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from importlib import import_module
import pandas as pd
import streamlit as st
import weave
from dotenv import load_dotenv
from guardrails_genie.guardrails import GuardrailManager
from guardrails_genie.llm import OpenAIModel
def initialize_session_state():
load_dotenv()
if "weave_project_name" not in st.session_state:
st.session_state.weave_project_name = "guardrails-genie"
if "uploaded_file" not in st.session_state:
st.session_state.uploaded_file = None
if "dataset_name" not in st.session_state:
st.session_state.dataset_name = None
if "preview_in_app" not in st.session_state:
st.session_state.preview_in_app = False
if "is_dataset_published" not in st.session_state:
st.session_state.is_dataset_published = False
if "publish_dataset_button" not in st.session_state:
st.session_state.publish_dataset_button = False
if "dataset_ref" not in st.session_state:
st.session_state.dataset_ref = None
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 "start_evaluations_button" not in st.session_state:
st.session_state.start_evaluations_button = False
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(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(llm_model=OpenAIModel(model_name=survey_guardrail_model))
)
elif guardrail_name == "PromptInjectionClassifierGuardrail":
classifier_model_name = st.sidebar.selectbox(
"Classifier Guardrail Model",
[
"",
"ProtectAI/deberta-v3-base-prompt-injection-v2",
"wandb://geekyrakshit/guardrails-genie/model-6rwqup9b:v3",
],
)
if classifier_model_name != "":
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(model_name=classifier_model_name)
)
elif guardrail_name == "PresidioEntityRecognitionGuardrail":
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(should_anonymize=True)
)
elif guardrail_name == "RegexEntityRecognitionGuardrail":
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(should_anonymize=True)
)
elif guardrail_name == "TransformersEntityRecognitionGuardrail":
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(should_anonymize=True)
)
elif guardrail_name == "RestrictedTermsJudge":
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(should_anonymize=True)
)
elif guardrail_name == "PromptInjectionLlamaGuardrail":
llama_guard_checkpoint_name = st.sidebar.text_input(
"Checkpoint Name", value=""
)
st.session_state.llama_guard_checkpoint_name = llama_guard_checkpoint_name
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)(
checkpoint=(
None
if st.session_state.llama_guard_checkpoint_name == ""
else st.session_state.llama_guard_checkpoint_name
)
)
)
else:
st.session_state.guardrails.append(
getattr(
import_module("guardrails_genie.guardrails"),
guardrail_name,
)()
)
st.session_state.guardrails_manager = GuardrailManager(
guardrails=st.session_state.guardrails
)
initialize_session_state()
st.title(":material/monitoring: Evaluation")
weave_project_name = st.sidebar.text_input(
"Weave project name", value=st.session_state.weave_project_name
)
st.session_state.weave_project_name = weave_project_name
if st.session_state.weave_project_name != "":
weave.init(project_name=st.session_state.weave_project_name)
uploaded_file = st.sidebar.file_uploader(
"Upload the evaluation dataset as a CSV file", type="csv"
)
st.session_state.uploaded_file = uploaded_file
if st.session_state.uploaded_file is not None:
dataset_name = st.sidebar.text_input("Evaluation dataset name", value=None)
st.session_state.dataset_name = dataset_name
preview_in_app = st.sidebar.toggle("Preview in app", value=False)
st.session_state.preview_in_app = preview_in_app
publish_dataset_button = st.sidebar.button("Publish dataset")
st.session_state.publish_dataset_button = publish_dataset_button
if st.session_state.publish_dataset_button and (
st.session_state.dataset_name is not None
and st.session_state.dataset_name != ""
):
with st.expander("Evaluation Dataset Preview", expanded=True):
dataframe = pd.read_csv(st.session_state.uploaded_file)
data_list = dataframe.to_dict(orient="records")
dataset = weave.Dataset(name=st.session_state.dataset_name, rows=data_list)
st.session_state.dataset_ref = weave.publish(dataset)
entity = st.session_state.dataset_ref.entity
project = st.session_state.dataset_ref.project
dataset_name = st.session_state.dataset_name
digest = st.session_state.dataset_ref._digest
dataset_url = f"https://wandb.ai/{entity}/{project}/weave/objects/{dataset_name}/versions/{digest}"
st.markdown(f"Dataset published to [**Weave**]({dataset_url})")
if preview_in_app:
st.dataframe(dataframe.head(20))
if len(dataframe) > 20:
st.markdown(
f"⚠️ Dataset is too large to preview in app, please explore in the [**Weave UI**]({dataset_url})"
)
st.session_state.is_dataset_published = True
if st.session_state.is_dataset_published:
guardrail_names = st.sidebar.multiselect(
"Select Guardrails",
options=[
cls_name
for cls_name, cls_obj in vars(
import_module("guardrails_genie.guardrails")
).items()
if isinstance(cls_obj, type) and cls_name != "GuardrailManager"
],
)
st.session_state.guardrail_names = guardrail_names
initialize_guardrails()
start_evaluations_button = st.sidebar.button("Start Evaluations")
st.session_state.start_evaluations_button = start_evaluations_button
if st.session_state.start_evaluations_button:
st.write(len(st.session_state.guardrails))
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