app.py

import random
from typing import AnyStr

import itertools
import streamlit as st
import torch.nn.parameter
from bs4 import BeautifulSoup
import numpy as np
import base64

import validators
from spacy_streamlit.util import get_svg
from validators import ValidationFailure

from custom_renderer import render_sentence_custom
from flair.data import Sentence
from flair.models import SequenceTagger

import spacy
from spacy import displacy
from spacy_streamlit import visualize_parser

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"""# 🤬 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 = [
    '😐', '🙂', '👶', '😇',
]
undecided_emojis = [
    '🤨', '🧐', '🥸', '🥴', '🤷',
]
potty_mouth_emojis = [
    '🤐', '👿', '😡', '🤬', '☠️', '☣️', '☢️',
]

# Page setup
st.set_page_config(
    page_title="Post-processing summarization fact checker",
    page_icon="",
    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 sorted(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 fetch_entity_specific_contents(filename: str) -> AnyStr:
    with open(f'./entity-specific-text/{filename.lower()}.txt', 'r') as f:
        data = f.read()
    return data


def fetch_dependency_specific_contents(filename: str) -> AnyStr:
    with open(f'./dependency-specific-text/{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)


def display_summary(article_name: str):
    summary_content = fetch_summary_contents(article_name)
    st.session_state.summary_output = summary_content
    soup = BeautifulSoup(summary_content, features="html.parser")
    HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
    return HTML_WRAPPER.format(soup)


##@st.cache(hash_funcs={preshed.maps.PreshMap: my_hash_func})
def get_spacy():
    nlp = spacy.load('en_core_web_lg')
    return nlp


# TODO: check the output mutation thingy
@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None}, allow_output_mutation=True)
def get_flair_tagger():
    tagger = SequenceTagger.load("flair/ner-english-ontonotes-fast")
    return tagger


def get_all_entities_per_sentence(text):
    # load all NER models
    nlp = get_spacy()
    tagger = get_flair_tagger()
    doc = nlp(text)

    sentences = list(doc.sents)

    entities_all_sentences = []
    for sentence in sentences:
        entities_this_sentence = []

        # SPACY ENTITIES
        for entity in sentence.ents:
            entities_this_sentence.append(str(entity))

        # FLAIR ENTITIES
        sentence_entities = Sentence(str(sentence))
        tagger.predict(sentence_entities)
        for entity in sentence_entities.get_spans('ner'):
            entities_this_sentence.append(entity.text)
        entities_all_sentences.append(entities_this_sentence)

    return entities_all_sentences


def get_all_entities(text):
    all_entities_per_sentence = get_all_entities_per_sentence(text)
    return list(itertools.chain.from_iterable(all_entities_per_sentence))


# TODO: this functionality can be cached (e.g. by storing html file output) if wanted (or just store list of entities idk)
def get_and_compare_entities(article_name: str):
    article_content = fetch_article_contents(article_name)
    all_entities_per_sentence = get_all_entities_per_sentence(article_content)
    # st.session_state.entities_per_sentence_article = all_entities_per_sentence
    entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence))

    summary_content = fetch_summary_contents(article_name)
    all_entities_per_sentence = get_all_entities_per_sentence(summary_content)
    # st.session_state.entities_per_sentence_summary = all_entities_per_sentence
    entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence))

    matched_entities = []
    unmatched_entities = []
    for entity in entities_summary:
        # TODO: currently substring matching but probably should do embedding method or idk?
        if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article):
            matched_entities.append(entity)
        else:
            unmatched_entities.append(entity)
    return matched_entities, unmatched_entities


def highlight_entities(article_name: str):
    summary_content = fetch_summary_contents(article_name)

    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
    markdown_end = "</mark>"

    matched_entities, unmatched_entities = get_and_compare_entities(article_name)

    for entity in matched_entities:
        summary_content = summary_content.replace(entity, markdown_start_green + entity + markdown_end)

    for entity in unmatched_entities:
        summary_content = summary_content.replace(entity, markdown_start_red + entity + markdown_end)
    soup = BeautifulSoup(summary_content, features="html.parser")

    HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; 
    margin-bottom: 2.5rem">{}</div> """

    return HTML_WRAPPER.format(soup)


def render_dependency_parsing(text: str):
    html = render_sentence_custom(text)
    html = html.replace("\n\n", "\n")
    st.write(get_svg(html), unsafe_allow_html=True)


