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| import gradio as gr | |
| import fitz # PyMuPDF | |
| import pdfplumber | |
| import pytesseract | |
| from pdf2image import convert_from_bytes | |
| from transformers import pipeline | |
| from PIL import Image | |
| import io | |
| # Load summarizer from Hugging Face (free model) | |
| summarizer = pipeline("summarization", model="Falconsai/text_summarization") | |
| # Optional: Configure Tesseract path for Windows users | |
| # pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe" | |
| def extract_text_from_pdf(pdf_file): | |
| try: | |
| pdf_file.seek(0) | |
| pdf_bytes = pdf_file.read() | |
| # Step 1: Try PyMuPDF | |
| with fitz.open(stream=pdf_bytes, filetype="pdf") as doc: | |
| text = "" | |
| for page in doc: | |
| text += page.get_text() | |
| if text.strip(): | |
| return "text", text | |
| # Step 2: Try pdfplumber | |
| pdf_file.seek(0) | |
| with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf: | |
| for page in pdf.pages: | |
| text += page.extract_text() or "" | |
| if text.strip(): | |
| return "text", text | |
| # Step 3: OCR via pdf2image + pytesseract | |
| images = convert_from_bytes(pdf_bytes) | |
| ocr_text = "" | |
| for img in images: | |
| ocr_text += pytesseract.image_to_string(img) | |
| if ocr_text.strip(): | |
| return "ocr", ocr_text | |
| return "error", "❌ Could not extract any text from PDF." | |
| except Exception as e: | |
| return "error", f"❌ Failed to read PDF. Error: {str(e)}" | |
| def process_pdf(pdf_file): | |
| method, extracted_text = extract_text_from_pdf(pdf_file) | |
| if method == "error": | |
| return extracted_text, "Error", "Error" | |
| try: | |
| # Shorten for model input | |
| short_text = extracted_text[:1000] | |
| summary = summarizer(short_text, max_length=120, min_length=30, do_sample=False)[0]["summary_text"] | |
| explanation = ( | |
| "This summary simplifies the medical content extracted from your report. " | |
| "If there are specific medical terms or values (e.g. Hemoglobin, WBC), the app tries to interpret them. " | |
| "For full interpretation, consult a doctor." | |
| ) | |
| return extracted_text, summary.strip(), explanation | |
| except Exception as e: | |
| return extracted_text, "❌ Summarization failed.", f"Error: {str(e)}" | |
| # Gradio UI | |
| iface = gr.Interface( | |
| fn=process_pdf, | |
| inputs=gr.File(label="Upload Medical Report (PDF)", type="file"), | |
| outputs=[ | |
| gr.Textbox(label="📄 Extracted Report Text"), | |
| gr.Textbox(label="🧠 AI-Generated Summary"), | |
| gr.Textbox(label="📘 Simplified Explanation") | |
| ], | |
| title="🧪 Medical Report Reader (Free)", | |
| description=( | |
| "Upload a medical report in PDF (scanned or digital). The app will extract the text, summarize it using AI, " | |
| "and give a simplified explanation." | |
| ) | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |