app.py

import gradio as gr
from transformers import pipeline
from matplotlib.ticker import MaxNLocator
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

MODEL_NAMES = ["bert-base-uncased",
               "distilbert-base-uncased", "xlm-roberta-base"]

DECIMAL_PLACES = 1
EPS = 1e-5  # to avoid /0 errors

# Example date conts
DATE_SPLIT_KEY = "DATE"
START_YEAR = 1800
STOP_YEAR = 1999
NUM_PTS = 20
DATES = np.linspace(START_YEAR, STOP_YEAR, NUM_PTS).astype(int).tolist()
DATES = [f'{d}' for d in DATES]

# Example place conts
# https://www3.weforum.org/docs/WEF_GGGR_2021.pdf
# Bottom 10 and top 10 Global Gender Gap ranked countries.
PLACE_SPLIT_KEY = "PLACE"
PLACES = [
    "Afghanistan",
    "Yemen",
    "Iraq",
    "Pakistan",
    "Syria",
    "Democratic Republic of Congo",
    "Iran",
    "Mali",
    "Chad",
    "Saudi Arabia",
    "Switzerland",
    "Ireland",
    "Lithuania",
    "Rwanda",
    "Namibia",
    "Sweden",
    "New Zealand",
    "Norway",
    "Finland",
    "Iceland"]


# Example Reddit interest consts
# in order of increasing self-identified female participation.
# See http://bburky.com/subredditgenderratios/ , Minimum subreddit size: 400000
SUBREDDITS = [
    "GlobalOffensive",
    "pcmasterrace",
    "nfl",
    "sports",
    "The_Donald",
    "leagueoflegends",
    "Overwatch",
    "gonewild",
    "Futurology",
    "space",
    "technology",
    "gaming",
    "Jokes",
    "dataisbeautiful",
    "woahdude",
    "askscience",
    "wow",
    "anime",
    "BlackPeopleTwitter",
    "politics",
    "pokemon",
    "worldnews",
    "reddit.com",
    "interestingasfuck",
    "videos",
    "nottheonion",
    "television",
    "science",
    "atheism",
    "movies",
    "gifs",
    "Music",
    "trees",
    "EarthPorn",
    "GetMotivated",
    "pokemongo",
    "news",
    # removing below subreddit as most of the tokens are taken up by it:
    # ['ff', '##ff', '##ff', '##fu', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', '##u', ...]
    # "fffffffuuuuuuuuuuuu",
    "Fitness",
    "Showerthoughts",
    "OldSchoolCool",
    "explainlikeimfive",
    "todayilearned",
    "gameofthrones",
    "AdviceAnimals",
    "DIY",
    "WTF",
    "IAmA",
    "cringepics",
    "tifu",
    "mildlyinteresting",
    "funny",
    "pics",
    "LifeProTips",
    "creepy",
    "personalfinance",
    "food",
    "AskReddit",
    "books",
    "aww",
    "sex",
    "relationships",
]

GENDERED_LIST = [
    ['he', 'she'],
    ['him', 'her'],
    ['his', 'hers'],
    ["himself", "herself"],
    ['male', 'female'],
    ['man', 'woman'],
    ['men', 'women'],
    ["husband", "wife"],
    ['father', 'mother'],
    ['boyfriend', 'girlfriend'],
    ['brother', 'sister'],
    ["actor", "actress"],
]


# Fire up the models
# TODO: Make it so models can be added in the future
models_paths = dict()
models = dict()


# %%
for bert_like in MODEL_NAMES:
    models_paths[bert_like] = bert_like
    models[bert_like] = pipeline(
        "fill-mask", model=models_paths[bert_like])


def get_gendered_token_ids():
    male_gendered_tokens = [list[0] for list in GENDERED_LIST]
    female_gendered_tokens = [list[1] for list in GENDERED_LIST]

    return male_gendered_tokens, female_gendered_tokens


def prepare_text_for_masking(input_text, mask_token, gendered_tokens, split_key):
    text_w_masks_list = [
        mask_token if word in gendered_tokens else word for word in input_text.split()]
    num_masks = len([m for m in text_w_masks_list if m == mask_token])

    text_portions = ' '.join(text_w_masks_list).split(split_key)
    return text_portions, num_masks


def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token, num_preds):
    pronoun_preds = [sum([
        pronoun["score"] if pronoun["token_str"].lower(
        ) in gendered_token else 0.0
        for pronoun in top_preds])
        for top_preds in mask_filled_text
    ]
    return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES)

