from utils.central_logging import get_logger
from langchain_openai import OpenAIEmbeddings
from pathlib import Path
import whisper
import threading
import os 


logger =  get_logger("whisper")

_whisper_model = None
_lock = threading.Lock()
_embedding = None
_embedding_lock = threading.Lock()

def get_whisper():
    global _whisper_model 

    if _whisper_model is  None:
        with _lock:
            if _whisper_model is None:
                _whisper_model = whisper.load_model("base")
                logger.info("Whisper model has been loaded")
    return _whisper_model

def get_embedding():
    global _embedding

    if _embedding is None:
        with _embedding_lock:
            if _embedding is None:
                _embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
                logger.info("Openai  embedding has been initialized")
    return _embedding


def transcribe_content(url_path:str) -> str:
    safe_path = Path(url_path).resolve().as_posix()
    model = get_whisper()
    result = model.transcribe(url_path)
    return  result["text"]


def save_file(file_name,result):
    with open(file_name,'w') as file:
        file.write(result)