app.py

import gradio as gr
from transformers import pipeline
import fitz  # PyMuPDF
import re
import pandas as pd

# Load detection models
bias_detector = pipeline("text-classification", model="himel7/bias-detector")
bias_type_classifier = pipeline("text-classification", model="maximuspowers/bias-type-classifier")

# Load neutralizer models (lazy load for speed)
neutralizer_models = {
    "BART Neutralizer": "himel7/bias-neutralizer-bart",
    "T5 Small Neutralizer": "himel7/bias-neutralizer-t5s"
}
neutralizers = {}

def get_neutralizer(model_name):
    if model_name not in neutralizers:
        neutralizers[model_name] = pipeline("text2text-generation", model=neutralizer_models[model_name])
    return neutralizers[model_name]

# Utils
def extract_text_from_pdf(pdf_file):
    text = ""
    with fitz.open(pdf_file) as pdf:
        for page in pdf:
            text += page.get_text("text")
    return text

def split_into_sentences(text):
    sentences = re.split(r'(?<=[.!?])\s+', text.strip())
    return [s for s in sentences if s]

def analyze_sentence(sentence):
    detection_result = bias_detector(sentence)[0]
    label = detection_result['label']
    score = detection_result['score']

    if label == "LABEL_1":  # Biased
        type_result = bias_type_classifier(sentence)[0]
        return {
            "sentence": sentence,
            "bias": "Biased",
            "bias_score": round(score, 2),
            "bias_type": type_result['label'],
            "bias_type_score": round(type_result['score'], 2)
        }
    else:
        return {
            "sentence": sentence,
            "bias": "Unbiased",
            "bias_score": round(score, 2),
            "bias_type": "-",
            "bias_type_score": "-"
        }

def analyze_pdf(pdf_file):
    text = extract_text_from_pdf(pdf_file)
    sentences = split_into_sentences(text)

    results = [analyze_sentence(s) for s in sentences]

    # Stats
    total = len(results)
    biased = sum(1 for r in results if r["bias"] == "Biased")
    unbiased = total - biased

    stats_md = f"""
    ### 📊 Bias Statistics
    - **Total Sentences:** {total}
    - **Biased Sentences:** {biased} ({(biased/total)*100:.1f}%)
    - **Unbiased Sentences:** {unbiased} ({(unbiased/total)*100:.1f}%)
    """

    df = pd.DataFrame(results)
    return stats_md, df

def analyze_text(text):
    return analyze_sentence(text)

# New: Neutralize Bias
def neutralize_text(text, model_choice):
    neutralizer = get_neutralizer(model_choice)
    result = neutralizer(text, max_length=512, do_sample=False)
    return result[0]["generated_text"]

def neutralize_pdf(pdf_file, model_choice):
    text = extract_text_from_pdf(pdf_file)
    sentences = split_into_sentences(text)

    neutralizer = get_neutralizer(model_choice)
    neutralized_sentences = [neutralizer(s, max_length=512, do_sample=False)[0]["generated_text"] for s in sentences]
    neutralized_text = " ".join(neutralized_sentences)
    return neutralized_text


# Top badges
badges_html = """
<p align="center">
  <a href="https://huggingface.co/himel7/bias-detector">
    <img src="https://img.shields.io/badge/🤗-Hugging%20Face-yellow.svg">
  </a>
  <a href="https://huggingface.co/himel7/bias-detector">
    <img src="https://img.shields.io/badge/Model-Homepage-purple.svg">
  </a>
  <a href="https://github.com/Himel1996/NewsBiasDetector/">
    <img src="https://img.shields.io/badge/GitHub-Repo-orange.svg">
  </a>
  <a href="https://arxiv.org/abs/2505.13010v1">
    <img src="https://img.shields.io/badge/arXiv-2505.13010-red.svg">
  </a>
</p>
"""

# Build UI
with gr.Blocks() as demo:
    gr.HTML(badges_html)
    gr.Markdown("## Bias Analyzer & Neutralizer")
    gr.Markdown("### This app helps you to detect biases in sentences, analyse them, and neutralize sentences.")

    with gr.Tab("Single Sentence"):
        text_input = gr.Textbox(lines=3, placeholder="Enter a sentence...")
        output = gr.JSON()
        btn = gr.Button("Analyze")
        btn.click(analyze_text, inputs=text_input, outputs=output)

        gr.Markdown("### Neutralize Bias")
        model_choice = gr.Dropdown(list(neutralizer_models.keys()), label="Neutralizer Model", value="BART Neutralizer")
        neutral_output = gr.Textbox(label="Neutralized Sentence", lines=3)
        neutral_btn = gr.Button("Neutralize")
        neutral_btn.click(neutralize_text, inputs=[text_input, model_choice], outputs=neutral_output)

    with gr.Tab("Analyze PDF"):
        pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
        stats_output = gr.Markdown()
        table_output = gr.Dataframe(headers=["Sentence", "Bias", "Bias Score", "Bias Type", "Bias Type Score"])
        analyze_btn = gr.Button("Analyze PDF")
        analyze_btn.click(analyze_pdf, inputs=pdf_input, outputs=[stats_output, table_output])

        gr.Markdown("### Neutralize Entire PDF")
        model_choice_pdf = gr.Dropdown(list(neutralizer_models.keys()), label="Neutralizer Model", value="BART Neutralizer")
        neutral_pdf_output = gr.Textbox(label="Neutralized PDF Text", lines=15)
        neutral_pdf_btn = gr.Button("Neutralize PDF")
        neutral_pdf_btn.click(neutralize_pdf, inputs=[pdf_input, model_choice_pdf], outputs=neutral_pdf_output)


if __name__ == "__main__":
    demo.launch()