src/streamlit_app.py

import os
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface/transformers"
import streamlit as st
from typing import List, Tuple
import re
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification

# Mapping of label to color
LABEL_COLORS = {
    'LABEL-0': '#cccccc',  # NONE
    'LABEL-1': '#ffadad',  # B-DATE
    'LABEL-2': '#ebdb98',  # I-DATE
    'LABEL-3': '#586492',  # B-TIME
    'LABEL-4': '#ffb788',  # I-TIME
    'LABEL-5': '#76abbb',  # B-DURATION
    'LABEL-6': '#a0c4ff',  # I-DURATION
    'LABEL-7': '#f84252',  # B-SET
    'LABEL-8': '#ebdb98',  # I-SET
}

LABEL_MEANINGS = {
    'LABEL-0': 'NONE',
    'LABEL-1': 'B-DATE',
    'LABEL-2': 'I-DATE',
    'LABEL-3': 'B-TIME',
    'LABEL-4': 'I-TIME',
    'LABEL-5': 'B-DURATION',
    'LABEL-6': 'I-DURATION',
    'LABEL-7': 'B-SET',
    'LABEL-8': 'I-SET',
}

@st.cache_resource(show_spinner=True)
def load_model():
    tokenizer = AutoTokenizer.from_pretrained('asdc/Bio-RoBERTime')
    model = AutoModelForTokenClassification.from_pretrained('asdc/Bio-RoBERTime')
    return tokenizer, model

def ner_with_robertime(text: str) -> List[Tuple[str, str]]:
    tokenizer, model = load_model()
    tokens = tokenizer(text, return_tensors="pt", truncation=True, is_split_into_words=False)
    with torch.no_grad():
        outputs = model(**tokens)
    predictions = torch.argmax(outputs.logits, dim=2)[0].tolist()
    labels = [model.config.id2label[pred] for pred in predictions]
    word_ids = tokens.word_ids(batch_index=0)
    input_ids = tokens["input_ids"][0]
    entities = []
    current_word_ids = []
    current_label = None
    last_word_id = None
    for idx, word_id in enumerate(word_ids):
        if word_id is None:
            continue
        label = labels[idx]
        if word_id != last_word_id and current_word_ids:
            word = tokenizer.decode([input_ids[i] for i in current_word_ids], skip_special_tokens=True)
            entities.append((word, current_label))
            current_word_ids = [idx]
            current_label = label
        else:
            current_word_ids.append(idx)
            current_label = label
        last_word_id = word_id
    if current_word_ids:
        word = tokenizer.decode([input_ids[i] for i in current_word_ids], skip_special_tokens=True)
        entities.append((word, current_label))
    return entities

def colorize_entities(ner_result: List[Tuple[str, str]]) -> str:
    html = ''
    for token, label in ner_result:
        norm_label = label.replace('_', '-')
        if norm_label != 'LABEL-0':
            color = LABEL_COLORS.get(norm_label, '#eeeeee')
            label_meaning = LABEL_MEANINGS.get(norm_label, norm_label)
            html += (
                f'<span class="ner-entity" style="background-color:{color};padding:2px 4px;border-radius:4px;margin:1px;" '
                f'data-tooltip="{label_meaning}">{token}</span> '
            )
        else:
            html += f'{token} '
    return html

def extract_entities(ner_result: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
    # Group consecutive tokens with the same entity label (not LABEL-0)
    entities = []
    current_entity = []
    current_label = None
    for token, label in ner_result:
        if label != 'LABEL-0':
            if current_label == label:
                current_entity.append(token)
            else:
                if current_entity:
                    entities.append((' '.join(current_entity), current_label))
                current_entity = [token]
                current_label = label
        else:
            if current_entity:
                entities.append((' '.join(current_entity), current_label))
                current_entity = []
                current_label = None
    if current_entity:
        entities.append((' '.join(current_entity), current_label))
    return entities

def legend_html() -> str:
    html = '<div style="display:flex;flex-wrap:wrap;gap:8px;">'
    for label, color in LABEL_COLORS.items():
        if label == 'LABEL-0':
            continue
        meaning = LABEL_MEANINGS[label]
        html += f'<span style="background-color:{color};padding:2px 8px;border-radius:4px;">{meaning} ({label})</span>'
    html += '</div>'
    return html

st.title('LLM-powered Named Entity Recognition (NER)')

st.markdown(
    '''
    <style>
    .ner-entity {
        position: relative;
        cursor: pointer;
    }
    .ner-entity[data-tooltip]:hover:after {
        content: attr(data-tooltip);
        position: absolute;
        left: 0;
        top: 100%;
        background: #222;
        color: #fff;
        padding: 2px 8px;
        border-radius: 4px;
        white-space: nowrap;
        z-index: 10;
        font-size: 0.9em;
        margin-top: 2px;
    }
    </style>
    ''',
    unsafe_allow_html=True
)

st.markdown('**Legend:**')
st.markdown(legend_html(), unsafe_allow_html=True)

user_text = st.text_area('Enter text for NER:', height=150)

if user_text:
    ner_result = ner_with_robertime(user_text)
    has_entity = any(label != 'LABEL-0' for _, label in ner_result)
    if has_entity:
        st.markdown('#### Entities Highlighted:')
        st.markdown(colorize_entities(ner_result), unsafe_allow_html=True)
        entities = extract_entities(ner_result)
        if entities:
            st.markdown('#### Detected Entities:')
            for ent, label in entities:
                norm_label = label.replace('_', '-')
                st.markdown(f'- <span style="background-color:{LABEL_COLORS[norm_label]};padding:2px 8px;border-radius:4px;">{ent}</span> <span style="color:#888;">({LABEL_MEANINGS[norm_label]})</span>', unsafe_allow_html=True)
        else:
            st.info('No entities detected.')
    else:
        st.info('No entities detected.')
    st.caption('Model: [asdc/Bio-RoBERTime](https://huggingface.co/asdc/Bio-RoBERTime)')