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 FilesystemBackend, LocalShellBackend, CompositeBackend

# --- LLM ------------------------------------------------------------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

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

# --- Tool -----------------------------------------------------------
@tool
def get_price(query: str) -> str:
    """Return a fake price table for a product in a city."""
    # In a real task this would call an API or database
    if "молоко" in query:
        return "| Продукт | Цена (руб.) | Магазин |\n| Молоко | 89 | Магнит |"
    if "хлеб" in query:
        return "| Продукт | Цена (руб.) | Магазин |\n| Хлеб | 35 | Перекресток |"
    return "No data found."

# --- Agent ----------------------------------------------------------
agent = create_deep_agent(
    model=llm,
    tools=[get_price],
    backend=backend,
    system_prompt="You are a helpful agent that can fetch product prices.",
)

# --- Stream handling -----------------------------------------------
async def main():
    # Prepare the initial message
    human_msg = HumanMessage(content="Какую цену у молока в Казани?")

    # Start streaming
    stream = agent.stream(
        {"messages": [human_msg]},
        stream_mode=["messages", "updates"],
    )

    step = 1

    def format_chunk_message(chunk):
        nonlocal step
        message, meta = chunk
        if meta.get("langgraph_step") != step:
            step = meta.get("langgraph_step")
            print("\n --- --- --- \n")
        if message.content:
            print(message.content, end="", flush=True)

    def format_message(message):
        if message.content:
            return message.content
        # If the message is a tool call, format it nicely
        if message.tool_calls:
            call = message.tool_calls[0]
            return f"{call['name']}({call['args']})"
        return ""

    for chunk in stream:
        chunk_type, chunk_data = chunk
        if chunk_type == "messages":
            format_chunk_message(chunk_data)
        elif chunk_type == "updates":
            if chunk_data.get("model"):
                last_msg = chunk_data["model"]["messages"][-1]
                print(format_message(last_msg))

    print("\n--- Stream finished ---")

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