from __future__ import annotations

from typing import TYPE_CHECKING, AsyncIterator

import gradio as gr
from langchain.chat_models import init_chat_model
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langgraph.prebuilt import create_react_agent

if TYPE_CHECKING:
    from langchain_core.language_models.chat_models import BaseChatModel
    from langgraph.graph.graph import CompiledGraph

MESSAGE_TYPE = BaseMessage | gr.ChatMessage | dict[str, str]


def create_agent(
    model_name: str, provider: str, api_key: str, tools: list
) -> CompiledGraph:
    """Create a React agent with the specified model."""
    model = _create_model(model_name, provider, api_key)
    return create_react_agent(
        model,
        tools=tools,
    )


async def call_agent(
    agent: CompiledGraph, messages: list[MESSAGE_TYPE], prompt: HumanMessage
) -> AsyncIterator[list[MESSAGE_TYPE]]:
    async for chunk in agent.astream(
        {
            "messages": [_convert_to_langchain_message(msg) for msg in messages[:-1]]
            + [prompt]
        }
    ):
        if "tools" in chunk:
            for step in chunk["tools"]["messages"]:
                messages.append(
                    gr.ChatMessage(
                        role="assistant",
                        content=step.content,
                        metadata={"title": f"🛠️ Used tool {step.name}"},
                    )
                )
                yield messages
        if "agent" in chunk:
            messages.append(
                gr.ChatMessage(
                    role="assistant",
                    content=_get_chunk_message_content(chunk),
                )
            )
            yield messages


def _create_model(model_name: str, provider: str, api_key: str) -> BaseChatModel:
    """Get the chat model based on the provider and model name."""
    if provider == "Anthropic":
        return init_chat_model(
            "anthropic:" + model_name,
            anthropic_api_key=api_key,
        )
    elif provider == "Mistral":
        return init_chat_model(
            "mistralai:" + model_name,
            mistral_api_key=api_key,
        )
    elif provider == "OpenAI":
        return init_chat_model(
            "openai:" + model_name,
            openai_api_key=api_key,
        )
    else:
        raise ValueError(f"Unsupported model provider: {provider}")


def _is_ai_message(message: MESSAGE_TYPE) -> bool:
    if isinstance(message, AIMessage):
        return True
    if isinstance(message, gr.ChatMessage):
        return message.role == "assistant"
    if isinstance(message, dict):
        return message.get("role") == "assistant"
    return False


def _convert_to_langchain_message(message: MESSAGE_TYPE) -> BaseMessage:
    if isinstance(message, BaseMessage):
        return message
    if isinstance(message, gr.ChatMessage):
        return (
            AIMessage(content=message.content)
            if _is_ai_message(message)
            else HumanMessage(content=message.content)
        )
    if isinstance(message, dict):
        return (
            AIMessage(content=message.get("content", ""))
            if _is_ai_message(message)
            else HumanMessage(content=message.get("content", ""))
        )
    raise ValueError(f"Unsupported message type: {type(message)}")


def _get_chunk_message_content(chunk: dict) -> str:
    msg_object = chunk["agent"]["messages"][0]
    message = msg_object.content
    if isinstance(message, list):
        message = message[0] if message else ""
    if isinstance(message, dict):
        message = message.get("text")
    return message or "Calling tool(s)"