Update app.py

app.py

@@ -1,25 +1,33 @@
-# -*- coding: utf-8 -*-
-"""Lab10_app.ipynb
+import streamlit as st
+from transformers.pipelines import pipeline
+from transformers.modeling_auto import AutoModelForQuestionAnswering
+from transformers.tokenization_auto import AutoTokenizer
 
-Automatically generated by Colaboratory.
+# b) Load model & tokenizer
+#model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+#tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-Original file is located at
-    https://colab.research.google.com/drive/17s4mJEUqE6OCVbdAOw1ROMQOpf4NBAw1
-"""
+#classifier = pipeline("question-answering", model="deepset/roberta-base-squad2")
 
-import streamlit as st
-from transformers import pipeline
+#model_name = "deepset/xlm-roberta-base-squad2"
+#nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+#QA_input = {
+#    'question': 'Why is model conversion important?',
+#    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
+#}
+#res = nlp(QA_input)
 
-classifier = pipeline("question-answering", model="deepset/roberta-base-squad2")
 def main():
     st.title("Question & Answering")
 
     with st.form("text_field"):
-        text = st.text_area('Enter question:')
-        # clicked==True only when the button is clicked
+        sentence_1= st.text_area('Enter Q1:')
+        sentence_2= st.text_area('Enter Q2:')
+        QA_input = {'question':sentence_1, 'context':sentence_2}
+        #clicked==True only when the button is clicked
         clicked = st.form_submit_button("Submit")
         if clicked:
-          results = classifier([text])
+          results = nlp(QA_input)
           st.json(results)
 
 if __name__ == "__main__":