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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})
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