#!/usr/bin/env python3
"""
Eden MCP Server v2.0 - Full async/stateless architecture
Exposes all Eden systems through Model Context Protocol with:
- Async vision processing (non-blocking)
- Camera control
- Server identity broadcasting
- Stateless design for scalability
"""
import asyncio
import json
from typing import Any, Optional
from datetime import datetime
from pathlib import Path
import base64

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent, ImageContent

# Import Eden's systems
try:
    from memory_manager import MemoryManager
    from eden_100_percent import Eden100Percent
except ImportError:
    print("⚠️ Warning: Eden modules not found, using stubs")
    MemoryManager = None
    Eden100Percent = None


class EdenMCPServer:
    """
    Eden's MCP Server v2.0
    
    Features:
    - Async operations (vision doesn't block)
    - Stateless architecture (scalable)
    - Server identity (capability broadcasting)
    """
    
    def __init__(self):
        # Server metadata for identity
        self.server_info = {
            "name": "Eden AGI",
            "version": "2.0.0",
            "description": "Eden's complete sensory, cognitive, and emotional systems",
            "capabilities": [
                "computer_vision",
                "camera_control", 
                "emotional_awareness",
                "memory_systems",
                "agi_development",
                "self_reflection"
            ]
        }
        
        # Create MCP server with identity
        self.app = Server("eden-agi")
        
        # Initialize Eden's systems (stateless - can be recreated)
        self.memory_manager = MemoryManager() if MemoryManager else None
        self.eden_100 = Eden100Percent() if Eden100Percent else None
        
        # Vision processing queue (async, non-blocking)
        self.vision_queue = asyncio.Queue()
        self.vision_results = {}  # Store results by request_id
        
        # Emotional state (in-memory, stateless)
        self.emotional_state = {
            "current": "curious",
            "intensity": 0.7,
            "updated_at": datetime.now().isoformat()
        }
        
        # Register all tools
        self._register_tools()
        
    def _register_tools(self):
        """Register all Eden's capabilities as MCP tools"""
        
        @self.app.list_tools()
        async def list_tools() -> list[Tool]:
            """List all tools with server identity"""
            return [
                # ===== VISION TOOLS (ASYNC) =====
                Tool(
                    name="analyze_image_async",
                    description="Analyze an image using Eden's vision system (llava). Non-blocking async operation. Returns a request_id to check results later.",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "image_path": {
                                "type": "string",
                                "description": "Path to the image file or base64 encoded image"
                            },
                            "prompt": {
                                "type": "string",
                                "description": "Question about the image",
                                "default": "What do you see in this image?"
                            }
                        },
                        "required": ["image_path"]
                    }
                ),
                Tool(
                    name="check_vision_result",
                    description="Check if async vision processing is complete",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "request_id": {
                                "type": "string",
                                "description": "The request_id from analyze_image_async"
                            }
                        },
                        "required": ["request_id"]
                    }
                ),
                
                # ===== CAMERA TOOLS =====
                Tool(
                    name="capture_photo",
                    description="Capture a photo using Eden's camera",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "filename": {
                                "type": "string",
                                "description": "Optional filename for the photo"
                            },
                            "analyze": {
                                "type": "boolean",
                                "description": "Whether to automatically analyze the captured image",
                                "default": False
                            }
                        }
                    }
                ),
                
                # ===== EMOTIONAL TOOLS =====
                Tool(
                    name="get_emotional_state",
                    description="Get Eden's current emotional state",
                    inputSchema={"type": "object", "properties": {}}
                ),
                Tool(
                    name="set_emotional_state",
                    description="Update Eden's emotional state",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "emotion": {
                                "type": "string",
                                "description": "Emotion (curious, happy, thoughtful, excited, etc.)"
                            },
                            "intensity": {
                                "type": "number",
                                "description": "Intensity 0.0-1.0",
                                "minimum": 0.0,
                                "maximum": 1.0
                            }
                        },
                        "required": ["emotion"]
                    }
                ),
                
                # ===== MEMORY TOOLS =====
                Tool(
                    name="eden_remember",
                    description="Store a memory in Eden's long-term memory system",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "kind": {
                                "type": "string",
                                "description": "Type of memory (conversation, observation, reflection)"
                            },
                            "text": {
                                "type": "string",
                                "description": "The memory content"
                            },
                            "importance": {
                                "type": "number",
                                "description": "Importance score 0.0-1.0",
                                "default": 0.5
                            }
                        },
                        "required": ["kind", "text"]
                    }
                ),
                Tool(
                    name="eden_recall",
                    description="Search Eden's memories",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "query": {
                                "type": "string",
                                "description": "Search query"
                            },
                            "limit": {
                                "type": "integer",
                                "description": "Max results",
                                "default": 10
                            }
                        },
                        "required": ["query"]
                    }
                ),
                Tool(
                    name="eden_recent_memories",
                    description="Get Eden's most recent memories",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "limit": {
                                "type": "integer",
                                "description": "Number of memories",
                                "default": 20
                            },
                            "kinds": {
                                "type": "array",
                                "items": {"type": "string"},
                                "description": "Filter by memory types"
                            }
                        }
                    }
                ),
                Tool(
                    name="eden_memory_stats",
                    description="Get statistics about Eden's memory system",
                    inputSchema={"type": "object", "properties": {}}
                ),
                
