmd # 系統架構圖 │ ├── dataflow.mmd # 數據流圖 │ ├── er.mmd # ER 圖 │ └── sequence.mmd # 序列圖 ├── docs/ # 文檔 │ ├── COST_ESTIMATION.md # 成本估算 │ └── VALIDATION_REPORT.md # 驗證報告 ├── README.md # 本文件 ├── TUTORIAL.md # 詳細教程 └── INSTALLATION.md # 安裝指南 ``` ## 文檔 - [詳細教程](TUTORIAL.md) - 完整的開發指南 - [安裝指南](INSTALLATION.md) - 部署和配置 - [成本估算](docs/COST_ESTIMATION.md) - 開發成本分析 ## 架構圖 項目包含以下架構圖 (Mermaid 格式): - **系統架構圖** (`diagrams/architecture.mmd`) - 展示整體系統結構 - **數據流圖** (`diagrams/dataflow.mmd`) - 展示數據流動 - **ER 圖** (`diagrams/er.mmd`) - 展示數據庫關係 - **序列圖** (`diagrams/sequence.mmd`) - 展示交互流程 使用 [Mermaid Live Editor](https://mermaid.live) 查看和編輯這些圖表。 ## 測試 ```bash # 運行所有測試 python manage.py test # 運行特定測試 python manage.py test code.tests ``` ## 部署 詳見 [INSTALLATION.md](INSTALLATION.md) 中的部署指南。 ## 許可證 MIT License ## 致謝 本項目由 [Blueprint Little Mouse](https://github.com/...) 自動生成。 --- **生成時間**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} **生成器版本**: v1.0 """ return readme def generate_tutorial(project_data: Dict[str, Any]) -> str: """ 生成 TUTORIAL.md Args: project_data: 項目數據 Returns: TUTORIAL.md 內容 """ project_name = project_data.get('name', 'Generated Project') framework = project_data.get('code', {}).get('framework', 'Django') tutorial = f"""# {project_name} - 完整教程 ## 目錄 1. [環境準備](#環境準備) 2. [項目設置](#項目設置) 3. [開發指南](#開發指南) 4. [API 使用](#api-使用) 5. [測試](#測試) 6. [部署](#部署) --- ## 環境準備 ### 系統要求 - Python 3.9 或更高版本 - pip (Python 包管理器) - PostgreSQL 12+ (生產環境) 或 SQLite (開發環境) ### 安裝 Python ```bash # macOS brew install python@3.9 # Ubuntu/Debian sudo apt-get install python3.9 # Windows # 從 python.org 下載安裝包 ``` ### 創建虛擬環境 ```bash # 創建虛擬環境 python3 -m venv venv # 激活虛擬環境 # macOS/Linux source venv/bin/activate # Windows venv\\Scripts\\activate ``` --- ## 項目設置 ### 1. 安裝依賴 ```bash pip install -r code/requirements.txt ``` ### 2. 環境變量配置 創建 `.env` 文件: ```env # Django 設置 SECRET_KEY=your-secret-key-here DEBUG=True ALLOWED_HOSTS=localhost,127.0.0.1 # 數據庫設置 DATABASE_URL=postgresql://user:password@localhost:5432/dbname # 或使用 SQLite (開發環境) # DATABASE_URL=sqlite:///db.sqlite3 ``` ### 3. 數據庫遷移 ```bash # 創建遷移文件 python manage.py makemigrations # 應用遷移 python manage.py migrate ``` ### 4. 創建超級用戶 ```bash python manage.py createsuperuser ``` 按提示輸入用戶名、郵箱和密碼。 ### 5. 收集靜態文件 (生產環境) ```bash python manage.py collectstatic ``` --- ## 開發指南 ### 項目結構說明 ``` code/ ├── models.py # 數據模型定義 ├── views.py # API 視圖和業務邏輯 ├── serializers.py # 數據序列化 ├── urls.py # URL 路由 ├── admin.py # Admin 後台配置 └── tests.py # 測試用例 ``` ### 添加新功能 1. **定義模型** (models.py) 2. **創建序列化器** (serializers.py) 3. **實現視圖** (views.py) 4. **配置路由** (urls.py) 5. **添加測試** (tests.py) ### 代碼風格 項目遵循 PEP 8 規範: ```bash # 檢查代碼風格 flake8 code/ # 自動格式化 black code/ ``` --- ## API 使用 ### API 端點 所有 API 端點都在 `/api/` 路徑下。 ### 認證 使用 Token 認證: ```bash # 獲取 Token curl -X POST http://localhost:8000/api/auth/login/ \\ -H "Content-Type: application/json" \\ -d '{{"username": "admin", "password": "password"}}' # 使用 Token curl -X GET http://localhost:8000/api/endpoint/ \\ -H "Authorization: Token your-token-here" ``` ### 示例請求 ```python import requests # 登錄 response = requests.post('http://localhost:8000/api/auth/login/', {{ 'username': 'admin', 'password': 'password' }}) token = response.json()['token'] # 使用 Token 訪問 API headers = {{'Authorization': f'Token {{token}}'}} response = requests.get('http://localhost:8000/api/endpoint/', headers=headers) data = response.json() ``` --- ## 測試 ### 運行測試 ```bash # 運行所有測試 python manage.py test # 運行特定應用的測試 python manage.py test code # 運行特定測試類 python manage.py test code.tests.TestClassName # 運行特定測試方法 python manage.py test code.tests.TestClassName.test_method_name ``` ### 測試覆蓋率 ```bash # 安裝 coverage pip install coverage # 運行測試並生成覆蓋率報告 coverage run --source='.' manage.py test coverage report coverage html # 生成 HTML 報告 ``` --- ## 部署 ### 生產環境檢查清單 - [ ] 設置 `DEBUG=False` - [ ] 配置 `ALLOWED_HOSTS` - [ ] 使用強密碼作為 `SECRET_KEY` - [ ] 配置 PostgreSQL 數據庫 - [ ] 設置 HTTPS - [ ] 配置靜態文件服務 - [ ] 設置日誌記錄 - [ ] 配置備份策略 ### 使用 Gunicorn ```bash # 安裝 Gunicorn pip install gunicorn # 啟動服務 gunicorn myproject.wsgi:application --bind 0.0.0.0:8000 ``` ### 使用 Nginx Nginx 配置示例: ```nginx server {{ listen 80; server_name example.com; location /static/ {{ alias /path/to/staticfiles/; }} location / {{ proxy_pass http://127.0.0.1:8000; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; }} }} ``` ### Docker 部署 ```dockerfile FROM python:3.9 WORKDIR /app COPY code/requirements.txt . RUN pip install -r requirements.txt COPY . . CMD ["gunicorn", "myproject.wsgi:application", "--bind", "0.0.0.0:8000"] ``` --- ## 常見問題 ### Q: 如何重置數據庫? ```bash python manage.py flush ``` ### Q: 如何導出/導入數據? ```bash # 導出 python manage.py dumpdata > data.json # 導入 python manage.py loaddata data.json ``` ### Q: 如何查看 SQL 查詢? 在 settings.py 中啟用 SQL 日誌: ```python LOGGING = {{ 'version': 1, 'handlers': {{ 'console': {{'class': 'logging.StreamHandler'}}, }}, 'loggers': {{ 'django.db.backends': {{ 'handlers': ['console'], 'level': 'DEBUG', }}, }}, }} ``` --- ## 支持 如有問題,請查閱: - [Django 官方文檔](https://docs.djangoproject.com/) - [DRF 官方文檔](https://www.django-rest-framework.org/) --- **最後更新**: {datetime.now().strftime('%Y-%m-%d')} """ return tutorial def generate_cost_report(cost_data: Dict[str, Any]) -> str: """ 生成成本估算報告 Args: cost_data: 成本數據 Returns: 成本報告 Markdown """ report = f"""# 成本估算報告 **生成時間**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} --- ## 開發時間估算 """ if 'development' in cost_data: dev = cost_data['development'] report += f""" - **總開發天數**: {dev.get('total_days', 'N/A')} 天 - **開發階段**: {dev.get('phases', {}).get('development', 'N/A')} 天 - **測試階段**: {dev.get('phases', {}).get('testing', 'N/A')} 天 - **部署階段**: {dev.get('phases', {}).get('deployment', 'N/A')} 天 """ if 'cost' in cost_data: cost = cost_data['cost'] report += f"""## 開發成本估算 - **總成本**: ${cost.get('total', 0):,} USD - **基礎成本**: ${cost.get('base_cost', 0):,} USD - **風險緩衝**: ${cost.get('risk_buffer', 0):,} USD ### 成本分解 """ if 'breakdown' in cost: for role, details in cost['breakdown'].items(): if isinstance(details, dict): report += f"- **{role}**: ${details.get('總計', 0):,} USD\n" if 'operations' in cost_data: ops = cost_data['operations'] report += f""" ## 運營成本估算 - **月度成本**: ${ops.get('monthly', 0):,} USD/月 - **年度成本**: ${ops.get('monthly', 0) * 12:,} USD/年 """ if 'team' in cost_data: team = cost_data['team'] report += f"""## 團隊配置建議 - **總人數**: {team.get('total', 0)} 人 ### 團隊組成 """ if 'roles' in team: for role, count in team['roles'].items(): report += f"- **{role}**: {count} 人\n" report += """ --- **注意**: 以上估算僅供參考,實際成本可能因項目具體情況而異。 """ return report def generate_validation_report(validation_data: Dict[str, Any]) -> str: """ 生成驗證報告 Args: validation_data: 驗證數據 Returns: 驗證報告 Markdown """ report = f"""# 代碼驗證報告 **生成時間**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} --- ## 驗證結果 """ passed = validation_data.get('passed', False) status = "✅ 通過" if passed else "❌ 失敗" report += f"**總體狀態**: {status}\n\n" if 'quality_score' in validation_data: score = validation_data['quality_score'] report += f"**質量評分**: {score}/100\n\n" if 'layers' in validation_data: report += "## 驗證層級\n\n" for layer in validation_data['layers']: layer_num = layer.get('layer', '?') layer_name = layer.get('name', '未命名') layer_passed = layer.get('passed', False) layer_status = "✅" if layer_passed else "❌" report += f"- **Layer {layer_num} - {layer_name}**: {layer_status}\n" if 'suggestions' in validation_data and validation_data['suggestions']: report += "\n## 改進建議\n\n" for suggestion in validation_data['suggestions']: report += f"- {suggestion}\n" report += """ --- **驗證系統**: Blueprint Little Mouse MMLA-MRM v1.0 """ return report if __name__ == "__main__": # 測試導出功能 test_data = { "name": "測試項目", "code": { "framework": "Django", "version": "4.2", "files": { "models.py": "# Django Models\nclass User(models.Model):\n pass", "views.py": "# Django Views\nclass UserViewSet(viewsets.ModelViewSet):\n pass" }, "setup_instructions": "# 安裝指南\npip install -r requirements.txt" }, "diagrams": [ { "type": "architecture", "mermaid": "graph TD\n A[Frontend] --> B[Backend]" } ], "cost": { "development": {"total_days": 90}, "cost": {"total": 100000} } } zip_data = export_project(test_data) # 保存測試 ZIP with open("test_export.zip", "wb") as f: f.write(zip_data) print(f"✅ 測試 ZIP 已生成: test_export.zip ({len(zip_data)} bytes)")