import os
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()

# Create reports directory
REPORTS_DIR = "reports"
os.makedirs(REPORTS_DIR, exist_ok=True)

# Email configuration from environment variables
SENDER_EMAIL = os.getenv("SENDER_EMAIL", "your-email@gmail.com")
SENDER_PASSWORD = os.getenv("SENDER_PASSWORD", "your-app-password")

# 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!")

    with open(file_path, "rb") as f:
        file_data = f.read()
        msg.add_attachment(file_data, maintype="application", subtype="pdf", filename="report.pdf")

    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 generate 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

    prediction = predict_fracture(xray)
    diagnosed_class = "Normal" if prediction > 0.5 else "Fractured"
    severity = "Mild" if prediction < 0.3 else "Moderate" if prediction < 0.7 else "Severe"

    # Save X-ray image for report
    img = Image.open(xray).resize((300, 300))
    img_path = os.path.join(REPORTS_DIR, "xray.png")
    img.save(img_path)

    # Generate PDF report
    report_path = os.path.join(REPORTS_DIR, "report.pdf")
    c = canvas.Canvas(report_path, pagesize=letter)
    c.setFont("Helvetica-Bold", 16)
    c.drawString(200, 770, "Bone Fracture Detection Report")

    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])
        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

    patient_table = format_table(patient_data)
    patient_table.wrapOn(c, 480, 500)
    patient_table.drawOn(c, 50, 620)

    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'}")

    c.save()

    # Send report via email
    send_email_with_attachment(email, report_path, name)
    
    return report_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")
        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")

    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()