# workflow_engine.py
from typing import Dict, List, Any
from enum import Enum
from datetime import datetime
import asyncio

class NodeType(Enum):
    START = "start"
    APPROVAL = "approval"
    ACTION = "action"
    CONDITION = "condition"
    END = "end"

class WorkflowNode:
    def __init__(self, node_id: str, node_type: NodeType, config: Dict):
        self.node_id = node_id
        self.node_type = node_type
        self.config = config
        self.next_nodes = []
    
    async def execute(self, context: Dict) -> Dict:
        """執行節點邏輯"""
        if self.node_type == NodeType.START:
            return {"status": "started", "next_nodes": self.next_nodes}
        
        elif self.node_type == NodeType.APPROVAL:
            return await self._handle_approval(context)
        
        elif self.node_type == NodeType.ACTION:
            return await self._handle_action(context)
        
        elif self.node_type == NodeType.CONDITION:
            return await self._handle_condition(context)

class WorkflowEngine:
    def __init__(self):
        self.workflows: Dict[str, Workflow] = {}
        self.executions: Dict[str, WorkflowExecution] = {}
    
    async def create_workflow(self, name: str, nodes: List[Dict]) -> str:
        """創建工作流"""
        workflow_id = f"wf_{datetime.now().timestamp()}"
        workflow = Workflow(workflow_id, name, nodes)
        self.workflows[workflow_id] = workflow
        return workflow_id
    
    async def execute_workflow(self, workflow_id: str, initial_data: Dict) -> str:
        """執行工作流"""
        execution_id = f"exec_{datetime.now().timestamp()}"
        execution = WorkflowExecution(execution_id, workflow_id, initial_data)
        self.executions[execution_id] = execution
        
        # 異步執行工作流
        asyncio.create_task(self._run_workflow(execution))
        
        return execution_id
    
    async def _run_workflow(self, execution: WorkflowExecution):
        """運行工作流實例"""
        workflow = self.workflows[execution.workflow_id]
        current_node = workflow.start_node
        
        while current_node:
            result = await current_node.execute(execution.context)
            execution.history.append({
                "node_id": current_node.node_id,
                "timestamp": datetime.now(),
                "result": result
            })
            
            if result.get("status") == "completed":
                current_node = self._get_next_node(current_node, result)
            else:
                # 等待人工審批或其他異步操作
                break

# 使用示例
engine = WorkflowEngine()

# 創建請假審批工作流
leave_workflow = await engine.create_workflow("請假審批", [
    {
        "type": "start",
        "id": "start",
        "next": "manager_approval"
    },
    {
        "type": "approval", 
        "id": "manager_approval",
        "approvers": ["manager@company.com"],
        "next": "hr_approval"
    },
    {
        "type": "approval",
        "id": "hr_approval", 
        "approvers": ["hr@company.com"],
        "next": "end"
    }
])

# 執行工作流
execution_id = await engine.execute_workflow(leave_workflow, {
    "applicant": "john@company.com",
    "leave_days": 5,
    "reason": "年假"
})

# ai_data_processor.py
import pandas as pd
import openai
from typing import Dict, List, Any
import json

class AIDataProcessor:
    def __init__(self):
        self.client = openai.OpenAI(api_key="your-api-key")
    
    async def smart_data_mapping(self, source_data: Dict, target_schema: Dict) -> Dict:
        """智能數據映射"""
        prompt = f"""
        根據源數據結構和目標schema，生成數據映射規則：
        
        源數據:
        {json.dumps(source_data, indent=2)}
        
        目標Schema:
        {json.dumps(target_schema, indent=2)}
        
        請返回JSON格式的映射規則，包含字段對應關係和轉換邏輯。
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1000
        )
        
        mapping_rules = self._extract_json(response.choices[0].message.content)
        return await self._apply_mapping_rules(source_data, mapping_rules)
    
    async def generate_data_transformations(self, data: pd.DataFrame, 
                                          objectives: List[str]) -> List[Dict]:
        """基於目標生成數據轉換建議"""
        prompt = f"""
        分析以下數據，為達成目標建議數據轉換操作：
        
