Update streamlit_app.py

streamlit_app.py

@@ -36,60 +36,28 @@ user_input = st.chat_input(prompt_label, key=input_key)
 deferred_response = None
 deferred_spinner_msg = ""
 
-# if user_input:
-#     st.session_state.chat_history.append(HumanMessage(content=user_input))
-
-#     if re.search(r'https?://\S+', user_input):
-#         accent_tool_obj.last_transcript = ""
-#         deferred_spinner_msg = "Analyzing new video..."
-#         def run_agent():
-#             return analysis_agent.invoke(st.session_state.chat_history)[-1].content
-#     else:
-#         deferred_spinner_msg = "Responding based on transcript..."
-#         def run_agent():
-#             return follow_up_agent.invoke(st.session_state.chat_history).content
-
-#     # Run response generation inside spinner after chat is rendered
-#     def process_response():
-#         with st.spinner(deferred_spinner_msg):
-#             try:
-#                 result = run_agent()
-#             except Exception as e:
-#                 result = f"Error: {str(e)}"
-#             st.session_state.chat_history.append(AIMessage(content=result))
-#             st.rerun() 
-
-if user_input and not st.session_state.get("processing_started", False):
+if user_input:
     st.session_state.chat_history.append(HumanMessage(content=user_input))
-    
+
     if re.search(r'https?://\S+', user_input):
         accent_tool_obj.last_transcript = ""
-        st.session_state.deferred_spinner_msg = "Analyzing new video..."
-        st.session_state.run_agent_type = "analysis"
+        deferred_spinner_msg = "Analyzing new video..."
+        def run_agent():
+            return analysis_agent.invoke(st.session_state.chat_history)[-1].content
     else:
-        st.session_state.deferred_spinner_msg = "Responding based on transcript..."
-        st.session_state.run_agent_type = "followup"
-    
-    st.session_state.processing_started = True
-    st.rerun() 
-
-for msg in st.session_state.chat_history:
-    with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
-        st.markdown(msg.content)
-
-if st.session_state.get("processing_started", False):
-    with st.spinner(st.session_state.deferred_spinner_msg):
-        try:
-            if st.session_state.run_agent_type == "analysis":
-                result = analysis_agent.invoke(st.session_state.chat_history)[-1].content
-            else:
-                result = follow_up_agent.invoke(st.session_state.chat_history).content
-        except Exception as e:
-            result = f"Error: {str(e)}"
-    
-    st.session_state.chat_history.append(AIMessage(content=result))
-    st.session_state.processing_started = False
-    st.rerun() 
+        deferred_spinner_msg = "Responding based on transcript..."
+        def run_agent():
+            return follow_up_agent.invoke(st.session_state.chat_history).content
+
+    # Run response generation inside spinner after chat is rendered
+    def process_response():
+        with st.spinner(deferred_spinner_msg):
+            try:
+                result = run_agent()
+            except Exception as e:
+                result = f"Error: {str(e)}"
+            st.session_state.chat_history.append(AIMessage(content=result))
+            st.rerun() 
 
 # Display full chat history (before running spinner)
 for msg in st.session_state.chat_history: