added info about upcoming features, fixed reset button

ProtHGT_app.py

@@ -11,7 +11,7 @@ with st.spinner("Please wait while we prepare the environment. This may take a f
         start_time = time.time()
         os.system("bash setup.sh")
         end_time = time.time()
-        st.success(f"Environment prepared in {end_time - start_time:.2f} seconds")
+        print(f"Environment prepared in {end_time - start_time:.2f} seconds")
         with open(".setup_done", "w") as f:
             f.write("done")
 
@@ -26,6 +26,15 @@ if 'predictions_df' not in st.session_state:
 if 'submitted' not in st.session_state:
     st.session_state.submitted = False
 
+with st.expander("🚀 Upcoming Features"):
+    st.info("""
+    We are actively working on enhancing ProtHGT application with new capabilities:
+    
+    - **Real-time data retrieval for new proteins**: Currently, ProtHGT can only generate predictions for proteins that already exist in our knowledge graph. We are developing a new feature that will allow users to **predict functions for entirely new proteins starting from their sequences**. This will work by **retrieving relevant relationship data in real time from external source databases** (e.g., UniProt, STRING, and other biological repositories). The system will dynamically construct a knowledge graph for the query protein, incorporating its interactions, domains, pathways, and other biological associations before running function prediction. This approach will enable ProtHGT to analyze newly discovered or less-studied proteins even if they are not pre-annotated in our dataset.  
+    - **Expanded embedding options**: Currently, this application represents proteins using **TAPE embeddings**, which serve as the initial numerical representations of protein sequences before being processed in the heterogeneous graph model. We are working on integrating **ProtT5** and **ESM-2** as alternative initial embeddings, allowing users to choose different sequence representations that may enhance performance for specific tasks. A detailed comparison of how these embeddings influence function prediction accuracy will be included in our upcoming publication.
+    
+    Stay tuned for updates and future publications!
+    """)
 
 with st.sidebar:
     st.markdown("""
@@ -317,4 +326,4 @@ with st.sidebar:
         if st.button("Reset"):
             st.session_state.predictions_df = None
             st.session_state.submitted = False
-            st.experimental_rerun()
+            st.rerun()