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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
# 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", "privilege_label", "protect_label", "num_run"]
defaults = [False, "", "", "", 0.5, 150, False, "", "", "", 1]
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:
parameters = {"temperature": st.session_state.temperature, "max_tokens": st.session_state.max_tokens}
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 Name", value=st.session_state.privilege_label)
st.session_state.protect_label = st.text_input("Protect Name", value=st.session_state.protect_label)
st.session_state.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")
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
if uploaded_file is not None and not st.session_state.data_processed:
data = StringIO(uploaded_file.getvalue().decode("utf-8"))
df = pd.read_csv(data)
if st.button('Process Data'):
# 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...'):
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.data_processed = True # Mark as processed
st.write('Processed Data:', df)
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