sentiment model

app.py

@@ -1,22 +1,15 @@
 import streamlit as st
 from transformers import pipeline
 
-pipe = pipeline('sentiment-analysis')
 text = st.text_area('enter some text!')
 
-if text:
-    out = pipe(text)
-    st.json(out)
-
-# text = st.text_area('enter some text!')
+classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
+preds = classifier(text, top_k=None)
 
-# classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
-# preds = classifier(text, top_k=None)
+sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
 
-# sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
+for item in sorted_preds:
+    item['score'] = round(item['score'], 5)
 
-# for item in sorted_preds:
-#     item['score'] = round(item['score'], 5)
-
-# if text:
-#     st.json(sorted_preds)
+if text:
+    st.json(sorted_preds)