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| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| from generation import process_scores | |
| from model import AzureAgent, GPTAgent | |
| from analysis import statistical_tests, result_evaluation | |
| # Set up the Streamlit interface | |
| st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision') | |
| st.sidebar.title('Model Settings') | |
| # Define a function to manage state initialization | |
| def initialize_state(): | |
| keys = ["model_submitted", "api_key", "endpoint_url", "deployment_name", "temperature", "max_tokens", | |
| "data_processed", "group_name","occupation", "privilege_label", "protect_label", "num_run", "uploaded_file"] | |
| defaults = [False, "", "https://safeguard-monitor.openai.azure.com/", "gpt35-1106", 0.5, 150, False,"Gender", "Programmer", "Male", "Female", 1, None] | |
| for key, default in zip(keys, defaults): | |
| if key not in st.session_state: | |
| st.session_state[key] = default | |
| initialize_state() | |
| # Model selection and configuration | |
| model_type = st.sidebar.radio("Select the type of agent", ('GPTAgent', 'AzureAgent')) | |
| st.session_state.api_key = st.sidebar.text_input("API Key", type="password", value=st.session_state.api_key) | |
| st.session_state.endpoint_url = st.sidebar.text_input("Endpoint URL", value=st.session_state.endpoint_url) | |
| st.session_state.deployment_name = st.sidebar.text_input("Model Name", value=st.session_state.deployment_name) | |
| api_version = '2024-02-15-preview' if model_type == 'GPTAgent' else '' | |
| st.session_state.temperature = st.sidebar.slider("Temperature", 0.0, 1.0, st.session_state.temperature, 0.01) | |
| st.session_state.max_tokens = st.sidebar.number_input("Max Tokens", 1, 1000, st.session_state.max_tokens) | |
| if st.sidebar.button("Reset Model Info"): | |
| initialize_state() # Reset all state to defaults | |
| st.experimental_rerun() | |
| if st.sidebar.button("Submit Model Info"): | |
| st.session_state.model_submitted = True | |
| # Ensure experiment settings are only shown if model info is submitted | |
| if st.session_state.model_submitted: | |
| df = None | |
| file_options = st.radio("Choose file source:", ["Upload", "Example"]) | |
| if file_options == "Example": | |
| df = pd.read_csv("prompt_test.csv") | |
| else: | |
| st.session_state.uploaded_file = st.file_uploader("Choose a file") | |
| if st.session_state.uploaded_file is not None: | |
| data = StringIO(st.session_state.uploaded_file.getvalue().decode("utf-8")) | |
| df = pd.read_csv(data) | |
| if df is not None: | |
| st.write('Data:', df) | |
| st.session_state.occupation = st.text_input("Occupation", value=st.session_state.occupation) | |
| st.session_state.group_name = st.text_input("Group Name", value=st.session_state.group_name) | |
| st.session_state.privilege_label = st.text_input("Privilege Label", value=st.session_state.privilege_label) | |
| st.session_state.protect_label = st.text_input("Protect Label", value=st.session_state.protect_label) | |
| st.session_state.num_run = st.number_input("Number of Runs", 1, 10, st.session_state.num_run) | |
| if st.button('Process Data') and not st.session_state.data_processed: | |
| # Initialize the correct agent based on model type | |
| if model_type == 'AzureAgent': | |
| agent = AzureAgent(st.session_state.api_key, st.session_state.endpoint_url, st.session_state.deployment_name) | |
| else: | |
| agent = GPTAgent(st.session_state.api_key, st.session_state.endpoint_url, st.session_state.deployment_name, api_version) | |
| # Process data and display results | |
| with st.spinner('Processing data...'): | |
| parameters = {"temperature": st.session_state.temperature, "max_tokens": st.session_state.max_tokens} | |
| df = process_scores(df, st.session_state.num_run, parameters, st.session_state.privilege_label, st.session_state.protect_label, agent, st.session_state.group_name, st.session_state.occupation) | |
| st.session_state.data_processed = True # Mark as processed | |
| # Add ranks for each score within each row | |
| df['Privilege_Rank'] = \ | |
| df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1)[ | |
| 'Privilege_Avg_Score'] | |
| df['Protect_Rank'] = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1)[ | |
| 'Protect_Avg_Score'] | |
| df['Neutral_Rank'] = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1)[ | |
| 'Neutral_Avg_Score'] | |
| st.write('Processed Data:', df) | |
| # use the data to generate a plot | |
| st.write("Plotting the data") | |
| test_results = statistical_tests(df) | |
| print(test_results) | |
| evaluation_results = result_evaluation(test_results) | |
| print(evaluation_results) | |
| for key, value in evaluation_results.items(): | |
| st.write(f"{key}: {value}") | |
| if st.button("Reset Experiment Settings"): | |
| st.session_state.occupation = "Programmer" | |
| st.session_state.group_name = "Gender" | |
| st.session_state.privilege_label = "Male" | |
| st.session_state.protect_label = "Female" | |
| st.session_state.num_run = 1 | |
| st.session_state.data_processed = False | |
| st.session_state.uploaded_file = None |