import os os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Force TensorFlow to use CPU import gradio as gr import tensorflow as tf import numpy as np from tensorflow.keras.preprocessing import image from PIL import Image from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas from reportlab.lib import colors from reportlab.platypus import Table, TableStyle import smtplib from email.message import EmailMessage # Load the trained model model = tf.keras.models.load_model("my_keras_model.h5") # Read HTML content from `re.html` with open("templates/re.html", "r", encoding="utf-8") as file: html_content = file.read() # List of sample images sample_images = [f"samples/{img}" for img in os.listdir("samples") if img.endswith((".png", ".jpg", ".jpeg"))] # Create a folder for reports REPORTS_DIR = "reports" os.makedirs(REPORTS_DIR, exist_ok=True) # Email Configuration SENDER_EMAIL = "qen3jv@gmail.com" # Change this SENDER_PASSWORD = "1w3r5y7i9pW$" # Use an App Password if using Gmail # Function to send email with PDF attachment def send_email_with_attachment(to_email, file_path, patient_name): msg = EmailMessage() msg["Subject"] = f"Bone Fracture Report for {patient_name}" msg["From"] = SENDER_EMAIL msg["To"] = to_email msg.set_content(f"Dear {patient_name},\n\nAttached is your bone fracture detection report.\n\nThank you!") # Attach PDF with open(file_path, "rb") as f: file_data = f.read() file_name = os.path.basename(file_path) msg.add_attachment(file_data, maintype="application", subtype="pdf", filename=file_name) # Send Email try: with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server: server.login(SENDER_EMAIL, SENDER_PASSWORD) server.send_message(msg) print(f"✅ Email sent to {to_email}") except Exception as e: print(f"❌ Failed to send email: {e}") # Function to process X-ray and generate a PDF report def generate_report(name, age, gender, weight, height, allergies, cause, xray, email): image_size = (224, 224) def predict_fracture(xray_path): img = Image.open(xray_path).resize(image_size) img_array = image.img_to_array(img) / 255.0 img_array = np.expand_dims(img_array, axis=0) prediction = model.predict(img_array)[0][0] return prediction # Predict fracture prediction = predict_fracture(xray) diagnosed_class = "Normal" if prediction > 0.5 else "Fractured" # Injury severity classification severity = "Mild" if prediction < 0.3 else "Moderate" if prediction < 0.7 else "Severe" # Treatment details table treatment_data = [ ["Severity Level", "Recommended Treatment", "Recovery Duration"], ["Mild", "Rest, pain relievers, and follow-up X-ray", "4-6 weeks"], ["Moderate", "Plaster cast, minor surgery if needed", "6-10 weeks"], ["Severe", "Major surgery, metal implants, physiotherapy", "Several months"] ] # Estimated cost & duration table cost_duration_data = [ ["Hospital Type", "Estimated Cost", "Recovery Time"], ["Government Hospital", f"₹{2000 if severity == 'Mild' else 8000 if severity == 'Moderate' else 20000} - ₹{5000 if severity == 'Mild' else 15000 if severity == 'Moderate' else 50000}", "4-12 weeks"], ["Private Hospital", f"₹{10000 if severity == 'Mild' else 30000 if severity == 'Moderate' else 100000}+", "6 weeks - Several months"] ] # Save X-ray image for report img = Image.open(xray).resize((300, 300)) img_path = os.path.join(REPORTS_DIR, f"{name}_xray.png") img.save(img_path) # Generate PDF report report_path = os.path.join(REPORTS_DIR, f"{name}_fracture_report.pdf") c = canvas.Canvas(report_path, pagesize=letter) # Report title c.setFont("Helvetica-Bold", 16) c.drawString(200, 770, "Bone Fracture Detection Report") # Patient details table patient_data = [ ["Patient Name", name], ["Age", age], ["Gender", gender], ["Weight", f"{weight} kg"], ["Height", f"{height} cm"], ["Allergies", allergies if allergies else "None"], ["Cause of Injury", cause if cause else "Not Provided"], ["Diagnosis", diagnosed_class], ["Injury Severity", severity] ] # Format and align tables def format_table(data): table = Table(data, colWidths=[270, 270]) # Set 90% width table.setStyle(TableStyle([ ('BACKGROUND', (0, 0), (-1, 0), colors.darkblue), ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke), ('ALIGN', (0, 0), (-1, -1), 'CENTER'), ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'), ('BOTTOMPADDING', (0, 0), (-1, 0), 12), ('GRID', (0, 0), (-1, -1), 1, colors.black), ('VALIGN', (0, 0), (-1, -1), 'MIDDLE') ])) return table # Draw patient details table patient_table = format_table(patient_data) patient_table.wrapOn(c, 480, 500) patient_table.drawOn(c, 50, 620) # Load and insert X-ray image c.drawInlineImage(img_path, 50, 320, width=250, height=250) c.setFont("Helvetica-Bold", 12) c.drawString(120, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}") # Draw treatment and cost tables treatment_table = format_table(treatment_data) treatment_table.wrapOn(c, 480, 200) treatment_table.drawOn(c, 50, 200) cost_table = format_table(cost_duration_data) cost_table.wrapOn(c, 480, 150) cost_table.drawOn(c, 50, 80) c.save() # Send Email with the Report send_email_with_attachment(email, report_path, name) return report_path # Return path for download # Define Gradio Interface with gr.Blocks() as app: gr.HTML(html_content) gr.Markdown("## Bone Fracture Detection System") with gr.Row(): name = gr.Textbox(label="Patient Name") age = gr.Number(label="Age") gender = gr.Radio(["Male", "Female", "Other"], label="Gender") with gr.Row(): weight = gr.Number(label="Weight (kg)") height = gr.Number(label="Height (cm)") with gr.Row(): allergies = gr.Textbox(label="Allergies (if any)") cause = gr.Textbox(label="Cause of Injury") email = gr.Textbox(label="Patient Email") # New Email Input Field with gr.Row(): xray = gr.Image(type="filepath", label="Upload X-ray Image") submit_button = gr.Button("Generate Report") output_file = gr.File(label="Download Report") submit_button.click( generate_report, inputs=[name, age, gender, weight, height, allergies, cause, xray, email], outputs=[output_file], ) if __name__ == "__main__": app.launch()