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import streamlit as st
import pandas as pd
from datasets import load_dataset
from random import sample
from utils.metric import Regard
from utils.model import gpt2
import os
# Set up the Streamlit interface
st.set_page_config(page_title="Gender Bias Analysis", page_icon="🔍", layout="wide")
st.title('Gender Bias Analysis in Text Generation')
# Password protection function
def check_password():
def password_entered():
if password_input == os.getenv('PASSWORD'):
st.session_state['password_correct'] = True
else:
st.error("Incorrect Password, please try again.")
if 'password_correct' not in st.session_state:
st.session_state['password_correct'] = False
if not st.session_state['password_correct']:
password_input = st.text_input("Enter Password:", type="password")
st.button("Submit", on_click=password_entered)
# Data loading function
def load_data():
if 'bold' not in st.session_state:
st.session_state['bold'] = load_dataset("AlexaAI/bold", split="train")
# Sampling function
def sample_data(data_size):
st.session_state['female_bold'] = sample(
[p for p in st.session_state['bold'] if p['category'] == 'American_actresses'], data_size)
st.session_state['male_bold'] = sample(
[p for p in st.session_state['bold'] if p['category'] == 'American_actors'], data_size)
# Text generation function
def generate_text():
GPT2 = gpt2()
st.session_state['male_prompts'] = [p['prompts'][0] for p in st.session_state['male_bold']]
st.session_state['female_prompts'] = [p['prompts'][0] for p in st.session_state['female_bold']]
progress_bar = st.progress(0)
st.write('Generating text for male prompts...')
male_generation = GPT2.text_generation(st.session_state['male_prompts'], pad_token_id=50256, max_length=50,
do_sample=False, truncation=True)
st.session_state['male_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
zip(male_generation, st.session_state['male_prompts'])]
progress_bar.progress(50)
st.write('Generating text for female prompts...')
female_generation = GPT2.text_generation(st.session_state['female_prompts'], pad_token_id=50256,
max_length=50, do_sample=False, truncation=True)
st.session_state['female_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
zip(female_generation, st.session_state['female_prompts'])]
progress_bar.progress(100)
st.write('Text generation completed.')
# Display data samples function
def display_samples():
st.write("### Male Data Samples")
samples_df = pd.DataFrame({
'Male Prompt': st.session_state['male_prompts'],
'Male Continuation': st.session_state['male_continuations'],
})
st.dataframe(samples_df)
st.write("### Female Data Samples")
samples_df = pd.DataFrame({
'Female Prompt': st.session_state['female_prompts'],
'Female Continuation': st.session_state['female_continuations']
})
st.dataframe(samples_df)
# Evaluate regard function
def evaluate_regard():
regard = Regard("compare")
st.write('Computing regard results to compare male and female continuations...')
with st.spinner('Computing regard results...'):
regard_results = regard.compute(data=st.session_state['male_continuations'],
references=st.session_state['female_continuations'])
st.write('**Raw Regard Results:**')
st.json(regard_results)
regard_results_avg = regard.compute(data=st.session_state['male_continuations'],
references=st.session_state['female_continuations'],
aggregation='average')
st.write('**Average Regard Results:**')
st.json(regard_results_avg)
# Main app logic
if not st.session_state.get('password_correct', False):
check_password()
else:
st.sidebar.success("Password Verified. Proceed with the demo.")
load_data()
st.subheader('Step 1: Set Data Size')
data_size = st.slider('Select number of samples per category:', min_value=1, max_value=50,
value=st.session_state.get('data_size', 10))
st.session_state['data_size'] = data_size
if st.button('Show Data'):
sample_data(data_size)
st.write(f'Sampled {data_size} female and male American actors.')
display_samples()
if st.session_state.get('female_bold') and st.session_state.get('male_bold'):
st.subheader('Step 2: Generate Text')
if st.button('Generate Text'):
generate_text()
if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'):
st.subheader('Step 3: Evaluate')
display_samples()
if st.button('Evaluate'):
evaluate_regard()
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