# If deps for article: True, otherwise deps for summary calc
def check_dependency(article: bool):
    nlp = spacy.load('en_core_web_lg')
    if article:
        text = st.session_state.article_text
        all_entities = get_all_entities_per_sentence(text)
        # all_entities = st.session_state.entities_per_sentence_article
    else:
        text = st.session_state.summary_output
        all_entities = get_all_entities_per_sentence(text)
        # all_entities = st.session_state.entities_per_sentence_summary
    doc = nlp(text)
    tok_l = doc.to_json()['tokens']
    # all_deps = ""
    test_list_dict_output = []

    sentences = list(doc.sents)
    for i, sentence in enumerate(sentences):
        start_id = sentence.start
        end_id = sentence.end
        for t in tok_l:
            # print(t)
            if t["id"] < start_id or t["id"] > end_id:
                continue
            head = tok_l[t['head']]
            if t['dep'] == 'amod' or t['dep'] == "pobj":
                object_here = text[t['start']:t['end']]
                object_target = text[head['start']:head['end']]
                if t['dep'] == "pobj" and str.lower(object_target) != "in":
                    continue
                # ONE NEEDS TO BE ENTITY
                if object_here in all_entities[i]:
                    # all_deps = all_deps.join(str(sentence))
                    identifier = object_here + t['dep'] + object_target
                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
                                                  "target_word_index": (t['head'] - sentence.start),
                                                  "identifier": identifier, "sentence": str(sentence)})
                elif object_target in all_entities[i]:
                    # all_deps = all_deps.join(str(sentence))
                    identifier = object_here + t['dep'] + object_target
                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
                                                  "target_word_index": (t['head'] - sentence.start),
                                                  "identifier": identifier, "sentence": str(sentence)})
                else:
                    continue
    # print(f'NOW TEST LIST DICT: {test_list_dict_output}')
    return test_list_dict_output
    # return all_deps


def is_valid_url(url: str) -> bool:
    result = validators.url(url)
    if isinstance(result, ValidationFailure):
        return False
    return True


# Start session
if 'results' not in st.session_state:
    st.session_state.results = []

# Page
st.title('Summarization fact checker')

# INTRODUCTION
st.header("Introduction")
st.markdown("""Recent work using transformers on large text corpora has shown great succes when fine-tuned on several 
different downstream NLP tasks. One such task is that of text summarization. The goal of text summarization is to 
generate concise and accurate summaries from input document(s). There are 2 types of summarization: extractive and 
abstractive. **Exstractive summarization** merely copies informative fragments from the input, whereas **abstractive 
summarization** may generate novel words. A good abstractive summary should cover principal information in the input 
and has to be linguistically fluent. This blogpost will focus on this more difficult task of abstractive summary 
generation.""")

st.markdown("""To generate summaries we will use the [PEGASUS] (https://huggingface.co/google/pegasus-cnn_dailymail) 
model, producing abstractive summaries from large articles. These summaries often still contain sentences with 
different kinds of errors. Rather than improving the core model, we will look at possible post-processing steps to 
improve the generated summaries by detecting such possible errors. By comparing contents of the summary with the 
source text, we can create some sort of factualness metric, indicating the trustworthiness of the generated 
summary.""")

# GENERATING SUMMARIES PART
st.header("Generating summaries")
st.markdown("Let’s start by selecting an article text for which we want to generate a summary, or you can provide "
            "text yourself. Note that it’s suggested to provide a sufficiently large text, as otherwise the summary "
            "generated might not be optimal to start from.")

# TODO: NEED TO CHECK ARTICLE TEXT INSTEAD OF ARTICLE NAME ALSO FREE INPUT OPTION
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='Full article text',
    value=st.session_state.article_text,
    height=150
)

st.markdown("Below you can find the generated summary for the article. The summaries of the example articles "
            "vary in quality, but are chosen as such. Based on some common errors, we will discuss possible "
            "methods to improve or rank the summaries in the following paragraphs. The idea is that in "
            "production, you could generate a set of summaries for the same article, with different "
            "parameters (or even different models). By using post-processing methods and metrics, "
            "we can detect some errors in summaries, and choose the best one to actually use.")
if st.session_state.article_text:
    with st.spinner('Generating summary...'):
        # classify_comment(article_text, selected_model)

        summary_displayed = display_summary(selected_article)

        st.write("**Generated summary:**", summary_displayed, unsafe_allow_html=True)
else:
    st.error('**Error**: No comment to classify. Please provide a comment.',
             help="Generate summary for the given article text")

if is_valid_url(article_text):
    print("YES")
else:
    print("NO")
def render_svg(svg_file):
    with open(svg_file, "r") as f:
        lines = f.readlines()
        svg = "".join(lines)

