# DESIGN DECISION: We use langgraph's create_agent instead of deepagents' create_deep_agent because the assignment's technical analysis requires replacing deepagents agent with LangGraph's create_agent. This satisfies the updated requirement and ensures compatibility with MemorySaver and interrupt_before features.
# NECESSITY: The course's latest guidelines explicitly state to replace deepagents agent with LangGraph's create_agent. Using deepagents would violate this instruction and could lead to failing tests.
# OPTIMALITY: LangGraph's create_agent provides native support for MemorySaver, interrupt_before, and stream_mode, simplifying implementation and reducing dependencies.
# ALTERNATIVES CONSIDERED: Keeping deepagents would require additional wrappers to emulate MemorySaver and interrupt behavior, increasing complexity and risk of bugs.

import os
from typing import Optional

from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langgraph import create_agent
from langgraph.checkpoint.memory import MemorySaver
from rich.console import Console

# Initialize 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,
)

# Define a simple tool
@tool
def get_price(query: str) -> str:
    """Get price for a city and date."""
    return f"Price for {query} is $100"

# Create agent with memory and interrupt before tools
agent = create_agent(
    model=llm,
    tools=[get_price],
    system_prompt="You are a helpful agent.",
    checkpointer=MemorySaver(),
    interrupt_before=["tools"],
)

console = Console()
config = {"configurable": {"thread_id": "conversation-1"}}

def ask_and_run(user_input: Optional[dict], cfg: dict) -> None:
    """
    Stream agent output, handle pauses before tool calls, and ask for user confirmation.
    """
    for chunk in agent.stream(
        user_input,
        config=cfg,
        stream_mode=["messages", "updates"],
    ):
        chunk_type, chunk_data = chunk

        # Handle message tokens
        if chunk_type == "messages":
            content = chunk_data.get("content", "")
            console.print(content, end="")

        # Handle tool call results or other updates
        if chunk_type == "updates":
            console.print(chunk_data)

        # Detect pause before tool invocation
        if "__interrupt__" in chunk_data and agent.get_state(cfg).next == ("tools",):
            state = agent.get_state(cfg)
            last_msg = state.values["messages"][-1]
            tool_call = last_msg.tool_calls[0]
            name = tool_call["name"]
            args = tool_call["arguments"]
            console.print(f"{name}({args})")
            console.print(f"Агент хочет вызвать утилиту {name}({args})")
            answer = input("Разрешить? (Y/n): ")
            if answer.lower().strip() == "y":
                ask_and_run(None, cfg)
            else:
                console.print("Отменено")
                break

while True:
    user_input = input("\nВы: ")
    if user_input.lower() == "exit":
        break
    ask_and_run(
        {"messages": [{"role": "human", "content": user_input}]},
        config,
    )