import os
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import create_agent
from pydantic import SecretStr

# 1. Connect to local LLM
llm = ChatOpenAI(
    model="<название модели в LM Studio>",
    base_url="http://localhost:1234/v1",
    api_key=SecretStr("fake"),
    temperature=0.7,
)

# 2. Sub-agent tool to get price
@tool
def get_price(product: str, city: str) -> str:
    """Return a realistic price table for a product in a city."""
    # Sub-agent that generates a price table
    sub_llm = ChatOpenAI(
        model="<название модели в LM Studio>",
        base_url="http://localhost:1234/v1",
        api_key=SecretStr("fake"),
        temperature=0.7,
    )
    sub_agent = create_agent(
        model=sub_llm,
        tools=[],
        system_prompt=f"You are a price estimator for {city}. Provide a table with columns: Продукт, Цена (руб.), Магазин. Use realistic Russian prices.",
    )
    prompt = f"Generate a price table for product '{product}' in city '{city}'."
    result = sub_agent.invoke({"messages": [{"role": "human", "content": prompt}]} )
    # Extract the last message content
    return result["messages"][-1]["content"]

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

# 4. Query
question = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
response = main_agent.invoke({"messages": [{"role": "human", "content": question}]})

# 5. Print all messages
for msg in response["messages"]:
    if "content" in msg:
        print(msg["content"])
    elif "tool_calls" in msg:
        for call in msg["tool_calls"]:
            print(f"{call['name']}({call['args']})")
    else:
        print(msg)

if __name__ == "__main__":
    pass