app.py

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

# Force TensorFlow to use CPU to prevent CUDA errors
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

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

# Load the trained model
model = tf.keras.models.load_model("my_keras_model.h5")

# Store generated report file paths
report_paths = {}

# Function to send email with the report
def send_email(patient_email, patient_name):
    if patient_name not in report_paths:
        return "Error: Generate the report first before sending."

    report_path = report_paths[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)

    return f"Report sent successfully to {patient_email}!"

# Function to generate report
def generate_report(name, age, gender, weight, height, allergies, cause, xray):
    if not name:
        return "Error: Please enter a patient name."

    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"

    # Generate PDF report
    report_path = f"{name}_fracture_report.pdf"
    c = canvas.Canvas(report_path, pagesize=letter)
    
    c.setFont("Helvetica-Bold", 16)
    c.drawString(200, 770, "Bone Fracture Detection Report")

    c.drawString(120, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}")

    # Save X-ray image for report
    img = Image.open(xray).resize((300, 300))
    img_path = f"{name}_xray.png"
    img.save(img_path)

    c.drawInlineImage(img_path, 50, 320, width=250, height=250)

    c.save()

    # Store file path for later use
    report_paths[name] = report_path

    return report_path  # Return file path

# Define Gradio Interface
with gr.Blocks() as app:
    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")
        cause = gr.Textbox(label="Cause of Injury")

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

    xray = gr.Image(type="filepath", label="Upload X-ray Image")

    with gr.Row():
        submit_button = gr.Button("Generate Report")
        send_email_button = gr.Button("Send Report via Email")

    output_file = gr.File(label="Download Report")

    # Generate Report Button
    submit_button.click(
        generate_report,
        inputs=[name, age, gender, weight, height, allergies, cause, xray],
        outputs=[output_file]
    )

    # Send Email Button
    send_email_button.click(
        send_email,
        inputs=[email, name],
        outputs=[gr.Textbox(label="Status")]
    )

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