import librosa, joblib, numpy as np, gradio as gr
from scipy.interpolate import interp1d
from pyAudioAnalysis import ShortTermFeatures
from pydub.silence import detect_nonsilent
from pydub import AudioSegment


def smart_resize(arr, target_size):
    current_size = arr.shape[1]

    current_idx = np.linspace(0, current_size - 1, current_size)
    target_idx = np.linspace(0, current_size - 1, target_size)

    # Interpolate/extrapolate
    interp_func = interp1d(current_idx, arr.squeeze(), kind='linear', fill_value="extrapolate")
    resized_arr = interp_func(target_idx)
    
    return resized_arr.reshape(1, target_size)

def remove_silence(wav_file):
    audSeg = AudioSegment.from_wav(wav_file)
    non_silence_ranges = detect_nonsilent(audSeg, min_silence_len=5, silence_thresh=-30)

    if not non_silence_ranges: 
        sound = audSeg
    else:
        start = non_silence_ranges[0][0]
        end = non_silence_ranges[-1][1]
        trimmed_sound = audSeg[start:end]
        sound = trimmed_sound

    sound.export('audio.wav', format="wav")

def transform_data(audio):
    remove_silence(audio)
    x, sr = librosa.load('audio.wav')

    result, f_names = ShortTermFeatures.feature_extraction(x, sr, 0.050*sr, 0.025*sr)

    resize_features = smart_resize(result.reshape(1,-1), 20) 

    return resize_features

def predict(newdf, loaded_model):
    
    prediction = loaded_model.predict(newdf)

    return prediction 
    
def get_label(newpred):
    if newpred == 0:
        return 'No'
    else:
        return 'Si'

def load_model():
    ram_for = joblib.load('models/sgd_90.pkl') 

    return ram_for
    
def main(audio):
    newdf = transform_data(audio)
    loaded_model = load_model()
    newpred = predict(newdf, loaded_model)
    final = get_label(newpred)
    
    return final

demo = gr.Interface(
    title = "Autoagent | YES or NO Classification - Layer7",
    description = "<h3>This model is useful to classify if the user says 'Si' or 'No'. 🎙️ </h3> <br> <b>Record your voice:</b>",
    allow_flagging = "never",
    fn = main,
    inputs = gr.Audio(
        sources=["microphone"],
        type="filepath",
    ), 
    outputs = gr.Textbox(label="Clasification")
    )
    
if __name__ == "__main__":
    demo.launch(show_api=False)