import gradio as gr
from transformers import pipeline
from textblob import TextBlob

# Load summarization pipeline
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def analyze(text):
    if not text.strip():
        return "⚠️ No input provided.", "Polarity: N/A | Subjectivity: N/A"
    try:
        summary = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
    except Exception as e:
        summary = f"⚠️ Summarization failed: {str(e)}"
    sentiment = TextBlob(text).sentiment
    polarity = f"Polarity: {sentiment.polarity:.2f}"
    subjectivity = f"Subjectivity: {sentiment.subjectivity:.2f}"
    return summary, polarity + " | " + subjectivity

with gr.Blocks() as demo:
    gr.Markdown("## ✨ Text Summarizer & Sentiment Analyzer")
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(lines=10, label="Enter your text", placeholder="Paste your article or paragraph here...")
            analyze_btn = gr.Button("Analyze")
        with gr.Column():
            output_summary = gr.Textbox(label="Summary")
            output_sentiment = gr.Textbox(label="Sentiment")
    analyze_btn.click(fn=analyze, inputs=input_text, outputs=[output_summary, output_sentiment])

demo.launch()