        數據樣本:
        {data.head().to_string()}
        
        數據列: {list(data.columns)}
        
        目標: {', '.join(objectives)}
        
        建議的數據清洗和轉換步驟：
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=800
        )
        
        return self._parse_transformation_suggestions(response.choices[0].message.content)
    
    async def automated_data_quality_check(self, data: pd.DataFrame) -> Dict:
        """自動化數據質量檢查"""
        quality_report = {
            "completeness": self._check_completeness(data),
            "consistency": self._check_consistency(data),
            "accuracy": await self._check_accuracy(data),
            "anomalies": await self._detect_anomalies(data)
        }
        
        # 使用AI生成改進建議
        improvement_suggestions = await self._generate_quality_improvements(quality_report)
        quality_report["improvement_suggestions"] = improvement_suggestions
        
        return quality_report

class SmartETL:
    """智能ETL處理器"""
    
    def __init__(self):
        self.ai_processor = AIDataProcessor()
    
    async def process_data_pipeline(self, source_config: Dict, 
                                  transformation_rules: List[Dict],
                                  target_config: Dict) -> Dict:
        """處理數據管道"""
        # 提取數據
        source_data = await self._extract_data(source_config)
        
        # 使用AI優化轉換規則
        optimized_rules = await self.ai_processor.optimize_transformations(
            source_data, transformation_rules
        )
        
        # 轉換數據
        transformed_data = await self._transform_data(source_data, optimized_rules)
        
        # 加載數據
        load_result = await self._load_data(transformed_data, target_config)
        
        return {
            "records_processed": len(transformed_data),
            "processing_time": load_result["duration"],
            "quality_metrics": load_result["quality_metrics"]
        }

# enterprise_connectors.py
from typing import Dict, List
import aiohttp
import asyncio

class ConnectorManager:
    def __init__(self):
        self.connectors = {
            "salesforce": SalesforceConnector(),
            "slack": SlackConnector(),
            "google_sheets": GoogleSheetsConnector(),
            "database": DatabaseConnector(),
            "api": APIConnector()
        }
    
    async def create_integration(self, source: str, target: str, 
                               mapping_rules: Dict) -> str:
        """創建系統集成"""
        integration_id = f"int_{datetime.now().timestamp()}"
        
        integration = {
            "id": integration_id,
            "source": source,
            "target": target,
            "mapping_rules": mapping_rules,
            "status": "active",
            "created_at": datetime.now()
        }
        
        # 啓動集成任務
        asyncio.create_task(self._run_integration(integration))
        
        return integration_id
    
    async def _run_integration(self, integration: Dict):
        """運行集成任務"""
        source_connector = self.connectors[integration["source"]]
        target_connector = self.connectors[integration["target"]]
        
        while integration["status"] == "active":
            try:
                # 從源系統獲取數據
                source_data = await source_connector.fetch_data()
                
                # 轉換數據
                transformed_data = await self._transform_data(
                    source_data, integration["mapping_rules"]
                )
                
                # 推送到目標系統
                await target_connector.push_data(transformed_data)
                
                # 等待下一次同步
                await asyncio.sleep(300)  # 5分鐘
                
            except Exception as e:
                print(f"集成錯誤: {e}")
                await asyncio.sleep(60)  # 錯誤後等待1分鐘

class SalesforceConnector:
    """Salesforce連接器"""
    
    async def fetch_data(self, query: str = None) -> List[Dict]:
        """從Salesforce獲取數據"""
        async with aiohttp.ClientSession() as session:
            async with session.get(
                "https://your-salesforce-instance.com/services/data/v50.0/query",
                headers={"Authorization": "Bearer your-token"},
                params={"q": query or "SELECT Id, Name FROM Account"}
            ) as response:
                data = await response.json()
                return data.get("records", [])
    
    async def push_data(self, data: List[Dict]):
        """推送數據到Salesforce"""
        async with aiohttp.ClientSession() as session:
            async with session.post(
                "https://your-salesforce-instance.com/services/data/v50.0/composite/sobjects",
                headers={"Authorization": "Bearer your-token"},
                json={"records": data}
            ) as response:
                return await response.json()

class SlackConnector:
    """Slack連接器"""
    
    async def send_message(self, channel: str, message: str, 
                          attachments: List[Dict] = None):
        """發送Slack消息"""
        payload = {
            "channel": channel,
            "text": message,
            "attachments": attachments or []
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.post(
                "https://slack.com/api/chat.postMessage",
                headers={"Authorization": "Bearer xoxb-your-token"},
                json=payload
            ) as response:
                return await response.json()

