import os
import re
import random
from scipy.io.wavfile import write, read
import numpy as np
import yt_dlp
import subprocess
from pydub import AudioSegment
from audio_separator.separator import Separator
from lib.infer import infer_audio
import edge_tts
import tempfile
import anyio
from pathlib import Path
from lib.language_tts import language_dict
import zipfile
import shutil
import urllib.request
import gdown
import streamlit as st

main_dir = Path().resolve()
print(main_dir)
os.chdir(main_dir)
models_dir = "models"

# Download audio using yt-dlp
def download_audio(url):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': 'ytdl/%(title)s.%(ext)s',
        'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', 'preferredquality': '192'}],
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        info_dict = ydl.extract_info(url, download=True)
        file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav'
        sample_rate, audio_data = read(file_path)
        audio_array = np.asarray(audio_data, dtype=np.int16)
        return sample_rate, audio_array


def separate_audio(input_audio, output_dir, model_voc_inst, model_deecho, model_back_voc):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    separator = Separator(output_dir=output_dir)

    vocals = os.path.join(output_dir, 'Vocals.wav')
    instrumental = os.path.join(output_dir, 'Instrumental.wav')
    vocals_reverb = os.path.join(output_dir, 'Vocals (Reverb).wav')
    vocals_no_reverb = os.path.join(output_dir, 'Vocals (No Reverb).wav')
    lead_vocals = os.path.join(output_dir, 'Lead Vocals.wav')
    backing_vocals = os.path.join(output_dir, 'Backing Vocals.wav')

    separator.load_model(model_filename=model_voc_inst)
    voc_inst = separator.separate(input_audio)
    os.rename(os.path.join(output_dir, voc_inst[0]), instrumental)
    os.rename(os.path.join(output_dir, voc_inst[1]), vocals)

    separator.load_model(model_filename=model_deecho)
    voc_no_reverb = separator.separate(vocals)
    os.rename(os.path.join(output_dir, voc_no_reverb[0]), vocals_no_reverb)
    os.rename(os.path.join(output_dir, voc_no_reverb[1]), vocals_reverb)

    separator.load_model(model_filename=model_back_voc)
    backing_voc = separator.separate(vocals_no_reverb)
    os.rename(os.path.join(output_dir, backing_voc[0]), backing_vocals)
    os.rename(os.path.join(output_dir, backing_voc[1]), lead_vocals)

    return instrumental, vocals, vocals_reverb, vocals_no_reverb, lead_vocals, backing_vocals


async def text_to_speech_edge(text, language_code):
    voice = language_dict.get(language_code, "default_voice")
    communicate = edge_tts.Communicate(text, voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path


# Streamlit UI
st.title("Hex RVC")

tabs = st.tabs(["Inference", "Download RVC Model", "Audio Separation"])

# Inference Tab
with tabs[0]:
    st.header("Inference")
    
    model_name = st.text_input("Model Name", placeholder="Enter model name")
    sound_path = st.text_input("Audio Path (Optional)", placeholder="Leave blank to upload audio")
    uploaded_audio = st.file_uploader("Upload Audio", type=["wav", "mp3"])

    if uploaded_audio is not None:
        with open("uploaded_audio.wav", "wb") as f:
            f.write(uploaded_audio.read())
        sound_path = "uploaded_audio.wav"

    f0_change = st.number_input("Pitch Change (semitones)", value=0)
    f0_method = st.selectbox("F0 Method", ["crepe", "harvest", "mangio-crepe", "rmvpe", "rmvpe+", "fcpe", "hybrid[rmvpe+fcpe]"], index=5)
    
    if st.button("Run Inference"):
        st.write("Running inference...")

# Download RVC Model Tab
with tabs[1]:
    st.header("Download RVC Model")
    url = st.text_input("Model URL")
    dir_name = st.text_input("Model Name")

    if st.button("Download Model"):
        try:
            download_online_model(url, dir_name)
            st.success(f"Model {dir_name} downloaded successfully!")
        except Exception as e:
            st.error(str(e))

# Audio Separation Tab
with tabs[2]:
    st.header("Audio Separation")
    input_audio = st.file_uploader("Upload Audio for Separation", type=["wav", "mp3"])

    if input_audio is not None:
        with open("input_audio.wav", "wb") as f:
            f.write(input_audio.read())
        st.write("Audio uploaded successfully.")

        if st.button("Separate Audio"):
            st.write("Separating audio...")
            output_dir = "./separated_audio"
            inst, voc, voc_rev, voc_no_rev, lead_voc, back_voc = separate_audio("input_audio.wav", output_dir, 
                                                                               'model_bs_roformer.ckpt',
                                                                               'UVR-DeEcho-DeReverb.pth',
                                                                               'mel_band_karaoke.ckpt')
            st.audio(inst)
            st.audio(voc)
            st.audio(voc_rev)
            st.audio(voc_no_rev)
            st.audio(lead_voc)
            st.audio(back_voc)