#!/usr/bin/env python # coding: utf-8 # In[1]: import validators, re from fake_useragent import UserAgent from bs4 import BeautifulSoup import streamlit as st from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer from sentence_transformers import SentenceTransformer import en_core_web_lg import time import base64 import requests import docx2txt from io import StringIO from PyPDF2 import PdfFileReader import warnings import nltk nltk.download('punkt') from nltk import sent_tokenize warnings.filterwarnings("ignore") # In[2]: time_str = time.strftime("%d%m%Y-%H%M%S") #Functions def article_text_extractor(url: str): '''Extract text from url and divide text into chunks if length of text is more than 500 words''' ua = UserAgent() headers = {'User-Agent':str(ua.chrome)} r = requests.get(url,headers=headers) soup = BeautifulSoup(r.text, "html.parser") title_text = soup.find_all(["h1"]) para_text = soup.find_all(["p"]) article_text = [result.text for result in para_text] try: article_header = [result.text for result in title_text][0] except: article_header = '' article = " ".join(article_text) article = article.replace(".", ".") article = article.replace("!", "!") article = article.replace("?", "?") sentences = article.split("") current_chunk = 0 chunks = [] for sentence in sentences: if len(chunks) == current_chunk + 1: if len(chunks[current_chunk]) + len(sentence.split(" ")) <= 500: chunks[current_chunk].extend(sentence.split(" ")) else: current_chunk += 1 chunks.append(sentence.split(" ")) else: print(current_chunk) chunks.append(sentence.split(" ")) for chunk_id in range(len(chunks)): chunks[chunk_id] = " ".join(chunks[chunk_id]) return article_header, chunks def chunk_clean_text(text): sentences = sent_tokenize(text) current_chunk = 0 chunks = [] for sentence in sentences: if len(chunks) == current_chunk + 1: if len(chunks[current_chunk]) + len(sentence.split(" ")) <= 500: chunks[current_chunk].extend(sentence.split(" ")) else: current_chunk += 1 chunks.append(sentence.split(" ")) else: print(current_chunk) chunks.append(sentence.split(" ")) for chunk_id in range(len(chunks)): chunks[chunk_id] = " ".join(chunks[chunk_id]) return chunks def preprocess_plain_text(x): x = x.encode("ascii", "ignore").decode() # unicode x = re.sub(r"https*\S+", " ", x) # url x = re.sub(r"@\S+", " ", x) # mentions x = re.sub(r"#\S+", " ", x) # hastags x = re.sub(r"\s{2,}", " ", x) # over spaces x = re.sub("[^.,!?A-Za-z0-9]+", " ", x) # special charachters except .,!? return x def extract_pdf(file): '''Extract text from PDF file''' pdfReader = PdfFileReader(file) count = pdfReader.numPages all_text = "" for i in range(count): page = pdfReader.getPage(i) all_text += page.extractText() return all_text def extract_text_from_file(file): '''Extract text from uploaded file''' # read text file if file.type == "text/plain": # To convert to a string based IO: stringio = StringIO(file.getvalue().decode("utf-8")) # To read file as string: file_text = stringio.read() # read pdf file elif file.type == "application/pdf": file_text = extract_pdf(file) # read docx file elif ( file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" ): file_text = docx2txt.process(file) return file_text def summary_downloader(raw_text): b64 = base64.b64encode(raw_text.encode()).decode() new_filename = "new_text_file_{}_.txt".format(time_str) st.markdown("#### Download Summary as a File ###") href = f'Click to Download!!' st.markdown(href,unsafe_allow_html=True) def get_all_entities_per_sentence(text): 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 (CURRENTLY NOT USED) # sentence_entities = Sentence(str(sentence)) # tagger.predict(sentence_entities) # for entity in sentence_entities.get_spans('ner'): # entities_this_sentence.append(entity.text) # XLM ENTITIES entities_xlm = [entity["word"] for entity in ner_model(str(sentence))] for entity in entities_xlm: entities_this_sentence.append(str(entity)) 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)) def get_and_compare_entities(article_content,summary_output): all_entities_per_sentence = get_all_entities_per_sentence(article_content) entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence)) all_entities_per_sentence = get_all_entities_per_sentence(summary_output) entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence)) matched_entities = [] unmatched_entities = [] for entity in entities_summary: if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article): matched_entities.append(entity) elif