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import importlib
import streamlit as st
import weave
from dotenv import load_dotenv
from guardrails_genie.guardrails import GuardrailManager
from guardrails_genie.llm import OpenAIModel
load_dotenv()
weave.init(project_name="guardrails-genie")
if "guardrails" not in st.session_state:
st.session_state.guardrails = []
if "guardrail_names" not in st.session_state:
st.session_state.guardrail_names = []
if "guardrails_manager" not in st.session_state:
st.session_state.guardrails_manager = None
def initialize_guardrails():
st.session_state.guardrails = []
for guardrail_name in st.session_state.guardrail_names:
if guardrail_name == "PromptInjectionSurveyGuardrail":
survey_guardrail_model = st.sidebar.selectbox(
"Survey Guardrail LLM", ["", "gpt-4o-mini", "gpt-4o"]
)
if survey_guardrail_model:
st.session_state.guardrails.append(
getattr(
importlib.import_module("guardrails_genie.guardrails"),
guardrail_name,
)(llm_model=OpenAIModel(model_name=survey_guardrail_model))
)
else:
st.session_state.guardrails.append(
getattr(
importlib.import_module("guardrails_genie.guardrails"),
guardrail_name,
)()
)
st.session_state.guardrails_manager = GuardrailManager(
guardrails=st.session_state.guardrails
)
openai_model = st.sidebar.selectbox(
"OpenAI LLM for Chat", ["", "gpt-4o-mini", "gpt-4o"]
)
chat_condition = openai_model != ""
guardrails = []
guardrail_names = st.sidebar.multiselect(
label="Select Guardrails",
options=[
cls_name
for cls_name, cls_obj in vars(
importlib.import_module("guardrails_genie.guardrails")
).items()
if isinstance(cls_obj, type) and cls_name != "GuardrailManager"
],
)
st.session_state.guardrail_names = guardrail_names
# 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:
with st.sidebar.status("Initializing Guardrails..."):
initialize_guardrails()
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})
guardrails_response, call = st.session_state.guardrails_manager.guard.call(
st.session_state.guardrails_manager, prompt=prompt
)
if guardrails_response["safe"]:
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})
else:
st.error("Guardrails detected an issue with the prompt.")
for alert in guardrails_response["alerts"]:
st.error(f"{alert['guardrail_name']}: {alert['response']}")
st.error(f"For details, explore in Weave at {call.ui_url}")
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