Update app.py

app.py

@@ -261,7 +261,7 @@ def generate_response(db, query_text, previous_context):
     
     return full_response
 
-def streamlit_app(db):
+def streamlit_app():
     st.title("BioModelsRAG")
     
     search_str = st.text_input("Enter search query:")
@@ -299,30 +299,29 @@ def streamlit_app(db):
                 
                 db = create_vector_db(final_items)
                 
-                if db:
-                    st.write("Models have been processed and added to the database.")
+                st.write("Models have been processed and added to the database.")
                     
-    if db:
-        @st.cache_resource
-        def get_messages(db):
-            if "messages" not in st.session_state:
-                st.session_state.messages = []
-            return st.session_state.messages
-        st.session_state.messages = get_messages()
 
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
+    @st.cache_resource
+    def get_messages(db):
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+        return st.session_state.messages
+    st.session_state.messages = get_messages()
 
-        if prompt := st.chat_input(query_text):
-            st.chat_message("user").markdown(prompt)
-            st.session_state.messages.append({"role": "user", "content":prompt})
-            response = generate_response(db, query_text, st.session_state)
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
 
-            with st.chat_message("assistant"):
-                st.markdown(response)
+    if prompt := st.chat_input(query_text):
+        st.chat_message("user").markdown(prompt)
+        st.session_state.messages.append({"role": "user", "content":prompt})
+        response = generate_response(db, query_text, st.session_state)
 
-            st.session_state.messages.append({"role":"assistant","content":response})
+        with st.chat_message("assistant"):
+            st.markdown(response)
+
+        st.session_state.messages.append({"role":"assistant","content":response})
 
 if __name__ == "__main__":
     streamlit_app()