any( np.inner(sentence_embedding_model.encode(entity, show_progress_bar=False), sentence_embedding_model.encode(art_entity, show_progress_bar=False)) > 0.9 for art_entity in entities_article): matched_entities.append(entity) else: unmatched_entities.append(entity) matched_entities = list(dict.fromkeys(matched_entities)) unmatched_entities = list(dict.fromkeys(unmatched_entities)) matched_entities_to_remove = [] unmatched_entities_to_remove = [] for entity in matched_entities: for substring_entity in matched_entities: if entity != substring_entity and entity.lower() in substring_entity.lower(): matched_entities_to_remove.append(entity) for entity in unmatched_entities: for substring_entity in unmatched_entities: if entity != substring_entity and entity.lower() in substring_entity.lower(): unmatched_entities_to_remove.append(entity) matched_entities_to_remove = list(dict.fromkeys(matched_entities_to_remove)) unmatched_entities_to_remove = list(dict.fromkeys(unmatched_entities_to_remove)) for entity in matched_entities_to_remove: matched_entities.remove(entity) for entity in unmatched_entities_to_remove: unmatched_entities.remove(entity) return matched_entities, unmatched_entities def highlight_entities(article_content,summary_output): markdown_start_red = "" markdown_start_green = "" markdown_end = "" matched_entities, unmatched_entities = get_and_compare_entities(article_content,summary_output) for entity in matched_entities: summary_content = summary_output.replace(entity, markdown_start_green + entity + markdown_end) for entity in unmatched_entities: summary_content = summary_output.replace(entity, markdown_start_red + entity + markdown_end) soup = BeautifulSoup(summary_content, features="html.parser") return HTML_WRAPPER.format(soup) def render_dependency_parsing(text: dict): html = render_sentence_custom(text, nlp) html = html.replace("\n\n", "\n") st.write(get_svg(html), unsafe_allow_html=True) def check_dependency(article: bool): if article: text = st.session_state.article_text all_entities = get_all_entities_per_sentence(text) else: text = st.session_state.summary_output all_entities = get_all_entities_per_sentence(text) doc = nlp(text) tok_l = doc.to_json()['tokens'] 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: 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]: 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]: 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 return test_list_dict_output 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'' % b64 return html def generate_abstractive_summary(text, type, min_len=120, max_len=512, **kwargs): text = text.strip().replace("\n", " ") if type == "top_p": text = summarization_model(text, min_length=min_len, max_length=max_len, top_k=50, top_p=0.95, clean_up_tokenization_spaces=True, truncation=True, **kwargs) elif type == "greedy": text = summarization_model(text, min_length=min_len, max_length=max_len, clean_up_tokenization_spaces=True, truncation=True, **kwargs) elif type == "top_k": text = summarization_model(text, min_length=min_len, max_length=max_len, top_k=50, clean_up_tokenization_spaces=True, truncation=True, **kwargs) elif type == "beam": text = summarization_model(text, min_length=min_len, max_length=max_len, clean_up_tokenization_spaces=True, truncation=True, **kwargs) summary = text[0]['summary_text'].replace("", " ") return summary def clean_text(text,doc=False,plain_text=False,url=False): """Return clean text from the various input sources""" if url: is_url = validators.url(text) if is_url: # complete text, chunks to summarize (list of sentences for long docs) article_title,chunks = article_text_extractor(url=url_text) return article_title, chunks elif doc: clean_text = chunk_clean_text(preprocess_plain_text(extract_text_from_file(text))) return None, clean_text elif plain_text: clean_text = chunk_clean_text(preprocess_plain_text(text)) return None, clean_text @st.cache(allow_output_mutation=True,suppress_st_warning=True) def get_spacy(): nlp = en_core_web_lg.load() return nlp @st.cache(allow_output_mutation=True,suppress_st_warning=True) def facebook_model(): summarizer = pipeline('summarization',model='facebook/bart-large-cnn') return summarizer @st.cache(allow_output_mutation=True,suppress_st_warning=True) def schleifer_model(): summarizer = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6') return summarizer @st.cache(allow_output_mutation=True,suppress_st_warning=True) def get_sentence_embedding_model(): return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') @st.cache(allow_output_mutation=True,suppress_st_warning=True) def