Fix debrief message format and remove duplicate bubbles

app.py

@@ -318,15 +318,14 @@ Journaling Prompt – Offer one reflective or integrative question to deepen the
 Tone: Warm, precise, emotionally attuned. Do not overuse praise, avoid pathologizing, and refrain from offering generic feedback."""
 
                 # Initialize debrief conversation with just the system message
-                st.session_state.debrief_messages = [
-                    {"role": "system", "content": debrief_system_message}
-                ]
+                st.session_state.debrief_messages = []
                 
                 try:
-                    # Get the initial response
+                    # Get the initial response using the system message as a parameter
                     response = client.messages.create(
                         model="claude-3-opus-20240229",
-                        messages=st.session_state.debrief_messages,
+                        system=debrief_system_message,
+                        messages=[{"role": "user", "content": "Please help me process this conversation."}],
                         max_tokens=1000
                     )
                     # Add the response to the messages
@@ -343,7 +342,7 @@ Tone: Warm, precise, emotionally attuned. Do not overuse praise, avoid pathologi
         st.markdown("## 🤝 Let's Process Together")
         
         # Display debrief conversation
-        for message in st.session_state.debrief_messages[1:]:  # Skip system message
+        for message in st.session_state.debrief_messages:
             with st.chat_message(message["role"]):
                 st.markdown(message["content"])
         
@@ -359,7 +358,12 @@ Tone: Warm, precise, emotionally attuned. Do not overuse praise, avoid pathologi
                     try:
                         response = client.messages.create(
                             model="claude-3-opus-20240229",
-                            messages=st.session_state.debrief_messages,
+                            system=debrief_system_message,
+                            messages=[
+                                {"role": "user", "content": msg["content"]} 
+                                for msg in st.session_state.debrief_messages 
+                                if msg["role"] == "user"
+                            ],
                             max_tokens=1000
                         )
                         assistant_response = response.content[0].text