app.py

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 = "[email protected]"  # 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()