import gradio as gr

from loguru import logger
from pydantic import BaseModel
from ast import literal_eval

from allofresh_chatbot import AllofreshChatbot
from utils import cut_dialogue_history
from prompts.mod_prompt import FALLBACK_MESSAGE

allo_chatbot = AllofreshChatbot(debug=True)

class Message(BaseModel):
    role: str
    content: str

def fetch_messages(history):
    """
    Fetch the messages from the chat history.
    """
    return [(history[i]["content"], history[i+1]["content"]) for i in range(0, len(history)-1, 2)]
    
def preproc_history(history):
    """
    Clean the chat history to remove the None values.
    """
    clean_history = [Message(**msg) for msg in history if msg["content"] is not None]
    return cut_dialogue_history(str(clean_history))

def user_input(input, history):
    """
    Add the user input to the chat history.
    """
    history.append({'role': 'user', 'content': input})
    history.append({'role': 'assistant', 'content': None})

    return fetch_messages(history), history

def predict_answer(input, history):
    """
    Answering component
    """
    answer = allo_chatbot.answer_optim_v2(input, preproc_history(history))

    history.append({'role': 'user', 'content': None})
    history.append({'role': 'assistant', 'content': answer})

    return fetch_messages(history), history

def predict_reco(history):
    """
    Reco component
    """
    if history[-1]["content"] != FALLBACK_MESSAGE:
        reco = allo_chatbot.reco_optim_v1(preproc_history(history))

        history.append({'role': 'user', 'content': None})
        history.append({'role': 'assistant', 'content': reco})

    return fetch_messages(history), history

"""
Gradio Blocks low-level API that allows to create custom web applications (here our chat app)
"""
with gr.Blocks() as app:
    logger.info("Starting app...")
    chatbot = gr.Chatbot(label="Allofresh Assistant")
    state = gr.State([])
    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Enter text, then press enter").style(container=False)
    txt.submit(
        user_input, [txt, state], [chatbot, state]
    ).success(
        predict_answer, [txt, state], [chatbot, state]
    ).success(
        predict_reco, [state], [chatbot, state]
    )

app.queue(concurrency_count=4)
app.launch()