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| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| import pytesseract | |
| from PyPDF2 import PdfReader | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.pdfgen import canvas | |
| import os | |
| # Load the medical analysis model (e.g., BioGPT or PubMedBERT) | |
| medical_analyzer = pipeline("text-classification", model="microsoft/biogpt") | |
| # Function to extract text from images or PDFs | |
| def extract_text(file_path): | |
| if file_path.endswith(".pdf"): | |
| # Extract text from PDF | |
| reader = PdfReader(file_path) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| return text.strip() | |
| else: | |
| # Extract text from image | |
| return pytesseract.image_to_string(Image.open(file_path)) | |
| # Function to generate a PDF report | |
| def create_pdf_report(analysis, output_path): | |
| c = canvas.Canvas(output_path, pagesize=letter) | |
| c.drawString(100, 750, "Blood Test Report Analysis") | |
| c.drawString(100, 730, "---------------------------") | |
| y_position = 700 | |
| for line in analysis.split("\n"): | |
| c.drawString(100, y_position, line) | |
| y_position -= 20 # Move down for the next line | |
| c.save() | |
| return output_path | |
| # Function to analyze blood test reports | |
| def analyze_blood_test(file): | |
| # Step 1: Extract text | |
| extracted_text = extract_text(file) | |
| if not extracted_text: | |
| return "Could not extract text. Please upload a valid file.", None | |
| # Step 2: Use medical model to analyze extracted text | |
| analysis_results = medical_analyzer(extracted_text) | |
| analysis_report = "🔍 Analysis Results:\n" | |
| for item in analysis_results[:5]: # Limit results for simplicity | |
| analysis_report += f"- {item['label']}: {item['score']:.2f}\n" | |
| # Step 3: Generate downloadable PDF report | |
| output_pdf = "analysis_report.pdf" | |
| create_pdf_report(f"Extracted Text:\n{extracted_text}\n\n{analysis_report}", output_pdf) | |
| return analysis_report, output_pdf | |
| # Gradio interface setup | |
| interface = gr.Interface( | |
| fn=analyze_blood_test, | |
| inputs=gr.File(label="Upload your blood test report (PNG, JPG, JPEG, or PDF)"), | |
| outputs=[ | |
| gr.Textbox(label="Analysis Results"), | |
| gr.File(label="Download PDF Report") | |
| ], | |
| title="MedAI Analyzer", | |
| description=( | |
| "Upload your blood test report in image (PNG, JPG, JPEG) or PDF format. " | |
| "The app will extract and analyze the values, flag abnormalities, and provide health recommendations. " | |
| "You can also download a detailed PDF report of the analysis." | |
| ), | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |