Create utils.py

utils.py

@@ -0,0 +1,133 @@
+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.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 = 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(".", ".<eos>")
+    ARTICLE = ARTICLE.replace("!", "!<eos>")
+    ARTICLE = ARTICLE.replace("?", "?<eos>")
+    sentences = ARTICLE.split("<eos>")
+    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.process(file)
+
+    return file_content