app.py

import json
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
import gradio as gr

# Initialize important things
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForTokenClassification.from_pretrained('maximuspowers/bias-detection-ner')
model.eval()
model.to('cuda' if torch.cuda.is_available() else 'cpu')

# IDs to labels we want to display
id2label = {
    0: 'O',
    1: 'B-STEREO',
    2: 'I-STEREO',
    3: 'B-GEN',
    4: 'I-GEN',
    5: 'B-UNFAIR',
    6: 'I-UNFAIR'
}

# Color map for entities
label_colors = {
    "B-STEREO": "#FFCDD2",
    "I-STEREO": "#E57373",
    "B-GEN": "#C8E6C9",
    "I-GEN": "#81C784",
    "B-UNFAIR": "#BBDEFB",
    "I-UNFAIR": "#64B5F6",
    "O": "#FFFFFF"  # Default for no label
}

# Predict function
def predict_ner_tags(sentence):
    inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
    input_ids = inputs['input_ids'].to(model.device)
    attention_mask = inputs['attention_mask'].to(model.device)

    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        probabilities = torch.sigmoid(logits)
        predicted_labels = (probabilities > 0.5).int()  # Threshold

    result = []
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    highlighted_sentence = ""
    for i, token in enumerate(tokens):
        if token not in tokenizer.all_special_tokens:
            label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
            labels = [id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
            # Get the most prominent label for coloring (arbitrary choice for multiple labels)
            primary_label = labels[0] if labels else "O"
            color = label_colors.get(primary_label, "#FFFFFF")
            highlighted_sentence += f"<span style='background-color:{color}'>{token}</span> "

    return highlighted_sentence.strip()

# Gradio Interface
iface = gr.Interface(
    fn=predict_ner_tags,
    inputs="text",
    outputs=gr.outputs.HTML(label="Highlighted Sentence"),
    title="Social Bias Named Entity Recognition (with BERT) 🕵",
    description=("Enter a sentence to predict biased parts of speech tags. This model uses multi-label BertForTokenClassification, to label the entities: (GEN)eralizations, (UNFAIR)ness, and (STEREO)types. Labels follow BIO format. Try it out :)."
                 "<br><br>Read more about how this model was trained in this <a href='https://huggingface.co/blog/maximuspowers/bias-entity-recognition' target='_blank'>blog post</a>."
                 "<br>Model Page: <a href='https://huggingface.co/maximuspowers/bias-detection-ner' target='_blank'>Bias Detection NER</a>."),
    allow_flagging="never"
)

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