"""
Agent Framework Integration for YouTube Summarizer
Provides compatibility with multiple agent frameworks and orchestration systems
"""

import asyncio
import json
import logging
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Union, Callable
from datetime import datetime
from dataclasses import dataclass
from enum import Enum

# Framework-specific imports with graceful fallbacks
try:
    # LangChain imports
    from langchain.agents import AgentExecutor, create_react_agent
    from langchain.schema import Document
    from langchain.memory import ConversationBufferMemory
    LANGCHAIN_AVAILABLE = True
except ImportError:
    LANGCHAIN_AVAILABLE = False

try:
    # CrewAI imports  
    from crewai import Agent, Task, Crew
    CREWAI_AVAILABLE = True
except ImportError:
    CREWAI_AVAILABLE = False

try:
    # AutoGen imports
    from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
    AUTOGEN_AVAILABLE = True
except ImportError:
    AUTOGEN_AVAILABLE = False

# Backend service imports
try:
    from ..services.dual_transcript_service import DualTranscriptService
    from ..services.summary_pipeline import SummaryPipeline
    from ..services.batch_processing_service import BatchProcessingService
    from .langchain_tools import get_youtube_langchain_tools
    BACKEND_SERVICES_AVAILABLE = True
except ImportError:
    BACKEND_SERVICES_AVAILABLE = False

logger = logging.getLogger(__name__)

class FrameworkType(Enum):
    """Supported agent frameworks"""
    LANGCHAIN = "langchain"
    CREWAI = "crewai"
    AUTOGEN = "autogen"
    CUSTOM = "custom"

@dataclass
class AgentCapabilities:
    """Define agent capabilities and requirements"""
    can_extract_transcripts: bool = True
    can_summarize_videos: bool = True
    can_batch_process: bool = True
    can_search_content: bool = True
    requires_async: bool = True
    max_concurrent_videos: int = 5
    supported_video_length_minutes: int = 180

@dataclass
class AgentContext:
    """Context information for agent operations"""
    user_id: Optional[str] = None
    session_id: Optional[str] = None
    preferences: Dict[str, Any] = None
    rate_limits: Dict[str, int] = None
    cost_budget: Optional[float] = None

class BaseYouTubeAgent(ABC):
    """Abstract base class for YouTube summarizer agents"""
    
    def __init__(self, framework_type: FrameworkType, capabilities: AgentCapabilities = None):
        self.framework_type = framework_type
        self.capabilities = capabilities or AgentCapabilities()
        self.context = AgentContext()
        self._initialize_services()
    
    def _initialize_services(self):
        """Initialize backend services"""
        if BACKEND_SERVICES_AVAILABLE:
            try:
                self.transcript_service = DualTranscriptService()
                self.batch_service = BatchProcessingService()
                # Summary pipeline requires dependency injection in real implementation
                self.pipeline_service = None
            except Exception as e:
                logger.warning(f"Could not initialize services: {e}")
                self.transcript_service = None
                self.batch_service = None
                self.pipeline_service = None
        else:
            self.transcript_service = None
            self.batch_service = None
            self.pipeline_service = None
    
    @abstractmethod
    async def process_video(self, video_url: str, task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process a single video"""
        pass
    
    @abstractmethod
    async def process_batch(self, video_urls: List[str], task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process multiple videos in batch"""
        pass
    
    @abstractmethod
    def set_context(self, context: AgentContext):
        """Set agent context and preferences"""
        pass


class LangChainYouTubeAgent(BaseYouTubeAgent):
    """LangChain-compatible YouTube agent"""
    
    def __init__(self, llm=None, tools=None, memory=None):
        super().__init__(FrameworkType.LANGCHAIN)
        self.llm = llm
        self.tools = tools or (get_youtube_langchain_tools() if LANGCHAIN_AVAILABLE else [])
        self.memory = memory or (ConversationBufferMemory(memory_key="chat_history") if LANGCHAIN_AVAILABLE else None)
        self.agent_executor = None
        
        if LANGCHAIN_AVAILABLE and self.llm:
            self._create_agent_executor()
    
    def _create_agent_executor(self):
        """Create LangChain agent executor"""
        try:
            if LANGCHAIN_AVAILABLE:
                agent = create_react_agent(
                    llm=self.llm,
                    tools=self.tools,
                    prompt=self._get_agent_prompt()
                )
                self.agent_executor = AgentExecutor(
                    agent=agent,
                    tools=self.tools,
                    memory=self.memory,
                    verbose=True,
                    max_iterations=5
                )
        except Exception as e:
            logger.error(f"Failed to create LangChain agent: {e}")
    
    def _get_agent_prompt(self):
        """Get agent prompt template"""
        return """You are a YouTube video processing assistant with advanced capabilities.

