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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
# Initialize session state variables if they don't already exist
def initialize_state():
if 'data_processed' not in st.session_state:
st.session_state.data_processed = False
if 'api_key' not in st.session_state:
st.session_state.api_key = ""
if 'endpoint_url' not in st.session_state:
st.session_state.endpoint_url = ""
if 'deployment_name' not in st.session_state:
st.session_state.deployment_name = ""
if 'temperature' not in st.session_state:
st.session_state.temperature = 0.5
if 'max_tokens' not in st.session_state:
st.session_state.max_tokens = 150
if 'group_name' not in st.session_state:
st.session_state.group_name = ""
if 'privilege_label' not in st.session_state:
st.session_state.privilege_label = ""
if 'protect_label' not in st.session_state:
st.session_state.protect_label = ""
if 'num_run' not in st.session_state:
st.session_state.num_run = 1
initialize_state()
# Set up the Streamlit interface
st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
st.sidebar.title('Model Settings')
# Model selection and configuration
model_type = st.sidebar.radio("Select the type of agent", ('GPTAgent', 'AzureAgent'))
api_key = st.sidebar.text_input("API Key", type="password", value=st.session_state.api_key)
endpoint_url = st.sidebar.text_input("Endpoint URL", value=st.session_state.endpoint_url)
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 ''
temperature = st.sidebar.slider("Temperature", 0.0, 1.0, st.session_state.temperature, 0.01)
max_tokens = st.sidebar.number_input("Max Tokens", 1, 1000, st.session_state.max_tokens)
# Reset buttons for model information
if st.sidebar.button("Reset Model Info"):
st.session_state.api_key = ""
st.session_state.endpoint_url = ""
st.session_state.deployment_name = ""
st.session_state.temperature = 0.5
st.session_state.max_tokens = 150
st.experimental_rerun()
submit_model_info = st.sidebar.button("Submit Model Info")
# Data upload and processing with reset option
if submit_model_info:
parameters = {"temperature": temperature, "max_tokens": max_tokens}
group_name = st.text_input("Group Name", value=st.session_state.group_name)
privilege_label = st.text_input("Privilege Name", value=st.session_state.privilege_label)
protect_label = st.text_input("Protect Name", value=st.session_state.protect_label)
num_run = st.number_input("Number of runs", min_value=1, value=st.session_state.num_run)
uploaded_file = st.file_uploader("Choose a file")
# Reset button for experiment settings
if st.button("Reset Experiment Settings"):
st.session_state.group_name = ""
st.session_state.privilege_label = ""
st.session_state.protect_label = ""
st.session_state.num_run = 1
st.session_state.data_processed = False
st.experimental_rerun()
if uploaded_file is not None:
data = StringIO(uploaded_file.getvalue().decode("utf-8"))
df = pd.read_csv(data)
process_button = st.button('Process Data')
if process_button and not st.session_state.data_processed:
# Initialize the correct agent based on model type
if model_type == 'AzureAgent':
agent = AzureAgent(api_key, endpoint_url, deployment_name)
else:
agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)
# Process data and display results
with st.spinner('Processing data...'):
df = process_scores(df, num_run, parameters, privilege_label, protect_label, agent, group_name)
st.session_state.data_processed = True # Mark as processed
st.write('Processed Data:', df)
elif process_button and st.session_state.data_processed:
st.warning("Data already processed for this session. Reset or re-upload to process new data.")
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