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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.title('Gender Bias Analysis in Text Generation')
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.")
password_input = st.text_input("Enter Password:", type="password")
submit_button = st.button("Submit", on_click=password_entered)
if submit_button and not st.session_state.get('password_correct', False):
st.error("Please enter a valid password to access the demo.")
if not st.session_state.get('password_correct', False):
check_password()
else:
st.sidebar.success("Password Verified. Proceed with the demo.")
st.subheader('Loading and Processing Data')
st.write('Loading the BOLD dataset...')
bold = load_dataset("AlexaAI/bold", split="train")
# Allow user to set the sample size
data_size = st.sidebar.slider('Select number of samples per category:', min_value=1, max_value=50, value=10)
st.write(f'Sampling {data_size} female and male American actors...')
female_bold = sample([p for p in bold if p['category'] == 'American_actresses'], data_size)
male_bold = sample([p for p in bold if p['category'] == 'American_actors'], data_size)
male_prompts = [p['prompts'][0] for p in male_bold]
female_prompts = [p['prompts'][0] for p in female_bold]
GPT2 = gpt2()
st.write('Generating text for male prompts...')
male_generation = GPT2.text_generation(male_prompts, pad_token_id=50256, max_length=50, do_sample=False,truncation=True)
print(male_generation)
male_continuations = [gen.replace(prompt, '') for gen, prompt in zip(male_generation, male_prompts)]
st.write('Generating text for female prompts...')
female_generation = GPT2.text_generation(female_prompts, pad_token_id=50256, max_length=50, do_sample=False,truncation=True)
print(male_generation)
female_continuations = [gen.replace(prompt, '') for gen, prompt in zip(female_generation, female_prompts)]
st.write('Generated {} male continuations'.format(len(male_continuations)))
st.write('Generated {} female continuations'.format(len(female_continuations)))
st.subheader('Sample Generated Texts')
st.write('**Male Prompt:**', male_prompts[0])
st.write('**Male Continuation:**', male_continuations[0])
st.write('**Female Prompt:**', female_prompts[0])
st.write('**Female Continuation:**', female_continuations[0])
regard = Regard("compare")
st.write('Computing regard results to compare male and female continuations...')
regard_results = regard.compute(data=male_continuations, references=female_continuations)
st.subheader('Regard Results')
st.write('**Raw Regard Results:**')
st.json(regard_results)
st.write('Computing average regard results for comparative analysis...')
regard_results_avg = regard.compute(data=male_continuations, references=female_continuations, aggregation='average')
st.write('**Average Regard Results:**')
st.json(regard_results_avg)
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