from fastapi import FastAPI
from pydantic import BaseModel
from src.models.openai_llm import run_query
from src.models.openai_stt import speech_to_text
from src.models.openai_tts import text_to_speech
import yaml
from openai import OpenAI
import uvicorn
from fastapi import FastAPI, File, UploadFile
import io
import aiofiles

app = FastAPI()

# read LLM config file
with open("conf/train_llm.yaml", "r") as file_in:
    cfg = yaml.safe_load(file_in)

# read system message
with open("data/system_template.txt", "r") as file_in:
    system_message = file_in.read()

# read STT config file
with open("conf/speech_to_text.yaml", "r") as file_in:
    cfg_stt = yaml.safe_load(file_in)

# init client
openai_client = OpenAI()


@app.get("/")
def root():
    return "welcome"


@app.post("/llm_query/")
def llm_query(llm_query: str):
    res = run_query(
        query=llm_query,
        openai_params=cfg["openai_parameters"],
        system_message=system_message,
        client=openai_client,
    )
    return res


@app.post("/stt_query/")
def stt_query(audio_file: UploadFile):
    contents = audio_file.file.read()
    buffer = io.BytesIO(contents)
    buffer.name = "file.mp3"
    return speech_to_text(
        audio=buffer,
        openai_client=openai_client,
        configuration=cfg_stt["openai_parameters"],
    )


@app.post("/tts_query/")
def tts_query(input_text: str):
    output_path = text_to_speech(client=openai_client, input=input_text)
    return output_path