app.py

import streamlit as st
from transformers import pipeline

# Load the grammar correction model pipeline
corrector = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")

# Load the text classification model pipeline
classifier = pipeline("text-classification", model="Alicewuu/bias_detection", return_all_scores=True)

# Streamlit application title
st.title("Text Bias Detection")
st.write("Detection for 2 classes")

# Text input for user to enter the text to classify
text = st.text_area("Enter the text to detect", "")

# Perform text classification when the user clicks the "Detect" button
if st.button("Detect"):
    
    corrected_text = corrector(text, num_beams=5, max_length=1000)[0]["generated_text"]

    result = classifier(corrected_text)

    bias_score = result[0][0]["score"]

    bias_detected = bias_score >= 0.5

    # Display the classification result
    st.write("The text with correct grammar:", corrected_text)
    st.write("Biased:", bias_detected)
    st.write("Score:", bias_score)