get_ner_pipeline(): tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") return pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) # Load all different models (cached) at start time of the hugginface space sentence_embedding_model = get_sentence_embedding_model() ner_model = get_ner_pipeline() nlp = get_spacy() #Streamlit App st.title("Article Text and Link Extractive Summarizer 📝") model_type = st.sidebar.selectbox( "Model type", options=["Facebook-Bart", "Sshleifer-DistilBart"] ) max_len= st.sidebar.slider("Maximum length of the summarized text",min_value=80,max_value=500,step=10) min_len= st.sidebar.slider("Minimum length of the summarized text",min_value=10,step=10) st.markdown( "Model Source: [Facebook-Bart-large-CNN](https://huggingface.co/facebook/bart-large-cnn) and [Sshleifer-distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)" ) st.markdown( """The app supports extractive summarization which aims to identify the salient information that is then extracted and grouped together to form a concise summary. For documents or text that is more than 500 words long, the app will divide the text into chunks and summarize each chunk. Please note when using the sidebar slider, those values represent the min/max text length per chunk of text to be summarized. If your article to be summarized is 1000 words, it will be divided into two chunks of 500 words first then the default max length of 100 words is applied per chunk, resulting in a summarized text with 200 words maximum. There are two models available to choose from:""") st.markdown(""" - Facebook-Bart, trained on large [CNN and Daily Mail](https://huggingface.co/datasets/cnn_dailymail) news articles. - Sshleifer-Distilbart, which is a distilled (smaller) version of the large Bart model.""" ) st.markdown("""Please do note that the model will take longer to generate summaries for documents that are too long.""") st.markdown( "The app only ingests the below formats for summarization task:" ) st.markdown( """- Raw text entered in text box. - URL of an article to be summarized. - Documents with .txt, .pdf or .docx file formats.""" ) st.markdown("---") if "text_area" not in st.session_state: c = st.empty() url_text = st.text_input("Please Enter a url here") if url_text: article_title, cleaned_text = clean_text(url_text, url=True) st.session_state.text_area = cleaned_text article_text = st.text_area( label='Full Article Text', placeholder="Full article text will be displayed here..", height=250, key='text_area' ) st.markdown( "

OR

", unsafe_allow_html=True, ) plain_text = st.text_input("Please Paste/Enter plain text here") st.markdown( "

OR

", unsafe_allow_html=True, ) upload_doc = st.file_uploader( "Upload a .txt, .pdf, .docx file for summarization" ) if plain_text: _, cleaned_text = clean_text(plain_text,plain_text=True) elif upload_doc: _, cleaned_text = clean_text(plain_text,doc=True) summarize = st.button("Summarize") # called on toggle button [summarize] if summarize: if model_type == "Facebook-Bart": if url_text: text_to_summarize =cleaned_text else: text_to_summarize = cleaned_text with st.spinner( text="Loading Facebook-Bart Model and Extracting summary. This might take a few seconds depending on the length of your text..." ): summarizer_model = facebook_model() summarized_text = summarizer_model(text_to_summarize, max_length=max_len, min_length=min_len) summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) elif model_type == "Sshleifer-DistilBart": if url_text: text_to_summarize = cleaned_text else: text_to_summarize = cleaned_text with st.spinner( text="Loading Sshleifer-DistilBart Model and Extracting summary. This might take a few seconds depending on the length of your text..." ): summarizer_model = schleifer_model() summarized_text = summarizer_model(text_to_summarize, max_length=max_len, min_length=min_len) summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) with st.spinner("Calculating and matching entities, this takes a few seconds..."): entity_match_html = highlight_entities(cleaned_text,summarized_text) st.subheader("Summarized text with matched entities in Green and mismatched entities in Red relative to the original text") st.markdown("####") if article_title: # view summarized text (expander) st.markdown(f"Article title: {article_title}") st.markdown("####") st.write(entity_match_html, unsafe_allow_html=True) st.markdown("####") summary_downloader(summarized_text) st.markdown(""" """) st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-article-text-summarizer)") # In[ ]: