app.py

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

# Initialize tokenizer and model
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')

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

label2id = {v: k for k, v in id2label.items()}

# Entity colors for highlights
label_colors = {
    "STEREO": "rgba(255, 0, 0, 0.2)",  # Light Red
    "GEN": "rgba(0, 0, 255, 0.2)",     # Light Blue
    "UNFAIR": "rgba(0, 255, 0, 0.2)"   # Light Green
}

# Post-process entity tags
def post_process_entities(result):
    prev_entity_type = None
    for token_data in result:
        labels = token_data["labels"]
        labels = list(set(labels))

        # Handle conflicting B- and I- tags for the same entity
        for entity_type in ["GEN", "UNFAIR", "STEREO"]:
            if f"B-{entity_type}" in labels and f"I-{entity_type}" in labels:
                labels.remove(f"I-{entity_type}")

        # Handle sequence rules
        current_entity_type = None
        current_label = None
        for label in labels:
            if label.startswith("B-") or label.startswith("I-"):
                current_label = label
                current_entity_type = label[2:]

        if current_entity_type:
            if current_label.startswith("B-") and prev_entity_type == current_entity_type:
                labels.remove(current_label)
                labels.append(f"I-{current_entity_type}")
            if current_label.startswith("I-") and prev_entity_type != current_entity_type:
                labels.remove(current_label)
                labels.append(f"B-{current_entity_type}")

            prev_entity_type = current_entity_type
        else:
            prev_entity_type = None

        token_data["labels"] = labels
    return result

# Generate JSON results
def generate_json(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()

    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    result = []
    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']
            result.append({"token": token.replace("##", ""), "labels": labels})

    result = post_process_entities(result)

    return json.dumps(result, indent=4)

# Predict function
def predict_ner_tags_with_json(sentence):
    json_result = generate_json(sentence)

    result = json.loads(json_result)

    word_row = []
    stereo_row = []
    gen_row = []
    unfair_row = []

    for token_data in result:
        token = token_data["token"]
        labels = token_data["labels"]

        word_row.append(f"<span style='font-weight:bold;'>{token}</span>")

        stereo_labels = [label[2:] for label in labels if "STEREO" in label]
        stereo_row.append(
            f"<span style='background:{label_colors['STEREO']}; border-radius:6px; padding:2px 5px;'>{', '.join(stereo_labels)}</span>"
            if stereo_labels else "&nbsp;"
        )

        gen_labels = [label[2:] for label in labels if "GEN" in label]
        gen_row.append(
            f"<span style='background:{label_colors['GEN']}; border-radius:6px; padding:2px 5px;'>{', '.join(gen_labels)}</span>"
            if gen_labels else "&nbsp;"
        )

        unfair_labels = [label[2:] for label in labels if "UNFAIR" in label]
        unfair_row.append(
            f"<span style='background:{label_colors['UNFAIR']}; border-radius:6px; padding:2px 5px;'>{', '.join(unfair_labels)}</span>"
            if unfair_labels else "&nbsp;"
        )

    matrix_html = f"""
    <table style='border-collapse:collapse; width:100%; font-family:monospace; text-align:left;'>
        <tr>
            <td><strong>Text Sequence</strong></td>
            {''.join(f"<td>{word}</td>" for word in word_row)}
        </tr>
        <tr>
            <td><strong>Generalizations</strong></td>
            {''.join(f"<td>{cell}</td>" for cell in gen_row)}
        </tr>
        <tr>
            <td><strong>Unfairness</strong></td>
            {''.join(f"<td>{cell}</td>" for cell in unfair_row)}
        </tr>
        <tr>
            <td><strong>Stereotypes</strong></td>
            {''.join(f"<td>{cell}</td>" for cell in stereo_row)}
        </tr>
    </table>
    """

    return f"{matrix_html}<br><pre>{json_result}</pre>"

# Gradio Interface
iface = gr.Blocks()

with iface:
    with gr.Row():
        gr.Markdown(
            """
            # Social Bias Named Entity Recognition (with BERT) 🕵
            Enter a sentence to predict biased parts of speech tags. This model uses multi-label `BertForTokenClassification` to label the entities:
            - **Generalizations (GEN)**
            - **Unfairness (UNFAIR)**
            - **Stereotypes (STEREO)**

            Labels follow the BIO format. Try it out!

            - **[Blog Post](https://huggingface.co/blog/maximuspowers/bias-entity-recognition)**
            - **[Model Page](https://huggingface.co/maximuspowers/bias-detection-ner)**
            """
        )
    with gr.Row():
        input_box = gr.Textbox(label="Input Sentence")
    with gr.Row():
        output_box = gr.HTML(label="Entity Matrix and JSON Output")

    input_box.change(predict_ner_tags_with_json, inputs=[input_box], outputs=[output_box])

iface.launch(share=True)