Update app.py

app.py

@@ -2,19 +2,8 @@ import streamlit as st
 from transformers import pipeline
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 
-def tras_sum(input):
-  model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
-  model = T5ForConditionalGeneration.from_pretrained(model_name)
-  tokenizer = T5Tokenizer.from_pretrained(model_name)
-  # text summary generate
-  prefix = 'summary to en: '
-  src_text = prefix + input
-  input_ids = tokenizer(src_text, return_tensors="pt")
-  generated_tokens = model.generate(**input_ids)
-  traslated_summary = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-  return traslated_summary
-    
 # Load the summarization & translation model pipeline
+tran_sum_pipe = pipeline("translation", model='utrobinmv/t5_summary_en_ru_zh_base_2048',return_all_scores=True)
 sentiment_pipeline = pipeline("text-classification", model='Howosn/Sentiment_Model',return_all_scores=True)
 
 # Streamlit application title