app.py

import gradio as gr

from transformers import pipeline

pipe = pipeline("translation_en_to_ml", model="t5-base")

def predict(text):
  return pipe(text)[0]["translation_text"]
  
iface = gr.Interface(
  fn=predict, 
  inputs=[gr.inputs.Textbox(label="text", lines=3)],
  outputs='text',
  examples=[["Hello! My name is Rajesh"], ["How are you?"]]
)

iface.launch()

# import gradio as gr
# from transformers import MBartForConditionalGeneration, MBart50TokenizerFast,MBartTokenizerFast,MBart50Tokenizer


# from transformers import MBartTokenizer,MBartForConditionalGeneration, MBartConfig
# model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
# tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt",src_lang="en_XX")

# def get_input(text):
#   models_input = tokenizer(text,return_tensors="pt")
#   generated_tokens = model.generate(**models_input,forced_bos_token_id=tokenizer.lang_code_to_id["ml_IN"])
#   translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
#   return translation
  
# iface = gr.Interface(fn=get_input,inputs="text",outputs="text", title = "English to Malayalam Translator",description="Get Malayalam translation for your text in English")  

# iface.launch()