"""
Social Amplification Agent implementation.
"""
from typing import Dict, Any, List, Optional
from datetime import datetime
from loguru import logger
from .base import SIFBaseAgent, TXTAI_AVAILABLE, Agent
from services.intelligence.agents.core_agent_framework import BaseALwrityAgent, TaskProposal

try:
    from services.intelligence.sif_integration import SIFIntegrationService
    SIF_AVAILABLE = True
except ImportError:
    SIF_AVAILABLE = False

class SocialAmplificationAgent(BaseALwrityAgent):
    """
    Agent responsible for social media monitoring, content adaptation, and distribution.
    """
    
    def __init__(self, user_id: str, shared_llm_name: str, llm: Any = None, **kwargs):
        super().__init__(user_id, "social_media_manager", shared_llm_name, llm, **kwargs)
        
        self.sif_service = None
        if SIF_AVAILABLE:
            try:
                self.sif_service = SIFIntegrationService(user_id)
            except Exception as e:
                logger.warning(f"Failed to initialize SIF service for SocialAmplificationAgent: {e}")

    def _create_txtai_agent(self):
        """Create a specialized txtai Agent for social media."""
        if not TXTAI_AVAILABLE or Agent is None:
            return None
            
        _llm_for_agent = getattr(self.llm, "llm", self.llm)
        return Agent(
            tools=[
                {
                    "name": "social_monitor",
                    "description": "Monitors social trends and conversations",
                    "target": self._social_monitor_tool
                },
                {
                    "name": "content_adapter",
                    "description": "Adapts long-form content for social platforms",
                    "target": self._content_adapter_tool
                },
                {
                    "name": "engagement_optimizer",
                    "description": "Optimizes posts for engagement (hashtags, timing)",
                    "target": self._engagement_optimizer_tool
                },
                {
                    "name": "distribution_manager",
                    "description": "Manages posting schedule",
                    "target": self._distribution_manager_tool
                }
            ],
            llm=_llm_for_agent,
            max_iterations=10,
            # Removed unsupported 'system' argument
            # Instruction will be provided via orchestrator context or initial prompt
            # Instruction should be provided during invocation or via orchestrator context
        )
    
    # Tool Implementations
    
    def _social_monitor_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Social monitoring tool using SIF.
        
        Args:
            context: Dictionary containing monitoring criteria like 'topics' or 'platforms'.
        """
        # Stub implementation
        return {
            "trends": ["AI in marketing", "Content automation"],
            "source": "stub",
            "timestamp": datetime.utcnow().isoformat()
        }

    def _content_adapter_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Adapts content for specific platforms.
        
        Args:
            context: Dictionary containing 'content' and 'platform' (e.g., 'linkedin', 'twitter').
        """
        # Stub implementation
        return {"adapted_content": "Social post"}

    def _engagement_optimizer_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Optimizes content for engagement (hashtags, timing, hook).
        
        Args:
            context: Dictionary containing 'content' to optimize.
        """
        # Stub implementation
        return {
            "optimization_suggestions": ["Use questions"],
            "estimated_engagement_score": 8.5,
            "timestamp": datetime.utcnow().isoformat()
        }

    def _distribution_manager_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Manages distribution (scheduling/posting).
        
        Args:
            context: Dictionary containing 'post_content' and 'schedule_time'.
        """
        # Stub implementation
        return {
            "distribution_plan": [],
            "status": "scheduled",
            "timestamp": datetime.utcnow().isoformat()
        }

    async def propose_daily_tasks(self, context: Dict[str, Any]) -> List[TaskProposal]:
        """
        Propose social media tasks.
        """
        proposals = []
        
        # 1. Social Post Creation
        proposals.append(TaskProposal(
            title="Create LinkedIn Thread",
            description="Summarize your latest blog post into a 5-tweet thread.",
            pillar_id="distribute",
            priority="medium",
            estimated_time=20,
            source_agent="SocialAmplificationAgent",
            reasoning="Repurpose existing content.",
            action_type="navigate",
            action_url="/content-planning-dashboard"
        ))
        
        return proposals