import gradio as gr
import os
os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')

import json
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader

import commons
import utils
from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
from models import SynthesizerTrn
from text.symbols import symbols
#from text.symbols import symbols_ftgra
from text import text_to_sequence

from scipy.io.wavfile import write

def get_text(text, hps):
    text_norm = text_to_sequence(text, hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm

def load_model(model_path, hps):
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model)
    _ = net_g.eval()
    _ = utils.load_checkpoint(model_path, net_g, None)
    return net_g

hps = utils.get_hparams_from_file("configs/vctk_base.json")

# Define a dictionary to store the model paths for each tab
model_paths = {
    "Phonemes_finetuned": "fr_wa_finetuned_pho/G_125000.pth",
    "Graphemes_finetuned": "fr_wa_finetuned/G_198000.pth",
    "Phonemes": "path_to_phonemes_model.pth",
    "Graphemes": "wa_graphemes/G_168000.pth"
}

# Load the initial model
net_g = load_model(model_paths["Phonemes_finetuned"], hps)

def tts(text, speaker_id, tab_name):
    global net_g
    net_g = load_model(model_paths[tab_name], hps)
    sid = torch.LongTensor([speaker_id])  # speaker identity
    stn_tst = get_text(text, hps)

    with torch.no_grad():
        x_tst = stn_tst.unsqueeze(0)
        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
            0, 0].data.float().numpy()
    return "Success", (hps.data.sampling_rate, audio)

def create_tab(tab_name):
    with gr.TabItem(tab_name):
        gr.Markdown(f"### {tab_name} TTS Model")
        tts_input1 = gr.TextArea(label="Text in Walloon (Depending on the model the input should be on phonemes or characters)", value="")
        tts_input2 = gr.Dropdown(label="Speaker", choices=["Male", "Female"], type="index", value="Male")
        tts_submit = gr.Button("Generate", variant="primary")
        tts_output1 = gr.Textbox(label="Message")
        tts_output2 = gr.Audio(label="Output")
        tts_submit.click(lambda text, speaker_id: tts(text, speaker_id, tab_name), [tts_input1, tts_input2], [tts_output1, tts_output2])

app = gr.Blocks()
with app:
    gr.Markdown(
        """
        # First Text to Speech (TTS) for Walloon
        Based on VITS (https://github.com/jaywalnut310/vits).

        Select the desired model and write the text in phonemes or graphemes depending on the model.

        For faster inference speed it is recommended to use short sentences.
        """
    )
    with gr.Tabs():
        create_tab("Phonemes_finetuned")
        create_tab("Graphemes_finetuned")
        create_tab("Phonemes")
        create_tab("Graphemes")

    gr.Markdown(
        """
        ### Examples
        | Input Text | Speaker | Input Method  |
        |------------|---------|---------------|
        | li biːç ɛ l sɔlja ɛstẽ ki s maʁɡajẽ pɔ sawɛ kiː ski , dɛ døː , ɛstøː l py fwaʁ . m ɛ̃ s koː la , la k i vɛjɛ õ tsminɔː k aʁivef pjim pjam , d ɛ̃ õ bja nuː tsoː paltɔ .  | Female | Phonemes |
        | Li bijhe et l’ solea estént ki s’ margayént po sawè kî çki, des deus, esteut l’ pus foirt. Mins ç’ côp la, la k’ i veyèt on tchminåd k' arivéve pyim piam, dins on bea noû tchôd paltot.  | Male | Graphemes |
        """
    )

app.launch()