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 configuration (OpenRouter)
# -------------------------------------------------
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 for tool execution
# -------------------------------------------------
backend = CompositeBackend(
    [
        LocalShellBackend(workspace_dir="./workspace"),
        FilesystemBackend(),
    ]
)

# -------------------------------------------------
# Example tool (can be replaced with any real tool)
# -------------------------------------------------
@tool
def get_price(product: str, city: str) -> str:
    """
    Return a mock price table for the given product in the specified city.
    """
    # In a real scenario this could call an external API or run a script.
    return f"""| Продукт | Цена (руб.) | Город |
| {product} | 89 | {city} |
"""

# -------------------------------------------------
# Create the deep agent
# -------------------------------------------------
agent = create_deep_agent(
    model=llm,
    tools=[get_price],
    backend=backend,
    system_prompt="You are a helpful assistant that can call tools when needed.",
)

# -------------------------------------------------
# Helper functions for streaming output
# -------------------------------------------------
def format_message(message) -> str:
    """
    Convert a LangChain message to a printable string.
    If the message contains tool calls, format them as a function call.
    """
    if getattr(message, "content", None):
        return message.content
    # Tool call case
    if getattr(message, "tool_calls", None):
        tool_call = message.tool_calls[0]
        name = tool_call["name"]
        args = tool_call["args"]
        return f"{name}({args})"
    return ""

def format_chunk_message(chunk):
    """
    Print token fragments from 'messages' chunks.
    Insert a separator when the LangGraph step changes.
    """
    message, meta = chunk
    global current_step
    step = meta.get("langgraph_step", 0)
    if step != current_step:
        current_step = step
        print("\n--- --- ---\n")
    if getattr(message, "content", None):
        print(message.content, end="", flush=True)

# -------------------------------------------------
# Main async entry point
# -------------------------------------------------
async def main():
    user_query = "Сколько стоит молоко в Казани?"
    stream = agent.stream(
        {"messages": [HumanMessage(content=user_query)]},
        stream_mode=["messages", "updates"],
    )

    global current_step
    current_step = -1  # initialize step counter

    for chunk_type, chunk_data in stream:
        if chunk_type == "messages":
            format_chunk_message(chunk_data)
        elif chunk_type == "updates":
            # When a model update contains a finished message, print it nicely
            model_info = chunk_data.get("model")
            if model_info and "messages" in model_info:
                last_msg = model_info["messages"][-1]
                print("\n" + format_message(last_msg))
    # Ensure final newline
    print()

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