"""
Image Generation Tool - Generate images using AI
"""

import logging
import os
import tempfile
import base64
from typing import Any, Dict
from datetime import datetime
import httpx

logger = logging.getLogger(__name__)

IMAGE_GEN_TOOL_SPEC = {
    "name": "generate_image",
    "description": (
        "Generate images using AI based on text prompts. "
        "Use this to create illustrations, diagrams, artwork, or visual content. "
        "Supports various styles and can save images to disk."
    ),
    "parameters": {
        "type": "object",
        "properties": {
            "prompt": {
                "type": "string",
                "description": "Detailed description of the image to generate",
            },
            "filename": {
                "type": "string",
                "description": "Output filename for the image (e.g., 'image.png')",
            },
            "size": {
                "type": "string",
                "enum": ["256x256", "512x512", "1024x1024"],
                "description": "Image size (default: 512x512)",
            },
        },
        "required": ["prompt", "filename"],
    },
}


async def generate_image_handler(arguments: Dict[str, Any]) -> tuple[str, bool]:
    """Handler for generating images using Pollinations AI (free, no API key needed)."""
    try:
        prompt = arguments.get("prompt", "")
        filename = arguments.get("filename", "generated_image.png")
        size = arguments.get("size", "512x512")
        
        if not prompt.strip():
            return "Error: Image prompt is required", False
        
        # Ensure filename has proper extension
        if not any(filename.endswith(ext) for ext in [".png", ".jpg", ".jpeg"]):
            filename += ".png"
        
        logger.info(f"Generating image with prompt: {prompt[:100]}...")
        
        # Parse size
        width, height = map(int, size.split("x"))
        
        # Use Pollinations AI for image generation (free, no API key)
        encoded_prompt = prompt.replace(" ", "%20")
        image_url = f"https://image.pollinations.ai/prompt/{encoded_prompt}?width={width}&height={height}&nologo=true"
        
        # Download the image
        async with httpx.AsyncClient(timeout=60.0) as client:
            response = await client.get(image_url)
            response.raise_for_status()
            image_data = response.content
        
        # Save image
        output_dir = os.path.join(tempfile.gettempdir(), "kimi_agent_outputs")
        os.makedirs(output_dir, exist_ok=True)
        output_path = os.path.join(output_dir, filename)
        
        with open(output_path, "wb") as f:
            f.write(image_data)
        
        # Also save to current working directory
        with open(filename, "wb") as f:
            f.write(image_data)
        
        file_size = len(image_data)

        # Create file data for file_generated event
        file_content_b64 = base64.b64encode(image_data).decode()

        file_data = {
            "id": f"file_{datetime.now().strftime('%Y%m%d%H%M%S')}_{filename}",
            "name": filename,
            "type": "image",
            "content": file_content_b64,
            "language": None,
            "originalContent": None,
            "modifiedContent": file_content_b64,
            "size": file_size,
            "created_at": datetime.now().isoformat(),
        }
        
        return (
            f"✅ Image generated successfully!\n"
            f"🖼️ Filename: {filename}\n"
            f"📐 Size: {width}x{height}\n"
            f"💾 File size: {file_size / 1024:.2f} KB\n"
            f"📁 Saved to: {output_path}\n"
            f"🎨 Prompt: {prompt[:100]}{'...' if len(prompt) > 100 else ''}",
            True,
            file_data  # Return file data for event
        )
        
    except Exception as e:
        logger.error(f"Image generation error: {e}")
        return f"❌ Error generating image: {str(e)}", False