Update apps/intro.py

apps/intro.py

@@ -16,6 +16,26 @@ def init_models():
     return model, umap_model
 
 def app():
+
+    with st.container():
+        st.markdown("<h1 style='text-align: center;  \
+                      color: black;'> Text Embedder</h1>", 
+                      unsafe_allow_html=True)
+        st.write(' ')
+        st.write(' ')
+
+    with st.expander("ℹ️ - About this app", expanded=True):
+
+        st.write(
+            """     
+            Information cartography - Get your word/phrase/sentence/paragraph embedded and visualized.
+            The (English) sentence-transformers model "all-MiniLM-L6-v2" maps sentences & paragraphs to a 384 dimensional dense vector space This is normally used for tasks like clustering or semantic search, but in this case, we use it to place your text to a 3D map. Before plotting, the dimension needs to be reduced to three so we can actually plot it, but preserve as much information as possible. For this, we use a technology called umap.
+            
+            Simply put in your text and press EMBED, your examples will add up. You can use the category for different coloring.
+            """)
+
+        st.markdown("")
+        
     word_to_embed_list =  st.session_state['embed_list']
     cat_list =  st.session_state['cat_list']