app.py

!pip install gradio transformers torch
import gradio as gr
from transformers import pipeline
translator = pipeline("translation_en_to_fr",model="Mr-Vicky-01/Fine_tune_english_to_tamil")
sentiment_analyzer = pipeline("sentiment-analysis")
summerizer = pipeline("summarization",model = "facebook/bart-large-cnn")

def translate_text(text):
  result = translator(text)
  return result[0]['translation_text']
def summarization(text):
  result = summerizer(text)
  return result[0]['summary_text']
def analyze_sentiment(text):
  result = sentiment_analyzer(text)
  a = result[0]['label']
  b = round(result[0]['score'],3)
  if (a == 'POSITIVE'):
    return 'Happy'
  elif(a == 'negative'):
    return 'Unhappy'

with gr.Blocks() as demo:
  gr.Markdown("Text Pipeline : Translation,summarization, and Sentiemnt Analysis")

  #TRANSLATION TAB
  with gr.Tab("Translation"):
    input_text = gr.Textbox(label = "Enter text for translation")
    output_text = gr.Textbox(label = "Translated text")
    translate_button = gr.Button("Translate")
    translate_button.click(fn=translate_text,inputs = input_text,outputs = output_text)
  #SUMMARIZATION TAB
  with gr.Tab("Summarization"):
    input_summary = gr.Textbox(label = "Enter text for Summarization")
    output_summary = gr.Textbox(label = "Summarized text")
    summarize_button = gr.Button("Summarize")
    summarize_button.click(fn=summarization,inputs = input_summary,outputs = output_summary)

  with gr.Tab("Sentiment Analysis"):
    input_sentiment = gr.Textbox(label = "Enter text for Sentiment Analysis")
    output_sentiement = gr.Textbox(label = "Sentiment Analysis Result")
    sentiment_button = gr.Button("Sentiment Analysis")
    sentiment_button.click(fn=analyze_sentiment,inputs = input_sentiment,outputs = output_sentiement)

demo.launch()