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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")
# Store generated report file path
report_paths = {}
# Function to send email
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"
# Injury severity classification
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 = 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)
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'}")
c.drawInlineImage(img_path, 50, 320, width=250, height=250)
c.save()
# Store the file path
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():
email = gr.Textbox(label="Patient Email", type="email")
with gr.Row():
xray = gr.Image(type="filepath", label="Upload X-ray Image")
submit_button = gr.Button("Generate Report")
send_email_button = gr.Button("Send Report via Email")
output_file = gr.File(label="Download Report")
# When clicking "Generate Report", save the file path and allow downloading
submit_button.click(
generate_report,
inputs=[name, age, gender, weight, height, allergies, cause, xray],
outputs=[output_file]
)
# When clicking "Send Report via Email", send the stored report file
send_email_button.click(
send_email,
inputs=[email, name],
outputs=[gr.Textbox(label="Status")]
)
# Launch the Gradio app
if __name__ == "__main__":
app.launch() |