app.py

import os
import smtplib
import ssl
from email.message import EmailMessage

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

# 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(patient_email, report_path, patient_name):
    sender_email = "[email protected]"
    sender_password = "your_email_password"

    subject = f"Bone Fracture Report for {patient_name}"
    body = f"""
    Dear {patient_name},

    Your bone fracture diagnosis report is attached.

    If you have any concerns, consult your doctor.

    Regards,
    Hospital Team
    """

    msg = EmailMessage()
    msg["From"] = sender_email
    msg["To"] = patient_email
    msg["Subject"] = subject
    msg.set_content(body)

    # Attach PDF file
    with open(report_path, "rb") as file:
        msg.add_attachment(file.read(), maintype="application", subtype="pdf", filename=os.path.basename(report_path))

    # Secure email sending
    context = ssl.create_default_context()
    with smtplib.SMTP_SSL("smtp.gmail.com", 465, context=context) as server:
        server.login(sender_email, sender_password)
        server.send_message(msg)

# 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 = 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.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 the report via email
    send_email(email, report_path, name)

    return report_path  # Return path for auto-download

# Function to select a sample image
def use_sample_image(sample_image_path):
    return sample_image_path  # Returns selected sample image filepath

# Define Gradio Interface
with gr.Blocks() as app:
    gr.HTML(html_content)  # Display `re.html` content in Gradio
    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")

    with gr.Row():
        email = gr.Textbox(label="Patient Email", type="email")

    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")

    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],
    )

# Launch the Gradio app
if __name__ == "__main__":
    app.launch()