# services/workflow_manager.py
"""
Workflow Manager for MasterLLM V3 Architecture

Manages saved workflows:
- Save user-approved pipelines to workflows collection
- NO deduplication (allow duplicates)
- Store workflow definitions in S3
- Retrieve and manage saved workflows
"""
import uuid
from typing import Dict, Any, List, Optional
from datetime import datetime
from pymongo import MongoClient
from services.s3_manager import get_s3_manager
from services.schemas import WorkflowSchema, schema_to_dict
import os


class WorkflowManager:
    """Manages saved workflows"""
    
    def __init__(self):
        """Initialize Workflow Manager"""
        # MongoDB connection
        mongodb_uri = os.getenv("MONGODB_URI")
        mongodb_db = os.getenv("MONGODB_DB", "masterllm")
        
        if not mongodb_uri:
            raise RuntimeError("MONGODB_URI environment variable not set")
        
        self.client = MongoClient(mongodb_uri)
        self.db = self.client[mongodb_db]
        self.workflows_collection = self.db["workflows"]
        
        # S3 manager
        self.s3 = get_s3_manager()
    
    def save_workflow(
        self,
        session_id: str,
        pipeline_definition: Dict[str, Any],
        user_message: str,
        source_pipeline_id: str = None,
        pipeline_status: str = None
    ) -> str:
        """
        Save a pipeline as a workflow
        
        NOTE: NO DEDUPLICATION - always creates a new workflow even if identical
        
        Args:
            session_id: Session where workflow was created
            pipeline_definition: Full pipeline definition
            user_message: User's message when confirming save
            source_pipeline_id: Pipeline ID this workflow came from
            pipeline_status: Pipeline status when saved ("proposed", "completed")
        
        Returns:
            workflow_id: Unique workflow ID
        """
        workflow_id = str(uuid.uuid4())
        now = datetime.utcnow()
        
        # Store pipeline definition in S3
        s3_key = f"workflows/{workflow_id}.json"
        self.s3.upload_json(s3_key, pipeline_definition, add_prefix=False)
        
        # Create pipeline preview (component names joined)
        components = pipeline_definition.get("components", []) or pipeline_definition.get("pipeline_steps", [])
        component_names = [comp.get("tool_name", "unknown") for comp in components]
        pipeline_preview = " → ".join(component_names) if component_names else "Empty Pipeline"
        
        # Create workflow record
        workflow_record = WorkflowSchema(
            workflow_id=workflow_id,
            session_id=session_id,
            saved_at=now,
            saved_by_user_message=user_message,
            pipeline_definition_s3_key=s3_key,
            pipeline_name=pipeline_definition.get("pipeline_name", "Untitled Workflow"),
            pipeline_preview=pipeline_preview,
            user_confirmed=True,
            source_pipeline_id=source_pipeline_id,
            pipeline_status=pipeline_status
        )
        
        # Insert into MongoDB
        self.workflows_collection.insert_one(schema_to_dict(workflow_record))
        
        return workflow_id
    
    def get_workflows(
        self,
        limit: int = 100,
        skip: int = 0
    ) -> List[Dict[str, Any]]:
        """
        Get all workflows
        
        Args:
            limit: Maximum number to return
            skip: Number to skip (for pagination)
        
        Returns:
            List of workflow records (latest first)
        """
        workflows = list(self.workflows_collection.find(
            {},
            {"_id": 0}  # Exclude MongoDB _id
        ).sort("saved_at", -1).skip(skip).limit(limit))
        
        return workflows
    
    def get_workflow(self, workflow_id: str) -> Optional[Dict[str, Any]]:
        """
        Get a specific workflow by ID
        
        Args:
            workflow_id: Workflow ID
        
        Returns:
            Workflow record with full definition from S3, or None if not found
        """
        workflow = self.workflows_collection.find_one(
            {"workflow_id": workflow_id},
            {"_id": 0}
        )
        
        if not workflow:
            return None
        
        # Load full pipeline definition from S3
        try:
            pipeline_definition = self.s3.download_json(
                workflow["pipeline_definition_s3_key"],
                add_prefix=False
            )
            workflow["pipeline_definition"] = pipeline_definition
        except Exception as e:
            workflow["pipeline_definition"] = None
            workflow["error"] = f"Failed to load pipeline definition: {str(e)}"
        
        return workflow
    
    def delete_workflow(self, workflow_id: str) -> bool:
        """
        Delete a workflow
        
        Args:
            workflow_id: Workflow ID
        
        Returns:
            True if deleted successfully
        """
        workflow = self.workflows_collection.find_one({"workflow_id": workflow_id})
        
        if not workflow:
            return False
        
        # Delete from S3
        try:
            self.s3.delete_object(
                workflow["pipeline_definition_s3_key"],
                add_prefix=False
            )
        except Exception:
            # Continue even if S3 deletion fails
            pass
        
        # Delete from MongoDB
        result = self.workflows_collection.delete_one({"workflow_id": workflow_id})
        
        return result.deleted_count > 0
    
    def get_session_workflows(
        self,
        session_id: str,
        limit: int = 10
    ) -> List[Dict[str, Any]]:
        """
        Get workflows created in a specific session
        
        Args:
            session_id: Session ID
            limit: Maximum number to return
        
        Returns:
            List of workflow records
        """
        workflows = list(self.workflows_collection.find(
            {"session_id": session_id},
            {"_id": 0}
        ).sort("saved_at", -1).limit(limit))
        
        return workflows
    
    def count_workflows(self) -> int:
        """Get total count of workflows"""
        return self.workflows_collection.count_documents({})


# Global singleton instance
_workflow_manager_instance: Optional[WorkflowManager] = None


def get_workflow_manager() -> WorkflowManager:
    """Get or create global WorkflowManager instance"""
    global _workflow_manager_instance
    
    if _workflow_manager_instance is None:
        _workflow_manager_instance = WorkflowManager()
    
    return _workflow_manager_instance