Update app.py

app.py

@@ -1,10 +1,13 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
-
+import smtplib
 import gradio as gr
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
 from PIL import Image
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
@@ -18,11 +21,14 @@ model = tf.keras.models.load_model("my_keras_model.h5")
 with open("templates/re.html", "r", encoding="utf-8") as file:
     html_content = file.read()
 
+# Ensure reports directory exists
+os.makedirs("reports", exist_ok=True)
+
 # List of sample images
 sample_images = [f"samples/{img}" for img in os.listdir("samples") if img.endswith((".png", ".jpg", ".jpeg"))]
 
 # Function to process X-ray and generate a PDF report
-def generate_report(name, age, gender, weight, height, allergies, cause, xray):
+def generate_report(name, age, gender, weight, height, allergies, cause, email, xray):
     image_size = (224, 224)
 
     def predict_fracture(xray_path):
@@ -34,7 +40,7 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray):
 
     # Predict fracture
     prediction = predict_fracture(xray)
-    diagnosed_class = "normal" if prediction > 0.5 else "Fractured"
+    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"
@@ -56,11 +62,11 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray):
 
     # Save X-ray image for report
     img = Image.open(xray).resize((300, 300))
-    img_path = f"{name}_xray.png"
+    img_path = f"reports/{name}_xray.png"
     img.save(img_path)
 
     # Generate PDF report
-    report_path = f"{name}_fracture_report.pdf"
+    report_path = f"reports/{name}_fracture_report.pdf"
     c = canvas.Canvas(report_path, pagesize=letter)
     
     # Report title
@@ -117,46 +123,57 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray):
 
     return report_path  # Return path for auto-download
 
-# Function to select a sample image
-def use_sample_image(sample_image_path):
-    return sample_image_path  # Returns selected sample image filepath
+# Function to send email with the report attached
+def send_email_report(email, report_path):
+    sender_email = "[email protected]"
+    sender_password = "1w3r5y7i9pW$"  # Use an app password or environment variable
+    subject = "Your Bone Fracture Detection Report"
+
+    msg = MIMEMultipart()
+    msg["From"] = sender_email
+    msg["To"] = email
+    msg["Subject"] = subject
+
+    body = "Dear Patient,\n\nPlease find attached your bone fracture detection report.\n\nBest Regards,\nYour Medical Team"
+    msg.attach(MIMEText(body, "plain"))
+
+    with open(report_path, "rb") as attachment:
+        part = MIMEBase("application", "octet-stream")
+        part.set_payload(attachment.read())
+        encoders.encode_base64(part)
+        part.add_header("Content-Disposition", f"attachment; filename={os.path.basename(report_path)}")
+        msg.attach(part)
+
+    try:
+        with smtplib.SMTP("smtp.gmail.com", 587) as server:
+            server.starttls()
+            server.login(sender_email, sender_password)
+            server.sendmail(sender_email, email, msg.as_string())
+        return "Email Sent Successfully"
+    except Exception as e:
+        return f"Failed to Send Email: {str(e)}"
 
 # Define Gradio Interface
 with gr.Blocks() as app:
-    gr.HTML(html_content)  # Display `re.html` content in Gradio
+    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")
 
-    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")
+    name = gr.Textbox(label="Patient Name")
+    age = gr.Number(label="Age")
+    gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
+    weight = gr.Number(label="Weight (kg)")
+    height = gr.Number(label="Height (cm)")
+    allergies = gr.Textbox(label="Allergies (if any)")
+    cause = gr.Textbox(label="Cause of Injury")
+    email = gr.Textbox(label="Patient Email")
+    xray = gr.Image(type="filepath", label="Upload X-ray Image")
 
     submit_button = gr.Button("Generate Report")
     output_file = gr.File(label="Download Report")
+    email_button = gr.Button("Send Email Report")
 
-    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],
-        outputs=[output_file],
-    )
+    submit_button.click(generate_report, inputs=[name, age, gender, weight, height, allergies, cause, email, xray], outputs=[output_file])
+    email_button.click(send_email_report, inputs=[email, output_file], outputs=[])
 
-# Launch the Gradio app
 if __name__ == "__main__":
     app.launch()