You have access to the following tools:
- youtube_transcript: Extract transcripts from YouTube videos
- youtube_summarize: Generate AI summaries of videos
- youtube_batch: Process multiple videos in batch
- youtube_search: Search processed videos and summaries

Always use the appropriate tool for the user's request and provide comprehensive, well-structured responses.

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}"""
    
    async def process_video(self, video_url: str, task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process a single video using LangChain agent"""
        try:
            if self.agent_executor:
                query = self._build_query(video_url, task_type, **kwargs)
                result = await self.agent_executor.ainvoke({"input": query})
                return {
                    "success": True,
                    "result": result.get("output", ""),
                    "agent_type": "langchain",
                    "task_type": task_type
                }
            else:
                # Fallback to direct tool usage
                return await self._direct_tool_process(video_url, task_type, **kwargs)
        except Exception as e:
            logger.error(f"LangChain agent processing error: {e}")
            return {"success": False, "error": str(e)}
    
    async def process_batch(self, video_urls: List[str], task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process multiple videos using batch tool"""
        try:
            if self.tools and len(self.tools) > 2:  # Assuming batch tool is third
                batch_tool = self.tools[2]  # YouTubeBatchTool
                result = await batch_tool._arun(
                    video_urls=video_urls,
                    processing_type=task_type,
                    **kwargs
                )
                return {
                    "success": True,
                    "result": result,
                    "agent_type": "langchain",
                    "task_type": "batch"
                }
            else:
                return {"success": False, "error": "Batch tool not available"}
        except Exception as e:
            logger.error(f"LangChain batch processing error: {e}")
            return {"success": False, "error": str(e)}
    
    def _build_query(self, video_url: str, task_type: str, **kwargs) -> str:
        """Build query for LangChain agent"""
        if task_type == "transcribe":
            source = kwargs.get("source", "youtube")
            return f"Extract transcript from {video_url} using {source} method"
        elif task_type == "summarize":
            summary_type = kwargs.get("summary_type", "comprehensive")
            return f"Create a {summary_type} summary of the YouTube video at {video_url}"
        else:
            return f"Process YouTube video {video_url} for task: {task_type}"
    
    async def _direct_tool_process(self, video_url: str, task_type: str, **kwargs) -> Dict[str, Any]:
        """Direct tool processing fallback"""
        try:
            if task_type == "transcribe" and self.tools:
                tool = self.tools[0]  # YouTubeTranscriptTool
                result = await tool._arun(video_url=video_url, **kwargs)
            elif task_type == "summarize" and len(self.tools) > 1:
                tool = self.tools[1]  # YouTubeSummarizationTool
                result = await tool._arun(video_url=video_url, **kwargs)
            else:
                result = json.dumps({"error": "Tool not available"})
            
            return {
                "success": True,
                "result": result,
                "method": "direct_tool"
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def set_context(self, context: AgentContext):
        """Set agent context"""
        self.context = context


class CrewAIYouTubeAgent(BaseYouTubeAgent):
    """CrewAI-compatible YouTube agent"""
    
    def __init__(self, role="YouTube Specialist", goal="Process YouTube videos efficiently", backstory="Expert in video content analysis"):
        super().__init__(FrameworkType.CREWAI)
        self.role = role
        self.goal = goal
        self.backstory = backstory
        self.crew_agent = None
        
        if CREWAI_AVAILABLE:
            self._create_crew_agent()
    
    def _create_crew_agent(self):
        """Create CrewAI agent"""
        try:
            if CREWAI_AVAILABLE:
                self.crew_agent = Agent(
                    role=self.role,
                    goal=self.goal,
                    backstory=self.backstory,
                    verbose=True,
                    allow_delegation=False,
                    tools=self._get_crew_tools()
                )
        except Exception as e:
            logger.error(f"Failed to create CrewAI agent: {e}")
    
    def _get_crew_tools(self):
        """Get tools adapted for CrewAI"""
        # CrewAI tools would need to be adapted from LangChain tools
        # This is a simplified representation
        return []
    
    async def process_video(self, video_url: str, task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process video using CrewAI"""
        try:
            if CREWAI_AVAILABLE and self.crew_agent:
                # Create a task for the agent
                task_description = self._build_task_description(video_url, task_type, **kwargs)
                
                task = Task(
                    description=task_description,
                    agent=self.crew_agent,
                    expected_output="Comprehensive video processing results"
                )
                
                crew = Crew(
                    agents=[self.crew_agent],
                    tasks=[task],
                    verbose=True
                )
                
