import re import requests import docx2txt from io import StringIO from PyPDF2 import PdfFileReader from bs4 import BeautifulSoup from nltk.tokenize import sent_tokenize emoji_pattern = re.compile( "[" u"\U0001F600-\U0001F64F" # emoticons u"\U0001F300-\U0001F5FF" # symbols & pictographs u"\U0001F680-\U0001F6FF" # transport & map symbols u"\U0001F1E0-\U0001F1FF" # flags (iOS) u"\U00002702-\U000027B0" u"\U000024C2-\U0001F251" "]+", flags=re.UNICODE, ) def clean_text(x): # x = x.lower() # lowercase 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 = x.replace("'", "") # remove ticks # x = re.sub("[%s]" % re.escape(string.punctuation), " ", x) # punctuation # x = re.sub(r"\w*\d+\w*", "", x) # numbers x = re.sub(r"\s{2,}", " ", x) # over spaces x = emoji_pattern.sub(r"", x) # emojis x = re.sub("[^.,!?A-Za-z0-9]+", " ", x) # special charachters except .,!? return x def fetch_article_text(url: str): r = requests.get(url) soup = BeautifulSoup(r.text, "html.parser") results = soup.find_all(["h1", "p"]) text = [result.text for result in results] ARTICLE = " ".join(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, chunks def preprocess_text_for_abstractive_summarization(tokenizer, text): sentences = sent_tokenize(text) # initialize length = 0 chunk = "" chunks = [] count = -1 for sentence in sentences: count += 1 combined_length = ( len(tokenizer.tokenize(sentence)) + length ) # add the no. of sentence tokens to the length counter if combined_length <= tokenizer.max_len_single_sentence: # if it doesn't exceed chunk += sentence + " " # add the sentence to the chunk length = combined_length # update the length counter # if it is the last sentence if count == len(sentences) - 1: chunks.append(chunk.strip()) # save the chunk else: chunks.append(chunk.strip()) # save the chunk # reset length = 0 chunk = "" # take care of the overflow sentence chunk += sentence + " " length = len(tokenizer.tokenize(sentence)) return chunks def read_pdf(file): pdfReader = PdfFileReader(file) count = pdfReader.numPages all_page_text = "" for i in range(count): page = pdfReader.getPage(i) all_page_text += page.extractText() return all_page_text def read_text_from_file(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_content = stringio.read() # read pdf file elif file.type == "application/pdf": file_content = read_pdf(file) # read docx file elif ( file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" ): file_content = docx2txt(file) return file_content