fix ui issue

app.py

@@ -126,4 +126,4 @@ with chat_col:
     render_chat_column(st, llm_client, model_option, max_tokens, BASE_PROMPT)
 
 # --- Footer ---
-st.markdown('<div style="text-align: center; margin-top: 2em; color: #888; font-size: 1.1em;">made with 💛 by baxin</div>', unsafe_allow_html=True)
+st.markdown('<div style="text-align: center; margin-top: 2em; color: #888; font-size: 1.1em;">made with 💙 by baxin</div>', unsafe_allow_html=True)

chat_column.py

@@ -10,23 +10,24 @@ def render_chat_column(st, llm_client, model_option, max_tokens, BASE_PROMPT):
     st.header("💬 Chat with the AI to generate JSON for Veo3")
 
     # --- Display Chat History ---
-    # (This part remains the same)
+    # Display all existing messages first
     for message in st.session_state.messages:
         avatar = '🤖' if message["role"] == "assistant" else '🦔'
         with st.chat_message(message["role"], avatar=avatar):
             st.markdown(message["content"])
 
-    # --- Chat Input and LLM Call ---
+    # --- Chat Input at the bottom ---
     if prompt := st.chat_input("Enter topic to generate JSON for Veo3..."):
         if len(prompt.strip()) == 0:
             st.warning("Please enter a topic.", icon="⚠️")
         elif len(prompt) > 4000:  # Example length limit
             st.error("Input is too long (max 4000 chars).", icon="🚨")
         else:
-            # Add user message to history and display FIRST
-            # It's important to add the user message *before* sending it to the API
+            # Add user message to history
             st.session_state.messages.append(
                 {"role": "user", "content": prompt})
+
+            # Display user message
             with st.chat_message("user", avatar='🦔'):
                 st.markdown(prompt)
 
@@ -37,41 +38,36 @@ def render_chat_column(st, llm_client, model_option, max_tokens, BASE_PROMPT):
                     response_placeholder.markdown("Generating prompt... ▌")
                     full_response = ""
 
-                    # --- MODIFICATION START ---
                     # Construct messages for API including the conversation history
-
                     # 1. Start with the system prompt
                     messages_for_api = [
                         {"role": "system", "content": BASE_PROMPT}]
 
                     # 2. Add all messages from the session state (history)
-                    #    This now includes the user message we just added above.
                     messages_for_api.extend(st.session_state.messages)
 
-                    # 3. Filter out any potential empty messages (just in case)
-                    #    This step might be less critical now but is good practice.
+                    # 3. Filter out any potential empty messages
                     messages_for_api = [
                         m for m in messages_for_api if m.get("content")]
-                    # --- MODIFICATION END ---
 
                     stream_kwargs = {
                         "model": model_option,
-                        "messages": messages_for_api,  # <--- Now contains history!
+                        "messages": messages_for_api,
                         "max_tokens": max_tokens,
                         "stream": True,
                     }
-                    # Assuming llm_client is correctly initialized (OpenAI or Cerebras)
+
                     response_stream = llm_client.chat.completions.create(
                         **stream_kwargs)
 
-                    # --- (Rest of the streaming and response handling code remains the same) ---
+                    # Stream the response
                     for chunk in response_stream:
                         chunk_content = ""
                         try:
                             if chunk.choices and chunk.choices[0].delta:
                                 chunk_content = chunk.choices[0].delta.content or ""
                         except (AttributeError, IndexError):
-                            chunk_content = ""  # Handle potential errors gracefully
+                            chunk_content = ""
 
                         if chunk_content:
                             full_response += chunk_content
@@ -81,29 +77,18 @@ def render_chat_column(st, llm_client, model_option, max_tokens, BASE_PROMPT):
                     response_placeholder.markdown(full_response)
 
                 # Add assistant response to history
-                # Check if the last message isn't already the assistant's response to avoid duplicates if rerun happens unexpectedly
                 if not st.session_state.messages or st.session_state.messages[-1]['role'] != 'assistant':
                     st.session_state.messages.append(
                         {"role": "assistant", "content": full_response})
                 elif st.session_state.messages[-1]['role'] == 'assistant':
-                    # If last message is assistant, update it (useful if streaming was interrupted/retried)
+                    # Update existing assistant message if needed
                     st.session_state.messages[-1]['content'] = full_response
 
-                # No longer updating image prompt text area here (based on previous request)
-
-                # Rerun might still cause subtle issues with message duplication if not handled carefully,
-                # The check above helps mitigate this. Consider removing rerun if it causes problems.
-                # st.rerun() # Keeping rerun commented out for now based on potential issues
-
             except Exception as e:
                 st.error(
                     f"Error during LLM response generation: {str(e)}", icon="🚨")
                 # Clean up potentially failed message
-                # Ensure we only pop if the *last* message is the user's (meaning the assistant failed)
                 if st.session_state.messages and st.session_state.messages[-1]["role"] == "user":
-                    # Maybe add a placeholder error message for the assistant instead of popping user?
-                    # For now, let's not pop the user's message. The error message itself indicates failure.
                     pass
-                # Or if the assistant message was partially added:
                 elif st.session_state.messages and st.session_state.messages[-1]["role"] == "assistant" and not full_response:
                     st.session_state.messages.pop()