import torch
import torchaudio
import torchaudio.functional as F
from torchaudio.utils import download_asset

from pesq import pesq
from pystoi import stoi
import mir_eval
from pydub import AudioSegment
import matplotlib.pyplot as plt

import streamlit as st 
from helper import plot_spectrogram,plot_mask,si_snr,generate_mixture,evaluate,get_irms

target_snr=3

#parameters for STFT
N_FFT = 1024
N_HOP = 256
stft = torchaudio.transforms.Spectrogram(
    n_fft=N_FFT,
    hop_length=N_HOP,
    power=None,
)
istft = torchaudio.transforms.InverseSpectrogram(n_fft=N_FFT, hop_length=N_HOP)
#defining a psd transform 
psd_transform = torchaudio.transforms.PSD()
mvdr_transform = torchaudio.transforms.SoudenMVDR()

#defining the reference microphone 
REFERENCE_CHANNEL = 0

#creating a random noise for better calculations 
SAMPLE_NOISE = download_asset("tutorial-assets/mvdr/noise.wav")
waveform_noise, sr2 = torchaudio.load(SAMPLE_NOISE)
waveform_noise = waveform_noise.to(torch.float32)
stft_noise = stft(waveform_noise)

def ui(): 
    st.title("Speech Enhancer")
    st.markdown("Made by Vageesh")
    #making an audio developer uploader: 
    audio_file = st.file_uploader("Upload an audio file in wav format", type=[ "wav"])

    if audio_file is not None:
        waveform_clean,sr=torchaudio.load(audio_file)
        waveform_clean = waveform_clean.to(torch.float32)
        stft_clean = stft(waveform_clean)
        st.text("Your uploaded audio")
        st.audio(audio_file)
        #creating a mixture of our audio file and the noise file 
        waveform_mix = generate_mixture(waveform_clean, waveform_noise, target_snr)
        #making the files into torch double format 
        waveform_mix = waveform_mix.to(torch.float32)
        #computing STFT
        stft_mix = stft(waveform_mix)
        #plotting the spectogram 
        spec_img=plot_spectrogram(stft_mix)
        # st.image(spec_img)
        #showing mixed audio in streamlit 
        
        torchaudio.save("./waveform_mix.wav", waveform_mix, sr)
        st.text("The noise mixed audio")
        st.audio("./waveform_mix.wav")
        #getting the irms 
        irm_speech, irm_noise = get_irms(stft_clean, stft_noise)
        #getting the psd speech
        psd_speech = psd_transform(stft_mix, irm_speech)
        psd_noise = psd_transform(stft_mix, irm_noise)
        stft_souden = mvdr_transform(stft_mix, psd_speech, psd_noise, reference_channel=REFERENCE_CHANNEL)
        waveform_souden = istft(stft_souden, length=waveform_mix.shape[-1])
        #plotting the cleaned audio and hearing it
        spec_clean_img=plot_spectrogram(stft_souden)
        waveform_souden = waveform_souden.reshape(1, -1)
        # st.image(spec_clean_img)
        torchaudio.save("./waveform_souden.wav", waveform_souden, sr)
        st.text("The cleaned Audio")
        st.audio("./waveform_souden.wav")

if __name__=="__main__": 
    ui()