Create Wikipedia-Article-SummarizerV2.py

Wikipedia-Article-SummarizerV2.py

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+#!/usr/bin/env python3
+#emreYbs
+# -*- coding: utf-8 -*-
+# Wikipedia Article Summarizer(Wikipedia Article Summarizer.py) v.0.02
+
+import urllib.request
+import pip
+import bs4 as bs
+import lxml
+import re
+import heapq
+import nltk
+pip.main(['install', 'pyfiglet'])
+import pyfiglet
+import re
+from time import sleep
+import csv
+
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+
+
+def get_article_summary(url):
+    print(" ")
+    raw_data = urllib.request.urlopen(url)
+    document = raw_data.read()
+
+    parsed_document = bs.BeautifulSoup(document, 'lxml')
+
+    article_paras = parsed_document.find_all('p')
+
+    scrapped_data = ""
+
+    for para in article_paras:
+        scrapped_data += para.text
+
+    scrapped_data = scrapped_data[:1500]
+
+    scrapped_data = re.sub(r'\[[0-9]*\]', ' ', scrapped_data)
+    scrapped_data = re.sub(r'\s+', ' ', scrapped_data)
+
+    formatted_text = re.sub('[^a-zA-Z]', ' ', scrapped_data)
+    formatted_text = re.sub(r'\s+', ' ', formatted_text)
+
+    all_sentences = nltk.sent_tokenize(scrapped_data)
+
+    stopwords = nltk.corpus.stopwords.words('english')
+
+    word_freq = {}
+    for word in nltk.word_tokenize(formatted_text):
+        if word not in stopwords:
+            if word not in word_freq.keys():
+                word_freq[word] = 1
+            else:
+                word_freq[word] += 1
+
+    max_freq = max(word_freq.values())
+
+    for word in word_freq.keys():
+        word_freq[word] = (word_freq[word] / max_freq)
+
+    sentence_scores = {}
+    for sentence in all_sentences:
+        for token in nltk.word_tokenize(sentence.lower()):
+            if token in word_freq.keys():
+                if len(sentence.split(' ')) < 25:
+                    if sentence not in sentence_scores.keys():
+                        sentence_scores[sentence] = word_freq[token]
+                    else:
+                        sentence_scores[sentence] += word_freq[token]
+
+    selected_sentences = heapq.nlargest(5, sentence_scores, key=sentence_scores.get)
+
+    text_summary = ' '.join(selected_sentences)
+    print(" ")
+    return text_summary
+    print(" ")
+
+print(" ")
+print("\033[94mWikipedia Article Summarizer by EmreYbs\033[0m")
+sleep(1)
+welcome = pyfiglet.figlet_format("Wikipedia Article Summarizer", font="standard")
+farewell = pyfiglet.figlet_format("Wiki", font="doh")
+print(welcome, sep="\n\n")
+
+
+
+def verify_url(url):
+    pattern = r'^https?://en\.wikipedia\.org/wiki/[\w()]+'
+    if re.match(pattern, url):
+        return True
+    else:
+        return False
+
+userLink = input("\n\033[35mWhich Wikipedia article would you want me to summarize: \033[0m")
+while not verify_url(userLink):
+    print("Invalid URL. Please enter a valid Wikipedia URL.")
+    print("Exitting...due to invalid Wikepedia URL")
+    exit()
+    sleep(0.5)
+
+
+summary = get_article_summary(userLink) 
+print(" ")
+sleep(1)
+print("\033[93mHere is the summary of the article you requested:\033[0m")
+sleep(0.5)
+print(" ")
+sleep(1)
+print(summary)
+
+# Save summary as CSV file
+filename = "summary.csv"
+with open(filename, "w", newline="", encoding="utf-8") as file:
+    writer = csv.writer(file)
+    writer.writerow(["Summary"])
+    writer.writerow([summary])
+    print(f"Summary saved as {filename}")
+print(" ")
+print(pyfiglet.figlet_format("                                   E.N.D", font="standard"))
+print("\t\t\t\tEnd of Automatic summarization")
+sleep(1)
+print(farewell + "\t\t\tWikipedia Article Summarizer by EmreYbs")
+print("\t\t\t\tHope you liked my summary\n", sep="  ")