"""MCP Server implementation for Flux Seamless Texture LoRA."""

import json
import logging
from typing import Any, Dict, List, Optional
from pathlib import Path
from datetime import datetime

from config.settings import MCP_ENABLED, EXAMPLES_DIR
from src.model_handler import ModelHandler
from src.presets import list_presets, get_preset, get_preset_prompt, get_preset_params
from src.image_processor import load_metadata, OUTPUT_DIR

logger = logging.getLogger(__name__)


class MCPServer:
    """MCP Server for exposing the Space via Model Context Protocol."""
    
    def __init__(self, model_handler: ModelHandler):
        """Initialize the MCP server.
        
        Args:
            model_handler: Instance of ModelHandler.
        """
        self.model_handler = model_handler
        self.history: List[Dict[str, Any]] = []
    
    # Tools (Ferramentas)
    
    def tool_generate_texture(
        self,
        prompt: str,
        negative_prompt: str = "",
        guidance_scale: float = 7.5,
        num_inference_steps: int = 50,
        seed: Optional[int] = None,
        width: int = 1024,
        height: int = 1024,
    ) -> Dict[str, Any]:
        """Generate a texture image.
        
        Args:
            prompt: Text prompt for generation.
            negative_prompt: Negative prompt.
            guidance_scale: Guidance scale.
            num_inference_steps: Number of inference steps.
            seed: Random seed.
            width: Image width.
            height: Image height.
            
        Returns:
            Dictionary with image path and metadata.
        """
        try:
            image, metadata = self.model_handler.generate(
                prompt=prompt,
                negative_prompt=negative_prompt,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                seed=seed,
                width=width,
                height=height,
            )
            
            # Add to history
            history_entry = {
                "timestamp": datetime.now().timestamp(),
                "prompt": prompt,
                "params": {
                    "negative_prompt": negative_prompt,
                    "guidance_scale": guidance_scale,
                    "num_inference_steps": num_inference_steps,
                    "seed": seed,
                    "width": width,
                    "height": height,
                },
                "image_path": metadata.get("image_path"),
            }
            self.history.append(history_entry)
            
            return {
                "success": True,
                "image_path": metadata.get("image_path"),
                "seed": metadata.get("seed"),
                "message": "Texture generated successfully",
            }
        except Exception as e:
            logger.error(f"Error in generate_texture: {e}")
            return {
                "success": False,
                "error": str(e),
            }
    
    def tool_generate_batch_textures(
        self,
        prompts: List[str],
        guidance_scale: float = 7.5,
        num_inference_steps: int = 50,
    ) -> Dict[str, Any]:
        """Generate multiple textures in batch.
        
        Args:
            prompts: List of prompts.
            guidance_scale: Guidance scale.
            num_inference_steps: Number of inference steps.
            
        Returns:
            Dictionary with results.
        """
        try:
            base_params = {
                "guidance_scale": guidance_scale,
                "num_inference_steps": num_inference_steps,
            }
            
            results = []
            for image, metadata, idx in self.model_handler.generate_batch(prompts, base_params):
                if image is not None:
                    results.append({
                        "index": idx,
                        "success": True,
                        "image_path": metadata.get("image_path"),
                    })
                else:
                    results.append({
                        "index": idx,
                        "success": False,
                        "error": metadata.get("error"),
                    })
            
            return {
                "success": True,
                "total": len(prompts),
                "results": results,
            }
        except Exception as e:
            logger.error(f"Error in generate_batch_textures: {e}")
            return {
                "success": False,
                "error": str(e),
            }
    
    def tool_get_presets(self) -> Dict[str, Any]:
        """Get list of available presets.
        
        Returns:
            Dictionary with preset names and details.
        """
        presets = list_presets()
        preset_details = {}
        
        for name in presets:
            preset = get_preset(name)
            if preset:
                preset_details[name] = {
                    "prompt": preset.get("prompt"),
                    "guidance_scale": preset.get("guidance_scale"),
                    "num_inference_steps": preset.get("num_inference_steps"),
                }
        
        return {
            "presets": presets,
            "details": preset_details,
        }
    
    def tool_get_history(self, limit: int = 10) -> Dict[str, Any]:
        """Get generation history.
        
        Args:
            limit: Maximum number of entries to return.
            
        Returns:
            Dictionary with history entries.
        """
        recent = self.history[-limit:] if len(self.history) > limit else self.history
        return {
            "total": len(self.history),
            "entries": recent,
        }
    
    def tool_download_image(self, image_path: str) -> Dict[str, Any]:
        """Get information about an image for download.
        
        Args:
            image_path: Path to the image.
            
        Returns:
            Dictionary with image information.
        """
        path = Path(image_path)
        if not path.exists():
            return {
                "success": False,
                "error": "Image not found",
            }
        
        metadata = load_metadata(path)
        return {
            "success": True,
            "image_path": str(path),
            "metadata": metadata,
        }
    
    # Resources (Recursos)
    
    def resource_presets(self) -> str:
        """Get presets as a resource.
        
        Returns:
            JSON string of presets.
        """
        presets = {}
        for name in list_presets():
            preset = get_preset(name)
            if preset:
                presets[name] = preset
        
        return json.dumps(presets, indent=2)
    
    def resource_history(self) -> str:
        """Get history as a resource.
        
        Returns:
            JSON string of history.
        """
        return json.dumps(self.history, indent=2)
    
    def resource_examples(self) -> str:
        """Get examples as a resource.
        
        Returns:
            JSON string of example information.
        """
        examples = []
        if EXAMPLES_DIR.exists():
            for img_file in EXAMPLES_DIR.glob("*.{png,jpg,jpeg}"):
                examples.append({
                    "filename": img_file.name,
                    "path": str(img_file),
                })
        
        return json.dumps(examples, indent=2)


def create_mcp_server(model_handler: ModelHandler) -> MCPServer:
    """Create an MCP server instance.
    
    Args:
        model_handler: Instance of ModelHandler.
        
    Returns:
        MCPServer instance.
    """
    return MCPServer(model_handler)