import logging
import os
import time
import uuid

import gradio as gr
import soundfile as sf

from model import get_pretrained_model, language_to_models

title = "# Next-gen Kaldi: Text-to-speech (TTS)"

description = """
This space shows how to convert text to speech with Next-gen Kaldi.

It is running on CPU within a docker container provided by Hugging Face.

See more information by visiting the following links:

- <https://github.com/k2-fsa/sherpa-onnx>

If you want to deploy it locally, please see
<https://k2-fsa.github.io/sherpa/>

If you want to use Android APKs, please see
<https://k2-fsa.github.io/sherpa/onnx/tts/apk.html>

If you want to use Android text-to-speech engine APKs, please see
<https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html>

If you want to download an all-in-one exe for Windows, please see
<https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models>
"""

css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""

# Simplified examples for Portuguese only
examples = [
    ["Portuguese", "csukuangfj/vits-mms-por", "Eu desejo uma versão simplificada para português.", 0, 1.0],
]

# Use only Portuguese as a language choice
language_choices = ["Portuguese"]

demo = gr.Blocks(css=css)

with demo:
    gr.Markdown(title)
    
    # Use Radio instead of Dropdown for language choice
    language_radio = gr.Radio(
        label="Language",
        choices=language_choices,
        value=language_choices[0],
    )

    # Initialize model_dropdown with Portuguese models
    model_dropdown = gr.Dropdown(
        choices=language_to_models["Portuguese"],
        label="Select a model",
        value=language_to_models["Portuguese"][0],
    )

    # No need to update model_dropdown for a single language

    with gr.Tabs():
        with gr.TabItem("Please input your text"):
            input_text = gr.Textbox(
                label="Input text",
                info="Your text",
                lines=3,
                placeholder="Please input your text here",
            )

            input_sid = gr.Textbox(
                label="Speaker ID",
                info="Speaker ID",
                lines=1,
                max_lines=1,
                value="0",
                placeholder="Speaker ID. Valid only for mult-speaker model",
            )

            input_speed = gr.Slider(
                minimum=0.1,
                maximum=10,
                value=1,
                step=0.1,
                label="Speed (larger->faster; smaller->slower)",
            )

            input_button = gr.Button("Submit")

            output_audio = gr.Audio(label="Output")

            output_info = gr.HTML(label="Info")

            gr.Examples(
                examples=examples,
                fn=process,
                inputs=[
                    language_radio,
                    model_dropdown,
                    input_text,
                    input_sid,
                    input_speed,
                ],
                outputs=[
                    output_audio,
                    output_info,
                ],
            )

        input_button.click(
            process,
            inputs=[
                language_radio,
                model_dropdown,
                input_text,
                input_sid,
                input_speed,
            ],
            outputs=[
                output_audio,
                output_info,
            ],
        )

    gr.Markdown(description)


def download_espeak_ng_data():
    os.system(
        """
    cd /tmp
    wget -qq https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
    tar xf espeak-ng-data.tar.bz2
    """
    )


if __name__ == "__main__":
    download_espeak_ng_data()
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)

    demo.launch()