Update app.py

app.py

@@ -1,12 +1,23 @@
 import streamlit as st
 from transformers import pipeline
 
-
+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')
-trans_pipe = pipeline("translation", model='liam168/trans-opus-mt-zh-en')
+#trans_pipe = pipeline("translation", model='liam168/trans-opus-mt-zh-en')
 sentiment_pipeline = pipeline("text-classification", model='Howosn/Sentiment_Model',return_all_scores=True)
-#tokenizer = AutoTokenizer.from_pretrained('Howosn/Sentiment_Model', use_fast=False)
+
 
 # Streamlit application title
 st.title("Emotion analysis")
@@ -18,7 +29,7 @@ text = st.text_area("Enter the text", "")
 # Perform analysis result when the user clicks the "Analyse" button
 if st.button("Analyse"):
     # Perform text classification on the input text
-    trans = trans_pipe(text)[0]
+    trans = tras_sum(text)[0]
     results = sentiment_pipeline(trans)[0]
     
     # Display the classification result