Update app.py

app.py

@@ -1,9 +1,31 @@
 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)
+  
 from transformers import pipeline
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter some text!')
+model_name = "xlm-roberta-base-squad2"
+
+qa_pl = pipeline('question-answering', model=model_name, tokenizer=model_name, device=0)
+
+#predictions = []
+
+# batches might be faster 
+context  = st.text_area('Gib context')
+question = st.text_area('Gib question')
+
+if context and question: 
+  result = qa_pl(context=ctx, question=q)
+  st.json(result)
+
+#for ctx, q in test_df[["context", "question"]].to_numpy():
 
-if text: 
-  out = pipe(text)
-  st.json(out)
+#    result = qa_pl(context=ctx, question=q)
+    
+#    predictions.append(result["answer"])