app.py

import gradio as gr
import requests
from newspaper import Article
from transformers import pipeline
import config
import os
import nltk


# Load summarization pipeline
summarizer = pipeline("summarization", model="harao-ml/flant5-finetuned-summarize")

# Function to split text into smaller chunks
def split_text(text, max_tokens=512):
    words = text.split()
    for i in range(0, len(words), max_tokens):
        yield ' '.join(words[i:i + max_tokens])

# Function to clean text
def clean_text(text):
    text = ' '.join(text.split())
    text = ' '.join(word for word in text.split() if len(word) < 100)
    return text


# Helper function to fetch and parse an article from a URL
def fetch_article_details(url):
    try:
        article = Article(url)
        article.download()
        article.parse()
        title = article.title or "Untitled"
        author = ", ".join(article.authors) if article.authors else "Unknown"
        pub_date = article.publish_date.strftime('%B %d, %Y') if article.publish_date else "Unknown"
        return title, author, pub_date, article.text
    except Exception as e:
        return None, None, None, f"Error fetching article: {str(e)}"

# Helper function to generate a summary
def generate_summary(content):
    if not content.strip():
            return "No input provided."
    text = content
    cleaned_text = clean_text(text)
    chunks = list(split_text(cleaned_text))
    cons_summary = ''.join([summarizer(chunk, do_sample=False)[0]['summary_text'] for chunk in chunks if chunk.strip()]) if chunks else ''
    summary = summarizer(text, do_sample=False)[0]['summary_text']
    return cons_summary

# Summarize from text or URL
def summarize_input(mixed_input):
    if mixed_input.startswith("http://") or mixed_input.startswith("https://"):
        title, author, pub_date, content = fetch_article_details(mixed_input)
        if content.startswith("Error"):
            return f"### Error\n\n{content}"
        summary = generate_summary(content)
        return f"**Title:** {title}\n\n**Author(s):** {author}\n\n**Published:** {pub_date}\n\n**📝 Summary** \n\n{summary}\n\n[🔗 Read more]({mixed_input})\n\n---"
    else:
        summary = generate_summary(mixed_input)
        return f"## 📝 Summary \n\n{summary}\n\n**Original Text:**\n\n{mixed_input}\n\n---"

# Function to fetch top headlines from NewsAPI and summarize them
def fetch_news():
    url = 'https://newsapi.org/v2/top-headlines'
    api_key = os.environ.get("api_key")
    params = {
        'apiKey': api_key,
        'language': 'en',
        'sources': 'associated-press',
        'pageSize': 10
    }
    try:
        response = requests.get(url, params=params)
        if response.status_code != 200:
            return f"Error: Failed to fetch news. Status code: {response.status_code}"

        articles = response.json().get("articles", [])
        summaries = []
        for article in articles:
            title = article.get("title", "No title")
            article_url = article.get("url", "#")
            author = article.get("author", "Unknown")
            pub_date = article.get("publishedAt", "Unknown")
            content = extract_full_content(article_url) or article.get("content") or article.get("description") or ""
            summary = generate_summary(content)
            summaries.append(f"**{title}** \n\n**Author(s):** {author}\n\n**Published:** {pub_date}\n\n**Summary:** {summary}\n\n [🔗 Read more]({article_url})\n\n---")

        if not summaries:
            return "### No articles could be summarized."
        return "\n\n".join(summaries)
    except Exception as e:
        return f"### Error fetching news\n\n{str(e)}"

# Helper function to extract full content using newspaper3k
def extract_full_content(url):
    try:
        article = Article(url)
        article.download()
        article.parse()
        return article.text
    except Exception:
        return None

# Gradio interface
with gr.Blocks(theme=gr.themes.Base()) as demo:
    gr.Markdown("# 📰 Sum Up! Stay Informed, Instantly")
    gr.Markdown(" ## A LLM based News Summarizer App")

    # Add a brief description
    gr.Markdown("Sum Up! condenses the latest headlines from trusted news sources into clear, concise and easy-to-read summaries, so you can stay informed in seconds.")
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Top Stories - A Snapshot ")
            gr.Markdown("**Source: Associated Press**")
            gr.Markdown("Click the button below to fetch the latest news articles.")
            news_btn = gr.Button("🗞️ News Now", variant="primary")
        with gr.Column(scale=1):
            input_box = gr.Textbox(label="Enter article text or URL", placeholder="Paste article text or link...")
            summarize_btn = gr.Button("🔍 Summarize", variant="secondary")

    # Output area for displaying results
    output_area = gr.Markdown()  # Use a valid output component

    # Link buttons to their respective functions
    summarize_btn.click(fn=summarize_input, inputs=input_box, outputs=output_area)
    news_btn.click(fn=fetch_news, inputs=[], outputs=output_area)


if __name__ == "__main__":
    demo.launch()