# -*- coding: utf-8 -*-

# !pip install gradio --quiet
# !pip install transformers --quiet

"""<h1> Sentiment Analysis"""

def get_sentiment(text):
  return sentiment(text)[0]['label'], sentiment(text)[0]['score']

from transformers import pipeline
import gradio as gr

sentiment = pipeline("sentiment-analysis", model='distilbert/distilbert-base-uncased-finetuned-sst-2-english')
sentiment_analysis = gr.Interface(fn=get_sentiment,
                         inputs=gr.Textbox(lines=1, label="Enter the review:"),
                         outputs=[gr.Text(label='Sentiment:'),
                                  gr.Text(label='Score:')])

"""<h1> Summarization"""

def get_summary(text):
  return summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']

summarizer = pipeline("summarization", model='sshleifer/distilbart-cnn-12-6')
summarization  = gr.Interface(fn=get_summary,
                              inputs=gr.Textbox(lines=1, label="Enter the text:"),
                              outputs=gr.Text(label="Summary:"))

"""<h1> Speech To Text"""

def speechToText(audio):
  extractTtext = pipeline(model='openai/whisper-tiny')
  return extractTtext(audio)['text']

speech_to_text = gr.Interface(fn=speechToText,
                              inputs=gr.Audio(sources="upload", type="filepath"),
                              outputs="text")

"""<h1> ChatBot"""

def get_chatbot(text):

  pipe = pipeline("text-generation", model="distilbert/distilgpt2")
  response = pipe(text)
  return response[0]['generated_text']

chatbot  = gr.Interface(fn=get_chatbot,
                              inputs=gr.Textbox(lines=1, label="Enter the text:"),
                              outputs=gr.Text(label="Response:"))

"""<h1> Creating the Tabbed Interface"""

demo = gr.TabbedInterface([sentiment_analysis, summarization, speech_to_text, chatbot], ["Sentiment Analysis", "Summarization", 'Speech to Text', 'Chat Bot'])

if __name__ == "__main__":
    demo.launch()