from transformers import pipeline
import gradio as gr
import numpy as np

def get_sentiment(text):
  sentiment_pipeline=pipeline('sentiment-analysis')
  result=sentiment_pipeline(text)
  output = gr.Textbox(label="Output Box")

  return result[0]['label'],result[0]['score']

def summraztion(text):
  summary_pipe = pipeline('summarization',model="cnicu/t5-small-booksum")
  result=summary_pipe(text)
  output = gr.Textbox(label="Output Box")

  return result[0]['summary_text']

def chat_bot(text,histroy):
  chat_pip=pipeline('text-generation')
  mes=chat_pip(text)
    
  return mes[0]['generated_text']

    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")

def transcribe(audio):
    sr, y = audio
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))

    return transcriber({"sampling_rate": sr, "raw": y})["text"]

Audio = gr.Interface(
    transcribe,
    gr.Audio(sources=["microphone"]),
    "text",
)

sentiment_a = gr.Interface(fn=get_sentiment , inputs=gr.Textbox(label="Enter the review ") , outputs=[gr.Textbox(label="sentiment") , gr.Textbox(label="Score")],description='sentiment-analysis')
summraztion = gr.Interface(fn=summraztion , inputs=gr.Textbox(label="Enter the text ") , outputs=gr.Textbox(label="summraztion") ,description='summraztion')
chatBot=gr.ChatInterface(chat_bot)


demo = gr.TabbedInterface([sentiment_a, summraztion,chatBot,Audio], ["Sentiment Analysis", "Summraztion","ChatBot",'Audio'])

demo.launch(debug=True)