import librosa
import numpy as np
from tensorflow.keras.models import load_model

# Load your trained model
model = load_model('/kaggle/working/Dubai_Audio.h5')
speaker_to_int = {
    'Brene Brown': 0,
    'Eckhart Tolle': 1,
    'Eric Thomas': 2,
    'Gary Vee': 3,
    'Jay Shetty': 4,
    'Les Brown': 5,
    'Mel Robbins': 6,
    'Nick Vujicic': 7,
    'Oprah Winfrey': 8,
    'Rabin Sharma': 9,
    'Simon Sinek': 10
}

def preprocess_audio(file_path, n_mfcc=40, max_pad_len=216):
    audio, sample_rate = librosa.load(file_path, sr=None)
    mfcc = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=n_mfcc)
    if mfcc.shape[1] < max_pad_len:
        pad_width = max_pad_len - mfcc.shape[1]
        mfcc = np.pad(mfcc, pad_width=((0,0), (0,pad_width)), mode='constant')
    else:
        mfcc = mfcc[:, :max_pad_len]
    mfcc = mfcc[np.newaxis, ..., np.newaxis]
    return mfcc

def predict_audio_class(file_path, model, int_to_speaker):
    processed_audio = preprocess_audio(file_path)
    predictions = model.predict(processed_audio)
    predicted_index = np.argmax(predictions, axis=1)[0]
    print(predicted_index)
    predicted_class = int_to_speaker[predicted_index]
    return predicted_class

# Reverse mapping dictionary
int_to_speaker = {v: k for k, v in speaker_to_int.items()}

# Example usage
uploaded_audio_path = '/kaggle/input/audio-classifier-dataset/augmented-audio/Mel Robbins/113Mel Robbins113.wav'

predicted_class = predict_audio_class(uploaded_audio_path, model, int_to_speaker)
print(f'Predicted class: {predicted_class}')