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| import asyncio | |
| from importlib import import_module | |
| import pandas as pd | |
| import streamlit as st | |
| import weave | |
| from dotenv import load_dotenv | |
| from guardrails_genie.llm import OpenAIModel | |
| from guardrails_genie.metrics import AccuracyMetric | |
| load_dotenv() | |
| weave.init(project_name="guardrails-genie") | |
| def initialize_session_state(): | |
| 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 = "" | |
| if "visualize_in_app" not in st.session_state: | |
| st.session_state.visualize_in_app = False | |
| if "dataset_ref" not in st.session_state: | |
| st.session_state.dataset_ref = None | |
| if "dataset_previewed" not in st.session_state: | |
| st.session_state.dataset_previewed = False | |
| if "guardrail_name" not in st.session_state: | |
| st.session_state.guardrail_name = "" | |
| if "guardrail" not in st.session_state: | |
| st.session_state.guardrail = None | |
| if "start_evaluation" not in st.session_state: | |
| st.session_state.start_evaluation = False | |
| if "evaluation_summary" not in st.session_state: | |
| st.session_state.evaluation_summary = None | |
| def initialize_guardrail(): | |
| if st.session_state.guardrail_name == "PromptInjectionSurveyGuardrail": | |
| survey_guardrail_model = st.sidebar.selectbox( | |
| "Survey Guardrail LLM", ["", "gpt-4o-mini", "gpt-4o"] | |
| ) | |
| if survey_guardrail_model: | |
| st.session_state.guardrail = getattr( | |
| import_module("guardrails_genie.guardrails"), | |
| st.session_state.guardrail_name, | |
| )(llm_model=OpenAIModel(model_name=survey_guardrail_model)) | |
| else: | |
| st.session_state.guardrail = getattr( | |
| import_module("guardrails_genie.guardrails"), | |
| st.session_state.guardrail_name, | |
| )() | |
| initialize_session_state() | |
| st.title(":material/monitoring: Evaluation") | |
| uploaded_file = st.sidebar.file_uploader( | |
| "Upload the evaluation dataset as a CSV file", type="csv" | |
| ) | |
| st.session_state.uploaded_file = uploaded_file | |
| dataset_name = st.sidebar.text_input("Evaluation dataset name", value="") | |
| st.session_state.dataset_name = dataset_name | |
| visualize_in_app = st.sidebar.toggle("Visualize in app", value=False) | |
| st.session_state.visualize_in_app = visualize_in_app | |
| if st.session_state.uploaded_file 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 | |
| st.markdown( | |
| f"Dataset published to [**Weave**](https://wandb.ai/{entity}/{project}/weave/objects/{dataset_name}/versions/{digest})" | |
| ) | |
| if visualize_in_app: | |
| st.dataframe(dataframe) | |
| st.session_state.dataset_previewed = True | |
| if st.session_state.dataset_previewed: | |
| guardrail_name = st.sidebar.selectbox( | |
| "Select Guardrail", | |
| 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_name = guardrail_name | |
| if st.session_state.guardrail_name != "": | |
| initialize_guardrail() | |
| if st.session_state.guardrail is not None: | |
| if st.sidebar.button("Start Evaluation"): | |
| st.session_state.start_evaluation = True | |
| if st.session_state.start_evaluation: | |
| evaluation = weave.Evaluation( | |
| dataset=st.session_state.dataset_ref, | |
| scorers=[AccuracyMetric()], | |
| streamlit_mode=True, | |
| ) | |
| with st.expander("Evaluation Results", expanded=True): | |
| evaluation_summary = asyncio.run( | |
| evaluation.evaluate(st.session_state.guardrail) | |
| ) | |
| st.write(evaluation_summary) | |
| st.session_state.evaluation_summary = evaluation_summary | |
| st.session_state.start_evaluation = False | |