import logging
logging.basicConfig(level=logging.WARNING)

import os
from langchain.schema import AIMessage, HumanMessage, SystemMessage

from dotenv import load_dotenv
load_dotenv(verbose=True)
assert os.getenv('GROQ_MODEL') is not None
assert os.getenv('GROQ_WHISPER_MODEL') is not None

import gradio as gr
from gradio import ChatMessage

from correctiveRag import app

logger = logging.getLogger(__name__)        # Child logger for this module
logger.setLevel(logging.INFO)

# Groq - Audio transcription
from groq import Groq
transcription_client = Groq()


system_message = "You are a helpful assistant who provides consice answers to questions."

def chat_response(message: str, history: list[dict], state: dict):
    logger.debug(f"session_id = {state['session_id']}")
    config = {"configurable": { "thread_id": state['session_id'] }}

    if message is not None:
        history.append( ChatMessage(role='user', content=message) )
        response = app.invoke({"messages": [HumanMessage(content=message)]}, config)
        answer = ''.join(response['generation'])
        history.append(ChatMessage(role='assistant', content=answer))
        return "", history


def transcribe_audio(filename):
    print('filename', filename, os.getenv('GROQ_WHISPER_MODEL'))
    with open(filename, "rb") as audio_file:
        transcription = transcription_client.audio.transcriptions.create(
            file=(filename, audio_file.read()),
            model=os.getenv('GROQ_WHISPER_MODEL'),  # Required model to use for transcription
            prompt="Preguntas sobre estructuras de datos y algoritmos.",   # Optional
            response_format="json",                 # Optional
            language="es",                          # Optional
            temperature=0.0                         # Optional
        )
        return transcription.text



def voice_input(audio, history, state):
    transcription = transcribe_audio(audio)
    return chat_response(transcription, history, state)