import random from typing import AnyStr import streamlit as st from bs4 import BeautifulSoup import spacy from spacy import displacy from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline import os from transformers_interpret import SequenceClassificationExplainer # Map model names to URLs model_names_to_URLs = { 'ml6team/distilbert-base-dutch-cased-toxic-comments': 'https://huggingface.co/ml6team/distilbert-base-dutch-cased-toxic-comments', 'ml6team/robbert-dutch-base-toxic-comments': 'https://huggingface.co/ml6team/robbert-dutch-base-toxic-comments', } about_page_markdown = f"""# ๐Ÿคฌ Dutch Toxic Comment Detection Space Made by [ML6](https://ml6.eu/). Token attribution is performed using [transformers-interpret](https://github.com/cdpierse/transformers-interpret). """ regular_emojis = [ '๐Ÿ˜', '๐Ÿ™‚', '๐Ÿ‘ถ', '๐Ÿ˜‡', ] undecided_emojis = [ '๐Ÿคจ', '๐Ÿง', '๐Ÿฅธ', '๐Ÿฅด', '๐Ÿคท', ] potty_mouth_emojis = [ '๐Ÿค', '๐Ÿ‘ฟ', '๐Ÿ˜ก', '๐Ÿคฌ', 'โ˜ ๏ธ', 'โ˜ฃ๏ธ', 'โ˜ข๏ธ', ] # Page setup st.set_page_config( page_title="Toxic Comment Detection Space", page_icon="๐Ÿคฌ", layout="centered", initial_sidebar_state="auto", menu_items={ 'Get help': None, 'Report a bug': None, 'About': about_page_markdown, } ) # Model setup @st.cache(allow_output_mutation=True, suppress_st_warning=True, show_spinner=False) def load_pipeline(model_name): with st.spinner('Loading model (this might take a while)...'): toxicity_pipeline = pipeline( 'text-classification', model=model_name, tokenizer=model_name) cls_explainer = SequenceClassificationExplainer( toxicity_pipeline.model, toxicity_pipeline.tokenizer) return toxicity_pipeline, cls_explainer # Auxiliary functions def format_explainer_html(html_string): """Extract tokens with attribution-based background color.""" inside_token_prefix = '##' soup = BeautifulSoup(html_string, 'html.parser') p = soup.new_tag('p', attrs={'style': 'color: black; background-color: white;'}) # Select token elements and remove model specific tokens current_word = None for token in soup.find_all('td')[-1].find_all('mark')[1:-1]: text = token.font.text.strip() if text.startswith(inside_token_prefix): text = text[len(inside_token_prefix):] else: # Create a new span for each word (sequence of sub-tokens) if current_word is not None: p.append(current_word) p.append(' ') current_word = soup.new_tag('span') token.string = text token.attrs['style'] = f"{token.attrs['style']}; padding: 0.2em 0em;" current_word.append(token) # Add last word p.append(current_word) # Add left and right-padding to each word for span in p.find_all('span'): span.find_all('mark')[0].attrs['style'] = ( f"{span.find_all('mark')[0].attrs['style']}; padding-left: 0.2em;") span.find_all('mark')[-1].attrs['style'] = ( f"{span.find_all('mark')[-1].attrs['style']}; padding-right: 0.2em;") return p def list_all_article_names() -> list: filenames = [] for file in os.listdir('./sample-articles/'): if file.endswith('.txt'): filenames.append(file.replace('.txt', '')) return filenames def fetch_article_contents(filename: str) -> AnyStr: with open(f'./sample-articles/{filename.lower()}.txt', 'r') as f: data = f.read() return data def fetch_summary_contents(filename: str) -> AnyStr: with open(f'./sample-summaries/{filename.lower()}.txt', 'r') as f: data = f.read() return data def classify_comment(comment, selected_model): """Classify the given comment and augment with additional information.""" toxicity_pipeline, cls_explainer = load_pipeline(selected_model) result = toxicity_pipeline(comment)[0] result['model_name'] = selected_model # Add explanation result['word_attribution'] = cls_explainer(comment, class_name="non-toxic") result['visualitsation_html'] = cls_explainer.visualize()._repr_html_() result['tokens_with_background'] = format_explainer_html( result['visualitsation_html']) # Choose emoji reaction label, score = result['label'], result['score'] if label == 'toxic' and score > 0.1: emoji = random.choice(potty_mouth_emojis) elif label in ['non_toxic', 'non-toxic'] and score > 0.1: emoji = random.choice(regular_emojis) else: emoji = random.choice(undecided_emojis) result.update({'text': comment, 'emoji': emoji}) # Add result to session st.session_state.results.append(result) # Start session if 'results' not in st.session_state: st.session_state.results = [] # Page # st.title('๐Ÿคฌ Dutch Toxic Comment Detection') # st.markdown("""This demo showcases two Dutch toxic comment detection models.""") # # # Introduction # st.markdown(f"""Both models were trained using a sequence classification task on a translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) which contains toxic online comments. # The first model is a fine-tuned multilingual [DistilBERT](https://huggingface.co/distilbert-base-multilingual-cased) model whereas the second is a fine-tuned Dutch RoBERTa-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base).""") # st.markdown(f"""For a more comprehensive overview of the models check out their model card on ๐Ÿค— Model Hub: [distilbert-base-dutch-toxic-comments]({model_names_to_URLs['ml6team/distilbert-base-dutch-cased-toxic-comments']}) and [RobBERT-dutch-base-toxic-comments]({model_names_to_URLs['ml6team/robbert-dutch-base-toxic-comments']}). # """) # st.markdown("""Enter a comment that you want to classify below. The model will determine the probability that it is toxic and highlights how much each token contributes to its decision: # # red # # tokens indicate toxicity whereas # # green # tokens indicate the opposite. # # Try it yourself! ๐Ÿ‘‡""", # unsafe_allow_html=True) # Demo # with st.form("dutch-toxic-comment-detection-input", clear_on_submit=True): # selected_model = st.selectbox('Select a model:', model_names_to_URLs.keys(), # )#index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False) # text = st.text_area( # label='Enter the comment you want to classify below (in Dutch):') # _, rightmost_col = st.columns([6,1]) # submitted = rightmost_col.form_submit_button("Classify", # help="Classify comment") with st.form("article-input"): # TODO: should probably set a minimum length of article or something selected_article = st.selectbox('Select an article or provide your own:', list_all_article_names(), )#index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False) st.session_state.article_text = fetch_article_contents(selected_article) article_text = st.text_area( label='Enter the comment you want to classify below (in Dutch):', value = st.session_state.article_text) _, rightmost_col = st.columns([6,1]) get_summary = rightmost_col.form_submit_button("Generate summary", help="Generate summary for the given article text") def display_summary(article_name: str): st.subheader("GENERATED SUMMARY") st.markdown("######") summary_content = fetch_summary_contents(article_name) nlp = spacy.load('en_core_web_sm') doc = nlp(summary_content) html = displacy.render(doc, style="ent") html = html.replace("\n", " ") HTML_WRAPPER = """
{}
""" st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True) st.markdown(summary_content) # Listener if get_summary: if article_text: with st.spinner('Generating summary...'): #classify_comment(article_text, selected_model) display_summary(selected_article) else: st.error('**Error**: No comment to classify. Please provide a comment.') # Results if 'results' in st.session_state and st.session_state.results: first = True for result in st.session_state.results[::-1]: if not first: st.markdown("---") st.markdown(f"Text:\n> {result['text']}") col_1, col_2, col_3 = st.columns([1,2,2]) col_1.metric(label='', value=f"{result['emoji']}") col_2.metric(label='Label', value=f"{result['label']}") col_3.metric(label='Score', value=f"{result['score']:.3f}") st.markdown(f"Token Attribution:\n{result['tokens_with_background']}", unsafe_allow_html=True) st.caption(f"Model: {result['model_name']}") first = False