Update app.py

app.py

@@ -189,13 +189,6 @@ if query:
     #     st.stop()
         st.markdown("---")
 
-        st.markdown(
-            f"<b><p style='font-family: Arial; font-size: 20px; font-style: Bold;'>Top <span style='color:red; font-style: italic;'>{value_word} "
-            f"</span>words contextually and semantically similar to "
-            f"<span style='color:red; font-style: italic;'>{query} </span>within the <span style='color:red; font-style: italic;'>{database_name}</span> corpus. "
-            f"Click on the squares to expand and also the PubMed and Wikipedia links for more word information</span></p></b>",
-            unsafe_allow_html=True)
-
         try:
             table = model.wv.most_similar_cosmul(query, topn=10000)
             table = (pd.DataFrame(table))
@@ -205,8 +198,11 @@ if query:
             pd.set_option('display.max_rows', None)
             table2 = table.copy()
 
-            st.markdown(f"Top 10000 words in an interactive embedding map for {query} in {database_name} PubMed corpus",
-                 unsafe_allow_html=True)
+            # st.markdown(f"<b><p style='font-family: Arial; font-size: 20px;'>Populate a treemap to visualize "
+            #             f"<span style='color:red; font-style: italic;'>words</span> contextually "
+            #             f"and semantically similar to <span style='color:red; font-style: italic;'>{query}</span> "
+            #             f"within the <span style='color:red; font-style: italic;'>{database_name}</span> corpus.</p></b>",
+            #     unsafe_allow_html=True)
 
             # Set the max number of words to display
             value_word = min(100, len(table2))
@@ -238,7 +234,7 @@ if query:
             # fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>Similarity score: %{customdata[0]:.2f}<extra></extra>')
             fig.update_layout(title=dict(
                 text=f"Top 10000 words in an interactive embedding map for {query} in {database_name} PubMed corpus"
-                     f": Zoom in to the black diamond to find {query}", x=0.5, y=.8, xanchor='center', yanchor='top',
+                     f": Zoom in to the black diamond to find {query}", x=0.5, y=1, xanchor='center', yanchor='top',
                 font=dict(color='black')))
             fig.update_coloraxes(colorbar_title="Similarity with query")
 
@@ -469,9 +465,6 @@ if query:
             st.warning(
                 f"This selection exceeds the number of similar proteins related to {query} within the {database_name} corpus, please choose a lower number")
 
-        st.markdown(f"3D interacive gene embedding map for {query} in {database_name} PubMed corpus",
-                 unsafe_allow_html=True)
-        
         try:
             # Get the top 50 similar genes to the query
             value_gene = min(df_len, 50)
@@ -506,7 +499,7 @@ if query:
         fig2.update_traces(
                 hovertemplate='<b>%{hovertext}</b><br>Similarity score: %{customdata[0]:.2f}<extra></extra>')
         fig2.update_layout(
-                title=dict(text=f"Word embedding map for {query} in {database_name} PubMed corpus", x=0.5, y=0.8,
+                title=dict(text=f"Word embedding map for {query} in {database_name} PubMed corpus", x=0.5, y=0.95,
                            xanchor='center', yanchor='top', font=dict(color='black')),
                 scene=dict(xaxis_title="Dimension 1", yaxis_title="Dimension 2", zaxis_title="Dimension 3"))
         fig2.update_coloraxes(colorbar_title="Similarity with query")