comit

app.py

@@ -1,6 +1,7 @@
 # pip install --upgrade pip
 # pip install transformers==4.11.3
 #pip install git+https://github.com/huggingface/transformers
+
 import torch
 import streamlit as st
 from transformers import BertForQuestionAnswering
@@ -77,13 +78,23 @@ import textwrap
 
 # Wrap text to 80 characters.
 wrapper = textwrap.TextWrapper(width=80) 
+context=st.text_area('Enter the context of your Topic')
 
-bert_abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
-wrapper.fill(bert_abstract)
+#bert_abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
+#wrapper.fill(bert_abstract)
+wrapper.fill(context)
 
 #question = "What does the 'B' in BERT stand for?"
 question = st.text_area('Enter your question...')
-
 answer=answer_question(question, bert_abstract)
+st.text(answer)
+while st.text_area().lower()!='q':
+    question = st.text_area('Enter your question...')
+    answer=answer_question(question, bert_abstract)
+    st.text(answer)
+    if st.text_area() =='q':
+        break
+
+
+
 
-st.text(answer)