import os
import gradio as gr
import whisper 
import requests 
import tempfile
from neon_tts_plugin_coqui import CoquiTTS
from datasets import load_dataset

dataset = load_dataset("ysharma/short_jokes")

# Language common in both the multilingual models - English, Chinese, Spanish, and French etc
# So it would make sense to test the App on these four prominently

# Whisper: Speech-to-text
model = whisper.load_model("base")
model_med = whisper.load_model("medium")
# Languages covered in Whisper - (exhaustive list) : 
#"en": "english", "zh": "chinese", "de": "german", "es": "spanish", "ru": "russian", 
#"ko": "korean", "fr": "french", "ja": "japanese", "pt": "portuguese", "tr": "turkish", 
#"pl": "polish", "ca": "catalan", "nl": "dutch", "ar": "arabic", "sv": "swedish", 
#"it": "italian", "id": "indonesian", "hi": "hindi", "fi": "finnish", "vi": "vietnamese", 
#"iw": "hebrew", "uk": "ukrainian", "el": "greek", "ms": "malay", "cs": "czech", 
#"ro": "romanian", "da": "danish", "hu": "hungarian", "ta": "tamil", "no": "norwegian", 
#"th": "thai", "ur": "urdu", "hr": "croatian", "bg": "bulgarian", "lt": "lithuanian", 
#"la": "latin", "mi": "maori", "ml": "malayalam", "cy": "welsh", "sk": "slovak", 
#"te": "telugu", "fa": "persian", "lv": "latvian", "bn": "bengali", "sr": "serbian", 
#"az": "azerbaijani", "sl": "slovenian", "kn": "kannada", "et": "estonian", 
#"mk": "macedonian", "br": "breton", "eu": "basque", "is": "icelandic", "hy": "armenian", 
#"ne": "nepali", "mn": "mongolian", "bs": "bosnian", "kk": "kazakh", "sq": "albanian", 
#"sw": "swahili", "gl": "galician", "mr": "marathi", "pa": "punjabi", "si": "sinhala", 
#"km": "khmer", "sn": "shona", "yo": "yoruba", "so": "somali", "af": "afrikaans", 
#"oc": "occitan", "ka": "georgian", "be": "belarusian", "tg": "tajik", "sd": "sindhi", 
#"gu": "gujarati", "am": "amharic", "yi": "yiddish", "lo": "lao", "uz": "uzbek", 
#"fo": "faroese", "ht": "haitian creole", "ps": "pashto", "tk": "turkmen", "nn": "nynorsk", 
#"mt": "maltese", "sa": "sanskrit", "lb": "luxembourgish", "my": "myanmar", "bo": "tibetan", 
#"tl": "tagalog", "mg": "malagasy", "as": "assamese", "tt": "tatar", "haw": "hawaiian", 
#"ln": "lingala", "ha": "hausa", "ba": "bashkir", "jw": "javanese", "su": "sundanese",


# Text-to-Speech
LANGUAGES = list(CoquiTTS.langs.keys())
coquiTTS = CoquiTTS()
print(f"Languages for Coqui are: {LANGUAGES}")
#Languages for Coqui are: ['en', 'es', 'fr', 'de', 'pl', 'uk', 'ro', 'hu', 'el', 'bg', 'nl', 'fi', 'sl', 'lv', 'ga']
# en - English, es - Spanish, fr -  French, de - German, pl - Polish
# uk - Ukrainian, ro - Romanian, hu - Hungarian, el - Greek, bg - Bulgarian,
# nl - dutch, fi - finnish, sl - slovenian, lv - latvian, ga - ??  


# Driver function
def driver_fun(audio) : 
  transcribe, translation, lang = whisper_stt(audio)
  #text1 = model.transcribe(audio)["text"]
  
  #For now only taking in English text for Bloom prompting as inference model is not high spec
  #text_generated = lang_model_response(transcribe, lang)
  #text_generated_en = lang_model_response(translation, 'en')
  
  if lang in ['es', 'fr']:
    speech = tts(transcribe, lang)
  else:
    speech = tts(translation, 'en') #'en')
  return transcribe, translation, speech


# Whisper - speech-to-text
def whisper_stt(audio):
  print("Inside Whisper TTS")
  # load audio and pad/trim it to fit 30 seconds
  audio = whisper.load_audio(audio)
  audio = whisper.pad_or_trim(audio)
  
  # make log-Mel spectrogram and move to the same device as the model
  mel = whisper.log_mel_spectrogram(audio).to(model.device)
  
  # detect the spoken language
  _, probs = model.detect_language(mel)
  lang = max(probs, key=probs.get)
  print(f"Detected language: {max(probs, key=probs.get)}")
  
  # decode the audio
  options_transc = whisper.DecodingOptions(fp16 = False, language=lang, task='transcribe') #lang
  options_transl = whisper.DecodingOptions(fp16 = False, language='en', task='translate') #lang
  result_transc = whisper.decode(model_med, mel, options_transc)
  result_transl = whisper.decode(model_med, mel, options_transl)
  
  # print the recognized text
  print(f"transcript is : {result_transc.text}")
  print(f"translation is : {result_transl.text}")

  return result_transc.text, result_transl.text, lang


# Coqui - Text-to-Speech
def tts(text, language):
  print(f"Inside tts - language is : {language}")
  coqui_langs = ['en' ,'es' ,'fr' ,'de' ,'pl' ,'uk' ,'ro' ,'hu' ,'bg' ,'nl' ,'fi' ,'sl' ,'lv' ,'ga']
  if language not in coqui_langs:
    language = 'en'
  with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
      coquiTTS.get_tts(text, fp, speaker = {"language" : language})
      return fp.name

demo = gr.Blocks()
with demo:
  gr.Markdown("<h1><center>Multilingual AI Assistant - Voice to Joke</center></h1>")
  gr.Markdown(
        """Model pipeline consisting of - <br>- [**Whisper**](https://github.com/openai/whisper) for Speech-to-text, <br>- [**CoquiTTS**](https://huggingface.co/coqui)  for Text-To-Speech. <br>- Front end is built using [**Gradio Block API**](https://gradio.app/docs/#blocks).<br>Both CoquiTTS and Whisper are Multilingual, there are several overlapping languages between them. Hence it would be suggested to test this ML-App using these two languages to get the best results</u>.<br>If you want to reuse the App, simply click on the small cross button in the top right corner of your voice record panel, and then press record again!
        """)
  with gr.Row():
    with gr.Column(): 
      in_audio = gr.Audio(source="microphone",  type="filepath", label='Record your voice command here in English, Spanish or French for best results-')  #type='filepath'
      b1 = gr.Button("AI response pipeline (Whisper - Bloom - Coqui pipeline)")
      out_transcript = gr.Textbox(label= 'English/Spanish/French Transcript of your Audio using OpenAI Whisper')
      out_translation_en = gr.Textbox(label= 'English Translation of audio using OpenAI Whisper')
    with gr.Column():
      out_audio = gr.Audio(label='AI response in Audio form in your language - This will be either in Spanish, or in French or in English for all other languages -')  
      out_generated_text = gr.Textbox(label= 'AI response to your query in your preferred language using Bloom! ')
      out_generated_text_en = gr.Textbox(label= 'AI response to your query in English using Bloom! ')
    
      b1.click(driver_fun,inputs=[in_audio], outputs=[out_transcript, out_translation_en, out_generated_text,out_generated_text_en, out_audio]) 
    
demo.launch(enable_queue=True, debug=True)