# core/ai_orchestrator.py
from typing import Dict, Any, List, Optional
from enum import Enum
import asyncio
from pydantic import BaseModel
import logging

class AIModelType(Enum):
    TEXT_GENERATION = "text_generation"
    CODE_GENERATION = "code_generation"
    IMAGE_GENERATION = "image_generation"
    TEXT_EMBEDDING = "text_embedding"
    SPEECH_TO_TEXT = "speech_to_text"
    TEXT_TO_SPEECH = "text_to_speech"

class ModelRequest(BaseModel):
    model_type: AIModelType
    provider: str
    model_name: str
    input_data: Dict[str, Any]
    parameters: Dict[str, Any]
    context: Optional[Dict[str, Any]] = None

class ModelResponse(BaseModel):
    success: bool
    output: Any
    metadata: Dict[str, Any]
    error: Optional[str] = None

class AIOrchestrator:
    def __init__(self):
        self.model_registry = {}
        self.providers = {}
        self.logger = logging.getLogger(__name__)
    
    async def register_model(self, model_config: Dict[str, Any]):
        """Register AI model with configuration"""
        model_id = model_config["id"]
        self.model_registry[model_id] = model_config
        self.logger.info(f"Registered model: {model_id}")
    
    async def execute_workflow_step(self, step_config: Dict[str, Any], input_data: Any) -> ModelResponse:
        """Execute a single AI workflow step"""
        try:
            model_request = ModelRequest(
                model_type=step_config["model_type"],
                provider=step_config["provider"],
                model_name=step_config["model_name"],
                input_data=input_data,
                parameters=step_config.get("parameters", {}),
                context=step_config.get("context", {})
            )
            
            return await self._call_model(model_request)
            
        except Exception as e:
            self.logger.error(f"Workflow step execution failed: {str(e)}")
            return ModelResponse(
                success=False,
                output=None,
                metadata={},
                error=str(e)
            )
    
    async def _call_model(self, request: ModelRequest) -> ModelResponse:
        """Make actual API call to AI model"""
        # Implementation for different providers
        if request.provider == "openai":
            return await self._call_openai(request)
        elif request.provider == "anthropic":
            return await self._call_anthropic(request)
        elif request.provider == "huggingface":
            return await self._call_huggingface(request)
        elif request.provider == "custom":
            return await self._call_custom_model(request)
        else:
            raise ValueError(f"Unsupported provider: {request.provider}")
    
    async def _call_openai(self, request: ModelRequest) -> ModelResponse:
        """Call OpenAI models"""
        # Implementation for OpenAI API calls
        pass
    
    async def _call_anthropic(self, request: ModelRequest) -> ModelResponse:
        """Call Anthropic Claude models"""
        # Implementation for Anthropic API calls
        pass
    
    async def _call_huggingface(self, request: ModelRequest) -> ModelResponse:
        """Call Hugging Face models"""
        # Implementation for Hugging Face API calls
        pass
    
    async def _call_custom_model(self, request: ModelRequest) -> ModelResponse:
        """Call custom deployed models"""
        # Implementation for custom model endpoints
        pass

# core/workflow_engine.py
from typing import Dict, List, Any, Optional
from pydantic import BaseModel
import networkx as nx
import asyncio
from datetime import datetime
import uuid

class WorkflowNode(BaseModel):
    id: str
    type: str
    config: Dict[str, Any]
    next_nodes: List[str]
    conditions: Optional[Dict[str, Any]] = None

class WorkflowExecution(BaseModel):
    execution_id: str
    workflow_id: str
    status: str
    start_time: datetime
    end_time: Optional[datetime] = None
    current_node: Optional[str] = None
    results: Dict[str, Any] = {}
    errors: List[str] = []

class WorkflowEngine:
    def __init__(self, ai_orchestrator):
        self.ai_orchestrator = ai_orchestrator
        self.workflows = {}
        self.executions = {}
        self.graph = nx.DiGraph()
    
    async def create_workflow(self, name: str, nodes: List[WorkflowNode]) -> str:
        """Create a new workflow"""
        workflow_id = str(uuid.uuid4())
        
        # Build workflow graph
        for node in nodes:
            self.graph.add_node(node.id, **node.dict())
            for next_node in node.next_nodes:
                self.graph.add_edge(node.id, next_node)
        
        self.workflows[workflow_id] = {
            "id": workflow_id,
            "name": name,
            "nodes": {node.id: node for node in nodes},
            "graph": self.graph.copy(),
            "created_at": datetime.now()
        }
        
        return workflow_id
    
    async def execute_workflow(self, workflow_id: str, initial_input: Dict[str, Any]) -> str:
        """Execute a workflow"""
        execution_id = str(uuid.uuid4())
        execution = WorkflowExecution(
            execution_id=execution_id,
            workflow_id=workflow_id,
            status="running",
            start_time=datetime.now(),
            results={"initial_input": initial_input}
        )
        
        self.executions[execution_id] = execution
        
        # Start execution in background
        asyncio.create_task(self._run_workflow(execution, initial_input))
        
        return execution_id
    
    async def _run_workflow(self, execution: WorkflowExecution, input_data: Any):
        """Execute workflow steps"""
        workflow = self.workflows[execution.workflow_id]
        graph = workflow["graph"]
        
