Update app.py

app.py

@@ -2,8 +2,19 @@ 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
@@ -28,6 +39,5 @@ if st.button("Analyse"):
             max_score = result['score']
             max_label = result['label']
 
-    st.write("Text:", trans)
     st.write("Label:", max_label)
     st.write("Score:", max_score)