app.py

import gradio as gr
from transformers import AutoProcessor, AutoModel
import torch
import torchaudio
import os

HF_TOKEN = os.getenv("hear2")

processor = AutoProcessor.from_pretrained("google/hear-pytorch", use_auth_token=HF_TOKEN)
model = AutoModel.from_pretrained("google/hear-pytorch", use_auth_token=HF_TOKEN)

def predict(audio):
    waveform, sample_rate = torchaudio.load(audio)
    inputs = processor(waveform, sampling_rate=sample_rate, return_tensors="pt")
    with torch.no_grad():
        embeddings = model(**inputs).last_hidden_state
    return f"Embedding shape: {embeddings.shape}"

iface = gr.Interface(
    fn=predict,
    inputs=gr.Audio(type="filepath"),
    outputs="text",
    title="HeAR PyTorch Demo",
    description="Upload an audio file to generate health acoustic embeddings."
)

if __name__ == "__main__":
    iface.launch()