import gradio as gr
from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM

MODELS = {
    "Tatar": {"model_id": "sammy786/wav2vec2-xlsr-tatar", "has_lm": False},
    "Chuvash": {"model_id": "sammy786/wav2vec2-xlsr-chuvash", "has_lm": False},
    "Bashkir": {"model_id": "AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt", "has_lm": True},
    "Erzya": {"model_id": "DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1", "has_lm": False}
    }
    
CACHED_MODELS_BY_ID = {}
    
LANGUAGES_ENG = list(MODELS.keys())
LANGUAGES_RUS = ["Татарский", "Чувашский", "Башкирский", "Эрзянский"]
RUS2ENG = {k:v for k,v in zip(LANGUAGES_RUS, LANGUAGES_ENG)}
LANG2YDX = {"Tatar": 'tt',
    "Chuvash": "ba",
    "Bashkir": "cv",
    "Erzya": None,
    "English": 'en',
    'Русский': 'ru'
}


def run(input_file, language, decoding_type, lang):
    language = RUS2ENG.get(language, language)
    model = MODELS.get(language, None)
    
    model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
    if model_instance is None:
        model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
        CACHED_MODELS_BY_ID[model["model_id"]] = model_instance

    if decoding_type == "LM":
        processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
        asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                        feature_extractor=processor.feature_extractor, decoder=processor.decoder)
    else:
        processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
        asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                        feature_extractor=processor.feature_extractor, decoder=None)

    transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]

    if LANG2YDX[language]:
      url = 'https://translate.yandex.ru/?lang=' + LANG2YDX[language] + '-' + LANG2YDX[lang] + '&text=' + transcription # ru-fr&text=
      if lang == "Русский":
        label = 'Посмотреть перевод'
      else: label = 'Check the translation'
      html = f'<a href="{url}"  target="_blank">{label}</a>'
    else: html = None

    return transcription, html
    
  
def update_decoding(language):
  language = RUS2ENG.get(language, language)
  if MODELS[language]['has_lm']:
    return gr.Radio.update(visible=True)
  else: return gr.Radio.update(visible=False, value='Greedy')


def update_interface(lang):
  if lang == 'Русский':
    languages = gr.Radio.update(label='Язык записи', choices=LANGUAGES_RUS)
    audio =  gr.Audio.update(label='Скажите что-нибудь...')
    # btn = gr.Button.update(value='Расшифровать')
    decoding = gr.Radio.update(label='Тип декодирования')
    
  elif lang == 'English':
    languages = gr.Radio.update(label='Language', choices=LANGUAGES_ENG)
    audio = gr.Audio.update(label='Say something...')
    # btn = gr.Button.update(value='Transcribe')
    decoding = gr.Radio.update(label='Decoding type')

  return languages, audio, decoding

with gr.Blocks() as blocks:
  lang = gr.Radio(label="Выберите язык интерфейса / Interface language", choices=['Русский','English'])
  languages = gr.Radio(label="Language", choices=LANGUAGES_RUS)
  audio = gr.Audio(source="microphone", type="filepath", label="Скажите что-нибудь...")
  decoding = gr.Radio(label="Тип декодирования", choices=["Greedy", "LM"], visible=False, type='index')
  btn = gr.Button('Расшифровать / Transcribe')
  output = gr.Textbox(show_label=False)

  translation = gr.HTML()
  
  languages.change(fn=update_decoding, inputs=[languages], outputs=[decoding])
  lang.change(fn=update_interface, inputs=[lang], outputs=[languages, audio, decoding])
  btn.click(fn=run, inputs=[audio, languages, decoding, lang], outputs=[output, translation])


blocks.launch(enable_queue=True, debug=True)