        # Find start nodes (nodes with no incoming edges)
        start_nodes = [node for node in graph.nodes() if graph.in_degree(node) == 0]
        
        for start_node in start_nodes:
            await self._execute_node(execution, start_node, input_data)
    
    async def _execute_node(self, execution: WorkflowExecution, node_id: str, input_data: Any):
        """Execute a single workflow node"""
        workflow = self.workflows[execution.workflow_id]
        node = workflow["nodes"][node_id]
        
        execution.current_node = node_id
        
        try:
            if node.type == "ai_model":
                result = await self.ai_orchestrator.execute_workflow_step(
                    node.config, input_data
                )
                
                if result.success:
                    execution.results[node_id] = result.output
                    
                    # Execute next nodes
                    for next_node in node.next_nodes:
                        await self._execute_node(execution, next_node, result.output)
                else:
                    execution.errors.append(f"Node {node_id} failed: {result.error}")
                    
            elif node.type == "condition":
                # Evaluate condition and choose next node
                condition_result = self._evaluate_condition(node.conditions, input_data)
                next_node = condition_result.get("next_node")
                if next_node:
                    await self._execute_node(execution, next_node, input_data)
                    
            elif node.type == "data_transform":
                # Transform data
                transformed_data = self._transform_data(node.config, input_data)
                for next_node in node.next_nodes:
                    await self._execute_node(execution, next_node, transformed_data)
                    
            elif node.type == "human_approval":
                # Wait for human approval
                await self._wait_for_approval(execution, node_id, input_data)
                
        except Exception as e:
            execution.errors.append(f"Node {node_id} execution error: {str(e)}")
            execution.status = "failed"
    
    def _evaluate_condition(self, conditions: Dict[str, Any], data: Any) -> Dict[str, Any]:
        """Evaluate conditional logic"""
        # Implementation for condition evaluation
        pass
    
    def _transform_data(self, config: Dict[str, Any], data: Any) -> Any:
        """Transform data according to configuration"""
        # Implementation for data transformation
        pass
    
    async def _wait_for_approval(self, execution: WorkflowExecution, node_id: str, data: Any):
        """Wait for human approval in the workflow"""
        # Implementation for human-in-the-loop approval
        pass

# api/main.py
from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Dict, Any, List
import uuid
from datetime import datetime

from core.ai_orchestrator import AIOrchestrator, ModelRequest
from core.workflow_engine import WorkflowEngine, WorkflowNode

app = FastAPI(title="Workflow-Fairbase API", version="1.0.0")

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize core components
ai_orchestrator = AIOrchestrator()
workflow_engine = WorkflowEngine(ai_orchestrator)

# API Models
class CreateWorkflowRequest(BaseModel):
    name: str
    description: str
    nodes: List[Dict[str, Any]]

class ExecuteWorkflowRequest(BaseModel):
    workflow_id: str
    input_data: Dict[str, Any]
    parameters: Dict[str, Any] = {}

class ModelRegistrationRequest(BaseModel):
    model_config: Dict[str, Any]

# API Routes
@app.get("/")
async def root():
    return {"message": "Workflow-Fairbase Generative AI Platform"}

@app.post("/workflows")
async def create_workflow(request: CreateWorkflowRequest):
    """Create a new workflow"""
    try:
        workflow_nodes = [
            WorkflowNode(**node_data) for node_data in request.nodes
        ]
        
        workflow_id = await workflow_engine.create_workflow(
            request.name, workflow_nodes
        )
        
        return {
            "workflow_id": workflow_id,
            "name": request.name,
            "status": "created"
        }
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@app.post("/workflows/{workflow_id}/execute")
async def execute_workflow(
    workflow_id: str,
    request: ExecuteWorkflowRequest,
    background_tasks: BackgroundTasks
):
    """Execute a workflow"""
    try:
        execution_id = await workflow_engine.execute_workflow(
            workflow_id, request.input_data
        )
        
        return {
            "execution_id": execution_id,
            "workflow_id": workflow_id,
            "status": "started"
        }
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@app.get("/executions/{execution_id}")
async def get_execution_status(execution_id: str):
    """Get execution status and results"""
    execution = workflow_engine.executions.get(execution_id)
    if not execution:
        raise HTTPException(status_code=404, detail="Execution not found")
    
    return execution.dict()

@app.post("/models/register")
async def register_model(request: ModelRegistrationRequest):
    """Register a new AI model"""
    try:
        await ai_orchestrator.register_model(request.model_config)
        return {"status": "model_registered"}
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@app.post("/models/execute")
async def execute_model(request: ModelRequest):
    """Execute a single model"""
    try:
        response = await ai_orchestrator._call_model(request)
        return response.dict()
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

# Health check
@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.now(),
        "version": "1.0.0"
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)