"""
FoodWise Inventory Agent using LangGraph's prebuilt React Agent.

This module creates and configures the inventory management agent using
LangGraph's create_react_agent() function with Notion-based tools.
"""
import os
from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langsmith import Client

from .tools import get_inventory_tools
from .prompts import INVENTORY_AGENT_PROMPT


def create_inventory_agent(
    model_name: str = "gpt-4.1",
    temperature: float = 0.1,
    enable_tracing: bool = True
) -> Any:
    """
    Creates and configures the FoodWise inventory agent using LangGraph's prebuilt React agent.

    This function initializes the language model, loads the inventory management tools,
    and sets up the agent for use with the FoodWise Notion database. Optionally enables
    LangSmith tracing for debugging and monitoring.

    Args:
        model_name: The LLM model to use (default: gpt-4o-mini)
        temperature: LLM temperature setting (default: 0.1 for consistency)
        enable_tracing: Whether to enable LangSmith tracing
        
    Returns:
        Any: A configured LangGraph React agent instance ready for execution.
    """
    # Initialize LLM
    llm = ChatOpenAI(
        model=model_name,
        temperature=temperature
    )

    # Load inventory management tools
    tools = get_inventory_tools()
    
    # Create the React agent with tools (system prompt handled in run_agent_query)
    agent = create_react_agent(
        model=llm,
        tools=tools
    )

    # Optionally enable LangSmith tracing for observability
    if enable_tracing and os.getenv("LANGCHAIN_API_KEY"):
        os.environ["LANGCHAIN_TRACING_V2"] = "true"
        os.environ["LANGCHAIN_PROJECT"] = "FoodWise-Inventory-Agent"

    return agent


def run_agent_query(query: str, agent: Any = None) -> Dict[str, Any]:
    """
    Execute a single query against the inventory agent.
    
    Args:
        query: User query/message
        agent: Pre-configured agent (will create one if None)
        
    Returns:
        Agent response including messages and any tool results
    """
    if agent is None:
        agent = create_inventory_agent()
    
    # Execute the agent with system prompt and user query
    result = agent.invoke({
        "messages": [
            ("system", INVENTORY_AGENT_PROMPT),
            ("user", query)
        ]
    })
    
    return result


if __name__ == "__main__":
    # Quick test of the agent
    test_agent = create_inventory_agent()
    response = run_agent_query("What items are expiring soon?", test_agent)
    print("Agent Response:", response)