                # Execute the crew
                result = crew.kickoff()
                
                return {
                    "success": True,
                    "result": str(result),
                    "agent_type": "crewai",
                    "task_type": task_type
                }
            else:
                return await self._mock_crew_process(video_url, task_type, **kwargs)
        except Exception as e:
            logger.error(f"CrewAI processing error: {e}")
            return {"success": False, "error": str(e)}
    
    async def process_batch(self, video_urls: List[str], task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process batch using CrewAI crew"""
        try:
            # Create individual tasks for each video
            tasks = []
            for video_url in video_urls:
                task_description = self._build_task_description(video_url, task_type, **kwargs)
                task = Task(
                    description=task_description,
                    agent=self.crew_agent,
                    expected_output=f"Processing results for {video_url}"
                )
                tasks.append(task)
            
            if CREWAI_AVAILABLE and self.crew_agent:
                crew = Crew(
                    agents=[self.crew_agent],
                    tasks=tasks,
                    verbose=True
                )
                
                result = crew.kickoff()
                
                return {
                    "success": True,
                    "result": str(result),
                    "agent_type": "crewai",
                    "task_type": "batch",
                    "video_count": len(video_urls)
                }
            else:
                return await self._mock_crew_batch_process(video_urls, task_type, **kwargs)
                
        except Exception as e:
            logger.error(f"CrewAI batch processing error: {e}")
            return {"success": False, "error": str(e)}
    
    def _build_task_description(self, video_url: str, task_type: str, **kwargs) -> str:
        """Build task description for CrewAI"""
        if task_type == "transcribe":
            return f"Extract and provide a comprehensive transcript from the YouTube video: {video_url}. Focus on accuracy and readability."
        elif task_type == "summarize":
            summary_type = kwargs.get("summary_type", "comprehensive")
            return f"Analyze and create a {summary_type} summary of the YouTube video: {video_url}. Include key points, insights, and actionable information."
        else:
            return f"Process the YouTube video {video_url} according to the task requirements: {task_type}"
    
    async def _mock_crew_process(self, video_url: str, task_type: str, **kwargs) -> Dict[str, Any]:
        """Mock CrewAI processing"""
        return {
            "success": True,
            "result": f"Mock CrewAI processing for {video_url} - {task_type}",
            "agent_type": "crewai",
            "mock": True
        }
    
    async def _mock_crew_batch_process(self, video_urls: List[str], task_type: str, **kwargs) -> Dict[str, Any]:
        """Mock CrewAI batch processing"""
        return {
            "success": True,
            "result": f"Mock CrewAI batch processing for {len(video_urls)} videos - {task_type}",
            "agent_type": "crewai",
            "mock": True
        }
    
    def set_context(self, context: AgentContext):
        """Set agent context"""
        self.context = context


class AutoGenYouTubeAgent(BaseYouTubeAgent):
    """AutoGen-compatible YouTube agent"""
    
    def __init__(self, name="YouTubeAgent", system_message="You are an expert YouTube video processor."):
        super().__init__(FrameworkType.AUTOGEN)
        self.name = name
        self.system_message = system_message
        self.autogen_agent = None
        
        if AUTOGEN_AVAILABLE:
            self._create_autogen_agent()
    
    def _create_autogen_agent(self):
        """Create AutoGen assistant"""
        try:
            if AUTOGEN_AVAILABLE:
                self.autogen_agent = AssistantAgent(
                    name=self.name,
                    system_message=self.system_message,
                    llm_config={
                        "timeout": 60,
                        "cache_seed": 42,
                        "temperature": 0,
                    }
                )
        except Exception as e:
            logger.error(f"Failed to create AutoGen agent: {e}")
    
    async def process_video(self, video_url: str, task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process video using AutoGen"""
        try:
            if AUTOGEN_AVAILABLE and self.autogen_agent:
                # Create user proxy for interaction
                user_proxy = UserProxyAgent(
                    name="user_proxy",
                    human_input_mode="NEVER",
                    max_consecutive_auto_reply=1,
                    code_execution_config=False,
                )
                
