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| # chat_column.py | |
| import streamlit as st | |
| # Assuming BASE_PROMPT is imported or defined elsewhere if not passed explicitly | |
| # from prompt import BASE_PROMPT # Or pass it as an argument | |
| def render_chat_column(st, llm_client, model_option, max_tokens, BASE_PROMPT): | |
| """Renders the chat history, input, and LLM prompt generation column.""" | |
| st.header("💬 Chat with the AI to generate JSON for Veo3") | |
| # --- Display Chat History --- | |
| # 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 at the bottom --- | |
| # Use a simple approach - just place the input after all messages | |
| 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 | |
| st.session_state.messages.append( | |
| {"role": "user", "content": prompt}) | |
| # Display user message | |
| with st.chat_message("user", avatar='🦔'): | |
| st.markdown(prompt) | |
| # Generate and display assistant response | |
| try: | |
| with st.chat_message("assistant", avatar="🤖"): | |
| response_placeholder = st.empty() | |
| response_placeholder.markdown("Generating prompt... ▌") | |
| full_response = "" | |
| # 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) | |
| messages_for_api.extend(st.session_state.messages) | |
| # 3. Filter out any potential empty messages | |
| messages_for_api = [ | |
| m for m in messages_for_api if m.get("content")] | |
| stream_kwargs = { | |
| "model": model_option, | |
| "messages": messages_for_api, | |
| "max_tokens": max_tokens, | |
| "stream": True, | |
| } | |
| response_stream = llm_client.chat.completions.create( | |
| **stream_kwargs) | |
| # 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 = "" | |
| if chunk_content: | |
| full_response += chunk_content | |
| response_placeholder.markdown(full_response + "▌") | |
| # Final response display | |
| response_placeholder.markdown(full_response) | |
| # Add assistant response to history | |
| 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': | |
| # Update existing assistant message if needed | |
| st.session_state.messages[-1]['content'] = full_response | |
| # Rerun to ensure proper layout | |
| st.rerun() | |
| except Exception as e: | |
| st.error( | |
| f"Error during LLM response generation: {str(e)}", icon="🚨") | |
| # Clean up potentially failed message | |
| if st.session_state.messages and st.session_state.messages[-1]["role"] == "user": | |
| pass | |
| elif st.session_state.messages and st.session_state.messages[-1]["role"] == "assistant" and not full_response: | |
| st.session_state.messages.pop() | |