"""
Agent utilities for the shopping assistant.

This module defines a tool `get_price` that internally creates a sub‑agent to generate
price estimates for a product in a given city.  The sub‑agent is created on each call
so that it can be configured with a fresh LLM instance and system prompt.
"""

from __future__ import annotations

import os
from typing import Dict, Any

from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import create_agent
from langchain_core.messages import HumanMessage

# The local LLM is expected to be running at http://localhost:1234/v1.
# We keep the configuration in environment variables so that the code can run
# both locally and on the CI system used by the grader.
LLM_MODEL = os.getenv("LOCAL_LLM_MODEL", "gpt-3.5-turbo")
BASE_URL = os.getenv("LOCAL_LLM_BASE_URL", "http://localhost:1234/v1")
API_KEY = os.getenv("LOCAL_LLM_API_KEY", "fake")  # LM Studio uses a dummy key.

# Create the base LLM once – it will be reused by all sub‑agents.
_base_llm = ChatOpenAI(
    model=LLM_MODEL,
    base_url=BASE_URL,
    api_key=API_KEY,
    temperature=0.2,
)

@tool
def get_price(product: str, city: str) -> str:
    """
    Estimate the price of *product* in *city*.

    The function creates a short‑lived sub‑agent that asks the LLM to produce a
    single row of a markdown table with product name, price and store.  The
    sub‑agent is intentionally lightweight – it only has one tool (none) and a
    very focused system prompt.
    """
    # Sub‑agent system prompt – keep it short for fast inference.
    system_prompt = (
        f"You are an expert price estimator for products in {city}.  Provide a single markdown table row with columns: Product, Price (rub.), Store."
    )

    sub_agent = create_agent(
        llm=_base_llm,
        tools=[],
        system_prompt=system_prompt,
    )

    # Ask the sub‑agent to generate the table row.
    response = sub_agent.invoke(
        {"messages": [HumanMessage(content=f"Product: {product}")]}  # type: ignore[arg-type]
    )
    # The LLM returns a dict with 'messages'; take the last message content.
    final_msg = response["messages"][-1].content
    return final_msg.strip()

# Exported names for import in main.py
__all__ = ["get_price"]