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| import gradio as gr | |
| import fitz # PyMuPDF | |
| from transformers import pipeline | |
| import re | |
| # Use faster summarization model | |
| summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") | |
| def extract_text_from_pdf(pdf_file): | |
| try: | |
| doc = fitz.open(pdf_file.name) # FIXED: Use .name instead of .read() | |
| text = "" | |
| for page in doc: | |
| text += page.get_text() | |
| return text | |
| except Exception as e: | |
| return None | |
| 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): | |
| summary = re.sub(r"(Mayo Hospital|Lahore Hospital|Dr\.\s+\w+)+.*", "", summary, flags=re.IGNORECASE) | |
| return "🩺 In simple terms:\n" + summary.strip() | |
| def process_report(pdf_file): | |
| raw_text = extract_text_from_pdf(pdf_file) | |
| if not raw_text: | |
| return "❌ Could not read PDF file.", "" | |
| chunks = chunk_text(raw_text, max_words=600) | |
| summaries = [] | |
| for chunk in chunks: | |
| try: | |
| result = summarizer(chunk, max_length=150, min_length=30, do_sample=False) | |
| summaries.append(result[0]['summary_text']) | |
| except: | |
| continue | |
| if not summaries: | |
| return "❌ Summarization failed. Try a smaller or clearer PDF.", "" | |
| full_summary = " ".join(summaries) | |
| return full_summary, simplify_summary(full_summary) | |
| 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="Upload a medical report PDF to get an AI summary and non-medical explanation." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |