Update app.py

app.py

@@ -189,6 +189,13 @@ 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))
@@ -198,11 +205,8 @@ if query:
             pd.set_option('display.max_rows', None)
             table2 = table.copy()
 
-            # 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)
+            st.markdown(f"Top 10000 words in an interactive embedding map for {query} in {database_name} PubMed corpus",
+                 unsafe_allow_html=True)
 
             # Set the max number of words to display
             value_word = min(100, len(table2))
@@ -232,10 +236,10 @@ if query:
                               yaxis=dict(gridcolor='#CCFFFF', color='blue'))
 
             # 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',
-                font=dict(color='black')))
+            # 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',
+            #     font=dict(color='black')))
             fig.update_coloraxes(colorbar_title="Similarity with query")
 
             # Represent query as a large red diamond
@@ -465,6 +469,9 @@ 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)
@@ -499,8 +506,8 @@ 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,
-                           xanchor='center', yanchor='top', font=dict(color='black')),
+                # title=dict(text=f"Word embedding map for {query} in {database_name} PubMed corpus", x=0.5, y=0.8,
+                #            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")