# ai_enhancements.py
import openai
from typing import Dict, List
import json

class AIEnhancements:
    def __init__(self):
        self.client = openai.OpenAI(api_key="your-api-key")
    
    async def generate_business_rules(self, business_description: str) -> List[Dict]:
        """從業務描述生成業務規則"""
        prompt = f"""
        根據以下業務需求，生成具體的業務規則：
        
        業務描述: {business_description}
        
        請返回JSON格式的業務規則列表，每個規則包含：
        - 規則名稱
        - 條件
        - 動作
        - 優先級
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1500
        )
        
        return self._extract_json(response.choices[0].message.content)
    
    async def optimize_workflow(self, current_workflow: Dict, 
                              performance_metrics: Dict) -> Dict:
        """優化工作流性能"""
        prompt = f"""
        分析以下工作流，基於性能指標提出優化建議：
        
        當前工作流:
        {json.dumps(current_workflow, indent=2)}
        
        性能指標:
        {json.dumps(performance_metrics, indent=2)}
        
        優化建議應包括：
        1. 瓶頸識別
        2. 並行化機會
        3. 自動化建議
        4. 預計性能提升
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1200
        )
        
        return self._parse_optimization_suggestions(response.choices[0].message.content)
    
    async def generate_documentation(self, app_design: Dict, 
                                   audience: str = "technical") -> str:
        """自動生成應用文檔"""
        prompt = f"""
        根據以下應用設計，生成{audience}文檔：
        
        應用設計:
        {json.dumps(app_design, indent=2)}
        
        文檔應包括：
        - 系統架構
        - 功能説明
        - 使用指南
        - API文檔（如適用）
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=2000
        )
        
        return response.choices[0].message.content

class NaturalLanguageQuery:
    """自然語言查詢處理器"""
    
    def __init__(self):
        self.ai_enhancements = AIEnhancements()
    
    async def process_query(self, natural_language_query: str, 
                          data_sources: List[Dict]) -> Dict:
        """處理自然語言查詢"""
        # 使用AI解析查詢意圖
        parsed_query = await self.ai_enhancements.parse_query_intent(
            natural_language_query, data_sources
        )
        
        # 生成SQL或API查詢
        generated_query = await self._generate_technical_query(parsed_query)
        
        # 執行查詢
        query_result = await self._execute_query(generated_query, data_sources)
        
        # 使用AI解釋結果
        interpreted_result = await self.ai_enhancements.interpret_results(
            query_result, natural_language_query
        )
        
        return {
            "original_query": natural_language_query,
            "generated_query": generated_query,
            "data": query_result,
            "interpretation": interpreted_result
        }

# admin_dashboard.py
from flask import Blueprint, render_template, jsonify, request
from typing import Dict, List
import json

admin_bp = Blueprint('admin', __name__, url_prefix='/admin')

@admin_bp.route('/')
def admin_dashboard():
    """管理員儀表板"""
    return render_template('admin_dashboard.html')

@admin_bp.route('/api/usage-metrics')
def get_usage_metrics():
    """獲取使用量指標"""
    metrics = {
        "active_users": 150,
        "apps_created": 45,
        "workflows_running": 12,
        "data_processes": 28,
        "api_calls": 1250
    }
    return jsonify(metrics)

@admin_bp.route('/api/integrations')
def get_integrations():
    """獲取集成狀態"""
    integrations = [
        {
            "name": "Salesforce CRM",
            "status": "active",
            "last_sync": "2024-01-15 10:30:00",
            "success_rate": 98.5
        },
        {
            "name": "Slack通知",
            "status": "active", 
            "last_sync": "2024-01-15 10:29:00",
            "success_rate": 99.2
        }
    ]
    return jsonify(integrations)

@admin_bp.route('/api/performance')
def get_performance_metrics():
    """獲取性能指標"""
    performance = {
        "response_time": {"avg": 125, "p95": 230},
        "throughput": {"requests_per_second": 45},
        "error_rate": 0.5,
        "uptime": 99.8
    }
    return jsonify(performance)