Updated app.py to add the cache code

app.py

@@ -80,7 +80,11 @@ def build_vectorstore():
     vectorstore = FAISS.from_documents(splits, embeddings)
     return vectorstore
 
-vectorstore = build_vectorstore()
+# Initialize vectorstore in session state to load only once
+@st.cache_resource
+def get_vectorstore():
+    """Get or create the vectorstore (cached)"""
+    return build_vectorstore()
 
 def run_rag_chat(query, vectorstore):
     """Run the Retrieval-Augmented Generation (RAG) for clinical questions"""
@@ -152,6 +156,16 @@ def main():
         layout="wide",
         initial_sidebar_state="expanded"
     )
+    
+    # Load vectorstore only once using session state
+    if 'vectorstore' not in st.session_state:
+        with st.spinner("Loading clinical knowledge base... This may take a minute."):
+            try:
+                st.session_state.vectorstore = get_vectorstore()
+                st.success("Clinical knowledge base loaded successfully!")
+            except Exception as e:
+                st.error(f"Error loading knowledge base: {str(e)}")
+                st.session_state.vectorstore = None
 
     # Custom CSS for modern look
     st.markdown("""
@@ -287,19 +301,25 @@ def main():
         
         # Process query
         if submit_button and user_input:
-            with st.spinner("Analyzing clinical data..."):
-                # Add a small delay for UX
-                time.sleep(0.5)
-                
-                # Run RAG
-                response = run_rag_chat(user_input, vectorstore)
-                response["retriever"] = vectorstore.as_retriever()
-                
-                # Add to chat history
-                st.session_state.chat_history.append((user_input, response))
-                
-                # Rerun to update UI
-                st.experimental_rerun()
+            if st.session_state.vectorstore is None:
+                st.error("Knowledge base not loaded. Please refresh the page and try again.")
+            else:
+                with st.spinner("Analyzing clinical data..."):
+                    try:
+                        # Add a small delay for UX
+                        time.sleep(0.5)
+                        
+                        # Run RAG
+                        response = run_rag_chat(user_input, st.session_state.vectorstore)
+                        response["retriever"] = st.session_state.vectorstore.as_retriever()
+                        
+                        # Add to chat history
+                        st.session_state.chat_history.append((user_input, response))
+                        
+                        # Rerun to update UI
+                        st.experimental_rerun()
+                    except Exception as e:
+                        st.error(f"Error processing query: {str(e)}")
     
     # About page
     elif st.session_state.page == 'about':