app.py

import streamlit as st
import pandas as pd
import numpy as np
import re
import string
import textwrap
from transformers import BertTokenizer, BertForSequenceClassification, AutoModelForCausalLM, AutoTokenizer, pipeline, AdamW
from happytransformer import HappyTextToText, TTSettings
import torch
from torch.nn import BCEWithLogitsLoss
from torch.utils.data import DataLoader, TensorDataset, random_split
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("saurabhg2083/model_bert")
model = AutoModelForSequenceClassification.from_pretrained("saurabhg2083/model_bert")
happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
args = TTSettings(num_beams=5, min_length=1)

gendered_pronouns = [
          'ambition', 'driven', 'lead', 'persist', 'principle', 'decision', 'superior', 'individual', 'assertive',
          'strong', 'hierarchical', 'rigid', 'silicon valley', 'stock options', 'takes risk', 'workforce', 'autonomous',
          'ping pong', 'pool table', 'must', 'competitive', 'he', 'his', 'himself', 'confident', 'active', 'aggressive',
          'ambitious', 'fearless', 'headstrong', 'defensive', 'independent', 'dominant', 'outspoken', 'leader', 'fast paced',
          'adventurous', 'analytical', 'decisive', 'determined', 'ninja', 'objective', 'rock star', 'boast', 'challenging', 'courage',
          'thoughtful', 'creative', 'adaptable', 'choose', 'curious', 'excellent', 'flexible', 'multitasking', 'health',
          'imaginative', 'intuitive', 'leans in', 'plans for the future', 'resilient', 'self-aware', 'socially responsible',
          'trustworthy', 'shup-to-date', 'wellness program', 'nurture', 'teach', 'dependable', 'community', 'serving', 'loyal',
          'enthusiasm', 'interpersonal', 'connect', 'commit', 'she', 'agree', 'empathy', 'sensitive', 'affectionate', 'feel',
          'support', 'collaborate', 'honest', 'trust', 'understand', 'compassion', 'share', 'polite', 'kind', 'caring', 'her',
          'hers', 'herself', 'feminine', 'cheer', 'communal', 'emotional', 'flatterable', 'gentle', 'interdependent', 'kinship',
          'modesty', 'pleasant', 'polite', 'quiet', 'sympathy', 'warm', 'dominant', 'yield',
          'native english speaker', 'professionally groomed hair', 'native', 'culture fit', 'non-white', 'clean-shaven',
          'neat hairstyle', 'master', 'slave', 'a cakewalk', 'brownbag session', 'spirit animal', 'digital native',
          'servant leadership', 'tribe', 'oriental', 'spic', 'english fluency', 'level native', 'illegals', 'eskimo',
          'latino', 'latina', 'migrant', 'blacklist', 'whitelist'
      ]

# List of neutral words
neutral_words = [
    "drive",
    "motivated",
    "guide",
    "continue",
    "ethic",
    "choice",
    "excellent",
    "person",
    "confident",
    "resilient",
    "structured",
    "inflexible",
    "tech industry",
    "equity options",
    "is adventurous",
    "employees",
    "independent",
    "table tennis",
    "billiards table",
    "should",
    "challenging",
    "they",
    "their",
    "themselves",
    "self-assured",
    "energetic",
    "assertive",
    "aspiring",
    "courageous",
    "determined",
    "protective",
    "self-reliant",
    "influential",
    "expressive",
    "guiding force",
    "high-speed",
    "daring",
    "logical",
    "resolute",
    "committed",
    "expert",
    "impartial",
    "outstanding performer",
    "brag",
    "demanding",
    "bravery",
    "considerate",
    "innovative",
    "flexible",
    "select",
    "inquisitive",
    "outstanding",
    "adaptable",
    "handling multiple tasks",
    "well-being",
    "creative",
    "instinctive",
    "long-term planning",
    "tough",
    "aware of oneself",
    "ethical",
    "reliable",
    "current",
    "health program",
    "foster",
    "instruct",
    "reliable",
    "society",
    "assisting",
    "devoted",
    "passion",
    "relational",
    "link",
    "dedicate",
    "they",
    "concur",
    "understanding",
    "responsive",
    "loving",
    "experience",
    "assist",
    "work together",
    "truthful",
    "confidence",
    "comprehend",
    "sympathy",
    "contribute",
    "courteous",
    "considerate",
    "supportive",
    "their",
    "theirs",
    "themselves",
    "androgynous",
    "encourage",
    "collective",
    "expressive",
    "complimentable",
    "tender",
    "mutual",
    "connection",
    "humility",
    "agreeable",
    "silent",
    "empathy",
    "friendly",
    "leading",
    "produce",
    "fluent English speaker",
    "well-groomed appearance",
    "indigenous",
    "cultural alignment",
    "diverse",
    "clean-cut",
    "tidy hair",
    "expert",
    "subordinate",
    "easy task",
    "informal meeting",
    "personal inspiration",
    "tech-savvy",
    "supportive leadership",
    "community",
    "eastern",
    "avoid using",
    "english proficiency",
    "fluent",
    "unauthorized individuals",
    "indigenous Northern people",
    "hispanic",
    "latinx",
    "mobile worker",
    "inclusion list",
]



def replace_gendered_pronouns(text):
    # Define a dictionary of gendered pronouns and their gender-neutral replacements
    word_dict = dict(zip(gendered_pronouns, neutral_words))

    # Use regular expressions to find and replace gendered pronouns in the text
    for pronoun, replacement in word_dict.items():
        # Use word boundaries to match whole words only
        pattern = r'\b' + re.escape(pronoun) + r'\b'
        text = re.sub(pattern, replacement, text, flags=re.IGNORECASE)

    return text

def model_eval(text):
    # Put the model in evaluation mode
    model.eval()

    # Input text
    input_text = text

    # Tokenize the input text
    inputs = tokenizer(input_text, padding='max_length', truncation=True, max_length=512, return_tensors="pt")

    # Make the prediction
    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    predicted_label = (logits > 0).int().item()
    
    return predicted_label
    

st.title("Job Bias Testing")

text1 = st.text_area("Enter Text 1")

if st.button("Check Bias"):
    if text1:
        predicted_label = model_eval(text1)
        # Convert 0 or 1 label back to a meaningful label if needed
        label_mapping = {0: "Negative", 1: "Positive"}
        predicted_label_text = label_mapping[predicted_label]
        #print(f"Predicted Label: {predicted_label_text}")
        if predicted_label_text == "Positive":
          rewritten_sentence = replace_gendered_pronouns(text1)
          words = rewritten_sentence.split()
          word_count = 0
          chunk = ""
          target_word_count = 35
          result = ""
        
          for word in words:
              # Add the sentence to the current chunk
              chunk += word + " "
        
              words_list = chunk.split()
              word_count = len(words_list)
              
              # Check if the word count exceeds the target
              if word_count >= target_word_count:          
                  grammar_text = happy_tt.generate_text("grammar: "+chunk, args=args)
                  result = result + grammar_text.text   
                  chunk = ""
                  word_count = 0
            
          # Add the prefix "grammar: " before each input
          #result = happy_tt.generate_text("grammar: "+rewritten_sentence, args=args)
          #print(result.text) # This sentence has bad grammar.
        st.success(f"Predicted Label: {predicted_label_text} \n new Text is: {result}")
    else:
        st.warning("Please enter text Job Description.")