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| import asyncio | |
| import os | |
| import time | |
| from importlib import import_module | |
| import pandas as pd | |
| import rich | |
| import streamlit as st | |
| import weave | |
| from dotenv import load_dotenv | |
| from guardrails_genie.guardrails import GuardrailManager | |
| from guardrails_genie.llm import OpenAIModel | |
| from guardrails_genie.metrics import AccuracyMetric | |
| from guardrails_genie.utils import EvaluationCallManager | |
| def initialize_session_state(): | |
| load_dotenv() | |
| 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 "preview_in_app" not in st.session_state: | |
| st.session_state.preview_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_names" not in st.session_state: | |
| st.session_state.guardrail_names = [] | |
| if "guardrails" not in st.session_state: | |
| st.session_state.guardrails = [] | |
| 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 | |
| if "guardrail_manager" not in st.session_state: | |
| st.session_state.guardrail_manager = None | |
| if "evaluation_name" not in st.session_state: | |
| st.session_state.evaluation_name = "" | |
| if "show_result_table" not in st.session_state: | |
| st.session_state.show_result_table = False | |
| if "weave_client" not in st.session_state: | |
| st.session_state.weave_client = weave.init( | |
| project_name=os.getenv("WEAVE_PROJECT") | |
| ) | |
| if "evaluation_call_manager" not in st.session_state: | |
| st.session_state.evaluation_call_manager = None | |
| if "call_id" not in st.session_state: | |
| st.session_state.call_id = None | |
| def initialize_guardrail(): | |
| 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: | |
| 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) | |
| ) | |
| st.session_state.guardrails = guardrails | |
| st.session_state.guardrail_manager = GuardrailManager(guardrails=guardrails) | |
| 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 | |
| preview_in_app = st.sidebar.toggle("Preview in app", value=False) | |
| st.session_state.preview_in_app = preview_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 preview_in_app: | |
| st.dataframe(dataframe) | |
| st.session_state.dataset_previewed = True | |
| if st.session_state.dataset_previewed: | |
| 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 | |
| if st.session_state.guardrail_names != []: | |
| initialize_guardrail() | |
| evaluation_name = st.sidebar.text_input("Evaluation name", value="") | |
| st.session_state.evaluation_name = evaluation_name | |
| if st.session_state.guardrail_manager 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, call = asyncio.run( | |
| evaluation.evaluate.call( | |
| evaluation, | |
| st.session_state.guardrail_manager, | |
| __weave={ | |
| "display_name": "Evaluation.evaluate:" | |
| + st.session_state.evaluation_name | |
| }, | |
| ) | |
| ) | |
| x_axis = list(evaluation_summary["AccuracyMetric"].keys()) | |
| y_axis = [ | |
| evaluation_summary["AccuracyMetric"][x_axis_item] | |
| for x_axis_item in x_axis | |
| ] | |
| st.bar_chart( | |
| pd.DataFrame({"Metric": x_axis, "Score": y_axis}), | |
| x="Metric", | |
| y="Score", | |
| ) | |
| st.session_state.evaluation_summary = evaluation_summary | |
| st.session_state.call_id = call.id | |
| st.session_state.start_evaluation = False | |
| if not st.session_state.start_evaluation: | |
| time.sleep(5) | |
| st.session_state.evaluation_call_manager = ( | |
| EvaluationCallManager( | |
| entity="geekyrakshit", | |
| project="guardrails-genie", | |
| call_id=st.session_state.call_id, | |
| ) | |
| ) | |
| for guardrail_name in st.session_state.guardrail_names: | |
| st.session_state.evaluation_call_manager.call_list.append( | |
| { | |
| "guardrail_name": guardrail_name, | |
| "calls": st.session_state.evaluation_call_manager.collect_guardrail_guard_calls_from_eval(), | |
| } | |
| ) | |
| rich.print( | |
| st.session_state.evaluation_call_manager.call_list | |
| ) | |
| st.dataframe( | |
| st.session_state.evaluation_call_manager.render_calls_to_streamlit() | |
| ) | |
| if st.session_state.evaluation_call_manager.show_warning_in_app: | |
| st.warning( | |
| f"Only {st.session_state.evaluation_call_manager.max_count} calls can be shown in the app." | |
| ) | |
| st.markdown( | |
| f"Explore the entire evaluation trace table in [Weave]({call.ui_url})" | |
| ) | |
| st.session_state.evaluation_call_manager = None | |