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import logging
from bs4 import BeautifulSoup
import requests
import nltk
from transformers import pipeline
import gradio as gr
from newsapi import NewsApiClient # Import NewsApiClient from newsapi library
# 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')
# Initialize the News API client with your API key
newsapi = NewsApiClient(api_key='your_newsapi_key_here')
# 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, value=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__":
iface.launch()
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