# backend.py
from transformers import pipeline
import torch

class AudioTranscriber:
    def __init__(self, model_name="openai/whisper-small"):
        self.device = 0 if torch.cuda.is_available() else -1
        print(f"Loading model '{model_name}' on device: {'GPU' if self.device == 0 else 'CPU'}")
        self.pipe = pipeline(
            "automatic-speech-recognition",
            model=model_name,
            chunk_length_s=30,
            device=self.device
        )

    def transcribe(self, audio_path):
        """
        Transcribe an audio file.
        Args:
            audio_path (str): Path to the audio file
        Returns:
            str: Transcribed text
        """
        if audio_path is None:
            return "No audio file provided."

        try:
            result = self.pipe(audio_path)
            return result.get("text", "").strip()
        except Exception as e:
            return f"Transcription error: {str(e)}"