        # """Renders the given svg string."""
        b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
        html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
        return html


# ENTITY MATCHING PART
st.header("Entity matching")
st.markdown("**Named entity recognition** (NER) is the task of identifying and categorising key information ("
            "entities) in text. An entity can be a singular word or a series of words that consistently refers to the "
            "same thing. Common entity classes are person names, organisations, locations and so on. By applying NER "
            "to both the article and its summary, we can spot possible **hallucinations**. Hallucinations are words "
            "generated by the model that are not supported by the source input. ")
with st.spinner("Calculating and matching entities..."):
    entity_match_html = highlight_entities(selected_article)
    st.write(entity_match_html, unsafe_allow_html=True)
    red_text = """<font color="black"><span style="background-color: rgb(238, 135, 135); opacity: 
    1;">red</span></font> """
    green_text = """<font color="black">
        <span style="background-color: rgb(121, 236, 121); opacity: 1;">green</span>
    </font>"""

    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
    st.markdown("Here you can see what this looks like when we apply entity-matching on the summary (compared to the "
                "original article). Entities in this summary are marked  " + green_text + " when the entity also "
                                                                                          "exists in the article, while unmatched entities are marked " + red_text + ".",
                unsafe_allow_html=True)
    entity_specific_text = fetch_entity_specific_contents(selected_article)
    st.markdown(entity_specific_text)

# DEPENDENCY PARSING PART
st.header("Dependency comparison")
st.markdown("**Dependency parsing** is the process in which the grammatical structure in a sentence is analysed, "
            "to find out related words as well as the type of the relationship between them. For the sentence “Jan’s "
            "wife is called Sarah” you would get the following dependency graph:")

# TODO: I wonder why the first doesn't work but the second does (it doesn't show deps otherwise)
# st.image("ExampleParsing.svg")
st.write(render_svg('ExampleParsing.svg'), unsafe_allow_html=True)
st.markdown("Here, “Jan” is the “poss” (possession modifier) of “wife”. If suddenly the summary would read “Jan’s "
            "husband…”, there would be a dependency in the summary that is non-existent in the article itself. "
            "However, it could be that such a new dependency is not per se correct, “The borders of Ukraine” have a "
            "different dependency between “borders” and “Ukraine” than “Ukraine’s borders”, while this would also be "
            "correct. So general matching between summary and article wont work.")
st.markdown("There is however a simple method that we found has potential in post-processing. Based on empirical "
            "results, we have found that when there are specific kinds of dependencies in the summary that are not in "
            "the article, these specific types are often an indication of a wrongly constructed sentence. Let’s take "
            "a look at an example:")
with st.spinner("Doing dependency parsing..."):
    summary_deps = check_dependency(False)
    article_deps = check_dependency(True)
    total_unmatched_deps = []
    for summ_dep in summary_deps:
        if not any(summ_dep['identifier'] in art_dep['identifier'] for art_dep in article_deps):
            total_unmatched_deps.append(summ_dep)
    # print(f'ALL UNMATCHED DEPS ARE: {total_unmatched_deps}')
    # render_dependency_parsing(check_dependency(False))
    if total_unmatched_deps:
        for current_drawing_list in total_unmatched_deps:
            render_dependency_parsing(current_drawing_list)
    dep_spec_text = fetch_dependency_specific_contents(selected_article)
    st.markdown(dep_spec_text)
    soup = BeautifulSoup("Example text option with box", features="html.parser")
    HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem;
    margin-bottom: 2.5rem">{}</div> """
    st.write(HTML_WRAPPER.format(soup), unsafe_allow_html=True)

# OUTRO/CONCLUSION
st.header("Wrapping up")
st.markdown("We have presented 2 methods that try to improve summaries via post-processing steps. Entity matching can "
            "be used to solve hallucinations, while checking if specific dependencies are matched between summary and "
            "article can be used to filter out some bad sentences (and thus worse summaries). Of course these are "
            "only basic methods which were empirically tested, but they are a start at actually making something good "
            "(???). (something about that we tested also RE and maybe other things).")
st.markdown("####")
st.markdown("Now based on these methods you can check summaries and whether they are “good” or “bad”. Below you can "
            "generate 5 different kind of summaries for the starting article (based on different model params) in "
            "which their ranks are estimated, and hopefully the best summary (read: the one that a human would prefer "
            "or indicate as the best one) will be at the top.")