import gradio as gr
from transformers import pipeline
classifier = pipeline('sentiment-analysis')

#################################################################
#1: Text to Speech
title = "Text to Speech Translation"
tts_examples = [
    "I love learning machine learning",
    "How do you do?",
]
tts_demo = gr.Interface.load(
    "huggingface/facebook/fastspeech2-en-ljspeech",
    title = title,
    examples=tts_examples,
    description="Give me something to say!",
)

#################################################################
#2: Sentiment Analysis
def get_sentiment(input_text):
  return classifier(input_text)
  
sentiment_demo= gr.Interface(fn=get_sentiment,  inputs = 'text',outputs=['text'],title = 'Sentiment Analysis', description = "Enter a sentence and know about it's sentiment",examples = [
    ["We are very happy to show you the 🤗 Transformers library."],
    ["I am happy with the performance of Indian Hockey team"],
    ["Pizza was not at all hot and restaurant was having the pathetic service"]
])
#sentiment_demo.launch(inline = False)
#################################################################    
#3 POS Tagging
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
def get_POS(input_text):
  sent_tag = nltk.word_tokenize(input_text)
  for token in sent_tag:

    return nltk.pos_tag([token])

title = 'Know about Parts of Speech (POS) Tags'
examples = [
    ["We are very happy to show you the 🤗 Transformers library."],
    ["I am happy with the performance of Indian Hockey team"],
    ["Pizza was not at all hot and restaurant was having the pathetic service"]
]
POS_demo = gr.Interface(fn=get_POS, inputs = 'text',outputs=['text'],title = title,description = 'Get POS tags',examples = examples)
#POS_demo.launch(debug=True)

#################################################################
#4 Language Translation
from language_translation import translation_demo


#################################################################
#5: Gradio ASR
from SpeechTranscription_ASR import ST_ASR_demo

#################################################################
#6 YouTube to Text Script generation

from Text_Script_from_YouTube import textScript_demo

########################################################################################################
demo = gr.TabbedInterface([tts_demo, sentiment_demo, POS_demo, translation_demo, ST_ASR_demo, textScript_demo], 
                          ["Text to speech","Sentiment Analysis","POS findings", "Language Translation","ASR", "Get Text Script from YouTube video"])
if __name__ == "__main__":
    demo.launch()