app.py

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 = "[email protected]"
    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()