add spinner

app.py

@@ -173,25 +173,26 @@ if prompt := st.chat_input("Tell me about YSA"):
 
     question = prompt
 
-    docs = db.similarity_search(question)
-    docs_2 = db.similarity_search_with_score(question)
-    docs_2_table = pd.DataFrame(
-        {
-            "source": [docs_2[i][0].metadata["source"] for i in range(len(docs))],
-            "content": [docs_2[i][0].page_content for i in range(len(docs))],
-            "distances": [docs_2[i][1] for i in range(len(docs))],
-        }
-    )
-    ref_from_db_search = docs_2_table["content"]
-
-    engineered_prompt = f"""
-        Based on the context: {ref_from_db_search},
-        answer the user question: {question}.
-        Answer the question directly (don't say "based on the context, ...")
-    """
-
-    answer = call_chatgpt(engineered_prompt)
-    response = answer
+    with st.spinner("Wait for it..."):
+        docs = db.similarity_search(question)
+        docs_2 = db.similarity_search_with_score(question)
+        docs_2_table = pd.DataFrame(
+            {
+                "source": [docs_2[i][0].metadata["source"] for i in range(len(docs))],
+                "content": [docs_2[i][0].page_content for i in range(len(docs))],
+                "distances": [docs_2[i][1] for i in range(len(docs))],
+            }
+        )
+        ref_from_db_search = docs_2_table["content"]
+
+        engineered_prompt = f"""
+            Based on the context: {ref_from_db_search},
+            answer the user question: {question}.
+            Answer the question directly (don't say "based on the context, ...")
+        """
+
+        answer = call_chatgpt(engineered_prompt)
+        response = answer
 
     # Display assistant response in chat message container
     with st.chat_message("assistant"):