app.py

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

# Load summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to extract text from a webpage
def fetch_url_text(url):
    try:
        headers_req = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers_req, timeout=10)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, "html.parser")
        text = soup.get_text(separator=" ", strip=True)
        text = " ".join(text.split())
        if len(text) < 100:
            return None, "❌ Extracted text from the webpage is too short to summarize."
        return text, None
    except Exception as e:
        return None, f"❌ URL error: {e}"

# Summarization function
def summarize_text(text_input, file_upload, url_input):
    text = ""

    if file_upload:
        try:
            with open(file_upload.name, "r", encoding="utf-8") as f:
                text = f.read()
        except Exception as e:
            return f"❌ File read error: {e}"

    elif url_input:
        text, error_msg = fetch_url_text(url_input)
        if error_msg:
            return error_msg

    elif text_input:
        text = text_input

    else:
        return "⚠️ Please provide some input."

    try:
        summary = summarizer(text[:1024], max_length=150, min_length=30, do_sample=False)
        return summary[0]["summary_text"]
    except Exception as e:
        return f"❌ Summarization error: {e}"

# Gradio Interface
demo = gr.Interface(
    fn=summarize_text,
    inputs=[
        gr.Textbox(label="✍️ Enter Text", lines=4, placeholder="Paste or type text here..."),
        gr.File(label="📄 Upload a .txt File", file_types=[".txt"]),
        gr.Textbox(label="🌐 Enter Webpage URL", placeholder="https://example.com/article")
    ],
    outputs="text",
    title="🧠 Multi-Input Text Summarizer",
    description="Summarize content from text, uploaded files, or web URLs using the BART model."
)

demo.launch()