import torch
import librosa
from transformers import WhisperProcessor, pipeline

def predict_from_file(audio_path, model_name="ahmad1703/whis_ee"):
    
    Make a prediction using the WhisperClassifier model
    
    Args:
        audio_path: Path to the audio file
        model_name: The Hugging Face model name
        
    Returns:
        Prediction (0 or 1)
    
    # Load audio
    audio, sr = librosa.load(audio_path, sr=16000)
    
    # Create pipeline
    classifier = pipeline("audio-classification", model=model_name)
    
    # Get prediction
    result = classifier(audio)
    
    # Convert probability to binary class
    prediction = 1 if result["score"] > 0.5 else 0
    
    return prediction

if __name__ == "__main__":
    # Example usage
    audio_path = "example.wav"  # Replace with your audio file
    prediction = predict_from_file(audio_path)
    print(f"Prediction: {prediction}")