import os import smtplib import gradio as gr import tensorflow as tf import numpy as np from email.message import EmailMessage 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 # 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"))] # Function to send email def send_email(receiver_email, file_path): sender_email = "your_email@example.com" sender_password = "your_email_password" msg = EmailMessage() msg["Subject"] = "Bone Fracture Detection Report" msg["From"] = sender_email msg["To"] = receiver_email msg.set_content("Please find attached your bone fracture detection report.") 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) try: with smtplib.SMTP_SSL("smtp.example.com", 465) as server: server.login(sender_email, sender_password) server.send_message(msg) return "Report sent successfully." except Exception as e: return f"Error sending email: {e}" # Function to process X-ray and generate a PDF report def generate_report(name, age, gender, weight, height, allergies, cause, xray, email): # Validate inputs name = name[:50] cause = " ".join(cause.split()[:100]) # Limit to 100 words 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 treatment_data = [ ["Severity Level", "Recommended Treatment", "Recovery Duration"], ["Mild", "Rest, pain relievers, 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"] ] # Cost & duration estimation 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 resized X-ray image img = Image.open(xray).resize((300, 300)) img_path = f"{name}_xray.png" img.save(img_path) # Generate PDF report report_path = f"{name}_fracture_report.pdf" c = canvas.Canvas(report_path, pagesize=letter) # Report title c.setFont("Helvetica-Bold", 16) c.drawCentredString(300, 770, "Bone Fracture Detection Report") # Patient details 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]) # 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) # Center X-ray image c.drawInlineImage(img_path, 150, 320, width=300, height=300) c.setFont("Helvetica-Bold", 12) c.drawCentredString(300, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}") # Draw treatment & 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 email_status = send_email(email, report_path) return report_path, email_status # Function to select a sample image def use_sample_image(sample_image_path): return sample_image_path # 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", max_length=50) 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", max_lines=5) with gr.Row(): email = gr.Textbox(label="Email Address") with gr.Row(): xray = gr.Image(type="filepath", label="Upload X-ray Image") with gr.Row(): sample_selector = gr.Dropdown(choices=sample_images, label="Use Sample Image") select_button = gr.Button("Load Sample Image") submit_button = gr.Button("Generate Report") output_file = gr.File(label="Download Report") email_status = gr.Textbox(label="Email Status", interactive=False) select_button.click(use_sample_image, inputs=[sample_selector], outputs=[xray]) submit_button.click( generate_report, inputs=[name, age, gender, weight, height, allergies, cause, xray, email], outputs=[output_file, email_status], ) # Launch the Gradio app if __name__ == "__main__": app.launch()