import wikipedia
from transformers import pipeline
import requests
from bs4 import BeautifulSoup
import re
import gradio as gr

# Initialize NLP model for understanding and generating text
model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
#nlp = pipeline("question-answering")
#summarizer = pipeline("summarization")
nlp = pipeline(model=model)
#summarizer = pipeline(model=model)

def fetch_and_summarize(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    content = soup.get_text()
    summary = nlp(content[:10000])  # Limit content to avoid overwhelming the model
    print(summary)
    print(summary[0])
    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 return feedback.
        return f"Content for {title} exists. Comparison:\n{new_content[:100]}..."
    except wikipedia.exceptions.PageError:
        # If the page doesn't exist, you could create it, but here we'll just inform
        return f"No page exists for {title}. New content could be:\n{new_content[:100]}..."
    except wikipedia.exceptions.DisambiguationError as e:
        # If there's ambiguity, handle it (for simplicity, we just return feedback)
        return f"Disambiguation needed for {title}. Options: {e.options}"

def wiki_contributor(topic):
    # 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
    feedback = check_and_update_wikipedia(topic, enhanced_content)
    return feedback

# Gradio Interface
iface = gr.Interface(
    fn=wiki_contributor,
    inputs=gr.Textbox(lines=1, placeholder="Enter topic here..."),
    outputs="text",
    title="AI Wikipedia Contributor",
    description="Enter a topic to get feedback on how an AI could contribute to Wikipedia.",
)

# Launch the interface
iface.launch()