import os
import yaml
import json
import logging
from typing import List, Dict, Any
from app.config import settings

logger = logging.getLogger(__name__)

class FileSystemSkillLoader:
    def __init__(self, base_dir: str):
        self.base_dir = base_dir

    def _ensure_metadata(self, folder_path: str, skill_name: str, is_system: bool = False):
        """
        Auto-generates a .metadata.json file if it's missing.
        Assigns 'admin' as default owner if it's auto-generated during boot.
        """
        meta_path = os.path.join(folder_path, ".metadata.json")
        if not os.path.exists(meta_path):
            default_metadata = {
                "owner_id": "admin",
                "is_system": is_system,
                "extra_metadata": {"emoji": "🛠️"}
            }
            try:
                with open(meta_path, "w") as f:
                    json.dump(default_metadata, f, indent=4)
                logger.info(f"Generated default .metadata.json for skill '{skill_name}'")
            except Exception as e:
                logger.error(f"Failed to generate metadata for {skill_name}: {e}")
            return default_metadata
            
        try:
            with open(meta_path, "r") as f:
                return json.load(f)
        except Exception as e:
            logger.error(f"Error reading {meta_path}: {e}")
            return {}

    def get_all_skills(self) -> List[Dict[str, Any]]:
        """
        Recursively walks the base direction and parses all SKILL.md rules 
        into the common dictionary schema used by the orchestrator.
        """
        skills = []
        if not os.path.exists(self.base_dir):
            return skills

        # Level 1 directories define Features (e.g., /data/skills/swarm_control)
        for feature in os.listdir(self.base_dir):
            feature_path = os.path.join(self.base_dir, feature)
            if not os.path.isdir(feature_path):
                continue
                
            # Level 2 directories define the specific Skills
            for skill_id in os.listdir(feature_path):
                skill_path = os.path.join(feature_path, skill_id)
                if not os.path.isdir(skill_path):
                    continue
                    
                skill_md_path = os.path.join(skill_path, "SKILL.md")
                if not os.path.exists(skill_md_path):
                    continue

                try:
                    with open(skill_md_path, "r", encoding='utf-8') as f:
                        skill_content = f.read()

                    # Create virtual file schema
                    vfs_files = []
                    for root, _, files in os.walk(skill_path):
                        for file in files:
                            # Hide .metadata.json and macOS .DS_Store from the VFS visible to LLMs
                            if file == ".metadata.json" or file.startswith('.'):
                                continue
                            file_abs = os.path.join(root, file)
                            file_rel = os.path.relpath(file_abs, skill_path).replace('\\', '/')
                            try:
                                with open(file_abs, "r", encoding='utf-8') as ff:
                                    vfs_files.append({"file_path": file_rel, "content": ff.read()})
                            except Exception:
                                pass # skip binary or unreadable
                                
                    # Extract or Generate Metadata
                    metadata = self._ensure_metadata(skill_path, skill_id, is_system=False)

                    # Extract Description directly from SKILL.md (Fallback or yaml frontmatter)
                    skill_name = skill_id
                    skill_desc = ""
                    
                    if skill_content.startswith("---"):
                        parts = skill_content.split("---", 2)
                        if len(parts) >= 3:
                            frontmatter = yaml.safe_load(parts[1]) or {}
                            skill_name = frontmatter.get("name", skill_name)
                            skill_desc = frontmatter.get("description", "")
                    
                    # Generate the legacy internal dict schema
                    skill_def = {
                        "id": f"fs-{skill_id}", 
                        "name": skill_name,
                        "description": skill_desc or f"Skill loaded dynamically from {skill_id}",
                        "skill_type": "local",
                        "is_enabled": True,
                        "features": [feature], # Sourced directly from folder hierarchy
                        "is_system": metadata.get("is_system", False),
                        "owner_id": metadata.get("owner_id", "admin"),
                        "extra_metadata": metadata.get("extra_metadata", {"emoji": "🛠️"}),
                        "files": vfs_files
                    }
                    skills.append(skill_def)
                    
                except Exception as e:
                    logger.error(f"Error parsing FS Skill at {skill_path}: {e}")

        return skills

# Create a global instance initialized with the data directory config
fs_loader = FileSystemSkillLoader(base_dir=os.path.join(settings.DATA_DIR, "skills"))