import os
from IPython.display import Image, display
from langchain_core.messages import SystemMessage
from langchain_openai import AzureChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import  tools_condition
from .state import State
from .custom_tool_node import CustomToolNode
from .tools import get_avaiable_tools


class CustomAgent:
    def __init__(self):
        print("CustomAgent initialized.")
        self.graph = build_graph()  # Build the state graph for the agent

    def __call__(self, question: str, task_id: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        system_prompt = SystemMessage(content=get_prompt())
        messages = self.graph.invoke({
            "messages": [
                system_prompt,
                {"role": "user", "content": question}
            ],
        "task_id": task_id
    })
        answer = messages['messages'][-1].content
        return answer[14:]


def get_prompt() -> str:
    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        return f.read()


def build_graph():
    """Builds the state graph for the React agent."""
    # Initialize our LLM
    llm = AzureChatOpenAI(
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),  # Corrected variable name
        openai_api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
        deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT"),  # Corrected variable name
        openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
        temperature=0.0,
    )
    avaiable_tools = get_avaiable_tools()
    llm_with_tools = llm.bind_tools(avaiable_tools)


    def assistant(state: State):
        """Assistant node"""
        response = llm_with_tools.invoke(state["messages"])
        if response.content == '':
            messages = [response]  # tool calling message
        else:
            final_message = response.content
            # final_message += f"\n\nTask ID: {state['task_id']}"
            messages = [final_message]
        return {"messages": messages, 
                "task_id": state["task_id"]
                }
    
    # Initialize the state graph
    graph_builder = StateGraph(State)

    # Add nodes
    # graph_builder.add_node("check_question_reversed", is_question_reversed)
    # graph_builder.add_node("reverse_text", reverse_text)
    graph_builder.add_node("assistant", assistant)
    tools_dict = {tool.name: tool for tool in avaiable_tools}

    graph_builder.add_node("tools", CustomToolNode(tools_dict))

    # graph_builder.add_edge(START, "check_question_reversed")
    # graph_builder.add_conditional_edges(
    #     "check_question_reversed",
    #     route_question,
    #     {
    #         "question_reversed": "reverse_text",
    #         "question_not_reversed": "assistant"
    #     }
    # )
    # graph_builder.add_edge("reverse_text", "assistant")
    graph_builder.add_edge(START, "assistant")
    graph_builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    graph_builder.add_edge("tools", "assistant")
    graph_builder.add_edge("assistant", END)
    return graph_builder.compile()

if __name__ == "__main__":
    # Build the graph
    react_graph = build_graph()

    # Display the graph visualization
    # graph = react_graph.get_graph(xray=True)
    # display(Image(graph.draw_mermaid_png(output_file_path='graph.png')))

    # Example question to test the agent
    # question = "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia."
    # question = ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI"
    # question = "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?"
    #question = """Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\""""
    # question = """Hi, I was out sick from my classes on Friday, so I'm trying to figure out what I need to study for my Calculus mid-term next week. My friend from class sent me an audio recording of Professor Willowbrook giving out the recommended reading for the test, but my headphones are broken :(\n\nCould you please listen to the recording for me and tell me the page numbers I'm supposed to go over? I've attached a file called Homework.mp3 that has the recording. Please provide just the page numbers as a comma-delimited list. And please provide the list in ascending order."""
    # question = """The attached Excel file contains the sales of menu items for a local fast-food chain. What were the total sales that the chain made from food (not including drinks)? Express your answer in USD with two decimal places."""
    question = """What is the final numeric output from the attached Python code?"""
    task_id = "f918266a-b3e0-4914-865d-4faa564f1aef"
    system_prompt = SystemMessage(content=get_prompt())
    messages = react_graph.invoke({
        "messages": [
            system_prompt,
            {"role": "user", "content": question}
        ],
        "task_id": task_id
    })
    for m in messages["messages"]:
        m.pretty_print()

    answer = messages['messages'][-1].content
    print(f"Final Answer: {answer[14:]}")
    # Stream the response from the agent
    # events = react_graph.stream(
    #     {"messages": [("user", question)]},
    #     config={"configurable": {"return_intermediate_steps": True}},
    #     stream_mode="values"
    # )
    
    # for event in events:
    #     print(event)  # Replace `_print_event(event, _printed)` with direct printing
    #     print("----\n---")