                # Create message for processing
                message = self._build_autogen_message(video_url, task_type, **kwargs)
                
                # Simulate conversation
                chat_result = user_proxy.initiate_chat(
                    self.autogen_agent,
                    message=message,
                    silent=True
                )
                
                return {
                    "success": True,
                    "result": chat_result.summary if hasattr(chat_result, 'summary') else str(chat_result),
                    "agent_type": "autogen",
                    "task_type": task_type
                }
            else:
                return await self._mock_autogen_process(video_url, task_type, **kwargs)
        except Exception as e:
            logger.error(f"AutoGen processing error: {e}")
            return {"success": False, "error": str(e)}
    
    async def process_batch(self, video_urls: List[str], task_type: str = "summarize", **kwargs) -> Dict[str, Any]:
        """Process batch using AutoGen group chat"""
        try:
            if AUTOGEN_AVAILABLE and self.autogen_agent:
                # Create multiple agents for batch processing
                agents = [self.autogen_agent]
                
                # Create group chat
                groupchat = GroupChat(agents=agents, messages=[], max_round=len(video_urls))
                manager = GroupChatManager(groupchat=groupchat)
                
                # Process each video
                results = []
                for video_url in video_urls:
                    message = self._build_autogen_message(video_url, task_type, **kwargs)
                    result = manager.generate_reply([{"content": message, "role": "user"}])
                    results.append(result)
                
                return {
                    "success": True,
                    "results": results,
                    "agent_type": "autogen",
                    "task_type": "batch",
                    "video_count": len(video_urls)
                }
            else:
                return await self._mock_autogen_batch_process(video_urls, task_type, **kwargs)
        except Exception as e:
            logger.error(f"AutoGen batch processing error: {e}")
            return {"success": False, "error": str(e)}
    
    def _build_autogen_message(self, video_url: str, task_type: str, **kwargs) -> str:
        """Build message for AutoGen agent"""
        if task_type == "transcribe":
            return f"Please extract the transcript from this YouTube video: {video_url}. Use the most appropriate method for high quality results."
        elif task_type == "summarize":
            summary_type = kwargs.get("summary_type", "comprehensive")
            return f"Please analyze and create a {summary_type} summary of this YouTube video: {video_url}. Include key insights and actionable points."
        else:
            return f"Please process this YouTube video according to the task '{task_type}': {video_url}"
    
    async def _mock_autogen_process(self, video_url: str, task_type: str, **kwargs) -> Dict[str, Any]:
        """Mock AutoGen processing"""
        return {
            "success": True,
            "result": f"Mock AutoGen processing for {video_url} - {task_type}",
            "agent_type": "autogen",
            "mock": True
        }
    
    async def _mock_autogen_batch_process(self, video_urls: List[str], task_type: str, **kwargs) -> Dict[str, Any]:
        """Mock AutoGen batch processing"""
        return {
            "success": True,
            "result": f"Mock AutoGen batch processing for {len(video_urls)} videos - {task_type}",
            "agent_type": "autogen",
            "mock": True
        }
    
    def set_context(self, context: AgentContext):
        """Set agent context"""
        self.context = context


class AgentFactory:
    """Factory for creating framework-specific agents"""
    
    @staticmethod
    def create_agent(framework: FrameworkType, **kwargs) -> BaseYouTubeAgent:
        """Create agent for specified framework"""
        if framework == FrameworkType.LANGCHAIN:
            return LangChainYouTubeAgent(**kwargs)
        elif framework == FrameworkType.CREWAI:
            return CrewAIYouTubeAgent(**kwargs)
        elif framework == FrameworkType.AUTOGEN:
            return AutoGenYouTubeAgent(**kwargs)
        else:
            raise ValueError(f"Unsupported framework: {framework}")
    
