import wikipedia from transformers import pipeline import requests from bs4 import BeautifulSoup import re # Initialize NLP model for understanding and generating text nlp = pipeline("question-answering") summarizer = pipeline("summarization") def fetch_and_summarize(url): response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') content = soup.get_text() summary = summarizer(content[:10000]) # Limit content to avoid overwhelming the model return summary[0]['summary'] def check_and_update_wikipedia(title, new_content): try: # Check if the page exists page = wikipedia.page(title) # Here, you would compare new_content with existing content # and decide if an update is necessary. For simplicity, we'll just print. print(f"Content for {title} exists. Comparing:\n{new_content[:100]}...") except wikipedia.exceptions.PageError: # If the page doesn't exist, create it wikipedia.create(title, new_content) print(f"Created new page for {title}.") except wikipedia.exceptions.DisambiguationError as e: # If there's ambiguity, handle it (for simplicity, we just print) print(f"Disambiguation needed: {e.options}") def main(): # Example topic to contribute to topic = "Quantum Entanglement" # Fetch and summarize content from an external source (e.g., arxiv.org) external_content = fetch_and_summarize(f"https://arxiv.org/search/?query={topic}&searchtype=all") # Generate or refine the content with NLP. Here's a placeholder for actual NLP operations: enhanced_content = f"Enhanced content on {topic}: {external_content}" # Check if Wikipedia needs updating or if we're creating a new entry check_and_update_wikipedia(topic, enhanced_content) if __name__ == "__main__": main()