Update app.py

app.py

@@ -3,7 +3,7 @@ import smtplib
 import ssl
 from email.message import EmailMessage
 
-# Force TensorFlow to use CPU to prevent CUDA errors
+# Force TensorFlow to use CPU
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 import gradio as gr
@@ -17,12 +17,12 @@ from reportlab.pdfgen import canvas
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
 
-# Store generated report file paths
+# Store generated report paths
 report_paths = {}
 
-# Function to send email with the report
+# Function to send email
 def send_email(patient_email, patient_name):
-    if patient_name not in report_paths:
+    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]
@@ -31,16 +31,7 @@ def send_email(patient_email, patient_name):
     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
-    """
+    body = f"Dear {patient_name},\n\nYour bone fracture diagnosis report is attached.\n\nBest Regards,\nHospital Team"
 
     msg = EmailMessage()
     msg["From"] = sender_email
@@ -48,11 +39,11 @@ def send_email(patient_email, patient_name):
     msg["Subject"] = subject
     msg.set_content(body)
 
-    # Attach PDF file
+    # Attach PDF
     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
+    # 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)
@@ -78,13 +69,12 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray):
     prediction = predict_fracture(xray)
     diagnosed_class = "Normal" if prediction > 0.5 else "Fractured"
 
-    # Generate PDF report
+    # 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
@@ -93,15 +83,14 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray):
     img.save(img_path)
 
     c.drawInlineImage(img_path, 50, 320, width=250, height=250)
-
     c.save()
 
-    # Store file path for later use
+    # Store file path for sending email
     report_paths[name] = report_path
 
     return report_path  # Return file path
 
-# Define Gradio Interface
+# Gradio Interface
 with gr.Blocks() as app:
     gr.Markdown("## Bone Fracture Detection System")
 
@@ -121,7 +110,9 @@ with gr.Blocks() as app:
     with gr.Row():
         email = gr.Textbox(label="Patient Email", type="email")
 
-    xray = gr.Image(type="filepath", label="Upload X-ray Image")
+    # Preloaded X-ray image
+    default_xray_path = "default_xray.png"  # Ensure this file exists in your project
+    xray = gr.Image(type="filepath", label="Upload X-ray Image", value=default_xray_path)
 
     with gr.Row():
         submit_button = gr.Button("Generate Report")
@@ -143,6 +134,6 @@ with gr.Blocks() as app:
         outputs=[gr.Textbox(label="Status")]
     )
 
-# Launch the Gradio app
+# Launch app
 if __name__ == "__main__":
     app.launch()