# Example of using the model via Hugging Face API
from huggingface_hub.inference_api import InferenceApi
import requests
import numpy as np
import librosa
import json

def predict_with_api(audio_path, api_token):
    
    Make a prediction using the Hugging Face API
    
    Args:
        audio_path: Path to the audio file
        api_token: Your Hugging Face API token
        
    Returns:
        Prediction (0 or 1)
    
    # Load and preprocess audio
    audio, sr = librosa.load(audio_path, sr=16000)
    
    # Convert to list for API call
    audio_list = audio.tolist()
    
    # API endpoint
    API_URL = "https://api-inference.huggingface.co/models/ahmad1703/whis_ee"
    headers = {"Authorization": f"Bearer {api_token}"}
    
    # Make API call
    payload = {"inputs": {"sampling_rate": 16000, "raw": audio_list}}
    response = requests.post(API_URL, headers=headers, json=payload)
    result = response.json()
    
    # Process result
    if "error" in result:
        print(f"Error: {result['error']}")
        return None
    
    # Extract probability and convert to class
    prob = result[0]["score"]
    prediction = 1 if prob > 0.5 else 0
    
    return prediction, prob

if __name__ == "__main__":
    # Example usage
    audio_path = "example.wav"  # Replace with your audio file
    api_token = "YOUR_API_TOKEN"  # Replace with your API token
    
    prediction, probability = predict_with_api(audio_path, api_token)
    print(f"Prediction: {prediction}, Probability: {probability:.4f}")