"""
Simple hierarchical LangChain agent for shopping list.
"""

from langchain_openai import ChatOpenAI
from pydantic import SecretStr
from langchain.tools import tool
from langchain.agents import create_agent, AgentExecutor
import json

# LLM configuration – replace <model_name> with your LM Studio model name
llm = ChatOpenAI(
    model="<model_name>",
    base_url="http://localhost:1234/v1",
    api_key=SecretStr("fake"),
    temperature=0.7,
)

# Sub‑agent that generates a price table for one product in a city
@tool(name="get_price", description="Return realistic price for a product in a city as a markdown table.")
def get_price(product: str, city: str) -> str:
    """
    Generates a markdown table with columns: Product | Price (руб.) | Store.
    The sub‑agent uses the same LLM to produce realistic values.
    """
    # Create a tiny agent that only returns the price table
    prompt = (
        f"You are a local market assistant. Provide a markdown table with columns:\n"
        f"| Продукт | Цена (руб.) | Магазин |\n"
        f"For product '{product}' in city '{city}'. Use realistic Russian prices and store names.")
    sub_agent = create_agent(
        model=llm,
        tools=[],
        system_prompt=prompt,
    )
    result = sub_agent.invoke({"messages": [{"role": "human", "content": "Generate table"}]})
    return result["messages"][-1]["content"]

# Main agent with get_price tool
main_agent = create_agent(
    model=llm,
    tools=[get_price],
    system_prompt="Ты помощник по планированию покупок.",
)

if __name__ == "__main__":
    user_query = (
        "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
    )
    response = main_agent.invoke({"messages": [{"role": "human", "content": user_query}]})
    # Print all messages
    for msg in response["messages"]:
        if msg.get("content"):
            print(msg["content"])
        elif msg.get("tool_calls"):
            call = msg["tool_calls"][0]
            print(f"{call['name']}({json.dumps(call['args'])})")
    # Final answer
    final = response["messages"][-1]["content"]
    print("\n---\n", final)