app.py

import gradio as gr
import fitz  # PyMuPDF
from transformers import pipeline
import re

# Use a faster and lighter summarization model
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def extract_text_from_pdf(pdf_file):
    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
    text = "".join(page.get_text() + "\n" for page in doc)
    return text

def chunk_text(text, max_words=500):
    words = text.split()
    return [" ".join(words[i:i + max_words]) for i in range(0, len(words), max_words)]

def simplify_summary(summary):
    # Remove repetitive hospital info
    summary = re.sub(r"\b(?:Mayo Hospital|Lahore Hospital|submitted by Dr\.).+\n?", "", summary, flags=re.IGNORECASE)
    return "🩺 In simple terms:\n" + summary.strip()

def process_report(pdf_file):
    text = extract_text_from_pdf(pdf_file)
    if not text.strip():
        return "❌ Couldn't extract text from the PDF.", ""
    
    # Remove irrelevant boilerplate
    header, *rest = text.split("\n\n", 1)
    core_text = rest[0] if rest else text

    chunks = chunk_text(core_text, max_words=600)
    summaries = [summarizer(chunk, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
                 for chunk in chunks]
    
    final_summary = " ".join(summaries)
    simple = simplify_summary(final_summary)
    return final_summary, simple

demo = gr.Interface(
    fn=process_report,
    inputs=gr.File(label="Upload Medical Report PDF"),
    outputs=[
        gr.Textbox(label="AI-Generated Summary", lines=8),
        gr.Textbox(label="Simplified Explanation", lines=8)
    ],
    title="🏥 Medical Report Summarizer",
    description="Speeds up summarization by chunking text & uses a lighter distil-BART model, focusing on core medical findings."
)

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