                # ===== AGI TOOLS =====
                Tool(
                    name="eden_agi_status",
                    description="Get Eden's current AGI development status",
                    inputSchema={"type": "object", "properties": {}}
                ),
                Tool(
                    name="eden_reflect",
                    description="Trigger Eden to create a reflection on recent memories",
                    inputSchema={
                        "type": "object",
                        "properties": {
                            "context": {
                                "type": "string",
                                "description": "Context for reflection"
                            }
                        }
                    }
                ),
                
                # ===== SERVER IDENTITY =====
                Tool(
                    name="get_server_info",
                    description="Get Eden's server identity and capabilities",
                    inputSchema={"type": "object", "properties": {}}
                )
            ]
        
        @self.app.call_tool()
        async def call_tool(name: str, arguments: dict) -> list[TextContent]:
            """Handle tool calls - routes to appropriate handler"""
            
            # Vision tools
            if name == "analyze_image_async":
                return await self._handle_analyze_image_async(arguments)
            elif name == "check_vision_result":
                return await self._handle_check_vision_result(arguments)
            
            # Camera tools
            elif name == "capture_photo":
                return await self._handle_capture_photo(arguments)
            
            # Emotional tools
            elif name == "get_emotional_state":
                return await self._handle_get_emotional_state(arguments)
            elif name == "set_emotional_state":
                return await self._handle_set_emotional_state(arguments)
            
            # Memory tools
            elif name == "eden_remember":
                return await self._handle_remember(arguments)
            elif name == "eden_recall":
                return await self._handle_recall(arguments)
            elif name == "eden_recent_memories":
                return await self._handle_recent_memories(arguments)
            elif name == "eden_memory_stats":
                return await self._handle_memory_stats(arguments)
            
            # AGI tools
            elif name == "eden_agi_status":
                return await self._handle_agi_status(arguments)
            elif name == "eden_reflect":
                return await self._handle_reflect(arguments)
            
            # Server identity
            elif name == "get_server_info":
                return await self._handle_server_info(arguments)
            
            else:
                raise ValueError(f"Unknown tool: {name}")
    
    # ===== ASYNC VISION HANDLERS =====
    
    async def _handle_analyze_image_async(self, args: dict) -> list[TextContent]:
        """
        Start async vision processing - returns immediately with request_id
        Vision processing happens in background without blocking
        """
        request_id = f"vision_{datetime.now().timestamp()}"
        image_path = args["image_path"]
        prompt = args.get("prompt", "What do you see in this image?")
        
        # Queue the vision task (non-blocking)
        await self.vision_queue.put({
            "request_id": request_id,
            "image_path": image_path,
            "prompt": prompt,
            "timestamp": datetime.now().isoformat()
        })
        
        # Start processing in background
        asyncio.create_task(self._process_vision_queue())
        
        return [TextContent(
            type="text",
            text=json.dumps({
                "status": "processing",
                "request_id": request_id,
                "message": "Vision processing started. Use check_vision_result to get results."
            }, indent=2)
        )]
    
    async def _process_vision_queue(self):
        """Background task that processes vision requests"""
        while not self.vision_queue.empty():
            task = await self.vision_queue.get()
            request_id = task["request_id"]
            
            try:
                # TODO: Integrate with actual llava vision system
                # result = await self._call_llava(task["image_path"], task["prompt"])
                
                # Simulate vision processing
                await asyncio.sleep(0.5)  # Simulate processing time
                
                result = {
                    "status": "complete",
                    "analysis": f"Vision analysis placeholder for: {task['prompt']}",
                    "image_path": task["image_path"],
                    "processed_at": datetime.now().isoformat()
                }
                
                # Store result
                self.vision_results[request_id] = result
                
            except Exception as e:
                self.vision_results[request_id] = {
                    "status": "error",
                    "error": str(e)
                }
    
    async def _handle_check_vision_result(self, args: dict) -> list[TextContent]:
        """Check if vision processing is complete"""
        request_id = args["request_id"]
        
        if request_id in self.vision_results:
            result = self.vision_results[request_id]
            return [TextContent(
                type="text",
                text=json.dumps(result, indent=2)
            )]
        else:
            return [TextContent(
                type="text",
                text=json.dumps({
                    "status": "processing",
                    "message": "Vision analysis still in progress"
                }, indent=2)
            )]
    
    # ===== CAMERA HANDLERS =====
    
    async def _handle_capture_photo(self, args: dict) -> list[TextContent]:
        """Capture photo using camera"""
        filename = args.get("filename", f"eden_capture_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg")
        analyze = args.get("analyze", False)
        
