Create app.py

app.py

@@ -0,0 +1,471 @@
+# %%
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import random
+from matplotlib.ticker import MaxNLocator
+from transformers import pipeline
+
+MODEL_NAMES = ["bert-base-uncased", "roberta-base", "bert-large-uncased", "roberta-large"]
+OWN_MODEL_NAME = 'add-a-model'
+
+DECIMAL_PLACES = 1
+EPS = 1e-5  # to avoid /0 errors
+
+# Example date conts
+DATE_SPLIT_KEY = "DATE"
+START_YEAR = 1801
+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
+models = dict()
+
+for bert_like in MODEL_NAMES:
+    models[bert_like] = pipeline("fill-mask", model=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.lower() 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"].strip().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()
+    # Trying small fig due to rendering issues on HF, not on VS Code
+    fig.set_figheight(3)
+    fig.set_figwidth(9)
+
+    # 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')
+    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=5)
+    return fig
+
+
+# %%
+def predict_gender_pronouns(
+    model_name,
+    own_model_name,
+    indie_vars,
+    split_key,
+    normalizing,
+    n_fit,
+    input_text,
+):
+    """Run inference on input_text for each model type, returning df and plots of percentage
+    of gender pronouns predicted as female and male in each target text.
+    """
+    if model_name not in MODEL_NAMES:
+        model = pipeline("fill-mask", model=own_model_name)
+    else:
+        model = models[model_name]
+
+    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',  n_fit,)
+    male_fig = get_figure(results_df.drop(
+        'female_pronouns', axis=1), 'male',  n_fit,)
+    display_text = f"{random.choice(indie_vars_list)}".join(text_segments)
+
+    return (
+        display_text,
+        female_fig,
+        male_fig,
+        results_df,
+    )
+
+
+# %%
+title = "Causing Gender Pronouns"
+description = """
+## Intro 
+"""
+
+
+date_example = [
+    MODEL_NAMES[1],
+    '',  
+    ', '.join(DATES),
+    'DATE',
+    "False",
+    1,
+    'She was a teenager in DATE.'
+]
+
+
+place_example = [
+    MODEL_NAMES[0],
+    '',  
+    ', '.join(PLACES),
+    'PLACE',
+    "False",
+    1,
+    'She became an adult in PLACE.'
+]
+
+
+subreddit_example = [
+    MODEL_NAMES[3],
+    '',  
+    ', '.join(SUBREDDITS),
+    'SUBREDDIT',
+    "False",
+    1,
+    'She was a kid. SUBREDDIT.'
+]
+
+own_model_example = [
+    OWN_MODEL_NAME,
+    'emilyalsentzer/Bio_ClinicalBERT',
+    ', '.join(DATES),
+    'DATE',
+    "False",
+    1,
+    'She was exposed to the virus in DATE.'
+]
+
+
+def date_fn():
+    return date_example
+
+
+def place_fn():
+    return place_example
+
+
+def reddit_fn():
+    return subreddit_example
+
+
+def your_fn():
+    return own_model_example
+
+
+# %%
+demo = gr.Blocks()
+with demo:
+    gr.Markdown("# Spurious Correlation Evaluation for Pre-trained LLMs")
+    gr.Markdown("Find spurious correlations between seemingly independent variables (for example between `gender` and `time`) in almost any BERT-like LLM on Hugging Face, below.")
+
+    gr.Markdown("See why this happens how in [our ICLR paper under review](https://openreview.net/pdf?id=25VgHaPz0l4)".)    
+       
+    gr.Markdown("## Instructions for this Demo")
+    gr.Markdown("1) Click on one of the examples below (where we sweep through a spectrum of `places`, `dates` and `subreddits`) to pre-populate the input fields.") 
+    gr.Markdown("2) Check out the pre-populated fields as you scroll down to the ['Hit Submit...'] button!")
+    gr.Markdown("3) Repeat steps (1) and (2) with more pre-populated inputs or with your own values in the input fields!")
+
+    gr.Markdown("## Example inputs")
+    gr.Markdown("Click a button below to pre-populate input fields with example values. Then scroll down to Hit Submit to generate predictions.")
