"""
Base components for the tools package: LLM dispatcher and central registry.
"""

import logging
import json
from langchain_core.tools import Tool, StructuredTool
from langchain_google_genai import ChatGoogleGenerativeAI
try:
    from ..config import LOG_LEVEL, GEMINI_API_KEY, OPENROUTER_API_KEY, DEFAULT_MODEL
except (ImportError, ValueError):
    from config import LOG_LEVEL, GEMINI_API_KEY, OPENROUTER_API_KEY, DEFAULT_MODEL
from langchain_openai import ChatOpenAI

logger = logging.getLogger(__name__)

# Global registry for tools
# Format: { "name": { "fn": callable, "description": str, "parameters": dict } }
TOOLS = {}

def register_tool(name: str, description: str, parameters: list):
    """Decorator to register a tool.
    parameters should be a list of dicts: [{"name": "...", "type": "...", "description": "...", "required": True/False}]
    """
    def decorator(func):
        TOOLS[name] = {
            "fn": func,
            "description": description,
            "parameters": parameters,
        }
        return func
    return decorator

def get_llm():
    """Initialize and return LLM (Prioritizing Gemini, then OpenRouter)."""
    if GEMINI_API_KEY:
        logger.info(f"Initializing Google Gemini LLM ({DEFAULT_MODEL})...")
        return ChatGoogleGenerativeAI(
            model=DEFAULT_MODEL,
            google_api_key=GEMINI_API_KEY,
            temperature=0.1,
            max_retries=5,
            timeout=60
        )
    
    if OPENROUTER_API_KEY:
        logger.info(f"Initializing OpenRouter LLM: {DEFAULT_MODEL}")
        return ChatOpenAI(
            model=DEFAULT_MODEL,
            openai_api_key=OPENROUTER_API_KEY,
            openai_api_base="https://openrouter.ai/api/v1",
            temperature=0.1,
            max_tokens=2048,
            max_retries=3,
            request_timeout=60
        )
        
    raise ValueError("Neither GEMINI_API_KEY nor OPENROUTER_API_KEY is configured. Check your .env file.")

def get_tool_schemas() -> list[dict]:
    """Return tool schemas in Anthropic API format."""
    schemas = []
    for name, tool in TOOLS.items():
        properties = {}
        required = []
        for p in tool["parameters"]:
            properties[p["name"]] = {"type": p["type"], "description": p.get("description", p["name"])}
            if p.get("required", True):
                required.append(p["name"])
                
        schemas.append({
            "name": name,
            "description": tool["description"],
            "input_schema": {
                "type": "object",
                "properties": properties,
                "required": required,
            },
        })
    return schemas

def execute_tool(name: str, args: dict) -> str:
    """Execute a tool by name and return result as string."""
    tool = TOOLS.get(name)
    if not tool:
        return f"Tool '{name}' does not exist"
    
    try:
        # Check if args is a string (sometimes LLMs pass JSON string instead of dict)
        if isinstance(args, str):
            try:
                args = json.loads(args)
            except:
                # If it's a pure string, try to map it to the first parameter of the tool
                # Only if the tool has at least one parameter
                if tool["parameters"]:
                    args = {tool["parameters"][0]["name"]: args}
                else:
                    args = {}

        if not isinstance(args, dict):
            return f"Error: Tool arguments must be a mapping (dict), got {type(args).__name__}"

        result = tool["fn"](**args)
        if isinstance(result, (dict, list)):
            return json.dumps(result, ensure_ascii=False, indent=2)
        return str(result)
    except Exception as e:
        logger.error(f"Error executing tool {name}: {e}")
        return f"Error: {e}"

def get_langchain_tools() -> list[StructuredTool]:
    """Return tools in LangChain-native StructuredTool objects for better multi-arg support."""
    lc_tools = []
    for name, tool in TOOLS.items():
        lc_tools.append(
            StructuredTool.from_function(
                func=tool["fn"],
                name=name,
                description=tool["description"]
            )
        )
    return lc_tools