app.py

import gradio as gr
from transformers import BartTokenizer, BartForConditionalGeneration
import torch

# -------------------------------
# MODEL LOADING
# -------------------------------
model_name = "facebook/bart-large-cnn"  # Hugging Face model
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

# -------------------------------
# SUMMARIZATION FUNCTION
# -------------------------------
def summarize_text(input_text):
    if not input_text.strip():
        return "⚠️ Please enter some text to summarize."

    inputs = tokenizer([input_text], max_length=1024, return_tensors="pt", truncation=True)

    summary_ids = model.generate(
        inputs["input_ids"],
        num_beams=4,
        min_length=50,
        max_length=200,
        early_stopping=True
    )

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# -------------------------------
# GRADIO INTERFACE
# -------------------------------
demo = gr.Interface(
    fn=summarize_text,
    inputs=gr.Textbox(lines=12, placeholder="Paste medical or long text here...", label="Input Text"),
    outputs=gr.Textbox(lines=10, label="Generated Summary"),
    title="🩺 Jan Arogya Summarizer",
    description="Summarize long medical or research text using the **facebook/bart-large-cnn** model.",
    theme="soft",
    examples=[
        ["COVID-19 is a respiratory disease caused by the SARS-CoV-2 virus. It spread rapidly across the globe..."],
        ["Hypertension is a chronic medical condition characterized by persistently high blood pressure levels..."]
    ],
)

# -------------------------------
# LAUNCH
# -------------------------------
if __name__ == "__main__":
    demo.launch()