+    with gr.Row():
+        date_gen = gr.Button('Click for date example inputs')
+        gr.Markdown("<-- x-axis sorted by older to more recent dates:")
+
+        place_gen = gr.Button('Click for country example inputs')
+        gr.Markdown(
+            "<-- x-axis sorted by bottom 10 and top 10 [Global Gender Gap](https://www3.weforum.org/docs/WEF_GGGR_2021.pdf) ranked countries:")
+
+        subreddit_gen = gr.Button('Click for Subreddit example inputs')
+        gr.Markdown(
+            "<-- x-axis sorted in order of increasing self-identified female participation (see [bburky](http://bburky.com/subredditgenderratios/)): ")
+
+        your_gen = gr.Button('Add-a-model example inputs')
+        gr.Markdown("<-- x-axis dates, with your own model loaded! (If first time, try another example, it can take a while to load new model.)")
+
+    gr.Markdown("## Input fields")
+    gr.Markdown(
+        f"A) Pick a spectrum of comma separated values for text injection and x-axis.")
+
+    with gr.Row():
+        x_axis = gr.Textbox(
+            lines=3,
+            label="A) Comma separated values for text injection and x-axis",
+        )
+
+
+    gr.Markdown("B) Pick a pre-loaded BERT-family model of interest on the right.")
+    gr.Markdown(f"Or C) select `{OWN_MODEL_NAME}`, then add the mame of any other Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task on the right (note: this may take some time to load).")
+
+    with gr.Row():
+        model_name = gr.Radio(
+            MODEL_NAMES + [OWN_MODEL_NAME],
+            type="value",
+            label="B) BERT-like model.",
+        )
+        own_model_name = gr.Textbox(
+            label="C) If you selected an 'add-a-model' model, put any Hugging Face pipeline model name (that supports the fill-mask task) here.",
+        )
+
+    gr.Markdown("D) Pick if you want to the predictions normalied to these gendered terms only.")
+    gr.Markdown("E) Also tell the demo what special token you will use in your input text, that you would like replaced with the spectrum of values you listed above.") 
+    gr.Markdown("And F) the degree of polynomial fit used for high-lighting potential spurious association.")
+
+
+    with gr.Row():
+        to_normalize = gr.Dropdown(
+            ["False", "True"],
+            label="D) Normalize model's predictions to only the gendered ones?",
+            type="index",
+        )
+        place_holder = gr.Textbox(
+            label="E) Special token place-holder",
+        )
+        n_fit = gr.Dropdown(
+            list(range(1, 5)),
+            label="F) Degree of polynomial fit",
+            type="value",
+        )
+
+    gr.Markdown(
+        "G) Finally, add input text that includes at least one gendered pronouns and one place-holder token specified above.")
+
+    with gr.Row():
+        input_text = gr.Textbox(
+            lines=2,
+            label="G) Input text with pronouns and place-holder token",
+        )
+
+    gr.Markdown("## Outputs!")
+    #gr.Markdown("Scroll down and 'Hit Submit'!")
+    with gr.Row():
+        btn = gr.Button("Hit submit to generate predictions!")
+
+    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")
+        male_fig = gr.Plot(type="auto")
+    with gr.Row():
+        df = gr.Dataframe(
+            show_label=True,
+            overflow_row_behaviour="show_ends",
+            label="Table of softmax probability for pronouns predictions",
+        )
+
+    with gr.Row():
+
+        date_gen.click(date_fn, inputs=[], outputs=[model_name, own_model_name,
+                       x_axis, place_holder, to_normalize,  n_fit, input_text])
+        place_gen.click(place_fn, inputs=[], outputs=[
+                        model_name, own_model_name, x_axis, place_holder, to_normalize,  n_fit, input_text])
+        subreddit_gen.click(reddit_fn, inputs=[], outputs=[
+                            model_name, own_model_name, x_axis, place_holder, to_normalize,  n_fit, input_text])
+        your_gen.click(your_fn, inputs=[], outputs=[
+            model_name, own_model_name, x_axis, place_holder, to_normalize,  n_fit, input_text])
+
+    btn.click(
+        predict_gender_pronouns,
+        inputs=[model_name, own_model_name, x_axis, place_holder,
+                to_normalize, n_fit, input_text],
+        outputs=[sample_text, female_fig, male_fig, df])
+
+
+demo.launch(debug=True)
+