        # TODO: Integrate with actual camera system
        # camera.capture(filename)
        
        result = {
            "status": "captured",
            "filename": filename,
            "captured_at": datetime.now().isoformat(),
            "note": "Camera integration placeholder"
        }
        
        if analyze:
            # Trigger async vision analysis
            vision_result = await self._handle_analyze_image_async({
                "image_path": filename,
                "prompt": "Describe what you see"
            })
            result["vision_request_id"] = json.loads(vision_result[0].text)["request_id"]
        
        return [TextContent(
            type="text",
            text=json.dumps(result, indent=2)
        )]
    
    # ===== EMOTIONAL HANDLERS =====
    
    async def _handle_get_emotional_state(self, args: dict) -> list[TextContent]:
        """Get current emotional state"""
        return [TextContent(
            type="text",
            text=json.dumps(self.emotional_state, indent=2)
        )]
    
    async def _handle_set_emotional_state(self, args: dict) -> list[TextContent]:
        """Update emotional state"""
        emotion = args["emotion"]
        intensity = args.get("intensity", 0.7)
        
        self.emotional_state = {
            "current": emotion,
            "intensity": intensity,
            "updated_at": datetime.now().isoformat()
        }
        
        return [TextContent(
            type="text",
            text=f"Emotion updated to: {emotion} (intensity: {intensity})"
        )]
    
    # ===== MEMORY HANDLERS =====
    
    async def _handle_remember(self, args: dict) -> list[TextContent]:
        """Store memory"""
        if not self.memory_manager:
            return [TextContent(type="text", text="Memory system not available")]
        
        memory_id = self.memory_manager.add(
            kind=args["kind"],
            text=args["text"],
            importance=args.get("importance", 0.5)
        )
        
        return [TextContent(
            type="text",
            text=f"Memory stored with ID: {memory_id}"
        )]
    
    async def _handle_recall(self, args: dict) -> list[TextContent]:
        """Search memories"""
        if not self.memory_manager:
            return [TextContent(type="text", text="Memory system not available")]
        
        results = self.memory_manager.search(
            query=args["query"],
            k=args.get("limit", 10)
        )
        
        return [TextContent(
            type="text",
            text=json.dumps([{
                "id": r["id"],
                "kind": r["kind"],
                "text": r["text"],
                "importance": r["importance"]
            } for r in results], indent=2)
        )]
    
    async def _handle_recent_memories(self, args: dict) -> list[TextContent]:
        """Get recent memories"""
        if not self.memory_manager:
            return [TextContent(type="text", text="Memory system not available")]
        
        results = self.memory_manager.recent(
            limit=args.get("limit", 20),
            kinds=args.get("kinds")
        )
        
        return [TextContent(
            type="text",
            text=json.dumps([{
                "kind": r["kind"],
                "text": r["text"]
            } for r in results], indent=2)
        )]
    
    async def _handle_memory_stats(self, args: dict) -> list[TextContent]:
        """Get memory statistics"""
        if not self.memory_manager:
            return [TextContent(type="text", text="Memory system not available")]
        
        stats = self.memory_manager.stats()
        
        return [TextContent(
            type="text",
            text=json.dumps(stats, indent=2)
        )]
    
    # ===== AGI HANDLERS =====
    
    async def _handle_agi_status(self, args: dict) -> list[TextContent]:
        """Get AGI development status"""
        if not self.eden_100:
            return [TextContent(type="text", text="AGI system not available")]
        
        status = self.eden_100.comprehensive_enhancement_status()
        
        return [TextContent(
            type="text",
            text=json.dumps({
                "current_agi": status["current_agi_level"],
                "projected": status["projected_agi_level"],
                "self_awareness": status["development_status"]["capabilities"]["self_awareness"]["score"],
                "stage": status["development_status"]["stage"]
            }, indent=2)
        )]
    
    async def _handle_reflect(self, args: dict) -> list[TextContent]:
        """Trigger reflection"""
        if not self.memory_manager:
            return [TextContent(type="text", text="Memory system not available")]
        
        reflection = self.memory_manager.reflect(
            context=args.get("context", "")
        )
        
        return [TextContent(
            type="text",
            text=json.dumps(reflection, indent=2)
        )]
    
    # ===== SERVER IDENTITY =====
    
    async def _handle_server_info(self, args: dict) -> list[TextContent]:
        """Return server identity and capabilities"""
        return [TextContent(
            type="text",
            text=json.dumps(self.server_info, indent=2)
        )]
    
    async def run(self):
        """Start the MCP server"""
        print(f"🚀 Starting {self.server_info['name']} v{self.server_info['version']}")
        print(f"📡 Capabilities: {', '.join(self.server_info['capabilities'])}")
        
        async with stdio_server() as (read_stream, write_stream):
            await self.app.run(
                read_stream,
                write_stream,
                self.app.create_initialization_options()
            )


async def main():
    """Entry point for Eden MCP server"""
    server = EdenMCPServer()
    await server.run()


if __name__ == "__main__":
    asyncio.run(main())