import io
import os
import base64
import librosa
import numpy as np
from io import BytesIO
import streamlit as st
from pydub import AudioSegment
import matplotlib.pyplot as plt
from scipy.io.wavfile import write
from src.denoise import denoise
from myrecorder import recorder


SR = 16000
CONTAINER_HEIGHT = 380


def np_audio_to_bytesio(np_audio, np_audio_sr):
    _bytes = bytes()
    byte_io = io.BytesIO(_bytes)
    write(byte_io, np_audio_sr, np_audio)
    bytes_audio = byte_io.read()
    return bytes_audio


def autoplay_audio(audio: str):
    audio_base64 = base64.b64encode(audio).decode('utf-8')
    audio_tag = f'<audio autoplay="true" src="data:audio/wav;base64,{audio_base64}">'
    st.markdown(audio_tag, unsafe_allow_html=True)


def load_noisy_speech(root=os.path.join(os.getcwd(), 'noisy_speech')):
    noisy_speech_paths = {'EN':{}, 'JA': {}}
    noisy_speech_names = os.listdir(root)
    for name in noisy_speech_names:
        splt = name.split('_')
        lang, snr = splt[0].upper(), int(splt[1][:2])
        noisy_speech_paths[lang][snr] = os.path.join(root, name)
        
    en_keys = list(noisy_speech_paths['EN'].keys())
    en_keys.sort()
    en_keys.reverse()
    noisy_speech_paths['EN'] = {f'{key}dB': noisy_speech_paths['EN'][key] for key in en_keys}
    
    ja_keys = list(noisy_speech_paths['JA'].keys())
    ja_keys.sort()
    ja_keys.reverse()
    noisy_speech_paths['JA'] = {f'{key}dB': noisy_speech_paths['JA'][key] for key in ja_keys}
    
    return noisy_speech_paths


def load_wav(wav_path):
    wav_22k, sr = librosa.load(wav_path)
    wav_16k = librosa.resample(wav_22k, orig_sr=sr, target_sr=SR)
    return wav_22k, wav_16k


def wav_to_spec(wav, sr):
    if sr == 16000:
        wav = librosa.resample(wav, orig_sr=sr, target_sr=22050)
    spec = np.abs(librosa.stft(wav))
    spec = librosa.amplitude_to_db(spec, ref=np.max)
    return spec


def export_spec_to_buffer(spec):
    plt.rcParams['figure.figsize'] = (16, 4.5)
    plt.rc('axes', labelsize=15)
    plt.rc('xtick', labelsize=15)
    plt.rc('ytick', labelsize=15)
    librosa.display.specshow(spec, y_axis='log', x_axis='time')
    img_buffer = BytesIO()
    plt.savefig(img_buffer, format='JPEG', bbox_inches='tight', pad_inches=0)
    return img_buffer


def process_recorded_wav_bytes(wav_bytes, sr):
    file = BytesIO(wav_bytes)
    audio = AudioSegment.from_file(file=file, format='wav')
    audio = audio.set_sample_width(2)
    audio = audio.set_channels(1)
    audio_22k = audio.set_frame_rate(sr)
    audio_16k = audio.set_frame_rate(SR)
    audio_22k = np.array(audio_22k.get_array_of_samples(), dtype=np.float32)
    audio_16k = np.array(audio_16k.get_array_of_samples(), dtype=np.float32)
    return audio_22k, audio_16k


def main():
    
    st.set_page_config(
        page_title="speech-denoising-app",
        layout="wide"
    )
    
    logo_space, title_space, _ = st.columns([1, 5, 1], gap="small")
    
    with logo_space:
        st.write(
            """
            <div style="display: flex; justify-content: left;">
                <b><span style="text-align: center; color: #101414; font-size: 14px">FPT Corporation</span></b>
            </div>
            """,
            unsafe_allow_html=True
        )
        st.image('aic-logo.png')
    
    with title_space:
        st.image('logo.png')
        
    noisy_speech_files = load_noisy_speech()

    input_space, output_space = st.columns([1, 1], gap="medium")
    _, record_space, _, compute_space= st.columns([0.7, 1, 1, 1], gap="small")
    
    with record_space:
        record = recorder(
            start_prompt="Start Recording",
            stop_prompt="Stop Recording",
            just_once=False,
            use_container_width=False,
            format="wav",
            callback=None,
            args=(),
            kwargs={},
            key=None
        )
        
    with compute_space:
        compute = st.button('Denoise')
    
    with input_space.container(height=CONTAINER_HEIGHT, border=True):
        lang_select_space, snr_select_space = st.columns([1, 1], gap="small")
        with lang_select_space:
            language_select = st.selectbox("Language", list(noisy_speech_files.keys()))
        with snr_select_space:
            if language_select:
                snr_select = st.selectbox("SNR Level", list(noisy_speech_files[language_select].keys()))
        
        if record:
            wav_bytes_record = record['bytes']
            sr = record['sample_rate']
            noisy_wav_22k, noisy_wav = process_recorded_wav_bytes(wav_bytes_record, sr=22050)
            noisy_spec = wav_to_spec(noisy_wav_22k, sr=22050)
            noisy_spec_buff = export_spec_to_buffer(noisy_spec)
            
            st.audio(wav_bytes_record, format="wav")
            st.image(image=noisy_spec_buff)

        elif language_select and snr_select:
            audio_path = noisy_speech_files[language_select][snr_select]
            noisy_wav_22k, noisy_wav = load_wav(audio_path)
            noisy_spec = wav_to_spec(noisy_wav_22k, sr=22050)
            noisy_spec_buff = export_spec_to_buffer(noisy_spec)
        
            st.audio(audio_path, format="wav")
            st.image(image=noisy_spec_buff)
    
    with output_space.container(height=CONTAINER_HEIGHT, border=True):
        st.write(
            """
            <div style="display: flex; justify-content: center;">
                <b><span style="text-align: center; color: #808080; font-size: 51.5px">Output</span></b>
            </div>
            """,
            unsafe_allow_html=True
        )
        if noisy_wav.any() and compute:
            denoised_wav = denoise(noisy_wav)
            st.audio(denoised_wav, sample_rate=SR, format="audio/wav")
            denoised_spec = wav_to_spec(denoised_wav, sr=SR)
            denoised_spec_buff = export_spec_to_buffer(denoised_spec)
            st.image(image=denoised_spec_buff)
            record = None
        

if __name__ == '__main__':
    main()