Update app.py

app.py

@@ -50,23 +50,19 @@ def main():
 
     info = '''
     :bookmark: **Paper:** [Making Sentence Embeddings Robust to User-Generated Content (Nishimwe et al., 2024)](https://arxiv.org/abs/2403.17220)  
-    :link: **Github:** [https://github.com/lydianish/RoLASER](https://github.com/lydianish/RoLASER)
-
-    This demo app computes text embeddings of sentence pairs using the LASER encoder and its robust students RoLASER and c-RoLASER. 
-    The pairwise cosine distances between the sentences are then computed and displayed. 
+    :link: **Github:** [https://github.com/lydianish/RoLASER](https://github.com/lydianish/RoLASER) 
     '''
     st.markdown(info)
             
-    st.header('Standard and Non-standard Text Input Pairs:')
+    st.header('Standard and Non-standard Text Input Pairs')
 
-    num_pairs = st.sidebar.number_input('Number of Text Input Pairs (1-10)', min_value=1, max_value=10, value=5)
+    cols = st.columns(3)
+    num_pairs = cols[1].number_input('Number of Text Input Pairs (1-10):', min_value=1, max_value=10, value=5)
 
     with st.form('text_input_form'):
         col1, col2 = st.columns(2)
-        with col1:
-            st.write('Enter standard text here:')
-        with col2:
-            st.write('Enter non-standard text here:')
+        col1.write('Enter standard text here:')
+        col2.write('Enter non-standard text here:')
 
         std_text_inputs = []
         ugc_text_inputs = []
@@ -103,14 +99,14 @@ def main():
     outputs['ugc'] = np.tile(ugc_text_inputs, 3)
     outputs['cos'] = np.concatenate([X_cos_laser, X_cos_rolaser, X_cos_c_rolaser])
 
-    st.header('Cosine Distance Scores:')
+    st.header('Cosine Distance Scores')
     st.caption('*This bar plot is interactive: Hover on the bars to display values. Click on the legend items to filter models.*')
     fig = px.bar(outputs, x='pair', y='cos', color='model', barmode='group', hover_data=['ugc', 'std'])
     fig.update_xaxes(title_text='Text Input Pair')
     fig.update_yaxes(title_text='Cosine Distance')
     st.plotly_chart(fig, use_container_width=True)
 
-    st.header('Average Cosine Distance Scores:')
+    st.header('Average Cosine Distance Scores')
     st.caption('*This data table is interactive: Click on a column header to sort values.*')
     st.write(outputs.groupby('model')['cos'].describe())