Delete app.py

app.py

@@ -1,150 +0,0 @@
-import os
-import smtplib
-import ssl
-from email.message import EmailMessage
-
-# Force TensorFlow to use CPU
-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 paths
-report_paths = {}
-
-# Function to send email
-def send_email(patient_email, patient_name):
-    if patient_name not in report_paths or not os.path.exists(report_paths[patient_name]):
-        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},\n\nYour bone fracture diagnosis report is attached.\n\nBest Regards,\nHospital Team"
-
-    msg = EmailMessage()
-    msg["From"] = sender_email
-    msg["To"] = patient_email
-    msg["Subject"] = subject
-    msg.set_content(body)
-
-    # Attach PDF
-    with open(report_path, "rb") as file:
-        msg.add_attachment(file.read(), maintype="application", subtype="pdf", filename=os.path.basename(report_path))
-
-    # Send email securely
-    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_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 sending email
-    report_paths[name] = report_path
-
-    return report_path  # Return file path
-
-# Path to samples folder
-samples_folder = "samples"
-
-# Ensure samples directory exists
-if os.path.exists(samples_folder) and os.path.isdir(samples_folder):
-    sample_images = [os.path.join(samples_folder, f) for f in os.listdir(samples_folder) if f.endswith(('.png', '.jpg', '.jpeg'))]
-else:
-    sample_images = []
-
-# Preloaded image (if available)
-preloaded_image = sample_images[0] if sample_images else None
-
-# 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")
-
-    # X-ray image upload with preloaded image from "samples"
-    xray = gr.Image(type="filepath", label="Upload X-ray Image", value=preloaded_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 app
-if __name__ == "__main__":
-    app.launch()