import torch
import gradio as gr
from transformers import pipeline
import tempfile
from neon_tts_plugin_coqui import CoquiTTS
from datetime import datetime
import time
import psutil
from mtranslate import translate
from gpuinfo import GPUInfo


MODEL_NAME = "cahya/whisper-medium-id"  # this always needs to stay in line 8 :D sorry for the hackiness
whisper_models = {
    "Indonesian Whisper Tiny": {
        "name": "cahya/whisper-tiny-id",
        "pipe": None,
    },
    "Indonesian Whisper Small": {
        "name": "cahya/whisper-small-id",
        "pipe": None,
    },
    "Indonesian Whisper Medium": {
        "name": "cahya/whisper-medium-id",
        "pipe": None,
    },
    "OpenAI Whisper Medium": {
        "name": "openai/whisper-medium",
        "pipe": None,
    },
}
lang = "id"
title = "Indonesian Whisperer"
description = "Cross Language Speech to Speech (Indonesian/English to 25 other languages) using OpenAI Whisper and Coqui TTS"
info = "This application uses [Indonesian Whisperer Medium](https://huggingface.co/cahya/whisper-medium-id) model"
badge = "https://img.shields.io/badge/Powered%20by-Indonesian%20Whisperer-red"
visitors = "https://visitor-badge.glitch.me/badge?page_id=cahya-hf-indonesian-whisperer"

languages = {
    'English': 'en',
    'German': 'de',
    'Spanish': 'es',
    'French': 'fr',
    'Portuguese': 'pt',
    'Polish': 'pl',
    'Dutch': 'nl',
    'Swedish': 'sv',
    'Italian': 'it',
    'Finnish': 'fi',
    'Ukrainian': 'uk',
    'Greek': 'el',
    'Czech': 'cs',
    'Romanian': 'ro',
    'Danish': 'da',
    'Hungarian': 'hu',
    'Croatian': 'hr',
    'Bulgarian': 'bg',
    'Lithuanian': 'lt',
    'Slovak': 'sk',
    'Latvian': 'lv',
    'Slovenian': 'sl',
    'Estonian': 'et',
    'Maltese': 'mt'
}

device = 0 if torch.cuda.is_available() else "cpu"

for model in whisper_models:
    whisper_models[model]["pipe"] = pipeline(
        task="automatic-speech-recognition",
        model=whisper_models[model]["name"],
        chunk_length_s=30,
        device=device,
    )
    whisper_models[model]["pipe"].model.config.forced_decoder_ids = \
        whisper_models[model]["pipe"].tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")


def transcribe(pipe, microphone, file_upload):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "WARNING: You've uploaded an audio file and used the microphone. "
            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        )

    elif (microphone is None) and (file_upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    file = microphone if microphone is not None else file_upload

    text = pipe(file)["text"]

    return warn_output + text


LANGUAGES = list(CoquiTTS.langs.keys())
default_lang = "en"

coquiTTS = CoquiTTS()


def process(language: str, model: str, audio_microphone: str, audio_file: str):
    language = languages[language]
    pipe = whisper_models[model]["pipe"]
    time_start = time.time()
    print(f"### {datetime.now()} TTS", language, audio_file)
    transcription = transcribe(pipe, audio_microphone, audio_file)
    print(f"### {datetime.now()} transcribed:", transcription)
    translation = translate(transcription, language, "id")
    # return output
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        coquiTTS.get_tts(translation, fp, speaker={"language": language})
        time_end = time.time()
        time_diff = time_end - time_start
        memory = psutil.virtual_memory()
        gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
        gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
        gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
        system_info = f"""
        *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.* 
        *Processing time: {time_diff:.5} seconds.*
        *GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.*
        """
        print(f"### {datetime.now()} fp.name:", fp.name)
        return transcription, translation, fp.name, system_info


with gr.Blocks() as blocks:
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
                + title
                + "</h1>")
    gr.Markdown(description)
    with gr.Row():  # equal_height=False
        with gr.Column():  # variant="panel"
            audio_microphone = gr.Audio(label="Microphone", source="microphone", type="filepath", optional=True)
            audio_upload = gr.Audio(label="Upload", source="upload", type="filepath", optional=True)
            language = gr.Dropdown([lang for lang in languages.keys()], label="Target Language", value="English")
            model = gr.Dropdown([model for model in whisper_models.keys()],
                                     label="Whisper Model", value="Indonesian Whisper Medium")
            with gr.Row():  # mobile_collapse=False
                submit = gr.Button("Submit", variant="primary")
            examples = gr.Examples(examples=["data/Jokowi - 2022.mp3", "data/Soekarno - 1963.mp3", "data/JFK.mp3"],
                                   label="Examples", inputs=[audio_upload])
        with gr.Column():
            text_source = gr.Textbox(label="Source Language")
            text_target = gr.Textbox(label="Target Language")
            audio = gr.Audio(label="Target Audio", interactive=False)
            memory = psutil.virtual_memory()
            system_info = gr.Markdown(f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB*")

    gr.Markdown(info)
    gr.Markdown("<center>"
                + f'<a href="https://github.com/cahya-wirawan/indonesian-whisperer"><img src={badge} alt="visitors badge"/></a>'
                + f'<img src={visitors} alt="visitors badge"/>'
                + "</center>")

    # actions
    submit.click(
        process,
        [language, model, audio_microphone, audio_upload],
        [text_source, text_target, audio, system_info],
    )

blocks.launch()