app.py

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()