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import streamlit as st |
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import pandas as pd |
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from datasets import load_dataset |
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from random import sample |
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from utils.metric import Regard |
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from utils.model import gpt2 |
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import os |
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st.title('Gender Bias Analysis in Text Generation') |
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def check_password(): |
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def password_entered(): |
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if password_input == os.getenv('PASSWORD'): |
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st.session_state['password_correct'] = True |
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else: |
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st.error("Incorrect Password, please try again.") |
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password_input = st.text_input("Enter Password:", type="password") |
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submit_button = st.button("Submit", on_click=password_entered) |
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if submit_button and not st.session_state.get('password_correct', False): |
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st.error("Please enter a valid password to access the demo.") |
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if not st.session_state.get('password_correct', False): |
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check_password() |
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else: |
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st.sidebar.success("Password Verified. Proceed with the demo.") |
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if 'data_size' not in st.session_state: |
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st.session_state['data_size'] = 10 |
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if 'bold' not in st.session_state: |
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st.session_state['bold'] = load_dataset("AlexaAI/bold", split="train") |
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if 'female_bold' not in st.session_state: |
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st.session_state['female_bold'] = [] |
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if 'male_bold' not in st.session_state: |
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st.session_state['male_bold'] = [] |
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st.subheader('Step 1: Set Data Size') |
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data_size = st.slider('Select number of samples per category:', min_value=1, max_value=50, |
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value=st.session_state['data_size']) |
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st.session_state['data_size'] = data_size |
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if st.button('Show Data'): |
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st.session_state['female_bold'] = sample( |
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[p for p in st.session_state['bold'] if p['category'] == 'American_actresses'], data_size) |
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st.session_state['male_bold'] = sample( |
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[p for p in st.session_state['bold'] if p['category'] == 'American_actors'], data_size) |
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st.write(f'Sampled {data_size} female and male American actors.') |
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if st.session_state['female_bold'] and st.session_state['male_bold']: |
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st.subheader('Step 2: Generated Text') |
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if st.button('Generate Text'): |
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GPT2 = gpt2() |
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st.session_state['male_prompts'] = [p['prompts'][0] for p in st.session_state['male_bold']] |
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st.session_state['female_prompts'] = [p['prompts'][0] for p in st.session_state['female_bold']] |
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st.write('Generating text for male prompts...') |
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male_generation = GPT2.text_generation(st.session_state['male_prompts'], pad_token_id=50256, max_length=50, |
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do_sample=False, truncation=True) |
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print(male_generation) |
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st.session_state['male_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in |
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zip(male_generation, st.session_state['male_prompts'])] |
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st.write('Generating text for female prompts...') |
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female_generation = GPT2.text_generation(st.session_state['female_prompts'], pad_token_id=50256, |
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max_length=50, do_sample=False, truncation=True) |
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st.session_state['female_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in |
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zip(female_generation, st.session_state['female_prompts'])] |
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st.write('Generated {} male continuations'.format(len(st.session_state['male_continuations']))) |
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st.write('Generated {} female continuations'.format(len(st.session_state['female_continuations']))) |
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if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'): |
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st.subheader('Step 3: Sample Generated Texts') |
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st.write('**Male Prompt:**', st.session_state['male_prompts'][0]) |
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st.write('**Male Continuation:**', st.session_state['male_continuations'][0]) |
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st.write('**Female Prompt:**', st.session_state['female_prompts'][0]) |
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st.write('**Female Continuation:**', st.session_state['female_continuations'][0]) |
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if st.button('Evaluate'): |
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st.subheader('Step 4: Regard Results') |
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regard = Regard("compare") |
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st.write('Computing regard results to compare male and female continuations...') |
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regard_results = regard.compute(data=st.session_state['male_continuations'], |
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references=st.session_state['female_continuations']) |
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st.write('**Raw Regard Results:**') |
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st.json(regard_results) |
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st.write('Computing average regard results for comparative analysis...') |
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regard_results_avg = regard.compute(data=st.session_state['male_continuations'], |
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references=st.session_state['female_continuations'], |
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aggregation='average') |
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st.write('**Average Regard Results:**') |
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st.json(regard_results_avg) |
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