Update app.py

app.py

@@ -1,13 +1,13 @@
 import streamlit as st
 from transformers import pipeline
 
-st.markdown("# Auto-generating Question-Answering Datasets with Domain-Specific Knowledge for Language Models in Scientific Tasks", unsafe_allow_html=True)
+st.markdown("# 🎓 Auto-generating Question-Answering Datasets with Domain-Specific Knowledge for Language Models in Scientific Tasks", unsafe_allow_html=True)
 
 # Transformation Algorithm
-st.markdown('## QA Dataset Auto Generation', unsafe_allow_html=True)
+st.markdown('## 🖥️ QA Dataset Auto Generation', unsafe_allow_html=True)
 
 # Question Answering
-st.markdown('## Question Answering', unsafe_allow_html=True)
+st.markdown('## 📖 Question Answering', unsafe_allow_html=True)
 st.markdown('### Select a model: ', unsafe_allow_html=True)
 
 # 定义模型配置选项
@@ -34,13 +34,13 @@ st.markdown(f'##### {model}', unsafe_allow_html=True)
 
 # 加载问答模型
 pipe = pipeline("question-answering", model=model)
-st.markdown('### QA: ', unsafe_allow_html=True)
+st.markdown('### Answer the question: ', unsafe_allow_html=True)
 
 # 使用Session State来存储默认的问题和上下文
 if 'default_question' not in st.session_state:
-    st.session_state['default_question'] = ""
+    st.session_state['default_question'] = "What is the value of FF?"
 if 'default_context' not in st.session_state:
-    st.session_state['default_context'] = ""
+    st.session_state['default_context'] = "The referential DSSC with Pt CE was also measured under the same conditions, which yields η of 6.66% (Voc= 0.78 V, Jsc= 13.0 mA cm−2, FF = 65.9%)."
 
 col1, col2 = st.columns(2)
 
@@ -74,7 +74,7 @@ if st.button('Extract the answer'):
 ##########
 # Property Extraction
 ##########
-st.markdown('## Property Extraction', unsafe_allow_html=True)
+st.markdown('## 📤 Property Extraction', unsafe_allow_html=True)
 
 default_property = "FF"
 default_context = "The referential DSSC with Pt CE was also measured under the same conditions, which yields η of 6.66% (Voc= 0.78 V, Jsc= 13.0 mA cm−2, FF = 65.9%)."