import gradio as gr
from huggingface_hub import InferenceClient

from agent.prompts import production_prompt
from agent.graph import graph
from langchain_core.messages import HumanMessage, SystemMessage


def respond(
    message,
    history: list[tuple[str, str]],
    #system_message,
    #max_tokens,
    #temperature,
    #top_p,
):
    
    messages = [{"role": "system", "content": production_prompt}]
    #messages = [
    #    SystemMessage(production_prompt),
    #    HumanMessage(message)
    #]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    #if len(message["files"]) > 0:
    #    pass
    #else:
    #messages.append({"role": "user", "content": message["text"]})
    messages.append({"role": "user", "content": message})

    print(messages)

    # Remember to change the filename when applying multimodality
    response = graph.invoke(
            input={'messages' : messages, 'filename' : "", 'file_extension' : ""},
            config={'recursion_limit' : 10}
        )
    
    return response["messages"][-1].content

    """
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response
    """

demo = gr.ChatInterface(
    respond,
    #examples = ["What is the capital of Letonia?", "How much people live in Paradis?"],
    #multimodal = True,
    #additional_inputs=[
    #    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()