app.py

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

            st.write('Processed Data:', df)

            # use the data to generate a plot
            st.write("Plotting the data")

            # 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']

            test_results = statistical_tests(df)
            evaluation_results = result_evaluation(test_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