# audio_utils.py

from transformers import pipeline
from pydub import AudioSegment
import os
import uuid
import sounddevice as sd
from scipy.io.wavfile import write
import tempfile

# تحميل نموذج Whisper
whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base")

def convert_to_wav(audio_file):
    sound = AudioSegment.from_file(audio_file)
    temp_filename = f"temp_{uuid.uuid4()}.wav"
    sound.export(temp_filename, format="wav")
    return temp_filename

def transcribe_audio(audio_path):
    if not audio_path.endswith(".wav"):
        audio_path = convert_to_wav(audio_path)

    result = whisper_pipeline(audio_path)
    text = result['text']
    
    # يمكن حذف الملف المؤقت بعد النسخ
    if os.path.exists(audio_path):
        os.remove(audio_path)

    return text

def record_audio(duration=5, fs=16000):
    """يسجل صوت من المايك لمدة محددة"""
    recording = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='int16')
    sd.wait()

    temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
    write(temp_wav.name, fs, recording)
    return temp_wav.name