from typing import  Dict, List, Any
from transformers import pipeline
import soundfile as sf
import torch
import logging
import base64

logger = logging.getLogger(__name__)

class EndpointHandler():
    def __init__(self, path=""):
        # load the optimized model
        # create inference pipeline
        self.pipeline = pipeline("text-to-audio", "facebook/musicgen-stereo-large", device="cuda", torch_dtype=torch.float16)

    def generate_audio(self, text: str):
        # Here you can implement your audio generation logic
        # For demonstration purposes, let's use your existing code
        logger.info("Generating audio for text: %s", text)
        try:
            music = self.pipeline(text, forward_params={"max_new_tokens": 256})
            return music["audio"][0].T, music["sampling_rate"]
        except Exception as e:
            logger.error("Error generating audio for text: %s", text, exc_info=True)
            raise e

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        input = data.pop("inputs", data)
        # parameters = data.pop("parameters",data) 

        audio_data, sampling_rate = self.generate_audio(input)

        # Create JSON response
        response = {
            "audio_data": audio_data.tolist(),
            "sampling_rate": sampling_rate
        }

        return response