import gradio as gr
from langchain.chat_models import init_chat_model
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

model = init_chat_model("gemini-2.0-flash", model_provider="google_genai")


def respond(
    user_input: str,
    dialog_history: list[dict],
    system_message: str,
    max_new_tokens: int,
    temperature: float,
    top_p: float,
) -> str:
    """
    Respond to user input using the model.
    """
    # Set the model parameters
    model.temperature = temperature
    model.max_output_tokens = max_new_tokens
    model.top_p = top_p

    history_langchain_format = []
    # Add the dialog history to the history
    for msg in dialog_history:
        if msg['role'] == "user":
            history_langchain_format.append(
                HumanMessage(content=msg['content']))
        elif msg['role'] == "assistant":
            history_langchain_format.append(AIMessage(content=msg['content']))

    # Combine the system message, history, and user input into a single list
    model_input = [SystemMessage(content=system_message)] + \
        history_langchain_format + [HumanMessage(content=user_input)]

    response = model.invoke(model_input)
    return response.content


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    fn=respond,
    type="messages",
    # save_history=True,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.",
                   label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512,
                  step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7,
                  step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()