import gradio as gr
from backend import backend_chat
from langchain_core.messages import HumanMessage, BaseMessage, AIMessage


def respond(message: str, history: list[dict]):
    """
    message: the latest user input
    history: openai-style list of dicts with keys 'role' and 'content'
    """

    backend_history = []
    for msg in history:
        if msg["role"] == "user":
            backend_history.append(HumanMessage(content=msg["content"]))
        elif msg["role"] == "assistant":
            backend_history.append(AIMessage(content=msg["content"]))
   
    updated_backend_history = backend_chat(backend_history, message)
    latest_assistant = None
    for msg in reversed(updated_backend_history):
        if not isinstance(msg, HumanMessage):
            latest_assistant = msg
            break

    if latest_assistant is None:
        return "Sorry, I couldn't generate a response."
    return latest_assistant.content

def main():
    demo = gr.ChatInterface(
        fn=respond,
        type="messages",
        title="ChatBot",
        description="Chatbot using langgraph backend and Memory Checkpointing",
    )
    demo.launch()

if __name__ == "__main__":
    main()