app.py

from transformers import pipeline
import gradio as gr
roberta_pipe = pipeline(
    "sentiment-analysis",
    model="siebert/sentiment-roberta-large-english",
    tokenizer="siebert/sentiment-roberta-large-english",
    return_all_scores = True
)

import requests

TRANS_API = "https://hussain-shk-indictrans-indic2english.hf.space/run/predict"

def trans(text: str, source:str, target:str):
    response = requests.post(TRANS_API, json={
    "data": [
      text,
      source,
      target
    ]
  }, timeout=None).json()
    return response["data"][0]

def analyse_sentiment(text, source):
   if source != "English":
        text = trans(text, source, "English")
   response = roberta_pipe(text)
   d = {}
   for i in response[0]:
     d[i['label'].lower()] = i['score']
   return d

languages = ["Assamese", "Bengali", "Gujarati", "Hindi", "Kannada","Malayalam", "Marathi", "Odia", "Punjabi", "Tamil", "Telugu", "English"]

input_text = gr.Textbox(placeholder="Enter a positive or negative sentence here...")
drop_down = gr.inputs.Dropdown(languages, type="value", default="English", label="Select Source Language")

examples = [["this book was a great book that i have read many times", "English"],
              ["एक महान अमेरिकी लेखक का एक आकर्षक संग्रह" , "Hindi"],
              ["हा आतापर्यंतचा सर्वात वाईट चित्रपट आहे यात शंका नाही", "Marathi"],
              ["இந்த தயாரிப்பு ஆச்சரியமாக இருக்கிறது", "Tamil"],
              ["તમારા માટે નહીં જો તમે વિના અવરોધે વીડિયો શોધી રહ્યા છો", "Gujrati"],]

demo = gr.Interface(
    enable_queue=True,
    fn=analyse_sentiment, 
    inputs=[input_text, drop_down], 
    outputs="label", 
    interpretation="default",
    title='IndiSent: Multilingual Sentiment Analysis',
    examples=examples)

demo.launch()