Update app.py

app.py

@@ -1,28 +1,28 @@
 import streamlit as st
-#from transformers import pipeline
+from transformers import pipeline
 
-#pipe = pipeline('sentiment-analysis')
-#text = st.text_area('enter some text!')
+pipe = pipeline('sentiment-analysis')
+text = st.text_area('enter some text!')
 
-#if text: 
- # out = pipe(text)
-  #st.json(out)
+if text: 
+ out = pipe(text)
+ st.json(out)
   
-from transformers import pipeline
+#from transformers import pipeline
 
-model_name = "deepset/xlm-roberta-large-squad2"
+#model_name = "deepset/xlm-roberta-large-squad2"
 
-qa_pl = pipeline('question-answering', model=model_name, tokenizer=model_name, device=0)
+#qa_pl = pipeline('question-answering', model=model_name, tokenizer=model_name, device=0)
 
 #predictions = []
 
 # batches might be faster 
-ctx  = st.text_area('Gib context')
-q = st.text_area('Gib question')
+#ctx  = st.text_area('Gib context')
+#q = st.text_area('Gib question')
 
-if context: 
-  result = qa_pl(context=ctx, question=q)
-  st.json(result["answer"])
+#if context: 
+#  result = qa_pl(context=ctx, question=q)
+#  st.json(result["answer"])
 
 #for ctx, q in test_df[["context", "question"]].to_numpy():