"""
from typing import Annotated, TypedDict

from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

from retriever import guest_info_tool
from tools import (absolute, add, divide, exponential, floor_divide,
                   get_current_time_in_timezone, logarithm, modulus, multiply,
                   power, roman_calculator_converter, square_root, subtract,
                   web_search)

# Generate the chat interface, including the tools
llm = HuggingFaceEndpoint(
    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
)

chat = ChatHuggingFace(llm=llm, verbose=True)
tools = [
    multiply,
    add,
    subtract,
    power,
    divide,
    modulus,
    square_root,
    floor_divide,
    absolute,
    logarithm,
    exponential,
    web_search,
    roman_calculator_converter,
    get_current_time_in_timezone,
]

chat_with_tools = chat.bind_tools(tools)

# Generate the AgentState and Agent graph
class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

def assistant(state: AgentState):
    return {
        "messages": [chat_with_tools.invoke(state["messages"])],
    }

## The graph
builder = StateGraph(AgentState)

# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))

# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    # If the latest message requires a tool, route to tools
    # Otherwise, provide a direct response
    tools_condition,
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()

"""