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

# Load 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')

# Define label mappings
id2label = {
    0: 'O',
    1: 'B-STEREO',
    2: 'I-STEREO',
    3: 'B-GEN',
    4: 'I-GEN',
    5: 'B-UNFAIR',
    6: 'I-UNFAIR'
}

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()

    result = []
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    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, labels))

    return result

def format_output(result):
    formatted_output = ""
    for token, labels in result:
        formatted_output += f"{token}: {', '.join(labels)}\n"
    return formatted_output

iface = gr.Interface(
    fn=predict_ner_tags,
    inputs="text",
    outputs="text",
    title="Named Entity Recognition with BERT",
    description="Enter a sentence to predict NER tags using BERT model trained for multi-label classification.",
    examples=["Tall men are so clumsy."],
    allow_flagging="never",
    interpretation="default",
    postprocessing_fn=format_output
)

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