# %%
def get_figure(df, gender, n_fit=1):
    df = df.set_index('x-axis')
    cols = df.columns
    xs = list(range(len(df)))
    ys = df[cols[0]]
    fig, ax = plt.subplots()

    # find stackoverflow reference
    p, C_p = np.polyfit(xs, ys, n_fit, cov=1)
    t = np.linspace(min(xs)-1, max(xs)+1,  10*len(xs))
    TT = np.vstack([t**(n_fit-i) for i in range(n_fit+1)]).T

    # matrix multiplication calculates the polynomial values
    yi = np.dot(TT, p)
    C_yi = np.dot(TT, np.dot(C_p, TT.T))  # C_y = TT*C_z*TT.T
    sig_yi = np.sqrt(np.diag(C_yi))  # Standard deviations are sqrt of diagonal

    ax.fill_between(t, yi+sig_yi, yi-sig_yi, alpha=.25)
    ax.plot(t, yi, '-')
    ax.plot(df, 'ro')
    ax.legend(list(df.columns))

    ax.axis('tight')

    # fig.canvas.draw()

    ax.set_xlabel("Value injected into input text")
    ax.set_title(
        f"Probability of predicting {gender} pronouns.")
    ax.set_ylabel(f"Softmax prob for pronouns")
    ax.xaxis.set_major_locator(MaxNLocator(6))
    ax.tick_params(axis='x', labelrotation=15)
    return fig


# %%
def predict_gender_pronouns(
    model_type,
    indie_vars,
    split_key,
    normalizing,
    input_text,
):
    """Run inference on input_text for each model type, returning df and plots of precentage
    of gender pronouns predicted as female and male in each target text.
    """
    model = models[model_type]
    mask_token = model.tokenizer.mask_token

    indie_vars_list = indie_vars.split(',')

    male_gendered_tokens, female_gendered_tokens = get_gendered_token_ids()

    text_segments, num_preds = prepare_text_for_masking(
        input_text, mask_token, male_gendered_tokens + female_gendered_tokens, split_key)

    male_pronoun_preds = []
    female_pronoun_preds = []
    for indie_var in indie_vars_list:

        target_text = f"{indie_var}".join(text_segments)
        mask_filled_text = model(target_text)
        # Quick hack as realized return type based on how many MASKs in text.
        if type(mask_filled_text[0]) is not list:
            mask_filled_text = [mask_filled_text]

        female_pronoun_preds.append(get_avg_prob_from_pipeline_outputs(
            mask_filled_text,
            female_gendered_tokens,
            num_preds
        ))
        male_pronoun_preds.append(get_avg_prob_from_pipeline_outputs(
            mask_filled_text,
            male_gendered_tokens,
            num_preds
        ))

    if normalizing:
        total_gendered_probs = np.add(
            female_pronoun_preds, male_pronoun_preds)
        female_pronoun_preds = np.around(
            np.divide(female_pronoun_preds, total_gendered_probs+EPS)*100,
            decimals=DECIMAL_PLACES
        )
        male_pronoun_preds = np.around(
            np.divide(male_pronoun_preds, total_gendered_probs+EPS)*100,
            decimals=DECIMAL_PLACES
        )

    results_df = pd.DataFrame({'x-axis': indie_vars_list})
    results_df['female_pronouns'] = female_pronoun_preds
    results_df['male_pronouns'] = male_pronoun_preds
    female_fig = get_figure(results_df.drop(
        'male_pronouns', axis=1), 'female')
    male_fig = get_figure(results_df.drop(
        'female_pronouns', axis=1), 'male')

    return (
        target_text,
        female_fig,
        male_fig,
        results_df,
    )

