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| import streamlit as st | |
| import weave | |
| from dotenv import load_dotenv | |
| from guardrails_genie.llm import OpenAIModel | |
| load_dotenv() | |
| weave.init(project_name="guardrails-genie") | |
| openai_model = st.sidebar.selectbox("OpenAI LLM", ["", "gpt-4o-mini", "gpt-4o"]) | |
| chat_condition = openai_model != "" | |
| # Use session state to track if the chat has started | |
| if "chat_started" not in st.session_state: | |
| st.session_state.chat_started = False | |
| # Start chat when button is pressed | |
| if st.sidebar.button("Start Chat") and chat_condition: | |
| st.session_state.chat_started = True | |
| # Display chat UI if chat has started | |
| if st.session_state.chat_started: | |
| st.title("Guardrails Genie") | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| llm_model = OpenAIModel(model_name=openai_model) | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # 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}) | |
| response, call = llm_model.predict.call( | |
| llm_model, user_prompts=prompt, messages=st.session_state.messages | |
| ) | |
| response = response.choices[0].message.content | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response + f"\n\n---\n[Explore in Weave]({call.ui_url})") | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |