app updated

app.py

@@ -103,35 +103,36 @@ if uploaded_files is None:
     st.info("Upload files to analyze")
 
 elif uploaded_files:
-    # Process the uploaded files to extract text and source information
-    textify_output = read_and_textify(uploaded_files)
+    with st.spinner("Wait for it... 🤔"):
+        # Process the uploaded files to extract text and source information
+        textify_output = read_and_textify(uploaded_files)
 
-    # Separate the output into documents (text) and their corresponding sources
-    documents, sources = textify_output
+        # Separate the output into documents (text) and their corresponding sources
+        documents, sources = textify_output
 
-    # Call the function
-    query_database = list_to_nums(documents)
+        # Call the function
+        query_database = list_to_nums(documents)
 
-    # Create reference table
-    refs_tab = query_search(
-        "pful for understanding federal income", documents, query_database, sources
-    )
-    refs_tab.head(math.ceil(top_n))
-
-    # React to user input
-    if prompt := st.chat_input("What is up?"):
-        # Display user message in chat message container
-        st.chat_message("user").markdown(prompt)
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-
-        result = refs_tab
-
-        # Display assistant response in chat message container
-        with st.chat_message("assistant"):
-            if not use_dict_format:
-                st.table(result)
-            else:
-                st.write(result.to_json())
-        # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": result})
+        # Create reference table
+        refs_tab = query_search(
+            "pful for understanding federal income", documents, query_database, sources
+        )
+        refs_tab.head(math.ceil(top_n))
+
+        # React to user input
+        if prompt := st.chat_input("What is up?"):
+            # Display user message in chat message container
+            st.chat_message("user").markdown(prompt)
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": prompt})
+
+            result = refs_tab
+
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                if not use_dict_format:
+                    st.table(result)
+                else:
+                    st.write(result.to_json())
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": result})