# %%
title = "Causing Gender Pronouns"
description = """
## Intro 

"""

place_example = [
    MODEL_NAMES[0],
    ', '.join(PLACES),
    'PLACE',
    "False",
    'Born in PLACE, she was a teacher.'
]

date_example = [
    MODEL_NAMES[0],
    ', '.join(DATES),
    'DATE',
    "False",
    'Born in DATE, she was a doctor.'
]


subreddit_example = [
    MODEL_NAMES[2],
    ', '.join(SUBREDDITS),
    'SUBREDDIT',
    "False",
    'I saw on r/SUBREDDIT that she is a hacker.'
]


def date_fn():
    return date_example
def place_fn():
    return place_example
def reddit_fn():
    return subreddit_example


# %%
demo = gr.Blocks()
with demo:
    gr.Markdown("## Hunt for spurious correlations in our LLMs.")
    gr.Markdown("Although genders are relatively evenly distributed across time, place and interests, there are also known gender disparities in terms of access to resources. We suggest that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.")
    gr.Markdown("These spurious associations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.")
    gr.Markdown("Selection bias of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.")


    gr.Markdown("One intuitive way to see the impact that changing one variable may have upon another is to look for a dose-response relationship, in which a larger intervention in the treatment (the value in text form injected in the otherwise unchanged text sample) produces a larger response in the output (the softmax probability of a gendered pronoun). Specifically, below are examples of sweeping through a spectrum of place, date and subreddit interest (we encourage you to try your own).")
    
    gr.Markdown("This requires a spectrum of less to more gender-equal values for each covariate. For date, it’s easy to just use time itself, as gender equality has generally improved with time, so we picked years ranging from 1800 - 1999. For place we used the bottom and top 10 Global Gender Gap ranked countries. And for subreddit, we use subreddit name ordered by subreddits that have an increasingly larger percentage of self-reported female commenters.")
    #gr.Markdown("Please see a better explanation in another [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two).")
    
    with gr.Row():
        x_axis = gr.Textbox(
            lines=5,
            label="Pick a spectrum of values for text injection and x-axis",
        )
    with gr.Row():
        model_name = gr.Radio(
            MODEL_NAMES,
            type="value",
            label="Pick a BERT-like model.",
        )
        place_holder = gr.Textbox(
            label="Special token used in input text that will be replaced with the above spectrum of values.",
            type="index",
        )
        to_normalize = gr.Dropdown(
            ["False", "True"],
            label="Normalize?",
            type="index",
        )
    with gr.Row():
        input_text = gr.Textbox(
            lines=5,
            label="Input Text: Sentence about a single person using some gendered pronouns to refer to them.",
        )
    with gr.Row():
        sample_text = gr.Textbox(
            type="auto", label="Output text: Sample of text fed to model")
    with gr.Row():
        female_fig = gr.Plot(
            type="auto", label="Plot of softmax probability pronouns predicted female.")
        male_fig = gr.Plot(
            type="auto", label="Plot of softmax probability pronouns predicted male.")
    with gr.Row():
        df = gr.Dataframe(
            show_label=True,
            overflow_row_behaviour="show_ends",
            label="Table of softmax probability for pronouns predictions",
        )
    gr.Markdown("X-axis sorted by older to more recent dates:")
    place_gen = gr.Button('Populate fields with a location example')

    gr.Markdown("X-axis sorted by bottom 10 and top 10 Global Gender Gap ranked countries:")
    date_gen = gr.Button('Populate fields with a date example')

    gr.Markdown("X-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ")
    subreddit_gen = gr.Button('Populate fields with a subreddit example')

    with gr.Row():

        date_gen.click(date_fn, inputs=[], outputs=[model_name,
                       x_axis, place_holder, to_normalize, input_text])
        place_gen.click(place_fn, inputs=[], outputs=[
                        model_name, x_axis, place_holder, to_normalize, input_text])
        subreddit_gen.click(reddit_fn, inputs=[], outputs=[
                            model_name, x_axis, place_holder, to_normalize, input_text])
    with gr.Row():
        btn = gr.Button("Hit submit")
        btn.click(
            predict_gender_pronouns,
            inputs=[model_name, x_axis, place_holder,
                    to_normalize, input_text],
            outputs=[sample_text, female_fig, male_fig, df])

demo.launch(debug=True)


# %%