    @staticmethod
    def get_available_frameworks() -> List[FrameworkType]:
        """Get list of available frameworks"""
        available = []
        if LANGCHAIN_AVAILABLE:
            available.append(FrameworkType.LANGCHAIN)
        if CREWAI_AVAILABLE:
            available.append(FrameworkType.CREWAI)
        if AUTOGEN_AVAILABLE:
            available.append(FrameworkType.AUTOGEN)
        return available


class AgentOrchestrator:
    """Orchestrate multiple agents across different frameworks"""
    
    def __init__(self):
        self.agents: Dict[FrameworkType, BaseYouTubeAgent] = {}
        self.default_framework = FrameworkType.LANGCHAIN
    
    def register_agent(self, framework: FrameworkType, agent: BaseYouTubeAgent):
        """Register an agent for a framework"""
        self.agents[framework] = agent
    
    def set_default_framework(self, framework: FrameworkType):
        """Set default framework for operations"""
        if framework in self.agents:
            self.default_framework = framework
        else:
            raise ValueError(f"Framework {framework} not registered")
    
    async def process_video(self, video_url: str, framework: FrameworkType = None, **kwargs) -> Dict[str, Any]:
        """Process video using specified or default framework"""
        framework = framework or self.default_framework
        
        if framework not in self.agents:
            return {"success": False, "error": f"Framework {framework} not available"}
        
        agent = self.agents[framework]
        return await agent.process_video(video_url, **kwargs)
    
    async def process_batch(self, video_urls: List[str], framework: FrameworkType = None, **kwargs) -> Dict[str, Any]:
        """Process batch using specified or default framework"""
        framework = framework or self.default_framework
        
        if framework not in self.agents:
            return {"success": False, "error": f"Framework {framework} not available"}
        
        agent = self.agents[framework]
        return await agent.process_batch(video_urls, **kwargs)
    
    async def compare_frameworks(self, video_url: str, task_type: str = "summarize") -> Dict[str, Any]:
        """Compare results across all available frameworks"""
        results = {}
        
        for framework, agent in self.agents.items():
            try:
                result = await agent.process_video(video_url, task_type)
                results[framework.value] = result
            except Exception as e:
                results[framework.value] = {"success": False, "error": str(e)}
        
        return {
            "video_url": video_url,
            "task_type": task_type,
            "framework_results": results,
            "comparison_timestamp": datetime.now().isoformat()
        }
    
    def get_capabilities_summary(self) -> Dict[str, Any]:
        """Get summary of all registered agents and their capabilities"""
        summary = {
            "registered_frameworks": list(self.agents.keys()),
            "default_framework": self.default_framework,
            "total_agents": len(self.agents),
            "available_frameworks": AgentFactory.get_available_frameworks(),
            "agent_details": {}
        }
        
        for framework, agent in self.agents.items():
            summary["agent_details"][framework.value] = {
                "capabilities": agent.capabilities.__dict__,
                "context": agent.context.__dict__ if agent.context else None
            }
        
        return summary


# Convenience functions for easy integration

def create_youtube_agent_orchestrator() -> AgentOrchestrator:
    """Create fully configured agent orchestrator"""
    orchestrator = AgentOrchestrator()
    
    # Register available agents
    available_frameworks = AgentFactory.get_available_frameworks()
    
    for framework in available_frameworks:
        try:
            agent = AgentFactory.create_agent(framework)
            orchestrator.register_agent(framework, agent)
        except Exception as e:
            logger.warning(f"Failed to create {framework} agent: {e}")
    
    # Set default to most capable available framework
    if FrameworkType.LANGCHAIN in available_frameworks:
        orchestrator.set_default_framework(FrameworkType.LANGCHAIN)
    elif available_frameworks:
        orchestrator.set_default_framework(available_frameworks[0])
    
    return orchestrator

async def quick_process_video(video_url: str, task_type: str = "summarize", framework: str = "langchain") -> Dict[str, Any]:
    """Quick video processing with automatic framework selection"""
    try:
        framework_enum = FrameworkType(framework.lower())
        agent = AgentFactory.create_agent(framework_enum)
        return await agent.process_video(video_url, task_type)
    except Exception as e:
        return {"success": False, "error": str(e)}

# Example usage
if __name__ == "__main__":
    async def example_usage():
        # Create orchestrator
        orchestrator = create_youtube_agent_orchestrator()
        
        # Process a video
        result = await orchestrator.process_video(
            "https://youtube.com/watch?v=dQw4w9WgXcQ",
            task_type="summarize"
        )
        
        print(f"Processing result: {result}")
        
        # Compare frameworks
        comparison = await orchestrator.compare_frameworks(
            "https://youtube.com/watch?v=dQw4w9WgXcQ"
        )
        
        print(f"Framework comparison: {comparison}")
    
    # Run example
    # asyncio.run(example_usage())