app.py

import gradio as gr
import torch
import librosa
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification

# 加载模型和特征提取器
model_path = "./music_genre_classifier/best_model"  # 你上传的模型文件夹名（保持与Space中文件夹一致）
feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
model = AutoModelForAudioClassification.from_pretrained(model_path)

# 使用GPU（如果可用）
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# 推理函数
def predict_genre(audio_path):
    try:
        audio, sr = librosa.load(audio_path, sr=16000)
        inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt").to(device)

        with torch.no_grad():
            logits = model(**inputs).logits
            predicted_label = torch.argmax(logits, dim=-1).item()
            genre = model.config.id2label.get(predicted_label, "Unknown")
            return f"🎧 Predicted Genre: {genre}"
    except Exception as e:
        return f"❌ Prediction failed: {str(e)}"

# 创建 Gradio 接口
demo = gr.Interface(
    fn=predict_genre,
    inputs=gr.Audio(type="filepath", label="🎵 Upload a music file (.mp3, .wav, .flac)"),
    outputs=gr.Textbox(label="Predicted Genre"),
    title="🎶 Music Genre Classifier",
    description="Upload a music file and this model will predict its genre using a fine-tuned Transformer model.",
)

# 启动Gradio界面
if __name__ == "__main__":
    demo.launch()  # 如果需要公开链接，使用 share=True