import soundfile as sf
import requests
import io
import numpy as np
import torch

class AudioLoadPath:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "path": ("STRING", {"default": "X://insert/path/here.mp4"}),
                              "sample_rate": ("INT", {"default": 22050, "min": 6000, "max": 192000, "step": 1}),
                              "offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001}),
                              "duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001})}}

    RETURN_TYPES = ("AUDIO", )
    CATEGORY = "Audio Reactor"
    FUNCTION = "load"

    def load(self, path: str, sample_rate: int, offset: float, duration: float|None):
        if duration == 0.0:
            duration = None

        if path.startswith(('http://', 'https://')):
            # 对于网络路径，直接从内存加载
            try:
                response = requests.get(path)
                response.raise_for_status()
                audio_data = io.BytesIO(response.content)

                # 使用 soundfile 从内存中读取音频数据
                audio, file_sr = sf.read(audio_data)

                # 如果需要重采样
                if file_sr != sample_rate:
                    # 这里需要添加重采样逻辑
                    # 可以使用 librosa.resample 或其他方法
                    pass

            except Exception as e:
                raise Exception(f"加载网络音频失败: {str(e)}")
        else:
            # 本地文件使用原有的 librosa 方式加载
            audio, _ = librosa.load(path, sr=sample_rate, offset=offset, duration=duration)

        # 转换为 torch tensor 并调整维度
        audio = torch.from_numpy(audio)[None,:,None]
        return (audio,)

NODE_CLASS_MAPPINGS = {
    "AudioLoadPath": AudioLoadPath,
}