import logging
from bs4 import BeautifulSoup
import requests
import nltk
from transformers import pipeline
import gradio as gr

# Configure logging
logging.basicConfig(level=logging.DEBUG)

# Initialize the summarization pipeline from Hugging Face Transformers
summarizer = pipeline("summarization")

# Initialize the NLTK sentence tokenizer
nltk.download('punkt')

# Function to fetch content from a given URL
def fetch_article_content(url):
    try:
        r = requests.get(url)
        soup = BeautifulSoup(r.text, 'html.parser')
        results = soup.find_all(['h1', 'p'])
        text = [result.text for result in results]
        return ' '.join(text)
    except Exception as e:
        logging.error(f"Error fetching content from {url}: {e}")
        return ""

# Function to summarize news articles based on a query
def summarize_news(query, num_results=3):
    logging.debug(f"Query received: {query}")
    logging.debug(f"Number of results requested: {num_results}")

    # Search for news articles
    logging.debug("Searching for news articles...")

    articles = []
    aggregated_content = ""
    try:
        news_results = newsapi.get_everything(q=query, language='en', page_size=num_results)
        logging.debug(f"Search results: {news_results}")
        
        for article in news_results['articles']:
            url = article['url']
            logging.debug(f"Fetching content from URL: {url}")
            content = fetch_article_content(url)
            aggregated_content += content + " "
    except Exception as e:
        logging.error(f"Error fetching news articles: {e}")

    # Summarize the aggregated content
    try:
        # Chunk the aggregated content into meaningful segments
        sentences = nltk.sent_tokenize(aggregated_content)
        
        # Summarize each sentence individually if it's meaningful
        summaries = []
        for sentence in sentences:
            if len(sentence) > 10:  # Adjust minimum length as needed
                summary = summarizer(sentence, max_length=120, min_length=30, do_sample=False)
                summaries.append(summary[0]['summary_text'])
        
        # Join all summaries to form final output
        final_summary = " ".join(summaries)
        
        logging.debug(f"Final summarized text: {final_summary}")
        return final_summary
    
    except Exception as e:
        logging.error(f"Error during summarization: {e}")
        return "An error occurred during summarization."
        
# Setting up Gradio interface
iface = gr.Interface(
    fn=summarize_news,
    inputs=[gr.Textbox(label="Query"), gr.Slider(minimum=1, maximum=10, default=3, label="Number of Results")],
    outputs="textbox",
    title="News Summarizer",
    description="Enter a query to get a consolidated summary of the top news articles."
)

if __name__ == "__main__":
    logging.debug("Launching Gradio interface...")
    iface.launch()