import os
import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend

# ---------- LLM ----------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.7,
)

# ---------- Backend ----------
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# ---------- Sub‑agent for price generation ----------
# The sub‑agent simply asks the LLM to produce a realistic price table.
# It is wrapped in a tool so that the main agent can call it.

@tool
def get_price(product: str, city: str) -> str:
    """Return a realistic price for a product in a given city.
    The response must be a Markdown table with columns: Продукт, Цена (руб.), Магазин.
    """
    # Create a tiny agent that only generates the table.
    from langchain.agents import create_agent
    from langchain_core.messages import HumanMessage

    system_prompt = (
        "You are a market price generator. "
        "Given a product and a city, produce a realistic price table in Markdown. "
        "Use plausible Russian store names and prices."
    )
    sub_agent = create_agent(
        model=llm,
        tools=[],
        system_prompt=system_prompt,
    )
    prompt = f"Product: {product}\nCity: {city}"
    result = sub_agent.invoke({"messages": [HumanMessage(content=prompt)]})
    # The sub‑agent returns a dict with 'messages'; take the last content.
    return result["messages"][-1].content

# ---------- Main agent ----------
agent = create_deep_agent(
    model=llm,
    tools=[get_price],
    backend=backend,
    system_prompt="Ты помощник по планированию покупок.",
)

# ---------- Run ----------
async def main():
    user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_query)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    # Print all messages in order
    for msg in result["messages"]:
        if msg.content:
            print(msg.content)
        elif msg.tool_calls:
            for call in msg.tool_calls:
                print(f"{call['name']}({call['args']})")

if __name__ == "__main__":
    asyncio.run(main())